Reviewerly/exHarmony huggingface dataset:
Exploiting Blockchains to improve Data Upload and Storage in the Cloud

Cloud computing is an information technology that enables different users to access a shared pool of configurable system resources and different services without physically acquiring them. Most industries nowadays such as banking, healthcare and education are migrating to the cloud due to its efficiency of services especially when it comes to data security and integrity. Cloud platforms encounter numerous challenges such as Data deduplication, Data Transmission, Data Integrity, VM Security, Data Availability, Bandwidth usage… etc. In this paper we have adopted the Blockchain technology - which is a relatively new technology - that emerged for the first time as the cryptocurrency Bitcoin and proved its efficiency in securing data and assuring data integrity. It is mostly a distributed public ledger that holds transactions data in case of Bitcoin. In our work blockchains are adopted in a different way than its regular use in bitcoin. Three of the major challenges in Cloud Computing and Cloud services are Data Deduplication, Storage and Bandwidth usage are discussed in this paper.
Baptista chaotic cryptosystem based on alternate superior dynamic lookup table

Chaotic cryptosystems are using for securing text data and images over the network. In 1998, M S Baptista suggested a chaotic cryptosystem based on partitioning the visiting interval of chaotic orbits of the logistic map, which attracted much attention. In 2002, K W Wong presented a prompt chaotic cryptosystem based onthe dynamic lookup table using a logistic maptoimprove the security in Baptista type cryptosystem. In 2004, Alvarezet al. [1] found that Wong’s cryptosystem [15] is not resistant tochosen-plaintext and known-plaintext attacks. To improve the security of the dynamic lookup table, weintroduce an alternate dynamic lookup table that will be more secure in context to the existing system.
Human Action Recognition Algorithm Based on DBPSO-SVM Classifier

In the context of population aging, the study of behavior recognition algorithms for the elderly has important social significance. This algorithm is designed to distinguish between Fall Action and Activity in Daily Life (ADL), which is important for the fall protection of older people. This paper makes full use of the improved Discrete Binary Particle Swarm Optimization (DBPSO) to select the optimal feature subset and parameters to train SVM (Support Vector Machine) algorithm. Experimental results show that this method can achieve higher precision than non-optimized SVM, the improved DBPSO-SVM algorithm can achieve more than 95% specificity and sensitivity for each action, especially when it comes to falls, the sensitivity is 98.44%, the specificity is 96.25%, and compared with the accuracy of 94.17% of the non-optimized SVM algorithm, there are good improvements.
Program-Oriented Logics of Renominative Level with Extended Renomination and Equality

The formalism of program logics is the main instrument for software verification. Such logics are based on formal program models and reflect main program properties. Among various program logics, Floyd-Hoare logic and its variants take a special place because of its naturalness and simplicity. But such logics are oriented on total pre- and post-conditions, and in the case of partial conditions they become unsound. Different methods to overcome this problem were proposed in our previous works. One of the methods involves extension of program algebras with the composition of predicate complement. This permits to modify rules of the logic making them sound. Such modification requires introduction of undefinedness conditions into logic rules. To work with such conditions, an underlying predicate logic should become more expressive. In this paper we continue our research of such logics. We investigate a special program-oriented predicate logic called logic of renominative (quantifier-free) level with the composition of predicate complement, extended renomination, and equality predicate. This logic is a constituent part of the program logic. We introduce a special consequence relation for this logic, construct a sequent calculus, and prove its soundness and completeness.
A Bargaining Games Approach to Information Security Interactions

This study models information security interactions in two scenarios: a) corporate and intermediary information security disclosure platforms and b) a direct interaction between heavyweight actors with high stakes. Using bargaining games, we analyze and discuss equilibrium conditions. Our results indicate that equilibrium conditions are easily attained when gains are small, vulnerabilities are shallow and when disclosure platforms are used to facilitate interactions between actors. Contrastingly, when stakes are high, our models arrive at conditions for equilibrium that are very difficult to achieve and maintain. We extend this model to a high stakes scenario to help explain the value proposition of Black Market Brokers.
InteractionGAN: Image-Level Interaction using Generative Adversarial Networks

