Reviewerly/exHarmony huggingface dataset:
Clean‐label poisoning attacks on federated learning for <scp>IoT</scp>

Abstract Federated Learning (FL) is suitable for the application scenarios of distributed edge collaboration of the Internet of Things (IoT). It can provide data security and privacy, which is why it is widely used in the IoT applications such as Industrial IoT (IIoT). Latest research shows that the federated learning framework is vulnerable to poisoning attacks in the case of an active attack by the adversary. However, the existing backdoor attack methods are easy to be detected by the defence methods. To address this challenge, we focus on edge‐cloud synergistic FL clean‐label attacks. Unlike common backdoor attack, to ensure the attack's concealment, we add a small perturbation to realize the clean label attack by judging the cosine similarity between the gradient of the adversarial loss and the gradient of the normal training loss. In order to improve the attack success rate and robustness, the attack is implemented when the global model is about to converge. The experimental results verified that 1% of poisoned data could make an attack successful with a high probability. Our method maintains stealth while performing model poisoning attacks, and the average Peak Signal‐to‐Noise Ratio (PSNR) of poisoning images reaches over 30 dB, and the average Structural SIMilarity (SSIM) is close to 0.93. Most importantly, our attack method can bypass the Byzantine aggregation defence.
Effectiveness of immersive virtual reality training in nasogastric tube feeding education: A randomized controlled trial

Given rapidly aging societies worldwide, improving the quality of long-term care through the cultivation of immense nursing assistants is critical. Accordingly, developing a satisfactory learning model to improve the learning outcomes of nursing assistant students is imperative.This study tested the hypothesis that students in long-term care departments who underwent immersive virtual reality (IVR) training would have significantly (1) higher levels of knowledge about the skills of nasogastric tube feeding, (2) higher learning motivations (i.e., intrinsic and extrinsic motivations, task values, and self-efficacy), (3) lower cognitive load, and (4) higher satisfaction than a control group.A randomized controlled trial with pretest and posttest design.We randomly assigned 107 students from the long-term care departments of two universities in central Taiwan to the IVR group (n = 54) or the control group (n = 53).The IVR group learned the procedure of nasogastric tube feeding through IVR, whereas the control group watched a 15-min 2D video. The participants filled pretest and posttest questionnaires on nasogastric tube feeding knowledge. After the experiment was completed, the participants answered another questionnaire on their learning motivations, cognitive load, and learning satisfaction.The nasogastric tube feeding knowledge improved significantly in the IVR and control groups after the intervention, with no significant between-group differences. The IVR group scored significantly higher than the control group on extrinsic goals, task value, and satisfaction; nevertheless, they also experienced a significantly higher cognitive load.Both the IVR training and the traditional 2D video improved the learning outcomes of the nursing assistant students. The students were more satisfied with IVR than with the conventional learning model and indicated that IVR inspired their extrinsic learning motivations and perceived task value. However, IVR incurred a high cognitive load, which must be addressed in future course designs.
Nonverbal communication with emojis in social media: dissociating hedonic intensity from frequency

As a popular means of nonverbal communication in social media, emojis provide quick predictions about public sentiments towards social events. Previous analyses of emojis reported that people use positive emojis more frequently than negative emojis. However, psychological research reveals a negativity bias in sentiment, as seen in the phenomenon of loss-aversion, where negative sentiment due to a loss possesses a greater psychological valence than positive sentiment due to a gain of an equal amount. We propose that the frequency and intensity of emojis are dissociable. Whereas the frequency of emojis reflects social norms in public communication, the intensity reflects hedonic values and loss-aversion. We first developed a text-free emoji sentiment lexicon based on a survey with more than 900 users of Weibo (a Chinese version of Twitter). Using the sentiment lexicon, we then analyzed 8822 Weibo comments containing the indexed emojis in reaction to three controversial events (i.e., a murder case in which public opinion largely opposed the final verdict, a manslaughter case in which public opinion was supportive of the final verdict, and a public debate on an award-winning subject). The results showed that positive or negative emoji frequency was consistent with the majority sentiment (social norm) towards a controversial event. In contrast, the average intensity of negative emojis was stronger than positive emojis across all three samples, revealing a public sentiment version of loss-aversion. In all three samples, emoji polarity analysis served as a proxy for text sentiment analysis to capture public attitudes towards a social event.
Predict Individuals’ Behaviors from Their Social Media Accounts, Different Approaches: A Survey

