Reviewerly/exHarmony huggingface dataset:
Ethics of AI: Do the Face Detection Models Act with Prejudice?

This work presents a study on an ethical issue in Artificial Intelligence related to the presence of racist biases by detecting faces in images. Our analyses were performed on a real-world system designed to detect fraud in public transportation in Salvador (Brazil). Our experiments were conducted by taking into account three steps. Firstly, we individually analyzed a sample of images and added specific labels related to the users’ gender and race. Then, we used well-defined detectors, based on different Convolutional Neural Network architectures, to find faces in the previously labeled images. Finally, we used statistical tests to assess whether or not there is some relation between the error rates and such labels. According to our results, we had noticed important biases, thus leading to higher error rates when images were taken from black people. We also noticed errors are more likely in both black men and women. Based on our conclusions, we recall the risk of deploying computational software that might affect minority groups that are historically neglected.
Towards Model-informed Precision Dosing with Expert-in-the-loop Machine Learning

Machine Learning (ML) and its applications have been transforming our lives but it is also creating issues related to the development of fair, accountable, transparent, and ethical Artificial Intelligence. As the ML models are not fully comprehensible yet, it is obvious that we still need humans to be part of algorithmic decision-making processes. In this paper, we consider a ML framework that may accelerate model learning and improve its interpretability by incorporating human experts into the model learning loop. We propose a novel human-in-the-loop ML framework aimed at dealing with learning problems that the cost of data annotation is high and the lack of appropriate data to model the association between the target tasks and the input features. With an application to precision dosing, our experimental results show that the approach can learn interpretable rules from data and may potentially lower experts' workload by replacing data annotation with rule representation editing. The approach may also help remove algorithmic bias by introducing experts' feedback into the iterative model learning process.
“Tell Me Your Story, I’ll Tell You What Makes It Meaningful’’: Characterization of Meaningful Social Interactions Between Intercultural Strangers and Design Considerations for Promoting Them

Positive meaningful interactions are encounters that promote positive attitudes and learning about others, which are needed to develop healthy social fabrics and cultural diversity. However, individuals tend to interact more with people like themselves often avoiding encounters with others that seem to be different, for example, with intercultural strangers. Though previous HCI work has been concerned with exploring meaningful experiences with products and technologies as a way of promoting product attachment, the field lacks studies exploring how design could facilitate intercultural MSI. Designing interventions to support intercultural MSI requires i) understanding what characteristics make these interactions meaningful and ii) how these characteristics can be addressed through design. In this study, we contribute to the literature by producing knowledge on these aspects. Based on an analysis of 56 real-life stories about intercultural MSI and an idea generation session with designers, we characterize intercultural MSI with four dimensions (outcomes, feelings, context, and elements) and we identify four design considerations to be taken into account when designing interventions to support intercultural MSI. Hence, our contribution is to formulate this knowledge while highlighting how the characteristics and perceptions of intercultural MSI can be applied to design new technologies that promote this kind of interaction.
A Feature-Weighted Rule for the K-Nearest Neighbor

The K-nearest Neighbor algorithm is a well-known non-parametric algorithm used for classifying. The algorithm is a simple, intuitive and preferable choice for many machine-learning models. Having that in mind, the negatives of the method should not be overlooked – the sensitivity of the k value, the choosing method of the neighbors and the voting mechanism.This paper reviews some state-of-art weight algorithms and motivated by their ideas proposes a solution for weight function. Unlike most weight functions, the proposed solution uses the features of the neighbors instead of just their distances. Some experiments are conducted on both real-world datasets and on well-known experimental ones. Some future improvements are targeted and the advantages and disadvantages are discussed.
A Watermarking Method Resilient Against Many Hybrid Attacks for Color Medical Images

This paper proposes a robust and blind medical image watermarking scheme that is resilient against many different hybrid attacks. The medical image is segmented into ROI, and RNOI and three different watermarks are embedded in the coefficients of the middle-frequencies of DCT of RONI. Due to the restoration techniques, ROI of the image is restored against six different hybrid attacks such as rotation and scaling, impulse noise, cropping and rotation, translation, JPEG and JPEG2000, impulse noise, Gaussian noise and speckle noise, histogram, blurring and sharpening and median, and average and wiener filters. The difference in the values of the original and regenerated PHV of ROI gives the amount of tampering. The proposed method has also source authenticity, accuracy, and ownership identity features. By comparing with existing methods, the proposed scheme gives better performance than other existing methods. Thus, the proposed scheme is more robust and can withstand against many hybrid attacks.
On the Innovation of Blended Teaching Mode of New Engineering Based on “Artificial Intelligence +”

