nils-herrmann/clean_scirepeval huggingface dataset:
Seventh sense technology

Seventh Sense Technology is a technology used for the human electronic interaction using natural hand gestures. The hand gestures can be used to control any micro controller based devices or robots at distant places using Zigbee or GSM Module or Satellite transceiver. The "Seventh Sense Technology" is easy to implement with reduced implementation cost and it opens a wide area of research for the next generation and a greater development in almost all areas of current interest. Seventh Sense Technology uses the Sixth Sense Technology and its advancements to control the autonomous robots or any microcontroller based devices on human will. Currently autonomous robots or any microcontroller based devices are designed for a single purpose, but with this technology robots and those devices can be made to achieve multidimensional purposes. Even the costlier sensors can be replaced with the low cost night vision cameras. It opens a way to control robots or any microcontroller based devices at any distance with natural hand gestures of human beings. It also has the provision of customizable virtual keyboard for giving more actions to distant devices. It is going to be a greater field of research that also paves a lot of attention in this current world of technology.
How does EMDR work ?

Eye movement desensitisation and reprocessing (EMDR) is an effective treatment for alleviating trauma symptoms, and the positive effects of this treatment have been scientifically confirmed under wellcontrolled conditions. This has provided an opportunity to explore how EMDR works. The present paper reports on the findings of a long series of experiments that disproved the hypothesis that eye movements or other ‘dual tasks’ are unnecessary. These experiments also disproved the idea that ‘bilateral stimulation’ is needed; moving the eyes up and down produces the same effect as horizontal eye movement, and so do tasks that require no eye movement at all. However, it is important that the dual task taxes working memory. Several predictions can be derived from the working memory explanation for eye movements in EMDR. These seem to hold up extremely well in critical experimental tests, and create a solid explanation on how eye movements work. This paper discusses the implications that this theory and the empirical findings may have for the EMDR technique.
A high-performance silicon-on-insulator MEMS gyroscope operating at atmospheric pressure

This paper presents a new, high-performance silicon-on-insulator (SOI) MEMS gyroscope with decoupled oscillation modes. The gyroscope tructure allows it to achieve matched-resonance-frequencies, large drive-mode oscillation amplitude, high sense-mode quality factor, and low echanical cross-talk. The gyroscope is fabricated through the commercially available SOIMUMPS process of MEMSCAP Inc. The fabricated yroscope has minimum capacitive sense gaps of 2.6 m and a structural silicon thickness of 25 m, and it fits into a chip area smaller than mm × 3 mm. The fabricated gyroscope is hybrid connected to a CMOS capacitive interface ASIC chip, which is fabricated in a standard 0.6 m MOS process. The characterization of the hybrid-connected gyroscope demonstrates a low measured noise-equivalent rate of 90◦/h/Hz1/2 at tmospheric pressure, eliminating the need for a vacuum package for a number of applications. R2-non-linearity of the gyroscope is measured to e better than 0.02%. The gyroscope has a low quadrature signal of 70◦/s and a short-term bias stability of 1.5◦/s. The angular rate sensitivity of the yroscope is 100 V/(◦/s) at atmospheric pressure, which improves 24 times to 2.4 mV/(◦/s) at vacuum. The noise-equivalent rate of the gyroscope t 20 mTorr vacuum is measured to be 35◦/h/Hz1/2, which can be improved further by reducing the electromechanical noise.
A Multi-View Fusion Neural Network for Answer Selection

Community question answering aims at choosing the most appropriate answer for a given question, which is important in many NLP applications. Previous neural network-based methods consider several different aspects of information through calculating attentions. These different kinds of attentions are always simply summed up and can be seen as a “single view”, causing severe information loss. To overcome this problem, we propose a Multi-View Fusion Neural Network, where each attention component generates a “view” of the QA pair and a fusion RNN integrates the generated views to form a more holistic representation. In this fusion RNN method, a filter gate collects important information of input and directly adds it to the output, which borrows the idea of residual networks. Experimental results on the WikiQA and SemEval-2016 CQA datasets demonstrate that our proposed model outperforms the state-of-the-art methods.
Comprehensive study of data analytics tools (RapidMiner, Weka, R tool, Knime)

