Reviewerly/exHarmony huggingface dataset:
Free-Space Phase-Matching Quantum Key Distribution

As a practical version of twin-field quantum key distribution (TF-QKD), phase-matching quantum key distribution (PM-QKD) is able to surpass the linear bound without quantum repeaters. Implementing PM-QKD through free space can guarantee two parties to share unconditionally secure keys between ground stations and mobile platforms. However, atmospheric turbulence decreases the free-space secure key rate as the result of fluctuations of transmittances caused by it. This paper includes two works we do. First, for generality, we derive the secure key rate formula of 4-intensity PM-QKD based on asymmetric channel transmittances. Second, we numerically simulate the performance of PM-QKD in free space and ideal circumstance to study the effect of the atmospheric turbulence.
Accurate Teaching Data Analysis of “Special Effects Production for Digital Films and TV Programmes” Based on Big Data

With the promotion and implementation of student-centered teaching concepts, traditional teaching methods do not meet teaching requirements in the new era. Because of the deficiency of pertinence and accuracy in traditional teaching activities, this paper proposes precision teaching based on big data by analyzing educational data. This paper proposes methods to reform the teaching methods from teaching design, teaching practice, and teaching assessment. Besides, data about students, courses, and other learning resources and course assessment are collected through normal analysis, Pearson correlation analysis, and linear regression analysis. Then it is found that personalized test scores are the main factor that affects students’ final learning effect. The interactive teaching factors are not perfect, so it is necessary to strengthen teaching design and embody individualized development.
Alpha channel-Based Indicator For Robustness Forward/Backward LSB Steganography

Steganography is important technique for protecting confidential media (text, image, audio, etc.) by embedding it on another media object calling a cover or carrier, subject to no one can discovered. There is a conflict between the developers of Steganography and Steganalysis (detecting hidden messages), Therefore, there must be a rapid development in the creation of complex algorithms to hide information in a way that is difficult to analyze and access. This research introduces a new image steganography technique, Alpha Channel Based Steganography, that differently others algorithms by hiding secret information depending on Alpha Channel where using RGB channels as search space and alpha channel as an indicator for hiding and retrieving confidential data. two least significance bits in alpha channel using to indicate which RGB channel have the secret bit and other two to indicate the rounds number and type (forward and backward search) until secret message end. Using Alpha Channel in the introduced algorithm increase robustness against Steganalysis. Moreover, the capacity of hidden information has also increased due to the quadrupling of the search field in one image, where no visual attack can detect stego image even with maximum hiding capacity. After testing, evaluating and comparing with other algorithms, the proposed algorithm scoring 0% in MSE test, infinite in PSNR test, and 1 for SSIM test, the result of testing indicate that the proposed algorithm achieves the best results according to steganography aspects: imperceptibility, capacity and robustness.
Development of Conceptual Framework for Reinforcement Learning Based Optimal Control

Gupta, Smriti Pal, Sabita Kumar, Kundan Ghosh, KuntalThe framework of reinforcement learning-based optimal control depends on a mathematical formulation of intelligent decision making. In this article, we demonstrated the comprehensive design framework for offline reinforcement learning algorithms that utilizes sparse and discrete data space for efficient decision-making purposes. Learning is often difficult with the sparse reward function under the absence of optimization. Hence, an optimized map can be used in the reward function to improve efficacy. Some reward functions outperform sparse reward, such as “map completeness” and “information gain”. “Map completeness” is proportional to the difference between the current time step and the previous time step, while “information gain” utilizes the entropy information for measuring the uncertainty in the map. On a whole. this article proposes a framework for the development of optimized reward function-based reinforcement learning based control strategy.
Russian Fingerspelling Recognition Using Leap Motion Controller

Russian Sign Language is a national linguistic system with its vocabulary and grammar used by deaf people. In this paper, we focus on fingerspelling - a separate component of sign language. The developed system is capable of recognizing Russian fingerspelling static gestures captured with a Leap Motion Controller. In addition, we collected a new dataset of 400 static images of signs to evaluate the accuracy of the deep learning sign recognition system. The system achieved a success rate of 97.5%.
GTOE: Anomaly Detection Geometric Transformation Network with Outlier Exposure for Real Industry Data

