Question: A fully integrated voltage boost converter with MPPT, designed for low-voltage energy harvesters, achieving a 0.21-V minimum input and 73.6% maximum efficiency.

In this paper, we present a 65nm bulk CMOS technology-based high-voltage charge pump design, capable of generating up to 34V output voltage. Traditionally, MEMS-based oscillators have gained popularity in timing applications previously dominated by quartz devices, but their use in high phase noise-sensitive applications like cellular, GPS, and high-speed serial links necessitates a higher DC voltage bias to the MEMS resonator to decrease its motional impedance and boost signal energy. However, realizing high-voltage charge pumps in bulk CMOS technology is constrained by the breakdown voltage of the well/substrate diodes, which diminishes with technology scaling and is less than 10V in 22nm CMOS nodes. To address this, systems with high-voltage needs often turn to older, high-voltage-tolerant nodes or specialized technologies, which can impede the integration of MEMS into SoCs. Our work introduces a charge pump design that triples the output voltage range in 65nm technology. This is made possible by the innovative well-biasing strategy and oxide isolation. The charge pump attains a 34V output using three distinct charge pump cells, each offering a trade-off between voltage range and power efficiency, with a peak efficiency of 38%. Furthermore, the design optimizes finger capacitors for reliability and efficiency retention.

**Question**: A 0.21-V minimum input, 73.6% maximum efficiency, fully integrated voltage boost converter with MPPT for low-voltage energy harvesters

**Documents**: A 2.5V to 5.5V, 93% peak efficiency, 200mA fully integrated boost converter in 0.35um CMOS
This paper presents a fully integrated boost converter that operates from a 2.5V to 5.5V supply voltage, achieving a peak efficiency of 93% at a 200mA output current. The converter is designed in a 0.35um CMOS process and incorporates several features to optimize its performance. It utilizes a high-frequency switching technique to minimize the size of the external components and reduce overall system cost. The converter also employs a low dropout regulator (LDO) to maintain a stable output voltage under varying load conditions. A current-mode control scheme is used to ensure fast transient response and improve stability. The design includes a soft-start function to prevent inrush current and a protection mechanism to safeguard against overcurrent and overvoltage

A novel method adaptively controls the comparator bias current in a boost converter for sensor-data storage on Resistive Random Access Memory (ReRAM). ReRAM is ideal for low-power sensors because it is faster and lower voltage than NAND flash. However, ReRAM's voltage design needs to consider program current variation due to data pattern dependency and cell reliability is sensitive to small voltage fluctuations. The proposed control method of the boost converter architecture has three proposals. First, low program voltage ripple (VRIPPLE) and high program energy efficiency is obtained with an adaptive 2 to 8 μA comparator bias-current (ICMP) control method. ICMP is adjusted depending on the number of program bits (NBITS) in the SET data. In 2 cases of NBITS = 1 and 16, and compared to a conventional scheme (ICMP = 8 μA and 2 μA), respectively, VRIPPLE decreases by 41.2% and energy efficiency improves by 6.6%, respectively. Secondly, as NBITS decrease during SET verify, ICMP also decreases, which further reduces SET energy by 8.9 pJ. Thirdly, although the conventional boost converter fails in the `SS' process condition, an additional cross-coupled charge-pump boosts the buffer circuit, which provides full functional operation over the input range of 0.6 to 1.0 V.

The main aim of this paper is to improve the performance of high current dual active bridge converters when operated over a wide voltage range. A typical application is for fuel cell vehicles where a bi-directional interface between a 12V battery and a high voltage DC bus is required. The battery side voltage ranges from 11V to 16V while the fuel cell is operated between 220V and 447V and the required power is typically 1kW. Careful analysis shows that the high currents on the battery side cause significant design issues in order to obtain a high efficiency. The standard phase shift modulation method can result in high conduction and switching losses. This paper proposes a combined triangular and trapezoidal modulation method to reduce losses over the wide operating range. Approximately, a 2% improvement in efficiency can be expected. An experimental system is used to verify the improved performance of the dual active bridge using the proposed advanced modulation method.

The demand for low voltage, low drop-out (LDO) regulators is increasing because of the growing demand for portable electronics, i.e., cellular phones, pagers, laptops, etc. LDOs are used coherently with dc-dc converters as well as standalone parts. In power supply systems, they are typically cascaded onto switching regulators to suppress noise and provide a low noise output. The need for low voltage is innate to portable low power devices and corroborated by lower breakdown voltages resulting from reductions in feature size. Low quiescent current in a battery operated system is an intrinsic performance parameter because it partially determines battery life. This paper discusses some techniques that enable the practical realizations of low quiescent current LDOs at low voltages and in existing technologies. The proposed circuit exploits the frequency response dependence on load-current to minimize quiescent current flow. Moreover, the output current capabilities of MOS power transistors are enhanced and drop-out voltages are decreased for a given device size. Other applications, like dc-dc converters, can also reap the benefits of these enhanced MOS devices. An LDO prototype incorporating the aforementioned techniques was fabricated. The circuit was operable down to input voltages of 1 V with a zero-load quiescent current flow of 23 μA. Moreover, the regulator provided 18 and 50 mA of output current at input voltages of 1 and 1.2 V respectively.

In this paper, a new current source gate drive circuit is proposed for high efficiency synchronous buck VRMs. The proposed circuit achieves quick turn-on and turn-off transition times to reduce switching loss and conduction loss in power MOSFETS. The driver circuit consists of two sets of four control switches and two very small inductors (typically 50 nH-300 nH each at 1 MHz). It drives both the control MOSFET and synchronous MOSFET in synchronous buck VRMs. An analysis, design procedure, optimization procedure and experimental results are presented for the proposed circuit. Experimental results demonstrate an efficiency of 86.6% at 15 A load and 81.9% at 30 A load for 12 V input and 1.3 V output at 1 MHz.

A low-power CMOS voltage reference was developed using a 0.35 m standard CMOS process technology. The device consists of MOSFET circuits operated in the subthreshold region and uses no resistors. It generates two voltages having opposite temperature coefficients and adds them to produce an output voltage with a near-zero temperature coefficient. The resulting voltage is equal to the extrapolated threshold voltage of a MOSFET at absolute zero temperature, which was about 745 mV for the MOSFETs we used. The temperature coefficient of the voltage was 7 ppm/ C at best and 15 ppm/ C on average, in a range from 20 to 80 C. The line sensitivity was 20 ppm/V in a supply voltage range of 1.4–3 V, and the power supply rejection ratio (PSRR) was 45 dB at 100 Hz. The power dissipation was 0.3 W at 80 C. The chip area was 0.05 mm . Our device would be suitable for use in subthreshold-operated, power-aware LSIs.

This paper presents an ultra low power rail-to-rail input/output operational amplifier (OpAmp) designed in a low cost 0.18 µm CMOS technology. In this OpAmp, rail-to-rail input operation is enabled by using complementary input pairs with gm control. To maximize the output swing a rail-to-rail output stage is employed. For low-voltage low-power operation, the operating transistors in the input and output stage are biased in the sub-threshold region. The simulated DC open loop gain is 51 dB, and the slew-rate is 0.04 V/µs with a 10 pF capacitive load connected to each of the amplifier outputs. For the same load, the simulated unity gain frequency is 131 kHz with a 64° phase margin. A common-mode feed-forward circuit (CMFF) increases CMRR, reducing drastically the variations in the output common mode voltage and keeping the DC gain almost constant. In fact, their relative error remains below 1.2 % for a (−20°C, +120°C) temperature span. In addition, the proposed OpAmp is very simple and consumes only 4 µW at 0.8 V supply.

A new multiple-loop design technique for high-performance low-dropout (LDO) regulator designs has been proposed and successfully implemented in many commercial products for portable smart phone and tablet PC applications. The proposed LDO is composed of five loops that allows designers to obtain a good tradeoff between quiescent current and other performances, such as undershoot, overshoot, and so on. A total of one bandgap reference and 38 LDOs (including n-type and p-type LDOs, which will be named NLDO and PLDO, respectively) were integrated in one power-management IC chip for powering an application processor inside mobile devices. The proposed LDO has been fabricated based on 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu {m}$ </tex-math></inline-formula> CMOS process and supplies various current capacities from 50 to 600 mA; 38 LDOs have been designed and supply different output voltage levels from 0.7 to 3.0 V. One of the proposed NLDOs consumes <inline-formula> <tex-math notation="LaTeX">$14~\mu {A}$ </tex-math></inline-formula> of the quiescent current and features under 56/24 mV of undershoot/overshoot at VOUT =1<italic>V</italic> as the load current steps up from 0 to 300 mA with 300 mA/1 <inline-formula> <tex-math notation="LaTeX">$\mu {s}$ </tex-math></inline-formula> on a 1-<inline-formula> <tex-math notation="LaTeX">$\mu {F}$ </tex-math></inline-formula> output capacitor. The measured output load and line regulations are 1.8 and 0.4 mV, respectively. The measured integrated output noise from 10 Hz to 100 kHz at <inline-formula> <tex-math notation="LaTeX">$\text{I}_{\mathrm{ LOAD}}=10\%$ </tex-math></inline-formula> of maximum current shows 80 <inline-formula> <tex-math notation="LaTeX">$\mu {V}$ </tex-math></inline-formula> rms. The package chip size is <inline-formula> <tex-math notation="LaTeX">$6.25 \times 6.25$ </tex-math></inline-formula> mm2 with 169 balls.

