nils-herrmann/clean_scirepeval huggingface dataset:
A primary side feedforward control scheme for low power LED driver compatible with Triac dimmer

This paper proposes a primary side feedforward control scheme for low power Triac dimmable LED driver. The LED driver is based on the Flyback topology operated in constant frequency (CF) DCM mode. The input power is controlled by the proposed control scheme based on the Triac dimming angle, which is not affected by the AC input voltage variation. Therefore the output power and current can be regulated without any isolated feedback circuit. Also, the input current automatically follows the input voltage due to CF DCM operation. With the proposed control method, high efficiency and high power factor can be achieved by eliminating the isolated feedback circuit and the dummy load required for traditional Triac dimmable LED driver. Furthermore, considering the parameters tolerance existed in the practical application, such as LED forward voltage drop and Flyback transformer magnetizing inductance, the output voltage compensation and primary side inductance compensation method for the proposed control schemes are also presented in the paper, which can further improve the control accuracy. The experimental results from a 27V/0.5A prototype verify the theoretical analysis.
An Algorithmic Solution of N-Person Games

function is a function which estimates what resulting value Two-person, perfect information, constant sum games have been studied in Artificial Intelligence. This paper opens up the issue of playing n-person games and proposes a procedure for constant sum or non-constant sum games. It is proved that a procedure, max”, locates an equilibrium point given the entire game tree. The minimax procedure for 2person games using look ahead finds a saddle point of approximations, while maxn finds an equilibrium point of the values of the evaluation function for n-person games using look ahead. Maz” is further analyzed with respect to some pruning schemes.
Learning Representations Using Complex-Valued Nets

Complex-valued neural networks (CVNNs) are an emerging field of research in neural networks due to their potential representational properties for audio, image, and physiological signals. It is common in signal processing to transform sequences of real values to the complex domain via a set of complex basis functions, such as the Fourier transform. We show how CVNNs can be used to learn complex representations of real valued time-series data. We present methods and results using a framework that can compose holomorphic and non-holomorphic functions in a multi-layer network using a theoretical result called the Wirtinger derivative. We test our methods on a representation learning task for real-valued signals, recurrent complex-valued networks and their real-valued counterparts. Our results show that recurrent complex-valued networks can perform as well as their realvalued counterparts while learning filters that are representative of the domain of the data.
Internet of Everything (Ioe): Analysing the Individual Concerns Over Privacy Enhancing Technologies (Pets)

This paper aims to investigate the effectiveness of the provision of privacy of individuals through privacy enhancing technologies (PETs). The successful evolution and emergence of cyberspace with the real world through “Internet of Everything (IoE)” has led to the speedy progress in research and development of predictive analysis of big data. The individual’s privacy has gained a considerable momentum in both industry and academia since privacy-enhancing technologies (PETs) constitute a technical means to protect information. Privacy regulations and state of law deemed this as an integral part in order to protect the individual’s private sphere when the infrastructure of Information Communication Technologies (ICT) is laid out. Modern organisations use consent forms to gather individual’s sensitive personal information for a specific purpose. The law prohibits using the person’s information for purposes other than that of when the consent was initially established. The infrastructure of ICT should be developed in alliance with the privacy laws and made compliant as well intelligent which learn by itself from the environment. This extra layer embedded in the system would educate the ICT structure and help system to authenticate as well as communicate with the perspective users. The existing literature on protecting individuals’ privacy through privacy-enhancing technologies (PETs) is still embryonic and does conclude that the individual’s concerns about privacy are not fully considered in the technological sense. Among other contributions, this research paper will devise a conceptual model to improve individual’s
A Generic Service Framework for Stock Market Prediction

Many researches have been done for stock market prediction. While there are many free and premium data sources available today, and there many new machine learning algorithms have been proposed, most of these researches do not focus on utilizing these data sources and algorithms. This paper proposes a stock market prediction service framework that allows users to choose different data sources and machine learning techniques. In particular, while most existing prediction approaches are based on neural networks, support vector machines or Naive Bayes, we illustrate the flexibility of our framework by including metric learning based methods to predict stock movement.
Secure ARP and Secure DHCP Protocols to Mitigate Security Attacks