The rapid rise and wide application of emerging information technologies such as artificial intelligence (AI), big data, cloud computing and Internet of things in the field of education technology, to a large extent, have changed the teaching mode of new engineering courses and rapidly promoted the reform of higher education. In this paper, the case method and logical empirical evaluation method are used to explore the full and flexible application of artificial intelligence technology means combined with new engineering courses, to create a new engineering intelligent class, and to carry out AI transformation, so as to make education and teaching deeply integrated with AI and develop towards intelligent and efficient development, promote blended teaching and build a deep integrated ecological chain, and form an interactive development mode of sustainable innovation and double helix evolution.
Design Virtual Reality Simulation System for Epidemic (Covid-19) Education to Public

In 2020, the COVID-19 epidemic swept the world and continued to spread. The global epidemic reflects the severe inadequacy of public education on infectious disease prevention in many countries. Therefore, it is necessary to popularize the knowledge of infectious disease prevention by designing a new epidemic education system. As a new interactive technology, Virtual Reality (VR) technology profoundly changes the human-computer interaction experience. According to the characteristics of VR, this research designed a virtual simulation system based on epidemic education research. This system is developed with Unreal 4 engine and simulates scenarios that require epidemic prevention education. The user interacts with the three-dimensional (3D) scene model through the VR controllers and visual user interface. This system allows users to effectively acquire knowledge about the epidemic and experience how medical staff deal with emergencies. This article discusses the methods of popularizing epidemic knowledge education by virtual simulation technology. It illustrates the system and knowledge popularization of the new epidemic health education in virtual simulation. The research will assist the public in acquiring epidemic knowledge effectively.
A data aggregation and real-time electricity billing scheme based on homomorphic encryption in smart grid

To realize more efficient load distribution, smart grid introduces a real-time electricity price billing strategy. In this kind of market, due to the interaction of a large amount of real-time data between the smart grid and users, the privacy of users has become a key issue. In the existing billing scheme based on homomorphic encryption, it usually introduces a third party to calculate the electricity bill, which leads to huge computing cost. We propose a data aggregation and realtime electricity price billing scheme on the basis of homomorphic encryption without a third party or electricity suppliers to participate in to reduce the computing cost. In our scheme, the local gateway uses hybrid multiplicative homomorphic algorithm for billing, which greatly reduces the system cost. Users' privacy is protected in the process of meter billing and bill verification in the meantime. Through experiments and performance analysis, the program has good security and better performance.
Challenges in Assistive Living Based on Tech Synergies: The Cooperation of a Wheelchair and A Wearable Device

The direct medical costs for falls only—what patients and insurance companies pay—was $34 billion [Center for Disease Control and Prevention, 2013]. The necessity for continuous monitoring and assisting of persons in risk, and the increasing prevalence of chronic conditions among our aging population present great challenges to our healthcare system. These costs are also expected to surpass $54 billion by 2020 ( http://www.cdc.gov/ncipc/factsheets/fallcost.htm , in 2007). In addition, the larger healthcare providers offer low quality services. Thus, making a robotic machine “lifting-up” elderly persons or persons with disabilities from a sitting position and transferring them to the bathroom is a very challenging scenario that urgently require innovated solutions. Thus, advanced technological achievements based on robotic nurses and wearable health monitoring devices may represent the up-coming hopeful and desirable solutions. Thus, the challenges here are based on the importance of IT-Engineering solutions to healthcare needs. In addition these challenges offer a paradigm on the already growing personalized/person-centered healthcare and the role of ‘smart homes’ and ‘smart beds’ for independent living and self-management of chronic conditions. While the importance of such care is recognized, not many efforts have been pursued to develop low-cost (and inconspicuous) technologies. Thus, the chapter presents issues for the development of a novel synergistic interactive intelligent framework-model between intelligent wearable health-monitoring devices (WS) and intelligent robotic wheelchairs (IRC) for assisting people (HS) in risk at smart homes, improving safety and quality of life, and reducing healthcare cost. In particular, it will focus on two challenging scenarios (lifting-transferring) reported above providing concepts but at the same time maintaining the generality of this synergistic model. The challenges are related with a new generation of intelligent synergistic models of inexpensive, unobtrusive intelligent wearable sensors, monitoring devices, intelligent robotic assistants, and surveillance methodologies. In addition, the healthcare advanced research technologies and educational training is expected to transform the way physicians and nurses provide care and the way of people at risk will safely stay at their homes. Concurrently, they will offer a ground for new research directions for engineers to contribute in healthcare applications. In addition, commercialization of these devices will certainly transform our healthcare monitoring systems for a large population, and transform the living conditions of people at need.
Solving IoT Security and Scalability Challenges with Blockchain

