nils-herrmann/clean_scirepeval huggingface dataset:
On the bipolarity of positive and negative affect.

Is positive affect (PA) the bipolar opposite of, or is it independent of, negative affect (NA)? Previous analyses of this vexing question have generally labored under the false assumption that bipolarity predicts an invariant latent correlation between PA and NA. The predicted correlation varies with time frame, response format, and items selected to define PA and NA. The observed correlation also varies with errors inherent in measurement. When the actual predictions of a bipolar model are considered and error is taken into account, there is little evidence for independence of what were traditionally thought opposites. Bipolarity provides a parsimonious fit to existing data.
Synthetic Occlusion Augmentation with Volumetric Heatmaps for the 2018 ECCV PoseTrack Challenge on 3D Human Pose Estimation

In this paper we present our winning entry at the 2018 ECCV PoseTrack Challenge on 3D human pose estimation. Using a fully-convolutional backbone architecture, we obtain volumetric heatmaps per body joint, which we convert to coordinates using soft-argmax. Absolute person center depth is estimated by a 1D heatmap prediction head. The coordinates are back-projected to 3D camera space, where we minimize the L1 loss. Key to our good results is the training data augmentation with randomly placed occluders from the Pascal VOC dataset. In addition to reaching first place in the Challenge, our method also surpasses the state-of-the-art on the full Human3.6M benchmark when considering methods that use no extra pose datasets in training. Code for applying synthetic occlusions is availabe at https://github.com/ isarandi/synthetic-occlusion.
Understanding churn in peer-to-peer networks

The dynamics of peer participation, or churn, are an inherent property of Peer-to-Peer (P2P) systems and critical for design and evaluation. Accurately characterizing churn requires precise and unbiased information about the arrival and departure of peers, which is challenging to acquire. Prior studies show that peer participation is highly dynamic but with conflicting characteristics. Therefore, churn remains poorly understood, despite its significance.In this paper, we identify several common pitfalls that lead to measurement error. We carefully address these difficulties and present a detailed study using three widely-deployed P2P systems: an unstructured file-sharing system (Gnutella), a content-distribution system (BitTorrent), and a Distributed Hash Table (Kad). Our analysis reveals several properties of churn: (i) overall dynamics are surprisingly similar across different systems, (ii) session lengths are not exponential, (iii) a large portion of active peers are highly stable while the remaining peers turn over quickly, and (iv) peer session lengths across consecutive appearances are correlated. In summary, this paper advances our understanding of churn by improving accuracy, comparing different P2P file sharingdistribution systems, and exploring new aspects of churn.
Coordinated multipoint transmission and reception in LTE-advanced: deployment scenarios and operational challenges

3GPP has completed a study on coordinated multipoint transmission and reception techniques to facilitate cooperative communications across multiple transmission and reception points (e.g., cells) for the LTE-Advanced system. In CoMP operation, multiple points coordinate with each other in such a way that the transmission signals from/to other points do not incur serious interference or even can be exploited as a meaningful signal. The goal of the study is to evaluate the potential performance benefits of CoMP techniques and the implementation aspects including the complexity of the standards support for CoMP. This article discusses some of the deployment scenarios in which CoMP techniques will likely be most beneficial and provides an overview of CoMP schemes that might be supported in LTE-Advanced given the modern silicon/DSP technologies and backhaul designs available today. In addition, practical implementation and operational challenges are discussed. We also assess the performance benefits of CoMP in these deployment scenarios with traffic varying from low to high load.
The Text Mining Handbook: Advanced Approaches to Analyzing Unstructured Data Ronen Feldman and James Sanger (Bar-Ilan University and ABS Ventures) Cambridge, England: Cambridge University Press, 2007, xii+410 pp; hardbound, ISBN 0-521-83657-3, $70.00

