Reviewerly/exHarmony huggingface dataset:
An Unsupervised Abnormal Crowd Behavior Detection Technique using Farneback Algorithm

Anomalous behavior of crowd in surveillance videos have gained tremendous interest in the past few years. Although a number of human detection and activity recognition systems are available today, there is a need for new method to address the key challenges in different scenarios. Intelligent video surveillance systems observe human activities within very crowded surroundings and have a vital role in public safety scenarios. `Abnormal behavior' in crowd scenes implies an event which is unexpected in nature. In this paper, a temporal abnormality situation is considered, in which a sudden random movement of people happens after an alarming situation such as, bomb explosion, terrorist attack, etc. arises. In this paper, using Farneback optical flow algorithm and an activity map based image segmentation, an unsupervised anomalous behavior detection technique among crowd scenes is depicted. Depending on the drastic variation in two metric parameters such as entropy and Temporal Occupancy Variation (TOV), the abnormality is detected. The proposed algorithm is compared with different state-of-the-art methods and shows a better performance based on various quantitative metric values.
Optimizing Social-Topic Engagement on Social Network and Knowledge Graph

Existing research on social networks manifests two crucial criteria to improve activity engagement of users: (1) user interests in the activity topics and (2) opportunities of making new friends with some acquaintances. However, current online platforms still involve massive manual selection for activity attendees and contents without proper recommendations. In this paper, therefore, we formulate a new activity organization problem, named Social Knowledge Group Query (SKGQ), to recommend attendees and topic-related contents simultaneously. We prove that SKGQ is NP-hard and design an approximation algorithm, named Social cOntent Knowledge Exploration (SOKE), to jointly choose the activity attendees and topic-related contents according to social-oriented and topic- oriented strategies. Simulation results manifest that the solution acquired by SOKE is close to the optimal solution and outperforms various baselines.
Using Blockchain Technology to Manage IoT Data for Smart City Initiatives: A Conceptual Framework and Initial Experiments Based on Smart Contracts

Blockchain technology is attracting the interest of professionals and academics across a variety of disciplines, including the interdisciplinary field of digital government. Such technology has the potential to transform the public sector by providing innovative ways to secure data and detect tampering. However, few studies have theorized the experimental applications of such technology and how it could be applied to data management practices in data-rich environments, such as the Internet of Things (IoT) applications in smart cities. This chapter proposes a workflow diagram for technical experiments that explore how blockchain technology can protect the integrity of data from sensors in a context where IoT functions as the underpinning infrastructure. This endeavor helps to contextualize this emerging technology and sheds light on opportunities, risks, and challenges of using blockchain technology in environments where intensive data collection is the norm. Contributions include a framework on data management for IoT that can be of special value to local governments that are considering blockchain as instrumental in engaging in or enhancing data-driven operations.
Image Encryption based on Pseudo Hadamard Transformation with Constant Co-efficient

In this paper, transformation and non-chaotic substitution based image cryptosystem has been proposed. The transformation phase involves 2-D Pseudo Hadamard Transform with constant co-efficient (PHTC) and substitution is performed using S-box with 128 bits of secrete key. Due to two stage encryption process, the strong correlation between adjacent pixels is effectively reduced and high entropy is observed in the cipher image. The cipher image is imperiled for several security analysis and the result proves strong resistivity against differential attacks. Even though the algorithm encrypts image in two stages, utilizes very less execution time. The results attained are relatively better compared to other prevailing algorithms.
Evasion Attacks Based on Wasserstein Generative Adversarial Network

Security issues have been accompanied by the development of the artificial intelligence industry. Machine learning has been widely used for fraud detection, spam detection, and malicious file detection, since it has the ability to dig the value of big data. However, for malicious attackers, there is a strong motivation to evade such algorithms. Because attackers do not know the specific parameters of the machine model, they can only carry out a black box attack. This paper proposes a method based on Wasserstein Generative Adversarial Network(WGAN) to generate malicious PDF files which are similar to benign ones and can evade the malicious file detection system. The experimental results show that the adversarial examples generated by our method can evade the PDF classifier-PDFrate of 100%. We also test their performance in different classifiers and the results show that our proposed method can evade the classifiers of different machine learning algorithms such as Support Vector Machine(SVM), Linear Regression, Decision Tree, Random Forest.
Deep Learning-Based Encoder for One-Bit Quantization

This paper proposes a deep learning-based error correction coding for AWGN channels under the constraint of one-bit quantization in receivers. An autoencoder is designed and integrated with a turbo code that acts as an implicit regularization. This implicit regularizer facilitates approaching the Shannon bound for the one-bit quantized AWGN channels even if the autoencoder is trained suboptimally, since one-bit quantization stymies ideal training. Our empirical results show that the proposed coding scheme gives better results at finite block lengths than conventional turbo codes even for QPSK modulation, which can achieve the Shannon bound at infinite block length despite one-bit quantization. Furthermore, the proposed coding method makes one- bit quantization operational even for 16-QAM, which is unprecedented.
Mobile Robot Autonomous Exploration and Navigation in Large-scale Indoor Environments

