nils-herrmann/clean_scirepeval huggingface dataset:
GLUE : A MultiTask Benchmark and Analysis Platform for Natural Language Understanding

For natural language understanding (NLU) technology to be maximally useful, both practically and as a scientific object of study, it must be general: it must be able to process language in a way that is not exclusively tailored to any one specific task or dataset. In pursuit of this objective, we introduce the General Language Understanding Evaluation benchmark (GLUE), a tool for evaluating and analyzing the performance of models across a diverse range of existing NLU tasks. GLUE is modelagnostic, but it incentivizes sharing knowledge across tasks because certain tasks have very limited training data. We further provide a hand-crafted diagnostic test suite that enables detailed linguistic analysis of NLU models. We evaluate baselines based on current methods for multi-task and transfer learning and find that they do not immediately give substantial improvements over the aggregate performance of training a separate model per task, indicating room for improvement in developing general and robust NLU systems.
Effects of a program to prevent social isolation on loneliness, depression, and subjective well-being of older adults: a randomized trial among older migrants in Japan.

Social isolation among the elderly is a concern in developed countries. Using a randomized trial, this study examined the effect of a social isolation prevention program on loneliness, depression, and subjective well-being of the elderly in Japan. Among the elderly people who relocated to suburban Tokyo, 63 who responded to a pre-test were randomized and assessed 1 and 6 months after the program. Four sessions of a group-based program were designed to prevent social isolation by improving community knowledge and networking with other participants and community "gatekeepers." The Life Satisfaction Index A (LSI-A), Geriatric Depression Scale (GDS), Ando-Osada-Kodama (AOK) loneliness scale, social support, and other variables were used as outcomes of this study. A linear mixed model was used to compare 20 of the 21 people in the intervention group to 40 of the 42 in the control group, and showed that the intervention program had a significant positive effect on LSI-A, social support, and familiarity with services scores and a significant negative effect on AOK over the study period. The program had no significant effect on depression. The findings of this study suggest that programs aimed at preventing social isolation are effective when they utilize existing community resources, are tailor-made based on the specific needs of the individual, and target people who can share similar experiences.
Multi-level Memory for Task Oriented Dialogs

Recent end-to-end task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. Current work makes simplifying assumptions about the structure of the knowledge base, such as the use of triples to represent knowledge, and combines dialog utterances (context) as well as knowledge base (KB) results as part of the same memory. This causes an explosion in the memory size, and makes the reasoning over memory harder. In addition, such a memory design forces hierarchical properties of the data to be fit into a triple structure of memory. This requires the memory reader to infer relationships across otherwise connected attributes. In this paper we relax the strong assumptions made by existing architectures and separate memories used for modeling dialog context and KB results. Instead of using triples to store KB results, we introduce a novel multi-level memory architecture consisting of cells for each query and their corresponding results. The multi-level memory first addresses queries, followed by results and finally each key-value pair within a result. We conduct detailed experiments on three publicly available task oriented dialog data sets and we find that our method conclusively outperforms current state-ofthe-art models. We report a 15-25% increase in both entity F1 and BLEU scores.
Implementing a Functional Spreadsheet in Clean

It has been claimed that recent developments in the research on the efficiency of code generation and on graphical input/output interfacing have made it possible to use a functional language to write efficient programs that can compete with industrial applications written in a traditional imperative language. As one of the early steps in verifying this claim, this paper describes a first attempt to implement a spreadsheet in a lazy, purely functional language. An interesting aspect of the design is that the language with which the user specifies the relations between the cells of the spreadsheet is itself a lazy, purely functional and higher order language as well, and not some special dedicated spreadsheet language. Another interesting aspect of the design is that the spreadsheet incorporates symbolic reduction and normalisation of symbolic expressions (including equations). This introduces the possibility of asking the system to prove equality of symbolic cell expressions: a property which can greatly enhance the reliability of a particular user-defined spreadsheet. The resulting application is by no means a fully mature product. It is not intended as a competitor to commercially available spreadsheets. However, with its higher order lazy functional language and its symbolic capabilities it may serve as an interesting candidate to fill the gap between calculators with purely functional expressions and full-featured spreadsheets with dedicated non-functional spreadsheet languages. This paper describes the global design and important implementation issues in the development of the application. The experience gained and lessons learnt during this project are treated. Performance and use of the resulting application are compared with related work.
High tech, high touch: The effect of employee skills and customer heterogeneity on customer satisfaction with enterprise system support services

