Reviewerly/exHarmony huggingface dataset:
Survey on Vision based Hand Gesture Recognition

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Adversarial Detection with Gaussian Process Regression-based Detector

Adversarial attack is a technique that causes a malfunction of classification models by adding noise that cannot be distinguished by humans, which poses a threat to a deep learning model.In this paper, we propose an efficient method to detect adversarial images using Gaussian process regression.Existing deep learning-based adversarial detection methods require numerous adversarial images for their training.The proposed method overcomes this problem by performing classification based on the statistical features of adversarial images and clean images that are extracted by Gaussian process regression with a small number of images.This technique can determine whether the input image is an adversarial image by applying Gaussian process regression based on the intermediate output value of the classification model.Experimental results show that the proposed method achieves higher detection performance than the other deep learning-based adversarial detection methods for powerful attacks.In particular, the Gaussian process regression-based detector shows better detection performance than the baseline models for most attacks in the case with fewer adversarial examples.
Game Sprite Generator Using a Multi Discriminator GAN

This paper proposes an image generation method using a Multi Discriminator Generative Adversarial Net (MDGAN) as a next generation 2D game sprite creation technique.The proposed GAN is an Autoencoder-based model that receives three areas of information-color, shape, and animation, and combines them into new images.This model consists of two encoders that extract color and shape from each image, and a decoder that takes all the values of each encoder and generates an animated image.We also suggest an image processing technique during the learning process to remove the noise of the generated images.The resulting images show that 2D sprites in games can be generated by independently learning the three image attributes of shape, color, and animation.The proposed system can increase the productivity of massive 2D image modification work during the game development process.The experimental results demonstrate that our MDGAN can be used for 2D image sprite generation and modification work with little manual cost.
Provenance for Biomedical Ontologies with RDF and Git.

The German Center for Lung Research (DZL) is a research network with the aim of researching respiratory diseases. In order to enable consortium-wide retrospective research and prospective patient recruitment, we perform data integration into a central data warehouse. The enhancements of the underlying ontology is an ongoing process for which we developed the Collaborative Metadata Repository (CoMetaR) tool. Its technical infrastructure is based on the Resource Description Framework (RDF) for ontology representation and the distributed version control system Git for storage and versioning. Ontology development involves a considerable amount of data curation. Data provenance improves its feasibility and quality. Especially in collaborative metadata development, a comprehensive annotation about "who contributed what, when and why" is essential. Although RDF and Git versioning repositories are commonly used, no existing solution captures metadata provenance information in sufficient detail. We propose an enhanced composition of standardized RDF statements for detailed provenance representation. Additionally, we developed an algorithm that extracts and translates provenance data from the repository into the proposed RDF statements.
Hate speech

This chapter explores understandings of online hate speech and its social control from institutional and corporate perspectives. Hate speech has increasingly become a source of societal and political concern across the globe, as witnessed by recent measures and initiatives to tackle it. The European institutions are major social actors with regard to hate speech, but they are fundamentally different. Bookmarks reproduces the Council of Europe’s 1997 definition and in so doing sustains an understanding of hate speech across time, 20 years, and also across medium since it serves to define hate speech online. Hate speech is clearly a global phenomenon and one which rests on some form of intergroup antagonism. Hate speech then feeds off conflict, but it also can feed into it as illustrated most strikingly by cases of genocide and atrocities in Nazi Germany, Rwanda, Kenya and the Balkans.
When Are We Done with Games?

From an early point, games have been promoted as important challenges within the research field of Artificial Intelligence (AI). Recent developments in machine learning have allowed a few AI systems to win against top professionals in even the most challenging video games, including Dota 2 and StarCraft. It thus may seem that AI has now achieved all of the long-standing goals that were set forth by the research community. In this paper, we introduce a black box approach that provides a pragmatic way of evaluating the fairness of AI vs. human competitions, by only considering motoric and perceptual fairness on the competitors’ side. Additionally, we introduce the notion of extrinsic and intrinsic factors of a game competition and apply these to discuss and compare the competitions in relation to human vs. human competitions. We conclude that Dota 2 and StarCraft II are not yet mastered by AI as they so far only have been able to win against top professionals in limited competition structures in restricted variants of the games.
Adversarial Patches Exploiting Contextual Reasoning in Object Detection

This work was performed under the following financial assistance award: 60NANB18D279 from U.S. Department of Commerce, National Institute of Standards and Technology, funding from SAP SE, and alsoNSF grant 1845216.
Embodied Language Grounding with Implicit 3D Visual Feature Representations

