nils-herrmann/clean_scirepeval huggingface dataset:
The cognitive benefits of interacting with nature.

We compare the restorative effects on cognitive functioning of interactions with natural versus urban environments. Attention restoration theory (ART) provides an analysis of the kinds of environments that lead to improvements in directed-attention abilities. Nature, which is filled with intriguing stimuli, modestly grabs attention in a bottom-up fashion, allowing top-down directed-attention abilities a chance to replenish. Unlike natural environments, urban environments are filled with stimulation that captures attention dramatically and additionally requires directed attention (e.g., to avoid being hit by a car), making them less restorative. We present two experiments that show that walking in nature or viewing pictures of nature can improve directed-attention abilities as measured with a backwards digit-span task and the Attention Network Task, thus validating attention restoration theory.
Please Share! Online Word of Mouth and Charitable Crowdfunding

During the last few years, charitable crowdfunding has become an increasingly popular method of online fundraising for personal and charitable causes. Many such crowdfunding platforms encourage and facilitate the use of online word of mouth (WOM) through social networks and social media, to spread the word about the crowdfunding campaigns. While online WOM is commonly used to share information about crowdfunding campaigns, there is hitherto limited understanding as to whether or how this information sharing affect individuals’ contribution behavior or the outcome of crowdfunding campaigns. In this study, using a unique dataset from 590 crowdfunding campaigns observed over 12 days, we examine to what extent, and how quickly online WOM affect the rate of contributions. In addition, we explore the effect of different phases of fundraising (over time or as they approach their target goals) and of the coverage of the campaigns in major online news websites.
The dimensions and mechanisms of mindfulness in regulating aggressive behaviors.

On the basis of the notion that the ability to exert self-control is critical to the regulation of aggressive behaviors, we suggest that mindfulness, an aspect of the self-control process, plays a key role in curbing workplace aggression. In particular, we note the conceptual and empirical distinctions between dimensions of mindfulness (i.e., mindful awareness and mindful acceptance) and investigate their respective abilities to regulate workplace aggression. In an experimental study (Study 1), a multiwave field study (Study 2a), and a daily diary study (Study 2b), we established that the awareness dimension, rather than the acceptance dimension, of mindfulness plays a more critical role in attenuating the association between hostility and aggression. In a second multiwave field study (Study 3), we found that mindful awareness moderates the association between hostility and aggression by reducing the extent to which individuals use dysfunctional emotion regulation strategies (i.e., surface acting), rather than by reducing the extent to which individuals engage in dysfunctional thought processes (i.e., rumination). The findings are discussed in terms of the implications of differentiating the dimensions and mechanisms of mindfulness for regulating workplace aggression.
Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos

Recent advances in media generation techniques have made it easier for attackers to create forged images and videos. Stateof-the-art methods enable the real-time creation of a forged version of a single video obtained from a social network. Although numerous methods have been developed for detecting forged images and videos, they are generally targeted at certain domains and quickly become obsolete as new kinds of attacks appear. The method introduced in this paper uses a capsule network to detect various kinds of spoofs, from replay attacks using printed images or recorded videos to computergenerated videos using deep convolutional neural networks. It extends the application of capsule networks beyond their original intention to the solving of inverse graphics problems.
Extracting Development Tasks to Navigate Software Documentation

Knowledge management plays a central role in many software development organizations. While much of the important technical knowledge can be captured in documentation, there often exists a gap between the information needs of software developers and the documentation structure. To help developers navigate documentation, we developed a technique for automatically extracting tasks from software documentation by conceptualizing tasks as specific programming actions that have been described in the documentation. More than 70 percent of the tasks we extracted from the documentation of two projects were judged meaningful by at least one of two developers. We present TaskNavigator, a user interface for search queries that suggests tasks extracted with our technique in an auto-complete list along with concepts, code elements, and section headers. We conducted a field study in which six professional developers used TaskNavigator for two weeks as part of their ongoing work. We found search results identified through extracted tasks to be more helpful to developers than those found through concepts, code elements, and section headers. The results indicate that task descriptions can be effectively extracted from software documentation, and that they help bridge the gap between documentation structure and the information needs of software developers.
One carbon metabolism and bone homeostasis and remodeling: A review of experimental research and population studies.

