nils-herrmann/clean_scirepeval huggingface dataset:
3D Human Pose Estimation in the Wild by Adversarial Learning

Recently, remarkable advances have been achieved in 3D human pose estimation from monocular images because of the powerful Deep Convolutional Neural Networks (DCNNs). Despite their success on large-scale datasets collected in the constrained lab environment, it is difficult to obtain the 3D pose annotations for in-the-wild images. Therefore, 3D human pose estimation in the wild is still a challenge. In this paper, we propose an adversarial learning framework, which distills the 3D human pose structures learned from the fully annotated dataset to in-the-wild images with only 2D pose annotations. Instead of defining hard-coded rules to constrain the pose estimation results, we design a novel multi-source discriminator to distinguish the predicted 3D poses from the ground-truth, which helps to enforce the pose estimator to generate anthropometrically valid poses even with images in the wild. We also observe that a carefully designed information source for the discriminator is essential to boost the performance. Thus, we design a geometric descriptor, which computes the pairwise relative locations and distances between body joints, as a new information source for the discriminator. The efficacy of our adversarial learning framework with the new geometric descriptor has been demonstrated through extensive experiments on widely used public benchmarks. Our approach significantly improves the performance compared with previous state-of-the-art approaches.
Self-Processing and the Default Mode Network: Interactions with the Mirror Neuron System

Recent evidence for the fractionation of the default mode network (DMN) into functionally distinguishable subdivisions with unique patterns of connectivity calls for a reconceptualization of the relationship between this network and self-referential processing. Advances in resting-state functional connectivity analyses are beginning to reveal increasingly complex patterns of organization within the key nodes of the DMN - medial prefrontal cortex and posterior cingulate cortex - as well as between these nodes and other brain systems. Here we review recent examinations of the relationships between the DMN and various aspects of self-relevant and social-cognitive processing in light of emerging evidence for heterogeneity within this network. Drawing from a rapidly evolving social-cognitive neuroscience literature, we propose that embodied simulation and mentalizing are processes which allow us to gain insight into another's physical and mental state by providing privileged access to our own physical and mental states. Embodiment implies that the same neural systems are engaged for self- and other-understanding through a simulation mechanism, while mentalizing refers to the use of high-level conceptual information to make inferences about the mental states of self and others. These mechanisms work together to provide a coherent representation of the self and by extension, of others. Nodes of the DMN selectively interact with brain systems for embodiment and mentalizing, including the mirror neuron system, to produce appropriate mappings in the service of social-cognitive demands.
Exploration-Exploitation Trade-off in Deep Reinforcement Learning

A fundamental dilemma in reinforcement learning is the exploration-exploitation trade-off. Deep reinforcement learning enables agents to act and learn in complex environments, but also introduces new challenges to both exploration and exploitation. Concepts like intrinsic motivation, hierarchical learning or curriculum learning all inspire different methods for exploration, while other agents profit from better methods to exploit current knowledge. In this work a survey of a variety of different approaches to exploration and exploitation in deep reinforcement learning is presented.
PHYSICAL ACTIVITY LEVEL AMONG UNIVERSITY STUDENTS: A CROSS SECTIONAL SURVEY

Physical inactivity is the fourth leading risk factor for global mortality. Physical inactivity levels are rising in developing countries and Malaysia is of no exception. Malaysian Adult Nutrition Survey 2003 reported that the prevalence of physical inactivity was 39.7% and the prevalence was higher for women (42.6%) than men (36.7%). In Malaysia, the National Health and Morbidity Survey 2006 reported that 43.7% (5.5 million) of Malaysian adults were physically inactive. These statistics show that physically inactive is an important public health concern in Malaysia. College students have been found to have poor physical activity habits. The objective of this study was to identify the physical activity level among students of Asia Metropolitan University (AMU) in Malaysia.
Security Challenges in Cloud Computing

Cloud Computing is one of the biggest buzzwords in the computer world these days. It allows resource sharing that includes software, platform and infrastructure by means of virtualization. Virtualization is the core technology behind cloud resource sharing. This environment strives to be dynamic, reliable, and customizable with a guaranteed quality of service. Security is as much of an issue in the cloud as it is anywhere else. Different people share different point of view on cloud computing. Some believe it is unsafe to use cloud. Cloud vendors go out of their way to ensure security. This paper investigates few major security issues with cloud computing and the existing counter measures to those security challenges in the world of cloud computing..
Cooccurrence of Multiple Sclerosis and Idiopathic Basal Ganglia Calcification

