nils-herrmann/clean_scirepeval huggingface dataset:
Non-Touch Character Input System Based on Hand Tapping Gestures Using Kinect Sensor

There have been a lot of studies on the text input system using the image-based hand gesture recognition. However, hand gesture languages such as sign languages, finger alphabets, and aerial handwriting treated in the previous works have some problems to be commonly used. The aerial handwriting requires much time for writing and recognition. The sign languages and finger alphabets demand quite a knowledge and practice for using it, which results in restricting the number of their users. As a solution to the problems, this paper proposes a new character input system based on hand tapping gestures for Japanese hiragana and English characters that can be used to facilitate human–computer interaction. The hand tapping gestures are motions for tapping keys on aerial virtual keypads by hands, which can be effectively used as a hand alphabet by anyone, including hearing impaired individuals. For hiragana characters, the hand used for tapping a key and the number of stretched fingers of the hand decide the consonant part of characters, and thereby the aerial virtual keypad. The character to be entered is determined by tapping the key on the virtual keypad corresponding to the desired vowel. Because we adopt a key layout similar to the Japanese and English flick keyboard of smart phones, our hand tapping gestures can be easily used by anyone with only a brief description. The users can effectively interact with computers by using our non-touch input system where only the Kinect sensor is used without any keyboard, mouse or body-worn device. We expect that our character input system will open a new channel for human-computer interaction.
EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining

High-utility itemset mining (HUIM) is an important data mining task with wide applications. In this paper, we propose a novel algorithm named EFIM (EFficient high-utility Itemset Mining), which introduces several new ideas to more efficiently discovers high-utility itemsets both in terms of execution time and memory. EFIM relies on two upper-bounds named sub-tree utility and local utility to more effectively prune the search space. It also introduces a novel array-based utility counting technique named Fast Utility Counting to calculate these upper-bounds in linear time and space. Moreover, to reduce the cost of database scans, EFIM proposes efficient database projection and transaction merging techniques. An extensive experimental study on various datasets shows that EFIM is in general two to three orders of magnitude faster and consumes up to eight times less memory than the state-of-art algorithms dHUP, HUI-Miner, HUP-Miner, FHM and UP-Growth+.
Digging Deeper Into the Outcomes of Social Media Use among College Students: The Mediating Effect of Academic Self-Efficacy

The use of social media is prevalent among college students. Additionally, youth and high school students, many of whom will eventually enroll in college, also use social media at high levels. With the nearubiquitous use of social media and the culture that comes with it, it is important for researchers to understand the effects and relationships that social media use has on students and the different aspects of students’ lives. This study specifically considers the outcomes of academic performance and satisfaction with life and digs deeper into these relationships by looking at the mediating role of academic self-efficacy and controlling for the effects of self-regulation. Results from a sample of 234 college students indicate that social media use is negatively associated with academic self-efficacy and academic performance and that academic self-efficacy mediates the relationships with academic performance and satisfaction with life.
Hand Spring Operated Movement Enhancer (HandSOME) device for hand rehabilitation after stroke

Hand rehabilitation after stroke is essential for restoring functional independent lifestyles. After stroke, patients often have flexor hypertonia, making it difficult to open their hand for functional grasp. The development and initial testing of a passive hand rehabilitation device is discussed. The device, Hand Spring Operated Movement Enhancer (HandSOME), assists with opening the patient's hand using a series of bungee cords that apply extension torques to the finger joints that compensate for the flexor hypertonia. This results in significant increase in range of motion and functional use when wearing HandSOME, even in severely impaired subjects. Device design, calibration, and range of motion are described as well as functional and usability testing with stroke subjects.
Widespread uplift and ‘trapdoor’ faulting on Galápagos volcanoes observed with radar interferometry

Volcanic uplift, caused by the accumulation of magma in subsurface reservoirs, is a common precursor to eruptions. But, for some volcanoes, uplift of metres or more has not yet led to an eruption. Here we present displacement maps of volcanoes in the Galápagos Islands, constructed using satellite radar interferometry, that might help explain this dichotomy. We show that all but one of the seven volcanoes on the islands of Isabela and Fernandina deformed during 1992–99.  Azul and Fernandina erupted during the observation period and show evidence of inflation, co-eruptive deflation and shallow dyke intrusion. In contrast, the largest volcano, Sierra Negra, has not erupted, yet exhibits spatially and temporally variable deformation, with a maximum uplift of 2.7 m between 1992 and 1999, which can be modelled by a shallow inflating sill. Inflation during 1997–98, however, was accompanied by ‘trapdoor’ faulting on a steeply dipping fracture system within the caldera. Repeated trapdoor faulting over geological time has formed an arcuate intra-caldera ridge within Sierra Negra and may have acted to relax stresses above the magma chamber, inhibiting summit eruptions. Similar processes may help explain large uplift unaccompanied by eruptive activity at other volcanoes.
American attitudes toward nudges