Homocysteine (HCY) is a degradation product of the methionine pathway. The B vitamins, in particular vitamin B12 and folate, are the primary nutritional determinant of HCY levels and therefore their deficiencies result in hyperhomocysteinaemia (HHCY). Prevalence of hyperhomocysteinemia (HHCY) and related dietary deficiencies in B vitamins and folate increase with age and have been related to osteoporosis and abnormal development of epiphyseal cartilage and bone in rodents. Here we provide a review of experimental and population studies. The negative effects of HHCY and/or B vitamins and folate deficiencies on bone formation and remodeling are documented by cell models, including primary osteoblasts, osteoclast and bone progenitor cells as well as by animal and human studies. However, underlying pathophysiological mechanisms are complex and remain poorly understood. Whether these associations are the direct consequences of impaired one carbon metabolism is not clarified and more studies are still needed to translate these findings to human population. To date, the evidence is limited and somewhat conflicting, however further trials in groups most vulnerable to impaired one carbon metabolism are required.
Energy-Harvesting Active Networked Tags (EnHANTs): Prototyping and Experimentation

This article focuses on a new type of wireless devices in the domain between RFIDs and sensor networks—Energy-Harvesting Active Networked Tags (EnHANTs). Future EnHANTs will be small, flexible, and self-powered devices that can be attached to objects that are traditionally not networked (e.g., books, furniture, toys, produce, and clothing). Therefore, they will provide the infrastructure for various tracking applications and can serve as one of the enablers for the Internet of Things. We present the design considerations for the EnHANT prototypes, developed over the past 4 years. The prototypes harvest indoor light energy using custom organic solar cells, communicate and form multihop networks using ultra-low-power Ultra-Wideband Impulse Radio (UWB-IR) transceivers, and dynamically adapt their communications and networking patterns to the energy harvesting and battery states. We describe a small-scale testbed that uniquely allows evaluating different algorithms with trace-based light energy inputs. Then, we experimentally evaluate the performance of different energy-harvesting adaptive policies with organic solar cells and UWB-IR transceivers. Finally, we discuss the lessons learned during the prototype and testbed design process.
Batch-normalized Maxout Network in Network

This paper reports a novel deep architecture referred to as Maxout network In Network (MIN), which can enhance model discriminability and facilitate the process of information abstraction within the receptive field. The proposed network adopts the framework of the recently developed Network In Network structure, which slides a universal approximator, multilayer perceptron (MLP) with rectifier units, to exact features. Instead of MLP, we employ maxout MLP to learn a variety of piecewise linear activation functions and to mediate the problem of vanishing gradients that can occur when using rectifier units. Moreover, batch normalization is applied to reduce the saturation of maxout units by pre-conditioning the model and dropout is applied to prevent overfitting. Finally, average pooling is used in all pooling layers to regularize maxout MLP in order to facilitate information abstraction in every receptive field while tolerating the change of object position. Because average pooling preserves all features in the local patch, the proposed MIN model can enforce the suppression of irrelevant information during training. Our experiments demonstrated the state-of-the-art classification performance when the MIN model was applied to MNIST, CIFAR-10, and CIFAR-100 datasets and comparable performance for SVHN dataset.
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction

One key task of fine-grained sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. This paper focuses on supervised aspect extraction using deep learning. Unlike other highly sophisticated supervised deep learning models, this paper proposes a novel and yet simple CNN model 1 employing two types of pre-trained embeddings for aspect extraction: general-purpose embeddings and domain-specific embeddings. Without using any additional supervision, this model achieves surprisingly good results, outperforming state-of-the-art sophisticated existing methods. To our knowledge, this paper is the first to report such double embeddings based CNN model for aspect extraction and achieve very good results.
A survey of cyber crime in India

Cybercrime is all about the crimes in which communication channel and communication device has been used directly or indirectly as a medium whether it is a Laptop, Desktop, PDA, Mobile phones, Watches, Vehicles. The report titled “Global Risks for 2012”, predicts cyber-attacks as one of the top five risks in the World for Government and business sector. Cyber crime is a crime which is harder to detect and hardest to stop once occurred causing a long term negative impact on victims. With the increasing popularity of online banking, online shopping which requires sensitive personal and financial data, it is a term that we hear in the news with some frequency. Now, in order to protect ourselves from this crime we need to know what it is and how it does works against us. This paper presents a brief overview of all about cyber criminals and crime with its evolution, types, case study, preventive majors and the department working to combat this crime.
On formal models for cognitive linguistics

