Reviewerly/exHarmony huggingface dataset:
Identification of adverse disease agents and risk analysis using frequent pattern mining

Life-threatening illnesses such as cancer, cirrhosis of the liver, and hepatitis have become crucial problems for humanity. The risk of mortality can be deflated by early detection of symptoms and providing the best possible diagnosis. This critical role of detection and/or diagnosis can be enhanced using one of the techniques used in data mining, such as periodic pattern mining, association rule mining, classification. Analyzing the commonly occurring possible patterns or signs followed by performing the correlation analysis among those patterns can be exhaustively practiced for early detection and improve the diagnosis. Towards the adoption of association rule mining, devising a cost-effective and time-saving algorithm for mining frequent patterns plays an important role. In this paper, we propose an approach to pattern mining called Improved Frequent Pattern Growth (Improved FP-Growth). Firstly, it constructs an improvised frequent pattern tree data structure called Improved FP-tree. Moreover, Improved FP-Growth introduces a construction of conditional FP-tree data structure layout called Improved Conditional Frequent Pattern Tree (Improved Conditional FP-Tree). Unlike the traditional FP-Growth method, it uses both top-down and bottom-up approaches to efficiently generate frequent patterns without recursively constructing the improved conditional FP-tree. The experimental results emphasize the significance of the proposed Improved FP-Growth algorithm over a few traditional frequent itemset mining algorithms those adopt the approach of recursive conditional FP-tree construction.
Sketch-QNet: A Quadruplet ConvNet for Color Sketch-based Image Retrieval

Architectures based on siamese networks with triplet loss have shown outstanding performance on the image-based similarity search problem. This approach attempts to discriminate between positive (relevant) and negative (irrelevant) items. However, it undergoes a critical weakness. Given a query, it cannot discriminate weakly relevant items, for instance, items of the same type but different color or texture as the given query, which could be a serious limitation for many real-world search applications. Therefore, in this work, we present a quadruplet-based architecture that overcomes the aforementioned weakness. Moreover, we present an instance of this quadruplet network, which we call Sketch-QNet, to deal with the color sketch-based image retrieval (CSBIR) problem, achieving new state-of- the-art results.
Violent Video Event Detection: A Local Optimal Oriented Pattern Based Approach

Video/Camera-based monitoring is a prominent and difficult research problem in the field of machine learning and pattern recognition and posed much interest in our safety in the private and public sectors. Therefore, surveillance cameras have been deploying to control suspicious activity. Consequently, many researchers have worked on developing an automatic surveillance system to detect violent events and assists security guards to take the right decision at the right time. Still, violent event detection is difficult to detect because of illumination, complex background, scale variation, blurriness, occlusion, and low resolution in a surveillance camera. In this paper, the Local Optimal Oriented Pattern (LOOP) texture-based feature descriptor is proposed. Eventually, eminent features are used with a support vector machine (SVM) classier for violent event detection. Experiments are conducted on the Hockey Fight dataset and Violent-Flows dataset. The five-Fold Cross-Validation approach is used to analyze the performance of the proposed method. The data and results are promising and encouraging.
Modified Particle Swarm Optimization with Unique Self-cognitive Learning for Global Optimization Problems

Although different modified versions of particle swarm optimization (PSO) were proposed in past decades to solve global optimization problems, the appropriate mechanism used to attain proper balancing of algorithm’s exploration and exploitation searches remains as an open-ended challenges. A modified PSO with unique self-cognitive learning (MPSO-USCL) is proposed in this paper to address this issue. For each particle, a unique exemplar can be generated by the proposed USCL module to replace the self-cognitive component of each particle and guide its search process towards the promising regions of search space with different levels of exploration and exploitation strengths. Extensive simulation studies are performed to compare the optimization performances of MPSO-USCL with six existing PSO variants using 12 benchmark functions. The proposed MPSO-USCL is reported to outperform its peer algorithms for all benchmark functions.
Knowledge memorization and generation for action recognition in still images