In today's era, data has been increasing in volume, velocity and variety. Due to large and complex collection of datasets, it is difficult to process on traditional data processing application. So, this leads to emerging of new technology called data analytics. Data analytics is a science of exploring raw data and elicitation the useful information and hidden pattern. The main aim of data analytics is to use advance analytics techniques for huge and different datasets. Datasets may vary in sizes from terabytes to zettabytes and can be structured or unstructured. The paper gives the comprehensive and theoretical analysis of four open source data analytics tools which are RapidMiner, Weka, R Tool and KNIME. The study describes the technical specification, features, and specialization for each selected tool. By employing the study the choice and selection of tools can be made easy. The tools are evaluated on basis of various parameters like volume of data used, response time, ease of use, price tag, analysis algorithm and handling.
Phase-Shifted Carrier-Based Synchronized Sinusoidal PWM Techniques for a Cascaded H-Bridge Multilevel Inverter

This paper analyzes synchronization strategy for cascaded H-bridge multilevel inverter (CHBMLI) topologies with a carrier-based sinusoidal phase-shifted pulse width modulation (PSPWM) technique. In the PSPWM technique, a separate carrier is used for each H-bridge (HB). The carriers are generally phase shifted from each other by π /x rad (x = No. of HBs) for unipolar PWM. With the carrier frequency being an integer (odd/even) multiple of the fundamental frequency, it is observed that the positions of zero crossings of the carriers with respect to the zero crossings of voltage references play an important role for maintaining quarter-wave symmetry among multilevel inverter pole voltage waveforms. This paper analytically shows the conditions for half-wave symmetry and quarter-wave symmetry and experimentally verifies those conditions for the PSPWM technique with a five-level CHBMLI laboratory prototype.
Grid Drawings of Four-Connected Plane Graphs

A grid drawing of a plane graph G is a drawing of G on the plane so that all vertices of G are put on plane grid points and all edges are drawn as straight line segments between their endpoints without any edge-intersection. In this paper we give a very simple algorithm to find a grid drawing of any given 4-connected plane graph G with four or more vertices on the outer face. The algorithm takes time O(n) and needs a rectangular grid of width dn/2e−1 and height dn/2e if G has n vertices. The algorithm is best possible in the sense that there are an infinite number of 4-connected plane graphs any grid drawings of which need rectangular grids of width dn/2e − 1 and height dn/2e.
Chipless RFID Sensor Tag for Metal Crack Detection and Characterization

Chipped radio-frequency identification (RFID) sensor systems have been studied for structural health monitoring (SHM) applications. However, the use of chip in sensor tags and its standardized narrowband operation contribute shortcomings in cost, durability, and detection capability. This paper presents a novel use of the frequency signature-based chipless RFID for metal crack detection and characterization operating in ultra-wideband frequency. The vision is to implement a low-cost and high-temperature-resistant passive wireless sensor able to monitor the crack on a metallic structure with multiparameter detection. We propose a chipless RFID sensor tag integrating four tip-loaded dipole resonators as a 4-bit ID encoder and a circular microstrip patch antenna (CMPA) resonator as a crack sensor. The radar cross section spectrum of the chipless RFID sensor tag generates four resonant frequencies from the dipole resonators and a resonant frequency from the CMPA resonator. Simulation and experimental results show that the resonant frequency shift of the CMPA is a useful feature to indicate the crack orientation and the crack width on a metallic structure. The direction of the resonant frequency shift represents the orientation of the crack, while the magnitude of the resonant frequency shift is proportional to the width of the crack. Furthermore, the experimentation with a natural fatigue crack sample proves that the proposed sensor tag is capable of detecting submillimeter cracks.
Guidelines for usability testing with children

