nils-herrmann/clean_scirepeval huggingface dataset:
Execution-Guided Neural Program Synthesis

Neural program synthesis from input-output examples has attracted an increasing interest from both the machine learning and the programming language community. Most existing neural program synthesis approaches employ an encoder-decoder architecture, which uses an encoder to compute the embedding of the given inputoutput examples, as well as a decoder to generate the program from the embedding following a given syntax. Although such approaches achieve a reasonable performance on simple tasks such as FlashFill, on more complex tasks such as Karel, the state-of-the-art approach can only achieve an accuracy of around 77%. We observe that the main drawback of existing approaches is that the semantic information is greatly under-utilized. In this work, we propose two simple yet principled techniques to better leverage the semantic information, which are execution-guided synthesis and synthesizer ensemble. These techniques are general enough to be combined with any existing encoder-decoder-style neural program synthesizer. Applying our techniques to the Karel dataset, we can boost the accuracy from around 77% to more than 90%, reducing the error rate by around 60%.
Performance Optimization of Aho-Corasick Algorithm on a GPU

Aho-Corasick (AC) algorithm is a multiple patternsmatching algorithm commonly used for applications such as computer and network security, bioinformatics, image processing, among others. These applications are computationally demanding, thus optimizing performance for AC algorithm is crucial. In this paper, we present a performance optimization strategy for the AC algorithm on a Graphic Processing Unit(GPU). Our strategy efficiently utilizes the high degree of the on chip parallelism and the complicated memory hierarchy of the GPU so that the aggregate performance (or throughput) for the AC algorithm can be optimized. The strategy significantly cuts down the effective memory access latencies and efficiently utilizes the memory bandwidth. Also, it maximizes the effects of the multithreading capability of the GPU through optimal thread scheduling. Experimental results on Nvidia GeForce GTX 285 GPU show that our approach delivers up to 127 Gbps throughput performance and 222-times speedup compared with a serial version running on single core of 2.2Ghz Core2Duo Intel processor.
Pricing for perishable goods in advance selling strategy

Perishable goods are those whose value decay rapidly over time. To reach the maximum profit, vendors should sell them as soon as possible ever since they are available on market. Advance selling strategy refers to sell a product before it is ready for sale and this helps sellers to observe the market response and adjust its operational plans. With the rapid development of E-commerce, it becomes much easier and cheaper for both sellers and buyers to search for desired information. Advance selling has become an effective and popular sales strategy for perishable goods selling. In this paper, we aim to develop a pricing model for perishable goods in the context of advance selling. Consumers are classified to two categories based on the shopping habit, namely strategic consumer and conventional consumer. The whole sales process is divided into two stages, which are advance selling stage and normal sales stage. A pricing model is proposed in advance selling strategy to achieve maximum profit for the seller in this paper. The best pricing of two phases can be obtained when the market reaches equilibrium. Numerical computation and sensitive analysis are conducted in the end of this paper. The results show that the best prices grow with the demand and depend on the ratio between the quantities of potential consumer for the two sales stages.
A Pattern Mining Approach to Sensor-Based Human Activity Recognition

Recognizing human activities from sensor readings has recently attracted much research interest in pervasive computing due to its potential in many applications, such as assistive living and healthcare. This task is particularly challenging because human activities are often performed in not only a simple (i.e., sequential), but also a complex (i.e., interleaved or concurrent) manner in real life. Little work has been done in addressing complex issues in such a situation. The existing models of interleaved and concurrent activities are typically learning-based. Such models lack of flexibility in real life because activities can be interleaved and performed concurrently in many different ways. In this paper, we propose a novel pattern mining approach to recognize sequential, interleaved, and concurrent activities in a unified framework. We exploit Emerging Pattern-a discriminative pattern that describes significant changes between classes of data-to identify sensor features for classifying activities. Different from existing learning-based approaches which require different training data sets for building activity models, our activity models are built upon the sequential activity trace only and can be applied to recognize both simple and complex activities. We conduct our empirical studies by collecting real-world traces, evaluating the performance of our algorithm, and comparing our algorithm with static and temporal models. Our results demonstrate that, with a time slice of 15 seconds, we achieve an accuracy of 90.96 percent for sequential activity, 88.1 percent for interleaved activity, and 82.53 percent for concurrent activity.
The Random Forest Based Detection of Shadowsock's Traffic

