Reviewerly/exHarmony huggingface dataset:
Resource Allocation for Minimized Power Consumption in Hardware Accelerated Clouds

In this paper we propose ACCLOUD-MAN, a novel resource manager for heterogeneous cloud data centers. In heterogeneous clouds a user request can be satisfied with more than one physical resource alternative. That is, the resource manager must decide which resource alternative will be chosen, along with the decision of the server the request will be assigned to. ACCLOUD-MAN's resource management objective is to reduce the power consumption of the cloud data center. To this end, the manager is modeled as an integer linear programming problem and is implemented in MATLAB, along with a cloud data center simulation platform. Simulation results show that the proposed ACCLOUD-MAN outperforms existing resource allocation methods such as Openstack.
Sarcasm Detection Using Feature-Variant Learning Models

Sentiment Analysis is the text classification tool that analyses a sentiment, message, emotion, attitude and tells whether the sentiment is positive, negative or neutral. The prime challenging aspect of sentiment analysis is the presence of sarcasm in message. Sarcasm is one kind of sentiment that is expressed verbally through the use of rolling of eyes and tonal stress. It consist of words mean the opposite of what user want to convey in order to be funny, or to show some irritation. The active online users and their reviews on websites are large in number so it is hard to detect even for humans, so in order to achieve error-free sentiment analysis it is imperative for machines to detect it accurately. The paper proposes the use of three different classes of features to help computers identify sarcasm reasonably well. In this paper, we intend to implement and empirically analyze number of computing techniques like Support Vector Machine, Decision Trees, Logistic Regression, Random Forest, K-Nearest Neighbors and Neural Networks for sarcasm detection on social media. The experimentation was done using three datasets i.e. SemEval 2015 Twitter benchmark dataset; random tweets collected using the Streaming API and a publicly available dataset of Reddit posts. The datasets provide interesting insights into how different forms of social media use the tool of sarcasm differently. The evaluated results were based on the performance measures like precision, recall, accuracy and F score. Amongst all, Twitter datasets had achieved the highest accuracy of around 91% to 92%, while the Reddit dataset had obtained peak accuracy of 80%.
Distant supervision and knowledge transfer for domain-oriented text classification in online social networks

Social networks are known to reflect users preferences and traits, for instance, in the form of posts and comments. This data can be used to solve various user profiling tasks that involve classifiers construction. State-of-the-art NLP models, despite their superior performance, still require manually labelled train and test datasets of significant, however, reduced size. The labelling with the help of user assessors may be impeded by the task complexity or lack of financial or time resources. Thus, we address an alternative distant supervision approach which helps to relatively easy acquire data, while producing noisy observations and imbalanced classes. In this work, we propose an approach to diminish the downsides of distant supervision by introducing how the subsets should be formed and how to optimize the training dataset structure.
Mutual Information Estimation: Independence Detection and Consistency

We address estimating the mutual information of variables X, Y from data. In particular, we consider a procedure that generates a sequence of contingency tables of quantized variables of X, Y, estimate the mutual information value for each of the contingency tables, and choose the largest value. This method estimates the mutual information regardless of whether each of X, Y is either discrete or continuous, and it was proved that the mutual information estimate is zero if and only if X, Y are independent, with probability one, as the sample size grows (independence detection). In this paper, we prove this method's strong consistency under a mild condition after deriving a formula of the probability that the estimate is positive when X, Y are discrete and independent. We also provide a simplified proof of independence detection using the formula.
An Efficient Profiling-Based Side-Channel Attack on Graphics Processing Units

The encryption/decryption algorithms have been ported to GPU platforms to take advantage of the GPUs' high-throughput computing capability. The downside of moving the cryptographic algorithms onto GPUs, however, is that the vulnerability of side-channel attacks for GPUs has not been well studied and the confidential information may be under a great risk by processing encryption on GPUs. In this paper, we proposed to leverage a profiling-based side-channel attack (SCA) to expose GPUs' side-channel vulnerability and the weakness of security services provided by GPUs. Our results show that GPUs are particularly vulnerable to profiling-based side-channel attacks and need to be protected against side-channel threats. Especially, for AES-128, the proposed method can recover all key bytes in less than 1 min, outperforming all prior SCAs we know.
The Real Time Face Recognition

The effectiveness of different modifications of local binary templates was investigated in relation to the problem of face recognition in real time. It has been found that centrally symmetric local binary patterns are practically inferior to other variations of the LBP as recognition attributes, and at the same time have a much higher speed. Based on the study, it was concluded that histograms of centrally symmetric local binary templates are an effective feature for the classification of individuals in real time.
A Transformer-Based Semantic Parser for NLPCC-2019 Shared Task 2