Consider utterance the tomato to left of pot. Humans answer numerous questions about situation described, as well as reason through counterfactuals and alternatives, such as, is pot larger than tomato ?, can we move to a viewpoint from which tomato completely hidden behind pot ?, can we have an object that both to left of tomato and to right of pot ?, would tomato fit inside pot ?, and so on. Such reasoning capability remains elusive from current computational models of language understanding. To link language processing with spatial reasoning, we propose associating natural language utterances to a mental workspace of their meaning, encoded as 3-dimensional visual feature representations of world scenes they describe. We learn such 3-dimensional visual representations---we call them visual imaginations--- by predicting images a mobile agent sees while moving around in 3D world. The input image streams agent collects are unprojected into egomotion-stable 3D scene feature maps of scene, and projected from novel viewpoints to match observed RGB image views in an end-to-end differentiable manner. We then train modular neural models to generate such 3D feature representations given language utterances, to localize objects an utterance mentions in 3D feature representation inferred from an image, and to predict desired 3D object locations given a manipulation instruction. We empirically show proposed models outperform by a large margin existing 2D models in spatial reasoning, referential object detection and instruction following, and generalize better across camera viewpoints and object arrangements.
Reducing Dimensionality of Data Using Autoencoders

Data dimensionality reduction is a long-standing pre-processing step in machine learning algorithms. Many of the efficient methods are devised to reduce the dimensionality of data very effectively; however, they are incapable of recovering the original data. But the autoencoders are effective not only in reducing the dimensionality but also reconstructing the original data. In this paper, we are attempting to explore the dimensionality reduction capability of autoencoders, and try to comprehend the difference between autoencoder and PCA dimensionality reduction methods. Experiments are conducted on both the methods using MNIST datasets. The results show that the autoencoder can indeed learn somewhat dissimilar from PCA method.
WebQual 4.0 dan Importance-Performance Analysis (IPA) : Eksplorasi Kualitas Situs Web e-Kuisioner

The e-questionnaire application that researchers built using CodeIgniter and React-Js which serves to support researchers' activities to survey respondents online and various evaluations of the quality of the application need to be carried out in accordance with the expectations of users, one of which is to analyze the quality of the website using the method WebQual 4.0 with the Importance-Performance Analysis (IPA) approach. The research method used by researchers is a survey method using the WebQual 4.0 method and Importance-Performance Analysis (IPA) to measure the level of performance and importance on the e-Karis website. The results of this study found that the level of importance and level of performance on the e-Questionnaire website that needs to be maximized in ease of use, appearance, and improvement on the information and communication pages that must be considered by the manager of the E-Questionnaire website.Keywords:WebQual 4.0, Importance-Performance Analysis (IPA), Exploration, Quality, Website, e-Questionnaire.
An Exploration of Distributed Access Control Mechanism Using BlockChain

To adequately take an interest in the current coordinated partnerships, associate companies ought to have the capacity to impart computerized resources to different collaborators, while guaranteeing their advanced resources is shielded from unseemly access. In the current literature, a significant measure of research on unified access control with regards to one company has been completed. Be that as it may, investigation on decentralized access control in a shared domain is rare. A model of a decentralized access control framework is implemented, which strengthens auditability, unchanging nature, and uniformity in a cooperation domain utilizing the multi-chain blockchain. The model was created to assess two essential measurements: throughput and response. To accomplish this, various analyses have been performed to quantify the two measurements on Amazon Web Services.
Comparison of Querying Performance of Neo4j on Graph and Hyper-graph Data Model

Graph databases are gaining wide use as they provide flexible mechanisms to model real world entities and the relationships among them. In the literature, there exists several studies that evaluate performance of graph databases and graph database query languages. However, there is limited work on comparing performance for graph database querying under different graph representation models. In this study, we focus on two graph representation models: ordinary graphs vs. hyper-graphs, and investigate the querying performance of Neo4j for various query types under each model. The analysis conducted on a benchmark data set reveal what type of queries perform better on each representation.
Lattice-Based Group Signatures with Verifier-Local Revocation: Achieving Shorter Key-Sizes and Explicit Traceability with Ease

