nils-herrmann/clean_scirepeval huggingface dataset:
Digital Image Security : Fusion of Encryption , Steganography and Watermarking

Digital images are widely communicated over the internet. The security of digital images is an essential and challenging task on shared communication channel. Various techniques are used to secure the digital image, such as encryption, steganography and watermarking. These are the methods for the security of digital images to achieve security goals, i.e. confidentiality, integrity and availability (CIA). Individually, these procedures are not quite sufficient for the security of digital images. This paper presents a blended security technique using encryption, steganography and watermarking. It comprises of three key components: (1) the original image has been encrypted using large secret key by rotating pixel bits to right through XOR operation, (2) for steganography, encrypted image has been altered by least significant bits (LSBs) of the cover image and obtained stego image, then (3) stego image has been watermarked in the time domain and frequency domain to ensure the ownership. The proposed approach is efficient, simpler and secured; it provides significant security against threats and attacks. Image security; Encryption; Steganography; Watermarking
Automatic Labelling of Topics with Neural Embeddings

Topics generated by topic models are typically represented as list of terms. To reduce the cognitive overhead of interpreting these topics for end-users, we propose labelling a topic with a succinct phrase that summarises its theme or idea. Using Wikipedia document titles as label candidates, we compute neural embeddings for documents and words to select the most relevant labels for topics. Compared to a state-of-the-art topic labelling system, our methodology is simpler, more efficient, and finds better topic labels.
Mindfulness meditation training for sport ( MMTS ) intervention : Impact of MMTS with division I female athletes

Mindfulness training has been considered an effective mode for optimizing sport performance. The purpose of this study was to examine the impact of a twelve-session, 30-minute mindfulness meditation training session for sport (MMTS) intervention. The sample included a Division I female collegiate athletes, using quantitative comparisons based on preand post-test ratings on the Mindfulness Attention Awareness Scale (MAAS), the Positive Affect Negative Affect Scale (PANAS), the Psychological Well-Being Scale and the Life Satisfaction Scale. Paired sample t-tests highlight significant increases in mindfulness scores for the intervention group (p < .01), while the comparison group score of mindfulness remained constant. Both groups remained stable in reported positive affect however the intervention group maintained stable reports of negative affect while the comparison group experienced a significant increase in Negative Affect (p < .001). Results are discussed in relation to existing theories on mindfulness and meditation.
CELLULOSIC NANOCOMPOSITES : A REVIEW

Because of their wide abundance, their renewable and environmentally benign nature, and their outstanding mechanical properties, a great deal of attention has been paid recently to cellulosic nanofibrillar structures as components in nanocomposites. A first major challenge has been to find efficient ways to liberate cellulosic fibrils from different source materials, including wood, agricultural residues, or bacterial cellulose. A second major challenge has involved the lack of compatibility of cellulosic surfaces with a variety of plastic materials. The water-swellable nature of cellulose, especially in its non-crystalline regions, also can be a concern in various composite materials. This review of recent work shows that considerable progress has been achieved in addressing these issues and that there is potential to use cellulosic nano-components in a wide range of high-tech applications.
World Conference on Earthquake Engineering October 12-17, 2008, Beijing, China SCALING OF EARTHQUAKE ACCELEROGRAMS FOR NON-LINEAR DYNAMIC ANALYSES TO MATCH THE EARTHQUAKE DESIGN SPECTRA

A complete seismic design of structures requires linear and nonlinear time-history analyses especially for special type buildings. Seismic design codes generally define ground shaking in the form of a response spectrum of acceleration and allow using response spectrum compatible time history records in linear and nonlinear time history analyses. These records can be obtained from natural earthquake records, or can be generated synthetically and artificially. Although using real earthquake records has many advantages, there may exist lack of strong motion earthquake records to satisfy seismological and geological conditions and requirements defined in seismic codes. Artificial accelerograms whose response spectra closely compatible to design response spectra can be generated in either time or frequency domain. Matching techniques are based on scaling of the selected time history in time domain; filtering actual motion in frequency domain by its spectral ratio with the design target spectrum; or elementary wavelets are added or subtracted from the real time history to match a target design spectrum. In this study, the spectrum matching procedures for real accelerograms are summarized and applied to selected real acceleration records to match the proposed Type 1 elastic design spectrum given in the Eurocode 8 for specified seismic region and soil type. Artificial accelerograms, which are compatible with the selected design spectrum, are generated according to specified scenario earthquake. The linear and nonlinear response of single degree of freedom system subjected to the modified and artificially generated time histories acceleration records are compared and the advantages and disadvantages of each one are discussed.
Data Augmentation in Emotion Classification Using Generative Adversarial Networks