Although firms have invested significant resources in implementing enterprise software systems (ESS) to modernize and integrate their business process infrastructure, customer satisfaction with ESS has remained an understudied phenomenon. In this exploratory research study, we investigate customer satisfaction for support services of ESS and focus on employee skills and customer heterogeneity. We analyze archival customer satisfaction data from 170 real-world customer service encounters of a leading ESS vendor. Our analysis indicates that the technical and behavioral skills of customer support representatives play a major role in influencing overall customer satisfaction with ESS support services. We find that the effect of technical skills on customer satisfaction is moderated by behavioral skills. We also find that the technical skills of the support personnel are valued more by repeat customers than by new customers. We discuss the implications of these findings for managing customer heterogeneity in ESS support services and for the allocation and training of ESS support personnel.
Automated testing of non-functional requirements based on behavioural scripts

To ensure high quality software, it is crucial that non‐functional requirements (NFRs) are well  specified  and  thoroughly  tested  in  parallel  with  functional  requirements  (FRs). Nevertheless, in requirement specification the focus is mainly on FRs, even though NFRs have a critical role  in the success of software projects. This study presents a systematic literature review of the NFR specification in order to identify the current state of the art and  needs  for  future  research.  The  systematic  review  summarizes  the  51  relevant papers found and discusses them within seven major sub categories with “combination of other approaches” being the one with most prior results.
Structural detection of android malware using embedded call graphs

The number of malicious applications targeting the Android system has literally exploded in recent years. While the security community, well aware of this fact, has proposed several methods for detection of Android malware, most of these are based on permission and API usage or the identification of expert features. Unfortunately, many of these approaches are susceptible to instruction level obfuscation techniques. Previous research on classic desktop malware has shown that some high level characteristics of the code, such as function call graphs, can be used to find similarities between samples while being more robust against certain obfuscation strategies. However, the identification of similarities in graphs is a non-trivial problem whose complexity hinders the use of these features for malware detection. In this paper, we explore how recent developments in machine learning classification of graphs can be efficiently applied to this problem. We propose a method for malware detection based on efficient embeddings of function call graphs with an explicit feature map inspired by a linear-time graph kernel. In an evaluation with 12,158 malware samples our method, purely based on structural features, outperforms several related approaches and detects 89% of the malware with few false alarms, while also allowing to pin-point malicious code structures within Android applications.
Occupational Therapy Interventions for Adolescents With Autism Spectrum Disorder.

Evidence Connection articles provide a clinical application of systematic reviews developed in conjunction with the American Occupational Therapy Association's (AOTA's) Evidence-Based Practice Project. In this Evidence Connection article, we describe a case report of an adolescent with autism spectrum disorder. The occupational therapy assessment and treatment processes for school, home, community, and transition settings are described. Findings from the systematic reviews on this topic were published in the September/October 2015 issue of the American Journal of Occupational Therapy and in AOTA's Occupational Therapy Practice Guidelines for Individuals With Autism Spectrum Disorder. Each article in this series summarizes the evidence from the published reviews on a given topic and presents an application of the evidence to a related clinical case. Evidence Connection articles illustrate how the research evidence from the reviews can be used to inform and guide clinical decision making.
A Dataset for Building Code-Mixed Goal Oriented Conversation Systems

There is an increasing demand for goal-oriented conversation systems which can assist users in various day-to-day activities such as booking tickets, restaurant reservations, shopping, etc. Most of the existing datasets for building such conversation systems focus on monolingual conversations and there is hardly any work on multilingual and/or code-mixed conversations. Such datasets and systems thus do not cater to the multilingual regions of the world, such as India, where it is very common for people to speak more than one language and seamlessly switch between them resulting in code-mixed conversations. For example, a Hindi speaking user looking to book a restaurant would typically ask, “Kya tum is restaurant mein ek table book karne mein meri help karoge?” (“Can you help me in booking a table at this restaurant?”). To facilitate the development of such code-mixed conversation models, we build a goal-oriented dialog dataset containing code-mixed conversations. Specifically, we take the text from the DSTC2 restaurant reservation dataset and create code-mixed versions of it in Hindi-English, Bengali-English, Gujarati-English and Tamil-English. We also establish initial baselines on this dataset using existing state of the art models. This dataset along with our baseline implementations is made publicly available for research purposes.
The adaptive radix tree: ARTful indexing for main-memory databases

