nils-herrmann/clean_scirepeval huggingface dataset:
An Isolated Three-Port Bidirectional DC-DC Converter With Decoupled Power Flow Management

An isolated three-port bidirectional dc-dc converter composed of three full-bridge cells and a high-frequency transformer is proposed in this paper. Besides the phase shift control managing the power flow between the ports, utilization of the duty cycle control for optimizing the system behavior is discussed and the control laws ensuring the minimum overall system losses are studied. Furthermore, the dynamic analysis and associated control design are presented. A control-oriented converter model is developed and the Bode plots of the control-output transfer functions are given. A control strategy with the decoupled power flow management is implemented to obtain fast dynamic response. Finally, a 1.5 kW prototype has been built to verify all theoretical considerations. The proposed topology and control is particularly relevant to multiple voltage electrical systems in hybrid electric vehicles and renewable energy generation systems.
Performance appraisal of software testers

To determine the effectiveness of software testers a suitable performance appraisal approach is necessary, both for research and practice purposes. However, review of relevant literature reveals little information of how software testers are appraised in practice.  (i) To enhance our knowledge of industry practice of performance appraisal of software testers; and (ii)to collect feedback from project managers on a proposed performance appraisal form for software testers.  A web-based survey with questionnaire was used to collect responses. Participants were recruited using cluster and snowball sampling. 18 software development project managers participated.  We found two broad trends in performance appraisal of software testers same employee appraisal process for all employees and a specialized performance appraisal method for software testers. Detailed opinions were collected and analysed on how performance of software testers should be appraised. Our proposed appraisal approach was generally well-received.  Factors such as number of bugs found after delivery and efficiency of executing test cases were considered important in appraising software testers’ performance. Our proposed approach was refined based on the feedback received.
Impact of Employee Motivation on Customer Satisfaction : Study of Airline Industry in Pakistan

In success or failure of any firm/industry or organization employees plays the most vital and important role. Airline industry is one of service industry the job of which is to sell seats to their travelers/costumers and passengers; hence employees inspiration towards their work plays a vital part in serving client’s requirements. This research focused on the influence of employee’s enthusiasm and its apparatuses e.g. pay and benefits, working atmosphere, vision of organization towards customer satisfaction and management systems in Pakistani airline industry. For analysis correlation and regression methods were used. Results of the research highlighted that workers motivation and its four major components e.g. pay and benefits, working atmosphere, vision of organization and management systems have a significant positive impact on customer’s gratification. Those employees of the industry who directly interact with client highly impact the client satisfaction level. It is obvious from results of this research that pay and benefits performs a key role in employee’s motivation towards achieving their organizational objectives of greater customer satisfaction.
Multi-Person Pose Estimation with Local Joint-to-Person Associations

Despite of the recent success of neural networks for human pose estimation, current approaches are limited to pose estimation of a single person and cannot handle humans in groups or crowds. In this work, we propose a method that estimates the poses of multiple persons in an image in which a person can be occluded by another person or might be truncated. To this end, we consider multiperson pose estimation as a joint-to-person association problem. We construct a fully connected graph from a set of detected joint candidates in an image and resolve the joint-to-person association and outlier detection using integer linear programming. Since solving joint-to-person association jointly for all persons in an image is an NP-hard problem and even approximations are expensive, we solve the problem locally for each person. On the challenging MPII Human Pose Dataset for multiple persons, our approach achieves the accuracy of a state-of-the-art method, but it is 6,000 to 19,000 times faster.
Kernelized Locality-Sensitive Hashing

Fast retrieval methods are critical for many large-scale and data-driven vision applications. Recent work has explored ways to embed high-dimensional features or complex distance functions into a low-dimensional Hamming space where items can be efficiently searched. However, existing methods do not apply for high-dimensional kernelized data when the underlying feature embedding for the kernel is unknown. We show how to generalize locality-sensitive hashing to accommodate arbitrary kernel functions, making it possible to preserve the algorithm's sublinear time similarity search guarantees for a wide class of useful similarity functions. Since a number of successful image-based kernels have unknown or incomputable embeddings, this is especially valuable for image retrieval tasks. We validate our technique on several data sets, and show that it enables accurate and fast performance for several vision problems, including example-based object classification, local feature matching, and content-based retrieval.
Simplified Successive-Cancellation List decoding of polar codes

