Reviewerly/exHarmony huggingface dataset:
Few Shot Learning For Infra-Red Object Recognition Using Analytically Designed Low Level Filters For Data Representation

It is well known that deep convolutional neural networks (CNNs) generalize well over large number of classes when ample training data is available. However, training with smaller datasets does not always achieve robust performance. In such cases, we show that using analytically derived filters in the lowest layer enables a network to achieve better performance than learning from scratch using a relatively small dataset. These class-agnostic filters represent the underlying manifold of the data space, and also generalize to new or unknown classes which may occur on the same manifold. This directly enables new classes to be learned with very few images by simply fine-tuning the final few layers of the network. We illustrate the advantages of our method using the publicly available set of infra-red images of vehicular ground targets. We compare a simple CNN trained using our method with transfer learning performed using the VGG-16 network, and show that when the number of training images is limited, the proposed approach not only achieves better results on the trained classes, but also outperforms a standard network for learning a new object class.
Real-Time Detection of Student Engagement: Deep Learning-Based System

Due to the spread of COVID-19, E-learning has become the only option for the teachers and students. However, it is difficult for a teacher to monitor each student’s engagement while teaching online. This paper aims to develop an automated real-time video-based system to detect student engagement during online classes effortlessly and efficiently. The MobileNet model is trained on an eye images dataset from Kaggle and achieved 99% accuracy on data validation. The result obtained from training the model is used with the Viola–Jones algorithm and OpenCV. By using the built-in camera of the laptop, the system can detect whether the student is engaged or disengaged from his/her eye gaze. In the disengagement state detection, a buzzer sound starts.
A Step-by-Step HHL Algorithm Walkthrough to Enhance Understanding of Critical Quantum Computing Concepts

After learning basic quantum computing concepts, it is desirable to reinforce the learning using an important and relatively complex algorithm through which students can observe and appreciate how qubits evolve and interact with each other.Harrow-Hassidim-Lloyd (HHL) quantum algorithm, which can solve linear system problems with exponential speed-up over the classical method and is the basis of many important quantum computing algorithms, is used to serve this purpose.The HHL algorithm is explained analytically followed by a 4-qubit numerical example in bra-ket notation.Matlab code corresponding to the numerical example is available for students to gain a deeper understanding of the HHL algorithm from a pure matrix point of view.A quantum circuit programmed using qiskit is also provided for real hardware execution in IBM quantum computers.After going through the material, students are expected to have a better appreciation of the concepts such as basis transformation, bra-ket and matrix representations, superposition, entanglement, controlled operations, measurement, quantum Fourier transformation, quantum phase estimation, and quantum programming.To help readers review these basic concepts, brief explanations augmented by the HHL numerical examples in the main text are provided in the Appendix.
Capturing Semantic Knowledge In Object Localization In Captioning Images

Image captioning is the process of generating the description of an image in a human-understandable form that needs to be meaningful, syntactically, and semantically correct. This process of generating description by understanding the semantic knowledge in the `scene' is very natural for humans since they can easily extract the common meaning from the perceived image focusing on salient objects and rejecting unimportant aspects and express the `story' behind it in meaningful words. The same task is much complex for machines because the semantic knowledge or context can be evenly or unevenly spread in an image as well as it can be local or global. To achieve the purpose of meaningful image caption generation there is a need of combining the research communities of natural language processing and computer vision. This paper presents a survey of various approaches used in image captioning to improve the context in description generation by capturing the semantics.
Discuss the Application of Informatization Data in the Electric Power Industry from the Perspective of Computer Data Analysis Technology

Abstract With the iterative development of social economy, the demand for the power industry continues to deepen. Traditional basic operations such as simple editing of information data in the power industry have been difficult to achieve the timely and effective processing of the growing information data, and can not effectively analyze and mine the data type and data value. Based on this, this paper first analyzes the characteristics and functions of computer data analysis technology, and then studies the application of power industry information data based on computer data analysis.
Risks and Opportunities for Information Hiding in DICOM Standard