For lattice-based group signatures (GS) with verifier-local revocation (VLR), it only requires the verifiers to possess up-to-date group information (i.e., a revocation list, RL, consists of a series of revocation tokens for revoked members), but not the signers. The first such scheme was introduced by Langlois et al. in 2014, and subsequently, a full and corrected version (to fix a flaw in the original revocation mechanism) was proposed by Ling et al. in 2018. However, both constructions are within the structure of a Bonsai Tree, and thus features bit-sizes of the group public-key and the member secret-key proportional to $$\log N$$ , where N is the maximum number of group members. On the other hand, the tracing algorithm for both schemes runs in a linear time in N (i.e., one by one, until the real signer is traced). Therefore for a large group, the tracing algorithm of conventional GS-VLR is not convenient and both lattice-based constructions are not that efficient. In this work, we propose a much more efficient lattice-based GS-VLR, which is efficient by saving the $$\mathcal {O}(\log N)$$ factor for both bit-sizes of the group public-key and the member secret-key. Moreover, we achieve this result in a relatively simple manner. Starting with Nguyen et al.’s efficient and compact identity-encoding technique in 2015 - which only needs a constant number of matrices to encode the member’s identity, we develop an improved identity-encoding function, and introduce an efficient Stern-type statistical zero-knowledge argument of knowledge (ZKAoK) protocol corresponding to our improved identity-encoding function, which may be of independent cryptographic interest. Furthermore, we demonstrate how to equip the obtained lattice-based GS-VLR with explicit traceability (ET) in some simple way. This attractive functionality, only satisfied in the non-VLR constructions, can enable the tracing authority in lattice-based GS-VLR to determine the signer’s real identity in a constant time, independent of N. In the whole process, we show that the proposed scheme is proven secure in the random oracle model (ROM) based on the hardness of the Short Integer Solution (SIS) problem, and the Learning With Errors (LWE) problem.
A baseline for NSFW video detection in e-learning environments

The broad use of video capture and services for its storage and transmission has enabled the production of a massive volume of video data. This usage presents a challenge in controlling the type of content that is loaded for these video storage services. The Internet slang NSFW (Not Safe For Work) is often used as a warning for media that contain inappropriate content, such as nudity, intense sexuality, violence, gore or other potentially disturbing subject matter. Convolutional Neural Network (CNNs) architectures, or ConvNets, have become the primary method used for audio-visual pattern recognition. In this work, we intend to: (1) create a CNN based model for video features extraction; And (2), validate these video features with baselines models for NSFW video classification using a multi-modal approach. In initial experimentation, our best model achieves a recall of 96.6% for NSFW class.
REVaMP2 Project: Towards Round-Trip Engineering of Software Product Lines - Approach, Intermediate Results and Challenges

The REVaMP2 Project is a major European effort towards Round-Trip Engineering of Software Product Lines for software intensive systems. Indeed, software is predominant in almost every modern industry. The importance of time-to-market has grown tremendously in many business domains. Organizations are in a constant search for approaches for mass production of highly customizable systems. The software product lines engineering approach promises to provide up to 10× speed increase benefits in time-to-market. Traditionally, automated tools proposed a top-down approach, i.e., variants were generated from a model of the product line. However, the industry used a bottom-up approach that helped to re-create a product line out of various clones of a system. This operation is very costly and error prone. The goal of REVaMP2 is to automate the process of extracting a product line from various system artifacts and help with verification and the co-evolution of the product line. The project involves 27 partners that contribute with diverse research and industrial practices to address case study challenges stemming from 11 application domains. In this paper, we would like to present the motivation for the project, the current approach, the intermediate results and challenges.
System susceptibility and bound-states in structured reservoirs

We propose a formulation to obtain the exact susceptibility for system arbitrary operators to the external fields by means of the whole-system Hamiltonian (system plus reservoir) diagonalization methods, where the dissipative effects directly reflect the nature of the structured non-Markovian reservoir. This treatment does not make the Born-Markovian approximation in structured non-Markovian reservoir. The relations between linear response function and bound-states for the system as well as structured reservoir are found, which shows the photon bound-states and continuous energy spectrum can be readout from the susceptibility, respectively. These results are then used to examine the validity of second-order Born-Markovian approximation, where we find interesting features (e.g., bound-states) are lost in the approximate treatments for open systems. We study the dependence of the response function on the type (spectrum density) of interaction between the system and structured reservoir. We also give the physical reasons behind the disappearance of the bound-states in the approximation method. Finally, these results are also extended to a more general quantum network involving an arbitrary number of coupled-bosonic system without rotating-wave approximation. The presented results might open a new door to understand the linear response and the energy spectrum for non-Markovian open systems with structured reservoirs.
Automated Criminal Identification System Using Face Generation

