nils-herrmann/clean_scirepeval huggingface dataset:
Electronic textiles: a platform for pervasive computing

In our era of converging technologies, it is significant that the second Industrial Revolution should meet the first on common ground: textiles. Modern data processing is, after all, an offshoot of technology first introduced in the 18th century to automate the production of woven textiles. The punched-card system developed by J.-M. Jacquard in 1804 to regulate the weaving of patterned fabrics was later adapted by the English inventor C. Babbage for his proposed analytical engine, the precursor of all binary-based digital computers. Now researchers are delving into a new technology that literally weaves a computing platform into wearable fabrics, creating electronic textiles, or e-textiles, that offer the wearer ready access to information anytime, anywhere, through the medium of an unobtrusive interface. This new field of research draws together an interdisciplinary cadre of specialists in information technology, computing and communications, microtechnology, textiles, and application domains. Their objective is to develop an economical process for the manufacture of large-area, flexible, conformable information systems that can be used in such diverse areas as medicine, sports, space research, firefighting, and law enforcement. In this paper, the authors explore the synergies accruing from wedding the capabilities of textiles with those of computing. In addition to the twin dimensions of functionality and aesthetics, clothing can now be woven to accommodate a third dimension: intelligence. Clothing, the authors point out, meets the user’s requirements for interactivity, connectivity, individuality, and adaptability. It is also probably the most universal of human–computer interfaces: it is familiar; it can be tailored to individual sizes, tastes, and budgets; and it is adaptable to varying climatic conditions. Textiles also provide the ultimate flexibility in systems design by offering a broad range of fibers, yarns, fabrics, and manufacturing techniques. Fabrics also have the spatial capacity needed for hosting the large numbers of sensors and processors demanded for covering broad terrains, such as a battlefield. The size of clothing further enables designers the latitude to build in redundant systems to ensure fault tolerance.
Automatic creation of WordNets from parallel corpora

In this paper we present the evaluation results for the creation of WordNets for five languages (Spanish, French, German, Italian and Portuguese) using an approach based on parallel corpora. We have used three very large parallel corpora for our experiments: DGT-TM, EMEA and ECB. The English part of each corpus is semantically tagged using Freeling and UKB. After this step, the process of WordNet creation is converted into a word alignment problem, where we want to align WordNet synsets in the English part of the corpus with lemmata on the target language part of the corpus. The word alignment algorithm used in these experiments is a simple most frequent translation algorithm implemented into the WN-Toolkit. The obtained precision values are quite satisfactory, but the overall number of extracted synset-variant pairs is too low, leading into very poor recall values. In the conclusions, the use of more advanced word alignment algorithms, such as Giza++, Fast Align or Berkeley aligner is suggested.
Manga content analysis using physiological signals

Recently, the physiological signals have been analyzed more and more, especially in the context of everyday life activities such as watching video or looking at pictures. Tracking these signals gives access to the mental state of the user (interest, tiredness, stress) but also to his emotions (sadness, fright, happiness). The analysis of the reader's physiological signals during reading can provide a better understanding of the reader's feelings but also a better understanding of the documents. Our main research direction is to find the relationship between a change in the reader's physiological signal and the content of the reading. As a first step, we investigate whether it is possible to distinguish a manga (Japanese comic book) from another by analyzing the physiological signals of the reader. We use 3 different manga genres (horror, romance, comedy) and try to predict which one is read by analyzing the features extracted from the physiological signals of the reader. Our method uses the blood volume pulse, the electrodermal activity and the skin temperature of the reader while reading. We show that by using these physiological signals with a support vector machine we can retrieve which manga has been read with a 90% average accuracy.
On the Distribution of Spacings Between Zeros of the Zeta Function By

A numerical study of the distribution of spacings between zeros of the Riemann zeta function is presented. It is based on values for the first 105 zeros and for zeros number 1012 + 1 to 1012 + 105 that are accurate to within +10"8, and which were calculated on the Cray-1 and Cray X-MP computers. This study tests the Montgomery pair correlation conjecture as well as some further conjectures that predict that the zeros of the zeta function behave like eigenvalues of random Hermitian matrices. Matrices of this type are used in modeling energy levels in physics, and many statistical properties of their eigenvalues are known. The agreement between actual statistics for zeros of the zeta function and conjectured results is generally good, and improves at larger heights. Several initially unexpected phenomena were found in the data and some were explained by relating them to the primes.
Media Exposure through the Funnel : A Model of Multi-Stage Attribution