Sequence-to-Sequence (seq2seq) approaches formalize semantic parsing as a translation task from a source sentence to its corresponding logical form. However, in the absence of large-scale annotated dataset, even the state-of-the-art seq2seq model, i.e., the Transformer may suffer from the data sparsity issue. In order to address this issue, this paper explores three techniques which are widely used in neural machine translation to better adapt seq2seq models for semantic parsing. First, we use byte pair encoding (BPE) to segment words into subwords to transfer rare words into frequent subwords. Second, we share word vocabulary on both the source and the target sides. Finally, we define heuristic rules to generate synthetic instances to increase the coverage of training dataset. Experimental results on the NLPCC 2019 shared task 2 show that our approach achieves state-of-the-art performance and gets the first place in the task from the current rankings.
Implementing Blockchain in Your Enterprise

Most executives are scrambling to educate themselves on how to lead their companies into the blockchain future. Adoption of technology by enterprises is on the rise. In a recent survey conducted by Juniper Research , employees of companies with more than 20,000 people were asked whether they were looking to incorporate blockchain. The survey found that 57% were interested in incorporating blockchain technology in their organizations. Additionally, 76% of the employees responded that blockchain could be "very useful" or "quite useful" to their company (Blockgeeks 2018).
Cross-scale fusion detection with global attribute for dense captioning

As a new task of image understanding, the dense caption model needs to locate and describe a salient region in the image simultaneously. It inevitably divides the dense caption model into two parts, one part for detecting the regions of interest and the other part for generating regional language caption. Previous methods are relatively simple to deal with these two parts, using the feature map on the last convolution layer of RPN network to predict object coordinates, and using LSTM for regional language modeling. However, the structure of RPN is insufficient to deal with a large number of objects in the complex dataset, and LSTM also fails to effectively utilize the global information of images in regional language training, which brings opportunity to improve the performance of dense caption. In this paper, we propose a novel Cross-scale Fusion with Global Attribute model (CSGA) that enables the two parts of the dense caption model to perform normal end-to-end training without mutual interference. Furthermore, our model uses a one-stage object detector with feature map fusion operation across multiple detection scales to improve the quality of object detection part, and combines image features with the global high-level attribute to improve regional language training. We design a variety of model architectures and conducted sufficient experiments. Empirical results on Visual Genome dataset show that our model achieves competitive results with mAP 8.33.
Comparing Lassoing Criteria and Modeling Straight-line and One-loop Lassoing Motions Considering Criteria

In graphical user interfaces, users can select multiple objects simultaneously via lasso selection. This can be implemented, for example, by having users select objects by looping around their centers or entire areas. Based on differences in lassoing criteria, we presume that the performance of the criteria also differs. In this study, we compare three lassoing criteria and model lassoing motions while considering these criteria. We conducted two experiments; participants steered through straight-line and one-loop paths by using three criteria. The participants handled the lassoing criteria correctly and performed lassoing at appropriate speeds for each path shape. Although the drawn trajectories varied depending on the lassoing criteria, the criteria in the performance and subjective evaluations did not differ significantly. Additionally, from our results, we build a baseline model to predict the movement time by considering the lassoing criteria. We also discuss further experiments to predict movement time under more complex conditions.
Systematic Mapping Study of Utility-Driven Platforms for Clouds

Cloud computing is a dynamic paradigm that applies utility driven platforms at all layers to provide on-demand elastic services to the users. Utility driven platforms on the cloud makes the services easy to use and access by customers. There are several utility platforms available and many more are being developed. This makes utility driven platforms on the cloud an interesting area to research on. However, deciding on a specific research area that relates to utility driven platforms on the cloud could be a cumbersome process for a researcher. A systematic mapping study allows an aggregation of publication in an area of interest, which provides a summary of the extent of work done in that field. The specific objective is to conduct a systematic mapping study of utility driven platforms for clouds. The study in this paper were carried out using three categories or facets; the topic, research, and contribution facets. The results obtained indicated that there were more publications in terms of scheduling and algorithms in the area of metric with 0.96%, more articles published on designs and architecture in terms of tool with 4.81% each, more papers published on design in the area of model with 18.27%, more paper on scheduling in the area of method with 6.73%, and more papers on allocations in terms of process with 6.73%. In addition, there were more articles published on design in the area of evaluation research with 9.24%, more articles published on algorithms in terms of validation research with 6.72%, more publications on design in terms of solution research with 11.76%, more articles published on design in relation to philosophical papers with 2.52%, more articles published on design in terms of experience with 3.36% and more publications on scheduling in terms of opinion with 1.68%. In this study, the clearly identified gaps in the field of utility driven platforms on the cloud should invigorate enthusiasm for further studies by researchers and industry professionals. This investigation plainly recognized holes in the field of utility driven stages on the cloud, which ought to invigorate enthusiasm for further examinations by scientists and industry professionals.
The specific features of perpetration of the criminal offence of fraud using information-communication technologies

