Reviewerly/exHarmony huggingface dataset:
Evaluation of hidden Markov models using deep CNN features in isolated sign recognition

Isolated sign recognition from video streams is a challenging problem due to the multi-modal nature of the signs, where both local and global hand features and face gestures needs to be attended simultaneously. This problem has recently been studied widely using deep Convolutional Neural Network (CNN) based features and Long Short-Term Memory (LSTM) based deep sequence models. However, the current literature is lack of providing empirical analysis using Hidden Markov Models (HMMs) with deep features. In this study, we provide a framework that is composed of three modules to solve isolated sign recognition problem using different sequence models. The dimensions of deep features are usually too large to work with HMM models. To solve this problem, we propose two alternative CNN based architectures as the second module in our framework, to reduce deep feature dimensions effectively. After extensive experiments, we show that using pretrained Resnet50 features and one of our CNN based dimension reduction models, HMMs can classify isolated signs with 90.15% accuracy in Montalbano dataset using RGB and Skeletal data. This performance is comparable with the current LSTM based models. HMMs have fewer parameters and can be trained and run on commodity computers fast, without requiring GPUs. Therefore, our analysis with deep features show that HMMs could also be utilized as well as deep sequence models in challenging isolated sign recognition problem.
Human Activity Recognition using Accelerometer and Gyroscope Data from Smartphones

Human Activity Recognition is a procedure for arranging the activity of an individual utilizing responsive sensors of the smartphone that are influenced by human activity. Its standouts among the most significant building blocks for numerous smartphone applications, for example, medical-related applications, tracking of fitness, context-aware mobile, survey system of human, and so forth. This investigation centers around acknowledgment of human activity utilizing sensors of the smartphone by some machine learning and deep learning characterization approaches. Data received from the accelerometer sensor and gyroscope sensor of the smartphone are grouped to recognize the human activity.
A New Machine Learning Algorithm Based on Optimization Method for Regression and Classification Problems

A convex minimization problem in the form of the sum of two proper lower-semicontinuous convex functions has received much attention from the community of optimization due to its broad applications to many disciplines, such as machine learning, regression and classification problems, image and signal processing, compressed sensing and optimal control. Many methods have been proposed to solve such problems but most of them take advantage of Lipschitz continuous assumption on the derivative of one function from the sum of them. In this work, we introduce a new accelerated algorithm for solving the mentioned convex minimization problem by using a linesearch technique together with a viscosity inertial forward–backward algorithm (VIFBA). A strong convergence result of the proposed method is obtained under some control conditions. As applications, we apply our proposed method to solve regression and classification problems by using an extreme learning machine model. Moreover, we show that our proposed algorithm has more efficiency and better convergence behavior than some algorithms mentioned in the literature.
Natural Language Generation Using Transformer Network in an Open-Domain Setting

Prior works on dialog generation focus on task-oriented setting and utilize multi-turn conversational utterance-response pairs. However, natural language generation (NLG) in the open-domain environment is more challenging. The conversations in an open-domain chit-chat model are mostly single-turn in nature. Current methods used for modeling single-turn conversations often fail to generate contextually relevant responses for a large dataset. In our work, we develop a transformer-based method for natural language generation (NLG) in an open-domain setting. Experiments on the utterance-response pairs show improvement over the baselines, both in terms of quantitative measures like BLEU and ROUGE and human evaluation metrics like fluency and adequacy.
A comparative study on using unsupervised learning based data analysis techniques for breast cancer detection

As stated by the World Health Organisation, breast cancer is the most frequent form of cancer among women, being responsible for 15% of all cancer-related deaths in this group. A lot of research has been carried out, so far, in using various machine learning models for breast cancer prediction, ranging from conventional classifiers to deep learning techniques. Three unsupervised learning models (t-Distributed Stochastic Neighbor Embedding, autoencoders and self-organizing maps) are comparatively analysed in this paper with the aim of unsupervisedly detecting the classes of benign and malignant instances. Experiments performed on data sets previously used in the literature for breast cancer detection reveal a good performance of the proposed unsupervised learning models. The best performance was obtained using autoencoders, which provided values higher than 0.935 for the area under the ROC curve evaluation measure.
Directional Pixogram: A New Approach for Video Steganography