Internet of Things (IoT) is one relatively new technology, which aims to make our lives easier by automating our daily processes. This article would aim to deliver an idea how to prevent the IoT technology, delivering maliciously and bad things and how to scale. The intention of this research is to explain how a specific implementation of a Blockchain network, enterprise-grade permissioned distributed ledger framework called Hyperledger Fabric, can be used to resolve the security and scalability issues in an IoT network.
A Prediction Method for Evolution Effects of Software Architecture

Software architecture evolution may lead to architecture erosion, resulting in the increase of software maintenance cost, the deterioration of software quality, the decline of software performance, and so on. In order to avoid software architecture erosion, we should evaluate the evolution effect of software architecture in time. This paper proposes a prediction method for the evolution effects of software architecture based on BP network. Firstly, this method proposes four evolution principles and evaluates the overall evolution effects based on the combined measurements. Then, we extract the evolutionary activities from release notes. Finally, we establish a prediction model for evolution effect based on BP network. Experimental results show that the proposed method can be used to predict the evolution effect.
Information Security Accountability in the Cloud Computing Context—A Comprehensive Review

Accountability is a main concern for information security within cloud computing; it represents the trust in service relationships between clients and cloud service providers. Without evidence of accountability, a lack of trust and confidence in cloud computing is to be expected from decision-makers. Furthermore, a lack of accountability is considered as an added level of risk, especially since a client’s essential services are controlled and managed by a third party. Therefore, this new outsourcing paradigm increases the challenge of maintaining data security and confidentiality, supporting data and service availability, and demonstrating compliance. This chapter presents a literature review on IS accountability. It sets out the different definitions of IS responsibility from the existing literature. This chapter reviews information security and cloud issues, and related security issues and how they relate to IS accountability in the context of cloud computing. The concept of Cloud computing along with the different types of cloud services is thoroughly described. The factors of accountability are also reviewed in this chapter. This chapter is made up of two main sections. The first section will focus on information security and explore IS Accountability in cloud services provisions. The second section will elaborate on the conceptual drivers of IS Accountability. These factors are Transparency, Responsibility, Assurance and Remediation. Finally, accountability for Cloud computing service relationships and all other aspects associated with IS Accountability will also be identified and explained.
Deep Learning based Real-Time Image Upscaling for Limited Data Rate and Prevalent Resources

Video streaming platforms have attracted substantial attention. The demand of maximizing image quality at the user end and in parallel, minimizing the amount of data transmission or date rate, is significantly increasing. On the other hand, compression algorithms have almost reached the upper bound; therefore, the only way to significantly decrease the amount of data transmission or data rate would be to decrease the image resolution itself on the transmitter (Tx) and to upscale the image on the receiver (Rx) or user side. The difficulty of this lies in the fact that users have a variety of resources available to perform upscaling, and normal upscaling does not yield sufficiently high-quality images. Deep learning has enabled the advent of image super-resolution techniques; however, such solutions require enormous time at the user end, which is inappropriate for real-time applications. With the aforementioned motivation, this paper proposes a deep learning network that can be run in real-time on the prevalent resources at the user end, e.g., a single core of the 2 GHz CPU, while providing the better quality than conventional methods of interpolation.
A Mixed-Initiative Visual Analytics Approach for Qualitative Causal Modeling

Modeling complex systems is a time-consuming, difficult and fragmented task, often requiring the analyst to work with disparate data, a variety of models, and expert knowledge across a diverse set of domains. Applying a user-centered design process, we developed a mixed-initiative visual analytics approach, a subset of the Causemos platform, that allows analysts to rapidly assemble qualitative causal models of complex socio-natural systems. Our approach facilitates the construction, exploration, and curation of qualitative models bringing together data across disparate domains. Referencing a recent user evaluation, we demonstrate our approach’s ability to interactively enrich user mental models and accelerate qualitative model building.
Chaotic system constructed by product trigonometric function and polynomial and applied to color image encryption