Text mining is a new and exciting area of computer science that tries to solve the crisis of information overload by combining techniques from data mining, machine learning, natural language processing, information retrieval, and knowledge management. The Text Mining Handbook presents a comprehensive discussion of the latest techniques in text mining and link detection. In addition to providing an in-depth examination of core text mining and link detection algorithms and operations, the book examines advanced pre-processing techniques, knowledge representation considerations, and visualization approaches, ending with real-world applications.
Convolutional neural networks applied to house numbers digit classification

We classify digits of real-world house numbers using convolutional neural networks (ConvNets). Con-vNets are hierarchical feature learning neural networks whose structure is biologically inspired. Unlike many popular vision approaches that are hand-designed, ConvNets can automatically learn a unique set of features optimized for a given task. We augmented the traditional ConvNet architecture by learning multi-stage features and by using Lp pooling and establish a new state-of-the-art of 95.10% accuracy on the SVHN dataset (48% error improvement). Furthermore, we analyze the benefits of different pooling methods and multi-stage features in ConvNets. The source code and a tutorial are available at eblearn.sf.net.
Neurobiology of intelligence: science and ethics

Human mental abilities, such as intelligence, are complex and profoundly important, both in a practical sense and for what they imply about the human condition. Understanding these abilities in mechanistic terms has the potential to facilitate their enhancement. There is strong evidence that the lateral prefrontal cortex, and possibly other areas, support intelligent behaviour. Variations in intelligence and brain structure are heritable, but are also influenced by factors such as education, family environment and environmental hazards. Cognitive, psychometric, genetic and neuroimaging studies are converging, and the emergence of mechanistic models of intelligence is inevitable. These exciting scientific advances encourage renewed responsiveness to the social and ethical implications of conducting such research.
J-Sim: a simulation and emulation environment for wireless sensor networks

Wireless sensor networks have gained considerable attention in the past few years. They have found application domains in battlefield communication, homeland security, pollution sensing, and traffic monitoring. As such, there has been an increasing need to define and develop simulation frameworks for carrying out high-fidelity WSN simulation. In this article we present a modeling, simulation, and emulation framework for WSNs in J-Sim - an open source, component-based compositional network simulation environment developed entirely in Java. This framework is built on the autonomous component architecture and extensible internetworking framework of J-Sim, and provides an object-oriented definition of target, sensor, and sink nodes, sensor and wireless communication channels, and physical media such as seismic channels, mobility models, and power models (both energy-producing and energy-consuming components). Application-specific models can be defined by subclassing classes in the simulation framework and customizing their behaviors. We also include in J-Sim a set of classes and mechanisms to realize network emulation. We demonstrate the use of the proposed WSN simulation framework by implementing several well-known localization, geographic routing, and directed diffusion protocols, and perform performance comparisons (in terms of the execution time incurred and memory used) in simulating WSN scenarios in J-Sim and ns-2. The simulation study indicates the WSN framework in J-Sim is much more scalable than ns-2 (especially in memory usage). We also demonstrate the use of the WSN framework in carrying out real-life full-fledged Future Combat System (FCS) simulation and emulation
SVM incremental learning, adaptation and optimization

The objective of machine learning is to identify a model that yields good generalization performance. This involves repeatedly selecting a hypothesis class, searching the hypothesis class by minimizing a given objective function over the model’s parameter space, and evaluating the generalization performance of the resulting model. This search can be computationally intensive as training data continuously arrives, or as one needs to tune hyperparameters in the hypothesis class and the objective function. In this paper, we present a framework for exact incremental learning and adaptation of support vector machine (SVM) classifiers. The approach is general and allows one to learn and unlearn individual or multiple examples, adapt the current SVM to changes in regularization and kernel parameters, and evaluate generalization performance through exact leave-one-out error estimation. I. I NTRODUCTION SVM techniques for classification and regression provide powerful tools for learning models that generalize well even in sparse, high dimensional settings. Their success can be attributed to Vapnik’s seminal work in statistical learning theory [15] which provided key insights into the factors affecting generalization performance. SVM learning can be viewed as a practical implementation of Vapnik’s structural risk minimizationinduction principle which involves searching over hypothesis classes of varying capacity to find the model with the best generalization performance. SVM classifiers of the formf(x) = w ·Φ(x)+b are learned from the data{(xi, yi) ∈ R I m × {−1, 1} ∀ i ∈ {1, . . . , N}} by minimizing min w,b,ξ 1 2 ‖w‖ + C N ∑
A MATLAB-based simulation program for indoor visible light communication system