The increasing application of ICT technologies to medicine opens new usage patterns. Among the various standards, the Digital Imaging and COmmunication in Medicine (DICOM) has been gaining momentum, mainly due to its complete coverage of the diagnostic pipeline, including key applications such as CT, MRI and ultrasound scanners. However, owing to its complex and multifaceted nature, DICOM is prone to many risks especially due to the vast and complex attack surface characterizing the composite interplay of services, formats and technologies at the basis of the standard. Luckily, DICOM exhibits some room for improving its security. Specifically, information hiding and steganography can be used in a twofold manner. On one hand, they can help to watermark diagnostic images to improve their resistance against tampering and alterations. On the other hand, the digital infrastructure at the basis of DICOM can lead to data leaks or malicious manipulations via artificial intelligence techniques. Therefore, in this work we introduce risks and opportunities when applying information-hiding-based techniques to the DICOM standard. Our investigation highlights some opportunities as well as introduces possibilities of exploiting DICOM images to set up covert channels, i.e., hidden communication paths that can be used to exfiltrate data or launch attacks. To prove the effectiveness of our vision, this paper also showcases the performance evaluation of a covert channel built by applying text steganography principles on realistic DICOM images.
Nighttime Haze Removal Using Saliency-Oriented Ambient Light And Transmission Estimation

The ambient light and transmission of light source regions and non-light source regions have different physical properties in nighttime scenes. However, existing nighttime dehazing algorithms ignored this phenomenon and got oversaturated results looked like in the daytime. In this paper, we propose a nighttime dehazing method using fused ambient light and transmission based on saliency detection. We first propose a multi-scale saliency detection algorithm to differentiate light source regions and non-light source regions. Then we estimate ambient light and transmission of the two regions based on different methods to acquire the fused ambient light and fused transmission. Finally, we get the haze-free results based on the nighttime imaging model. Experimental results show that our method outperforms other state-of-the-art methods in color recovery and our haze-free results still look like in the nighttime.
Covid-19 and tracing methodologies: A lesson for the future society

As the new coronavirus (SARS-CoV-2) surged across the globe, new technical solutions have supported policy makers and health authorities to plan and modulate containment measures. The introduction of these solutions provoked a large debate which has focused on risks for privacy and data protection. In this paper we offer an analysis of the available technical approaches and provide new arguments to move beyond the ongoing discussions. In particular, we argue that the past debate missed the opportunity to highlight the societal aspects of privacy and to stimulate a broader reflection on the actions needed to serve the good of society. With this paper, as well as providing an accessible review of the technical and legal aspects of the proposed solutions, we aim to offer new stimuli to reconsider contact tracing and its role in helping countries navigate the current pandemic.
Methods of Using Cases from the Life of Outstanding Mathematicians in the Training of Future Teacher

An appeal to the personalistic component of the history of mathematics, stories about scientists who have contributed to its development, contribute to the implementation of the educational aspect in the teaching of mathematics. A question arises: how can a mathematics teacher shape the moral qualities of a child, including patriotic and international feelings; what pedagogical means he can use at the same time; how to prepare a teacher for this type of activity at a university. Based on the purpose of the study, the case study method was chosen as the main tool, which allowed us to carefully study the data (biographical materials) in a certain context (in terms of their educational potential). Biographies of outstanding mathematicians were analyzed and cases related to the manifestation of their qualities in various life situations were identified. The publications and experience of teachers were considered in terms of the possibility of including this material in the process of teaching mathematics. The empirical experience of implementing the process of training a teacher-educator in the framework of teaching methodological and mathematical disciplines and organizing extracurricular activities of a professional orientation was generalized. As a result, the main directions of work on the implementation of educational activities through the appeal to the patterns of behavior of the creators of mathematics were identified: the creation of biographical essays and the preparation of presentations that reveal personal experience and career trajectory; the inclusion in the discussion of the actions and facts from the life of outstanding mathematicians; the inclusion in the discussion of actions and facts from the life of these wonderful people; preparation of brief historical notes on the discoveries of Russian mathematicians of different historical periods and other near-mathematical events; making presentations, wall newspapers (traditional or interactive) or video clips, organizing mathematical holidays, etc. The practical significance of the materials of this article lies in the fact that the developed system of work, including getting to know the cases from the life of scientists in the framework of training sessions, setting practice-oriented tasks for organizing independent work of students and organizing extracurricular educational activities in the form of group projects, contributes to the formation of the future teacher's readiness to use the educational potential of cases from the life of scientists in educational work.
Research on Financial Informatization Construction in Colleges and Universities under the Background of "Internet+"

NOTICE OF RETRACTION: While investigating potential publication-related misconduct in connection with the ICIMTech 2021 Conference Proceedings, serious concerns were raised that cast doubt on the integrity of the peer-review process and all papers published in the Proceedings of this Conference. The integrity of the entire Conference has been called into question. As a result, of its investigation, ACM has decided to retract the Entire Conference Proceedings and all related papers from the ACM Digital Library.
Supporting refactoring of BDD specifications—An empirical study