It is a difficult task to classify images with multiple class labels using only a small number of labeled examples, especially when the label (class) distribution is imbalanced. Emotion classification is such an example of imbalanced label distribution, because some classes of emotions like disgusted are relatively rare comparing to other labels like happy or sad. In this paper, we propose a data augmentation method using generative adversarial networks (GAN). It can complement and complete the data manifold and find better margins between neighboring classes. Specifically, we design a framework using a CNN model as the classifier and a cycle-consistent adversarial networks (CycleGAN) as the generator. In order to avoid gradient vanishing problem, we employ the least-squared loss as adversarial loss. We also propose several evaluation methods on three benchmark datasets to validate GAN’s performance. Empirical results show that we can obtain 5%∼10% increase in the classification accuracy after employing the GAN-based data augmentation techniques.
Quantitative InfraRed Thermography July 2-5 , 2008 , Krakow-Poland Calibration for 3 D Reconstruction of Thermal Images

The calibration system presented in this article enables to calculate optical parameters i.e. intrinsic and extrinsic of both thermal and visual cameras used for 3D reconstruction of thermal images. Visual cameras are in stereoscopic set and provide a pair of stereo images of the same object which are used to perform 3D reconstruction of the examined object . The thermal camera provides information about temperature distribution on the surface of an examined object. In this case the term of 3D reconstruction refers to assigning to each pixel of one of the stereo images (called later reference image) a 3D coordinate in the respective camera reference frame . The computed 3D coordinate is then re-projected on to the thermograph and thus to the known 3D position specific temperature is assigned. In order to remap the 3D coordinates on to thermal image it is necessary to know the position of thermal camera against visual camera and therefore a calibration of the set of the three cameras must be performed. The presented calibration system includes special calibration board (fig.1) whose characteristic points of well known position are recognizable both by thermal and visual cameras. In order to detect calibration board characteristic points’ image coordinates, especially in thermal camera, a new procedure was designed.
Polymers for 3D Printing and Customized Additive Manufacturing

Additive manufacturing (AM) alias 3D printing translates computer-aided design (CAD) virtual 3D models into physical objects. By digital slicing of CAD, 3D scan, or tomography data, AM builds objects layer by layer without the need for molds or machining. AM enables decentralized fabrication of customized objects on demand by exploiting digital information storage and retrieval via the Internet. The ongoing transition from rapid prototyping to rapid manufacturing prompts new challenges for mechanical engineers and materials scientists alike. Because polymers are by far the most utilized class of materials for AM, this Review focuses on polymer processing and the development of polymers and advanced polymer systems specifically for AM. AM techniques covered include vat photopolymerization (stereolithography), powder bed fusion (SLS), material and binder jetting (inkjet and aerosol 3D printing), sheet lamination (LOM), extrusion (FDM, 3D dispensing, 3D fiber deposition, and 3D plotting), and 3D bioprinting. The range of polymers used in AM encompasses thermoplastics, thermosets, elastomers, hydrogels, functional polymers, polymer blends, composites, and biological systems. Aspects of polymer design, additives, and processing parameters as they relate to enhancing build speed and improving accuracy, functionality, surface finish, stability, mechanical properties, and porosity are addressed. Selected applications demonstrate how polymer-based AM is being exploited in lightweight engineering, architecture, food processing, optics, energy technology, dentistry, drug delivery, and personalized medicine. Unparalleled by metals and ceramics, polymer-based AM plays a key role in the emerging AM of advanced multifunctional and multimaterial systems including living biological systems as well as life-like synthetic systems.
Improving GAN with neighbors embedding and gradient matching

We propose two new techniques for training Generative Adversarial Networks (GANs). Our objectives are to alleviate mode collapse in GAN and improve the quality of the generated samples. First, we propose neighbor embedding, a manifold learning-based regularization to explicitly retain local structures of latent samples in the generated samples. This prevents generator from producing nearly identical data samples from different latent samples, and reduces mode collapse. We propose an inverse t-SNE regularizer to achieve this. Second, we propose a new technique, gradient matching, to align the distributions of the generated samples and the real samples. As it is challenging to work with high-dimensional sample distributions, we propose to align these distributions through the scalar discriminator scores. We constrain the difference between the discriminator scores of the real samples and generated ones. We further constrain the difference between the gradients of these discriminator scores. We derive these constraints from Taylor approximations of the discriminator function. We perform experiments to demonstrate that our proposed techniques are computationally simple and easy to be incorporated in existing systems. When Gradient matching and Neighbour embedding are applied together, our GN-GAN achieves outstanding results on 1D/2D synthetic, CIFAR-10 and STL-10 datasets, e.g. FID score of 30.80 for the STL-10 dataset. Our code is available at: https://github.com/tntrung/gan
Certifiable Distributional Robustness with Principled Adversarial Training