We report a simulation program for indoor visible light communication environment based on MATLAB and Simulink. The program considers the positions of the transmitters and the reflections at each wall. For visible light communication environment, the illumination light-emitting diode is used not only as a lighting device, but also as a communication device. Using the simulation program, the distributions of illuminance and root-mean-square delay spread are analyzed at bottom surface.
SECURITY ENHANCEMENT OF ADVANCED ENCRYPTION STANDARD (AES) USING TIME-BASED DYNAMIC KEY GENERATION

Login is the first step which is conducted every time we access to a system and only granted to those who are entitled. Login is very important and it is a part of the security of a system. Registered user or guest and administrator are two kinds of users that have the privilege and access. Nowadays, wrongdoers are always on the dark side and frequently try to gain access to the system. To provide a better security, it is important to enhance the access mechanism and to evaluate the authentication process. Message-Digest 5 (MD5) is one of the algorithms that commonly used in the login system. Although it has been so popular, but the algorithm is still vulnerable to dictionary attacks and rainbow tables. In addition to the hash function algorithm, Advanced Encryption Standard (AES) algorithm alternatively can be the best choice in the login authentication process. This study is proposed to develop a dynamic key generation on the AES algorithm using the function of time. The experimental results obtained that AES key can be generated at random based on the value of the time when a user logs in with a particular active period. In this implementation, the security authentication becomes stronger because of changes in key generating chipertext changes for each encryption process. Based on the time as a valuable benchmark, the result shown that AES encryption-decryption process is relatively fast with a time average about 0.0023 s.
Diversified Top-k Subgraph Querying in a Large Graph

Subgraph querying in a large data graph is interesting for different applications. A recent study shows that top-k diversified results are useful since the number of matching subgraphs can be very large. In this work, we study the problem of top-k diversified subgraph querying that asks for a set of up to k subgraphs isomorphic to a given query graph, and that covers the largest number of vertices. We propose a novel level-based algorithm for this problem which supports early termination and has a theoretical approximation guarantee. From experiments, most of our results on real datasets used in previous works are near optimal with a query time within 10ms on a commodity machine.
Egocentric Spatial Memory Network

Inspired by neurophysiological discoveries of navigation cells in the mammalian brain, we introduce the first deep neural network architecture for modeling Egocentric Spatial Memory (ESM). It learns to estimate the pose of the agent and progressively construct top-down 2D global maps from egocentric views in a spatially extended environment. During the exploration, our proposed ESM network model updates belief of the global map based on local observations using a recurrent neural network. It also augments the local mapping with a novel external memory to encode and store latent representations of the visited places based on their corresponding locations in the egocentric coordinate. This enables the agents to perform loop closure and mapping correction. This work contributes in the following aspects: first, our proposed ESM network provides an accurate mapping ability which is vitally important for embodied agents to navigate to goal locations. In the experiments, we demonstrate the functionalities of the ESM network in random walks in complicated 3D mazes by comparing with several competitive baselines and state-of-the-art Simultaneous Localization and Mapping (SLAM) algorithms. Secondly, we faithfully hypothesize the functionality and the working mechanism of navigation cells in the brain. Comprehensive analysis of our model suggests the essential role of individual modules in our proposed architecture and demonstrates efficiency of communications among these modules. We hope this work would advance research in the collaboration and communications over both fields of computer science and computational neuroscience.
CFOA-Based Lossless and Lossy Inductance Simulators F ı rat