We present a novel strategy for image-level interaction that is applicable to a single image without any prior structural knowledge, such as object status or reconstructed 3D models. By training sets of input image and interaction pairs using a target image, our model can generate result images by applying the desired interaction to new unseen images. The proposed method is differentiated from previous approaches for changing poses, which requires absolute statuses for training images or estimated 3D model with reconstruction errors. Based on the conceptual analysis of encoder-decoder networks, we propose a novel generator network architecture containing a feature converter network, which is suitable for applying interactions to images. We also implement a discriminator network for training, which is a well-known technique for generative adversarial networks. Experimental results demonstrate that the proposed method successfully generates result images with applied interactions without any prior knowledge. We expect that our method will provide insights into novel interaction schemes for augmented reality by reflecting interactions onto real scenes and providing more realistic user experiences.
Depth Image Based Object Localization Using Binocular Camera and Dual-stream Convolutional Neural Network

With the development of depth sensing devices, depth image based localization has received much attentions. In this paper, a depth image based object localization algorithm by the dual-stream convolutional neural network (CNN) is proposed. In the off-line phase, at each reference position, the grayscale image and its corresponding depth image pairs are collected by the binocular camera. The using the image preprocessing technique, the grayscale image and depth image are transformed to the three-channel images. Then, the dual-stream CNN with the shared weight coefficients are used for offline regression learning. At last, the distance based regression model is obtained. In the on-line phase, after the preprocessing of the grayscale image and depth image, the final distance can be estimated by the distance based regression model. Experiment are carried out to evaluate the performance of the proposed algorithm. The test results illustrated that the proposed algorithm has better localization performance than traditional image approaches.
Semantic role labeling for opinion target extraction from chinese social network

Social network is a good resource to collect public opinions considering the diversity and variety in fashion, especially user generated content (UGC). Extracting the opinions from UGC can be the base of commercial policy, so how to extract the opinions correctly is an important problem. However, there are two problems: Are the opinions really talking about the target entities? Or the amount of opinions is enough for network volume analysis? In this study, we combine rule-based method and Semantic Role Labeling (SRL) to detect the opinion target (OTD) from UGC. In SRL task, we design a neural network model to extract the opinion words. Considering gradient vanish problem, we use highway connection to control the percentage of data. To improve the performance of SRL, we use additional features to help model learn the features between verb and other phrases. Finally, we design a rule to filter the result of SRL for OTD task. The experimental results show that our SRL model obtains 71% F1, and our method on OTD task obtains 73% precision which outperforms LTP. This research can be the basis of hot topic prediction for sponsors and helps them to decide marketing strategies
Efficient GPU Implementations to Compute the Diameter of a Graph

The Floyd-Warshall algorithm is a well-known algorithm to compute the distance of all pairs of nodes of a graph. The Blocked Floyd-Warshall algorithm, a variant of the Floyd-Warshall has been proposed to accelerate the Floyd-Warshall algorithm by means of a GPU architecture. The previously published GPU implementations for the Blocked Floyd-Warshall algorithm perform many separated kernel calls for costly barrier synchronization. The main contribution of this paper is to present a more sophisticated implementation of the Blocked Floyd-Warshall algorithm, which performs no barrier synchronization and invokes only one kernel call. Experimental results using NVIDIA Tesla V100 show that our implementation is 1.02-1.09 times faster than the previously published one. Our implementation with SIMD functions also runs 1.22-2.02 times faster than it. Furthermore, we propose efficient GPU implementations to execute the Blocked Floyd-Warshall algorithm for many graphs at the same time. From the experimental results, our implementation is 1.02-1.46 times faster than an extended implementation of previous one.
Old Man Fall Detection Based on Surveillance Video Object Tracking

Image recognition technology based on deep learning has made great progress, which makes object detection technology work in many fields. The number of elderly people in China has risen year by year, proclaiming the arrival of an aging society. "The old man can't fall" is a consensus. Using object detection algorithm to detect the fall of the elderly is a research hotspot in the field of object detection. Through the analysis of the object detection algorithm and the object tracking algorithm, Deep-sort and YOLOv3 algorithms are used to achieve the real-time fall detection of the surveillance video. The experimental results prove that combined with YOLOv3 and the Deep-sort algorithms can detect the fall of the elderly.
Salient Object Detection based on Multiple Priors Fusion

Salient object detection can be utilized to detect the most significant regions in various environments, which has been regarded as foundation of computer vision. Different saliency models use different prior or knowledge. We propose a multi priors fusion method for saliency measure, which integrates background prior with foreground prior and center prior. Firstly, through each boundary of the image, we can get four saliency maps, and fuse them to get the background prior saliency map. Secondly, we utilize boundary extension method to highlight regions, and these regions can be regarded as the queries of manifold ranking for the foreground prior saliency map. Thirdly, the corners on the image are obtained, filtered by the foreground region, and then clustered into a point as the center of Gaussian model, which is used to calculate the center prior saliency map. Finally, the above three kinds of prior-based saliency maps are fused via the proposed fusion framework to gain a better final saliency map. Compared with fifteen methods, the experimental results on ECSSD and MSRA10K show that our proposed method achieves better saliency detection results.
Exploring the Training and Execution Acceleration of a Neural Network in a Reconfigurable General-purpose Processor for Embedded Systems