Anomaly detection is a technology that is actively applied to many industries. Supervised learning-based anomaly detection requires a large amount of the normal and the abnormal data. However, it is difficult to get the abnormal data compared to the normal data. Therefore, in this paper, we propose a network for anomaly detection using only the normal data based on semi-supervised learning. It is a method that uses only the normal data to learn to fit 72 geometric transformations after using 72 geometric transformations. Furthermore, we improve performance by applying outlier exposure methods, which are methods to learn together by exposing the abnormal data. The abnormal data is not obtained additionally, but uses some from the normal data. The abnormal data are trained using a uniform distribution to avoid affecting the normal data. By using the abnormal data, we generalize anomaly detectors to improve performance on anomaly detection.
Budgeting System In Event Management Application Using Web-Based Genetic Algorithms

The increasing number of events usually formed a committee in small to large fields involving event organizers. In this study, the survey results were used to Telkom University students in Roadshow, Try Out and other committees. However, there are often problems in the implementation process caused by poor planning and supervision processes to have less clear financial statements. To anticipate this problem and create a web application-based event management application that provides appropriate budget recommendations. So, by using Genetics Algorithm is an optimization algorithm that can provide optimal results. This application is expected to help financial management reduce errors in the budgeting creation process and provide recommendations to users. Based on the test results, three experiments were carried out to see the optimal fitness value. In the first test, the densest optimal fitness value is between the ± 18 th generation out of a total of 50 generations. In the second experiment, the optimal fitness value is between the 20th generation to the 30th generation. In the last experiment, the optimal fitness value is between the 2nd generation to the 10th generation. This difference occurs because the random values used in each experiment are different. In this study, the genetic algorithm succeeded in recommending goods that match the maximum budget parameters of the division, indoor or outdoor category.
Users’ experiences of enhancing underwater images: an empirical study

Abstract Within the worldwide diving community, underwater photography is becoming increasingly popular. However, the marine environment presents certain challenges for image capture, with resulting imagery often suffering from colour distortions, low contrast and blurring. As a result, image enhancement software is used not only to enhance the imagery aesthetically, but also to address these degradations. Although feature-rich image enhancement software products are available, little is known about the user experience of underwater photographers when interacting with such tools. To address this gap, we conducted an online questionnaire to better understand what software tools are being used, and face-to-face interviews to investigate the characteristics of the image enhancement user experience for underwater photographers. We analysed the interview transcripts using the pragmatic and hedonic categories from the frameworks of Hassenzahl (Funology, Kluwer Academic Publishers, Dordrecht, pp 31–42, 2003; Funology 2, Springer, pp 301–313, 2018) for positive and negative user experience. Our results reveal a moderately negative experience overall for both pragmatic and hedonic categories. We draw some insights from the findings and make recommendations for improving the user experience for underwater photographers using image enhancement tools.
GGB Color Normalization and Faster-RCNN Techniques for Malaria Parasite Detection

Malaria is a disease transmitted by a female mosquito anopheles bite. Malaria commonly occurs in tropical and sub-tropical regions that having minimum health facilities. Promising news for us, early malaria diagnosis is a proven effective preventive a malaria-related mortality. In addition, automated malaria detection studies have shown a promising performance in reducing the manual microscopy-based examination times. However, since the quality input image is not standardized, a proper image preprocessing technique is notable in recognizing the object. Therefore, this study applies green, green, blue (GGB) color normalization as a preprocessing step in malaria detection. We evaluate our technique in a large public dataset containing 2,418 infected thin blood smear images by 49,900 parasites. The results show that our technique has malaria detection performance consistently better sensitivity and consistently similar precision in several intersection over union (IoU) thresholds. Furthermore, it indicates that using GGB color normalization in malaria parasite detection is valuable in reducing the false positive error.
<scp><i>k</i></scp>‐anonymity based location privacy protection method for location‐based services in Internet of Thing