Historically speaking, along with the development of ICT, the ways of their misuse developed as well. Whoever may have bad intentions will keep finding new methods to abuse these technologies. These methods are based on vulnerabilities of ICT systems, whether they are technical or related to human factor. This paper describes the phenomena of the so-called social engineering and phishing, as modes of performing the criminal offences of fraud and computer fraud, which are used for illegal collection and abuse of the data of ICT system users, in order to mislead them towards acting in a way that causes harm to their own or someone else's possession, with the intention of illegal gain for the perpetrator. Contemporary phishing attacks are very sophisticated, adapted to the potential victims and adjusted to their affinities. The collecting of information regarding a potential victim is most often done via social networks. Therefore, this paper describes the process of planning acts of fraud and explains two most common techniques for data collecting - via social networks and via the victim's illegal router hijacking. A special emphasis is put on the criminal law treatment of these phenomena in the legislation of the Republic of Serbia, as well as on solving the dilemmas about technical terms in fraudulent acts in Serbian and English languages. The authors present different forms of social engineering and phishing in ICT systems, i.e. cyberspace, and discuss if and in which cases their acts have legal characteristics of the aforementioned criminal offences. The authors also present and analyze Serbian criminal legislation,with the emphasis on the crime of fraud and similar crimes, which are, in their essence, special forms of fraud. The stress is put on the crime of computer fraud and its characteristic features in comparison with the crime of fraud. The authors consider the changes in legislation that would lead to reformulating the crime of computer fraud so as to encompass various acts carried out with the use of the information-communication technologies.
A Robust Image Watermarking Through Bi-empirical Mode Decomposition and Discrete Wavelet Domain

This paper presents a watermarking technique using combination of discrete wavelet transform (DWT) and bi-dimensional empirical mode decomposition (BEMD) for the ownership authentication. The method uses DWT and BEMD to imperceptibly hide watermark in to original data. Research established that BEMD is combined with the watermarking technique to provide much better visual quality than other decomposition techniques. The BEMD distinguish the image from the least fragile frequency bands to the most robust frequency bands. Experimental result shows that the value of peak-signal to noise ratio (PSNR) is greater than 40 dB without any attacks and robust for different attacks. Further, the performance comparison with other competing approaches shows that our scheme is found superior results for different attacks.
Three-Dimensional Image-Guided Techniques for Minimally Invasive Surgery

Minimally invasive surgery (MIS) has attracted significant interest in current medicine for less operative pain and complications, smaller incisions, and faster recovery times. Image-guided surgery (IGS) is a general term used for any surgical procedure with indirect vision to realize the MIS. However, in current IGS, most three-dimensional (3D) medical images used for surgical navigation are displayed in 2D plane monitors, which lack intuitive 3D spatial information. Moreover, surgeons easily suffer from eye–hand coordination problems due to that the 2D navigation screen being away from the surgical area. This chapter focuses on 3D medical image rendering and display as well as augmented reality (AR)-based 3D image-guided techniques. Among the various 3D image rendering and display techniques, integral videography (IV) is promising due to its full parallax, continuous viewing points, autostereoscopic displayed images, and simple implementation without dedicated glasses or tracking devices. An AR-based 3D image-guided system using IV technique can present intuitive “see-through” surgical scenes to improve eye–hand coordination in diagnosis, surgical planning, operation, and telemedicine. With the assistance of IV image-guided techniques, MIS with enhanced safety, efficiency, and accuracy can be utilized widely for improving the quality of life.
Authenticating IoT Devices with Blockchain

There has been a tremendous effort in recent years to adopt everything smartly for personal, industrial, and social use. This provides the emerging Internet of Things (IoT) more impact, potential, and wide acceptance. However, the privacy and security concerns hamper its further adoption and development; this is mainly due to its reliance on a centralized cloud, fog, intrusion detection systems, firewall, and antivirus software for data, identity processing, and secure communication. On the one hand, IoT devices’ have some authentication and authorization flaws in the heterogeneous deployment and also relatively “vulnerable” facing malicious hackers due to resource constraints. If the single IoT device is compromised, the whole network becomes faulty. The challenges of security issues are increasing by the central authorities or third parties like cloud, fog, firewall, etc. To provide a cryptographic technique for both authentication and communication problems, to avoid the use of central authority we need a platform which is decentralized and cryptographically strong with immutability. The emerging blockchain technology effectively resolves the issues and provides extra novel facilities like distributed, publically viewable to strengthen security.
Co-Saliency Detection for RGBD Images Based on Effective Propagation Mechanism