Neural networks are getting more and more present in our daily lives. Whether to classify, optimize, or predict results, the use of neural networks proves to be a great and powerful choice to solve these problems. However, the time spent in the training process can be an issue. Although it can be accelerated by Graphics Processing Units (GPUs), coprocessors or even Field Programmable Gate Array (FPGA), these approaches may not be the best choices to improve training performance in embedded systems due to the large area and power consumption. In this paper, we present a customized instruction set architecture with fixed-point arithmetic considering a lightweight general purpose processor in order to accelerate the training and execution of neural networks. Cadence <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> LX7 Processor and Xtensa <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">®</sup> software are used to identify run-time bottlenecks and optimize specific pieces of hardware. Experimental results show that our proposed architecture achieves about 24X runtime reduction with respect to a floating-point implementation, and were compared against other architectures.
Sentiment Analysis of Tweet Messages using Hybrid Approach Algorithm

Communication is a vital component of everyday life. Through technology via social media, communication becomes more dynamic generating huge volume of data. Each data represents sentiments toward a public issue. Sentiment analysis algorithms able classify whether positive, negative or neutral. This paper introduces a hybrid algorithm combining two lexicon-based algorithms namely SentiWordNet and VADER algorithms. Three algorithms were tested using different data sources. It achieved an accuracy of 88.83% which 21.44% improvement from most commonly used algorithm SentiWordNet.
Visualising alignment to support students’ judgment of confidence in open learner models

Abstract Knowledge monitoring is a component of metacognition which can help students regulate their own learning. In adaptive learning software, the system’s model of the student can be presented as an open learner model (OLM) which is intended to enable monitoring processes. We explore how presenting alignment, between students’ self-assessed confidence and the system’s model of the student, supports knowledge monitoring. When students can see their confidence and their performance (either combined within one skill meter or expanded as two separate skill meters), their knowledge monitoring and performance improves, particularly for low-achieving students. These results indicate the importance of communicating the alignment between the system’s evaluation of student performance and student confidence in the correctness of their answers as a means to support metacognitive skills.
Adaptive Sampling for Human-aware Path Planning in Dynamic Environments

Nowadays, robots are increasingly used in densely populated dynamic environments. Robots not only need to complete the navigation tasks quickly, but also need to take into account the human trajectories and the constraints of social rules. In order to avoid the robot going into the crowed areas and improve robot acceptance in the crowded public environment, we propose a human-aware motion planning algorithm that is based on sampling method. Firstly, human will be annoyed and stressed if robots disturb them during operation. To alleviate this uncomfort brought by the robot, we use probabilistic representations to build the Human Domain Zone (HDZ) of individual or crowd behaviors. Besides, we propose a sampling strategy that is capable of biasing the sampling in the area where human feel comfortable or crowd is sparse. Moreover, we put forward an evaluation function to select the optimum trajectory. This function can avoid robot falling into the crowded area through VDM which is used to model the relationship between human and robot. The proposed approach is verified with extensive experiments in simulated environments. The results show that our method has the promising performance in crowded environment. It can also generate a smooth path with higher success rate.
Intelligent, Adaptive and Social e-Learning in POLYGLOT

Intelligence and adaptivity are core characteristics of sophisticated e-learning systems which in many cases have social networking functionalities. They involve the development of modeling, diagnostic, feedback and reasoning techniques towards creating a student-centered learning environment. Therefore, there have been systems that combine all of the above technologies for tutoring specific domains. However, there have also been domain independent tutoring platforms with social characteristics that serve as learning management systems within the technology of social media. In this chapter, we give a short presentation of the concepts of domain independent mechanisms, including student modeling techniques, as well as of learners’ characteristics for building efficient student models. The described concepts are used in the following chapters for the implementation of the social networking-based language learning application, called POLYGLOT. In addition, this chapter presents well-known web-based platforms that have been used for social e-learning.
Gesture Interpretation Control System Using Convolutional Neural Networks

This paper proposes a non-invasive control system for electrical wheelchairs utilizing facial gestures of individuals captured by real-time monocular camera. The images are interpreted with a convolutional neural network that achieves up to ~99.5% overall accuracy. The control system uses an embedded system with a graphics processing unit for predicting real-time throughput with fast inference time. This solution offers great versatility, where the user can make a gesture to depict a command of his choice.
Marker Based Pedestrian Detection Using Augmented Reality