Neural networks are vulnerable to adversarial examples and researchers have proposed many heuristic attack and defense mechanisms. We take the principled view of distributionally robust optimization, which guarantees performance under adversarial input perturbations. By considering a Lagrangian penalty formulation of perturbation of the underlying data distribution in a Wasserstein ball, we provide a training procedure that augments model parameter updates with worst-case perturbations of training data. For smooth losses, our procedure provably achieves moderate levels of robustness with little computational or statistical cost relative to empirical risk minimization. Furthermore, our statistical guarantees allow us to efficiently certify robustness for the population loss. We match or outperform heuristic approaches on supervised and reinforcement learning tasks.
Learning to Hash with Binary Deep Neural Network

Define hash function as a deep network • In order to deal with binary constraints, instead of involving the sgn or step function as in previous work, our design constrains one layer to directly output the binary codes • Propose a framework to solve the resulting NP-hard optimization with binary constraints • Directly incorporate the independence and balance properties on the codes • Ensure the similarity preserving → Binary Deep Neural Network for both unsupervised and supervised hashing
Early adolescent Internet game addiction in context: How parents, school, and peers impact youth

PAR, parent–adolescent relationship; IGA, internet game addiction; SC, school connectedness; DPA, deviant peer affiliation. ⇑ Corresponding author at: School of Psychology & Center for Studies of Psychological Application, South China Normal University, Guangzhou, Guangdong 510631, PR China.  E-mail address: zhangwei@scnu.edu.cn (W. Zhang).  Zhu, Wei Zhang ⇑, Chengfu Yu, Zhenzhou Bao
Interventions to reduce social isolation amongst older people : where is the evidence ?

As the population ages and more people are living alone, social isolation amongst older people is emerging as one of the major issues facing the industrialised world because of the adverse impact it can have on health and wellbeing. This article reviews the empirical literature published over the last 20 years on the effectiveness of interventions that target social isolation amongst older people. The results reveal that although numerous such interventions have been implemented worldwide, there is very little evidence to show that they work. It is concluded that future intervention programmes aimed at reducing social isolation should have evaluation built into them at inception, and that the results of the evaluation studies, whether positive or negative, should be widely disseminated. Where possible, as a cost-effective measure, pilot or demonstration projects should precede these interventions. Some key elements of successful interventions to counter social isolation amongst older people are presented.
Boolean kernels for collaborative filtering in top-N item recommendation

In many personalized recommendation problems available data consists only of positive interactions (implicit feedback) between users and items. This problem is also known as One-Class Collaborative Filtering (OC-CF). Linear models usually achieve state-of-the-art performances on OC-CF problems and many efforts have been devoted to build more expressive and complex representations able to improve the recommendations.Recent analysis show that collaborative filtering (CF) datasets have peculiar characteristics such as high sparsity and a long tailed distribution of the ratings. In this paper we propose a boolean kernel, called Disjunctive kernel, which is less expressive than the linear one but it is able to alleviate the sparsity issue in CF contexts. The embedding of this kernel is composed by all the combinations of a certain arity d of the input variables, and these combined features are semantically interpreted as disjunctions of the input variables. Experiments on several CF datasets show the effectiveness and the efficiency of the proposed kernel.
A Deeper Look at Experience Replay

Experience replay plays an important role in the success of deep reinforcement learning (RL) by helping stabilize the neural networks. It has become a new norm in deep RL algorithms. In this paper, however, we showcase that varying the size of the experience replay buffer can hurt the performance even in very simple tasks. The size of the replay buffer is actually a hyper-parameter which needs careful tuning. Moreover, our study of experience replay leads to the formulation of the Combined DQN algorithm, which can significantly outperform primitive DQN in some tasks.
Fully Connected Object Proposals for Video Segmentation

We present a novel approach to video segmentation using multiple object proposals. The problem is formulated as a minimization of a novel energy function defined over a fully connected graph of object proposals. Our model combines appearance with long-range point tracks, which is key to ensure robustness with respect to fast motion and occlusions over longer video sequences. As opposed to previous approaches based on object proposals, we do not seek the best per-frame object hypotheses to perform the segmentation. Instead, we combine multiple, potentially imperfect proposals to improve overall segmentation accuracy and ensure robustness to outliers. Overall, the basic algorithm consists of three steps. First, we generate a very large number of object proposals for each video frame using existing techniques. Next, we perform an SVM-based pruning step to retain only high quality proposals with sufficiently discriminative power. Finally, we determine the fore-and background classification by solving for the maximum a posteriori of a fully connected conditional random field, defined using our novel energy function. Experimental results on a well established dataset demonstrate that our method compares favorably to several recent state-of-the-art approaches.
Securing virtual machine orchestration with blockchains

