Reviewerly/exHarmony huggingface dataset:
Sea-Land Clutter Classification Based on Graph Spectrum Features

In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application.
Academic Customer Service Chatbot Development using TelegramBot API

The growth of social media pushes every company, even academic institutions to own more than 1 official social media account, which requires more resources, especially in the Customer Service department. One of the ways to avoid this is by using chatbot, as it can increase cost-efficiency while also giving good responses while interacting with the consumers. The chatbot created in this study is developed using Telegram API and webhook method. Other than that, Telegram Bot is used to exchange messages with the users (in this case, the students), higher education institution administrator, and the chatbot. Simple testing shows that all chatbot's functions run well.
Face Recognition With Partial Occlusion Based on Attention Mechanism

Face recognition is widely used in our life, face recognition with partial occlusion is of great significance to the improvement of face recognition accuracy in practical application scenarios. For improve the accuracy of face recognition in partial occlusion scene, we propose a MobilenetV1-CBAM-Facenet (MC-Facenet) model. which uses Multi-Task Cascaded Convolutional Neural Networks to complete the detection of partial occlusion, then introduces MobilenetV1 and Attention Mechanism to solve the face recognition with partial occlusion. We designs a new Loss function which combines the Triplet Loss and cross-entropy Loss to increase the convergence rate. We carried out comparative experiments based on the public data set LFW, and demonstrated the effectiveness of MC-Facenet under different occlusion types and different occlusion areas. The experimental results show that the occlusion type is glasses and the occlusion area is 30%, the recognition accuracy is improved by 10.92% and 9.39% respectively compared with the original model. It is proved that MC-Facenet can greatly improve the accuracy of local occlusion face recognition.
Computer simulations for the denoising effect of morphological reconstructions for CT images on hexagonal grids and regular hexagonal regions

In this paper, morphological operations on hexagonal lattices and regular hexagonal regions are applied to 2D parallel beam computerized tomography (CT) image denoising. To show some denoising effects of the morphological operations, for an input image, we add certain amount of noise to get the noised image; and perform Radon transform and inverse Radon transform on the noised image to obtain a CT image. To compare hexagonal lattices with square lattices fairly, the CT image is resampled to an image defined on a certain randomized grid, which is in turn resampled to an image defined on a hexagonal lattice and an image defined on the corresponding square lattice (with the same sampling rate), respectively. For the morphological operations, we use a structuring element defined on a hexagonal lattice and a structuring element defined on a square lattice such that those two structuring elements have the same number of ones. Each of those two images performs an erosion with the corresponding structuring element. Then use those two images as masks and use the two corresponding eroded images as makers to perform morphological image reconstructions to obtain the two reconstructed images. Finally, both usual and Phantom images are used as test images. For many different tested noise levels, the output data show that the image reconstructed on the hexagonal lattice is usually more similar to the input image than the one reconstructed on the square lattice.
Digital Education: Pros and Cons (Instrumental and Axiological Aspects)

The purpose of this work is to show the students’ opinion of different educational formats, digitalization of the higher education. The scientific relevance of this research is determined by the forced transition to the digital training forms during the pandemic and the problems that we faced in this period. The revealed contradictions showed that the education digitalization in the reality is not the same in the state programs and reports. The new, “digital” reality requires not formal, but “life”, “working” instruments of digitalization. The research was conducted in 2021 and involved 5 Russian universities (the Samara Region and the Krasnodar territory). The used research methods are survey, analysis and generalization of theoretical and survey materials.
Introducing K-Anonymity Principles to Adversarial Attacks for Privacy Protection in Image Classification Problems

The network output activation values for a given input can be employed to produce a sorted ranking. Adversarial attacks typically generate the least amount of perturbation required to change the classifier label. In that sense, generated adversarial attack perturbation only affects the output in the 1st sorted ranking position. We argue that meaningful information about the adversarial examples i.e., their original labels, is still encoded in the network output ranking and could potentially be extracted, using rule-based reasoning. To this end, we introduce a novel adversarial attack methodology inspired by the K-anonymity principles, that generates adversarial examples that are not only misclassified, but their output sorted ranking spreads uniformly along K different positions. Any additional perturbation arising from the strength of the proposed objectives, is regularized by a visual similarity-based term. Experimental results denote that the proposed approach achieves the optimization goals inspired by K-anonymity with reduced perturbation as well.
Social media: the double-edged sword for perpetuating and preventing criminal activities in Zimbabwe

