Reviewerly/exHarmony huggingface dataset:
A Novel Hybrid Keypoint Detection Algorithm for Gradual Shot Boundary Detection

In the recent period, video summarization plays a vital role in the video retrieval process especially in the video surveillance system. The summarization process comprises of several activities such as preprocessing, features extraction, frame similarity analysis, shot boundary detection (SBD), etc. This paper presents a novel hybrid keypoint detection algorithm for the gradual shot boundary detection. The proposed technique combines the Hessian matrix of point of interest and the minimum Eigen values of the region of interest. The proposed technique has been tested on several video data sets and the results shows better performance in terms of accuracy when compared to the previous methods such as MEV, CD, FAST, SURF, MSER and BRISK techniques.
MERIT: Tensor Transform for Memory-Efficient Vision Processing on Parallel Architectures

Computationally intensive deep neural networks (DNNs) are well- suited to run on GPUs, but newly developed algorithms usually require the heavily optimized DNN routines to work efficiently, and this problem could be even more difficult for specialized DNN architectures. In this article, we propose a mathematical formulation that can be useful for transferring the algorithm optimization knowledge across computing platforms. We discover that data movement and storage inside parallel processor architectures can be viewed as tensor transforms across memory hierarchies, making it possible to describe many memory optimization techniques mathematically. Such transform, which we call memory-efficient ranged inner-product tensor (MERIT) transform, can be applied to not only DNN tasks but also many traditional machine learning and computer vision computations. Moreover, the tensor transforms can be readily mapped to existing vector processor architectures. In this article, we demonstrate that many popular applications can be converted to a succinct MERIT notation on GPUs, speeding up GPU kernels up to 20 times while using only half as many code tokens. We also use the principle of the proposed transform to design a specialized hardware unit called MERIT-z processor. This processor can be applied to a variety of DNN tasks as well as other computer vision tasks while providing comparable area and power efficiency to dedicated DNN application-specific integrated circuits (ASICs).
Comparative Analysis of AES &amp; RSA Cryptographic Techniques

In today's era, communication is very important and most of communications are done using the modern technologies which are making the communication better day by day. Data Security, here plays a vital role and protects the data from any malicious attacks. Cryptography is the science which protects the data against the third parties and enables a confidential communication through a secure channel. Cryptography is a science of secret codes which is associated with the process of converting the simple plain texts into the text which is impossible to understand (unintelligible) by unauthorized parties and vice-versa. Cryptography is a method which is used of sorting and transmitting the data in a specific form such that everyone would not be able to read that. Only those who are intended can read the data. Prior Cryptography was adequately synonymous with encryption but nowadays it is mainly working on mathematical theory and computer science practices. This paper proposed an implementation of a complete and practical AES and RSA encrypt/decrypt algorithm and will get to know that how RSA is better algorithm than AES.
Improving k-Nearest Neighbor Pattern Recognition Models for Privacy-Preserving Data Analysis

Supervised learning classification models use labeled data to train models on a discrete form for generating predictions. A major challenge addressed in this paper is training a machine learning model to the recognition of a pattern data perspective of the original datasets and privacy-preserving datasets to improve predictive models. The model training process, the training datasets, and validation datasets are mixed with data and privacy-preserving data cause overfitting from high variance in the machine learning algorithm. This paper addresses a k-Nearest Neighbor algorithm to build models, apply an automated hyperparameter tuning method to determine the optimal parameters based on the characteristics before the training process of a large volume datasets. Evaluating the model to achieve goals based on a high score of accuracy results on quality prediction and performance models. The experiments from our real datasets and the UCI machine learning repository show the best method for all of the training data and conduct difference experiments for improving accuracy, feasibility, correctness and reliability of the scheme.
A New Type of Two-dimensional Anti-counterfeit Code for Document Authentication Using Neural Networks

With the development of technology, the precision of scanning and printing is on the increase. Nowadays, People can easily forge prints with the facility. We need an effective anti-counterfeiting method. Graphical codes are an inexpensive method in the production of anti-counterfeiting. It is generally composed of random-like binary images and printed with high precision. After scanning and reprinting, the information on the pattern will irreversibly be damaged. Thus we can verify the authenticity of the print based on the loss rate of the information of the graphical codes. In this paper, we present a new anti-counterfeiting code, called Two-Level Dot Code (2LD-code), by replacing each dot in the 2D dot code with a particular anti-counterfeiting pattern. This 2LD-code realizes the anti-counterfeiting function while storing extra information. A deep neural network is applied to verify the authorization by extracting the anti-counterfeiting pattern and distinguish original prints and duplicate ones. Experimental results demonstrated the efficiency of the proposed method.
Tweedle: Sensitivity Check in Health-related Social Short Texts based on Regret Theory