This paper presents a slightly different concept of energy harvesting method as compared to the conventional energy harvesters. The new energy harvester of interest is known as Hybrid Energy Harvester (HEH). It comprises of the Piezoelectric and Magnetostrictive/Electromagnetic transducers integrated into a system. Theoretically, the hybrid energy harvester device developed is targeted to expand the vibration bandwidth as compared to the standalone energy harvesters, hence higher output power will be generated. To develop the hybrid energy harvester, mathematical models of each respective transducers are formulated, then simulations are carried out using the MATLAB/Simulink analysis tool. Next, a macro-scale prototype device is designed, fabricated and validated. From the experimental characterizations, it is unveiled that the maximum open circuit output voltage produced by the hybrid energy harvester is 6.23 V at a resonant frequency of 59.6 Hz.

A novel on-chip impedance matching and powercombining method, the distributed active transformer is presented. It combines several low-voltage push–pull amplifiers efficiently with their outputs in series to produce a larger output power while maintaining a 50match. It also uses virtual ac grounds and magnetic couplings extensively to eliminate the need for any offchip component, such as tuned bonding wires or external inductors. Furthermore, it desensitizes the operation of the amplifier to the inductance of bonding wires making the design more reproducible. To demonstrate the feasibility of this concept, a 2.4-GHz 2-W 2-V truly fully integrated power amplifier with 50input and output matching has been fabricated using 0.35m CMOS transistors. It achieves a power added efficiency (PAE) of 41% at this power level. It can also produce 450 mW using a 1-V supply. Harmonic suppression is 64 dBc or better. This new topology makes possible a truly fully integrated watt-level gigahertz range low-voltage CMOS power amplifier for the first time.

We need electricity to use wireless information. If we reduce amount of batteries or electrical wires with a wireless power transmission technology via microwave (MPT), it is a green communication system. We Kyoto University propose a Fixed Wireless Access (FWA) system with the MPT with NTT, Japan. In this paper, we show mainly development results of 24GHz rectennas, rectifying antenna, for FWA. We developed some types of the rectennas. Finally we achieve 65% of RF-DC conversion efficiency with output filter of harmonic balance.


Question: A fully integrated voltage boost converter with MPPT, designed for low-voltage energy harvesters, achieving a 73.6% maximum efficiency and a 0.21-V minimum input.

This paper presents an innovative on-chip ultra-high-voltage charge pump circuit developed using low-voltage bulk CMOS processes with polysilicon diodes. The key advantage of utilizing polysilicon diodes lies in their complete isolation from the silicon substrate, which prevents the output voltage of the charge pump circuit from being constrained by the junction breakdown voltage of MOSFETs. Importantly, these polysilicon diodes can be seamlessly integrated into standard CMOS processes without necessitating additional fabrication steps. The proposed circuit has been successfully implemented in a 0.25-micron 2.5V standard CMOS process. Remarkably, the four-stage charge pump circuit, operating with a 2.5V power supply (VDD=2.5V), can generate an output voltage of up to 28.08V, significantly exceeding the n-well/p-substrate breakdown voltage of approximately 18.9V typically observed in a 0.25-micron 2.5V bulk CMOS process. This achievement underscores the circuit's capability to deliver ultra-high voltages within the constraints of low-voltage CMOS processes, making it a promising solution for applications requiring high voltage levels in compact, integrated designs.

A novel method adaptively controls the comparator bias current in a boost converter for sensor-data storage on Resistive Random Access Memory (ReRAM). ReRAM is ideal for low-power sensors because it is faster and lower voltage than NAND flash. However, ReRAM's voltage design needs to consider program current variation due to data pattern dependency and cell reliability is sensitive to small voltage fluctuations. The proposed control method of the boost converter architecture has three proposals. First, low program voltage ripple (VRIPPLE) and high program energy efficiency is obtained with an adaptive 2 to 8 μA comparator bias-current (ICMP) control method. ICMP is adjusted depending on the number of program bits (NBITS) in the SET data. In 2 cases of NBITS = 1 and 16, and compared to a conventional scheme (ICMP = 8 μA and 2 μA), respectively, VRIPPLE decreases by 41.2% and energy efficiency improves by 6.6%, respectively. Secondly, as NBITS decrease during SET verify, ICMP also decreases, which further reduces SET energy by 8.9 pJ. Thirdly, although the conventional boost converter fails in the `SS' process condition, an additional cross-coupled charge-pump boosts the buffer circuit, which provides full functional operation over the input range of 0.6 to 1.0 V.

This paper proposes an interleaved zero voltage transition (ZVT) PWM buck converter. The proposed converter consists of two identical buck converter modules and an auxiliary circuit. The auxiliary circuit provides zero voltage switching condition for the main switches. Also, zero current switching condition for the auxiliary switch and diodes is achieved. The interleaved buck converter with the proposed auxiliary switch is analyzed. The simulation results of a 300W ZVT interleaved buck converter operating at 100 KHz are presented to justify the validity of theoretical analysis.

This paper investigates the analysis and design of a vibration-based energy harvester for rotary motion applications. The energy harvester consists of a cantilever beam with a tip mass and a piezoelectric ceramic attached along the beam that is mounted on a rotating shaft. Using this system, mechanical vibration energy is induced in the flexible beam due to the gravitational force applied to the tip mass while the hub is rotating. The piezoelectric transducer is used to convert the induced mechanical vibration energy into electricity. The equations of motion of the flexible structure are utilized along with the physical characteristics of the piezoelectric transducer to derive expressions for the electrical power. Furthermore, expressions for the optimum load resistance and maximum output power are obtained and validated experimentally using PVDF and PZT transducers. The results indicate that a maximum power of 6.4 mW at a shaft speed of 138 rad/s can be extracted by using a PZT transducer with dimensions 50.8 mm × 38.1 mm × 0.13 mm. This amount of power is sufficient to provide power for typical wireless sensors such as accelerometers and strain gauges.

We demonstrate a new flexible multilayered triboelectric nanogenerator (TENG) with extremely low cost, simple structure, small size (3.8 cm×3.8 cm×0.95 cm) and lightweight (7 g) by innovatively integrating five layers of units on a single flexible substrate. Owing to the unique structure and nanopore-based surface modification on the metal surface, the instantaneous short-circuit current (Isc) and the open-circuit voltage (Voc) could reach 0.66 mA and 215 V with an instantaneous maximum power density of 9.8 mW/cm2 and 10.24 mW/cm3. This is the first 3D integrated TENG for enhancing the output power. Triggered by press from normal walking, the TENG attached onto a shoe pad was able to instantaneously drive multiple commercial LED bulbs. With the flexible structure, the TENG can be further integrated into clothes or even attached onto human body without introducing sensible obstruction and discomfort to human motions. The novel design of TENG demonstrated here can be applied to potentially achieve self-powered portable electronics.

In this paper, four types control methods have been proposed for buck-boost cascade topology. This converter used in hybrid electric vehicle can step the battery voltage level either up or down. The target is to find the most appropriate control method for the converter with certain specifications. Critical conduction mode control, fixed frequency control, the buck/boost control and the buck + boost control have been mentioned. The inductor design is presented according to the given parameters. The relationship among the parameters for buck/boost mode, buck mode and boost mode in critical conduction mode are proposed. The efficiency comparison for four control methods is given. The loss breakdown of different control methods is also presented by mathematical calculation.

This paper proposes a new soft-switched continuous-conduction-mode (CCM) boost converter suitable for high-power applications such as power factor correction, hybrid electric vehicles, and fuel cell power conversion systems. The proposed converter achieves zero-voltage-switched (ZVS) turn-on of active switches in CCM and zero-current-switched turn-off of diodes leading to negligible reverse-recovery loss. The components' voltage ratings and energy volumes of passive components of the proposed converter are greatly reduced compared to the conventional zero-voltage-transition converter. Voltage conversion ratio is almost doubled compared to the conventional boost converter. Extension of the proposed concept to realize multiphase dc-dc converters is discussed. Experimental results from a 1.5-kW prototype are provided to validate the proposed concept.

This paper presents a new compact CMOS Li-Ion battery charger for portable applications that uses a charge-pump technique. The proposed charger features a small chip size and a simple circuit structure. Additionally, it provides basic functions with voltage/current detection, end-of-charge detection, and charging speed control. The charger operates in dual-mode and is supported in the trickle/large constant-current mode to constant-voltage mode with different charging rates. This charger is implemented using a TSMC 0.35-mum CMOS process with a 5-V power supply. The output voltage is almost 4.2 V, and the maximum charging current reaches 700 mA. It has 67.89% power efficiency, 837-mW chip power dissipation, and only 1.455times1.348 mm2 in chip area including pads

This paper presents an enhanced Maximum Power Point Tracking (MPPT) of Photovoltaic (PV) systems by means of Model Predictive Control (MPC) techniques. The PV array can feed power to the load through a DC/DC converter boosting the output voltage. Due to stochastic behavior of solar energy, MPPT control technique of PV arrays is required to operate at maximum power point. Extracting the maximum power from PV systems has been widely investigated within the literature. The main contribution of this paper is enhancement of the Incremental Conductance (INC) method through a fixed step predictive control under measured fast solar radiation variation. The proposed predictive control to achieve Maximum Power Point (MPP) speeds up the control loop since it predicts error before the switching signal is applied to the selected high gain multilevel DC-DC converter. Comparing the developed technique to the conventional INC method shows significant improvement in PV system performance. Experimental validation is presented using the dSpace CP 1103 to implement the proposed MPC-MPPT.

