nils-herrmann/clean_scirepeval huggingface dataset:
Deep Learning Assisted Heuristic Tree Search for the Container Pre-marshalling Problem

One of the key challenges for operations researchers solving real-world problems is designing and implementing high-quality heuristics to guide their search procedures. In the past, machine learning techniques have failed to play a major role in operations research approaches, especially in terms of guiding branching and pruning decisions. We integrate deep neural networks into a heuristic tree search procedure to decide which branch to choose next and to estimate a bound for pruning the search tree of an optimization problem. We call our approach Deep Learning assisted heuristic Tree Search (DLTS) and apply it to a well-known problem from the container terminals literature, the container pre-marshalling problem (CPMP). Our approach is able to learn heuristics customized to the CPMP solely through analyzing the solutions to CPMP instances, and applies this knowledge within a heuristic tree search to produce the highest quality heuristic solutions to the CPMP to date.
SHPR-Net: Deep Semantic Hand Pose Regression From Point Clouds

3-D hand pose estimation is an essential problem for human–computer interaction. Most of the existing depth-based hand pose estimation methods consume 2-D depth map or 3-D volume via 2-D/3-D convolutional neural networks. In this paper, we propose a deep semantic hand pose regression network (SHPR-Net) for hand pose estimation from point sets, which consists of two subnetworks: a semantic segmentation subnetwork and a hand pose regression subnetwork. The semantic segmentation network assigns semantic labels for each point in the point set. The pose regression network integrates the semantic priors with both input and late fusion strategy and regresses the final hand pose. Two transformation matrices are learned from the point set and applied to transform the input point cloud and inversely transform the output pose, respectively, which makes the SHPR-Net more robust to geometric transformations. Experiments on NYU, ICVL, and MSRA hand pose data sets demonstrate that our SHPR-Net achieves high performance on par with the start-of-the-art methods. We also show that our method can be naturally extended to hand pose estimation from the multi-view depth data and achieves further improvement on the NYU data set.
Open Source: Concepts, Benefits, and Challenges

With the emergence of free and open source software (F/OSS) projects (e.g. Linux) as serious contenders to well-established proprietary software, advocates of F/OSS are quick to generalize the superiority of this approach to software development. On the other hand, some wellestablished software development firms view F/OSS as a threat and vociferously refute the claims of F/OSS advocates. This article represents a tutorial on F/OSS that tries objectively to identify and present open source software’s concepts, benefits, and challenges. From our point of view, F/OSS is more than just software. We conceptualize it as an IPO system that consists of the license as the boundary of the system, the community that provides the input, the development process, and the software as the output. After describing the evolution and definition of F/OSS, we identify three approaches to benefiting from F/OSS that center on (1) the software, (2) the community, and (3) the license respectively. Each approach is fit for a specific situation and provides a unique set of benefits and challenges. We further illustrate our points by refuting common misconceptions associated with F/OSS based upon our conceptual framework.
Communities of Practice A Research Paradigm for the Mixed Methods Approach

The mixed methods approach has emerged as a ‘‘third paradigm’’ for social research. It has developed a platform of ideas and practices that are credible and distinctive and that mark the approach out as a viable alternative to quantitative and qualitative paradigms. However, there are also a number of variations and inconsistencies within the mixed methods approach that should not be ignored. This article argues the need for a vision of research paradigm that accommodates such variations and inconsistencies. It is argued that the use of ‘‘communities of practice’’ as the basis for such a research paradigm is (a) consistent with the pragmatist underpinnings of the mixed methods approach, (b) accommodates a level of diversity, and (c) has good potential for understanding the methodological choices made by those conducting mixed methods research.
A Review of the Research on Internet Addiction

Research indicates that maladaptive patterns of Internet use constitute behavioral addiction. This article explores the research on the social effects of Internet addiction. There are four major sections. The Introduction section overviews the field and introduces definitions, terminology, and assessments. The second section reviews research findings and focuses on several key factors related to Internet addiction, including Internet use and time, identifiable problems, gender differences, psychosocial variables, and computer attitudes. The third section considers the addictive potential of the Internet in terms of the Internet, its users, and the interaction of the two. The fourth section addresses current and projected treatments of Internet addiction, suggests future research agendas, and provides implications for educational psychologists.
PACIS ) 2014 EXPLORING MOBILE PAYMENT ADOPTION IN CHINA