Twitter helps us to know what is happening in the world and what people are talking right now. Every day, millions of Twitteraties tweet something personal or impersonal to express their emotions and valuable knowledge. In the health domain, disclosure of personal health information will have a long-term effect to common individuals either directly or indirectly, which emphasize the presence of unrealistic social boundaries and the need of sensitivity analysis in social media. The proposed Tweedle framework was built with 100K tweets extracted based on a set of 20 health-related cyber-keywords. The framework of Tweedle was bounded with Regret Theory for tweet annotation, content and contextual feature scores for feature selection and various machine learning algorithms for sensitivity classification. The tweets annotated in accordance with Regret Theory by domain experts of Amazon Mechanical Turkresulted in 61.5% of sensitive tweets with health data. The context and content-oriented features scoresare introduced in terms of Primary / Secondary tweet score, Named Entity Recognition Score of tweets, Term Frequency-Inverse Document Frequency(TF-IDF), Cyber-KeywordRatioin tweets, hashtag mentions, user mentions as features for classification.The Tweedle experimented Regret Theory in combination with various classifiers like Support Vector Machine, Naïve Bayes, Random Forest, Decision Tree, Logistic Regression and Recurrent Neural Network + Long Short-Term Memory for sensitivity classificationin health domain tweets.The training and testing results proved RNN + LSTM as the better performing model to identify tweets with sensitive health data.
3-Phase Adiabatic Logic and its Sound SCA Evaluation

Nowadays there is no doubt on the susceptibility of pure cryptographic devices to side-channel analysis attacks. During the last decade, integration of corresponding countermeasures into devices which deal with security and privacy of the users has become a must. This motivated several research on designing countermeasures, one of which is to make use of power-balancing feature of adiabatic logic families which have been originally motivated for low-power applications. In this work we introduce the first such a construction which operates in three phases compared to the entire state-of-the-art schemes requiring at least four phases. We especially designed our proposed scheme to harden power-analysis SCA attacks. To this end, we considered several relevant design criteria. For example, we payed particular attention to the symmetry of the fundamental gates of our proposed 3-phase construction. Based on simulation results and information-theoretic based SCA evaluations we claim that our construction can harden power analysis attacks more than the state-of-the-art adiabatic logic families. We further highlight the importance of the way power clocks are generated for an adiabatic circuit, and show the negative effect of the widely-known stepwise charge sharing power clock generator on the SCA security. Accordingly, we present an adjustment on such a circuit followed by corresponding SCA evaluation of its application on various adiabatic logic families.
Arbitrated Dynamic Ensemble with Abstaining for Time-Series Forecasting on Data Streams

A well-known challenge in mining temporal data streams is their dynamic nature where changes and recurrent concepts are likely to happen. Ensemble methods are powerful techniques to improve overall accuracy and tackle the aforementioned challenges by combining several classifiers. Dynamic Ensemble Selection allows selecting, on the fly, the most accurate classifiers only to contribute to the final output. This is motivated by the assumption that components of the ensemble have different degrees of expertise on different sub-spaces of the data. Existing Dynamic Ensemble Selection methods are tailored to batch learning or classification tasks but less suited to stream mining and forecasting tasks. In this paper, we propose a new Arbitrated Dynamic Ensemble Selection technique STREAMING-ADE for time-series forecasting on data streams that uses meta-learning to monitor the predictive power of ensemble components and accordingly select and weight experts. Our selection is based on an abstaining policy where poorly performing classifiers are excluded from the experts' committee. Our contribution is twofold: (i) We introduce two different approaches of abstaining: threshold-based and random-based selection and (ii) We conduct an extensive experimental study to compare different methods on both real and synthetic time-series.
A Study on Software Metric Selection for Software Fault Prediction

For most software systems, superfluous software metrics are often collected. Sometimes, metrics that are collected may be redundant or irrelevant to fault prediction results. Feature (software metric) selection helps separating relevant software metrics from irrelevant or redundant ones, thereby identifying the small set of software metrics that are best predictors of fault proneness for new components, modules, or releases. In this study, we compare three forms of feature selection techniques (filter-and wrapper-based subset evaluators along with two search techniques (Best First (BF) and Greedy Stepwise (GS)), and feature ranking on four datasets from a real world software project. Five learners are used to build fault prediction models with the selected software metrics. Each model is assessed using the Area Under the Receiver Operating Characteristic Curve (AUC). We find that wrapper-based subset evaluators performed best and feature ranking performed worst. In addition, the model built with the logistic regression (LR) learner performs best in terms of the AUC performance metric. This leads us to recommend the use of the wrapper-based subset evaluators to select software metric subsets and the LR learner for building software fault prediction models.
Virtual Reality as Educational Technology