Consumers are exposed to advertisers across a number of channels. As such, a conversion or a sale may be the result of a series of ads that were displayed to the consumer. This raises the key question of attribution: which ads get credit for a conversion and how much credit does each of these ads get? This is one of the most important questions facing the advertising industry today. Although the issue is well documented, current solutions are often simplistic; for e.g., attributing the sale to the most recent ad exposure. In this paper, we address the problem of attribution by developing a Hidden Markov Model (HMM) of an individual consumer’s behavior based on the concept of a conversion funnel. We apply the model to a unique data-set from the online campaign for the launch of a car. We observe that different ad formats, e.g. display and search ads, affect consumers differently based on their states in the decision process. Display ads usually have an early impact on the consumer, moving him from a disengaged state to an state in which he interacts with the campaign. On the other hand, search ads have a pronounced effect across all stages. Further, when the consumer interacts with these ads (e.g. by clicking on them), the likelihood of a conversion increases considerably. Finally, we show that attributing conversions based on the HMM provides fundamentally different insights into ad effectiveness relative to the commonly used approaches for attribution. Contrary to the common belief that display ads as are not useful, our results show that display ads affect early stages of the conversion process. Furthermore, we show that only a fraction of online conversions are driven by online ads.
A Novel Hybrid Spiking Neuron: Bifurcations, Responses, and On-Chip Learning

We present a novel hybrid spiking neuron that is a wired system of shift registers and behaves like an analog spiking neuron model. The presented neuron exhibits various bifurcation phenomena and response characteristics to an input spike train. We derive continuous discrete hybrid maps that can describe the neuron dynamics analytically. By using these maps, the typical mechanisms of bifurcations and responses are clarified. We also present a novel field-programmable gate-array-friendly online learning algorithm for the neuron. It is shown that the algorithm enables the neuron to reconstruct the response characteristics of another neuron with unknown parameter values. Typical learning functions are also validated by experimental measurements.
Incorporating Expert Feedback into Active Anomaly Discovery

Unsupervised anomaly detection algorithms search for outliers and then predict that these outliers are the anomalies. When deployed, however, these algorithms are often criticized for high false positive and high false negative rates. One cause of poor performance is that not all outliers are anomalies and not all anomalies are outliers. In this paper, we describe an Active Anomaly Discovery (AAD) method for incorporating expert feedback to adjust the anomaly detector so that the outliers it discovers are more in tune with the expert user's semantic understanding of the anomalies. The AAD approach is designed to operate in an interactive data exploration loop. In each iteration of this loop, our algorithm first selects a data instance to present to the expert as a potential anomaly and then the expert labels the instance as an anomaly or as a nominal data point. Our algorithm updates its internal model with the instance label and the loop continues until a budget of B queries is spent. The goal of our approach is to maximize the total number of true anomalies in the B instances presented to the expert. We show that when compared to other state-of-the-art algorithms, AAD is consistently one of the best performers.
Predictive Control in Power Electronics and Drives

Predictive control is a very wide class of controllers that have found rather recent application in the control of power converters. Research on this topic has been increased in the last years due to the possibilities of today's microprocessors used for the control. This paper presents the application of different predictive control methods to power electronics and drives. A simple classification of the most important types of predictive control is introduced, and each one of them is explained including some application examples. Predictive control presents several advantages that make it suitable for the control of power converters and drives. The different control schemes and applications presented in this paper illustrate the effectiveness and flexibility of predictive control.
Multi-level direction of autonomous creatures for real-time virtual environments

There have been several recent efforts to build behavior-based autonomous creatures. While competent autonomous action is highly desirable, there is an important need to integrate autonomy with “directability”. In this paper we discuss the problem of building autonomous animated creatures for interactive virtual environments which are also capable of being directed at multiple levels. We present an approach to control which allows an external entity to “direct” an autonomous creature at the motivational level, the task level, and the direct motor level. We also detail a layered architecture and a general behavioral model for perception and action-selection which incorporates explicit support for multi-level direction. These ideas have been implemented and used to develop several autonomous animated creatures.
Rule Engine based on improvement Rete algorithm

Rete algorithm is the mainstream of the algorithm in the rules engine; it provides efficient local entities data and rules pattern matching method. But Rete algorithm applied in rules engine has defects in performance and demand aspect, this paper applies three methods to improve the Rete algorithm in the rule engine: rule decomposition, Alpha-Node-Hashing and Beta-Node-Indexing. Rules engine is enterprise applications, business logic framework for extracting rules from software application, and make it more flexible. This paper describes the principle of Rule Engine at first, secondly show the Rete algorithm, and finally we do some improvement to Rete algorithm. It makes Rule Engine widely meet the application demand and be greatly improved the efficiency.
Distributed Deep Learning for Answer Selection