The Successive-Cancellation List (SCL) decoding algorithm is one of the most promising approaches towards practical polar code decoding. It is able to provide a good trade-off between error-correction performance and complexity, tunable through the size of the list. In this paper, we show that in the conventional formulation of SCL, there are redundant calculations which do not need to be performed in the course of the algorithm. We simplify SCL by removing these redundant calculations and prove that the proposed simplified SCL and the conventional SCL algorithms are equivalent. The simplified SCL algorithm is valid for any code and can reduce the time-complexity of SCL without affecting the space complexity.
A Cost-Effective Method of Electric Brake With Energy Regeneration for Electric Vehicles

This paper proposes a simple but effective method of electric brake with energy regeneration for a brushless DC motor of an electric vehicle (EV). During the braking period, the proposed method only changes the switching sequence of the inverter to control the inverse torque so that the braking energy will return to the battery. Compared with the presented methods, the proposed solution simultaneously achieves dual goals of the electric brake and the energy regeneration without using additional converter, ultracapacitor, or complex winding-changeover technique. Since the braking kinetic energy is converted into the electrical energy and then returns to the battery, the energy regeneration could increase the driving range of an EV. In addition to the braking period, the duration of release throttle is also included in the energy-regenerative mechanism such that the EV is similar to engine vehicles having the engine brake. Therefore, the electric brake can improve rider's comfort and enhance the EV's safety. Finally, the feasibility of the proposed method is demonstrated by experimental results. It shows that the driving range of the EV could be increased to about 16.2%.
Business Process-Based Regulation Compliance: The Case of the Sarbanes-Oxley Act

Balance Sheets and Annual Financial Reports play a major role in determining the public worth of any company. In the wake of corporate scandals such as Enron and WorldCom, the US and other countries passed legislation governing reporting processes. The Sarbanes Oxley Act of 2002 (hereafter SOX) requires US national securities exchange and US national security associations not to list any securities of any issuer that is not in compliance with the act. In this paper, we present a business process-based solution to the SOX compliance problem and offer evidence that such a solution is feasible through an industrial case study. The proposed solution aims to support SOX reporting requirements based on core business processes and a continuous improvement of the company's adopted business processes. This means that the solution integrates SOX-related tasks into the "daily work" of a company, rather than achieve compliance on a project basis.
Can online behavior unveil deceivers? - an exploratory investigation of deception in instant messaging

Deception has been extensively studied in many disciplines in social science. With the increasing use of instant messaging (IM) in both informal communication and performing tasks in work place, deception in IM is emerging as an important issue. In this study, we aimed to explore the online behavior of deception in a group IM setting. The empirical results from triadic groups showed that two types of online behavior under investigation could significantly differentiate deceivers from truth-tellers. The findings can potentially broaden our knowledge of behavioral indicators of deception in human interaction and improve deception detection in cyberspace.
Using neural networks and data mining techniques for the financial distress prediction model

The operating status of an enterprise is disclosed periodically in a financial statement. As a result, investors usually only get information about the financial distress a company may be in after the formal financial statement has been published. If company executives intentionally package financial statements with the purpose of hiding the actual status of the company, then investors will have even less chance of obtaining the real financial information. For example, a company can manipulate its current ratio by up to 200% so that its liquidity deficiency will not show up as a financial distress in the short run. To improve the accuracy of the financial distress prediction model, this paper adopted the operating rules of the Taiwan stock exchange corporation (TSEC) which were violated by those companies that were subsequently stopped and suspended, as the range of the analysis of this research. In addition, this paper also used financial ratios, other non-financial ratios, and factor analysis to extract adaptable variables. Moreover, the artificial neural network (ANN) and data mining (DM) techniques were used to construct the financial distress prediction model. The empirical experiment with a total of 37 ratios and 68 listed companies as the initial samples obtained a satisfactory result, which testifies for the feasibility and validity of our proposed methods for the financial distress prediction of listed companies. This paper makes four critical contributions: (1) The more factor analysis we used, the less accuracy we obtained by the ANN and DM approach. (2) The closer we get to the actual occurrence of financial distress, the higher the accuracy we obtain, with an 82.14% correct percentage for two seasons prior to the occurrence of financial distress. (3) Our empirical results show that factor analysis increases the error of classifying companies that are in a financial crisis as normal companies. (4) By developing a financial distress prediction model, the ANN approach obtains better prediction accuracy than the DM clustering approach. Therefore, this paper proposes that the artificial intelligent (AI) approach could be a more suitable methodology than traditional statistics for predicting the potential financial distress of a company.
Methodological challenges in cross-language qualitative research: a research review.