To successfully select and implement nudges, policy makers need a psychological understanding of who opposes nudges, how they are perceived, and when alternative methods (e.g., forced choice) might work better. Using two representative samples, we examined four factors that influence U.S. attitudes toward nudges – types of nudges, individual dispositions, nudge perceptions, and nudge frames. Most nudges were supported, although opt-out defaults for organ donations were opposed in both samples. “System 1” nudges (e.g., defaults and sequential orderings) were viewed less favorably than “System 2” nudges (e.g., educational opportunities or reminders). System 1 nudges were perceived as more autonomy threatening, whereas System 2 nudges were viewed as more effective for better decision making and more necessary for changing behavior. People with greater empathetic concern tended to support both types of nudges and viewed them as the “right” kind of goals to have. Individualists opposed both types of nudges, and conservatives tended to oppose both types. Reactant people and those with a strong desire for control opposed System 1 nudges. To see whether framing could influence attitudes, we varied the description of the nudge in terms of the target (Personal vs. Societal) and the reference point for the nudge (Costs vs. Benefits). Empathetic people were more supportive when framing highlighted societal costs or benefits, and reactant people were more opposed to nudges when frames highlighted the personal costs of rejection.
A Railway Measurement System to Evaluate the Wheel–Rail Interaction Quality

This paper deals with the problem of measuring the wheel-rail interaction quality in real time using a suitably designed and realized railway measurement system. More specifically, the measured parameter is the equivalent conicity, as defined in the international union of railways UIC 518 Standard, and the measurement system is based on suitable processing of geometric data that is acquired by a contactless optical unit. The measurement system has been verified according to the test procedures described in the UIC 519 Standard. This paper shows how it is possible to obtain, in real time and with comparatively simple algorithms, measurements that are perfectly compliant with the UIC 519 Standard, with regard to the required measurement uncertainty as well.
IBM POWER8 processor core microarchitecture

core microarchitecture B. Sinharoy J. A. Van Norstrand R. J. Eickemeyer H. Q. Le J. Leenstra D. Q. Nguyen B. Konigsburg K. Ward M. D. Brown J. E. Moreira D. Levitan S. Tung D. Hrusecky J. W. Bishop M. Gschwind M. Boersma M. Kroener M. Kaltenbach T. Karkhanis K. M. Fernsler The POWER8i processor is the latest RISC (Reduced Instruction Set Computer) microprocessor from IBM. It is fabricated using the company’s 22-nm Silicon on Insulator (SOI) technology with 15 layers of metal, and it has been designed to significantly improve both single-thread performance and single-core throughput over its predecessor, the POWER7A processor. The rate of increase in processor frequency enabled by new silicon technology advancements has decreased dramatically in recent generations, as compared to the historic trend. This has caused many processor designs in the industry to show very little improvement in either single-thread or single-core performance, and, instead, larger numbers of cores are primarily pursued in each generation. Going against this industry trend, the POWER8 processor relies on a much improved core and nest microarchitecture to achieve approximately one-and-a-half times the single-thread performance and twice the single-core throughput of the POWER7 processor in several commercial applications. Combined with a 50% increase in the number of cores (from 8 in the POWER7 processor to 12 in the POWER8 processor), the result is a processor that leads the industry in performance for enterprise workloads. This paper describes the core microarchitecture innovations made in the POWER8 processor that resulted in these significant performance benefits.
Self-Training for Jointly Learning to Ask and Answer Questions

Building curious machines that can answer as well as ask questions is an important challenge for AI. The two tasks of question answering and question generation are usually tackled separately in the NLP literature. At the same time, both require significant amounts of supervised data which is hard to obtain in many domains. To alleviate these issues, we propose a self-training method for jointly learning to ask as well as answer questions, leveraging unlabeled text along with labeled question answer pairs for learning. We evaluate our approach on four benchmark datasets: SQUAD, MS MARCO, WikiQA and TrecQA, and show significant improvements over a number of established baselines on both question answering and question generation tasks. We also achieved new state-of-the-art results on two competitive answer sentence selection tasks: WikiQA and TrecQA.
Emotion and motivation: the role of the amygdala, ventral striatum, and prefrontal cortex