This paper presents a new power regulation method for contactless power transfer systems that combines current magnitude control of a series tuned resonant converter with energy injection concepts suitable for constant load or single pick-up applications. In the latter, the control of the primary converter operates with asymmetrical control, to regulate the magnitude of the high frequency primary track current according to the load requirement of the secondary power pickup. The feedback control algorithm is developed by monitoring the output voltage of the secondary power pickup. Switching losses and EMI are reduced due to the ZCS (Zero Current Switching) operation of the converter. Simulation and experimental results show that the primary converter has a fast and smooth start-up transient response, and the magnitudes of the primary current and the output voltage of a secondary power pickup are both fully controllable against load variations.

This paper involves designing a small scale battery charging system which is powered via a photovoltaic panel. This work aims at the usage of solar energy for charging the battery and optimizing the power of the system. Implementation is done using Artificial Neural Network (ANN) on FPGA. To develop this system an Artificial Neural Network is trained and its result is further used for the PWM technique. PWM pulse generation has been done using Papilio board which is based on XILINX Spartan 3E FPGA. The ANN with PWM technique is ported on FPGA which is programmed using VHDL. This able to automatically control the whole charging system operation without requirement of external sensory unit. The simulation results are achieved by using MATLAB and XILINX. These results allowed demonstrating the charging of the battery using proposed ANN and PWM technique. KeywordsPhotovoltaic battery charger, PWM, ANN, FPGA


Question: A fully integrated voltage boost converter with MPPT, designed for low-voltage energy harvesters, achieving a 0.21-V minimum input and 73.6% maximum efficiency.

This paper delves into the realm of complex thermoelectric materials, which have the potential to significantly impact sustainable energy solutions by converting waste heat into electricity or serving as efficient solid-state cooling devices. The quest for materials with superior thermoelectric efficiency is pivotal, yet it presents a complex challenge due to the inherent trade-offs between necessary material properties. Despite these difficulties, advancements in modern synthesis and characterization techniques, particularly for materials at the nanoscale, are ushering in a new era of sophisticated thermoelectric materials. Here, we summarize the latest breakthroughs in the field, emphasizing the innovative strategies employed to enhance thermopower while simultaneously reducing thermal conductivity, crucial steps toward achieving higher efficiency in thermoelectric energy conversion.

**Question**: A 0.21-V minimum input, 73.6% maximum efficiency, fully integrated voltage boost converter with MPPT for low-voltage energy harvesters

**Documents**: A novel, ultra-low power, fully integrated voltage boost converter for energy harvesting applications
In this paper, we present a novel voltage boost converter designed specifically for energy harvesting applications that require ultra-low power consumption. The converter is fully integrated, making it ideal for compact and portable devices. It operates at a minimum input voltage of 0.21V, enabling it to function effectively with low-voltage energy sources. The converter's maximum efficiency is 73.6%, demonstrating its ability to efficiently manage power in energy-constrained systems. A key feature of the design is the inclusion of a Maximum Power Point Tracking (MPPT) algorithm, which optimizes power extraction from the energy harvester, ensuring the system operates at its peak efficiency under varying input conditions. The converter's design and performance are validated through simulation and experimental results, confirming its suitability for a wide range of energy harvesting scenarios, particularly those involving low-voltage sources such as thermoelectric generators or photovoltaic cells.

**Similar Documents**: This paper introduces an innovative, fully integrated voltage boost converter tailored for energy harvesting systems that demand minimal power usage. Designed with compactness and portability in mind, the converter is capable of operating at a low minimum input voltage of 0.21V, making it compatible with a variety of low-voltage energy sources. Notably, it achieves a maximum efficiency of 73.6%, showcasing its proficiency in power management within energy-limited environments. A standout feature of this converter is the incorporation of a Maximum Power Point Tracking (MPPT) mechanism, which dynamically adjusts the system's operation to maximize power extraction

We need electricity to use wireless information. If we reduce amount of batteries or electrical wires with a wireless power transmission technology via microwave (MPT), it is a green communication system. We Kyoto University propose a Fixed Wireless Access (FWA) system with the MPT with NTT, Japan. In this paper, we show mainly development results of 24GHz rectennas, rectifying antenna, for FWA. We developed some types of the rectennas. Finally we achieve 65% of RF-DC conversion efficiency with output filter of harmonic balance.

The main aim of this paper is to improve the performance of high current dual active bridge converters when operated over a wide voltage range. A typical application is for fuel cell vehicles where a bi-directional interface between a 12V battery and a high voltage DC bus is required. The battery side voltage ranges from 11V to 16V while the fuel cell is operated between 220V and 447V and the required power is typically 1kW. Careful analysis shows that the high currents on the battery side cause significant design issues in order to obtain a high efficiency. The standard phase shift modulation method can result in high conduction and switching losses. This paper proposes a combined triangular and trapezoidal modulation method to reduce losses over the wide operating range. Approximately, a 2% improvement in efficiency can be expected. An experimental system is used to verify the improved performance of the dual active bridge using the proposed advanced modulation method.

This paper presents a novel single-phase grid-connected photovoltaic (PV) converter based on current-fed dual active bridge (CF-DAB) dc-dc converter. The proposed converter allows a minimal DC-link capacitor with large voltage variation without low-frequency effect on maximum power point tracking (MPPT). An advanced control system is developed based on the converter operation principles. Minimized transformer peak current is capable to be achieved through a symmetrical dc voltage control. Simulation results are presented to validate the performance of the proposed PV converter. A 5kW laboratorial prototype is also implemented and the results will be presented later.

Modular photovoltaic (PV) power conditioning systems (PCSs) require a high-efficiency dc-dc converter stage capable of regulation over a wide input voltage range for maximum power point tracking. In order to mitigate ground leakage currents and to be able to use a high-efficiency, nonisolated grid-tied inverter, it is also desirable for this microconverter to provide galvanic isolation between the PV module and the inverter. This paper presents a novel, isolated topology that is able to meet the high efficiency over a wide input voltage range requirement. This topology yields high efficiency through low circulating currents, zero-voltage switching (ZVS) and low-current switching of the primary side devices, ZCS of the output diodes, and direct power transfer to the load for the majority of switching cycle. This topology is also able to provide voltage regulation through basic fixed-frequency pulsewidth modulated (PWM) control. These features are able to be achieved with the simple addition of a secondary-side bidirectional ac switch to the isolated series resonant converter. Detailed analysis of the operation of this converter is discussed along with a detailed design procedure. Experimental results of a 300-W prototype are given. The prototype reached a peak power stage efficiency of 98.3% and a California Energy Commission (CEC) weighted power stage efficiency of 98.0% at the nominal input voltage.

A 2:1 351 V hard-switched boost converter was constructed using high-voltage GaN high-electron-mobility transistors grown on Si substrates and GaN Schottky diodes grown on Sapphire substrates. The high speed and low on-resistance of the GaN devices enables extremely fast switching times and low losses, resulting in a high conversion efficiency of 95% with 425-W output power at 1 MHz. The boost converter has a power density of 175 W/in3. To our knowledge, these results are the best reported on GaN devices, and the highest for 1MHz switching.

This paper presents a study of reception and rectification of broad-band statistically time-varying low-power-density microwave radiation. The applications are in wireless powering of industrial sensors and recycling of ambient RF energy. A 64-element dual-circularly-polarized spiral rectenna array is designed and characterized over a frequency range of 2-18 GHz with single-tone and multitone incident waves. The integrated design of the antenna and rectifier, using a combination of full-wave electromagnetic field analysis and harmonic balance nonlinear circuit analysis, eliminates matching and filtering circuits, allowing for a compact element design. The rectified dc power and efficiency is characterized as a function of dc load and dc circuit topology, RF frequency, polarization, and incidence angle for power densities between 10/sup -5/-10/sup -1/ mW/cm/sup 2/. In addition, the increase in rectenna efficiency for multitone input waves is presented.

We present a dynamic programming technique for solving the multiple supply voltage scheduling problem in both non-pipelined and functionally pipelined data-paths. The scheduling problem refers to the assignment of a supply voltage level to each operation in a data ow graph so as to minimize the average energy consumption for given computation time or throughput constraints or both. The energy model is accurate and accounts for the input pattern dependencies, re-convergent fanout induced dependencies, and the energy cost of level shifters. Experimental results show that using four supply voltage levels on a number of standard benchmarks, an average energy saving of 53% (with a computation time constraint of 1.5 times the critical path delay) can be obtained compared to using one fixed supply voltage level.