Predicting individual behavior has been among the key objectives in the social sciences, helping derive important insights, such as the individuals to target with specific marketing material. For many years, rudiments behavioral, geographic, and demographic methods were used for prediction. However, different researchers have developed advanced, computerized methods to quantify emotional and sentimental intensity from social media posts. Therefore, the research employed a literature review methodology to determine the main approaches proposed in the last five years. IEEE Xplore was used to select reliable research articles about different prediction methods. Four techniques were identified: the Lexicon approach, the Louvain algorithm, Naïve Bayes classification, and MCDM. Based on collected information, the Lexicon approach can be used to arrange data into neutral, negative, and positive labels that depict prevailing sentiments. It can also be combined with Multi-Criteria Decision Making to detect emotions. Conversely, the Louvain method is a clustering algorithm that can be employed for topic modeling, the process of extracting a group of words from a set of documents that best represent the contained information. The Naïve Bayes approach can also predict personality and emotions from typical social media posts. The best results are attained when the method is combined with statistical tests.
Recent Advances in Algorithmic Biases and Fairness in Financial Services: A Survey

Artificial intelligence capabilities and machine learning algorithms have been widely used in different applications including financial services. Many financial services such as loan or credit limit approval and credit score estimation rely on automated algorithms to offer efficient and best possible services to customers. However, algorithms suffer from intentional and unintentional biases and produce unfair outcomes. This paper presents a survey of algorithmic biases and fairness in financial services. We study the sources of bias and the different instances of bias existing in the prominent areas of the financial industry. We also discuss on the detection and mitigation techniques that have been proposed, developed and used to enhance transparency and accountability.
Orfeon: An AIOps framework for the goal-driven operationalization of distributed analytical pipelines

The use of Artificial Intelligence solutions keeps raising in the business domain. However, this adoption has not brought the expected results to companies so far. There are several reasons that make Artificial Intelligence solutions particularly complicated to adopt by businesses, such as the knowledge gap between the data science and operations teams. In this paper, we tackle the operationalization of distributed analytical pipelines in heterogeneous production environments, which span across different computational layers. In particular, we present a system called Orfeon, which can leverage different objectives and yields an optimized deployment for these pipelines. In addition, we offer the mathematical formulation of the problem alongside the objectives in hand (i.e. resilience, performance, and cost). Next, we propose a scenario utilizing cloud and edge infrastructural devices, in which we demonstrate how the system can optimize these objectives, without incurring scalability issues in terms of time nor memory. Finally, we compare the usefulness of Orfeon with a variety of tools in the field of machine learning operationalization and conclude that it is able to outperform these tools under the analyzed criteria, making it an appropriate system for the operationalization of machine learning pipelines.
Reliable Integrity Preservation Analysis of Video Contents with Support of Blockchain Systems

In this article, we propose an integrity preservation analysis scheme of video contents working on the blockchain systems. The proposed scheme stores the core points of video contents analysis in the blockchain system permanently so that any user can easily verify the results of the proposed analysis procedure and their reliability. The scheme first examines the codec software characteristics of digital camera devices and video editing tools in advance, and stores them in the blockchain systems. Next, the scheme extracts the codec software characteristic from the target video file and compares it with the prepared characteristics in the blockchain system. With a matched characteristic, the scheme finds out the source camera device or the source video editing tool that generates the target video file. We also propose an integrity preservation trace scheme to record the transformation history of video contents into the blockchain systems. This scheme compares the original video and its transformed video frame by frame, and stores the comparison result with a hash value of the transformed video in the blockchain system. Then, the integrity analysis and transformation history of the target file can be easily searched in the blockchain system, where the hash value of the video file is used as the index of searching operation. We implement the proposed scheme into a practical tool upon a commercial blockchain system, Klaytn. Experimental results show that the proposed scheme carries out the integrity analysis of video contents with 100% accuracy, and provides the transformation history of non-original video contents with 100% accuracy when a proper parameter is given. It is also shown that the proposed scheme completes the integrity analysis within at most one second, and the search operation for transformation history within at most four seconds.
Quantum algorithms for Schrieffer-Wolff transformation

The Schrieffer-Wolff transformation aims to solve degenerate perturbation problems and give an effective Hamiltonian that describes the low-energy dynamics of the exact Hamiltonian in the low-energy subspace of unperturbed Hamiltonian. This unitary transformation decoupling the low-energy and high-energy subspaces for the transformed Hamiltonian can be realized by quantum circuits. We give a fully quantum algorithm for realizing the SW transformation. We also propose a hybrid quantum-classical algorithm for finding the effective Hamiltonian on NISQ hardware, where a general cost function is used to indicate the decoupling degree. Numerical simulations without or with noise and experiments on quantum computer ibmq_manila are implemented for a Heisenberg chain model. Our results verify the algorithm and show its validity on near-term quantum computers.
Stellarium Software as a Means of Development of Students' Research Competence While Studying Physics and Astronomy