For network computers to communicate to one another, they need to know one another’s IP address and MAC address. Address Resolution Protocol (ARP) is developed to find the Ethernet address that map to a specific IP address. The source computer broadcasts the request for Ethernet address and eventually the target computer replies. The IP to Ethernet address mapping would later be stored in an ARP Cache for some time duration, after which the process is repeated. Since ARP is susceptible to ARP poisoning attacks, we propose to make it unicast, centralized and secure, along with a secure design of DHCP protocol to mitigate MAC spoofing. The secure protocol designs are explained in detail. Lastly we also discuss some performance issues to show how the proposed protocols work.
The challenge problem for automated detection of 101 semantic concepts in multimedia

We introduce the challenge problem for generic video indexing to gain insight in intermediate steps that affect performance of multimedia analysis methods, while at the same time fostering repeatability of experiments. To arrive at a challenge problem, we provide a general scheme for the systematic examination of automated concept detection methods, by decomposing the generic video indexing problem into 2 unimodal analysis experiments, 2 multimodal analysis experiments, and 1 combined analysis experiment. For each experiment, we evaluate generic video indexing performance on 85 hours of international broadcast news data, from the TRECVID 2005/2006 benchmark, using a lexicon of 101 semantic concepts. By establishing a minimum performance on each experiment, the challenge problem allows for component-based optimization of the generic indexing issue, while simultaneously offering other researchers a reference for comparison during indexing methodology development. To stimulate further investigations in intermediate analysis steps that inuence video indexing performance, the challenge offers to the research community a manually annotated concept lexicon, pre-computed low-level multimedia features, trained classifier models, and five experiments together with baseline performance, which are all available at
Continuous heart rate and body temperature monitoring system using Arduino UNO and Android device

An integrated portable device for continuous heart rate and body temperature monitoring system development is presented in this paper. Heart related diseases are increasing day by day; therefore, an accurate, affordable and portable heart rate and body temperature measuring device is essential for taking action in proper time. Such a device is more essential in a situation where there is no doctor or clinic nearby (e.g., rural area) and patients are unable not recognize their actual condition. The developed system of this study consists of Arduino UNO microcontroller system, transmission system and Android based application. The system gives information of heart rate and body temperature simultaneously acquired on the portable device in real time and shows it through the connected Android application instantly. The developed system is more affordable with low price compared to other developed devices due to use of easy available Arduino UNO and smart phone as Android device. The developed device is shown acceptable outcomes when compared with other measuring devices.
BitDeposit: Deterring Attacks and Abuses of Cloud Computing Services through Economic Measures

Dependability in cloud computing applications can be negatively affected by various attacks or service abuses. To come ahead of this threat, we propose an economic measure to deter attacks and various service abuses in cloud computing applications. Our proposed defense is based on requiring a service user to pay a small deposit, using digital currency, before invoking the service. Once they are done using the service, and there has been no detected abuse or attack, the deposit is paid back by the service provider to the service user. If an attack or an abuse is detected, the service user is not paid back and the service provider gets to keep the deposit. We propose the use of micro payments with a decentralized nature and small transaction fees, such as the Bit coin digital currency. Moreover, thanks to the existence of money exchanges which convert the Bit coin currency to real world currency, service providers can recoup loses when they exchange the confiscated deposits for real world currency.
Silicon carbide Schottky diodes and MOSFETs: Solutions to performance problems

Silicon carbide has long been hailed as the successor to silicon in many power electronics applications. Its superior electrical and thermal properties have delivered devices that operate at higher voltages, higher temperatures and with lower on-resistances than silicon devices. However, SiC Schottky diodes are still the only devices commercially available today. Though SiC Schottkys are now being used with silicon IGBTs in dasiahybridpsila inverter modules, the real advantages will be seen when silicon switching devices can be replaced by SiC. This paper describes the current state of SiC diode and MOSFET technology, discussing possible solutions to making these devices commercially viable.
A point-based MDP for robust single-lane autonomous driving behavior under uncertainties

In this paper, a point-based Markov Decision Process (QMDP) algorithm is used for robust single-lane autonomous driving behavior control under uncertainties. Autonomous vehicle decision making is modeled as a Markov Decision Process (MDP), then extended to a QMDP framework. Based on MDP/QMDP, three kinds of uncertainties are taken into account: sensor noise, perception constraints and surrounding vehicles' behavior. In simulation, the QMDP-based reasoning framework makes the autonomous vehicle perform with differing levels of conservativeness corresponding to different perception confidence levels. Road tests also indicate that the proposed algorithm helps the vehicle in avoiding potentially unsafe situations under these uncertainties. In general, the results indicate that the proposed QMDP-based algorithm makes autonomous driving more robust to limited sensing ability and occasional sensor failures.
Performance Comparison between Five NoSQL Databases