This paper presents a recursive digital low-dropout (RLDO) regulator that improves response time, quiescent power, and load regulation dynamic range over prior digital LDO designs by 1–2 orders of magnitude. The proposed RLDO enables a practical digital replacement to analog LDOs by using an SAR-like binary search algorithm in a coarse loop and a sub-LSB pulse width modulation duty control scheme in a fine loop. A proportional-derivative compensation scheme is employed to ensure stable operation independent of load current, the size of the output decoupling capacitor, and clock frequency. Implemented in 0.0023 mm2 in 65 nm CMOS, the 7-bit RLDO achieves, at a 0.5-V input, a response time of 15.1 ns with a figure of merit of 199.4 ps, along with stable operation across a 20 000 $\times $  dynamic load range.

A zero-voltage switching-zero-current switching interleaved boost converter is proposed in this paper. An active circuit branch in parallel with the main switches is added and it is composed of an auxiliary switch and a snubber capacitor. By using this interleaved converter topology, zero current turn-on and zero voltage turn-off of the main switches can be achieved and the reverse-recovery loss of boost diode can be reduced. In addition, the auxiliary switches are zero-voltage transmission during the whole switching transition. A prototype of boost converter rated at 1.2kW has been built to confirm the effectiveness of the converter

In this paper, four types control methods have been proposed for buck-boost cascade topology. This converter used in hybrid electric vehicle can step the battery voltage level either up or down. The target is to find the most appropriate control method for the converter with certain specifications. Critical conduction mode control, fixed frequency control, the buck/boost control and the buck + boost control have been mentioned. The inductor design is presented according to the given parameters. The relationship among the parameters for buck/boost mode, buck mode and boost mode in critical conduction mode are proposed. The efficiency comparison for four control methods is given. The loss breakdown of different control methods is also presented by mathematical calculation.


Question: SKILL: A system designed for the identification and normalization of skills.

This paper presents two innovative model frameworks for the efficient computation of continuous vector word representations derived from extensive data sets. The efficacy of these representations is assessed through a word similarity task, with outcomes juxtaposed against prior state-of-the-art techniques that employ various neural network architectures. Notably, our models achieve substantial enhancements in accuracy with significantly reduced computational overhead; specifically, high-quality word vectors can be learned from a 1.6 billion words corpus in under 24 hours. Moreover, we illustrate that these vector representations outperform existing methods in our comprehensive test set, which evaluates both syntactic and semantic word similarities, thereby establishing a new benchmark in the field.

We have witnessed a great interest in ontologies as an emerging technology within the Semantic Web. Its foundation that was laid in the last decade paved the way for the development of ontologies and systems that use ontologies in various domains, including the E-Learning domain. In this article, we survey key contributions related to the development and use of ontologies in the domain of E-Learning systems. We provide a framework for classification of the literature that is useful for the community of practice in categorizing work in this field and for determining possible research lines. We also discuss future trends in this area.

We propose a method to create process flow graphs automatically from textbooks for cooking programs. This is realized by understanding context by narrowing down the domain to cooking, and making use of domain specific constraints and knowledge. Since it is relatively easy to extract significant keywords from cooking procedures, we create a domain specific dictionary by statistical methods, and propose a structural analysis method using the dictionary. In order to evaluate the ability of the proposed method, we applied the method to actual procedures as an experiment, which showed effective results. The same experiment was also performed on a different program, which showed lower accuracy but also showed realistic results.

Accessing the wealth of structured data available on the Data Web is still a key challenge for lay users. Keyword search is the most convenient way for users to access information (e.g., from data repositories). In this paper we introduce a novel approach for determining the correct resources for user-supplied keyword queries based on a hidden Markov model. In our approach the user-supplied query is modeled as the observed data and the background knowledge is used for parameter estimation. Instead of learning parameter estimation from training data, we leverage the semantic relationships between data items for computing the parameter estimations. In order to maximize accuracy and usability, query segmentation and resource disambiguation are mutually tightly interwoven. First, an initial set of potential segmentations is obtained leveraging the underlying knowledge base; then the final correct set of segments is determined after the most likely resource mapping was computed using a scoring function. While linguistic methods like named entity, multi-word unit recognition and POS-tagging fail in the case of an incomplete sentences (e.g. for keyword-based queries), we will show that our statistical approach is robust with regard to query expression variance. Our experimental results when employing the hidden Markov model for resource identification in keyword queries reveal very promising results.

0306-4573/$ see front matter Published by Elsevi doi:10.1016/j.ipm.2008.07.005 * Corresponding author. Tel.: +1 601 266 4035; fa E-mail addresses: Yuelin.Li@usm.edu (Y. Li), nick 1 Tel.: +1 732 932 7500x8271; fax: +1 732 932 69 The nature of the task that leads a person to engage in information interaction, as well as of information seeking and searching tasks, have been shown to influence individuals’ information behavior. Classifying tasks in a domain has been viewed as a departure point of studies on the relationship between tasks and human information behavior. However, previous task classification schemes either classify tasks with respect to the requirements of specific studies or merely classify a certain category of task. Such approaches do not lead to a holistic picture of task since a task involves different aspects. Therefore, the present study aims to develop a faceted classification of task, which can incorporate work tasks and information search tasks into the same classification scheme and characterize tasks in such a way as to help people make predictions of information behavior. For this purpose, previous task classification schemes and their underlying facets are reviewed and discussed. Analysis identifies essential facets and categorizes them into Generic facets of task and Common attributes of task. Generic facets of task include Source of task, Task doer, Time, Action, Product, and Goal. Common attributes of task includes Task characteristics and User’s perception of task. Corresponding sub-facets and values are identified as well. In this fashion, a faceted classification of task is established which could be used to describe users’ work tasks and information search tasks. This faceted classification provides a framework to further explore the relationships among work tasks, search tasks, and interactive information retrieval and advance adaptive IR systems design. Published by Elsevier Ltd.

Answering questions correctly from standardized eighth-grade science tests is itself a test of machine intelligence.

We present the components of the ColumbiaGWU contribution to the 2016 TAC KBP BeSt Evaluation.

In this paper we introduce a novel computational technique of extraction of personality traits (HEXACO) of employees from Enterprise Social Media posts. We deal with challenges such as not being able to use existing survey instruments for scoring and not being able to directly use existing psychological studies on written text due to lack of overlapping words between the existing dictionary and words used in Enterprise Social Media. Using our approach we are able to infer personality traits (HEXACO) from posts and find better coverage and usage of the extended dictionary.

Personal Attributes Extraction in Chinese Text Task is designed to extract person specific attributes, for example, spouse/husband, children, education, title etc. from unstructured Chinese texts. According to the characteristics of the personal attributes extraction, we propose a new method based on deep learning to extract attributes from the documents in this paper. Experiments show that this method can improve the accuracy of personal attributes extraction effectively.

As increasingly powerful techniques emerge for machine tagging multimedia content, it becomes ever more important to standardize the underlying vocabularies. Doing so provides interoperability and lets the multimedia community focus ongoing research on a well-defined set of semantics. This paper describes a collaborative effort of multimedia researchers, library scientists, and end users to develop a large standardized taxonomy for describing broadcast news video. The large-scale concept ontology for multimedia (LSCOM) is the first of its kind designed to simultaneously optimize utility to facilitate end-user access, cover a large semantic space, make automated extraction feasible, and increase observability in diverse broadcast news video data sets

Early phases of information systems engineering include the understanding of the enterprise’s context and the construction of models at different levels of decomposition, required to design the system architecture. These time-consuming activities are usually conducted by relatively large teams, composed of groups of non-technical stakeholders playing mostly an informative role (i.e. not involved in documentation and even less in modelling), led by few experienced technical consultants performing most of the documenting and modelling effort. This paper evaluates the ability of non-technical stakeholders to create strategic dependency diagrams written with the i* language in the design of the context model of a system architecture, and find out which difficulties they may encounter and what the quality of the models they build is. A case study involving non-technical stakeholders from 11 organizational areas in an Ecuadorian university held under the supervision and coordination of the two authors acting as consultants. The non-technical stakeholders identified the majority of the dependencies that should appear in the case study’s context model, although they experienced some difficulties in declaring the type of dependency, representing such dependencies graphically and applying the description guidelines provided in the training. Managers were observed to make more mistakes than other more operational roles. From the observations of these results, a set of methodological advices were compiled for their use in future, similar endeavours. It is concluded that non-technical stakeholders can take an active role in the construction of the context model. This conclusion is relevant for both researchers and practitioners involved in technology transfer actions with use of i*.


Question: SKILL: A framework for the identification and normalization of skills.

This research introduces an extensive framework for recognizing and resolving the semantic ambiguity of named entities, utilizing data sourced from a vast encyclopedic repository and web search outcomes. It delves into the specifics of the disambiguation strategy adopted and the intricate extraction of information from Wikipedia. The system achieves notable precision in disambiguation by optimizing the alignment between contextual data derived from Wikipedia and the textual context of the document under analysis, alongside harmonizing the category labels associated with potential entities. This approach is validated through high-performance benchmarks on both news articles and Wikipedia entries, demonstrating its efficacy in large-scale named entity disambiguation.