A video signal can be expressed as a 3D signal where the rows and columns of pixels represent the first and the second dimension, while the third dimension is the time. The 3D nature of video signals has produced an additional source of data redundancy; that is, the temporal redundancy. Utilizing signal redundancy is the fundamental driving force for steganography techniques. In this paper, the Directional Pixogram is proposed to optimally exploit the redundancy in a video segment. The Directional Pixogram is a 1D vector that starts from a certain initial position and then grows in the direction of the motion vector associated with that initial position. It is expected that this temporal vector will contain highly correlated pixels. Therefore, the Discrete Cosine Transform (DCT) can express this vector within few significant DCT coefficients leaving a large amount of insignificant DCT coefficients. Thus, experimental results have shown that the proposed Directional Pixogram is able to obtain outstanding stego quality while hiding with very high hiding capacities.
Modified First Fit Decreasing Method for Load Balancing in Mobile Clouds

Mobile Cloud Computing has the expertise to combine the services offered to mobile gadgets and the concept of cloud computing together. There are several amnesties of clouds rendered to Mobile Cloud Computing. Those amnesties are used to dispense tasks to the users. There is a strenuous task done by Mobile Clouds which will send and receive data in a stipulated time. There are several issues to be addressed here such as heterogeneity, bandwidth issues, scalability, etc. Load balancing is one of a primary issue must be dealt with the highest priority in Mobile Cloud Computing. The static and dynamic algorithms are responsible to dispense the load evenly to the available VMs. In this research paper, an offline algorithm is planned to balance the under-utilized and over-utilized processors in MCC.
Optimization of Network Coverage and Lifetime of the Wireless Sensor Network based on Pareto Optimization using Non-dominated Sorting Genetic Approach

Evolutionary algorithms are developed systematically to solve optimization problems with one, several, and many objectives. NSGA-II is one of the universally used algorithm for superior performance and adaptability to many purposes, especially the most constant and combinatorial difficulties. NSGA-III following the configuration of classical genetic algorithms: reproductive and selection cycles. This ensures optimal placement and ensures complete coverage and connectivity with a minimum number of sensors. The NSGA-III method is very powerful in solving nonlinear feature problems and achieving many goals and this method should solve multi-target problems and solve all three problem classes. In this article we use and applies one of the most attractive ways to optimize the use of wireless sensor networks based on a meta-heuristic search called non-dominant genetic classification algorithm III. Limit connectivity while maximizing network life by achieving maximum range and minimizing power consumption. The comparison shows that the NSGA-III algorithm surpasses the Pareto-based multipurpose evolutionary approach used as the basis for this study. These results recommend applying NSGA-III to real deployment issues, and the importance of this method can be solved for a variety of purposes.
3D Context-Aware Convolutional Neural Network for False Positive Reduction in Clustered Microcalcifications Detection

False positives (FPs) reduction is indispensable for clustered microcalcifications (MCs) detection in digital breast tomosynthesis (DBT), since there might be excessive false candidates in the detection stage. Considering that DBT volume has an anisotropic resolution, we proposed a novel 3D context-aware convolutional neural network (CNN) to reduce FPs, which consists of a 2D intra-slices feature extraction branch and a 3D inter-slice features fusion branch. In particular, 3D anisotropic convolutions were designed to learn representations from DBT volumes and inter-slice information fusion is only performed on the feature map level, which could avoid the influence of anisotropic resolution of DBT volume. The proposed method was evaluated on a large-scale Chinese women population of 877 cases with 1754 DBT volumes and compared with 8 related methods. Experimental results show that the proposed network achieved the best performance with an accuracy of 92.68% for FPs reduction with an AUC of 97.65%, and the FPs are 0.0512 per DBT volume at a sensitivity of 90%. This also proved that making full use of 3D contextual information of DBT volume can improve the performance of the classification algorithm.
Deep Reinforcement Learning with Noise Injection for UAV Path Planning

Intelligent aerial vehicles became useful and serve as assistance to the civilian missions. These innovative techniques have evolved and consistently improve their capacity and performance. Thus, effective path planning is necessary to achieve these missions. This paper explores the Deep Reinforcement Learning with the use of Gaussian noise injection in path planning of unmanned aerial vehicle. A desirable result processed designing an effective UAV path planning by getting the lowest duration to reach the target destination point using Double Dueling Deep Q Network (D3QN), a combination of double Q - learning and dueling architectures were used in this paper. The D3QN was used to approximate the Q values of actions to select the best action while the Gaussian noise (GN) layer is also added after the convolutional neural network and dense layer of the dueling network. The Gaussian Noise layer applies additive zero-centered Gaussian noise using a standard deviation of 1.0. The D3QN with Gaussian noise is more stable in learning than the D3QN baseline and resulted in the shortest duration and avoiding the obstacles in the environment.
A novel robust approach for image copyright protection based on concentric rectangles