Recently NoSQL databases and their related technologies are developing rapidly and are widely applied in many scenarios with their BASE (Basic Availability, Soft state, Eventual consistency) features. At present, there are more than 225 kinds of NoSQL databases. However, the overwhelming amount and constantly updated versions of databases make it challenging for people to compare their performance and choose an appropriate one. This paper is trying to evaluate the performance of five NoSQL clusters (Redis, MongoDB, Couchbase, Cassandra, HBase) by using a measurement tool – YCSB (Yahoo! Cloud Serving Benchmark), explain the experimental results by analyzing each database's data model and mechanism, and provide advice to NoSQL developers and users.
Fighting Accounting Fraud Through Forensic Data Analytics

Accounting fraud is a global concern representing a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Several tricks can be used to commit accounting fraud, hence the need for non-static regulatory interventions that take into account different fraudulent patterns. Accordingly, this study aims to improve the detection of accounting fraud via the implementation of several machine learning methods to better differentiate between fraud and non-fraud companies, and to further assist the task of examination within the riskier firms by evaluating relevant financial indicators. Out-of-sample results suggest there is a great potential in detecting falsified financial statements through statistical modelling and analysis of publicly available accounting information. The proposed methodology can be of assistance to public auditors and regulatory agencies as it facilitates auditing processes, and supports more targeted and effective examinations of accounting reports.
Political Facebook groups: Micro-activism and the digital front stage

The growth of social networking sites (SNS), like Facebook, has caused many to rethink how we understand political activism and citizen engagement. In 2010, 43% of Internet users reported using social networking sites " several times a day, " a sizeable increase from 2009 where only 34% reported using social networking at the same rate (Pew 2010). Research on digital activism has emphasized the use of websites, listservs, and other forums by formal organizations to raise awareness for their specific causes (McCaughey and Ayers 2003; Van de Donk 2004). Information communication technologies (ICT's) have been regarded as particularly effective tools in mobilizing disparate, resource-poor counter-spheres in collective off-line activity (Van de Donk 2004). In recent years, scholars have moved away from viewing information communication technologies (ICT's) as simply a tool for formal activist organizations. Breindl (2010) argues that on-line digital activism has largely been examined through a classic " social movement paradigm. " She calls for scholarship that looks at how the meaning and structure of activism has been transformed by ICT's. One key way it has been transformed is by allowing for small scale, many-to-many forms of politically oriented communication, what I conceptualize as a new term: microactivism. Examples include the formation of political Facebook groups, re-tweeting of articles of political interest and sharing politically relevant videos on YouTube. These acts reflect micro-level intentions and are not necessarily geared towards mobilization like more traditional forms of digital activism. This microactivism has helped bring about a radical reformulation of the political arena. One major change that emerges from the ease of many-to-many communication brought about by SMS is the reduced incentive to join social movements (Earl and Schussman 2003). These new applications encourage movement entrepreneurs (Garrett 2006) not affiliated with formal social movements but interested in fomenting social change. The ease with which individuals can create content and connect with one another to share content is viewed by others as a harbinger of a more democratic and egalitarian society (Benkler 2006, Jenkins 2007, Shirky 2010). Salter (2005) argues that sites like SNS' provide new radical public spheres that provide additional spaces for voice cultivation and political citizenship formation. More importantly, in his view information technologies help shift political discourse to more informal venues that are less subject to elite control (Dahlberg 2004). Social critics like Morozov (2009) suggest, however, that microactivism might do more harm than good. Morozov (2009) refers …
Team MIT Urban Challenge Technical Report