Emotions are multifaceted, but a key aspect of emotion involves the assessment of the value of environmental stimuli. This article reviews the many psychological representations, including representations of stimulus value, which are formed in the brain during Pavlovian and instrumental conditioning tasks. These representations may be related directly to the functions of cortical and subcortical neural structures. The basolateral amygdala (BLA) appears to be required for a Pavlovian conditioned stimulus (CS) to gain access to the current value of the specific unconditioned stimulus (US) that it predicts, while the central nucleus of the amygdala acts as a controller of brainstem arousal and response systems, and subserves some forms of stimulus-response Pavlovian conditioning. The nucleus accumbens, which appears not to be required for knowledge of the contingency between instrumental actions and their outcomes, nevertheless influences instrumental behaviour strongly by allowing Pavlovian CSs to affect the level of instrumental responding (Pavlovian-instrumental transfer), and is required for the normal ability of animals to choose rewards that are delayed. The prelimbic cortex is required for the detection of instrumental action-outcome contingencies, while insular cortex may allow rats to retrieve the values of specific foods via their sensory properties. The orbitofrontal cortex, like the BLA, may represent aspects of reinforcer value that govern instrumental choice behaviour. Finally, the anterior cingulate cortex, implicated in human disorders of emotion and attention, may have multiple roles in responding to the emotional significance of stimuli and to errors in performance, preventing responding to inappropriate stimuli.
Gender classification by combining clothing, hair and facial component classifiers

In this paper, we propose a novel gender classification framework, which utilizes not only facial features, but also external information, i.e. hair and clothing. Instead of using the whole face, we consider five facial components: forehead, eyes, nose, mouth and chin. We also design feature extraction methods for hair and clothing; these features have seldom been used in previous work because of their large variability. For each type of feature, we train a single support vector machine classifier with probabilistic output. The outputs of these classifiers are combined using various strategies, namely fuzzy integral, maximal, sum, voting, and product rule. The major contributions of this paper are (1) investigating the gender discriminative ability of clothing information; (2) using facial components instead of the whole face to obtain higher robustness for occlusions and noise; (3) exploiting hair and clothing information to facilitate gender classification. Experimental results show that our proposed framework improves classification accuracy, even when images contain occlusions, noise, and illumination changes. & 2011 Elsevier B.V. All rights reserved.
k-Pattern Set Mining under Constraints

We introduce the problem of k-pattern set mining, concerned with finding a set of k related patterns under constraints. This contrasts to regular pattern mining, where one searches for many individual patterns. The k-pattern set mining problem is a very general problem that can be instantiated to a wide variety of well-known mining tasks including concept-learning, rule-learning, redescription mining, conceptual clustering and tiling. To this end, we formulate a large number of constraints for use in k-pattern set mining, both at the local level, that is, on individual patterns, and on the global level, that is, on the overall pattern set. Building general solvers for the pattern set mining problem remains a challenge. Here, we investigate to what extent constraint programming (CP) can be used as a general solution strategy. We present a mapping of pattern set constraints to constraints currently available in CP. This allows us to investigate a large number of settings within a unified framework and to gain insight in the possibilities and limitations of these solvers. This is important as it allows us to create guidelines in how to model new problems successfully and how to model existing problems more efficiently. It also opens up the way for other solver technologies.
Boosting Coded Dynamic Features for Facial Action Units and Facial Expression Recognition

It is well known that how to extract dynamical features is a key issue for video based face analysis. In this paper, we present a novel approach of facial action units (AU) and expression recognition based on coded dynamical features. In order to capture the dynamical characteristics of facial events, we design the dynamical haar-like features to represent the temporal variations of facial events. Inspired by the binary pattern coding, we further encode the dynamic haar-like features into binary pattern features, which are useful to construct weak classifiers for boosting learning. Finally the Adaboost is performed to learn a set of discriminating coded dynamic features for facial active units and expression recognition. Experiments on the CMU expression database and our own facial AU database show its encouraging performance.
CMOS-based sensors

This paper provides an overview of CMOS-based sensor technology with specific attention placed on devices made through micromachining of CMOS substrates and thin films. Microstructures may be formed using either pre-CMOS, intra-CMOS and post-CMOS fabrication approaches. To illustrate and motivate monolithic integration, a handful of microsystem examples, including inertial sensors, gravimetric chemical sensors, microphones, and a bone implantable sensor will be highlighted. Design constraints and challenges for CMOS-MEMS devices will be covered
Positive psychology. An introduction.