Abstract The wide application of Internet of Things technology has promoted the application of location‐based services (LBS). Users can enjoy various conveniences brought by LBS. However, mobile users should submit their location information to LBS which may lead to privacy disclosure. Therefore, users need to protect their privacy while enjoying the service. If the privacy protection problem cannot be solved, the development of mobile internet business will be greatly affected. The existing location privacy protection methods take little account of real‐time road conditions and are unable to resist link attacks. In this article, a k ‐anonymous location privacy protection method based on Voronoi map was proposed. First, the Voronoi diagram was divided according to road network structure and real‐time road network data, and Voronoi unit was established with the intersection as the base point. Then, the base point is sent as an anchor instead of the user's actual location to the third‐party server. The Voronoi diagram generated by the k ‐anonymity constituted an anonymous space to resist multi‐query attacks and protected the location. k ‐1 user location points that are communicating were selected to form k ‐anonymous set with the current user, and send the anonymous set to LBS. Theoretical analysis and experimental results show that the proposed method not only efficiently protected location privacy, but also provided high‐quality service.
Cloud-based fall Detection System with Passive RFID Sensor Tags and Supervised Learning

Falls in older adults are a severe worldwide issue that threatens healthy living with a high risk of fatal accidents. In order to maintain a healthy life for them, it is necessary to detect fall accidents early to rescue them quickly. This paper proposes a fall detection system with passive RFID sensor tags measuring Received Signal Strength and Pressure values. By placing sensors on daily items, the system is expected to be non-invasive to the daily lives of older adults, compared to systems using cameras or wearable sensors. In this paper, we have compared the classification performance of several learning algorithms and have developed a prototype system based on a cloud.
Revisiting IoT Fingerprinting behind a NAT

The growing usage of Network Address Translation (NAT) over the past couple of years has become a double-edged sword. On one hand, it provides an added measure of security for legitimate users. On the other hand, the anonymity provided by NAT could undoubtedly be leveraged by malicious actors. To this end, the objective of fingerprinting devices behind a NAT aims at properly comprehending the nature of such devices while aiding in proper network and security provisioning and characterization. While the problem scope is certainly not new, it has been evolving quite rapidly given the wide macroscopic and microscopic deployments of IoT devices, and have recently attracted significant attention from the research community. Throughout this paper, we revisit the task of classifying IoT devices deployed within a localized IoT realm. In contrast to the state-of-the-art, we explore the capabilities of unsupervised and semi-supervised shallow and deep learning methodologies in capturing the nature of such NATed devices. Initial results using empirical data indicate the failure of clustering methods in fingerprinting both IoT and non-IoT nodes behind a NAT. Further, and knowing that IoT devices typically possess relatively consistent traffic patterns, the results shed light on the unexpected capability of autoencoders in better capturing non-IoT devices (in contrast to IoT nodes) behind a NAT. In an effort to comprehend such results, we implement and evaluate an explainable mechanism that provides preliminary insights into this phenomena. Additionally, the developed semi-supervised Restricted Boltzmann Machine (RBM) approach generated comparative results to the state-of-the-art, without relying on the stringent and adhoc process of features’ engineering, with relatively very good algorithm complexity and scalability. Results from this work put forward interesting future work in the area of network traffic analysis of NATed IoT devices, while highlighting the need for addressing the notions of explainability and concept drift.
Cybersecurity maturity model for providing services in the financial sector in Peru

In this paper, we proposed a cybersecurity maturity model for providing services to organizations in the financial sector. To develop this proposal, existing frameworks and models have been taken as a reference, expanding their scope by integrating cloud security and privacy capabilities. In this way, the proposed model is supported by a measurement tool that facilitates diagnosis and result visualization. The evaluation process is performed based on the fulfillment of controls, which are evaluated on a scale of five levels of maturity: 1. Initial, 2. In Development, 3. Defined, 4. Managed and 5. Optimized. The results are grouped by domains and functions. The model was validated with pilot studies in two entities from the financial sector in Lima, Peru. In the performed validation, the preliminary results showed an average acceptance level of 4.3 on a 1 to 5 scale while the obtained maturity level was 3. Those preliminary results allowed us to propose activities which eliminate the gaps found as well as improve the organization's capabilities in the evaluated process.
Design and Evaluation of a Hands-Free Video Game Controller for Individuals With Motor Impairments

Over the past few decades, video gaming has evolved at a tremendous rate although game input methods have been slower to change. Game input methods continue to rely on two-handed control of the joystick and D-pad or the keyboard and mouse for simultaneously controlling player movement and camera actions. Bi-manual input poses a significant play impediment to those with severe motor impairments. In this work, we propose and evaluate a hands-free game input control method that uses real-time facial expression recognition. Through our novel input method, our goal is to enable and empower individuals with neurological and neuromuscular diseases, who may lack hand muscle control, to be able to independently play video games. To evaluate the usability and acceptance of our system, we conducted a remote user study with eight severely motor-impaired individuals. Our results indicate high user satisfaction and greater preference for our input system with participants rating the input system as easy to learn. With this work, we aim to highlight that facial expression recognition can be a valuable input method.
Directives of Communicability for Software Artifacts