A Although user-centered design is a well-supported concept in the literature on adult computer products, not until recently have publications begun to appear addressing the need to include the user in the design process of children's computer products. Good examples are a recent panel discussion in interactions on the importance of understanding the perspectives and needs of children, and the energizing work of Allison Druin and Cynthia Solomon [1, 2]. Growth has also occurred in evaluation research in both the industrial and academic communities, assessing the effectiveness or appeal of various types of (6 to 10 years), and middle-school-aged children (11 to 14 years). These age divisions are arbitrary , and many behaviors will overlap. In our experience, most children younger than 2 1/2 years of age are not proficient enough with standard input devices (e.g., mouse, trackball, or keyboard) to interact with the technology and provide useful data. Children older than 14 years of age will likely behave as adults in a testing situation and should be treated accordingly. Preschool (ages 2 to 5 years) Preschoolers require the most extensive adaptations of usability testing because their attention span, their motivation to please adults, and their ability to adjust to strange surroundings and new people may change from one moment to the next. In general, children in this age range should be allowed to explore the computer according to their own interests and pacing instead of performing a series of directed tasks. They will often be happy to show you what they know, and what they can do on the computer independently. When assessing appeal or engagement, testers will need to closely observe children's behavior such as sighing, smiling, or sliding under the table. Children this age often have difficulty expressing their likes and dislikes in words. Elementary School (ages 6 to 10 years) Children in this age range are relatively easy to include in software usability testing. Their experience in school makes them ready to sit at a task and follow directions from an adult, and they are generally not self-conscious about being observed as they play on the computer. They will answer questions and try new things with ease. In this age range, children will develop more sophistication about how they can describe the things they see and do. Six-and seven-year-old children will be more hands-on—ready to work on the computer but a little shy or inarticulate when …
Video Captioning by Adversarial LSTM

In this paper, we propose a novel approach to video captioning based on adversarial learning and long short-term memory (LSTM). With this solution concept, we aim at compensating for the deficiencies of LSTM-based video captioning methods that generally show potential to effectively handle temporal nature of video data when generating captions but also typically suffer from exponential error accumulation. Specifically, we adopt a standard generative adversarial network (GAN) architecture, characterized by an interplay of two competing processes: a “generator” that generates textual sentences given the visual content of a video and a “discriminator” that controls the accuracy of the generated sentences. The discriminator acts as an “adversary” toward the generator, and with its controlling mechanism, it helps the generator to become more accurate. For the generator module, we take an existing video captioning concept using LSTM network. For the discriminator, we propose a novel realization specifically tuned for the video captioning problem and taking both the sentences and video features as input. This leads to our proposed LSTM–GAN system architecture, for which we show experimentally to significantly outperform the existing methods on standard public datasets.
Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

As a key technique for enabling artificial intelligence, machine learning (ML) has been shown to be capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition and recommendation systems, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design, and computation resource management, while ML-based networking focuses on the applications in base station (BS) clustering, BS switching control, user association, and routing. Each aspect is further categorized according to the adopted ML techniques. Additionally, given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where the topics of ML-based network slicing, infrastructure update to support MLbased paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation, and so on are discussed.
Deep learning features at scale for visual place recognition

The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using generic features from networks that were trained for other types of recognition tasks. In this paper, we train, at large scale, two CNN architectures for the specific place recognition task and employ a multi-scale feature encoding method to generate condition- and viewpoint-invariant features. To enable this training to occur, we have developed a massive Specific PlacEs Dataset (SPED) with hundreds of examples of place appearance change at thousands of different places, as opposed to the semantic place type datasets currently available. This new dataset enables us to set up a training regime that interprets place recognition as a classification problem. We comprehensively evaluate our trained networks on several challenging benchmark place recognition datasets and demonstrate that they achieve an average 10% increase in performance over other place recognition algorithms and pre-trained CNNs. By analyzing the network responses and their differences from pre-trained networks, we provide insights into what a network learns when training for place recognition, and what these results signify for future research in this area.
One-Sided Unsupervised Domain Mapping

In unsupervised domain mapping, the learner is given two unmatched datasets A and B. The goal is to learn a mapping GAB that translates a sample in A to the analog sample in B. Recent approaches have shown that when learning simultaneously both GAB and the inverse mapping GBA, convincing mappings are obtained. In this work, we present a method of learning GAB without learning GBA. This is done by learning a mapping that maintains the distance between a pair of samples. Moreover, good mappings are obtained, even by maintaining the distance between different parts of the same sample before and after mapping. We present experimental results that the new method not only allows for one sided mapping learning, but also leads to preferable numerical results over the existing circularity-based constraint. Our entire code is made publicly available at https://github.com/sagiebenaim/DistanceGAN.
Modeling memory and perception