This paper is an empirical study of the distributed deep lear ning for a question answering subtask: answer selection. Comparison studies of S GD, MSGD, DOWNPOUR and EASGD/EAMSGD algorithms have been presented. Expe rimental results show that the message passing interface based distr ibuted framework can accelerate the convergence speed at a sublinear scale. This paper demonstrates the importance of distributed training: with 120 workers, an 83 x speedup is achievable and running time is decreased from 107.9 hours to 1.3 hours, w hich will benefit the productivity significantly.
T-S Fuzzy Path Controller Design for the Omnidirectional Mobile Robot

This paper focuses on the design of T-S fuzzy path controller for improving the high-speed tracking accuracy of a four-wheel omnidirectional mobile robot (ODMR). Usually, when an ODMR moves in high speed, the adverse effects induced by Coriolis force significantly deteriorate the tracking performances. In this paper, a T-S fuzzy control designed with both the state feedback and path tracking error is proposed to meet the design specifications. Moreover, a new approach of kinematics inversion considering the nonlinear term of coordinate transform is proposed to simplify the analysis of ODMR motion. Finally, an example is provided to compare the present T-S fuzzy path controller with the well-tuned PID controller. The simulation results indicate that the proposed approach provides much better tracking performances as the speed of ODMR increases
A mobile application of augmented reality for aerospace maintenance training

Aircraft maintenance technicians (AMTs) must obtain new levels of job task skill and knowledge to effectively work with modem computer-based avionics and advanced composite materials. Traditional methods of training, such as on-the-job training (OJT), may not have potential to fulfill the training requirements to meet future trends in aviation maintenance. A new instruction delivery system could assist AMTs with job task training and job tasks. The purpose of this research is to analyze the use of an augmented reality (AR) system as a training medium for novice AMTs. An AR system has the potential to enable job task training and job task guidance for the novice technician in a real world environment. An AR system could reduce the cost for training and retraining of AMTs by complementing human information processing and assisting with performance of job tasks. An AR system could eliminate the need to leave the aircraft for the retrieval of information from maintenance manuals for inspection and repair procedures. AR has the potential to supply rapid and accurate feedback to an AMT with any information that he/she needs to successfully complete a job task. New technologies that promote smaller computer-based systems make the application of a mobile AR system possible in the near future.
Adaptive Clock Gating Technique for Low Power IP Core in SoC Design

Clock gating is a well-known technique to reduce chip dynamic power. This paper analyzes the disadvantages of some recent clock gating techniques and points out that they are difficult in system-on-chip (SoC) design. Based on the analysis of the intellectual property (IP) core model, an adaptive clock gating (ACG) technique which can be easily realized is introduced for the low power IP core design. ACG can automatically enable or disable the IP clock to reduce not only dynamic power but also leakage power with power gating technique. The experimental results on some IP cores in a real SoC show an average of 62.2% dynamic power reduction and 70.9% leakage power reduction without virtually performance impact.
Radoop : Analyzing Big Data with RapidMiner and Hadoop

Working with large data sets is increasingly common in research and industry. There are some distributed data analytics solutions like Hadoop, that offer high scalability and fault-tolerance, but they usually lack a user interface and only developers can exploit their functionalities. In this paper, we present Radoop, an extension for the RapidMiner data mining tool which provides easy-to-use operators for running distributed processes on Hadoop. We describe integration and development details and provide runtime measurements for several data transformation tasks. We conclude that Radoop is an excellent tool for big data analytics and scales well with increasing data set size and the number of nodes in the cluster.
Parsing Videos of Actions with Segmental Grammars

Real-world videos of human activities exhibit temporal structure at various scales, long videos are typically composed out of multiple action instances, where each instance is itself composed of sub-actions with variable durations and orderings. Temporal grammars can presumably model such hierarchical structure, but are computationally difficult to apply for long video streams. We describe simple grammars that capture hierarchical temporal structure while admitting inference with a finite-state-machine. This makes parsing linear time, constant storage, and naturally online. We train grammar parameters using a latent structural SVM, where latent subactions are learned automatically. We illustrate the effectiveness of our approach over common baselines on a new half-million frame dataset of continuous YouTube videos.
A 0.65 THz Focal-Plane Array in a Quarter-Micron CMOS Process Technology