Multiple sclerosis (MS) is a chronic inflammatory demyelinating and neurodegenerative disease of central nervous system that affects both white and gray matter. Idiopathic calcification of the basal ganglia is a rare neurodegenerative disorder of unknown cause that is characterized by sporadic or familial brain calcification. Concurrence of multiple sclerosis (MS) and idiopathic basal ganglia calcification (Fahr's disease) is very rare event. In this study, we describe a cooccurrence of idiopathic basal ganglia calcification with multiple sclerosis. The association between this disease and MS is unclear and also maybe probably coincidental.
’ s repository of research publications and other research outputs State-OfThe-Art and Prospects for Peer-To-Peer Transaction-Based Energy System

Transaction-based energy (TE) management and control has become an increasingly relevant topic, attracting considerable attention from industry and the research community alike. As a result, new techniques are emerging for its development and actualization. This paper presents a comprehensive review of TE involving peer-to-peer (P2P) energy trading and also covering the concept, enabling technologies, frameworks, active research efforts and the prospects of TE. The formulation of a common approach for TE management modelling is challenging given the diversity of circumstances of prosumers in terms of capacity, profiles and objectives. This has resulted in divergent opinions in the literature. The idea of this paper is therefore to explore these viewpoints and provide some perspectives on this burgeoning topic on P2P TE systems. This study identified that most of the techniques in the literature exclusively formulate energy trade problems as a game, an optimization problem or a variational inequality problem. It was also observed that none of the existing works has considered a unified messaging framework. This is a potential area for further investigation.
Abusive supervision and workplace deviance and the moderating effects of negative reciprocity beliefs.

In this study, the authors examine the relationship between abusive supervision and employee workplace deviance. The authors conceptualize abusive supervision as a type of aggression. They use work on retaliation and direct and displaced aggression as a foundation for examining employees' reactions to abusive supervision. The authors predict abusive supervision will be related to supervisor-directed deviance, organizational deviance, and interpersonal deviance. Additionally, the authors examine the moderating effects of negative reciprocity beliefs. They hypothesized that the relationship between abusive supervision and supervisor-directed deviance would be stronger when individuals hold higher negative reciprocity beliefs. The results support this hypothesis. The implications of the results for understanding destructive behaviors in the workplace are examined.
Knowledge Graph Completion to Predict Polypharmacy Side Effects

The polypharmacy side effect prediction problem considers cases in which two drugs taken individually do not result in a particular side effect; however, when the two drugs are taken in combination, the side effect manifests. In this work, we demonstrate that multirelational knowledge graph completion achieves state-of-the-art results on the polypharmacy side effect prediction problem. Empirical results show that our approach is particularly effective when the protein targets of the drugs are well-characterized. In contrast to prior work, our approach provides more interpretable predictions and hypotheses for wet lab validation.
Triple Non-negative Matrix Factorization Technique for Sentiment Analysis and Topic Modeling

​ Topic modeling refers to the process of algorithmically sorting documents into categories based on some common relationship between the documents. This common relationship between the documents is considered the “topic” of the documents. Sentiment analysis refers to the process of algorithmically sorting a document into a positive or negative category depending whether this document expresses a positive or negative opinion on its respective topic. In this paper, I consider the open problem of document classification into a topic category, as well as a sentiment category. This has a direct application to the retail industry where companies may want to scour the web in order to find documents (blogs, Amazon reviews, etc.) which both speak about their product, and give an opinion on their product (positive, negative or neutral). My solution to this problem uses a Non-negative Matrix Factorization (NMF) technique in order to determine the topic classifications of a document set, and further factors the matrix in order to discover the sentiment behind this category of product. Introduction to Sentiment Analysis:​ In the United States, the incredible accessibility of the internet gives a voice to every consumer. Furthermore, internet blogs and common review sites are the first places the majority of consumers turn to when researching the pros and cons behind a product they are looking to purchase. Discovering sentiment and insights behind a company's products is hardly a new challenge, but a constantly evolving one given the complexity and sheer number of reviews buried inside a multitude of internet domains. Internet reviews, perhaps more frequently than a traditionally written and formatted magazine or newspaper review, are riddled with sarcasm, abbreviations, and slang. Simple text classification techniques based on analyzing the number of positive and negative words that occur in a document are error prone because of this. The internet requires a new solution to the trend of “reviews in 140 characters or less” which will necessitate unsupervised or semi-supervised machine learning and natural language processing techniques. Observed in [Ng et al., 2009] semi-supervised dictionary based approaches yield unsatisfactory results, with resulting lexicons of large coverage and low precision, or limited coverage and higher precision. In this paper, I will attempt to utilize these previously created dictionaries (of positive and negative words) and incorporate them into a machine learning approach to classify unlabeled documents.  Non-negative Matrix Factorization and Topic Modeling:​ Non-negative Matrix Factorization has applications to many fields such as computer vision, but we are interested in the specific application to topic modeling (often referred to as document clustering). NMF is the process of factoring a matrix into (usually) two parts where a [A x B] matrix is approximated by [A x r] x [r x B] where r is a chosen value, less than A and B. Every element in [A x r] and [r x B] must be non-negative throughout this process.
Building Encoder and Decoder with Deep Neural Networks: On the Way to Reality