Main memory capacities have grown up to a point where most databases fit into RAM. For main-memory database systems, index structure performance is a critical bottleneck. Traditional in-memory data structures like balanced binary search trees are not efficient on modern hardware, because they do not optimally utilize on-CPU caches. Hash tables, also often used for main-memory indexes, are fast but only support point queries. To overcome these shortcomings, we present ART, an adaptive radix tree (trie) for efficient indexing in main memory. Its lookup performance surpasses highly tuned, read-only search trees, while supporting very efficient insertions and deletions as well. At the same time, ART is very space efficient and solves the problem of excessive worst-case space consumption, which plagues most radix trees, by adaptively choosing compact and efficient data structures for internal nodes. Even though ART's performance is comparable to hash tables, it maintains the data in sorted order, which enables additional operations like range scan and prefix lookup.
Deep Facial Expression Recognition: A Survey

With the transition of facial expression recognition (FER) from laboratory-controlled to challenging in-the-wild conditions and the recent success of deep learning techniques in various fields, deep neural networks have increasingly been leveraged to learn discriminative representations for automatic FER. Recent deep FER systems generally focus on two important issues: overfitting caused by a lack of sufficient training data and expression-unrelated variations, such as illumination, head pose and identity bias. In this paper, we provide a comprehensive survey on deep FER, including datasets and algorithms that provide insights into these intrinsic problems. First, we introduce the available datasets that are widely used in the literature and provide accepted data selection and evaluation principles for these datasets. We then describe the standard pipeline of a deep FER system with the related background knowledge and suggestions of applicable implementations for each stage. For the state of the art in deep FER, we review existing novel deep neural networks and related training strategies that are designed for FER based on both static images and dynamic image sequences, and discuss their advantages and limitations. Competitive performances on widely used benchmarks are also summarized in this section. We then extend our survey to additional related issues and application scenarios. Finally, we review the remaining challenges and corresponding opportunities in this field as well as future directions for the design of robust deep FER systems.
Towards an insider threat prediction specification language

This concept paper presents the process of constructing a language tailored to describing insider threat incidents, for the purposes of mitigating threats originating from legitimate users in an IT infrastructure. Various information security surveys indicate that misuse by legitimate (insider) users has serious implications for the health of IT environments. A brief discussion of survey data and insider threat concepts is followed by an overview of existing research efforts to mitigate this particular problem. None of the existing insider threat mitigation frameworks provide facilities for systematically describing the elements of misuse incidents, and thus all threat mitigation frameworks could benefit from the existence of a domain specific language for describing legitimate user actions. The paper presents a language development methodology which centres upon ways to abstract the insider threat domain and approaches to encode the abstracted information into language semantics. Due to lack of suitable insider case repositories, and the fact that most insider misuse frameworks have not been extensively implemented in practice, the aforementioned language construction methodology is based upon observed information security survey trends and the study of existing insider threat and intrusion specification frameworks. The development of a domain specific language goes through various stages of refinement that might eventually contradict these preliminary findings.  This paper summarizes the picture of the insider threat in IT infrastructures and provides a useful reference for insider threat modeling researchers by indicating ways to abstract insider threats. The problems of constructing insider threat signatures and utilizing them in insider threat models are also discussed.
Using a Knowledge Graph to Combat Human Trafficking

There is a huge amount of data spread across the web and stored in databases that we can use to build knowledge graphs. However, exploiting this data to build knowledge graphs is difficult due to the heterogeneity of the sources, scale of the amount of data, and noise in the data. In this work, we developed a system for building knowledge graphs by exploiting semantic technologies to reconcile the data continuously crawled from diverse sources, to scale to billions of triples extracted from the crawled content, and to support interactive queries on the data. We applied our approach, implemented in the DIG system, to the problem of combating human trafficking and deployed it to six law enforcement agencies and several non-governmental organizations to assist them with finding traffickers and helping victims. The demonstration will show the resulting application that is currently in use by these law enforcement agencies. ? This is a demonstration paper for the In-Use Paper titled Building and Using a Knowledge Graph to Combat Human Trafficking by Szekely et al. presented at ISWC 2015 2 Szekely, Knoblock et al.
Tweet modeling with LSTM recurrent neural networks for hashtag recommendation