Given a group of images, co-saliency detection aims at highlighting the common and salient foreground regions. To optimally explore the complementary information among images, we propose an effective propagation mechanism for RGBD images. First, we design a depth optimization map guided by image global saliency, which generates a superpixel-level saliency propagation label to express the primary saliency propagation confidence. Then, we further get the superior saliency propagation confidence based on the corresponding probabilities of the external contrast between images and the image internal regions. Finally, the primary and superior saliency propagation confidence are integrated to optimize the saliency propagation and get the final co-saliency value. The proposed method enables the complementary information among images to be reasonably propagated in a group of images. The relevance of depth information is enhanced and the co-salient objects are closer to the truth values. Experiments on two RGBD co-saliency datasets demonstrate the effectiveness of the proposed model.
Hiding Information Along Fractal in a Digital Cover to Improve Capacity

Steganography is the art and science of secret communication where the message to be hidden is in an invisible form. The message to be transferred is hidden inside an image, known as cover. Once the message is hidden inside the image using a stego key, it becomes a stego image. We need to take care that inserting the secret message inside the cover does not produce any detectable artifacts. If any suspicion is raised our steganography fails. The information needs to be hidden inside the host image with minimal degradation of the host image. The information to be embedded inside the image can be in the form of text, image, audio or video. Generally, a large amount of data can be stored inside the cover. In this paper, we have selected a JPEG image as the cover. Selecting an appropriate cover decides the effectiveness of the steganographic technique. The cover selected should have the large variety of colors. The high-intensity pixel values are selected for hiding data as it is not easy for human eyes to make out the difference in small changes in RGB values. Newton’s fractal is chosen as the stego key to finding redundancy in a digital image and a secret message is embedded in it. The amendment of the host image must not raise any suspicion during transmission. The JPEG image is used to camouflage the information inside it.
An Optimized Static Propositional Function Model to Detect Software Vulnerability

Due to the lack of appropriate theory to accurately characterize vulnerabilities, the current static detection technologies have two key challenges, i.e., limited applicability, and the problem of state space explosion. In this paper, we put forward a static detection model based on the proposition function. Furthermore, a new program intermediate representation called Vulnerability Executable Path Set (VEPS) is proposed to optimize our model which compresses the program state space distinctly. In addition, in order to confirm the reliability of the static detection model, we conduct three terms of contrast experiments to estimate the results with the vulnerability disclosed by NIST. The results obtained from extensive experiments show that the proposed model effectively detects more Wireshark bugs than NIST, and reveals a higher detection efficiency than FindBugs.
On the Computational Complexity of Blind Detection of Binary Linear Codes

In this work, we study the computational complexity of the MINIMUM DISTANCE CODE DETECTION problem. In this problem, we are given a set of noisy codeword observations and we wish to find a code in a set of linear codes C of a given dimension k, for which the sum of distances between the observations and the code is minimized. We prove that, for the practically relevant case when the set C only contains a fixed number of candidate linear codes, the detection problem is NPhard and we identify a number of interesting open questions related to the code detection problem.
neomento - Towards Building a Universal Solution for Virtual Reality Exposure Psychotherapy

We present the neomento project, a solution for virtual reality (VR) exposure therapy (VRET). In our work we have created specific rendering methods and virtual environments (VEs), designed and published a novel form of behavioural control for virtual agents (VA), included biophysiological measurements directly into the experience, and created an asymmetric gameplay system to assure the correct progress of a therapy session. The combination of above allows us to overcome multiple of the limitations that were hindering the previously published VRET solutions.
Enhancing Battle Maps through Flow Graphs

So-called battle maps are an appropriate way to visually summarize the flow of battles as they happen in many team-based combat games. Such maps can be a valuable tool for retrospective analysis of battles for the purpose of training or for providing a summary representation for spectators. In this paper an extension to the battle map algorithm previously proposed by the author [1] and which addresses a shortcoming in the depiction of troop movements is described. The extension does not require alteration of the original algorithm and can easily be added as an intermediate step before rendering. The extension is illustrated using gameplay data from the team-based multiplayer game World of Tanks.