The goal of the project is to aid the police department to ease the process of criminal recognition. We first initiate the recognition by the generation of face images based on image generation by using their sketch and visual attributes. The images we are generating are hyper-realistic in nature. This is followed by facial identification based on the image generated in the previous phase. The Model we have chosen here is a Generative Adversarial Network. The model learns the association by the use of a generator and a discriminator. We are using a context based Generative Adversarial Network to do the same. Then, we are running the process of facial identification for recognizing the person from the image generated with the help of facial recognition. For this we use the HOG Face recognition algorithm. We work towards aiding the police department in reducing their sketching time and criminal recognition time.
Designing Videogames to Crowdsource Accelerometer Data Annotation for Activity Recognition Research

Human activity recognition using wearable accelerometers can enable in-situ detection of physical activities to support novel human-computer interfaces and interventions. However, developing valid algorithms that use accelerometer data to detect everyday activities often requires large amounts of training datasets, precisely labeled with the start and end times of the activities of interest. Acquiring annotated data is challenging and time-consuming. Applied games, such as human computation games (HCGs) have been used to annotate images, sounds, and videos to support advances in machine learning using the collective effort of "non-expert game players." However, their potential to annotate accelerometer data has not been formally explored. In this paper, we present two proof-of-concept, web-based HCGs aimed at enabling game players to annotate accelerometer data. Using results from pilot studies with Amazon Mechanical Turk players, we discuss key challenges, opportunities, and, more generally, the potential of using applied videogames for annotating raw accelerometer data to support activity recognition research.
Enhancing Branch Predictors using Genetic Algorithm

Dynamic branch prediction is a hardware technique used to speculate the direction of control branches. Inaccurate prediction will make all speculative works useless while accurate prediction will significantly improve microprocessors performance. In this work, we have shown that Genetic Algorithm (GA) can be used to select (near) optimal parameters for branch predictors in most cases. The GA-enhanced predictors take time to find suitable parameters, but once the values of these parameters are determined, the GA-enhanced predictors take the same time to execute as the basic predictors with increased accuracy.
Device-free Sleep Stage Recognition through Bed Frame Vibration Sensing

Sleep disorder impairs people's health. To better analyze user's sleep quality, it is important to monitor people's sleep stages. Prior methods, such as polysomnography (PSG) and Fitbit, are often intrusive and having potential to change user's daily sleep routine. We present a non-intrusive approach to identify sleep stages through bed-frame vibrations. The proposed system detects the movement of users during their sleep and estimates their sleep stages via their movements induced vibration on the bed frame. Our system extracts features from the vibration signal and distinguishes sleep stages between rapid eye movement (REM) and non-rapid eye movement sleep (NREM). We evaluated our system in two ways 1) to understand the data distribution over different sleep stages, and 2) to explore the feasibility of distinguishing different movement characteristics over different sleep stages. The system achieved an area under curve (AUC) score of 0.84 when classifying REM sleep stages over NREM stages.
Natural language description of images using hybrid recurrent neural network

We presented a learning model that generated natural language description of images. The model utilized the connections between natural language and visual data by produced text line based contents from a given image. Our Hybrid Recurrent Neural Network model is based on the intricacies of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Bi-directional Recurrent Neural Network (BRNN) models. We conducted experiments on three benchmark datasets, e.g., Flickr8K, Flickr30K, and MS COCO. Our hybrid model utilized LSTM model to encode text line or sentences independent of the object location and BRNN for word representation, this reduced the computational complexities without compromising the accuracy of the descriptor. The model produced better accuracy in retrieving natural language based description on the dataset.
Client-Side Vulnerabilities in Commercial VPNs

Internet users increasingly rely on commercial virtual private network (VPN) services to protect their security and privacy. The VPN services route the client’s traffic over an encrypted tunnel to a VPN gateway in the cloud. Thus, they hide the client’s real IP address from online services, and they also shield the user’s connections from perceived threats in the access networks. In this paper, we study the security of such commercial VPN services. The focus is on how the client applications set up VPN tunnels, and how the service providers instruct users to configure generic client software. We analyze common VPN protocols and implementations on Windows, macOS and Ubuntu. We find that the VPN clients have various configuration flaws, which an attacker can exploit to strip off traffic encryption or to bypass authentication of the VPN gateway. In some cases, the attacker can also steal the VPN user’s username and password. We suggest ways to mitigate each of the discovered vulnerabilities.