This technical report describes Team MIT's approach to the DARPA Urban Challenge. We have developed a novel strategy for using many inexpensive sensors, mounted on the vehicle periphery, and calibrated with a new cross­modal calibration technique. Lidar, camera, and radar data streams are processed using an innovative, locally smooth state representation that provides robust perception for real­ time autonomous control. A resilient planning and control architecture has been developed for driving in traffic, comprised of an innovative combination of well­proven algorithms for mission planning, situational planning, situational interpretation, and trajectory control. These innovations are being incorporated in two new robotic vehicles equipped for autonomous driving in urban environments, with extensive testing on a DARPA site visit course. Experimental results demonstrate all basic navigation and some basic traffic behaviors, including unoccupied autonomous driving, lane following using pure­pursuit control and our local frame perception strategy, obstacle avoidance using kino­dynamic RRT path planning, U­turns, and precedence evaluation amongst other cars at intersections using our situational interpreter. We are working to extend these approaches to advanced navigation and traffic scenarios.  This technical report describes Team MIT's approach to the DARPA Urban Challenge. We have developed a novel strategy for using many inexpensive sensors, mounted on the vehicle periphery, and calibrated with a new cross-modal calibration technique. Lidar, camera, and radar data streams are processed using an innovative, locally smooth state representation that provides robust perception for real-time autonomous control. A resilient planning and control architecture has been developed for driving in traffic, comprised of an innovative combination of well-proven algorithms for mission planning, situational planning, situational interpretation, and trajectory control. These innovations are being incorporated in two new robotic vehicles equipped for autonomous driving in urban environments, with extensive testing on a DARPA site visit course. Experimental results demonstrate all basic navigation and some basic traffic behaviors, including unoccupied autonomous driving, lane following using pure-pursuit control and our local frame perception strategy, obstacle avoidance using kino-dynamic RRT path planning, U-turns, and precedence evaluation amongst other cars at intersections using our situational interpreter. We are working to extend these approaches to advanced navigation and traffic scenarios.  The information contained in this paper does not represent the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency (DARPA) or the Department of Defense. DARPA does not guarantee the accuracy or reliability of the information in this paper. Additional support …
SQuAD: 100, 000+ Questions for Machine Comprehension of Text

We present the Stanford Question Answering Dataset (SQuAD), a new reading comprehension dataset consisting of 100,000+ questions posed by crowdworkers on a set of Wikipedia articles, where the answer to each question is a segment of text from the corresponding reading passage. We analyze the dataset to understand the types of reasoning required to answer the questions, leaning heavily on dependency and constituency trees. We build a strong logistic regression model, which achieves an F1 score of 51.0%, a significant improvement over a simple baseline (20%). However, human performance (86.8%) is much higher, indicating that the dataset presents a good challenge problem for future research. The dataset is freely available at https://stanford-qa.com.
Building a smart home environment for service robots based on RFID and sensor networks

This paper is concerned with constructing a prototype smart home environment which has been built in the research building of Korea Institute of Industrial Technology (KITECH) to demonstrate the practicability of a robot-assisted future home environment. Key functionalities that a home service robot must provide are localization, navigation, object recognition and object handling. A considerable amount of research has been conducted to make the service robot perform these operations with its own sensors, actuators and a knowledge database. With all heavy sensors, actuators and a database, the robot could have performed the given tasks in a limited environment or showed the limited capabilities in a natural environment. We initiated a smart home environment project for light-weight service robots to provide reliable services by interacting with the environment through the wireless sensor networks. This environment consists of the following three main components: smart objects with an radio frequency identification (RFID) tag and smart appliances with sensor network functionality; the home server that connects smart devices as well as maintains information for reliable services; and the service robots that perform tasks in collaboration with the environment. In this paper, we introduce various types of smart devices which are developed for assisting the robot by providing sensing and actuation, and present our approach on the integration of these devices to construct the smart home environment. Finally, we discuss the future directions of our project.
Interpretation and trust: designing model-driven visualizations for text analysis

Statistical topic models can help analysts discover patterns in large text corpora by identifying recurring sets of words and enabling exploration by topical concepts. However, understanding and validating the output of these models can itself be a challenging analysis task. In this paper, we offer two design considerations - interpretation and trust - for designing visualizations based on data-driven models. Interpretation refers to the facility with which an analyst makes inferences about the data through the lens of a model abstraction. Trust refers to the actual and perceived accuracy of an analyst's inferences. These considerations derive from our experiences developing the Stanford Dissertation Browser, a tool for exploring over 9,000 Ph.D. theses by topical similarity, and a subsequent review of existing literature. We contribute a novel similarity measure for text collections based on a notion of "word-borrowing" that arose from an iterative design process. Based on our experiences and a literature review, we distill a set of design recommendations and describe how they promote interpretable and trustworthy visual analysis tools.