This work is intended to study the stages of exploring, localization and mapping of autonomous mobile robots and vehicles. In addition to the use of integrated and standard software, ROS has the possibility of creating small map data files recorded with the data provided by 2D Light Detection And Ranging (LiDAR) sensors. The low data density favours the increased efficiency during data processing. The metric maps register just enough information to create the topological nodes and edges in a relational map. Extensive experiments in both simulated environments and real-world applications show the effectiveness of the proposed method.
RCE-HIL

Entailment recognition is an important paradigm of reasoning that judges if a hypothesis can be inferred from given premises. However, previous efforts mainly concentrate on text-based reasoning as recognizing textual entailment (RTE) , where the hypotheses and premises are both textual. In fact, humans’ reasoning process has the characteristic of cross-media reasoning . It is naturally based on the joint inference with different sensory organs, which represent complementary reasoning cues from unique perspectives as language, vision, and audition. How to realize cross-media reasoning has been a significant challenge to achieve the breakthrough for width and depth of entailment recognition. Therefore, this article extends RTE to a novel reasoning paradigm: recognizing cross-media entailment (RCE) , and proposes heterogeneous interactive learning (HIL) approach. Specifically, HIL recognizes entailment relationships via cross-media joint inference, from image-text premises to text hypotheses. It is an end-to-end architecture with two parts: (1) Cross-media hybrid embedding is proposed to perform cross embedding of premises and hypotheses for generating their fine-grained representations. It aims to achieve the alignment of cross-media inference cues via image-text and text-text interactive attention. (2) Heterogeneous joint inference is proposed to construct a heterogeneous interaction tensor space and extract semantic features for entailment recognition. It aims to simultaneously capture the interaction between cross-media premises and hypotheses and distinguish their entailment relationships. Experimental results on widely used Stanford natural language inference (SNLI) dataset with image premises from Flickr30K dataset verify the effectiveness of HIL and the intrinsic inter-media complementarity in reasoning.
Quantum Computing and Algorithmic Law

Quantum computing technology will greatly enhance the abilities of the emerging field of computational law to express, model, and operationalize law in algorithmic form. In anticipation of the legal sector’s utilization of quantum computing technology, this essay identifies, with reference to computational complexity theory, the categories of computational problems which quantum computers are better equipped to deal with than are classical computers (‘quantum supremacy’). Subsequently, the essay demarcates the possible contours of legal ‘quantum supremacy’ by showcasing three anticipated legal fields of quantum technology: optimization problems, standards of proof, and machine learning. Acknowledging that the exact manifestation of quantum computing technology in the legal sector remains difficult to predict, the essay posits that the meaningful utilization of quantum computing technology at a later stage presupposes the creative imagination of possible use-cases at the present.
Two Novel Design Approaches for Optimized Reversible Multiplier Circuit

Reversible logic is one of the stepping stones in low power circuit design. Due to low power consumption and minimum bit loss features, future quantum computing devices are estimated to be reversible. Compared to addition, multiplication is a step further in the ladder of abstraction. It is the manifestation of a pattern generation system capable of representing 3-D patterns and transforming them by rotation, projection, reflection etc. The use of reversible logic in this field can reduce the wastage of energy, power and improve many factors. In this paper, an efficient multiplication algorithm has been proposed which is a modification of conventional shift-and-add multiplication method. Implementing proposed algorithm two reversible multiplier circuit has been proposed. For producing partial product, Peres gate has been used for the first design and Toffoli gate for the second design. Both of the circuits use Peres gate as Half-Adder and HNG gate as Full-Adder for addition of partial products. The first design is improved radically in terms of quantum cost, delay and the second design is notably improved in terms of transistor count and hardware complexity compared to the existing best known multipliers.
Contextual Anomaly Detection in Time Series Using Dynamic Bayesian Network

In this paper, we propose a novel method to identify contextual anomaly in time series using Dynamic Bayesian Networks (DBN). DBN is a powerful machine learning approach that captures temporal characteristics of time series data. In order to detect contextual anomaly we integrate contextual information to the DBN framework, referred to as Contextual DBN (CxDBN). The efficacy of CxDBN is shown using a case study of the identification of contextual anomaly in real-time oil well drilling data.
Dynamic Time Warping-based Korean Spoken Word Detection System Using Euclidean Distance in Intelligent Personal Assistants