Deep learning has been a groundbreaking technology in various fields as well as in communications systems. In spite of the notable advancements of deep neural network (DNN) based technologies in recent years, the high computational complexity has been a major obstacle to apply DNN in practical communications systems which require real-time operation. In this sense, challenges regarding practical implementation must be addressed before the proliferation of DNN-based intelligent communications becomes a reality. To the best of the authors’ knowledge, for the first time, this article presents an efficient learning architecture and design strategies including link level verification through digital circuit implementations using hardware description language (HDL) to mitigate this challenge and to deduce feasibility and potential of DNN for communications systems. In particular, DNN is applied for an encoder and a decoder to enable flexible adaptation with respect to the system environments without needing any domain specific information. Extensive investigations and interdisciplinary design considerations including the DNN-based autoencoder structure, learning framework, and low-complexity digital circuit implementations for real-time operation are taken into account by the authors which ascertains the use of DNN-based communications in practice.
Control-Flow Bending: On the Effectiveness of Control-Flow Integrity

Control-Flow Integrity (CFI) is a defense which prevents control-flow hijacking attacks. While recent research has shown that coarse-grained CFI does not stop attacks, fine-grained CFI is believed to be secure. We argue that assessing the effectiveness of practical CFI implementations is non-trivial and that common evaluation metrics fail to do so. We then evaluate fullyprecise static CFI — the most restrictive CFI policy that does not break functionality — and reveal limitations in its security. Using a generalization of non-control-data attacks which we call Control-Flow Bending (CFB), we show how an attacker can leverage a memory corruption vulnerability to achieve Turing-complete computation on memory using just calls to the standard library. We use this attack technique to evaluate fully-precise static CFI on six real binaries and show that in five out of six cases, powerful attacks are still possible. Our results suggest that CFI may not be a reliable defense against memory corruption vulnerabilities. We further evaluate shadow stacks in combination with CFI and find that their presence for security is necessary: deploying shadow stacks removes arbitrary code execution capabilities of attackers in three of six cases.
TV Ads and Search Spikes : Toward a Deeper Understanding

Television advertisements lead some multitasking viewers to take immediate, measurable actions online. We analyze minute-by-minute TV ad insertion and online search data for daily fantasy sports and pick-up truck brands. TV ads with small audiences can produce detectable search spikes for the advertised brand, with 75% of incremental searches occurring within two minutes. TV ads also generate post-ad searches for competitor brands. Search spikes vary with ad content: they are larger after brandfocused ads than after price-focused ads, and after less-informative ads than after more-informative ads. Search spikes also vary with contextual media factors (e.g., TV network, daypart, program genre), but their effects differ across brands. Taken as a whole, our findings suggest that marketers should consider post-ad search spikes in conjunction with other metrics when evaluating and purchasing TV ads.
Support vector machines classification with a very large-scale taxonomy

Very large-scale classification taxonomies typically have hundreds of thousands of categories, deep hierarchies, and skewed category distribution over documents. However, it is still an open question whether the state-of-the-art technologies in automated text categorization can scale to (and perform well on) such large taxonomies. In this paper, we report the first evaluation of Support Vector Machines (SVMs) in web-page classification over the full taxonomy of the Yahoo! categories. Our accomplishments include: 1) a data analysis on the Yahoo! taxonomy; 2) the development of a scalable system for large-scale text categorization; 3) theoretical analysis and experimental evaluation of SVMs in hierarchical and non-hierarchical settings for classification; 4) an investigation of threshold tuning algorithms with respect to time complexity and their effect on the classification accuracy of SVMs. We found that, in terms of scalability, the hierarchical use of SVMs is efficient enough for very large-scale classification; however, in terms of effectiveness, the performance of SVMs over the Yahoo! Directory is still far from satisfactory, which indicates that more substantial investigation is needed.
Liposomes and nanoparticles: nanosized vehicles for drug delivery in cancer.