With the development of the integration between mobile communication and Internet technology, China is expected to have a large number of M-payment users due to its population size with a large number of mobile users. However, the number of M-payment users in China is still low and currently there are limited in-depth studies exploring the adoption of M-payment in China. This study aims to explore reasons for individuals to use M-payment in China through a qualitative study. The research results indicated that M-payment adoption was influenced by various reasons related to system quality, service quality, usefulness, social influence, trust, among others. The study findings indicate that the influence of system quality and service quality on individual’s decision to use in China appear to be the most important. A particular individual lifestyle, need and promotion offered by service providers have also been identified as important reasons for using M-payment in China. The outcomes of this study enhance the current knowledge about the M-payment adoption particularly in China. They can also be used by service providers to devise appropriate strategies to encourage wider adoption of M-payment.
Podcasting in Education: Student Attitudes, Behaviour and Self-Efficacy

The aim of the present study was to describe the characteristics of podcast users, compare uptake across courses, examine preferred modes of use and satisfaction, assess the impact of podcasts on lecture attendance, and evaluate reasons for use and non-use. Participants were 273 undergraduate students enrolled in six diverse courses at a large Australian university. Results suggested differences in uptake and satisfaction across courses, with later year students more satisfied than first year students. Although podcast users were older, worked longer hours in paid employment, and attended fewer lectures than those who did not use podcasts, results also suggest that users had more contact with staff and reported higher levels of academic self-efficacy than nonusers. Suggestions for improvements to current podcasting provisions are offered and directions for future research are provided. In particular the need to tap into the use of podcasts for examination revision is highlighted.
Understand Short Texts by Harvesting and Analyzing Semantic Knowledge

Understanding short texts is crucial to many applications, but challenges abound. First, short texts do not always observe the syntax of a written language. As a result, traditional natural language processing tools, ranging from part-of-speech tagging to dependency parsing, cannot be easily applied. Second, short texts usually do not contain sufficient statistical signals to support many state-of-the-art approaches for text mining such as topic modeling. Third, short texts are more ambiguous and noisy, and are generated in an enormous volume, which further increases the difficulty to handle them. We argue that semantic knowledge is required in order to better understand short texts. In this work, we build a prototype system for short text understanding which exploits semantic knowledge provided by a well-known knowledgebase and automatically harvested from a web corpus. Our knowledge-intensive approaches disrupt traditional methods for tasks such as text segmentation, part-of-speech tagging, and concept labeling, in the sense that we focus on semantics in all these tasks. We conduct a comprehensive performance evaluation on real-life data. The results show that semantic knowledge is indispensable for short text understanding, and our knowledge-intensive approaches are both effective and efficient in discovering semantics of short texts.
A Review of Wearable Technologies for Elderly Care that Can Accurately Track Indoor Position, Recognize Physical Activities and Monitor Vital Signs in Real Time

Rapid growth of the aged population has caused an immense increase in the demand for healthcare services. Generally, the elderly are more prone to health problems compared to other age groups. With effective monitoring and alarm systems, the adverse effects of unpredictable events such as sudden illnesses, falls, and so on can be ameliorated to some extent. Recently, advances in wearable and sensor technologies have improved the prospects of these service systems for assisting elderly people. In this article, we review state-of-the-art wearable technologies that can be used for elderly care. These technologies are categorized into three types: indoor positioning, activity recognition and real time vital sign monitoring. Positioning is the process of accurate localization and is particularly important for elderly people so that they can be found in a timely manner. Activity recognition not only helps ensure that sudden events (e.g., falls) will raise alarms but also functions as a feasible way to guide people's activities so that they avoid dangerous behaviors. Since most elderly people suffer from age-related problems, some vital signs that can be monitored comfortably and continuously via existing techniques are also summarized. Finally, we discussed a series of considerations and future trends with regard to the construction of "smart clothing" system.
Language selection in bilingual speech: evidence for inhibitory processes.

Although bilinguals rarely make random errors of language when they speak, research on spoken production provides compelling evidence to suggest that both languages are active when only one language is spoken (e.g., [Poulisse, N. (1999). Slips of the tongue: Speech errors in first and second language production. ). Moreover, the parallel activation of the two languages appears to characterize the planning of speech for highly proficient bilinguals as well as second language learners. In this paper, we first review the evidence for cross-language activity during single word production and then consider the two major alternative models of how the intended language is eventually selected. According to language-specific selection models, both languages may be active but bilinguals develop the ability to selectively attend to candidates in the intended language. The alternative model, that candidates from both languages compete for selection, requires that cross-language activity be modulated to allow selection to occur. On the latter view, the selection mechanism may require that candidates in the nontarget language be inhibited. We consider the evidence for such an inhibitory mechanism in a series of recent behavioral and neuroimaging studies.
An Attention-Based Approach for Single Image Super Resolution