This paper proposes a Dynamic Time Warping (DTW)-based Korean spoken word detection system using Euclidean distance (ED) in intelligent personal assistants. The proposed system searches a spoken word from users in a reference utterance and generates a recognized word. For searching, DTW algorithm with ED is used. The accuracy of the proposed system was measured by 91.42% in terms of word error rate (WER).
Spatial mismatch calibration based on fast partial phase correlation for interferograms in dynamic Fizeau interferometer

For a simultaneous phase-shifting interferometer (SPSI), typically four interferograms with different phase shifts are captured at different areas on the detector target by a single-shot capture method. Prior to calculating phase distribution, the region segmentation should be conducted to obtain four separated interferograms. A registration method is also necessary to eliminate the mismatch errors between the interferograms. A spatial mismatch calibration method based on fast partial phase correlation is proposed to register the spatial positions between the phase-shifting interferograms. By tilting the reflective flat, four carrier interferograms are captured to extract four phase distributions using the Fourier transform technique. Partial phase distribution is used as match characteristics to register the interferograms rapidly by employing the correlation operation. The simulation and experimental results show that the ripple error generated by spatial mismatch is suppressed well by the proposed method.
Synthesis of Semantic Model of Subject Area at Integration of Relational Databases

The article considers the problem of synthesizing a semantic model of the subject area at integration of heterogeneous information resources. In this case, emphasis is placed on ensuring the universality of the means of description, without regard to artificial limitations on the data typification and categorization. Two types of logical existence rules are introduced: functional and structural ones, which allow analyzing not only explicitly defined, but also logically deducible information objects, that is, determining the boundary of the subject area. Information about all possible information objects of the subject area makes it possible to determine the area of intersection of the integrable data semantics.
psi-collect: A Python module for post-storm image collection and cataloging

This imagery aids in recovery efforts as well as rapid assessment of storm impacts along developed and undeveloped coastlines (Madore, Imahori, Kum, White, & Worthem, 2018).
Thrombus Localization in Middle Cerebral Artery of Patient with Acute Ischemic Stroke on ncCT Image

A common cause of an Acute Ischemic Stroke (AIS) is a thrombus in the middle cerebral artery (MCA). Localizing the thrombus on a set of non-contrast computerized tomography (ncCT) image for some composite type is not easy. This work presents an algorithm for localizing thrombus in MCA region on ncCT using an image processing method. Given an ncCT image P, brain region is extracted by considering a homogeneous intensity area using k-mean cluster. A set of candidate regions is obtained by considering size and average intensity of each connected region. Also, location of the MCA is located to filter the candidate regions that are in the interested region. Experiment is perform on a number of images of the different composite of thrombus. The result shows that the proposed image-processing-based thrombus localization algorithm is able to automatically locate the thrombus on ncCT of both red blood cell-rich and fibrin-rich composite.
Proof-of-Lottery: Design for Block Producing Algorithm Based on PoS for Scalability

As blockchain technology has become popular, blockchain services have emerged. Blockchain has the potential to change the industry paradigm. However, decentralized P2P environment of blockchain creates a number of problems. In this paper, we suggest a design for a PoS, block producing algorithm, named Proof-of-Lottery(PoL) to solve these problems. The idea is that selecting block producer in a similar way to drawing a lottery. We expect to improve scalability in blockchain while not hurting decentralization.
Random Seed Generation For IoT Key Generation and Key Management System Using Blockchain

Recently, the Internet of Things (IoT) is growing rapidly. IoT sensors are attached to various devices, and information is detected, collected and utilized through various wired and wireless communication environments. As the IoT is used in various places, IoT devices face a variety of malicious attacks such as MITM and reverse engineering. To prevent these, encryption is required for device-to-device communication, and keys required for encryption must be properly managed. We propose a scheme to generate seed needed for key generation and a scheme to manage the public key using blockchain.
Immutable and Secure IP Address Protection Using Blockchain

IP addresses can be passed on to new recipients even with a damaged reputation score. It takes a lot of effort to defend a network against storing IP address data using current required practices. A blockchain network offers a decentralized, immutable ledger to record IP data which ensures that the data will not be tampered with and the data is trustworthy. Master nodes and digital IDs are two ways the blockchain can be used to preserve a digital identity. Master nodes can act as a mediator and store the entire blockchain to improve the validity of the blockchain. Digital IDs can make it easier to verify an identity on the blockchain. In this chapter, we explore how blockchain technology can preserve user’s digital identity more effectively. This chapter also offers a sample program that can be used to store and retrieve information with the use of the Solidity language used on the Ethereum network. Data pertaining to IP addresses is added to a solidity contract with several methods offered for reading data. The IP address data ends up more secure than if it was simply stored in a central database.
Comparison of Decision Tree-Based Learning Algorithms Using Breast Cancer Data