This paper presents an adaptive and robust method for image copyright protection to control ownership and prevent unauthorized usage of image property. The image is transformed into a wavelet domain using a multi-level of lifting wavelet transform; the lowest frequency band is globally divided into several concentric rectangles to withstand cropping. Pixels of a user-selected rectangle are designated for embedding flag bits to protect against rotational attacks. The proposed embedding operation takes into consideration the coefficient of high energy to be identified as potential positions for the embedding process. This procedure reduces the total error presented by the embedding process and protects hidden data against noise and jpeg-compression attacks. A reversible scrambling is applied to a pre-defined region from the watermarked image to prevent unauthorized users from attaining a high quality watermarked image. Experimental results show that the proposed algorithm achieves a range of PSNR (44–50) dB and a normalized correlation range of (1–0.9) for the watermarked image and reconstructed logo. Results also demonstrate the robustness against various attacks, such as rotation, cropping, noise adding, and JPEG-compression.
Blockchain and Stackelberg Game Model for Roaming Fraud Prevention and Profit Maximization

Roaming fraud is one of the most significant financial losses for mobile service providers. The inefficiency of current exchanging data management methods among mobile service providers is the main obstacle for roaming fraud prevention. In this paper, we introduce a novel blockchain-based data exchange management system to address roaming fraud problems in mobile networks. This system provides a secure and automatic data exchange service among mobile service providers and mobile subscribers. In addition, we introduce an emerging Proof-of-Stake (PoS) consensus mechanism for the proposed blockchain-based roaming fraud prevention system, which can significantly reduce the delay in exchanging information as well as implementation costs for mobile service providers. To further enhance benefits and security efficiency for the proposed blockchain system, we develop an economic model based on Stackelberg game. This game model is very effective in maximizing profits for both the stakeholders and stake pool and useful in designing a robust blockchain-based mobile roaming management system. Through performance analysis and numerical results, we show that our proposed framework not only provides an effective solution to prevent mobile roaming fraud but also opens many business opportunities for future mobile networks.
Augmented reality based criminal investigation system (ARCRIME)

Crime scene investigation and preservation are fundamentally the pillars of forensics. Numerous cases have been discussed in this paper where mishandling of evidence or improper investigation leads to lengthy trials and even worse incorrect verdicts. Whether the problem is lack of training of first responders or any other scenario, it is essential for police officers to properly preserve the evidence. Second problem is the criminal profiling where each district department has its own method of storing information about criminals. ARCRIME intends to digitally transform the way police combat crime. It will allow police officers to create a copy of the scene of crime so that it can be presented in courts or in forensics labs. It will be in the form of wearable glasses for officers on site whereas officers during training will be wearing a headset. The trainee officers will be provided with simulations of cases which have already been resolved. Officers on scene would be provided with intelligence about the crime and the suspect they are interviewing. They would be able to create a case file with audio recording and images which can be digitally sent to a prosecution lawyer. This paper also explores the risks involved with ARCRIME and also weighs in their impact and likelihood of happening. Certain contingency plans have been highlighted in the same section as well to respond to emergency situations.
Person anomaly detection-based videos surveillance system in urban integrated pipe gallery

(2021). Person anomaly detection-based videos surveillance system in urban integrated pipe gallery. Building Research & Information: Vol. 49, Smart Cities: Policy making and applications for better living, pp. 55-68.
Exploring the Non-Use of Mobile Devices in Families through Provocative Design

Mobile devices are fast becoming an integral part of family life. While mobile technology provides constant connectivity to a world outside the home, it inevitably disrupts family dynamics and the social notion of being together. In this paper, we explore "non-use" of mobile technology in a family setting. To do this, we designed the Pup-Lock provotype, a design provocation intended to challenge established expectations and practices around mobile device use at home. We report on a five-week in-depth study of using Pup-Lock with three families reflecting on their mobile device usage and their experience of non-use. Our findings illustrate how mobile use shapes social expectations and how over-use creates tensions in families. We contribute by showing how provoking non-use through design results in desirable and meaningful ways to increase family interaction. We discuss implications of designing for non-use to challenge established domestic practices around technology use.
Multi-Agent System of Knowledge Representation and Processing

The article reviews the functional and structural organization of the Multi-Agent System of Knowledge Representation and Processing (MASKRP). The architecture of the MASKRP, the models of reactive and cognitive software agents are described. The examples of the interaction, states and transitions diagrams of the software agents, the systems of rules and the queries of the problem-oriented multi-agent solver are given. A classical logic solver and the multi-agent solver, which is developed on the basis of the software agents models described in this article, are compared. The results of fuzzy queries to the knowledge base, which were realized according to the sets of fuzzy rules and membership functions specified in the example, are shown. The results demonstrate the practical viability and efficiency of the presented approach to implementation of a multi-agent system based on fuzzy knowledge. At the heart of design decisions at creation of the MASKRP the requirement of support of users of different competence level is put. To support the process of knowledge acquisition and user’s comfort setting up mechanisms of automated reasoning developed the problem-oriented toolkit of visual design.
Security Analysis of Lightweight Encryption based on Advanced Encryption Standard for Wireless Sensor Networks