Pedestrian detection is a popular research topic from the last decade. Most of the pedestrian identification models are based on face recognition algorithms. It is a difficult task to detect and track individual pedestrians based on these algorithms because there is a compulsion that their faces should be towards the camera. This limitation makes face recognition algorithms inefficient to detect pedestrians from the backsides. In this paper, we proposed a method for pedestrian detection using marker recognition. Multiple pedestrians are detected and then tracked based on markers attached to their backsides. Attaching markers on back-side of pedestrians helps to recognize them even when they are looking the other way. After the marker is recognized, the unique character related to that marker is displayed as a 3D object. This marker-based pedestrian detection is carried out using a mobile phone system and can be applied to embedded systems. The proposed method makes it possible to recognize up to three pedestrians located at different positions from the camera.
An Improved Dynamic Alignment Method for Vehicle Re-Identification

In this paper, we propose an improved vehicle re-identification method based on the combination between the AlignedRelD and the Stochastic Weight Averaging (SWA). AlignedRelD extracts a global feature and local features of a vehicle’s image and performs joint learning. Local automatic alignment is achieved by computing the shortest path between the two sets of local features, so that global feature learning can benefit from local feature learning. By running an optimizer with a high constant learning rate, the SWA averages the weight of the model to ensure that a better weight combination can be found. Our method achieves rank-1 accuracy of 94.4% on VeRi-776 and 95.1% on VehiclelD(small), outperforming state-of-the-art methods by a large margin. In order to better solve the task of vehicle re-identification in residential area, we have made the Oeasy-Parking dataset and experimented with our methods, and achieved good results.
Gradient-Based Pooling for Convolutional Neural Networks

Pooling layers are an important part of convolutional neural networks (CNNs). They reduce the dimensionality of feature maps and pass salient information to subsequent layers. In this paper, we introduce a novel gradient-based feature pooling method that can down-sample feature maps while better preserving key information. This method considers the spatial gradient of the pixels within a pooling region as a key to select the most possible descriptive information in contrast to the current practice of existing methods that mostly rely on the pixel values. Extensive experiments on different benchmark image classification tasks and CNN architectures demonstrate that the proposed method achieves superior results over existing pooling approaches.
Quantitative Analysis of Bitcoin Transferred in Bitcoin Exchange

In this paper, a new Bitcoin address clustering algorithm is proposed for Bitcoin exchanges. The proposed algorithm aims to classify the cold wallets, hot wallets and user wallets in the Bitcoin exchanges, which are verified by off-chain information from the Internet. By analyzing the structures of different Bitcoin exchanges, we find that most Bitcoin exchanges exist weakness in managing the reasonable amounts of bitcoins kept in hot wallets. A large amount of Bitcoins stored in hot wallets can meet the users’ withdrawal demands but may increase the risk of attack. However, a small amount of bitcoins in hot wallet may be inconvenient to frequently transfer for cold wallet. The problem then is modeled as a Bitcoin withdrawal prediction problem for hot wallets. We adopt traditional and classific supervised learning methods to solve the problems. Numerical experiments show that the proposed approach provides reasonable prediction results. Furthermore, we simulate two processes to analyze our results. The first process shows that Facebook-prophet outperforms other methods if there is no transaction occurred from cold wallets to hot wallet. The second process shows that the more transactions from cold wallets to hot wallets, the smaller Bitcoins required for hot wallets. Overall, our work is valuable and useful for the Bitcoin exchanges’ business.
Moving Target Defense Against Injection Attacks

With the development of network technology, web services become more convenient and popular. However, web services are also facing serious security threats, especially SQL injection attack(SQLIA). Due to the diversity of attack techniques and the static of defense configurations, it is difficult for existing passive defence methods to effectively defend against all SQLIAs. To reduce the risk of successful SQLIAs and increase the difficulty of the attacker, an effective defence technique based on moving target defence (MTD) called dynamic defence to SQLIA (DTSA) was presented in this article. DTSA diversifies the types of databases and implementation languages dynamically, turns the Web server into an untraceable and unpredictable moving target and slows down SQLIAs. Moreover, the period of mutation was determined by the concept of dynamic programming so as to reduce the hazards caused by SQLIAs and minimize the impact on normal users as much as possible. Final, the experimental results showed that the proposed defence method can effectively defend against injection attacks in relational databases.