The article analyzes the concept of "competence", "research competence", outlines that the components of research competence of students are: knowledge, abilities, skills, abilities and experience of research activities. Based on the analysis of scientific and methodological literature, it has been determined that research competence is the ability of participants in the educational process to perform tasks involving modeling and research of physical and astronomical phenomena and processes using specialized software environment. The study focuses on the possibilities of information and communication technologies (ICT) for the formation and development of research competence of students in the study of physics. The authors analyze the possibilities of the Stellarium software environment and present an algorithm for using a virtual planetarium to develop research competence in the study of astronomy. For educational purposes, Stellarium is used to form ideas about the apparent annual motion of the Sun and the motions of the planets; to determine distances to celestial objects; as an interactive multimedia for understanding and motivating students in the study of astronomy, etc. Given that one of the important tasks of modern astronomy is the study of exoplanets, the authors describe the method of studying the planetary system TOI 700. The proposed algorithm for the study of exoplanets can be used by subjects of educational activities as didactic material for creative and individual educational and research tasks.
An Introduction to Detection of Hate Speech and Offensive Language in Slovak

The paper introduces a very current topic in the field of natural language processing oriented to the automatic detection of hate speech and offensive language performed in the Slovak language. In this work, we describe the creation and processing database of short texts composed of posts and comments written in Slovak and published on social media. The proposed approach is based on sentiment analysis and implementing a tool for detecting hate speech using a convolutional neural network with elements of a recursive neural network, applied to a created database of comments. We achieved 61.32% detection accuracy only on a small set of training data balanced in the number of positive, neutral, and negative sentiments.
CMOS Simulation of Mixed-Polarity Generalized Fredkin Gates

Processors have become increasingly overloaded with transistors in the last years. It causes heat dissipation and, as a result, error occurrence. One of the ways to solve those problems is to use reversible logic instead of the classical one. Therefore, reversible gates implemented in CMOS are valuable assets in electronic circuitry. The paper raises an approach for simulating generalized mixed-polarity Fredkin gate on CMOS transistors. Estimation formulas of complexity, delay, and power consumption were obtained for the circuits. A simulation of the proposed n-bit Fredkin gate with mixed control in Multisim 14.2 confirmed its functional correctness.
Software Solution for Energy Smart Analyzer with IoT Module

Software and hardware solution with the usage of IoT module for efficient and optimized forecasting of energy consumption in the production of linoleum is described in this paper. New software and hardware architecture was proposed to automate the collection of electricity consumption data and do an analysis with the ability to visualize and detail certain indicators for minimizing the costs of final products.
Libra: A Stateful Layer-4 Load Balancer with Fair Load Distribution

Layer-4 (L4) load balancers (LBs) are essential for data centers, dispatching incoming connections among thousands of servers. There are two critical requirements for L4 LBs: i) load balancing fairness, i.e., the ability to assign the load to servers in proportion to their capacity; ii) per-connection consistency (PCC), i.e., all the packets belonging to the same connection should be forwarded to the same server. Howbeit, existing LBs at large sacrifice load balancing fairness to mitigate PCC violations, which cannot satisfy both requirements in the meantime. In this paper, we present Libra, a stateful L4 LB that supports fair load distribution, PCC, memory efficiency, and resilience to resource depletion attacks. Libra makes load balancing decisions resorting to the proposed Weighted M-Least-Connection First (WMLCF) mechanism considering the real-time load and available processing capacity of servers, hence enabling load balancing fairness. We prototype Libra in a programmable software switch—BMv2 using P4 language and conduct extensive flow-level simulation to evaluate the performance. The evaluation indicates that Libra significantly improves load balancing fairness (over 95%), fully ensures PCC, and reduces the average flow completion time by 17.27–42.55% compared to existing mechanisms.
Exploring Adversarial Attacks on Neural Networks: An Explainable Approach