In the applied research of nonlinear system, the low degree of chaos in the dynamical system leads to the limitation of using the chaos method to solve some practical problems. In this paper, we use the product trigonometric function and ternary polynomial to build a dynamical system, which has strong chaotic characteristics. The dynamical system is constructed by two product trigonometric functions and a ternary linear equation, and its chaotic properties are verified by bifurcation diagrams, Lyapunov exponents, fractal dimensions, etc. The system has many parameters and large parameter intervals and is not prone to cycles. The conditions for the non-divergence of this system are given by mathematical derivation, and it is found that the linear part of the system can be replaced by an arbitrary ternary polynomial system and still not diverge, and the bifurcation diagram is drawn to verify it. Finally, the chaotic sequence is distributed more uniformly in the value domain space by adding the modulo operation. Then, the bit matrix of multiple images is directly permuted by the above system, and the experiment confirms that the histogram, information entropy, and pixel correlation of its encrypted images are satisfactory, as well as a very large key space.
Quantifying Geometric Measure of Entanglement of Multi-qubit Graph States on the IBM’s Quantum Computer

Quantum entanglement gives rise to a range of non-classical effects, which are extensively exploited in quantum computing and quantum communication. Therefore, detection and quantification of entanglement as well as preparation of highly entangled quantum states remain the fundamental objectives in these fields. Much attention has been devoted to the studies of graph states, which play a role of a central resource in quantum error correction, quantum cryptography and practical quantum metrology in the presence of noise.We examine multi-qubit graph states generated by the action of controlled phase shift operators on a separable quantum state of a system, in which all the qubits are in arbitrary identical states. Analytical expression is obtained for the geometric measure of entanglement of a qubit with other qubits in graph states represented by arbitrary graphs. We conclude that this quantity depends on the degree of the vertex corresponding to the qubit, the absolute values of the parameter of the phase shift gate and the parameter of the initial state the gate is acting on. Moreover, the geometric measure of entanglement of certain types of graph states is quantified on the IBM’s quantum computer ibmq_athens based on the measurements of the mean spin. Namely, we consider states associated with the native connectivity of ibmq_athens, the claw and the complete graphs. Appropriate protocols are proposed to prepare these states on the quantum computer. The results of quantum computations verify our theoretical findings [1].
Institute of Philosophy as a State Organization and Social Institution

The article examines the 100-year history of the Institute of Philosophy of the Russian Academy of Sciences (IF) as a state organization and a special social institution. It traces the history of systematization of philosophical knowledge in an encyclopedic form: the initial plan (early 1920s) of G.G. Shpet to focus ef­forts on the question “What is philosophy?”; the project of the historical study of philosophy in the light of materialistic dialectics as its pinnacle (1928–1930); a partly realized plan (late 1930 – early 1940) of writing a 7-volume world his­tory of philosophy; “Philosophical Encyclopedia” in 5 volumes (1960–1970); “New Philosophical Encyclopedia” (2000–2001) in 4 volumes. The article shows the contradictory interaction of the tasks of the study of philosophy and its development. In the history of IF, the following stages are highlighted: the dog­matism of 1930–1940, the humanistic turn of the mid-1950s, rejection of the mo­nopoly of Marxist philosophy, pluralism of post-Soviet philosophy. The contem­porary situation has put into question the traditional beliefs what a human being is. The challenges from the brain and cognitive sciences highlight a new under­standing of consciousness and reason, which creates a common field of research for the IF and exciting creative perspectives for its staff
Research on the Construction of Music Teaching Cloud Platform in Universities Based on Data Mining Technology

From the beginning of music education to now, it has gone through different stages of development and carried out different forms of reform. On the whole, the advantages of music teaching mode are becoming more and more prominent. The progress of teaching has overcome many difficulties in music learning and effectively promoted students' enthusiasm and interest in learning. There are a lot of spiritual interactions in the process of music education. Perceptual education, which pays attention to the individual differences in cognitive activities and the importance of self-learning, is one of the important theories to study the efficiency improvement of music education. In music teaching, taking the network as the basic platform can not only enrich the classroom contents, but also enrich the students' music knowledge reserves. This paper analyzes the role of music teaching cloud platform based on data mining technology as an auxiliary tool for music teaching, and explores the ways to improve the efficiency of music education in Chinese universities from the perspective of improving the utilization efficiency of music education resources.