Inductance simulator is a useful component in the circuit synthesis theory especially for analog signal processing applications such as filter, chaotic oscillator design, analog phase shifters and cancellation of parasitic element. In this study, new four inductance simulator topologies employing a single current feedback operational amplifier are presented. The presented topologies require few passive components. The first topology is intended for negative inductance simulation, the second topology is for lossy series inductance, the third one is for negative lossy parallel inductance (-R) (-L) and the fourth topology is for negative parallel (-R) (-L) (-C) simulation. The performance of the proposed CFOA based inductance simulators is demonstrated on both a second-order low-pass filter and inductance cancellation circuit. PSPICE simulations are given to verify the theoretical analysis.
Facial expression recognition using deep convolutional neural networks

Facial expressions convey non-verbal information between humans in face-to-face interactions. Automatic facial expression recognition, which plays a vital role in human-machine interfaces, has attracted increasing attention from researchers since the early nineties. Classical machine learning approaches often require a complex feature extraction process and produce poor results. In this paper, we apply recent advances in deep learning to propose effective deep Convolutional Neural Networks (CNNs) that can accurately interpret semantic information available in faces in an automated manner without hand-designing of features descriptors. We also apply different loss functions and training tricks in order to learn CNNs with a strong classification power. The experimental results show that our proposed networks outperform state-of-the-art methods on the well-known FERC-2013 dataset provided on the Kaggle facial expression recognition competition. In comparison to the winning model of this competition, the number of parameters in our proposed networks intensively decreases, that accelerates the overall performance speed and makes the proposed networks well suitable for real-time systems.
Cyber-Physical System Security of a Power Grid : State-ofthe-Art

As part of the smart grid development, more and more technologies are developed and deployed on the power grid to enhance the system reliability. A primary purpose of the smart grid is to significantly increase the capability of computer-based remote control and automation. As a result, the level of connectivity has become much higher, and cyber security also becomes a potential threat to the cyber-physical systems (CPSs). In this paper, a survey of the state-of-the-art is conducted on the cyber security of the power grid concerning issues of: (1) the structure of CPSs in a smart grid; (2) cyber vulnerability assessment; (3) cyber protection systems; and (4) testbeds of a CPS. At Washington State University (WSU), the Smart City Testbed (SCT) has been developed to provide a platform to test, analyze and validate defense mechanisms against potential cyber intrusions. A test case is provided in this paper to demonstrate how a testbed helps the study of cyber security and the anomaly detection system (ADS) for substations.
Defending against malicious peripherals

Attacks on host computers by malicious peripherals are a growing problem. Inexpensive and powerful peripherals, which attach to plug-and-play buses, have made such attacks easy to mount. Making matters worse, commodity operating systems lack systematic defenses, and users are often not aware of the scope of the problem. We present Cinch, a pragmatic response to this threat. Cinch uses virtualization to place the hardware in a logically separate, untrusted machine, and includes an interposition layer between the untrusted machine and the protected one. This layer accepts or rejects interaction with devices and enforces security policies that are easily configured and extended by users. We show that Cinch integrates with existing OSes, enforces policies that thwart real world attacks, and has low overhead.
Analysis of Financial Time Series with EViews

4 GARCH Models 7 4.1 Basic GARCH Specifications . . . . . . . . . . . . . . . . . . . 8 4.2 Diagnostic Checking . . . . . . . . . . . . . . . . . . . . . . . 11 4.3 Regressors in the Variance Equation . . . . . . . . . . . . . . . 12 4.4 The GARCH–M Model . . . . . . . . . . . . . . . . . . . . . . 12 4.5 The Threshold GARCH (TARCH) Model . . . . . . . . . . . . 12 4.6 The Exponential GARCH (EGARCH) Model . . . . . . . . . 13 4.7 The Power ARCH (PARCH) Model . . . . . . . . . . . . . . . 13 4.8 The Component GARCH (CGARCH) Model . . . . . . . . . .
Contraction of the abdominal muscles associated with movement of the lower limb.