The word “ontology” is used with different senses in different communities. The most radical difference is perhaps between the philosophical sense, which has of course a well-established tradition, and the computational sense, which emerged in the recent years in the knowledge engineering community, starting from an early informal definition of (computational) ontologies as “explicit specifications of conceptualizations”. In this paper we shall revisit the previous attempts to clarify and formalize such original definition, providing a detailed account of the notions of conceptualization and explicit specification, while discussing at the same time the importance of shared explicit specifications.

Ontology have become a relevant representation formalism and many application domains are  considering adopting them. This attention claims for methods for reusing domain knowledge resources in the development of domain ontologies. Accordingly, in this paper we discuss a general methodology to create domain ontology for more than one object oriented language (OOP) like Java, PHP and C++. A lot of software development methods specially Web applications have presented most of these methods that are focusing on the structure of distributed systems and security, in which they are connected through networks and the internet; resulting in more valuable business and critical assets stored, searched and manipulated by World Wide Web. The aims of this study building domain ontology for OOP language classes for different OOP languages or different versions of the same language is an exciting opportunity for researchers to access the information required under the constant increase in the volume of information disseminated on the Internet. By creating Ontology domain for OOP, we can Improve methods of viewing and 1 E-mail: iabuhassan@newsoft.ps 2 E-mail: akram.othman@gmail.com Article Info: Received : October 14, 2012. Revised : November 19, 2012 Published online : December 30, 2012 76 Domain Ontology for Programming Languages organizing information, improve the way of processing, in addition to increasing the vocabulary and their relationship to terminology as well as the rules used in natural language with OOP languages. The clear identification of the properties and relations of terms is the starting point to become Ontology domain. The importance of the domain Ontology among object oriented programming languages is that through the synthesis of these relationships or Ontology an OOP can be achieved through web by any junior programmers.

In this paper we introduce the TTCS system, so named after Terms To Conceptual Spaces, that exploits a resource-driven approach relying on BabelNet, NASARI and ConceptNet. TTCS takes in input a term and its context of usage and produces as output a specific type of vector-based semantic representation, where conceptual information is encoded through the Conceptual Spaces (a geometric framework for common-sense knowledge representation and reasoning). The system has been evaluated in a twofold experimentation. In the first case we assessed the quality of the extracted common-sense conceptual information with respect to human judgments with an online questionnaire. In the second one we compared the performances of a conceptual categorization system that was run twice, once fed with extracted annotations and once with hand-crafted annotations. In both cases the results are encouraging and provide precious insights to make substantial improvements.

This paper discusses long-term prospects of AI-ED research with the aim of giving a clear view of what we need for further promotion of the research from both the AI and ED points of view. An analysis of the current status of AI-ED research is done in the light of intelligence, conceptualization, standardization and theory-awareness. Following this, an ontology-based architecture with appropriate ontologies is proposed. Ontological engineering of IS/ID is next discussed followed by a road map towards an ontology-aware authoring system. Heuristic design patterns and XML-based documentation are also discussed.

We report on a four year academic research project to build a natural language processing platform in support of a large media company. The Computable News platform processes news stories, producing a layer of structured data that can be used to build rich applications. We describe the underlying platform and the research tasks that we explored building it. The platform supports a wide range of prototype applications designed to support different newsroom functions. We hope that this qualitative review provides some insight into the challenges involved in this type of project.

We introduce a problem class which we term activity monitoring. Such problems involve monitoring the behavior of a large population of entities for interesting events requiring action. We present a framework within which each of the individual problems has a natural expression, as well as a methodology for evaluating performance of activity monitoring techniques. We show that two superficially different tasks, news story monitoring and intrusion detection, can be expressed naturally within the framework, and show that key differences in solution methods can be compared.

This paper proposes a new dataset, Frames, composed of 1369 human-human dialogues with an average of 15 turns per dialogue. This corpus contains goal-oriented dialogues between users who are given some constraints to book a trip and assistants who search a database to find appropriate trips. The users exhibit complex decision-making behaviour which involve comparing trips, exploring different options, and selecting among the trips that were discussed during the dialogue. To drive research on dialogue systems towards handling such behaviour, we have annotated and released the dataset and we propose in this paper a task called frame tracking. This task consists of keeping track of different semantic frames throughout each dialogue. We propose a rule-based baseline and analyse the frame tracking task through this baseline.

The gathering of stakeholder requirements comprises an early, but continuous and highly critical stage in system development. This phase in development is subject to a large degree of error, influenced by key factors rooted in communication problems. This pilot study builds upon an existing theory-based categorisation of these problems through presentation of a four-dimensional framework on communication. Its structure is validated through a content analysis of interview data, from which themes emerge, that can be assigned to the dimensional categories, highlighting any problematic areas. The paper concludes with a discussion on the utilisation of the framework for requirements elicitation exercises.

In a life-long learning society, learning scenarios can be categorized into five types, which are “classroom learning”, “self-learning”, “inquiry learning”, “learning in doing” and “learning in working”. From a life-wide learning perspective, all these scenarios play vital roles for personal development. How to recognize these learning scenarios (including learning time, learning place, learning peers, learning activities, etc.) and provide the matched learning ways (including learning path, resources, peers, teachers, etc.) are the basis for smart learning environments, however few research could be found to address this problem. In order to solve this problem, we propose a conceptual framework of smart learning engine that is the core of integrated, interactive and intelligent (i) learning environments. The smart learning engine consists of three main functions. The first function is to identify data from student, teacher, subject area, and the environment using wireless sensors, the established learning resources and scenarios, and a learner modeling technology. The acquired data includes prior knowledge, theme-based context, leaner/teacher profile, physical environments, etc. The second function is to compute the best ways of learning based on the learning scenario and learning preference. In detail, this function includes modeling learner’s affective data, building knowledge structure, optimizing knowledge module, and connecting learners. The third function is to deploy personalized and adaptive strategy, resources and tools for students and teachers based on the computed results in the second function. Deploy interactive strategies, learning paces, learning resources, and delivery approaches are the core elements for this function.

For a machine to be able to ‘see’, it must know something about the object it is ‘looking’ at, A common method in machine vision is to provide the machine with general rather than specific knowledge about the object. An alternative technique, and the one used in thti paper, b a model-based approach in which particulars aboat the object are given and this drives the analysis. The computer program described here, the WALKER model, maps images into a description in which a person is represented by the series of hierarchical levels, i.e. a person has an arm which has a lower-arm which has a hand. l7te performance of the program ti illustrated by superimposing the machinegenerated picture over the original photographic images.


Question: SKILL: A framework for identifying and normalizing skills.

In a recent study, an exhaustive evaluation was conducted to compare context-predicting models, also known as embeddings or neural language models, against traditional count-vector-based distributional semantic methods. These context-predicting models have recently gained significant attention in the field of distributional semantics, yet there has been a notable absence of comprehensive analyses that directly pit these models against the established count-based approaches. The research fills this gap by systematically assessing the performance of context-predicting models across various lexical semantics tasks and under different parameter settings. Surprisingly, the findings unequivocally demonstrate that the hype surrounding context-predicting models is well-founded, as these models outperform their count-based rivals across the board, achieving clear and decisive victories in all tested scenarios.

Ontology have become a relevant representation formalism and many application domains are  considering adopting them. This attention claims for methods for reusing domain knowledge resources in the development of domain ontologies. Accordingly, in this paper we discuss a general methodology to create domain ontology for more than one object oriented language (OOP) like Java, PHP and C++. A lot of software development methods specially Web applications have presented most of these methods that are focusing on the structure of distributed systems and security, in which they are connected through networks and the internet; resulting in more valuable business and critical assets stored, searched and manipulated by World Wide Web. The aims of this study building domain ontology for OOP language classes for different OOP languages or different versions of the same language is an exciting opportunity for researchers to access the information required under the constant increase in the volume of information disseminated on the Internet. By creating Ontology domain for OOP, we can Improve methods of viewing and 1 E-mail: iabuhassan@newsoft.ps 2 E-mail: akram.othman@gmail.com Article Info: Received : October 14, 2012. Revised : November 19, 2012 Published online : December 30, 2012 76 Domain Ontology for Programming Languages organizing information, improve the way of processing, in addition to increasing the vocabulary and their relationship to terminology as well as the rules used in natural language with OOP languages. The clear identification of the properties and relations of terms is the starting point to become Ontology domain. The importance of the domain Ontology among object oriented programming languages is that through the synthesis of these relationships or Ontology an OOP can be achieved through web by any junior programmers.

We present the components of the ColumbiaGWU contribution to the 2016 TAC KBP BeSt Evaluation.

MC& CYC project is the building, over the coming decade, of a large knowledge base (or KB) of real world facts and heuristics and-as a part of the KB itself-methods for efficiently reasoning over the KB. As the title of this article suggests, our hypothesis is that the two major limitations to building large intelligent programs might be overcome by using such a system. We briefly illustrate how common sense reasoning and analogy can widen the knowledge acquisition bottleneck The next section (“How CYC Works”) illustrates how those same two abilities can solve problems of the type that stymie current expert systems. We then report how the project is being conducted currently: its strategic philosophy, its tactical methodology, and a case study of how we are currently putting that into practice. We conclude with a discussion of the project’s feasibility and timetable.