Human action recognition in visual data is one of the most fundamental challenges in computer vision. Existing approaches for this primary goal have been based on video data, often incorporating both color and dynamic flow information. Nevertheless, the majority of the visual data constitute still images, and for this reason, being able to recognize actions in still image is an ultimate objective of visual understanding with an extended list of applications. In this paper, we present a novel method that transfers the knowledge learned from action videos onto images to allow recognition of the principal action depicted in still image. Our intuition is that a generative model for knowledge transfer can be learned by taking advantage of the available action videos in the training stage to bridge images to videos. Based on this, we propose two complementary knowledge-transfer models utilizing fully connected networks to deliver the knowledge extracted from color and motion flow sequences to still images. We introduce a weighted reconstruction and classification loss to steer the generation procedure of the networks. In addition, we describe and analyze the influence of different data augmentation techniques, initialization strategies, and weighting coefficients for improving the performance. We observe that: both the transferred knowledge from color sequences and motion flow sequences can improve the performance of still image based human action recognition; the latter one which provides complementary dynamic information improves the performance a lot. We evaluate our models on two publicly available video based human action recognition datasets: UCF101 and HMDB51. To further validate the generalization ability of the proposed solution, we test the learned models from UCF101 dataset on two still image based human action recognition benchmarks: Willow7 Actions and the Sports. Our results demonstrate that the proposed method outperforms the baseline approaches with more than 2% accuracy, 3% accuracy, 3% accuracy and 5% mAP on UCF101, HMDB51, Sports and Willow 7 Actions datasets, respectively.
Does agri-business/small and medium enterprise investment scheme (AGSMEIS) impact on youth entrepreneurship development in sub-Saharan Africa? Evidence from Nigeria

Purpose The purpose of this paper is to critically examine the agri-business/small and medium investment schemes (AGSMEIS) in Nigeria. Its special focus is to investigate the impact of the AGSMEIS on youth entrepreneurship development in Nigeria. Design/methodology/approach This paper adopts a survey research technique, aimed at gathering information from a representative sample of the population, as it is essentially cross-sectional, describing and interpreting the current situation. A total of 1,200 respondents were sampled across the six geopolitical zones of Nigeria. Findings The results from the use of a combined propensity score matching (PSM) and logit model indicate that AGSMEIS initiative generates significance gains in empowering youths in enterprise development, and if enhanced will help many young people become entrepreneurs. Practical implications This suggests that AGSMEIS initiative can facilitate youth's access to credit and help them become owners of small and medium enterprises. Social implications It implies that investing in young people for small and medium enterprises could bring Nigeria into the modern economy and lift sub-Saharan Africa out of poverty. Originality/value This research adds to the literature on youth entrepreneurship development’s debate in developing countries. It concludes that targeting the young people in AGSMEIS should form the foundation of public policy for entrepreneurship, poverty alleviation, and economic development.
IndoFashion : Apparel Classification for Indian Ethnic Clothes

Cloth categorization is an important research problem that is used by e-commerce websites for displaying correct products to the end-users. Indian clothes have a large number of clothing categories both for men and women. The traditional Indian clothes like "Saree" and "Dhoti" are worn very differently from western clothes like t-shirts and jeans. Moreover, the style and patterns of ethnic clothes have a very different distribution from western outfits. Thus the models trained on standard cloth datasets fail on ethnic outfits. We introduce the first large-scale ethnic dataset of over 106K images with 15 different categories for fine-grained classification of Indian ethnic clothes. We gathered a diverse dataset from a large number of Indian e-commerce websites. We then evaluate several baselines for the cloth classification task on our dataset. We obtain 88.43% classification accuracy. We hope that our dataset would foster research in the development of several algorithms such as cloth classification, landmark detection, especially for ethnic clothes. The dataset is publicly available here <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Image Processing For Face Recognition Using HAAR, HOG, and SVM Algorithms

Abstract Face recognition is one of the most active areas of research from the past two decades. Attempts are being made to understand how a human recognizes another human face. It is widely accepted that facial recognition can be based on structural information and nonstructural / spatial details. In the present study, he is applying differential observations using Eigen / docking characteristics of many built-in facial features and artificial neural networks. The proposed method aims to obtain a facial feature by reducing facial features such as eyes, nose, mouth, and face depending on the importance of facial features. The face recognition system developed in this paper will inform the human face and assess the current percentage of accuracy. Therefore, this work is for human facial recognition and includes a percentage of facial expressions. The implementation of this function also offers many applications such as photography, bio-metric in bank Lockers, etc.
Securing Remote Access to Information Systems of Critical Infrastructure Using Two-Factor Authentication