With the development of anonymous communication technology, it has led to the fact that the network monitoring is becoming more and more difficult. If the anonymous traffic can be effectively identified, the abuse of such technology can be prevented. Since the study of machine learning is rapidly developing these years, this paper applies the Random Forest Algorithm --- a semi-supervised learning method --- into the traffic detection of Shadowsocks. We can get over 85% detection accuracy rate in our experiments after applying Random Forest Algorithm by collecting train set, gathering features, training models and predicting results. With the scale of train set and test set increase, the detection accuracy rate gradually increases until it becomes constant. We will make several adjustments on train set, test set and feature set to reduce the false alarm rate and false rate when detecting
The effect of site quality on repurchase intention in Internet shopping through mediating variables: The case of university students in South Korea

We performed a study to determine the influence that site quality has on repurchase intention of Internet shopping through customer satisfaction, customer trust, and customer commitment. Appropriate measures were developed and tested on 230 university students of Gyeongnam province in South Korea with a cross-sectional questionnaire survey. The results of the empirical analysis confirmed that site quality can be conceptualized as a composite of six dimensions of shopping convenience, site design, information usefulness, transaction security, payment system, and customer communication. Second, site quality positively affected customer satisfaction and customer trust, but did not affect customer commitment and repurchase intention. Third, site quality can affect repurchase intention by enhancing or attenuating customer satisfaction, customer trust, and customer commitment in online transaction situation. The mediating effect of customer satisfaction, customer trust, and customer commitment between site quality and repurchase intention is identified. Fourth, site quality indirectly affected customer commitment through customer satisfaction. Customer satisfaction indirectly affected repurchase intention through customer trust and customer commitment. Thus, it is found that site quality can be a very important factor to enhance repurchase intention in the customer perspective.
Is the cerebellum a smith predictor?

The motor system may use internal predictive models of the motor apparatus to achieve better control than would be possible by negative feedback. Several theories have proposed that the cerebellum may form these predictive representations. In this article, we review these theories and try to unify them by reference to an engineering control model known as a Smith Predictor. We suggest that the cerebellum forms two types of internal model. One model is a forward predictive model of the motor apparatus (e.g., limb and muscle), providing a rapid prediction of the sensory consequences of each movement. The second model is of the time delays in the control loop (due to receptor and effector delays, axonal conductances, and cognitive processing delays). This model delays a copy of the rapid prediction so that it can be compared in temporal register with actual sensory feedback from the movement. The result of this comparison is used both to correct for errors in performance and as a training signal to learn the first model. We discuss evidence that the cerebellum could form both of these models and suggest that the cerebellum may hold at least two separate Smith Predictors. One, in the lateral cerebellum, would predict the movement outcome in visual, egocentric, or peripersonal coordinates. Another, in the intermediate cerebellum, would predict the consequences in motor coordinates. Generalization of the Smith Predictor theory is discussed in light of cerebellar involvement in nonmotor control systems, including autonomic functions and cognition.
Impact of Social Media on Social Anxiety: A Systematic Review

Online social networking sites are being used all around the world. However, only recently researchers have started to investigate their relationship with mental health. Evidence coming from literature suggests that they have both advantages and disadvantages for individuals. The aim of this study is to critically review the existent research conducted on the relationship between online social networking and social anxiety.  According to PRISMA guidelines, comprehensive systematic searches of electronic databases were conducted (PsychInfo, Cochrane, PubMed, Scopus, Web of Science). Terms related to online social networking were combined with social anxiety terms. After identifying relevant papers, the relationship between social media networking and social anxiety as well as limitations of each study were presented. All of the papers included were cross-sectional, relying on self-report assessments.  There are several papers published on the relationship between social media and social anxiety; however, more research needs to be done in order to clarify the influence of one variable over the other. Rigorous experimental studies need to be conducted with different time-point assessments, and bidirectionality should be investigated. Important mediators and moderators have to be considered in order to explain the relationship between social media networking and social anxiety.
ROBHAZ-rescue: rough-terrain negotiable teleoperated mobile robot for rescue mission

This paper presents design and integration of the ROBHAZ-DT3, which is a newly developed mobile robot system with chained double-track mechanisms. It is designed to carry out military and civilian missions in various hazardous environments. A passive adaptation mechanism equipped between the front and rear body enables the ROBHAZ-DT3 to have good adaptability to uneven terrains including stairs. The passive adaptation mechanism reduces energy consumption when moving on uneven terrain as well as its simplicity in design and remote control, since no actuator is necessary for adaptation. Based on this novel mobile platform, a rescue version of the ROBHAZ-DT3 with appropriate sensors and a semi-autonomous mapping and localization algorithm is developed to participate in the RoboCup2004 US-Open: Urban Search and Rescue Competition. From the various experiments in the realistic rescue arena, we can verify that the ROBHAZ-DT3 is reliable in travelling rugged terrain and the proposed mapping and localization algorithm are effective in the unstructured environment with uneven ground.
Chemical and Physical Behavior of Human Hair