The main challenge of single image super resolution (SISR) is the recovery of high frequency details such as tiny textures. However, most of the state-of-the-art methods lack specific modules to identify high frequency areas, causing the output image to be blurred. We propose an attention-based approach to give a discrimination between texture areas and smooth areas. After the positions of high frequency details are located, high frequency compensation is carried out. This approach can incorporate with previously proposed SISR networks. By providing high frequency enhancement, better performance and visual effect are achieved. We also propose our own SISR network composed of DenseRes blocks. The block provides an effective way to combine the low level features and high level features. Extensive benchmark evaluation shows that our proposed method achieves significant improvement over the state-of-the-art works in SISR.
Design and Simulation of Gate Driver Circuit Using Pulse Transformer

Pulse transformer is always used to be the isolator between gate driver and power MOSFET. There are many topologies about the peripheral circuit. This paper proposes a new topology circuit that uses pulse transformer to transfer driving signal and driving power, energy storage capacitor to supply secondary side power and negative voltage. Without auxiliary power source, it can realize rapidly switch and off state with negative voltage. And a simulation model has been used to verify it. The simulation results prove that the new driver has a better anti-interference, faster switching speed, lower switching loss, and higher reliability than the current drive circuits.
Seakeeping analysis for preliminary design

In recent years, greater attention has been given to vessels’ seakeeping characteristics. This is due to a number of factors: proliferation of high-speed semi-displacement passenger vessels; increasing demand for passenger comfort (passengers are often able to vote with their feet by taking alternative transport, e.g. English Channel Tunnel); deployment of increasingly sophisticated systems on ever smaller naval vessels (Hunt 1999); greater pressure from regulatory bodies and the broader public for safer vessels; staggering advancements in desktop computer power; and developments in prediction and analysis tools.
Neural Joking Machine : Humorous image captioning

What is an effective expression that draws laughter from human beings? In the present paper, in order to consider this question from an academic standpoint, we generate an image caption that draws a “laugh” by a computer. A system that outputs funny captions based on the image caption proposed in the computer vision field is constructed. Moreover, we also propose the Funny Score, which flexibly gives weights according to an evaluation database. The Funny Score more effectively brings out “laughter” to optimize a model. In addition, we build a self-collected BoketeDB, which contains a theme (image) and funny caption (text) posted on “Bokete”, which is an image Ogiri website. In an experiment, we use BoketeDB to verify the effectiveness of the proposed method by comparing the results obtained using the proposed method and those obtained using MS COCO Pre-trained CNN+LSTM, which is the baseline and idiot created by humans. We refer to the proposed method, which uses the BoketeDB pre-trained model, as the Neural Joking Machine (NJM).
New global optimization methods for ship design problems

The aim of this paper is to solve optimal design problems for industrial applications when the objective function value requires the evaluation of expensive simulation codes and its first derivatives are not available. In order to achieve this goal we propose two new algorithms that draw inspiration from two existing approaches: a filled function based algorithm and a Particle Swarm Optimization method. In order to test the efficiency of the two proposed algorithms, we perform a numerical comparison both with the methods we drew inspiration from, and with some standard Global Optimization algorithms that are currently adopted in industrial design optimization. Finally, a realistic ship design problem, namely the reduction of the amplitude of the heave motion of a ship advancing in head seas (a problem connected to both safety and comfort), is solved using the new codes and other global and local derivativeThis work has been partially supported by the Ministero delle Infrastrutture e dei Trasporti in the framework of the research plan “Programma di Ricerca sulla Sicurezza”, Decreto 17/04/2003 G.U. n. 123 del 29/05/2003, by MIUR, ). E.F. Campana ( ) · D. Peri · A. Pinto INSEAN—Istituto Nazionale per Studi ed Esperienze di Architettura Navale, Via di Vallerano 139, 00128 Roma, Italy e-mail: E.Campana@insean.it G. Liuzzi Consiglio Nazionale delle Ricerche, Istituto di Analisi dei Sistemi ed Informatica “A. Ruberti”, Viale Manzoni 30, 00185 Roma, Italy S. Lucidi Dipartimento di Informatica e Sistemistica “A. Ruberti”, Università degli Studi di Roma “Sapienza”, Via Ariosto 25, 00185 Roma, Italy V. Piccialli Dipartimento di Ingegneria dell’Impresa, Università degli Studi di Roma “Tor Vergata”, Via del Policlinico 1, 00133 Roma, Italy 534 E.F. Campana et al. free optimization methods. All the numerical results show the effectiveness of the two new algorithms.
An evaluation of a hybrid occupational therapy and supported employment program in Japan for persons with schizophrenia.