A focal-plane array (FPA) for room-temperature detection of 0.65-THz radiation has been fully integrated in a low-cost 0.25 mum CMOS process technology. The circuit architecture is based on the principle of distributed resistive self-mixing and facilitates broadband direct detection well beyond the cutoff frequency of the technology. The 3 timesZ 5 pixel array consists of differential on-chip patch antennas, NMOS direct detectors, and integrated 43-dB voltage amplifiers. At 0.65 THz the FPA achieves a responsivity (Rv) of 80 kV/W and a noise equivalent power (NEP) of 300 pW/ radic{Hz}. Active multi-pixel imaging of postal envelopes demonstrates the FPAs potential for future cost-effective terahertz imaging solutions.
Object recognition and full pose registration from a single image for robotic manipulation

Robust perception is a vital capability for robotic manipulation in unstructured scenes. In this context, full pose estimation of relevant objects in a scene is a critical step towards the introduction of robots into household environments. In this paper, we present an approach for building metric 3D models of objects using local descriptors from several images. Each model is optimized to fit a set of calibrated training images, thus obtaining the best possible alignment between the 3D model and the real object. Given a new test image, we match the local descriptors to our stored models online, using a novel combination of the RANSAC and Mean Shift algorithms to register multiple instances of each object. A robust initialization step allows for arbitrary rotation, translation and scaling of objects in the test images. The resulting system provides markerless 6-DOF pose estimation for complex objects in cluttered scenes. We provide experimental results demonstrating orientation and translation accuracy, as well a physical implementation of the pose output being used by an autonomous robot to perform grasping in highly cluttered scenes.
Chip and PIN is Broken

EMV is the dominant protocol used for smart card payments worldwide, with over 730 million cards in circulation. Known to bank customers as “Chip and PIN”, it is used in Europe; it is being introduced in Canada; and there is pressure from banks to introduce it in the USA too. EMV secures credit and debit card transactions by authenticating both the card and the customer presenting it through a combination of cryptographic authentication codes, digital signatures, and the entry of a PIN. In this paper we describe and demonstrate a protocol flaw which allows criminals to use a genuine card to make a payment without knowing the card’s PIN, and to remain undetected even when the merchant has an online connection to the banking network. The fraudster performs a man-in-the-middle attack to trick the terminal into believing the PIN verified correctly, while telling the card that no PIN was entered at all. The paper considers how the flaws arose, why they remained unknown despite EMV’s wide deployment for the best part of a decade, and how they might be fixed. Because we have found and validated a practical attack against the core functionality of EMV, we conclude that the protocol is broken. This failure is significant in the field of protocol design, and also has important public policy implications, in light of growing reports of fraud on stolen EMV cards. Frequently, banks deny such fraud victims a refund, asserting that a card cannot be used without the correct PIN, and concluding that the customer must be grossly negligent or lying. Our attack can explain a number of these cases, and exposes the need for further research to bridge the gap between the theoretical and practical security of bank payment systems. It also demonstrates the need for the next version of EMV to be engineered properly.
An empirical analysis of the distribution of unit test smells and their impact on software maintenance

Unit testing represents a key activity in software development and maintenance. Test suites with high internal quality facilitate maintenance activities, such as code comprehension and regression testing. Several guidelines have been proposed to help developers write good test suites. Unfortunately, such rules are not always followed resulting in the presence of bad test code smells (or simply test smells). Test smells have been defined as poorly designed tests and their presence may negatively affect the maintainability of test suites and production code. Despite the many studies that address code smells in general, until now there has been no empirical evidence regarding test smells (i) distribution in software systems nor (ii) their impact on the maintainability of software systems. This paper fills this gap by presenting two empirical studies. The first study is an exploratory analysis of 18 software systems (two industrial and 16 open source) aimed at analyzing the distribution of test smells in source code. The second study, a controlled experiment involving twenty master students, is aimed at analyzing whether the presence of test smells affects the comprehension of source code during software maintenance. The results show that (i) test smells are widely spread throughout the software systems studied and (ii) most of the test smells have a strong negative impact on the comprehensibility of test suites and production code.
The Fast Research Interface for the KUKA Lightweight Robot

KUKA lightweight robot (LWR) provides many unique features for robotic researchers. To give full access to these features, a new interface was developed that gives direct low-level real-time access to the KUKA robot controller (KRC) at high rates of up to 1 kHz. On the other hand, all industrial strength features, like teaching, motion script features, fieldbus I/O and safety are provided. Using standard UDP socket technology, the user is not limited to one specific runtime system. This paper describes the capabilities of the interface, the practical realization within the LWR control architecture and first applications of the interface.