Information systems of critical infrastructure provide services on which the core functions of a state and its economy depend as well as welfare of society. Such systems are becoming an increasingly common target for crimes and attacks in cyberspace, as their vulnerabilities can be exploited for malicious activities seeking financial or political gain. One of the main reasons that threatens the security of these systems is the weak control of remote access, otherwise defined as management of a system’s user identity. Management of user identity depends on user authentication, authorization and the assignment of certain rights in the digital space. This paper provides the proposed two-factor (2FA) digital authentication method for remote access to an information system of a critical infrastructure. Results of testing the method’s usability and resilience to cyber threats have shown that the system, in which the method was implemented, is protected from dangerous HTTP requests and publicly available system’s endpoints are protected from threatening inputs that could cause malicious activities on the critical infrastructure. Additionally, the implementation of the authentication API application ensures the rapidity of the method for less than 500 ms for 100 users working in parallel with the system at the same time.
Counterexample-Guided Synthesis of Perception Models and Control

Recent advances in learning-based perception systems have led to drastic improvements in the performance of robotic systems like autonomous vehicles and surgical robots. These perception systems, however, are hard to analyze and errors in them can propagate to cause catastrophic failures. In this paper, we consider the problem of synthesizing safe and robust controllers for robotic systems which rely on complex perception modules for feedback. We propose a counterexample-guided synthesis framework that iteratively builds simple surrogate models of the complex perception module and enables us to find safe control policies. The framework uses a falsifier to find counterexamples, or traces of the systems that violate a safety property, to extract information that enables efficient modeling of the perception modules and errors in it. These models are then used to synthesize controllers that are robust to errors in perception. If the resulting policy is not safe, we gather new counterexamples. By repeating the process, we eventually find a controller which can keep the system safe even when there is a perception failure. We demonstrate our framework on two scenarios in simulation, namely lane keeping and automatic braking, and show that it generates controllers that are safe, as well as a simpler model of a deep neural network-based perception system that can provide meaningful insight into operations of the perception system.
Deep Learning Video Analytics on Edge Computing Devices

The rapid progress of deep learning-based techniques such as Convolutional Neural Network (CNN) has enabled many emerging applications related to video analytics and running them on mobile devices can help improve our daily lives in many ways. However, there are many challenges for video analytics on mobile devices using multiple CNN models. CNN models are resource hungry, and each model requires a large amount of computational power and occupies a large portion of memory space. Although video processing can be offloaded to reduce the computation time, transmitting large amount of video data is time consuming. Thus, offloading is not always the best option. Moreover, different CNN models have different memory usage and processing time, making the scheduling problem more complex. As a result, besides deciding which task to be offloaded, we must decide which CNN model should reside in the memory and for how long, and which CNN model should be switched out due to memory constraint. In this paper, we propose resource aware scheduling algorithms to address these challenges. We identify the task scheduling problem for running multiple CNN models on mobile devices under resource constraints and formulate it as an integer programming problem. We propose resource-aware scheduling algorithms which combine offloading and local processing methods to minimize the completion time of video processing. We implement the proposed scheduling algorithms on Android-based smartphones and demonstrate its effectiveness through extensive experiments.
Rule‐based preprocessing for data stream mining using complex event processing

Abstract Data preprocessing is known to be essential to produce accurate data from which mining methods are able to extract valuable knowledge. When data constantly arrives from one or more sources, preprocessing techniques need to be adapted to efficiently handle these data streams. To help domain experts to define and execute preprocessing tasks for data streams, this paper proposes the use of active rule‐based systems and, more specifically, complex event processing (CEP) languages and engines. The main contribution of our approach is the formulation of preprocessing procedures as event detection rules, expressed in an SQL‐like language, that provide domain experts a simple way to manipulate temporal data. This idea is materialized into a publicly available solution that integrates a CEP engine with a library for online data mining. To evaluate our approach, we present three practical scenarios in which CEP rules preprocess data streams with the aim of adding temporal information, transforming features and handling missing values. Experiments show how CEP rules provide an effective language to express preprocessing tasks in a modular and high‐level manner, without significant time and memory overheads. The resulting data streams do not only help improving the predictive accuracy of classification algorithms, but also allow reducing the complexity of the decision models and the time needed for learning in some cases.
Hyperanalytic Wavelet-Based Robust Edge Detection

The imperfections of image acquisition systems produce noise. The majority of edge detectors, including gradient-based edge detectors, are sensitive to noise. To reduce this sensitivity, the first step of some edge detectors’ algorithms, such as the Canny’s edge detector, is the filtering of acquired images with a Gaussian filter. We show experimentally that this filtering is not sufficient in case of strong Additive White Gaussian or multiplicative speckle noise, because the remaining grains of noise produce false edges. The aim of this paper is to improve edge detection robustness against Gaussian and speckle noise by preceding the Canny’s edge detector with a new type of denoising system. We propose a two-stage denoising system acting in the Hyperanalytic Wavelet Transform Domain. The results obtained in applying the proposed edge detection method outperform state-of-the-art edge detection results from the literature.
The Meant, the Said, and the Understood: Conversational Argument Search and Cognitive Biases