Purpose This study aims to evaluate social media’s role in facilitating criminal activities in Zimbabwe and determine how social media platforms have been embraced by the Zimbabwean police to curb criminal activities. Design/methodology/approach A total of 72 participants from the Zimbabwean police and 5 non-police security experts were invited to participate in this study through questionnaires and in-depth interviews. The study was confined to an urban policing area, which is policed by approximately 400 police officers. Findings The facilitation of violent protests was found to be the major threat that is posed by social media in Zimbabwe, with social media platforms playing a catalytic role in fuelling these violent protests. Social media is also playing a significant role in the spread of fake news, with the intention of causing fear and panic amongst citizens. Findings also reveal that the Zimbabwe police have embraced social media platforms for sharing crime prevention tips, receiving crime reports, engaging with citizens and gathering crime intelligence. Originality/value The study provides context-specific information on the threats posed by social media on a developing nation that has been characterised by a myriad of socio-economic challenges, as well as how these social media platforms have been embraced for crime prevention and control. Given limited empirical research on social media and national security in Zimbabwe, this study adds to the existing body of knowledge.
Enhancement of phase sensitivity by the additional resource in a Mach-Zehnder interferometer

Improving the phase sensitivity of the optical interferometer plays a key role in quantum metrology. We propose a novel scheme that two Mach-Zehnder interferometers (MZIs) are parallelly placed for investigating the single-phase and two-phase sensitivities. For single-phase estimation, we study the performance of phase sensitivity by difference-intensity detection. The results show that the external loss in the additional resource plays the role of enhancing the phase sensitivity. And the sub shot-noise phase sensitivity is obtained by adjusting the photon loss of the additional resource in the MZI even with the inputs of a vacuum state plus a coherent state. For two-phase estimation, we investigate the ultimate bounds of estimation precision and compare them with the case of single-phase estimation under optimized detection method. Our results could yield potential applications to quantum sensor, quantum imaging and quantum tomography.
Classification of Natural Disaster on Online News Data Using Machine Learning

We know neither what the future holds nor what will occur in the future. It is impossible to anticipate what the next instant may bring, and it could be awful. A natural disaster is an unforeseen event, which can have a tremendous impact on human life and the environment. The internet provides many sources that generate vast amounts of news articles daily. As the amount of news stories online increases, it's becoming more difficult for people to access disaster-relevant news, which makes it necessary to extract and classify news to be easily accessed. This paper presents an automated system that scraps news from various online sources and identifies disaster-relevant news. This paper also states the performance evaluation for classifying the natural disaster types based on machine learning in Indonesia's online news. Our results show that relevant Indonesian's online news about natural disasters can be the accuracy around 96% using the Support Vector Machine for three classes of natural disasters. In the Indonesian news data set, the machine learning algorithm that gets the highest value out of all the parameters is the best.
Recent Advances and Future Directions of Assistive Technologies for Alzheimer’s Patients

With the technological advancements in the domain of computer science, Medicinal and engineering fields, Intelligent Assistive Technologies (IAT) have been developed to mitigate the burden of caretakers and patients of Alzheimer’s. A wide variety of IAT has been developed. Caretakers and patients of Alzheimer’s will be much relieved if there are tools that assist them in performing their daily activities like bathing, cooking, hand wash, brushing and medication aids etc. Further, a safety system that ensures the safety of the patients like fall detection, gas leakage detection and fire detection etc. is also needed. This paper mainly reviews various existing IAT for medication aids, brushing and fall detection. Thus, this provides new research challenges and future directions for other researchers working in this area.
Process capability indexes: Trends and developments in the manufacturing of blood components