Behavior-driven development (BDD) is a variant of test-driven development where specifications are described in a structured domain-specific natural language. Although refactoring is a crucial activity of BDD, little research is available on the topic. To support practitioners in refactoring BDD specifications by (1) proposing semi-automated approaches to identify refactoring candidates; (2) defining refactoring techniques for BDD specifications; and (3) evaluating the proposed identification approaches in an industry context. Using Action Research, we have developed an approach for identifying refactoring candidates in BDD specifications based on two measures of similarity and applied the approach in two projects of a large software organization. The accuracy of the measures for identifying refactoring candidates was then evaluated against an approach based on machine learning and a manual approach based on practitioner perception. We proposed two measures of similarity to support the identification of refactoring candidates in a BDD specification base; (1) normalized compression similarity (NCS) and (2) similarity ratio (SR). A semi-automated approach based on NCS and SR was developed and applied to two industrial cases to identify refactoring candidates. Our results show that our approach can identify candidates for refactoring 6o times faster than a manual approach. Our results furthermore showed that our measures accurately identified refactoring candidates compared with a manual identification by software practitioners and outperformed an ML-based text classification approach. We also described four types of refactoring techniques applicable to BDD specifications; merging candidates, restructuring candidates, deleting duplicates, and renaming specification titles. Our results show that NCS and SR can help practitioners in accurately identifying BDD specifications that are suitable candidates for refactoring, which also decreases the time for identifying refactoring candidates.
Deep Learning-Based Approach for Sentiment Classification of Hotel Reviews

As everything is shifting online, the demand for sentiment analysis has expanded tremendously in the recent years. Sentiment analysis is an automated process of analyzing people’s opinions and feelings using natural language processing tools. Organizations in the field of tourism can benefit from sentiment analysis to accurately track their customers’ opinions. A deep learning-based approach is introduced in this paper that automatically performs sentiment analysis for hotel reviews using word embedding and gated recurrent unit. The performance of our deep learning-based model outperformed the performance of the traditional machine learning methods in sentiment classification of hotel reviews with 89% accuracy and 92% F-score.
Deep restricted and additive homomorphic ElGamal privacy preservations over big healthcare data

Purpose The purpose of this paper is to improve the privacy in healthcare datasets that hold sensitive information. Putting a stop to privacy divulgence and bestowing relevant information to legitimate users are at the same time said to be of differing goals. Also, the swift evolution of big data has put forward considerable ease to all chores of life. As far as the big data era is concerned, propagation and information sharing are said to be the two main facets. Despite several research works performed on these aspects, with the incremental nature of data, the likelihood of privacy leakage is also substantially expanded through various benefits availed of big data. Hence, safeguarding data privacy in a complicated environment has become a major setback. Design/methodology/approach In this study, a method called deep restricted additive homomorphic ElGamal privacy preservation (DR-AHEPP) to preserve the privacy of data even in case of incremental data is proposed. An entropy-based differential privacy quasi identification and DR-AHEPP algorithms are designed, respectively, for obtaining privacy-preserved minimum falsified quasi-identifier set and computationally efficient privacy-preserved data. Findings Analysis results using Diabetes 130-US hospitals illustrate that the proposed DR-AHEPP method is more significant in preserving privacy on incremental data than existing methods. A comparative analysis of state-of-the-art works with the objective to minimize information loss, false positive rate and execution time with higher accuracy is calibrated. Originality/value The paper provides better performance using Diabetes 130-US hospitals for achieving high accuracy, low information loss and false positive rate. The result illustrates that the proposed method increases the accuracy by 4% and reduces the false positive rate and information loss by 25 and 35%, respectively, as compared to state-of-the-art works.
Measurement Study of Real-Time Virtual Reality Contents Streaming over IEEE 802.11ac Wireless Links

Experience sharing among multiple users in virtual reality (VR) is one of the key applications in next generation wireless systems. In this VR application, one object can be reproduced as a virtual object based on recorded/captured multiple real-time images from multiple observation points. At this time, VR applications require a lot of bandwidth to provide seamless services to users in wireless links, and thus, a certain level of data rates should be maintained. As the number of users increases, the server allocates more data rates to users on top of the limited bandwidth in wireless networks. At this time, users who utilize the VR streaming services will suffer from a lower quality, due to the limited bandwidth. This paper reports the measurement study and also analyzes the fluctuations in terms of the data rates as the number of users increases while sharing point cloud information in real-time authorized reality environments over IEEE 802.11ac wireless networks. Moreover, it measures and analyzes fluctuations in terms of frames-per-second and Jitters, which are practical quality reduction indicators.
An Improved Convolutional Neural Network Based on Noise Layer