At or near its surface, hair fibers contain a thick protective cover consisting of six to eight layers of flat overlapping scale-like structures called cuticle or scales which consists of high sulfur KAPs, keratin proteins and structural lipids. The cuticle layers surround the cortex, but the cortex contains the major part of the fiber mass. The cortex consists of spindle-shaped cells that are aligned parallel with the fiber axis. Cortical cells consist of both Type I and Type II keratins (IF proteins) and KAP proteins. Coarser hairs often contain one or more loosely packed porous regions called the medulla, located near the center of the fiber. The cell membrane complex, the “glue” that binds or holds all of the cells together, is a highly laminar structure consisting of both structural lipid and protein structures. Hair fibers grow in cycles consisting of three distinct stages called anagen (growth), catagen (transition) and telogen (rest). Each stage is controlled by molecular signals/regulators acting first on stem cells and then on the newly formed cells in the bulb and subsequently higher up in differentiation in the growing fiber. The effects and incidence of hair growth and hair loss (normal and diseased) for both males and females are described in detail. Molecular structures controlling hair fiber curvature (whether a fiber is straight or curly) and the effects of the different structural units of the fiber on stress–strain and swelling behavior are described in detail.
The boundaries between: Parental involvement in a teen's online world

The increasing popularity of the Internet and social media is creating new and unique challenges for parents and adolescents regarding the boundaries between parental control and adolescent autonomy in virtual spaces. Drawing on developmental psychology and Communication Privacy Management (CPM) theory, we conduct a qualitative study to examine the challenge between parental concern for adolescent online safety and teens’ desire to independently regulate their own online experiences. Analysis of 12 parent–teen pairs revealed five distinct challenges: (a) increased teen autonomy and decreased parental control resulting from teens’ direct and unmediated access to virtual spaces, (b) the shift in power to teens who are often more knowledgeable about online spaces and technology, (c) the use of physical boundaries by parents as a means to control virtual spaces, (d) an increase in indirect boundary control strategies such as covert monitoring, and (e) the blurring of lines in virtual spaces between parents’ teens and teens’ friends.
A Tale of Two Sieves

I t is the best of times for the game of factoring large numbers into their prime factors. In 1970 it was barely possible to factor “hard” 20-digit numbers. In 1980, in the heyday of the Brillhart-Morrison continued fraction factoring algorithm, factoring of 50-digit numbers was becoming commonplace. In 1990 my own quadratic sieve factoring algorithm had doubled the length of the numbers that could be factored, the record having 116 digits. By 1994 the quadratic sieve had factored the famous 129-digit RSA challenge number that had been estimated in Martin Gardner’s 1976  American column to be safe for 40 quadrillion years (though other estimates around then were more modest). But the quadratic sieve is no longer the champion. It was replaced by Pollard’s number field sieve in the spring of 1996, when that method successfully split a 130-digit RSA challenge number in about 15% of the time the quadratic sieve would have taken. In this article we shall briefly meet these factorization algorithms—these two sieves—and some of the many people who helped to develop them. In the middle part of this century, computational issues seemed to be out of fashion. In most books the problem of factoring big numbers was largely ignored, since it was considered trivial. After all, it was doable in principle, so what else was there to discuss? A few researchers ignored the fashions of the time and continued to try to find fast ways to factor. To these few it was a basic and fundamental problem, one that should not be shunted to the side. But times change. In the last few decades we have seen the advent of accessible and fast computing power, and we have seen the rise of cryptographic systems that base their security on our supposed inability to factor quickly (and on other number theoretic problems). Today there are many people interested in factoring, recognizing it not only as a benchmark for the security of cryptographic systems, but for computing itself. In 1984 the Association for Computing Machinery presented a plaque to the Institute for Electrical and Electronics Engineers (IEEE) on the occasion of the IEEE centennial. It was inscribed with the prime factorization of the number 2251 − 1 that was completed that year with the quadratic sieve. The president of the ACM made the following remarks:
Denoising auto-encoders for learning of objects and tools affordances in continuous space