The current paper emphasizes on explaining how Virtual Reality (VR) technology works and why it is important to be implemented for education purposes. The main goal of VR in education is to make studying process more involving and effective. The essential distinction of VR from other computer technologies for displaying information is that there is a “feedback” corresponding to the user's actions, i.e., the virtual environment is changing appropriately to his/her reactions. In the paper, a comparison between the different implementations of VR is made and the key elements of a VR system are listed. Also, a case study is presented in the last part of the paper. It consists of a description of the process of choosing which VR technology is best used in different education scenarios and shows a specific example of how VR technology is implemented in the teaching process at “Prof. Dr. Assen Zlatarov” University - Burgas and thus making the education process of students, and especially those from medical and technical science departments, more complete and engaging.
DSSLP: A Distributed Framework for Semi-supervised Link Prediction

Link prediction is widely used in a variety of industrial applications, such as merchant recommendation, fraudulent transaction detection, and so on. However, it's a great challenge to train and deploy a link prediction model on industrial-scale graphs with billions of nodes and edges. In this work, we present a scalable and distributed framework for semi-supervised link prediction problem (named DSSLP), which is able to handle industrial-scale graphs. Instead of training model on the whole graph, DSSLP is proposed to train on the k-hops neighborhood of nodes in a mini-batch setting, which helps reduce the scale of the input graph and distribute the training procedure. In order to generate negative examples effectively, DSSLP contains a distributed batched runtime sampling module. It implements uniform and dynamic sampling approaches, and is able to adaptively construct positive and negative examples to guide the training process. Moreover, DSSLP proposes a model-split strategy to accelerate the speed of inference process of the link prediction task. Experimental results demonstrate that the effectiveness and efficiency of DSSLP in serval public datasets as well as real-world datasets of industrial-scale graphs.
An Interaction Design Model for Information Visualization in Immersive Augmented Reality platform

Recent advances in immersive technologies such as Augmented Reality (AR) provide new opportunities to explore, analyze and present data through Immersive Analytics (IA). This study presents a thorough overview of current research in immersive AR and further proposes a preliminary model for design of immersive AR applications. The goal is to present data in AR settings where users can rely on familiar perceptions to draw in-depth conclusions on the data. This will be achieved by developing a tangible AR system for information visualization in future. The results obtained from this study will help researchers and designers in the field of Information Visualization, immersive Analytics, Virtual Reality (VR), Augmented Reality (AR), HCI, and Natural User Interfaces (NUI).
On the probability of overlap of random subsequences of pseudorandom number generators

We analyze in detail the probability that sequences of equal length generated by a pseudorandom number generator starting from random points of the state space overlap, providing for the first time an exact result and manageable bounds. While the computation of the probability is almost elementary, the value has been reported erroneously several times in the literature.
Fully-automatic inverse tone mapping algorithm based on dynamic mid-level tone mapping

Abstract High Dynamic Range (HDR) displays can show images with higher color contrast levels and peak luminosities than the common Low Dynamic Range (LDR) displays. However, most existing video content is recorded and/or graded in LDR format. To show LDR content on HDR displays, it needs to be up-scaled using a so-called inverse tone mapping algorithm. Several techniques for inverse tone mapping have been proposed in the last years, going from simple approaches based on global and local operators to more advanced algorithms such as neural networks. Some of the drawbacks of existing techniques for inverse tone mapping are the need for human intervention, the high computation time for more advanced algorithms, limited low peak brightness, and the lack of the preservation of the artistic intentions. In this paper, we propose a fully-automatic inverse tone mapping operator based on mid-level mapping capable of real-time video processing. Our proposed algorithm allows expanding LDR images into HDR images with peak brightness over 1000 nits, preserving the artistic intentions inherent to the HDR domain. We assessed our results using the full-reference objective quality metrics HDR-VDP-2.2 and DRIM, and carrying out a subjective pair-wise comparison experiment. We compared our results with those obtained with the most recent methods found in the literature. Experimental results demonstrate that our proposed method outperforms the current state-of-the-art of simple inverse tone mapping methods and its performance is similar to other more complex and time-consuming advanced techniques.
ReJection: A AST-Based Reentrancy Vulnerability Detection Method

