nils-herrmann/clean_scirepeval huggingface dataset:
Anthropomorphic Movement Analysis and Synthesis: A Survey of Methods and Applications

The anthropomorphic body form is a complex articulated system of links/limbs and joints, simultaneously redundant and underactuated, and capable of a wide range of sophisticated movement. The human body and its movement have long been a topic of study in physiology, anatomy, biomechanics, and neuroscience and have served as inspiration for humanoid robot design and control. This survey paper reviews the literature on robotics research using anthropomorphic design principles as an inspiration, at both the design and control levels. Next, anthropomorphic body modeling, motion analysis, and synthesis techniques are overviewed. Finally, key applications arising at the intersection of robotics and human movement science are introduced. The survey ends with a discussion of open research questions and directions for future work.
Separate face and body selectivity on the fusiform gyrus.

Recent reports of a high response to bodies in the fusiform face area (FFA) challenge the idea that the FFA is exclusively selective for face stimuli. We examined this claim by conducting a functional magnetic resonance imaging experiment at both standard (3.125 x 3.125 x 4.0 mm) and high resolution (1.4 x 1.4 x 2.0 mm). In both experiments, regions of interest (ROIs) were defined using data from blocked localizer runs. Within each ROI, we measured the mean peak response to a variety of stimulus types in independent data from a subsequent event-related experiment. Our localizer scans identified a fusiform body area (FBA), a body-selective region reported recently by Peelen and Downing (2005) that is anatomically distinct from the extrastriate body area. The FBA overlapped with and was adjacent to the FFA in all but two participants. Selectivity of the FFA to faces and FBA to bodies was stronger for the high-resolution scans, as expected from the reduction in partial volume effects. When new ROIs were constructed for the high-resolution experiment by omitting the voxels showing overlapping selectivity for both bodies and faces in the localizer scans, the resulting FFA* ROI showed no response above control objects for body stimuli, and the FBA* ROI showed no response above control objects for face stimuli. These results demonstrate strong selectivities in distinct but adjacent regions in the fusiform gyrus for only faces in one region (the FFA*) and only bodies in the other (the FBA*).
A strategic framework for good governance through e-governance optimization: A case study of Punjab in India

The purpose of this paper is to attempt to find out whether the new information and communication technologies can make a significant contribution to the achievement of the objective of good governance. The study identifies the factors responsible for creating a conducive environment for effective and successful implementation of e-governance for achieving good governance and the possible barriers in the implementation of e governance applications. Based on the comprehensive analysis it proposes a strategic policy framework for good governance in Punjab in India. Punjab is a developed state ranked amongst some of the top states of India in terms of per capita income and infrastructure.  The study designs a framework for good governance by getting the shared vision of all stakeholders about providing good quality administration and governance in the Indian context through “Participatory Stakeholder Assessment”. The study uses descriptive statistics, perception gap, ANOVA and factor analysis to identify the key factors for good governance, the priorities of public regarding e-services, the policy makers’ perspectives regarding good governance to be achieved through e-governance.  The study captures the good governance factors mainly contributing to the shared vision. The study further highlights that most Indian citizens in Punjab today believe in the power of information and communication technology (ICT) and want to access e-governance services. Major factors causing pain and harassment to the citizens in getting the services from various government departments include: unreasonable delay, multiple visits even for small services; poor public infrastructure and its maintenance in government offices. In the understanding of citizens the most important factors for the success of e-governance services are: overall convenience and experience of the citizens; reduction in the corruption levels by improvement in the transparency of government functioning and awareness about the availability of service amongst general masses.  The present study has evolved a shared vision of all stakeholders on good governance in the Indian context. It has opened up many new possibilities for the governments, not only to use ICTs and help them in prioritizing the governance areas for focused attention, but also help to understand the mindset of the modern citizenry, their priorities and what they consider as good governance. The study will help policy makers focus on these factors for enhancing speedy delivery of prioritized services and promote good governance in developing countries similar to India.
Towards Automatic Topical Question Generation