Activity of the trunk muscles is essential for maintaining stability of the lumbar spine because of the unstable structure of that portion of the spine. A model involving evaluation of the response of the lumbar multifidus and abdominal muscles to leg movement was developed to evaluate this function.



To examine this function in healthy persons, 9 male and 6 female subjects (mean age = 20.6 years, SD = 2.3) with no history of low back pain were studied.



Fine-wire and surface electromyography electrodes were used to record the activity of selected trunk muscles and the prime movers for hip flexion, abduction, and extension during hip movements in each of those directions.



Trunk muscle activity occurring prior to activity of the prime mover of the limb was associated with hip movement in each direction. The transversus abdominis (TrA) muscle was invariably the first muscle that was active. Although reaction time for the TrA and oblique abdominal muscles was consistent across movement directions, reaction time for the rectus abdominis and multifidus muscles varied with the direction of limb movement.



Results suggest that the central nervous system deals with stabilization of the spine by contraction of the abdominal and multifidus muscles in anticipation of reactive forces produced by limb movement. The TrA and oblique abdominal muscles appear to contribute to a function not related to the direction of these forces.
NEUROBIOLOGICAL SEQUELAE OF CHILDHOOD TRAUMA : Post-traumatic Stress Disorders in Children

The Cybrary is an interactive, multi-media resource on the Internet. This electronic library collects, stores, links and distributes a wide array of multi-media materials and resources targeted for key audiences. Materials specifically focused for different disciplines (e.g., pediatrics, psychology, social work, law enforcement) as well as "translated" interdisciplinary materials developed by CIVITAS Academy projects will be available. Distance consultation and cybercast training activities linked to ongoing CIVITAS Academy training will provide real-time access to the expertise of the CIVITAS Initiative staff and partners. Special sections of the Cybrary will be developed for children and families. This unique collection of crucial, understandable and entertaining materials and activities will be available via the Internet.
Mapping the Americanization of English in space and time

As global political preeminence gradually shifted from the United Kingdom to the United States, so did the capacity to culturally influence the rest of the world. In this work, we analyze how the world-wide varieties of written English are evolving. We study both the spatial and temporal variations of vocabulary and spelling of English using a large corpus of geolocated tweets and the Google Books datasets corresponding to books published in the US and the UK. The advantage of our approach is that we can address both standard written language (Google Books) and the more colloquial forms of microblogging messages (Twitter). We find that American English is the dominant form of English outside the UK and that its influence is felt even within the UK borders. Finally, we analyze how this trend has evolved over time and the impact that some cultural events have had in shaping it.
NON-UNIFORM TRANSMISSION LINE TRANSFORMERS AND THEIR APPLICATION IN THE DESIGN OF COMPACT MULTI-BAND BAGLEY POWER DIVIDERS WITH HARMONICS SUPPRESSION

In this paper, the application of compact non-uniform transmission line transformers (NTLTs) in suppressing and controlling the odd harmonics of the fundamental frequency is presented. A design example showing the complete suppression of the odd harmonics of the fundamental frequency is given. In addition, several compact NTLTs are designed showing the possibility of controlling the existence of a fundamental frequency’s odd harmonics. Moreover, multi-band operation using NTLTs is investigated. Specifically, a design example of a miniaturized triple-frequency NTLT is introduced. Based on these compact NTLTs, a 3-way triple-frequency modified Bagley power divider (BPD) with a size reduction of 50%, and a 5-way modified BPD with harmonics suppression and size reduction of 34%, are designed. For verification purposes, both dividers are simulated using the two full-wave simulators IE3D and HFSS. Moreover, the modified 5-way BPD with harmonics suppression is fabricated and measured. Both the simulation and measurement results validate the design approach.