Deep Learning (DL) is being applied in various domains, especially in safety-critical applications such as autonomous driving. Consequently, it is of great significance to ensure the robustness of these methods and thus counteract uncertain behaviors caused by adversarial attacks. In this paper, we use gradient heatmaps to analyze the response characteristics of the VGG-16 model when the input images are mixed with adversarial noise and statistically similar Gaussian random noise. In particular, we compare the network response layer by layer to determine where errors occurred. Several interesting findings are derived. First, compared to Gaussian random noise, intentionally generated adversarial noise causes severe behavior deviation by distracting the area of concentration in the networks. Second, in many cases, adversarial examples only need to compromise a few intermediate blocks to mislead the final decision. Third, our experiments revealed that specific blocks are more vulnerable and easier to exploit by adversarial examples. Finally, we demonstrate that the layers Block4_conv1 and Block5_ cov1 of the VGG-16 model are more susceptible to adversarial attacks. Our work could potentially provide useful insights into developing more reliable Deep Neural Network (DNN) models.
A Scalable Nested Blockchain Framework with Dynamic Node Selection Approach for IoT

A high level of scalability is needed to support the large-scale Internet-of-Things (IoT) networks. To address the issue of distributed trust in different IoT devices, blockchain technology can be effectively used to safely manage IoT data due to its ability to provide transactions traceability and security. However, massive real-time IoT application data has brought huge challenges to the scalability of the integration framework of blockchain and IoT. This paper proposes a nested-chain architecture, which consists of one main chain and multiple sub chains to address the aforementioned challenges. The main chain stores identity credential used for distributed identity (DID) management, while the sub chain stores the IoT data. A notary module that involves access nodes from both chains is designed for cross-chain transactions. In addition, considering the transaction information, node characteristics, and network status, we further introduce a node selection algorithm based on Graph Convolutional Network (GCN), which can effectively reduce the cost of cross-chain communications. We implement and evaluate a prototype of our framework on the Hyperledger Fabric platform to demonstrate its feasibility and superiority. The analyzed results have shown that our proposed framework outperforms traditional schemes, by reducing system latency up to 23.2% and increasing system throughput up to 12.5%.
Analysis of Load Balancing Algorithms Used in the Cloud Computing Environment: Advantages and Limitations

Cloud computing as an advanced technology in the IT infrastructure presents nowadays a big concern of researches. It’s no longer a matter of on-demand successful delivery of computing resources. Throughput, performance, server response time, and cost had become the metrics that enable the quality-of-service agreement. Technically, cloud service provider guarantees to deliver computing resources (storage, servers and applications) through back-end data center. It consists of several hosts distributed geographically to answer the client requests. To ensure the service level agreement between clients and providers, cloud infrastructure software need to schedule and optimally manage the workload of several demands. Here, Load balancing technology enters as a major key with a set of algorithms to handle the most effectively and fairly the allocation and scheduling of computational resources, to serve the large amount of calling jobs. This review presents a comparative and comprehensive study that covers the principal concepts of cloud computing, and the well-known algorithms used for load balancing which are classified into static and dynamic sets. The objectives of this survey are to (1) mention, explain, compare and analyze some developed methods for load balancing by systematically reviewing papers from the years 2018 to 2021, (2) analyze the level of maturity of the solutions proposed in the literature and (3) present an insight into the actual solutions which may help with future improvements.
Immernet As a Natural Environment for the Development of Immersive Technological and Vocational Training

The modern environment surrounding a young person is multidimensional and contains a significant number of attractive factors that make it difficult to focus on educational technologies. This negatively affects the effectiveness of training. Therefore, it is important to minimize such distracting phenomena in the learning process. The solution of these problems, according to the authors, lies in the development of immersive learning, which uses an artificial environment. This minimizes distracting phenomena, activates motivation through increased involvement in the learning process, and provides stimulating visualization. The article is devoted to the presentation of the results of the analysis of definitions, the role and connections of the components of immersive technological and vocational training, including the "immernet" as a natural environment for the development of immersive learning. It is shown that today the time factor is becoming relevant not only for specific individuals, but also for enterprises and for the state. The rapid pace of technological development leads to the fact that most formal training programs become obsolete almost immediately after its launch. The programs are poorly modifiable and the students studying on them do not acquire the skills of adaptability and mobility. This is due to the fact that software developers do not always take into account the interests of production or business, that is, they are not sensitive to the learning processes at the enterprise. In other words, the "traditional formal education system" does not meet the urgent needs of today's graduates. Most courses are organized not by tasks, but by topics. Therefore, many students are not able to solve rapidly changing production tasks. The content of the subjects around the topic gives an additional load, which in many cases is unjustified. In a 3D learning system, information in specific situational contexts is more important than outside of any problem or practice of action. As a result, many teachers began to identify a set of strict practical procedures in the content of formal training, allowing students to adapt to production processes at specific workplaces.