Deep reinforcement learning has demonstrated increasing capabilities for continuous control problems, including agents that can move with skill and agility through their environment. An open problem in this setting is that of developing good strategies for integrating or merging policies for multiple skills, where each individual skill is a specialist in a specific skill and its associated state distribution. We extend policy distillation methods to the continuous action setting and leverage this technique to combine expert policies, as evaluated in the domain of simulated bipedal locomotion across different classes of terrain. We also introduce an input injection method for augmenting an existing policy network to exploit new input features. Lastly, our method uses transfer learning to assist in the efficient acquisition of new skills. The combination of these methods allows a policy to be incrementally augmented with new skills. We compare our progressive learning and integration via distillation (PLAID) method against three alternative baselines.

Autonomous exploration of motor skills is a key capability of learning robotic systems. Learning motor skills can be formulated as inverse modeling problem, which targets at finding an inverse model that maps desired outcomes in some task space, e.g., via points of a motion, to appropriate actions, e.g., motion control policy parameters. In this paper, autonomous exploration of motor skills is achieved by incrementally learning inverse models starting from an initial demonstration. The algorithm is referred to as skill babbling, features sample-efficient learning, and scales to high-dimensional action spaces. Skill babbling extends ideas of goal-directed exploration, which organizes exploration in the space of goals. The proposed approach provides a modular framework for autonomous skill exploration by separating the learning of the inverse model from the exploration mechanism and a model of achievable targets, i.e. the workspace. The effectiveness of skill babbling is demonstrated for a range of motor tasks comprising the autonomous bootstrapping of inverse kinematics and parameterized motion primitives.

This paper presents the results of the wearable computing prototype supporting trainingand qualification activities at the SKODA production facilities in Czech Republic. The emerged prototype is based upon the first of the 2 main “Variant Production Showcases” (training and assembly-line) which are to be implemented in the WearIT@work project (EC IP 004216). As an introduction, the authors of this paper investigate current training processes at Skoda, and derive the potential benefits and risks of applying wearable computing technology. Accordingly, the approach of creating the wearable prototypes, via usability experiments at the Skoda production site, is explained in detail. As a preliminary result, the first functional prototypes, including a task recognition prototype, based upon the components of the European Wearable Computing Platform, are described. The paper is rounded up by providing a short outlook regarding the second envisaged test case, which is focussed upon selected assembly line operations of blue collar workers.

extraordinarily difficult and poses challenges for both integration and grounding. Here, I propose a unifying idea that meets both challenges: language games. A language game is a sequence of verbal interactions between two agents situated in a specific environment. Language games both integrate the various activities required for dialogue and ground unknown words or phrases in a specific context, which helps constrain possible meanings. Over the past five years, I have been working with a team to develop and test language games on progressively more sophisticated systems, from relatively simple camera-based systems to humanoid robots. The results of our work show that language games are a useful way to both understand and design human–robot interaction.

In this article we summarize the strengths and weaknesses of empirical research in software engineering. We argue that in order to improve the current situation we must create better studies and draw more credible interpretations from them. We finally present a roadmap for this improvement, which includes a general structure for software empirical studies and concrete steps for achieving these goals: designing better studies, collecting data more effectively, and involving others in our empirical enterprises.

Given that in practice training data is scarce for all but a small set of problems, a core question is how to incorporate prior knowledge into a model. In this paper, we consider the case of prior procedural knowledge for neural networks, such as knowing how a program should traverse a sequence, but not what local actions should be performed at each step. To this end, we present an end-to-end differentiable interpreter for the programming language Forth which enables programmers to write program sketches with slots that can be filled with behaviour trained from program input-output data. We can optimise this behaviour directly through gradient descent techniques on user-specified objectives, and also integrate the program into any larger neural computation graph. We show empirically that our interpreter is able to effectively leverage different levels of prior program structure and learn complex behaviours such as sequence sorting and addition. When connected to outputs of an LSTM and trained jointly, our interpreter achieves state-of-the-art accuracy for end-to-end reasoning about quantities expressed in natural language stories.

A key concern for any e-assessment tool (computer assisted assessment) is its efficiency in assessing the learner’s knowledge, skill set and ability. Multiple-choice questions are the most common means of assessment used in e-assessment systems, and are also successful. An efficient e-assessment system should use variety of question types including shortanswers, essays etc. and modes of response to assess learner’s performance. In this paper, we consider the task of assessing short-answer questions. Several researches have been performed on the evaluation and assessment of short-answer questions and many products are deployed to assess the same as part of e-learning systems. We propose an automated system for assessing short-answers using ontology mapping. We also compare our approach with some existing systems and give an overall evaluation of experiment results, which shows that our approach using ontology mapping gives an optimized result.


Question: SKILL: A system designed for skill identification and normalization.

This paper introduces an innovative technique aimed at automating the creation and expansion of dictionaries and phrase tables, which are fundamental components of contemporary statistical machine translation systems. Our approach leverages large monolingual datasets to learn the structural nuances of languages and employs a small bilingual dataset to establish mappings between them. This methodology translates absent word and phrase entries by utilizing the distributed representations of words and identifying a linear transformation between the vector spaces of different languages. Despite its straightforwardness, our technique proves remarkably efficient, achieving a precision@5 of nearly 90% for word translations between English and Spanish. Given its minimal assumptions about the languages involved, this method holds potential for enhancing and refining dictionaries and translation tables across any language pair.

Research from numerous corners of psychological inquiry suggests that self-assessments of skill and character are often flawed in substantive and systematic ways. We review empirical findings on the imperfect nature of self-assessment and discuss implications for three real-world domains: health, education, and the workplace. In general, people's self-views hold only a tenuous to modest relationship with their actual behavior and performance. The correlation between self-ratings of skill and actual performance in many domains is moderate to meager-indeed, at times, other people's predictions of a person's outcomes prove more accurate than that person's self-predictions. In addition, people overrate themselves. On average, people say that they are "above average" in skill (a conclusion that defies statistical possibility), overestimate the likelihood that they will engage in desirable behaviors and achieve favorable outcomes, furnish overly optimistic estimates of when they will complete future projects, and reach judgments with too much confidence. Several psychological processes conspire to produce flawed self-assessments. Research focusing on health echoes these findings. People are unrealistically optimistic about their own health risks compared with those of other people. They also overestimate how distinctive their opinions and preferences (e.g., discomfort with alcohol) are among their peers-a misperception that can have a deleterious impact on their health. Unable to anticipate how they would respond to emotion-laden situations, they mispredict the preferences of patients when asked to step in and make treatment decisions for them. Guided by mistaken but seemingly plausible theories of health and disease, people misdiagnose themselves-a phenomenon that can have severe consequences for their health and longevity. Similarly, research in education finds that students' assessments of their performance tend to agree only moderately with those of their teachers and mentors. Students seem largely unable to assess how well or poorly they have comprehended material they have just read. They also tend to be overconfident in newly learned skills, at times because the common educational practice of massed training appears to promote rapid acquisition of skill-as well as self-confidence-but not necessarily the retention of skill. Several interventions, however, can be introduced to prompt students to evaluate their skill and learning more accurately. In the workplace, flawed self-assessments arise all the way up the corporate ladder. Employees tend to overestimate their skill, making it difficult to give meaningful feedback. CEOs also display overconfidence in their judgments, particularly when stepping into new markets or novel projects-for example, proposing acquisitions that hurt, rather then help, the price of their company's stock. We discuss several interventions aimed at circumventing the consequences of such flawed assessments; these include training people to routinely make cognitive repairs correcting for biased self-assessments and requiring people to justify their decisions in front of their peers. The act of self-assessment is an intrinsically difficult task, and we enumerate several obstacles that prevent people from reaching truthful self-impressions. We also propose that researchers and practitioners should recognize self-assessment as a coherent and unified area of study spanning many subdisciplines of psychology and beyond. Finally, we suggest that policymakers and other people who makes real-world assessments should be wary of self-assessments of skill, expertise, and knowledge, and should consider ways of repairing self-assessments that may be flawed.

An enhanced technique for risk categorization is presented. This technique, PCA-ANN, provides an improved capability to discriminate high-risk software. The approach draws on the combined strengths of pattern recognition, multivariate statistics and neural networks. Principal component analysis is utilized to provide a means of normalizing and orthogonalizing the input data, thus eliminating the ill effects of multicollinearity. A neural network is used for risk determination/classification. A significant feature of this approach is a procedure, herein termed cross-normalization. This procedure provides the technique with capability to discriminate data sets that include disproportionately large numbers of high-risk software modules.

Decoding the important differences in terminology, underlying philosophy, and value systems between two similar categories of software.

Within the Human-Computer Interaction community there is a growing interest in designing for the whole body in interaction design. The attempts aimed at addressing the body have very different outcomes spanning from theoretical arguments for understanding the body in the design process, to more practical examples of designing for bodily potential. This paper presents Kinesthetic Interaction as a unifying concept for describing the body in motion as a foundation for designing interactive systems. Based on the theoretical foundation for Kinesthetic Interaction, a conceptual framework is introduced to reveal bodily potential in relation to three design themes --- kinesthetic development, kinesthetic means and kinesthetic disorder; and seven design parameters --- engagement, sociality, movability, explicit motivation, implicit motivation, expressive meaning and kinesthetic empathy. The framework is a tool to be utilized when analyzing existing designs, as well as developing designs exploring new ways of designing kinesthetic interactions.