Blockchain is deeply integrated into the vertical industry, and gradually forms an application ecosphere of blockchain in various industries. However, the security incidents of blockchain occur frequently, and especially smart contracts have become the badly-disastered area. So avoiding security incidents caused by smart contracts has become an essential topic for blockchain developing. Up to now, there is not generic method for the security auditing of smart contracts and most researchers have to use existing vulnerability detection technology. To reduce the high false rate of smart contract vulnerability detection, we use ReJection, a detection method based on abstract syntax tree (AST), to focus on the reentrancy vulnerability with obvious harm and features in smart contracts. ReJection consists of four steps. Firstly, ReJection obtains the AST corresponding to the contract by the smart contract compiler solc. Then, AST is preprocessed to eliminate redundant information. Thirdly, ReJection traverses the nodes of the AST and records the notations related to reentrancy vulnerabilities during the traversal, such as Danger-Transfer function, Checks-Effects-Interactions pattern and mutex mechanism. Finally, ReJection uses record information and predefined rules to determine whether the reentrancy vulnerability is occurred. ReJection is implemented based on Slither, which is an open-source smart contract vulnerability detection tool. Furthermore, we also use the open-source smart contract code as the test program to compare experimental results to verify the effects with the ReJection and Slither. The result highlights that the ReJection has higher detection accuracy for reentrancy vulnerability.
Visual Flight Rules-Based Collision Avoidance Systems for UAV Flying in Civil Aerospace

The operation of Unmanned Aerial Vehicles (UAVs) in civil airspace is restricted by the aviation authorities, which require full compliance with regulations that apply for manned aircraft. This paper proposes control algorithms for a collision avoidance system that can be used as an advisory system or a guidance system for UAVs that are flying in civil airspace under visual flight rules. A decision-making system for collision avoidance is developed based on the rules of the air. The proposed architecture of the decision-making system is engineered to be implementable in both manned aircraft and UAVs to perform different tasks ranging from collision detection to a safe avoidance manoeuvre initiation. Avoidance manoeuvres that are compliant with the rules of the air are proposed based on pilot suggestions for a subset of possible collision scenarios. The proposed avoidance manoeuvres are parameterized using a geometric approach. An optimal collision avoidance algorithm is developed for real-time local trajectory planning. Essentially, a finite-horizon optimal control problem is periodically solved in real-time hence updating the aircraft trajectory to avoid obstacles and track a predefined trajectory. The optimal control problem is formulated in output space, and parameterized by using B-splines. Then the optimal designed outputs are mapped into control inputs of the system by using the inverse dynamics of a fixed wing aircraft.
Multilingual and multimode phone recognition system for Indian languages

The aim of this paper is to develop a flexible framework capable of automatically recognizing phonetic units present in a speech utterance of any language spoken in any mode. In this study, we considered two modes of speech: conversation and read modes in four Indian languages, namely, Telugu, Kannada, Odia, and Bengali. The proposed approach consists of two stages: (i) Automatic speech mode classification (SMC) and (ii) Automatic phoneme recognition using mode-specific multilingual phone recognition system (MMPRS). The vocal tract and excitation source features are considered for classifying speech modes using feed forward neural networks (FFNNs). The vocal tract, excitation source, and tandem features are used in training deep neural network (DNN)-based multilingual phone recognition systems (MPRSs). The performance of the proposed approach is compared with baseline mode-dependent and mode-independent MPRSs. Experimental results show that the proposed approach which combines both SMC and MMPRS into a single system outperforms the baseline phone recognition systems.
Fast and Accurate SimRank Computation via Forward Local Push and its Parallelization

Measuring similarity among data objects is important in data analysis and mining. SimRank is a popular link-based similarity measurement among nodes in a graph. To compute the all-pairs SimRank matrix accurately, iterative methods are usually used. For static graphs, current iterative solutions are not efficient enough, both in time and space, due to the unnecessary cost and storage by the nature of iterative updating. For dynamic graphs, all current incremental solutions for updating the SimRank matrix are based on an approximated SimRank definition, and thus have no accuracy guarantee. In this paper, we propose a novel local push based algorithm for computing and tracking all-pairs SimRank. Furthermore, we develop an iterative parallel two-step framework for local push to take advantage of modern hardwares with multicore CPUs. We show that our algorithms outperform the state-of-the-art methods.
Listening While Speaking and Visualizing: Improving ASR Through Multimodal Chain

Previously, a machine speech chain, which is based on sequence-to-sequence deep learning, was proposed to mimic speech perception and production behavior. Such chains separately processed listening and speaking by automatic speech recognition (ASR) and text-to-speech synthesis (TTS) and simultaneously enabled them to teach each other in semi-supervised learning when they received unpaired data. Unfortunately, this speech chain study is limited to speech and textual modalities. In fact, natural communication is actually multimodal and involves both auditory and visual sensory systems. Although the said speech chain reduces the requirement of having a full amount of paired data, in this case we still need a large amount of unpaired data. In this research, we take a further step and construct a multimodal chain and design a closely knit chain architecture that combines ASR, TTS, image captioning, and image production models into a single framework. The framework allows the training of each component without requiring a large number of parallel multimodal data. Our experimental results also show that an ASR can be further trained without speech and text data and cross-modal data augmentation remains possible through our proposed chain, which improves the ASR performance.