We address the challenge of automatically generating questions from topics. We consider that each topic is associated with a body of texts containing useful information about the topic. Questions are generated by exploiting the named entity information and the predicate argument structures of the sentences present in the body of texts. To measure the importance of the generated questions, we use Latent Dirichlet Allocation (LDA) to identify the sub-topics (which are closely related to the original topic) in the given body of texts and apply the Extended String Subsequence Kernel (ESSK) to calculate their similarity with the questions. We also propose the use of syntactic tree kernels for computing the syntactic correctness of the questions. The questions are ranked by considering their importance (in the context of the given body of texts) and syntactic correctness. To the best of our knowledge, no other study has accomplished this task in our setting before. Experiments show that our approach can significantly outperform the state-of-the-art results.
Embedding wikipedia title based on its wikipedia text and categories

Distributed word representation is widely used in many NLP tasks and knowledge-based resources also provide valuable information. Comparing to conventional knowledge bases, Wikipedia provides semi-structural data other than structural data. We argue that a Wikipedia title's categories can help complement the title's meaning besides Wikipedia text, so the categories should be utilized to improve the title's embedding. We propose two directions of using categories, cooperating with conventional context-based approaches, to generate embeddings of Wikipedia titles. We conduct extensively large scale experiments on the generated title embeddings on Chinese Wikipedia. Experiments on word similarity task and analogical reasoning task show that our approaches significantly outperform conventional context-based approaches.
Novel Design of a 2.5-GHz Fully Integrated CMOS Butler Matrix for Smart-Antenna Systems

This paper presents a novel design of monolithic 2.5-GHz 4times4 Butler matrix in 0.18-mum CMOS technology. To achieve a full integration of smart antenna system monolithically, the proposed Butler matrix is designed with the phase-compensated transformer-based quadrature couplers and reflection-type phase shifters. The measurements show an accurate phase distribution of 45plusmn3deg, 135 plusmn 4deg, -45 plusmn 3deg, and -135 plusmn 4deg with amplitude imbalance less than 1.5 dB. The antenna beamforming capability is also demonstrated by integrating the Butler matrix with a 1 X 4 monopole antenna array. The generated beams are pointing to -45deg, -15deg, 15deg, and 45deg, respectively, with less than 1deg error, which agree very well with the predictions. This Butler matrix consumes no dc power and only occupies the chip area of 1.36 times 1.47 mm2. To our knowledge, this is the first demonstration of the single-chip Butler matrix in CMOS technology.
Celeb-500K: A Large Training Dataset for Face Recognition

In this paper, we propose a large training dataset named Celeb-500K for face recognition, which contains 50M images from 500K persons. To better facilitate academic research, we clean Celeb-500K to obtain Celeb-500K-2R, which contains 25M aligned face images from 365K persons. Based on the developed dataset, we achieve state-of-the-art face recognition performance and reveal two important observations on face recognition study. First, metric learning methods have limited performance gain when the training dataset contains a large number of identities. Second, in order to develop an efficient training dataset, the number of identities is more important than the average image number of each identity from the perspective of face recognition performance. Extensive experimental results show the superiority of Celeb-500K and provide a strong support to the two observations.
A qualitative examination of movie piracy behaviors and their impact

The piracy of copyrighted movies in the digital disc format at any stage of the motion picture industry's value chain without permission of the copyright holders is an ongoing problem that is prevalent and adversely impacts industry revenues. Drawing on the Theory of Planned Behavior, we qualitatively examine the factors impacting domestic movie piracy behaviors in US respondents between 17 and 70 years of age. We discuss differences across groups and examine the underlying rationale for piracy behaviors. In order to move closer to mitigating some of the damages caused by movie piracy, it is recommended that the Motion Picture Association of America (MPAA) should find ways to communicate with each group through effective marketing, education and enforcement of its piracy policies and penalties.
Deobfuscating Android Applications through Deep Learning

Android applications are nearly always obfuscated before release, making it difficult to analyze them for malware presence or intellectual property violations. Obfuscators might hide the true intent of code by renaming variables, modifying the control flow of methods, or inserting additional code. Prior approaches toward automated deobfuscation of Android applications have relied on certain structural parts of apps remaining as landmarks, un-touched by obfuscation. For instance, some prior approaches have assumed that the structural relationships between identifiers (e.g. that A represents a class, and B represents a field declared directly in A) are not broken by obfuscators; others have assumed that control flow graphs maintain their structure (e.g. that no new basic blocks are added). Both approaches can be easily defeated by a motivated obfuscator. We present a new approach to deobfuscating Android apps that leverages deep learning and topic modeling on machine code, MACNETO. MACNETO makes few assumptions about the kinds of modifications that an obfuscator might perform, and we show that it has high precision when applied to two different state-of-the-art obfuscators: ProGuard and Allatori.
Preventing Shoulder-Surfing Attack with the Concept of Concealing the Password Objects' Information