Sketch recognition aims to automatically classify human hand sketches of objects into known categories. This has become increasingly a desirable capability due to recent advances in human computer interaction on portable devices. The problem is nontrivial because of the sparse and abstract nature of hand drawings as compared to photographic images of objects, compounded by a highly variable degree of details in human sketches. To this end, we present a method for the representation and matching of sketches by exploiting not only local features but also global structures of sketches, through a star graph based ensemble matching strategy. Different local feature representations were evaluated using the star graph model to demonstrate the effectiveness of the ensemble matching of structured features. We further show that by encapsulating holistic structure matching and learned bag-of-features models into a single framework, notable recognition performance improvement over the state-of-the-art can be observed. Extensive comparative experiments were carried out using the currently largest sketch dataset released by Eitz et al. [15], with over 20,000 sketches of 250 object categories generated by AMT (Amazon Mechanical Turk) crowd-sourcing.

This paper presents strategies and lessons learned from the use of natural language annotations to facilitate question answering in the START information access system.

We present an athlete identification module forming part of a system for the personalization of sport video broadcasts. The aim of this module is the localization of athletes in the scene, their identification through the reading of names or numbers printed on their uniforms, and the labelling of frames where athletes are visible. Building upon a previously published algorithm we extract text from individual frames and read these candidates by means of an optical character recognizer (OCR). The OCR-ed text is then compared to a known list of athletes’ names (or numbers), to provide a presence score for each athlete. Text regions are tracked in subsequent frames using a template matching technique. In this way blurred or distorted text, normally unreadable by the OCR, is exploited to provide a denser labelling of the video sequences. Extensive experiments show that the method proposed is fast, robust and reliable, out-performing results of other systems in the literature.

A typical goal for transfer learning algorithms is to utilize knowledge gained in a source task to learn a target task faster. Recently introduced transfer methods in reinforcement learning settings have shown considerable promise, but they typically transfer between pairs of very similar tasks. This work introduces Rule Transfer, a transfer algorithm that first learns rules to summarize a source task policy and then leverages those rules to learn faster in a target task. This paper demonstrates that Rule Transfer can effectively speed up learning in Keepaway, a benchmark RL problem in the robot soccer domain, based on experience from source tasks in the gridworld domain. We empirically show, through the use of three distinct transfer metrics, that Rule Transfer is effective across these domains.

similarity. Units not yet reached asymptote: 0 Goodness of network: 0.61 Calculating the best mappings after 72 cycles. Best mapping of SMART is HUNGRY. 0.70 Best mapping of TALL is FRIENDLY. 0.71 Best mapping of TIMID is FRISKY. 0.54 Best mopping of TOM is BLACKIE. 0.70 Best mapping of STEVE is FIDO. 0.54 Best mopping of BILL is ROVER. 0.71 by ACME for this example. The five sentences corresponding to the five propositions in each analog (e.g., “Bill is smart”) were listed in adjacent colunms on a piece of paper. Sentences related to the same individual were listed consecutively; otherwise, the order was scrambled. Across subjects two different orders were used. The instructions simply stated, “Your task is to figure out what in the left set of sentences corresponds to what in the right set of sentences.” Subjects were also told that the meaning of the words was irrelevant. The three individuals and three attributes of the analog on the left were listed on the bottom of the page; for each element, subjects were to write down what they believed to be the corresponding element of the analog on the right. Three minutes were allowed for completion of the task. A group of 8 UCLA students in an undergraduate psychology class served as subjects. Five subjects produced the same set of six correspondences identified by ACME, 2 subjects produced four of the six, and 1 subject was unable to understand the task. These results indicate that finding the isomorphism for this example is within the capability of many college students. 344 HOLYOAK AND THAGARD Structure and Pragmatics in Metaphor To explore the performance of ACME in metaphorical mapping, the program was given predicate-calculus representations of the knowledge underlying a metaphor that has been analyzed in detail by Kittay (1987). The metaphor is derived from a passage in Plato’s Theuetetus in which Socrates declares himself to be a “midwife of ideas,” elaborating the metaphor at length. Table 19 contains predicate-calculus representations based upon Kittay’s analysis of the source analog r.?ncerning the role of a midwife and of the target analog concerning the role of a philosopher-teacher. Roughly, Socrates claims that he is like a midwife in that he introduces the student to intellectual partners, just as a midwife often serves first as a matchmaker; Socrates helps the student evaluate the truth or falsity of his ideas much as a midwife helps a mother to deliver a child. This metaphor was used to provide an illustration of the manner in which structural and pragmatic constraints interact in ACME. Table 19 presents predicate-calculus representations of two versions of the metaphor: an isomorphic version based directly upon Kittay’s analysis, and a nonisomorphic version created by adding irrelevant and misleading information to the representation of the “Socrates” target analog. The best mappings obtained for each object and predicate in the target, produced by three runs of ACME, are reported in Table 20. The asymptotic activations of the best mappings are also presented. A mapping of “none” means that no mapping unit had an asymptotic activation greater than .20. The run reported in the first column used the isomorphic version without any pragmatic weights. The network settles with a correct set of mappings after 34 cycles. Thus Socrates maps to the midwife, his student o the mother, the student’s intellectual partner to the father, and the idea to the child. (Note that there is a homomorphic mapping of the predicates thinks-about and tests-truth to in-labor-with.) The propositions expressing causal relations in the two analogs are not essential here; deletion of them still allows a complete mapping to be discovered. A very different set of mappings is reported in the middle column of Table 20 for the nonisomorphic version of the “Socrates” analog. This version provides additional knowledge about Socrates that would be expected to produce major interference with discovery of the metaphoric relation between the two analogs. The nonisomorphic version contains the information that Socrates drinks hemlock juice, which is of course irrelevant to the metaphor. Far worse, the representation encodes the information that Socrates him’self was matched to his wife by a midwife; and that Socrates’ wife had a child with the help of this midwife. Clearly, this nonisomorphic extension will cause the structural and semantic constraints on mapping to support a much more superficial set of correspondences between the two situations. And indeed, in this second run ACME finds only the barest fragments of the intended metaphoric mappings when the network settles ANALOGICAL MAPPING 345 TABLE 19 Predicate-Calculus Representatlans of Knowledge Underlying the Metaphor “Socrates is a Mldwlfe of Ideas”‘(lsamarphlc and Nanlsomorphlc Verslons)

Since 2009, DevOps, the combination of development and operation, has been adopted within organizations in industry, such as Netflix, Flickr, and Fotopedia. Configuration management tools have been used to support DevOps. However, in this paper we investigate which Knowledge, Skills, and Abilities (KSA) have been employed in developing and deploying modern web applications and how these KSAs support DevOps. By applying a qualitative analysis approach, namely grounded theory, to three web application development projects, we discover that the KSAs for both Software Development and IT Operator practitioners support the four perspectives of DevOps: collaboration culture, automation, measurement, and sharing.


Question: SKILL: A framework for skill identification and standardization.

This paper delves into the core computational challenge associated with deriving and leveraging the essence or 'gist' of sentences, conversations, or documents for language processing. It posits that understanding the general idea of a text can significantly aid in anticipating relevant concepts and resolving word ambiguities. By framing this essence extraction as a rational statistical inference problem, the paper proposes a unique perspective on semantic representation. This representation is grounded in a probabilistic topic model, where word meanings are characterized through a set of topics, each with an associated probability. The model's efficacy is demonstrated through its ability to predict word associations accurately and its effectiveness in addressing the impact of semantic association and ambiguity on various language-processing and memory tasks. Moreover, this probabilistic topic model lays the groundwork for developing more sophisticated statistical models of language, as its generative process can be readily expanded to encompass additional semantic and syntactic structures.

**Question**: A Comparative Analysis of Clustering Algorithms for Big Data

**Documents**: An empirical study of big data clustering algorithms
Clustering is a key technique in data mining, pattern recognition, and machine learning, often used for data segmentation and summarization. In this paper, we present an empirical study of several clustering algorithms on big data. The algorithms include K-means, DBSCAN, BIRCH, and CURE. We evaluate their performance in terms of clustering quality, scalability, and execution time. The experimental results show that K-means is the fastest algorithm but has the poorest clustering quality, while DBSCAN has the best clustering quality but is the slowest. BIRCH and CURE are in between, with good clustering quality and reasonable execution time. We also find that the choice of the number of clusters has a significant impact on the performance of K-means and CURE, while the choice of the neighborhood radius has a significant impact on the performance of DBSCAN and BIRCH.

**Similar Documents**: This study offers an in-depth empirical analysis of prominent clustering algorithms when applied to big data scenarios. The focus is on evaluating K-means, DBSCAN, BIRCH, and CURE in terms of their performance, specifically looking at clustering accuracy, scalability, and processing time. Results indicate that while K-means boasts the quickest execution, it falls short in clustering precision. Conversely, DBSCAN exhibits superior clustering quality but is the most time-consuming. BIRCH and CURE, meanwhile, strike a balance, offering a commendable level of clustering accuracy with reasonably efficient execution times. A noteworthy finding is that the performance of K-means and

OBJECTIVE
To evaluate the performance of a natural language processing system in extracting pneumonia-related concepts from chest x-ray reports.