A vocational rehabilitation program (occupational therapy and supported employment) for promoting the return to the community of long-stay persons with schizophrenia was established at a psychiatric hospital in Japan. The purpose of the study was to evaluate the program in terms of hospitalization rates, community tenure, and social functioning with each individual serving as his or her control.



Fifty-two participants, averaging 8.9 years of hospitalization, participated in the vocational rehabilitation program consisting of 2 to 6 hours of in-hospital occupational therapy for 6 days per week and a post-discharge supported employment component. Seventeen years after the program was established, a retrospective study was conducted to evaluate the impact of the program on hospitalizations, community tenure, and social functioning after participants' discharge from hospital, using an interrupted time-series analysis. The postdischarge period was compared with the period from onset of illness to the index discharge on the three outcome variables.



After discharge from the hospital, the length of time spent by participants out of the hospital increased, social functioning improved, and risk of hospitalization diminished by 50%. Female participants and those with supportive families spent more time out of the hospital than participants who were male or came from nonsupportive families.



A combined program of occupational therapy and supported employment was successful in a Japanese psychiatric hospital when implemented with the continuing involvement of a clinical team. Interventions that improve the emotional and housing supports provided to persons with schizophrenia by their families are likely to enhance the outcome of vocational services.
Toward a Psychological Science for a Cultural Species.

Humans are a cultural species, and the study of human psychology benefits from attention to cultural influences. Cultural psychology's contributions to psychological science can largely be divided according to the two different stages of scientific inquiry. Stage 1 research seeks cultural differences and establishes the boundaries of psychological phenomena. Stage 2 research seeks underlying mechanisms of those cultural differences. The literatures regarding these two distinct stages are reviewed, and various methods for conducting Stage 2 research are discussed. The implications of culture-blind and multicultural psychologies for society and intergroup relations are also discussed.
A Novel Method for Tuning Configuration Parameters of Spark Based on Machine Learning

Apache Spark is an open source distributed data processing platform, which can use distributed memory abstraction to process large volume of data efficiently. With the application of Apache Spark more and more widely, some problems are exposed. One of the most important aspects is the performance problem. Apache Spark has more than 180 configuration parameters, which can be adjusted by users according to their own specific application so as to optimize the performance. Currently these parameters are tuned manually by trial and error, which is ineffective due to the large parameter space and the complex interactions among the parameters. In this paper, in order to make the parameter tuning process of Spark more effective, a novel method for tuning configuration of Spark based on machine learning is proposed, which is composed of binary classification and multi-classification. This method can be used to auto-tune the configuration parameters of Spark. Furthermore, several common machine learning algorithms based on the proposed method are explored, and experimental results show that decision tree model (C5.0) is the best model considering the accuracy and computational efficiency. Finally, the experimental results also show that the performance can get average 36% gain with the proposed method compared with the default configuration of Spark.
Pushover analysis of multi-storey cantilever wall systems

Midto high-rise buildings are often braced by slender reinforced concrete (RC) walls, which are coupled by RC floor diaphragms. In design it is typically assumed that the walls act independently and the design base shear demand is computed neglecting any compatibility forces between the walls. Pushover analysis of systems comprising walls of different lengths have, however, shown that large compatibility forces can develop between walls of different length, which should be considered in design, but also that the magnitude of the computed forces is very sensitive to the modelling assumptions. The paper explores by means of a case study of an eight storey structure with two walls of different lengths the shear forces developing at the base of the wall. It compares and discusses the analysis results from different models including simple hand calculations, a lumped plasticity beam element model and a complex shell element model. It concludes that numerical and analytical approaches which are based on the lumped plasticity model tend to overestimate the shear force demand on the shorter wall.
Entity Tracking Improves Cloze-style Reading Comprehension

Reading comprehension tasks test the ability of models to process long-term context and remember salient information. Recent work has shown that relatively simple neural methods such as the Attention Sum-Reader can perform well on these tasks; however, these systems still significantly trail human performance. Analysis suggests that many of the remaining hard instances are related to the inability to track entity-references throughout documents. This work focuses on these hard entity tracking cases with two extensions: (1) additional entity features, and (2) training with a multi-task tracking objective. We show that these simple modifications improve performance both independently and in combination, and we outperform the previous state of the art on the LAMBADA dataset, particularly on difficult entity examples.