Many questions of public interest do not have a single answer but come with a set of choices, each of which with its pros and cons. An “objective” information system can help explore the underlying argument space, and, if equipped with a conversational interface, it can create the experience of lively discussions resembling those from our daily lives. However, users will (subconsciously) extend the provided information by assumptions that adhere to their cognitive biases. In this regard, note that biases do not arise only from the underlying data or the employed algorithms, but also from the way the information is presented—especially in audio-only channels. Our paper brings attention to bias-related challenges of conversational interfaces for argument search systems. We identify research questions that address these challenges, and we propose ideas and methods to tackle them.
Conditional Identity Disentanglement for Differential Face Morph Detection

We present the task of differential face morph attack detection using a conditional generative network (cGAN). To determine whether a face image in an identification document, such as a passport, is morphed or not, we propose an algorithm that learns to implicitly disentangle identities from the morphed image conditioned on the trusted reference image using the cGAN. Furthermore, the proposed method can also recover some underlying information about the second subject used in generating the morph. We performed experiments on AMSL face morph, MorGAN, and EMorGAN datasets to demonstrate the effectiveness of the proposed method. We also conducted cross-dataset and cross-attack detection experiments. We obtained promising results of 3% BPCER @ 10% APCER on intra-dataset evaluation, which is comparable to existing methods; and 4.6% BPCER @ 10% APCER on cross-dataset evaluation, which outperforms state-of-the-art methods by at least 13.9%.
End-to-End Model Based on RNN-T for Kazakh Speech Recognition

Automatic speech recognition is a rapidly developing area in machine learning. The most popular speech recognition systems today are end-to-end systems, especially those models that directly output a sequence of words taking into account the input sound in real time, which are online end-to-end models. Stream speech recognition allows to transfer the audio stream to speech-to-text conversion and get the results of stream speech recognition in real time as the audio is processed. This article discusses and implements a popular RNN-T-based model for recognizing Kazakh speech. The analysis of works related to recognition of Kazakh speech based on the CTC model is also given. The findings demonstrated that an RNN-T-based model can work well without additional components, like a language model and showed the best outcome on our dataset. As a result of the research, the system reached 10.6% CER, which is the best indicator among other end-to-end systems for recognizing Kazakh speech.
Learning with error‐based key agreement and authentication scheme for satellite communication

Summary Satellite communication is one of the essential communication mechanisms that can be used for significant distance and under circumstances where the other communication mechanisms cannot work. Therefore, the security of this communication method is highly needed. Because in this era where information plays an important role, information security becomes the first priority for everyone. When we talk about information security, the key exchange and authentication are two key factors of information security. Whitfield Diffie and Martin Hellman 1 proposed the first key exchange protocol. In the last two or three decades, various authentication schemes have been proposed to create a secure network that mainly depends on classical number–theoretical hard assumptions (factorization or discrete logarithm), but due to the Shor's 2 algorithm, above‐mentioned scheme is no longer secure because any discrete logarithm or factorization problems can be solved by Shor's algorithm in polynomial time if the quantum computer becomes the reality soon. As far as our knowledge goes, there is no authentication protocol for satellite communication, which is secure against quantum computer attacks. Therefore, in this paper, we first proposed authentication protocol for satellite communication based on ring learning with error which is secure against quantum attacks.
Mind2Mind: Transfer Learning for GANs

Training generative adversarial networks (GANs) on high quality (HQ) images involves important computing resources. This requirement represents a bottleneck for the development of applications of GANs. We propose a transfer learning technique for GANs that significantly reduces training time. Our approach consists of freezing the low-level layers of both the critic and generator of the original GAN. We assume an auto-encoder constraint to ensure the compatibility of the internal representations of the critic and the generator. This assumption explains the gain in training time as it enables us to bypass the low-level layers during the forward and backward passes. We compare our method to baselines and observe a significant acceleration of the training. It can reach two orders of magnitude on HQ datasets when compared with StyleGAN. We provide a theorem, rigorously proven within the framework of optimal transport, ensuring the convergence of the learning of the transferred GAN. We moreover provide a precise bound for the convergence of the training in terms of the distance between the source and target dataset.