METHODS

DESIGN
Four physicians, three lay persons, a natural language processing system, and two keyword searches (designated AAKS and KS) detected the presence or absence of three pneumonia-related concepts and inferred the presence or absence of acute bacterial pneumonia from 292 chest x-ray reports. Gold standard: Majority vote of three independent physicians. Reliability of the gold standard was measured.

OUTCOME MEASURES
Recall, precision, specificity, and agreement (using Finn's R: statistic) with respect to the gold standard. Differences between the physicians and the other subjects were tested using the McNemar test for each pneumonia concept and for the disease inference of acute bacterial pneumonia.

RESULTS
Reliability of the reference standard ranged from 0.86 to 0.96. Recall, precision, specificity, and agreement (Finn R:) for the inference on acute bacterial pneumonia were, respectively, 0.94, 0.87, 0.91, and 0.84 for physicians; 0.95, 0.78, 0.85, and 0.75 for natural language processing system; 0.46, 0.89, 0.95, and 0.54 for lay persons; 0.79, 0.63, 0.71, and 0.49 for AAKS; and 0.87, 0.70, 0.77, and 0.62 for KS. The McNemar pairwise comparisons showed differences between one physician and the natural language processing system for the infiltrate concept and between another physician and the natural language processing system for the inference on acute bacterial pneumonia. The comparisons also showed that most physicians were significantly different from the other subjects in all pneumonia concepts and the disease inference.

CONCLUSION
In extracting pneumonia related concepts from chest x-ray reports, the performance of the natural language processing system was similar to that of physicians and better than that of lay persons and keyword searches. The encoded pneumonia information has the potential to support several pneumonia-related applications used in our institution. The applications include a decision support system called the antibiotic assistant, a computerized clinical protocol for pneumonia, and a quality assurance application in the radiology department.

PURPOSE OF THE STUDY
Computers and the Internet have the potential to enrich the lives of seniors and aid in the performance of important tasks required for independent living. A prerequisite for reaping these benefits is having the skills needed to use these systems, which is highly dependent on proper training. One prerequisite for efficient and effective training is being able to gauge current levels of proficiency. We developed a new measure (the Computer Proficiency Questionnaire, or CPQ) to measure computer proficiency in the domains of computer basics, printing, communication, Internet, calendaring software, and multimedia use. Our aim was to develop a measure appropriate for individuals with a wide range of proficiencies from noncomputer users to extremely skilled users.

DESIGN AND METHODS
To assess the reliability and validity of the CPQ, a diverse sample of older adults, including 276 older adults with no or minimal computer experience, was recruited and asked to complete the CPQ.

RESULTS
The CPQ demonstrated excellent reliability (Cronbach's α = .98), with subscale reliabilities ranging from .86 to .97. Age, computer use, and general technology use all predicted CPQ scores. Factor analysis revealed three main factors of proficiency related to Internet and e-mail use; communication and calendaring; and computer basics. Based on our findings, we also developed a short-form CPQ (CPQ-12) with similar properties but 21 fewer questions.

IMPLICATIONS
The CPQ and CPQ-12 are useful tools to gauge computer proficiency for training and research purposes, even among low computer proficient older adults.

The paper presents some novel concepts and models derived from Activity Theory for to identify a potential CSCW application. It is suggested that the six elements of the structure of the activity concept might be useful for differentiating between areas of support, and that three levels of support are needed in order to cope with both routine and emergent features of cooperative work situations. Thus a 3x6 support type classification is formed and its usefulness studied by means of a real-world example. A work situation is analyzed, problems identified and possible areas of support defined. A temporary solution is produced and, by evaluating it, possible directions for the development of a “real” new CSCW application and the usefulness of the classification am discussed.

In this position paper, we argue that building operational automated scoring systems is a task that has disciplinary complexity above and beyond standard competitive shared tasks which usually involve applying the latest machine learning techniques to publicly available data in order to obtain the best accuracy. Automated scoring systems warrant significant cross-discipline collaboration of which natural language processing and machine learning are just two ofmany important components. Such systems have multiple stakeholders with different but valid perspectives that can often times be at odds with each other. Our position is that it is essential for us as NLP researchers to understand and incorporate these perspectives into our research and work towards a mutually satisfactory solution in order to build automated scoring systems that are accurate, fair, unbiased, and useful. 1 What is Automated Scoring? Automated scoring is an NLP application usually deployed in the educational domain. It involves automatically analyzing a student’s response to a question and generating either (a) a score in order to assess the student’s knowledge and/or other skills and/or (b) some actionable feedback on how the student can improve the response (Page, 1966; Burstein et al., 1998; Burstein et al., 2004; Zechner et al., 2009; Bernstein et al., 2010). It is considered an NLP application since typically the core technology behind the automated analysis of the student response enlists NLP techniques. The student responses can include essays, short answers, or spoken responses and the two most common kinds of automated scoring are the automated evaluation of writing quality and content knowledge. Both the scores and feedback are usually based on linguistic characteristics of the responses including but not limited to: (i) Lower-level errors in the response (e.g., pronunciation errors in spoken responses and grammatical/spelling errors in written responses), (ii) The discourse structure and/or organization of the response, (iii) Relevance of the response to the question that was asked.

Ontology have become a relevant representation formalism and many application domains are  considering adopting them. This attention claims for methods for reusing domain knowledge resources in the development of domain ontologies. Accordingly, in this paper we discuss a general methodology to create domain ontology for more than one object oriented language (OOP) like Java, PHP and C++. A lot of software development methods specially Web applications have presented most of these methods that are focusing on the structure of distributed systems and security, in which they are connected through networks and the internet; resulting in more valuable business and critical assets stored, searched and manipulated by World Wide Web. The aims of this study building domain ontology for OOP language classes for different OOP languages or different versions of the same language is an exciting opportunity for researchers to access the information required under the constant increase in the volume of information disseminated on the Internet. By creating Ontology domain for OOP, we can Improve methods of viewing and 1 E-mail: iabuhassan@newsoft.ps 2 E-mail: akram.othman@gmail.com Article Info: Received : October 14, 2012. Revised : November 19, 2012 Published online : December 30, 2012 76 Domain Ontology for Programming Languages organizing information, improve the way of processing, in addition to increasing the vocabulary and their relationship to terminology as well as the rules used in natural language with OOP languages. The clear identification of the properties and relations of terms is the starting point to become Ontology domain. The importance of the domain Ontology among object oriented programming languages is that through the synthesis of these relationships or Ontology an OOP can be achieved through web by any junior programmers.

Speaker: Mrinmaya Sachan Thesis Committee: Eric P. Xing, Chair Jaime Carbonell Tom Mitchell Dan Roth (University of Pennsylvania) Thesis Proposal Jan. 22, 2018 1:00pm 8102 GHC Link to draft document: http://www.cs.cmu.edu/~mrinmays/thesis_proposal.pdf Standardized tests are often used to test students as they progress in the formal education system. These tests are widely available and measurable with clear evaluation procedures and metrics. Hence, these can serve as good tests for AI. We propose approaches for solving some of these tests. We broadly categorize these tests into two categories: open domain question answering tests such as reading comprehensions and elementary school science tests, and closed domain question answering tests such as intermediate or advanced math and science tests. We present alignment based approach with multi-task learning for the former. For closed domain tests, we propose a parsing to programs approach which can be seen as a natural language interface to expert systems. We also describe approaches for question generation based on instructional material in both open domain as well as closed domain settings. Finally, we show that we can improve both the question answering and question generation models by learning them jointly. This mechanism also allows us to leverage cheap unlabelled data for learning the two models. Our work can potentially be applied for the social good in the education domain. We perform studies on human subjects who found our approaches useful as assistive tools in education.

This paper discusses long-term prospects of AI-ED research with the aim of giving a clear view of what we need for further promotion of the research from both the AI and ED points of view. An analysis of the current status of AI-ED research is done in the light of intelligence, conceptualization, standardization and theory-awareness. Following this, an ontology-based architecture with appropriate ontologies is proposed. Ontological engineering of IS/ID is next discussed followed by a road map towards an ontology-aware authoring system. Heuristic design patterns and XML-based documentation are also discussed.

Examining large-scale, long-term application use is critical to understanding how an application meets the needs of its user community. However, there have been few published analyses of long-term use of desktop applications, and none that have examined applications that support creating and modifying content using direct manipulation. In this paper, we present an analysis of 2 years of usage data from an instrumented version of the GNU Image Manipulation Program, including data from over 200 users. In the course of our analysis, we show that previous findings concerning the sparseness of command use and idiosyncrasy of users’ command vocabularies extend to a new domain and interaction style. These findings motivate continued research in adaptive and mixed-initiative interfaces. We also describe the novel application of a clustering technique to characterize a user community’s higher-level tasks from low-level logging data.

We trace in pragmatic terms some of what we know about knowledge, information technology, knowledge management practice and research, and provide two complementary frameworks that highlight potential opportunities for building a research agenda in this area. The papers in this special issue are then discussed.