Nanoscale drug delivery systems using liposomes and nanoparticles are emerging technologies for the rational delivery of chemotherapeutic drugs in the treatment of cancer. Their use offers improved pharmacokinetic properties, controlled and sustained release of drugs and, more importantly, lower systemic toxicity. The commercial availability of liposomal Doxil and albumin-nanoparticle-based Abraxane has focused attention on this innovative and exciting field. Recent advances in liposome technology offer better treatment of multidrug-resistant cancers and lower cardiotoxicity. Nanoparticles offer increased precision in chemotherapeutic targeting of prostate cancer and new avenues for the treatment of breast cancer. Here we review current knowledge on the two technologies and their potential applications to cancer treatment.
The Digital Disruption of Strategic Paths: An Experimental Study

In this paper, we draw on the literatures on path dependence and disruptive innovation to examine in an experimental setting how path-dependent firms respond to digital disruption. As our results indicate, in the face of digital disruption, path-dependent firms tend to renovate the technological foundation on which their strategic path is based if they have the opportunity to reproduce their established strategic path. Our findings also suggest that path-dependent firms equally tend to renovate their technological foundation or the targeted market segment in the face of digital disruption if they are unable to reproduce their established strategic path. Our findings provide insights into the challenges that digitization imposes on established firms, complement the literature on path dependence with insights into path disruption, contribute an integrated view to the literature on disruptive innovation, and offer some guidance to practitioners.
Multifaceted Nature of Intrinsic Motivation : The Theory of 16 Basic Desires

R. W. White (1959) proposed that certain motives, such as curiosity, autonomy, and play (called intrinsic motives, or IMs), have common characteristics that distinguish them from drives. The evidence that mastery is common to IMs is anecdotal, not scientific. The assertion that “intrinsic enjoyment” is common to IMs exaggerates the significance of pleasure in human motivation and expresses the hedonistic fallacy of confusing consequence for cause. Nothing has been shown scientifically to be common to IMs that differentiates them from drives. An empirically testable theory of 16 basic desires is put forth based on psychometric research and subsequent behavior validation. The desires are largely unrelated to each other and may have different evolutionary histories.
An Overview of Cross-Media Retrieval: Concepts, Methodologies, Benchmarks, and Challenges

Multimedia retrieval plays an indispensable role in big data utilization. Past efforts mainly focused on single-media retrieval. However, the requirements of users are highly flexible, such as retrieving the relevant audio clips with one query of image. So challenges stemming from the “media gap,” which means that representations of different media types are inconsistent, have attracted increasing attention. Cross-media retrieval is designed for the scenarios where the queries and retrieval results are of different media types. As a relatively new research topic, its concepts, methodologies, and benchmarks are still not clear in the literature. To address these issues, we review more than 100 references, give an overview including the concepts, methodologies, major challenges, and open issues, as well as build up the benchmarks, including data sets and experimental results. Researchers can directly adopt the benchmarks to promptly evaluate their proposed methods. This will help them to focus on algorithm design, rather than the time-consuming compared methods and results. It is noted that we have constructed a new data set XMedia, which is the first publicly available data set with up to five media types (text, image, video, audio, and 3-D model). We believe this overview will attract more researchers to focus on cross-media retrieval and be helpful to them.
Multi-dimensional Graph Convolutional Networks

Convolutional neural networks (CNNs) leverage the great power in representation learning on regular grid data such as image and video. Recently, increasing attention has been paid on generalizing CNNs to graph or network data which is highly irregular. Some focus on graph-level representation learning while others aim to learn node-level representations. These methods have been shown to boost the performance of many graph-level tasks such as graph classification and node-level tasks such as node classification. Most of these methods have been designed for single-dimensional graphs where a pair of nodes can only be connected by one type of relation. However, many real-world graphs have multiple types of relations and they can be naturally modeled as multi-dimensional graphs with each type of relation as a dimension. Multi-dimensional graphs bring about richer interactions between dimensions, which poses tremendous challenges to the graph convolutional neural networks designed for single-dimensional graphs. In this paper, we study the problem of graph convolutional networks for multi-dimensional graphs and propose a multi-dimensional convolutional neural network model mGCN aiming to capture rich information in learning node-level representations for multi-dimensional graphs. Comprehensive experiments on real-world multi-dimensional graphs demonstrate the effectiveness of the proposed framework.