Cross-language qualitative research occurs when a language barrier is present between researchers and participants. The language barrier is frequently mediated through the use of a translator or interpreter. The purpose of this analysis of cross-language qualitative research was threefold: (1) review the methods literature addressing cross-language research; (2) synthesize the methodological recommendations from the literature into a list of criteria that could evaluate how researchers methodologically managed translators and interpreters in their qualitative studies; (3) test these criteria on published cross-language qualitative studies.



A group of 40 purposively selected cross-language qualitative studies found in nursing and health sciences journals.



The synthesis of the cross-language methods literature produced 14 criteria to evaluate how qualitative researchers managed the language barrier between themselves and their study participants. To test the criteria, the researcher conducted a summative content analysis framed by discourse analysis techniques of the 40 cross-language studies.



The evaluation showed that only 6 out of 40 studies met all the criteria recommended by the cross-language methods literature for the production of trustworthy results in cross-language qualitative studies. Multiple inconsistencies, reflecting disadvantageous methodological choices by cross-language researchers, appeared in the remaining 33 studies. To name a few, these included rendering the translator or interpreter as an invisible part of the research process, failure to pilot test interview questions in the participant's language, no description of translator or interpreter credentials, failure to acknowledge translation as a limitation of the study, and inappropriate methodological frameworks for cross-language research.



The finding about researchers making the role of the translator or interpreter invisible during the research process supports studies completed by other authors examining this issue. The analysis demonstrated that the criteria produced by this study may provide useful guidelines for evaluating cross-language research and for novice cross-language researchers designing their first studies. Finally, the study also indicates that researchers attempting cross-language studies need to address the methodological issues surrounding language barriers between researchers and participants more systematically.
All-Moves-As-First Heuristics in Monte-Carlo Go

We present and explore the effectiveness of several variations on the All-Moves-As-First (AMAF) heuristic in Monte-Carlo Go. Our results show that: • Random play-outs provide more information about the goodness of moves made earlier in the play-out. • AMAF updates are not just a way to quickly initialize counts, they are useful after every play-out. • Updates even more aggressive than AMAF can be even
Monitoring vital signs using millimeter wave

Continuous monitoring of human's breathing and heart rates is useful in maintaining better health and early detection of many health issues. Designing a technique that can enable contactless and ubiquitous vital sign monitoring is a challenging research problem. This paper presents mmVital, a system that uses 60 GHz millimeter wave (mmWave) signals for vital sign monitoring. We show that the mmWave signals can be directed to human's body and the RSS of the reflections can be analyzed for accurate estimation of breathing and heart rates. We show how the directional beams of mmWave can be used to monitor multiple humans in an indoor space concurrently. mmVital relies on a novel human finding procedure where a human can be located within a room by reflection loss based object/human classification. We evaluate mmVital using a 60 GHz testbed in home and office environment and show that it provides the mean estimation error of 0.43 Bpm (breathing rate) and 2.15 bpm (heart rate). Also, it can locate the human subject with 98.4% accuracy within 100 ms of dwell time on reflection. We also demonstrate that mmVital is effective in monitoring multiple people in parallel and even behind the wall.
High-capacity millimetre-wave communications with orbital angular momentum multiplexing

One property of electromagnetic waves that has been recently explored is the ability to multiplex multiple beams, such that each beam has a unique helical phase front. The amount of phase front 'twisting' indicates the orbital angular momentum state number, and beams with different orbital angular momentum are orthogonal. Such orbital angular momentum based multiplexing can potentially increase the system capacity and spectral efficiency of millimetre-wave wireless communication links with a single aperture pair by transmitting multiple coaxial data streams. Here we demonstrate a 32-Gbit s(-1) millimetre-wave link over 2.5 metres with a spectral efficiency of ~16 bit s(-1) Hz(-1) using four independent orbital-angular momentum beams on each of two polarizations. All eight orbital angular momentum channels are recovered with bit-error rates below 3.8 × 10(-3). In addition, we demonstrate a millimetre-wave orbital angular momentum mode demultiplexer to demultiplex four orbital angular momentum channels with crosstalk less than -12.5 dB and show an 8-Gbit s(-1) link containing two orbital angular momentum beams on each of two polarizations.
Specific language impairment as a period of extended optional infinitive.