I present a framework for modeling memory, retrieval, perception, and their interactions. Recent versions of the models were inspired by Bayesian induction: We chose models that make optimal decisions conditioned on a memory/perceptual system with inherently noisy storage and retrieval. The resultant models are, fortunately, largely consistent with my models dating back to the 1960s, and are therefore natural successors. My recent articles have presented simplified models in order to focus on particular applications. This article takes a larger perspective and places the individual models in a more global framework. I will discuss (1) the storage of episodic traces, the accumulation of these into knowledge (e.g., lexical/semantic traces in the case of words), and the changes in knowledge caused by learning; (2) the retrieval of information from episodic memory and from general knowledge; (3) decisions concerning storage, retrieval, and responding. Examples of applications include episodic recognition and cued and free recall, perceptual identification (naming, yes–no and forced-choice), lexical decision, and long-term and short-term priming.
The long quest for computational thinking

Computational thinking (CT) is a popular phrase that refers to a collection of computational ideas and habits of mind that people in computing disciplines acquire through their work in designing programs, software, simulations, and computations performed by machinery. Recently a computational thinking for K-12 movement has spawned initiatives across the education sector, and educational reforms are under way in many countries. However, modern CT initiatives should be well aware of the broad and deep history of computational thinking, or risk repeating already refuted claims, past mistakes, and already solved problems, or losing some of the richest and most ambitious ideas in CT. This paper presents an overview of three important historical currents from which CT has developed: evolution of computing's disciplinary ways of thinking and practicing, educational research and efforts in computing, and emergence of computational science and digitalization of society. The paper examines a number of threats to CT initiatives: lack of ambition, dogmatism, knowing versus doing, exaggerated claims, narrow views of computing, overemphasis on formulation, and lost sight of computational models.
The Rise of Emotion-aware Conversational Agents: Threats in Digital Emotions

A future where the conversation with machines can potentially involve mutual emotions between the parties may be not so far in time. Inspired by the episode of Black Mirror “Be Right Back” and Replika, a futuristic app that promises to be “your best friend”, in this work we are considering the positive and negative points of including an automated learning conversational agent inside the personal world of feelings and emotions. These systems can impact both single individuals and society, worsening an already critical situation. Our conclusion is that a regulation on the artificial emotional content should be considered before actually going beyond some one-way-only limits.
Lipemia: causes, interference mechanisms, detection and management

In the clinical laboratory setting, interferences can be a significant source of laboratory errors with potential to cause serious harm for the patient. After hemolysis, lipemia is the most frequent endogenous interference that can influence results of various laboratory methods by several mechanisms. The most common preanalytical cause of lipemic samples is inadequate time of blood sampling after the meal or parenteral administration of synthetic lipid emulsions. Although the best way of detecting the degree of lipemia is measuring lipemic index on analytical platforms, laboratory experts should be aware of its problems, like false positive results and lack of standardization between manufacturers. Unlike for other interferences, lipemia can be removed and measurement can be done in a clear sample. However, a protocol for removing lipids from the sample has to be chosen carefully, since it is dependent on the analytes that have to be determined. Investigation of lipemia interference is an obligation of manufacturers of laboratory reagents; however, several literature findings report lack of verification of the declared data. Moreover, the acceptance criteria currently used by the most manufacturers are not based on biological variation and need to be revised. Written procedures for detection of lipemia, removing lipemia interference and reporting results from lipemic samples should be available to laboratory staff in order to standardize the procedure, reduce errors and increase patient safety.
On the Design of Adaptive Automation for Complex Systems

This article presents a constrained review of human factors issues relevant to adaptive automation (AA), including designing complex system interfaces to support AA, facilitating human–computer interaction and crew interactions in adaptive system operations, and considering workload associated with AA management in the design of human roles in adaptive systems. Unfortunately, these issues have received limited attention in earlier reviews of AA. This work is aimed at supporting a general theory of human-centered automation advocating humans as active information processors in complex system control loops to support situation awareness and effective performance. The review demonstrates the need for research into user-centered design of dynamic displays in adaptive systems. It also points to the need for discretion in designing transparent interfaces to facilitate human awareness of modes of automated systems. Finally, the review identifies the need to consider critical human–human interactions in designing adaptive systems. This work describes important branches of a developing framework of AA research and contributes to the general theory of human-centered automation.
Understanding "watchers" on GitHub

Users on GitHub can watch repositories to receive notifications about project activity. This introduces a new type of passive project membership. In this paper, we investigate the behavior of watchers and their contribution to the projects they watch. We find that a subset of project watchers begin contributing to the project and those contributors account for a significant percentage of contributors on the project. As contributors, watchers are more confident and contribute over a longer period of time in a more varied way than other contributors. This is likely attributable to the knowledge gained through project notifications.