The blockchain technology is gaining momentum because of its possible application to other systems than the cryptocurrency one. Indeed, blockchain, as a de-centralized system based on a distributed digital ledger, can be utilized to securely manage any kind of assets, constructing a system that is independent of any authorization entity. In this paper, we briefly present blockchain and our work in progress, the VMOA blockchain, to secure virtual machine orchestration operations for cloud computing and network functions virtualization systems. Using tutorial examples, we describe our design choices and draw implementation plans.
On the performance of artificial bee colony (ABC) algorithm

Artificial bee colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behaviour of honeybee swarms. This work compares the performance of ABC algorithm with that of differential evolution (DE), particle swarm optimization (PSO) and evolutionary algorithm (EA) for multi-dimensional numeric problems. The simulation results show that the performance of ABC algorithm is comparable to those of the mentioned algorithms and can be efficiently employed to solve engineering problems with high dimensionality.
Drugs which influence serotonin transporter and serotonergic receptors: Pharmacological and clinical properties in the treatment of depression.

Depression is nowadays a major contributor to global burden of disease. The most commonly prescribed drugs influence monoaminergic pathways, mainly concentrating on serotonin. Unfortunately, there are several drawbacks associated with these drugs, namely late onset of action, risk of suicide and adverse effects: mainly nausea, vomiting and sexual dysfunction. Therefore there is still need for new drugs with possibly high efficacy and fewer side effects. In this paper selected compounds which inhibit serotonin reuptake by acting on the serotonin transporter (SERT) and various serotoninergic receptors are presented. We also discuss the ways in which their mechanism of action can be modified to improve pharmacological profile. Here, we focus on describing drugs' potency, efficacy and adverse effects. Additional applications, apart from depression, are also discussed.
The ContrastMedium Algorithm: Taxonomy Induction From Noisy Knowledge Graphs With Just A Few Links

In this paper, we present ContrastMedium, an algorithm that transforms noisy semantic networks into full-fledged, clean taxonomies. ContrastMedium is able to identify the embedded taxonomy structure from a noisy knowledge graph without explicit human supervision such as, for instance, a set of manually selected input root and leaf concepts. This is achieved by leveraging structural information from a companion reference taxonomy, to which the input knowledge graph is linked (either automatically or manually). When used in conjunction with methods for hypernym acquisition and knowledge base linking, our methodology provides a complete solution for end-to-end taxonomy induction. We conduct experiments using automatically acquired knowledge graphs, as well as a SemEval benchmark, and show that our method is able to achieve high performance on the task of taxonomy induction.
Scaling the Security Wall: Developing a Security Behavior Intentions Scale (SeBIS)

Despite the plethora of security advice and online education materials offered to end-users, there exists no standard measurement tool for end-user security behaviors. We present the creation of such a tool. We surveyed the most common computer security advice that experts offer to end-users in order to construct a set of Likert scale questions to probe the extent to which respondents claim to follow this advice. Using these questions, we iteratively surveyed a pool of 3,619 computer users to refine our question set such that each question was applicable to a large percentage of the population, exhibited adequate variance between respondents, and had high reliability (i.e., desirable psychometric properties). After performing both exploratory and confirmatory factor analysis, we identified a 16-item scale consisting of four sub-scales that measures attitudes towards choosing passwords, device securement, staying up-to-date, and proactive awareness.
IGCV$2$: Interleaved Structured Sparse Convolutional Neural Networks

In this paper, we study the problem of designing efficient convolutional neural network architectures with the interest in eliminating the redundancy in convolution kernels. In addition to structured sparse kernels, low-rank kernels and the product of low-rank kernels, the product of structured sparse kernels, which is a framework for interpreting the recently-developed interleaved group convolutions (IGC) and its variants (e.g., Xception), has been attracting increasing interests. Motivated by the observation that the convolutions contained in a group convolution in IGC can be further decomposed in the same manner, we present a modularized building block, IGCV2: interleaved structured sparse convolutions. It generalizes interleaved group convolutions, which is composed of two structured sparse kernels, to the product of more structured sparse kernels, further eliminating the redundancy. We present the complementary condition and the balance condition to guide the design of structured sparse kernels, obtaining a balance among three aspects: model size, computation complexity and classification accuracy. Experimental results demonstrate the advantage on the balance among these three aspects compared to interleaved group convolutions and Xception, and competitive performance compared to other state-of-the-art architecture design methods.