Data security in Wireless Sensor Networks (WSNs) is very essential. However, because of the design of WSN devices with constrained resources; it is a challenge to develop and implement an encryption algorithm that is secure but will not consume much of the devices limited resources. This paper, presents a lightweight encryption based from Advanced Encryption Standard (AES) - LAES. In the proposed cipher, the MixColumns function of original AES is replaced by a 128-bit permutation to lessen the computational complexity of the algorithm and in order to reduce the resource utilization of the WSN node. During synthesis, results have shown that LAES have lesser device utilization as compared to recent implementation of traditional AES. The security of both LAES and AES were tested using the same sets of data to obtain the avalanche effect. Results have shown that on Plaintext bit flip, LAES obtained an average of 53.0469% which is 4.2969% higher than AES. On key bit flip, LAES obtained an average of 50.9375% which 3.4375% higher than AES. This shows that replacing MixColumns function with 128-bit permutation lessens the resource utilization in the hardware implementation of the encryption algorithm without compromising the security.
Genre Classification of Movie Trailers using 3D Convolutional Neural Networks

There is a constant influx of movies every year in the entertainment industry. A crucial ingredient that helps movies succeed at the box office is its trailer. A trailer helps a viewer form an opinion about the movie and is often an accurate representation of the movie. Thus, genre identification of movie trailers is a significant problem that can be solved for movie categorization and censorship. In this paper, we propose using a 3D CNN for solving this problem. The advantage of 3D CNN is its ability to capture the spatial as well as the temporal information present in a movie trailer. We achieve an accuracy of 82.14% which is greater than other models that utilize just 2D CNNs.
Feature Extraction and Classification of Movie Reviews using Advanced Machine Learning Models

The usage of the internet has grown in different systems since the mid-90s. Individuals express various comments on the internet depending on their emotions. Twitter is the most well-established small-scale blogging. It has 330 million diverse users every month and 145 million daily active users. For this research, we're attempting to evaluate the thoughts of the users in the IMDB movie reviews on tweets obtained from different outlets. The experiment forecasts correctly that the analysis feeling would be of a negative or optimistic polarity. We came across various approaches like word2vec, Doc2vec for matching and SVM, ANN, RF for classification, and defined different strategies from which we could identify the features of the reviews either considering it as a single entity or considering it as a whole text. We also applied a methodology named distributed representation of sentences and records, which eventually gives us the highest precision of all the models introduced.
Affording Extremes

In this mixed-methods study of political discourse, we study the affordances of Twitter in the context of free speech in India. We critically examine specific cases of the legal prosecution of free speech and the use of extreme speech in attacks on people to document the risks to citizens when they engage in antagonistic online discourse, particularly against the state or political institutions. We follow this up with quantitative study of the use of extreme speech through 477 hashtags used by a random sample of thousand political actors on Twitter and find that politicians are rewarded, through higher retweet rates, when they engage in extreme or uncivil messaging. We contextualize these findings to the postcolonial history of India and the laws and institutions that enable differential consequences for engaging in various forms of speech. In conclusion, we propose that the affordances of new ICTs like social media need to be carefully considered for their unintended consequences, and that functional access to free speech may differ dramatically based on one's access to institutions.
Surrogate-assisted parallel tempering for Bayesian neural learning

Due to the need for robust uncertainty quantification, Bayesian neural learning has gained attention in the era of deep learning and big data. Markov Chain Monte-Carlo (MCMC) methods typically implement Bayesian inference which faces several challenges given a large number of parameters, complex and multimodal posterior distributions, and computational complexity of large neural network models. Parallel tempering MCMC addresses some of these limitations given that they can sample multimodal posterior distributions and utilize high-performance computing. However, certain challenges remain given large neural network models and big data. Surrogate-assisted optimization features the estimation of an objective function for models which are computationally expensive. In this paper, we address the inefficiency of parallel tempering MCMC for large-scale problems by combining parallel computing features with surrogate assisted likelihood estimation that describes the plausibility of a model parameter value, given specific observed data. Hence, we present surrogate-assisted parallel tempering for Bayesian neural learning for simple to computationally expensive models. Our results demonstrate that the methodology significantly lowers the computational cost while maintaining quality in decision making with Bayesian neural networks. The method has applications for a Bayesian inversion and uncertainty quantification for a broad range of numerical models.