In order to solve the over-fitting problem in Convolutional Neural Networks (CNN), a new method to improve the performance of CNN with noise layer on the basis of the previous studies has been proposed. This method improves the generalization performance of the CNN model by adding corresponding noise to the feature image obtained after convolution operation. The constructed noise layer can be flexibly embedded in a certain position of the CNN structure, and with each iteration of training, the added noise is also constantly changing, which makes the interference to the CNN model more profound, thus the more essential features of the input image are obtained. The experimental results show that the improved CNN model based on the noise layer has better recognition effect on some test images than the CNN model without any improvement; for different CNN models, the position of the noise layer which can improve the recognition accuracy is different; as the number of layers deepens, to improve the generalization performance of the CNN model, the position of the noise layer needs to be moved back. The improved CNN model based on the noise layer proposed in this paper solves the overfitting problem to a certain extent, and it has a certain reference significance for studying how to improve the generalization performance of CNN.
Strengthening of Homomorphic Encryption Scheme for Cloud Environment Using Particle Optimization Algorithm

The cloud computing is amongst the most trending fields with several lines of research possibilities, driven by cost-benefits offered by cloud adoption to businesses around the globe. Cloud adoption is more pressing than ever before, especially in the current context of global health emergency caused by pandemic outbreaks of Covid-19, where in “WFH - Work from Home” is the new normal and businesses require “Any-Time, Any-Where” access to IT. However, one of the major barriers to cloud adoption viz “Security of Data” continues to be a cause of concern. In this study, authors have attempted to address this concern by studying encryption techniques available in the literature and generally adopted by the Cloud Service Providers, identifying attributes and aspects that may be enhanced further, for strengthening of solution in used for security of Data in Motion. Through this study, authors have proposed a methodology for addressing key allocation and key management issues by way of optimization and opened a new dimension of research possibilities in future.
REAL 2021 – Robot open-Ended Autonomous Learning: A Competition and Benchmark

Open-ended learning is a core research field of developmental robotics that aims to build learning machines and robots that can autonomously acquire knowledge and skills incrementally as infants and children do. Here we present a competition, in its third edition, that aims to create a standard benchmark within the field. The competition focuses on a simulated camera-arm robot that: (a) in a first ‘intrinsic phase’ acquires sensorimotor competence by autonomously interacting with objects through mechanisms such as curiosity, intrinsic motivations, self-generated goals, and reinforcement learning; (b) in a second ‘extrinsic phase’ is tested with tasks unknown in the intrinsic phase to measure the quality of knowledge and skills acquired in the first phase. Participants can install a simulated environment on their machines to handily develop and test their systems, then submit these to the online platform for evaluation and public ranking. As for infants, the benchmark requires the contextual solution of a number of challenges usually tackled in isolation, such as exploration, sparse rewards, object learning, generalisation, task/goal self-generation, and autonomous skill learning. The competition is contributing to foster the research in the field within different scientific communities, and to operationally define the multiple challenges posed by open-ended learning so as to compare how alternative systems can solve them.
Exploring Topic-language Preferences in Multilingual Swahili Information Retrieval in Tanzania

Habitual switching of languages is a common behaviour among polyglots when searching for information on the Web. Studies in information retrieval (IR) and multilingual information retrieval (MLIR) suggest that part of the reason for such regular switching of languages is the topic of search. Unlike survey-based studies, this study uses query and click-through logs. It exploits the querying and results selection behaviour of Swahili MLIR system users to explore how topic of search (query) is associated with language preferences—topic-language preferences. This article is based on a carefully controlled study using Swahili-speaking Web users in Tanzania who interacted with a guided multilingual search engine. From the statistical analysis of queries and click-through logs, it was revealed that language preferences may be associated with the topics of search. The results also suggest that language preferences are not static; they vary along the course of Web search from query to results selection. In most of the topics, users either had significantly no language preference or preferred to query in Kiswahili and changed their preference to either English or no preference for language when selecting/clicking on the results. The findings of this study might provide researchers with more insights in developing better MLIR systems that support certain types of users and in certain scenarios.