The hash symbol, called a hashtag, is used to mark the keyword or topic in a tweet. It was created organically by users as a way to categorize messages. Hashtags also provide valuable information for many research applications such as sentiment classification and topic analysis. However, only a small number of tweets are manually annotated. Therefore, an automatic hashtag recommendation method is needed to help users tag their new tweets. Previous methods mostly use conventional machine learning classifiers such as SVM or utilize collaborative filtering technique. A bottleneck of these approaches is that they all use the TF-IDF scheme to represent tweets and ignore the semantic information in tweets. In this paper, we also regard hashtag recommendation as a classification task but propose a novel recurrent neural network model to learn vector-based tweet representations to recommend hashtags. More precisely, we use a skip-gram model to generate distributed word representations and then apply a convolutional neural network to learn semantic sentence vectors. Afterwards, we make use of the sentence vectors to train a long short-term memory recurrent neural network (LSTM-RNN). We directly use the produced tweet vectors as features to classify hashtags without any feature engineering. Experiments on real world data from Twitter to recommend hashtags show that our proposed LSTM-RNN model outperforms state-of-the-art methods and LSTM unit also obtains the best performance compared to standard RNN and gated recurrent unit (GRU).
Impact of Human Resource Management Practices on Employees Performance Miss

Human Resource is the most important asset for any organization and it is the resource of achieving competitive advantage. Managing human resources is very challenging as compared to managing technology or capital and for its effective management, organization requires effective HRM system. HRM system should be backed up by strong HRM practices. HRM practices refer to organizational activities directed at managing the group of human resources and ensuring that the resources are employed towards the fulfillment of organizational goals. The purpose of this study is to explore contribution of Human Resource Management (HRM) practices including selection, training, career planning, compensation, performance appraisal, job definition and employee participation on perceived employee performance. This research describe why human resource management (HRM) decisions are likely to have an important and unique influence on organizational performance. This research forum will help advance research on the link between HRM and organizational performance. Unresolved questions is trying to identify in need of future study and make several suggestions intended to help researchers studying these questions build a more cumulative body of knowledge that will have key implications for body theory and practice. This study comprehensively evaluated the links between systems of High Performance Work Practices and firm performance. Results based on a national sample of firms indicate that these practices have an economically and statistically significant impact on employee performance. Support for predictions that the impact of High Performance Work Practices on firm performance is in part contingent on their interrelationships and links with competitive strategy was limited.
Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback

Machine translation is a natural candidate problem for reinforcement learning from human feedback: users provide quick, dirty ratings on candidate translations to guide a system to improve. Yet, current neural machine translation training focuses on expensive human-generated reference translations. We describe a reinforcement learning algorithm that improves neural machine translation systems from simulated human feedback. Our algorithm combines the advantage actor-critic algorithm (Mnih et al., 2016) with the attention-based neural encoderdecoder architecture (Luong et al., 2015). This algorithm (a) is well-designed for problems with a large action space and delayed rewards, (b) effectively optimizes traditional corpus-level machine translation metrics, and (c) is robust to skewed, high-variance, granular feedback modeled after actual human behaviors.
Bayesian Policy Gradients via Alpha Divergence Dropout Inference

Policy gradient methods have had great success in solving continuous control tasks, yet the stochastic nature of such problems makes deterministic value estimation difficult. We propose an approach which instead estimates a distribution by fitting the value function with a Bayesian Neural Network. We optimize an α-divergence objective with Bayesian dropout approximation to learn and estimate this distribution. We show that using the Monte Carlo posterior mean of the Bayesian value function distribution, rather than a deterministic network, improves stability and performance of policy gradient methods in continuous control MuJoCo simulations.
A survey: Internet of Things (IOT) technologies, applications and challenges

The main aim of this paper is to discuss the Internet of things in wider sense and prominence on protocols, technologies and application along related issues. The main factor IoT concept is the integration of different technologies. The IoT is empowered by the hottest developments in RFID, smart sensors, communication technologies, and Internet protocols. Primary hypothesis is to have smart sensor dealing directly to deliver a class of applications without any external or human participation. Recently development in Internet and smart phone and machine-to-machine M2M technologies can be consider first phase of the IoT. In the coming years IoT is expected to be one of the main hub between various technologies by connecting smart physical objects together and allow different applications in support of smart decision making. In this paper we discuss IoT architecture and technical aspect that relate to IoT. Then, give over view about IoT technologies, protocols and applications and related issues with comparison of other survey papers. Our main aim to provide a framework to researcher and application developer that how different protocols works, over view of some key issues of IoT and the relation between IoT and other embryonic technologies including big data analytics and cloud computing.