Recent times are witness to tremendous developments occurring in the field of Machine Learning. Data Mining applications are finding widespread acceptance, as they extend to encompass several sectors, including health care, education, weather forecasting, finance, etc. Data mining algorithms like classification algorithms are being integrated into the Knowledge Discovery in Databases (KDD) process to build optimized predictive models useful to healthcare and other professionals. Decision tree is considered to be one of the simplest and most widely used classifier algorithms. In the current study, we have compared six decision tree-based learning algorithms on breast cancer dataset. The algorithms used include, J48 decision tree, decision stump, random forest tree, REP tree, hoeffding tree and Logistic Model Tree (LMT). The dataset is obtained from Mizoram Cancer Institute, Aizawl, Mizoram, India and it contain 575 records and 24 attributes. Infogain attribute evaluator has been computed to determine the rank of an attribute. The analysis is performed in two steps. First step involves the building of the model by training data, while during the second step, the dataset is evaluated using 10-fold cross validation technique. Our computations indicate that of all the algorithms evaluated, J48 decision tree performs with the highest accuracy (84.2105%), while random tree demonstrates the lowest accuracy (76.4912%).
Evidential Data Association: Benchmark of Belief Assignment Models

Data association is an important task in Multiple Target Tracking (MTT) systems. The purpose is to assign the sensor detections to the known objects. However, sensor data can be inaccurate and incomplete. The Evidential theory provides interesting tools to manage ignorance and data imperfection. In fact, the Evidential theory is considered as a generalized form of the Bayesian theory which enables reasoning with uncertainty. The Evidential data association quantifies sensor information by belief masses. Subsequently, the combination of these masses provides more accurate data to make-decision on associations. Most published studies focus on decision-making algorithms. In this paper, the importance is done to belief masses estimation which is a crucial step in data fusion. There are two estimation models: antagonist and non-antagonist. The objective of this paper is to benchmark both models on real-data. The obtained results show that the non-antagonist mass function is more suited for data association problems.
Computer-Aided Diagnosis of Acute Myocardial Infarction using Time-Dependent Plasma Metabolites

Acute myocardial infarction (MI) is complicated, and multiple etiologies can result in this clinical condition. Guidelines recognize two categories of MI: Thrombotic (Type 1) and non-thrombotic (Type 2), that have quite same prevalence but require unlike treatment. Unfortunately, diagnostic criteria to differentiate between Type 1 and Type 2 require invasive procedures. This results in inefficient and sub-optimal care of patients suspected of MI. This paper presents a novel machine-learning system that detects biomarkers of thrombus formation by analyzing the association between plasma metabolites with the formation of thrombosis in cohort of MI patients at multiple time-points. Study data are collected by a newly introduced non-targeted technique that evaluates the quantities of both known and unknown metabolites from blood samples. Our system uses recursive feature elimination (RFE) and multi-layer perceptron (MLP) neural network to detect associated metabolites at each time-point followed by weighted-voting algorithm using ensemble learning. Our experiment achieves an accuracy of 91%, sensitivity of 89%, and specificity of 94% for MI diagnosis.
Selecting suitable deep learning network for face recognition

In this paper is given problems in face recognition methods and algorithms, ways of improving of recognition accuracy and decreasing errors. There are false acceptance and rejection errors in face recognition and these errors are decreased by normalization method when detected occlusion faces. For increasing face recognition accuracy and speed is proposed combining of deep learning networks and optimization of filter size.
Real-time facial expressions identification using supervised descent method

Human's facial expressions are related to the position of the facial features. In this paper, we use Supervised Descent Method to find the position of the features which converges several times by a template and can let the feature points to be on the right place. After getting all the feature points, we find out the relationship between these points and the facial expressions. At the meanwhile, we also need the neutral face of the users to be used as the basis for identification.
Comparative Analysis of Proposed Artificial Neural Network (ANN) Algorithm With Other Techniques

The mortality rate among women is increasing progressively due to cancer. Generally, women around 45 years old are vulnerable from this disease. Early detection is hope for patients to survive otherwise it may reach to unrecoverable stage. Currently, there are numerous techniques available for diagnosis of such a disease out of which mammography is the most trustworthy method for detecting early cancer stage. The analysis of these mammogram images are difficult to analyze due to low contrast and nonuniform background. The mammogram images are scanned and digitized for processing that further reduces the contrast between Region of Interest and background. Presence of noise, glands and muscles leads to background contrast variations. Boundaries of suspected tumor area are fuzzy &amp; improper. Aim of paper is to develop robust edge detection technique which works optimally on mammogram images to segment tumor area. Output results of proposed technique on different mammogram images of MIAS database are presented and compared with existing techniques in terms of both Qualitative &amp; Quantitative parameters.