English-speaking children with specific language impairment (SLI) are known to have particular difficulty with the acquisition of grammatical morphemes that carry tense and agreement features, such as the past tense -ed and third-person singular present -s. In this study, an Extended Optional Infinitive (EOI) account of SLI is evaluated. In this account, -ed, -s, BE, and DO are regarded as finiteness markers. This model predicts that finiteness markers are omitted for an extended period of time for nonimpaired children, and that this period will be extended for a longer time in children with SLI. At the same time, it predicts that if finiteness markers are present, they will be used correctly. These predictions are tested in this study. Subjects were 18 5-year-old children with SLI with expressive and receptive language deficits and two comparison groups of children developing language normally: 22 CA-equivalent (5N) and 20 younger, MLU-equivalent children (3N). It was found that the children with SLI used nonfinite forms of lexical verbs, or omitted BE and DO, more frequently than children in the 5N and 3N groups. At the same time, like the normally developing children, when the children with SLI marked finiteness, they did so appropriately. Most strikingly, the SLI group was highly accurate in marking agreement on BE and DO forms. The findings are discussed in terms of the predictions of the EOI model, in comparison to other models of the grammatical limitations of children with SLI.
Sentiment analysis on E-commerce application by using opinion mining

Social media is becoming a major and popular technological platform that allows users to express personal opinions toward the subjects with shared interests, opinion are good for decision making to People would want to know others' opinion before taking a decision, while corporate would like to monitor pulse of people in a social media about their products and services and take appropriate actions. This paper reviewed about world are realizing that e-commerce is not just buying and selling over Internet, rather it is improve the efficiency to compete with other giants in the market. Their opinions on specific topic are inevitably dependent on many social effects such as user preference on topics, peer influence, user profile information.
Deep Learning on FPGAs: Past, Present, and Future

The rapid growth of data size and accessibility in recent years has instigated a shift of philosophy in algorithm design for artificial intelligence. Instead of engineering algorithms by hand, the ability to learn composable systems automatically from massive amounts of data has led to groundbreaking performance in important domains such as computer vision, speech recognition, and natural language processing. The most popular class of techniques used in these domains is called deep learning , and is seeing significant attention from industry. However, these models require incredible amounts of data and compute power to train, and are limited by the need for better hardware acceleration to accommodate scaling beyond current data and model sizes. While the current solution has been to use clusters of graphics processing units (GPU) as general purpose processors (GPGPU), the use of field programmable gate arrays (FPGA) provide an interesting alternative. Current trends in design tools for FPGAs have made them more compatible with the high-level software practices typically practiced in the deep learning community, making FPGAs more accessible to those who build and deploy models. Since FPGA architectures are flexible, this could also allow researchers the ability to explore model-level optimizations beyond what is possible on fixed architectures such as GPUs. As well, FPGAs tend to provide high performance per watt of power consumption, which is of particular importance for application scientists interested in large scale server-based deployment or resource-limited embedded applications. This review takes a look at deep learning and FPGAs from a hardware acceleration perspective, identifying trends and innovations that make these technologies a natural fit, and motivates a discussion on how FPGAs may best serve the needs of the deep learning community moving forward.
Average and small-signal modeling of self-oscillating flyback converter with applied switching delay

This paper presents a consistent methodology to assess the effect of variable-frequency operation on the dynamics of a flyback converter used in low-cost, high-volume, battery-charger applications. The self-oscillation is typically implemented using peak-current control in boundary conduction mode with a deterministic delay before switching on the next cycle. The modeling is based on the modified state-space-averaging method, where the effect of the varying cycle time and peak-current-mode control is included using special cycle-time and on-time constraints derived from the inductor-current waveforms. The method leads to accurate full-order models, where the effect of the circuit parasitics and the switching delay may also be taken into account. In most of the cases, the effect of parasitics and switching delay is, however, minimal, and therefore, simple small-signal models may be used. The applied switching delay affects the dc operating point in terms of duty ratio and switching frequency, and shall be, therefore, carefully considered.
Number 2002-01-3006 HUD Symbology for Surface Operations : Command Guidance vs . Situation Guidance Formats

This study investigated pilots' taxi performance, situation awareness and workload while taxiing with three different head-up display (HUD) symbology formats: Command-guidance, Situation-guidance and Hybrid. Command-guidance symbology provided the pilot with required control inputs to maintain centerline position; Situation-guidance symbology provided conformal, scene-linked navigation information; while the Hybrid symbology combined elements of both symbologies. Taxi speed, centerline tracking accuracy, workload and situation awareness were assessed. Taxi speed, centerline accuracy, and situation awareness were highest and workload lowest with Situation-guidance and Hybrid symbologies. These results are thought to be due to cognitive tunneling induced by the Commandguidance symbology. The conformal route information of the Situation-guidance and Hybrid HUD formats provided a common reference with the environment, which may have supported better distribution of attention.