The production of blood components takes place in a workflow where conditions should be controlled and stabilized. The quality control claims and the principles of statistical process control should be understood to apply a control policy properly. Control typically employs charts complemented by a set of rules that allow taking well-supported actions. The use of capability indexes is not systematically used in blood establishments. They allow characterizing the production process. The data analyzed refer to producing a type of blood component over three years in two blood establishments. One or more capability levels may be typical of a given analyte for a specific blood component in a given environment. Some recommendations for adequate use of capability indexes to control blood components production properly are assumed.
Towards Robust Knowledge Graph Embedding via Multi-Task Reinforcement Learning

Nowadays, Knowledge graphs (KGs) have been playing a pivotal role in AI-related applications. Despite the large sizes, existing KGs are far from complete and comprehensive. In order to continuously enrich KGs, automatic knowledge construction and update mechanisms are usually utilized, which inevitably bring in plenty of noise. However, most existing knowledge graph embedding (KGE) methods assume that all the triple facts in KGs are correct, and project both entities and relations into a low-dimensional space without considering noise and knowledge conflicts. This will lead to low-quality and unreliable representations of KGs. To this end, in this paper, we propose a general multi-task reinforcement learning framework, which can greatly alleviate the noisy data problem. In our framework, we exploit reinforcement learning for choosing high-quality knowledge triples while filtering out the noisy ones. Also, in order to take full advantage of the correlations among semantically similar relations, the triple selection processes of similar relations are trained in a collective way with multi-task learning. Moreover, we extend popular KGE models TransE, DistMult, ConvE and RotatE with the proposed framework. Finally, the experimental validation shows that our approach is able to enhance existing KGE models and can provide more robust representations of KGs in noisy scenarios.
An Analysis of Enrollment and Query Attacks on Hierarchical Bloom Filter-Based Biometric Systems

A Hierarchical Bloom Filter (HBF) -based biometric framework was recently proposed to provide compact storage, noise tolerance, and fast query processing for resource-constrained environments, e.g., Internet of things (IoT). While security and privacy were also touted as features of the HBF, it was not thoroughly evaluated. Compared to the classical BFs, the HBF uses a threshold parameter to make robust authentication decisions when the HBF encounters noise in the biometric input which one would think might lead to security issues. In this paper, the attack vectors that could compromise the HBF security by increasing the false positive authentication of non-members and by leaking soft information about enrolled members are explored. With quantitative analyses, HBF-based biometric system security under these well-defined attack vectors is evaluated and it is concluded that the framework is more difficult to attack than the classical Bloom Filter. Further, experimental results show that soft biometric information is also kept private.
Enriching WordNet with Subject Specific Out of Vocabulary Terms Using Existing Ontology

WordNet is a huge repository being used as a tool in various fields. With an increasing number of applications referring to WordNet as a dictionary, several attempts have been made to update it. The paper proposes to extend the huge repository by adding words and relationships derived from students' class notes through wikidata. These terms can be phrases, technical terms, or any subject specific terminology appearing in students' notes of a specific subject. Although various WordNet enriching techniques are available, it is for the first time that subject specific terminology is being added. The resulting version of WordNet has some very common phrases and technical terms along with the generic terms. Making subject specific and generic terms available in a hierarchy can improve the accuracy of various applications like text summarization and clustering for text belonging to a specific domain.
Temporally Consistent Online Depth Estimation in Dynamic Scenes

Temporally consistent depth estimation is crucial for online applications such as augmented reality. While stereo depth estimation has received substantial attention as a promising way to generate 3D information, there is relatively little work focused on maintaining temporal stability. Indeed, based on our analysis, current techniques still suffer from poor temporal consistency. Stabilizing depth temporally in dynamic scenes is challenging due to concurrent object and camera motion. In an online setting, this process is further aggravated because only past frames are available. We present a framework named Consistent Online Dynamic Depth (CODD) to produce temporally consistent depth estimates in dynamic scenes in an online setting. CODD augments per-frame stereo networks with novel motion and fusion networks. The motion network accounts for dynamics by predicting a per-pixel SE3 transformation and aligning the observations. The fusion network improves temporal depth consistency by aggregating the current and past estimates. We conduct extensive experiments and demonstrate quantitatively and qualitatively that CODD outperforms competing methods in terms of temporal consistency and performs on par in terms of per-frame accuracy.
SODA: A Semantics-Aware Optimization Framework for Data-Intensive Applications Using Hybrid Program Analysis