Software artifacts created in the early stages of the development process describe the proposed solutions for the system, such as UML and prototypes. For this reason, these artifacts are important for team communication. Miscommunication via artifacts occurs because practitioners typically focus on modeling, without reflecting on how other software development team members interpret them. Incorrect information during the development process can be introduced in the system due to miscommunication, impacting its quality. In this context, this work aimed to answer the following research question: How to support effective communication between producers and consumers of artifacts developed in the early stages of software development? To answer this question, we proposed the Directives of Communicability (DCs), based on theories that investigate different communication perspectives and exploratory studies. We designed the DCs to support practitioners to analyze characteristics that affect the artifact's content on communication. We conducted different empirical studies with our proposal, with 105 participants from industry and academia. From the empirical studies results, we noticed the DCs supported the reflection of producers’ artifacts, contributing to effective communication and reducing defects caused by miscommunication.
Bringing Home Recursions

This report is about an explorative co-crafting course applying the notion of recursive publics to adult learning and pro-environmental activation, which aimed to engage a diverse cohort of learners towards patterns of eating, living, and engaging that promoted wellbeing and a healthy environment. This two-month-long, university-endorsed study in Hong Kong saw 22 participants fermenting their urine in which to grow an edible plant (Lactuca sativa), thereby creating a material relationship between their bodies and the environment. Technologies were employed to bring people physically together for greater emancipatory engagement inside the shared material condition. When analyzed, these technologies revealed their potential for opening or restricting the synergies from combined purpose, expertise, and immanent life processes in recursively profound and playful ways. This civic-tech study offers a recursive self-implication approach to design education as a collective negotiation process for navigating unknown territory to converge a myriad of expertise and intended beneficiaries.
Psychological and pedagogical conditions for the formation of the subjective attitude of students to educational and professional activities

The article touches upon the actual problem of vocational education the development of the subjective attitude of students to educational and professional activities in the light of the requirements of the dynamic development of socio-economic processes, the modern labor market. The current stage of a dynamic civilization, total informatization, places high demands on the self-development, self-education, self-improvement of a professional. One of the important skills of our time is the skill of managing one's own resources, a conscious attitude to building life trajectories (self-skills), respectively, the formation of such skills should be the focus of attention in the process of professional training of the younger generation. The purpose of this article is to present the experience of organizing psychological and pedagogical conditions for the development of a conscious attitude of students to educational and professional activities, the development of subjectivity, the formation of supra professional skills. The main objectives of the article are to reveal the methodological basis for the construction of the author's course for first-year students "Psychology of personal and educational and professional effectiveness", to justify the choice of content, pedagogical methods and technologies used during the implementation of the program, to identify the effective aspects of the discipline and shortcomings indicating the need for further improvement of the course program. The methodological basis for the formation of the content of the author's training course was the research of the subject and subjectivity of the individual in domestic psychology, the specifics of the competence paradigm of education, the requirements of the modern labor market and the problems of the formation of universal skills in the modern system of vocational education. The presented article substantiates the selection of the content of the training course modules, self-diagnosis and self-development practices, and reveals the features and main difficulties of implementing the training course in offline and online learning. The practice of implementing the author's course program has shown the effectiveness of forming a subjective attitude to educational and professional activities, self-skills, as basic skills for the formation of universal competencies of future professionals.
Intelligent Execution through Plan Analysis

Intelligent robots need to generate and execute plans. In order to deal with the complexity of real environments, planning makes some assumptions about the world. When executing plans, the assumptions are usually not met. Most works have focused on the negative impact of this fact and the use of replanning after execution failures. Instead, we focus on the positive impact, or opportunities to find better plans. When planning, the proposed technique finds and stores those opportunities. Later, during execution, the monitoring system can use them to focus perception and repair the plan, instead of replanning from scratch. Experiments in several paradigmatic robotic tasks show how the approach outperforms standard replanning strategies.