Cognitive linguistics is an emerging discipline that studies the cognitive properties of natural languages and the cognitive models of languages in computational linguistics, cognitive computing, and computational intelligence. This paper presents the theoretical framework of cognitive linguistics in order to systematically formalize the syntaxes and grammars of natural languages. An abstract language model of cognitive linguistics is created at the top level. Based on it, the cognitive structures of languages at the levels of lexis, phrase, clauses, sentence, paragraph, and essay are formally modeled from the bottom up. Using contemporary denotational mathematics, the deductive grammar of English is formally modeled and rigorously analyzed. The basic research provides support for a wide range of applications in computational linguistics, cognitive informatics, online text processing, web search engines, machine language comprehension, autonomous machine learning, smart cell phones, semantic computing, and computing with words.
Object Detectors Emerge in Deep Scene CNNs

With the success of new computational architectures for visual processing, such as convolutional neural networks (CNN) and access to image databases with millions of labeled examples (e.g., ImageNet, Places), the state of the art in computer vision is advancing rapidly. One important factor for continued progress is to understand the representations that are learned by the inner layers of these deep architectures. Here we show that object detectors emerge from training CNNs to perform scene classification. As scenes are composed of objects, the CNN for scene classification automatically discovers meaningful objects detectors, representative of the learned scene categories. With object detectors emerging as a result of learning to recognize scenes, our work demonstrates that the same network can perform both scene recognition and object localization in a single forward-pass, without ever having been explicitly taught the notion of objects.
Distributed Constraint Optimization Problems and Applications: A Survey

The field of multi-agent system (MAS) is an active area of research within artificial intelligence, with an increasingly important impact in industrial and other real-world applications. In a MAS, autonomous agents interact to pursue personal interests and/or to achieve common objectives. Distributed Constraint Optimization Problems (DCOPs) have emerged as a prominent agent model to govern the agents’ autonomous behavior, where both algorithms and communication models are driven by the structure of the specific problem. During the last decade, several extensions to the DCOP model have been proposed to enable support of MAS in complex, real-time, and uncertain environments. This survey provides an overview of the DCOP model, offering a classification of its multiple extensions and addressing both resolution methods and applications that find a natural mapping within each class of DCOPs. The proposed classification suggests several future perspectives for DCOP extensions, and identifies challenges in the design of efficient resolution algorithms, possibly through the adaptation of strategies from different areas.
Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition

A common practice to gain invariant features in object recognition models is to aggregate multiple low-level features over a small neighborhood. However, the differences between those models makes a comparison of the properties of different aggregation functions hard. Our aim is to gain insight into different functions by directly comparing them on a fixed architecture for several common object recognition tasks. Empirical results show that a maximum pooling operation significantly outperforms subsampling operations. Despite their shift-invariant properties, overlapping pooling windows are no significant improvement over non-overlapping pooling windows. By applying this knowledge, we achieve state-of-the-art error rates of 4.57% on the NORB normalized-uniform dataset and 5.6% on the NORB jittered-cluttered dataset.
Emerging Frontiers in Drug Delivery.

Medicine relies on the use of pharmacologically active agents (drugs) to manage and treat disease. However, drugs are not inherently effective; the benefit of a drug is directly related to the manner by which it is administered or delivered. Drug delivery can affect drug pharmacokinetics, absorption, distribution, metabolism, duration of therapeutic effect, excretion, and toxicity. As new therapeutics (e.g., biologics) are being developed, there is an accompanying need for improved chemistries and materials to deliver them to the target site in the body, at a therapeutic concentration, and for the required period of time. In this Perspective, we provide an historical overview of drug delivery and controlled release followed by highlights of four emerging areas in the field of drug delivery: systemic RNA delivery, drug delivery for localized therapy, oral drug delivery systems, and biologic drug delivery systems. In each case, we present the barriers to effective drug delivery as well as chemical and materials advances that are enabling the field to overcome these hurdles for clinical impact.
Do E-Athletes Move?: A Study on Training and Physical Exercise in Elite E-Sports

This article offers possibly the first peer-reviewed study on the training routines of elite eathletes, with special focus on the subjects’ physical exercise routines. The study is based on a sample of 115 elite e-athletes. According to their responses, e-athletes train approximately 5.28 hours every day around the year on the elite level. Approximately 1.08 hours of that training is physical exercise. More than half (55.6%) of the elite e-athletes believe that integrating physical exercise in their training programs has a positive effect on esport performance; however, no less than 47.0% of the elite e-athletes do their physical exercise chiefly to maintain overall health. Accordingly, the study indicates that elite e-athletes are active athletes as well, those of age 18 and older exercising physically more than three times the daily 21-minute activity recommendation given by World Health Organization.
Multi-Object Tracking with Correlation Filter for Autonomous Vehicle