A science of positive subjective experience, positive individual traits, and positive institutions promises to improve quality of life and prevent the pathologies that arise when life is barren and meaningless. The exclusive focus on pathology that has dominated so much of our discipline results in a model of the human being lacking the positive features that make life worth living. Hope, wisdom, creativity, future mindedness, courage, spirituality, responsibility, and perseverance are ignored or explained as transformations of more authentic negative impulses. The 15 articles in this millennial issue of the American Psychologist discuss such issues as what enables happiness, the effects of autonomy and self-regulation, how optimism and hope affect health, what constitutes wisdom, and how talent and creativity come to fruition. The authors outline a framework for a science of positive psychology, point to gaps in our knowledge, and predict that the next century will see a science and profession that will come to understand and build the factors that allow individuals, communities, and societies to flourish.
Importance masks for revealing occluded objects in augmented reality

When simulating "X-ray vision" in Augmented Reality, a critical aspect is ensuring correct perception of the occluded objects position. Naïve overlay rendering of occluded objects on top of real-world occluders can lead to a misunderstanding of the visual scene and a poor perception of the depth. We present a simple technique to enhance the perception of the spatial arrangements in the scene. An importance mask associated with occluders informs the rendering what information can be overlaid and what should be preserved. This technique is independent of scene properties such as illumination and surface properties, which may be unknown. The proposed solution is computed efficiently in a single-pass fragment shaders on the GPU.
Clustering Web services to facilitate service discovery

Clustering Web services would greatly boost the ability of Web service search engine to retrieve relevant services. The performance of traditional Web service description language (WSDL)-based Web service clustering is not satisfied, due to the singleness of data source. Recently, Web service search engines such as Seekda! allow users to manually annotate Web services using tags, which describe functions of Web services or provide additional contextual and semantical information. In this paper, we cluster Web services by utilizing both WSDL documents and tags. To handle the clustering performance limitation caused by uneven tag distribution and noisy tags, we propose a hybrid Web service tag recommendation strategy, named WSTRec, which employs tag co-occurrence, tag mining, and semantic relevance measurement for tag recommendation. Extensive experiments are conducted based on our real-world dataset, which consists of 15,968 Web services. The experimental results demonstrate the effectiveness of our proposed service clustering and tag recommendation strategies. Specifically, compared with traditional WSDL-based Web service clustering approaches, the proposed approach produces gains in both precision and recall for up to 14 % in most cases.
On The Mathematical Foundations Of Theoretical Statistics

On the Mathematical   Philosophical Transactions of the Royal Society of London. Series A Solutions to Exercises. 325. Bibliography. 347. Index Discrete mathematics is an essential part of the foundations of (theoretical) computer science, statistics . 2) Statistical Methods by S.P.Gupta. 3) Mathematical Statistics by Saxena & Kapoor. 4) Statistics by Sancheti & Kapoor. 5) . 2. R.V and and .
Pervasive Healthcare and Wireless Health Monitoring

With an increasingly mobile society and the worldwide deployment of mobile and wireless networks, the wireless infrastructure can support many current and emerging healthcare applications. This could fulfill the vision of “Pervasive Healthcare” or healthcare to anyone, anytime, and anywhere by removing locational, time and other restraints while increasing both the coverage and the quality. In this paper, we present applications and requirements of pervasive healthcare, wireless networking solutions and several important research problems. The pervasive healthcare applications include pervasive health monitoring, intelligent emergency management system, pervasive healthcare data access, and ubiquitous mobile telemedicine. One major application in pervasive healthcare, termed comprehensive health monitoring is presented in significant details using wireless networking solutions of wireless LANs, ad hoc wireless networks, and, cellular/GSM/3G infrastructureoriented networks.Many interesting challenges of comprehensive wireless health monitoring, including context-awareness, reliability, and, autonomous and adaptable operation are also presented along with several high-level solutions. Several interesting research problems have been identified and presented for future research.
Adaptive Weight Allocation-Based Subpixel Rendering Algorithm

In this letter, a new approach is presented for adaptive weight allocation-based subpixel rendering in organic light-emitting diode displays. Subpixel rendering is used to enhance the apparent resolution without changing a pixel structure. Existing methods have blurring and color fringing artifact after subpixel rendering. The proposed method, on the other hand, dynamically controls weights of current and neighboring pixels based on color difference. Thus, it preserves the image quality, while increasing the resolution. In experiments, the proposed subpixel rendering improved luminance sharpness by up to 0.043, when compared with the benchmark methods. For chrominance blending, the peak signal-to-noise ratio of the proposed method was up to 6.192 dB higher than those of benchmark methods.
Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learning

Instability and variability of Deep Reinforcement Learning (DRL) algorithms tend to adversely affect their performance. Averaged-DQN is a simple extension to the DQN algorithm, based on averaging previously learned Q-values estimates, which leads to a more stable training procedure and improved performance by reducing approximation error variance in the target values. To understand the effect of the algorithm, we examine the source of value function estimation errors and provide an analytical comparison within a simplified model. We further present experiments on the Arcade Learning Environment benchmark that demonstrate significantly improved stability and performance due to the proposed extension.