The concept of affordances facilitates the encoding of relations between actions and effects in an environment centered around the agent. Such an interpretation has important impacts on several cognitive capabilities and manifestations of intelligence, such as prediction and planning. In this paper, a new framework based on denoising Auto-encoders (dA) is proposed which allows an agent to explore its environment and actively learn the affordances of objects and tools by observing the consequences of acting on them. The dA serves as a unified framework to fuse multi-modal data and retrieve an entire missing modality or a feature within a modality given information about other modalities. This work has two major contributions. First, since training the dA is done in continuous space, there will be no need to discretize the dataset and higher accuracies in inference can be achieved with respect to approaches in which data discretization is required (e.g. Bayesian networks). Second, by fixing the structure of the dA, knowledge can be added incrementally making the architecture particularly useful in online learning scenarios. Evaluation scores of real and simulated robotic experiments show improvements over previous approaches while the new model can be applied in a wider range of domains.
Snap, Eat, RepEat: A Food Recognition Engine for Dietary Logging

We present a system to assist users in dietary logging habits, which performs food recognition from pictures snapped on their phone in two different scenarios. In the first scenario, called "Food in context", we exploit the GPS information of a user to determine which restaurant they are having a meal at, therefore restricting the categories to recognize to the set of items in the menu. Such context allows us to also report precise calories information to the user about their meal, since restaurant chains tend to standardize portions and provide the dietary information of each meal. In the second scenario, called "Foods in the wild" we try to recognize a cooked meal from a picture which could be snapped anywhere. We perform extensive experiments on food recognition on both scenarios, demonstrating the feasibility of our approach at scale, on a newly introduced dataset with 105K images for 500 food categories.
A distributed ADMM approach for mobile data offloading in software defined network

Mobile data offloading has been introduced to alleviate the congestion of cellular networks and to improve the quality of service for mobile end users. This paper presents a distributed mechanism for mobile data offloading in software defined network (SDN) at the network edge. In SDN, the data traffic of base stations (BSs) can be dynamically offloaded to access points (APs), which is enabled by the SDN controller. The SDN controller formulates a revenue maximization problem to optimize the data offloading decision, and solves the problem in a fully distributed fashion. The proposed mechanism is based on the proximal Jacobian multi-block alternating direction method of multipliers (ADMM). BSs and APs perform the offloading decision update concurrently, and are coordinated by the SDN controller through dual variables to reach a consensus on the offloading demand and supply. Numerical simulations validate the effectiveness of the proposed algorithm.
Physical and psychological factors predict outcome following whiplash injury

Predictors of outcome following whiplash injury are limited to socio-demographic and symptomatic factors, which are not readily amenable to secondary and tertiary intervention. This prospective study investigated the predictive capacity of early measures of physical and psychological impairment on pain and disability 6 months following whiplash injury. Motor function (ROM; kinaesthetic sense; activity of the superficial neck flexors (EMG) during cranio-cervical flexion), quantitative sensory testing (pressure, thermal pain thresholds, brachial plexus provocation test), sympathetic vasoconstrictor responses and psychological distress (GHQ-28, TSK, IES) were measured in 76 acute whiplash participants. The outcome measure was Neck Disability Index scores at 6 months. Stepwise regression analysis was used to predict the final NDI score. Logistic regression analyses predicted membership to one of the three groups based on final NDI scores (<8 recovered, 10-28 mild pain and disability, >30 moderate/severe pain and disability). Higher initial NDI score (1.007-1.12), older age (1.03-1.23), cold hyperalgesia (1.05-1.58), and acute post-traumatic stress (1.03-1.2) predicted membership to the moderate/severe group. Additional variables associated with higher NDI scores at 6 months on stepwise regression analysis were: ROM loss and diminished sympathetic reactivity. Higher initial NDI score (1.03-1.28), greater psychological distress (GHQ-28) (1.04-1.28) and decreased ROM (1.03-1.25) predicted subjects with persistent milder symptoms from those who fully recovered. These results demonstrate that both physical and psychological factors play a role in recovery or non-recovery from whiplash injury. This may assist in the development of more relevant treatment methods for acute whiplash.
Minitrack Introduction

The Minitrack "IT Governance and its Mechanisms" within the "Organizational Systems and Technology" Track is introduced at the HICSS conference in 2002. The goal of this Minitrack is to enhance publications on the issue of IT governance and its mechanisms. It is expected to get submissions from academics and practitioners involved in research on IT governance, Business/IT alignment, IT Balanced Scorecard, COBIT implementations, ITIL implementations,, applications of the Capability Maturity Model for IT processes, and typical IT governance mechanisms such as IT steering committees.