Multi-object tracking is a crucial problem for autonomous vehicle. Most state-of-the-art approaches adopt the tracking-by-detection strategy, which is a two-step procedure consisting of the detection module and the tracking module. In this paper, we improve both steps. We improve the detection module by incorporating the temporal information, which is beneficial for detecting small objects. For the tracking module, we propose a novel compressed deep Convolutional Neural Network (CNN) feature based Correlation Filter tracker. By carefully integrating these two modules, the proposed multi-object tracking approach has the ability of re-identification (ReID) once the tracked object gets lost. Extensive experiments were performed on the KITTI and MOT2015 tracking benchmarks. Results indicate that our approach outperforms most state-of-the-art tracking approaches.
Learning Convolutional Feature Hierarchies for Visual Recognition

We propose an unsupervised method for learning multi-stage hierarchies of sparse convolutional features. While sparse coding has become an in creasingly popular method for learning visual features, it is most often traine d at the patch level. Applying the resulting filters convolutionally results in h ig ly redundant codes because overlapping patches are encoded in isolation. By tr aining convolutionally over large image windows, our method reduces the redudancy b etween feature vectors at neighboring locations and improves the efficienc y of the overall representation. In addition to a linear decoder that reconstruct s the image from sparse features, our method trains an efficient feed-forward encod er that predicts quasisparse features from the input. While patch-based training r arely produces anything but oriented edge detectors, we show that convolution al training produces highly diverse filters, including center-surround filters, corner detectors, cross detectors, and oriented grating detectors. We show that using these filters in multistage convolutional network architecture improves perfor mance on a number of visual recognition and detection tasks.
A Systematic Study on Agile Software Development Methodologies and Practices

Software engineering techniques have been employed for many years to create software products. The selections of appropriate software development methodologies for a given project, and tailoring the methodologies to a specific requirement have been a challenge since the establishment of software development as a discipline. In the late 1990’s, the general trend in software development techniques has changed from traditional waterfall approaches to more iterative incremental development approaches with different combination of old concepts, new concepts, and metamorphosed old concepts. Nowadays, the aim of most software companies is to produce software in short time period with minimal costs, and within unstable, changing environments that inspired the birth of Agile. Agile software development practice have caught the attention of software development teams and software engineering researchers worldwide during the last decade but scientific research and published outcomes still remains quite scarce. Every agile approach has its own development cycle that results in technological, managerial and environmental changes in the software companies. This paper explains the values and principles of ten agile practices that are becoming more and more dominant in the software development industry. Agile processes are not always beneficial, they have some limitations as well, and this paper also discusses the advantages and disadvantages of Agile processes.
Better bitmap performance with Roaring bitmaps

Bitmap indexes are commonly used in databases and search engines. By exploiting bit-level parallelism, they can significantly accelerate queries. However, they can use much memory, and thus we might prefer compressed bitmap indexes. Following Oracle’s lead, bitmaps are often compressed using run-length encoding (RLE). Building on prior work, we introduce the Roaring compressed bitmap format: it uses packed arrays for compression instead of RLE. We compare it to two high-performance RLE-based bitmap encoding techniques: WAH (Word Aligned Hybrid compression scheme) and Concise (Compressed ‘n’ Composable Integer Set). On synthetic and real data, we find that Roaring bitmaps (1) often compress significantly better (e.g., 2×) and (2) are faster than the compressed alternatives (up to 900× faster for intersections). Our results challenge the view that RLE-based bitmap compression is best.
Multichannel End-to-end Speech Recognition

The field of speech recognition is in the midst of a paradigm shift: end-to-end neural networks are challenging the dominance of hidden Markov models as a core technology. Using an attention mechanism in a recurrent encoder-decoder architecture solves the dynamic time alignment problem, allowing joint end-to-end training of the acoustic and language modeling components. In this paper we extend the end-to-end framework to encompass microphone array signal processing for noise suppression and speech enhancement within the acoustic encoding network. This allows the beamforming components to be optimized jointly within the recognition architecture to improve the end-to-end speech recognition objective. Experiments on the noisy speech benchmarks (CHiME-4 and AMI) show that our multichannel end-to-end system outperformed the attention-based baseline with input from a conventional adaptive beamformer.