Traditionally, picture-based password systems employ password objects (pictures/icons/symbols) as input during an authentication session, thus making them vulnerable to "shoulder-surfing" attack because the visual interface by function is easily observed by others. Recent software-based approaches attempt to minimize this threat by requiring users to enter their passwords indirectly by performing certain mental tasks to derive the indirect password, thus concealing the user's actual password. However, weaknesses in the positioning of distracter and password objects introduce usability and security issues. In this paper, a new method, which conceals information about the password objects as much as possible, is proposed. Besides concealing the password objects and the number of password objects, the proposed method allows both password and distracter objects to be used as the challenge set's input. The correctly entered password appears to be random and can only be derived with the knowledge of the full set of password objects. Therefore, it would be difficult for a shoulder-surfing adversary to identify the user's actual password. Simulation results indicate that the correct input object and its location are random for each challenge set, thus preventing frequency of occurrence analysis attack. User study results show that the proposed method is able to prevent shoulder-surfing attack.
Goal-Oriented Chatbot Dialog Management Bootstrapping with Transfer Learning

Goal-Oriented (GO) Dialogue Systems, colloquially known as goal oriented chatbots, help users achieve a predefined goal (e.g. book a movie ticket) within a closed domain. A first step is to understand the user’s goal by using natural language understanding techniques. Once the goal is known, the bot must manage a dialogue to achieve that goal, which is conducted with respect to a learnt policy. The success of the dialogue system depends on the quality of the policy, which is in turn reliant on the availability of high-quality training data for the policy learning method, for instance Deep Reinforcement Learning. Due to the domain specificity, the amount of available data is typically too low to allow the training of good dialogue policies. In this paper we introduce a transfer learning method to mitigate the effects of the low in-domain data availability. Our transfer learning based approach improves the bot’s success rate by 20% in relative terms for distant domains and we more than double it for close domains, compared to the model without transfer learning. Moreover, the transfer learning chatbots learn the policy up to 5 to 10 times faster. Finally, as the transfer learning approach is complementary to additional processing such as warm-starting, we show that their joint application gives the best outcomes.
RenderNet: A deep convolutional network for differentiable rendering from 3D shapes

Traditional computer graphics rendering pipelines are designed for procedurally generating 2D images from 3D shapes with high performance. The nondifferentiability due to discrete operations (such as visibility computation) makes it hard to explicitly correlate rendering parameters and the resulting image, posing a significant challenge for inverse rendering tasks. Recent work on differentiable rendering achieves differentiability either by designing surrogate gradients for non-differentiable operations or via an approximate but differentiable renderer. These methods, however, are still limited when it comes to handling occlusion, and restricted to particular rendering effects. We present RenderNet, a differentiable rendering convolutional network with a novel projection unit that can render 2D images from 3D shapes. Spatial occlusion and shading calculation are automatically encoded in the network. Our experiments show that RenderNet can successfully learn to implement different shaders, and can be used in inverse rendering tasks to estimate shape, pose, lighting and texture from a single image.
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-Related Applications

Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research.
Two Storage Mechanisms in Free Recall I

Two experiments were carried out to test the hypothesis that the bimodal serial position curve in free recall is produced by. output from two storage mechanisms--short-term and long-term. Experimental operations were applied that were predicted to have a distinct effect on each of these mechanisms, and the changes in the serial position curve were observed. In the first experiment, presentation rate and repetition of individual words were varied in order to affect long-term storage and thereby affect the beginning sections of the serial position curve. Presentation rate has the predicted effect of differentially raising the beginning section of the serial position curve. It does not affect the end section. Repetition, however, did not have any effect that could not be ascribed to presentation rate. It could not, therefore, be used to demonstrate independently the predicted differential effect. In the second experiment, delay between end of list and recall was varied in order to affect short-term storage and, thereby, the end section of the serial position curve. The predicted effect was clearly demonstrated. The results make it possible to systematize a number of findings in the literature.
New E-Commerce User Interest Patterns