In the era of data explosion, a growing number of data-intensive computing frameworks, such as Apache Hadoop and Spark, have been proposed to handle the massive volume of unstructured data in parallel. Since programming models provided by these frameworks allow users to specify complex and diversified user-defined functions (UDFs) with predefined operations, the grand challenge of tuning up entire system performance arises if programmers do not fully understand the semantics of code, data, and runtime systems. In this paper, we design a holistic semantics-aware (optimization for data-intensive applications using hybrid program analysis (SODA) to assist programmers to tune performance issues. SODA is a two-phase framework: the offline phase is a static analysis that analyzes code and performance profiling data from the online phase of prior executions to generate a parameterized and instrumented application; the online phase is a dynamic analysis that keeps track of the application's execution and collects runtime information of data and system. Extensive experimental results on four real-world Spark applications show that SODA can gain up to 60%, 10%, 8%, faster than its original implementation, with the three proposed optimization strategies, i.e., cache management, operation reordering, and element pruning, respectively.
Non-adaptive Stochastic Score Classification and Explainable Halfspace Evaluation

We consider the stochastic score classification problem. There are several binary tests, where each test i is associated with a probability $$p_i$$ of being positive, a cost $$c_i$$ , and a weight $$a_i$$ . The score of an outcome is a weighted sum of all positive tests, and the range of possible scores is partitioned into intervals corresponding to different classes. The goal is to perform tests sequentially (and possibly adaptively) so as to identify the class at the minimum expected cost. We provide the first constant-factor approximation algorithm for this problem, which improves over the previously-known logarithmic approximation ratio. Moreover, our algorithm is non adaptive: it just involves performing tests in a fixed order until the class is identified. Our approach also extends to the d-dimensional score classification problem and the “explainable” stochastic halfspace evaluation problem (where we want to evaluate some function on d halfspaces). We obtain an $$O(d^2\log d)$$ -approximation algorithm for both these extensions. Finally, we perform computational experiments that demonstrate the practical performance of our algorithm for score classification. We observe that, for most instances, the cost of our algorithm is within $$50\%$$ of an information-theoretic lower bound on the optimal value.
Dependency Distance and Its Probability Distribution: Are They the Universals for Measuring Second Language Learners’ Language Proficiency?

Previous studies have shown that dependency distance and its probability distribution can be applied as syntactic indicators of English as interlanguage. However, the universal application of these indicators has not been verified from the perspective of language typology. The issues are addressed in the present study based on a treebank of Chinese interlanguage of English and Japanese native speakers. The findings are as follows: (1) with the improvement of L2 proficiency, the MDDs of learners with different native language backgrounds gradually approach that of the target language in different patterns, and dependency distance is of universal significance as a metric to measure the development of interlanguage's syntactic complexity; (2) Chinese interlanguage also follows the principle of least effort, and its probability distribution of dependency distance, like those of natural languages, presents a power–law distribution, which can successfully fit the Zipf-Alekseev distribution; (3) the right truncated modified Zipf-Alekseev distribution can be used to measure Chinese interlanguage proficiency, and the fitting parameters of the probability distribution of dependency distance as a metric of interlanguage proficiency are also of universal value.
Automated Face Detection and Control System Using Computer Vision based Video Analytics to Avoid the Spreading of COVID-19

The classification of COVID-19 as global pandemic has led researchers and scientists to design solutions in order to reduce the fast spreading of the virus.This paper presents a novel detection and control system that utilizes Computer Vision based video analytics to help in reducing the speed of the spreading of the virus by recognizing people and detecting masks.The system uses the body temperature and other user biometrics to give access to a particular environement.The proposed system is able to identify a person who wants to access an environment and tracks his movement.The system can also control the door of the main entrance, the elevator, or any access zone, and generate audio notifications to alert user(s) to put their mask(s).The implementation results show that the proposed system has the advantages of a high sensitivity of 98.8% for front faces and 90.3% for turned faces, and ensure a safe environment while preserving the benefits of being modular and low cost.