The number of online purchases is increasing constantly. Companies have recognized the related opportunities and they are using online channels progressively. In order to acquire potential customers, companies often try to gain a better understanding through the use of web analytics. One of the most useful sources are web log files. Basically, these provide an abundance of important information about the user behavior on a website, such as the path or access time. Mining this so-called clickstream data in the most comprehensive way has become an important task in order to predict the behavior of online customers, optimize webpages, and give personalized recommendations. As the number of customers constantly rises, the volume of the generated data log files also increases, both in terms of size and quantity. Thus, for certain companies, the currently used technologies are no longer sufficient. In this work, a comprehensive workflow will be proposed using a clustering algorithm in a Hadoop ecosystem to investigate user interest patterns. The complete workflow will be demonstrated on an application scenario of one of the largest business-to-business (B2B) electronic commerce websites in Germany. Furthermore, an experimental evaluation method will be applied to verify the applicability and efficiency of the used algorithm, along with the associated framework.
Knowledge Management Capability and Firm Performance: the Mediating Role of Organizational Agility

Implementing knowledge management capability (KMC) effectively is becoming an important strategic issue for organizational success. However, our understanding about the underlying mechanism of KMC on firm performance is still limited. Based on the dynamic capabilities perspective, this study tries to explore how KMC (i.e., exploration KMC and exploitation KMC) affects firm performance through the mediating role of operational adjustment agility and market capitalizing agility. Survey data from 211 firms indicate that both operational adjustment agility and market capitalizing agility can fully mediate the influence of KMC on firm performance. In addition, the relationship intensions of these two KMC on organizational agility are distinguishing. We conclude with implications and limitations for future research.
Smart contract-based campus demonstration of decentralized transactive energy auctions

Transactive energy paradigms will enable the exchange of energy from a distributed set of prosumers. While prosumers have access to distributed energy resources, these resources are intermittently available. There is a need for distributed markets to enable the exchange of energy in transactive environments, however, the large number of potential prosumers introduces challenges in the establishment of trust between prosumers. Markets for transactive environments create other challenges, such as establishing clearing prices for energy and exchanging money between prosumers. Blockchains provide a unique technology to address this distributed trust problem through the use of a distributed ledger, cryptocurrencies, and the execution of smart contracts. This paper introduces a smart contract that implements a transactive energy auction that operates without the need for a trusted entitys oversight. The auction mechanism implements a Vickrey second price auction, which guarantees bidders will submit honest bids. The contract is implemented on transactive agents on the WSU campus interacting with a 72kW PV array and the Ethereum blockchain. The contract is then used to execute auctions based on the energy from the the PV array and simulated building loads to demonstrate the auctions operations.
Comparison of automatic shot boundary detection algorithms

Various methods of automatic shot boundary detection have been proposed and claimed to perform reliably. A the detection of edits is fundamental to any kind of video analysis since it segments a video into its basic com the shots, only few comparative investigations on early shot boundary detection algorithms have been pu These investigations mainly concentrate on measuring the edit detection performance, however, do not con algorithms’ ability to classify the types and to locate the boundaries of the edits correctly. This paper extend comparative investigations. More recent algorithms designed explicitly to detect specific complex editing ope such as fades and dissolves are taken into account, and their ability to classify the types and locate the boun such edits are examined. The algorithms’ performance is measured in terms of hit rate, number of false hits, a rate for hard cuts, fades, and dissolves over a large and diverse set of video sequences. The experiments while hard cuts and fades can be detected reliably, dissolves are still an open research issue. The false hit rat solves is usually unacceptably high, ranging from 50% up to over 400%. Moreover, all algorithms seem to fai roughly the same conditions.
Review of Face Recognition Techniques

Use of Biometrics and widespread acceptability for person authentication has instigated several techniques. Now a day’s security is very important. Biometrics has received a great attention. Face biometrics, is useful person’s authentication that recognizes face. In this paper first we represent an overview of face recognition and discuss the methodology and functioning. Thereafter we represent face recognition techniques which are recently used including their advantages and disadvantages. A large number of face recognition algorithms have been developed. In this paper an attempt is made to review a wide range of methods including PCA, Eigen face, LDA for recognition and hybrid combination if this techniques. Under the various illumination and expression condition of face images some techniques specified here also improves the efficiency of face recognition.  Face Recognition, Principal Component Analysis, Eigen Face, Linear Discriminant Analysis