Reviewerly/exHarmony huggingface dataset:
Digital interaction literacy model – Conceptualizing competencies for literate interactions with voice-based AI systems

AI-based systems, and especially voice-based AI systems, are gaining popularity and set novel requirements for end users. Research has provided first approaches postulating and conceptualizing AI-related user competencies. However, voice-based technology utilizing spoken language for operation has been neglected so far. This study presents qualitative interviews of 12 experts in research, development, or design of voice-based AI and explores the skills and competencies which are needed to interact with VBAI in a self-determined and mindful manner. Analyses highlight that, contrary to existing assumptions and providers' advertising promises, not only language skills but a wider variety of competencies is required. As a result, ten skills and competencies are derived and integrated into the model of "Digital Interaction Literacy - DIL". The current paper contributes to a holistic model of AI Literacy using the example of voice-based AI technology. Wider implications for practice and science are discussed.
Forecasting Ethereum Price by Tuned Long Short-Term Memory Model

Cryptocurrencies have established a firm position in the economic world in the past decade, with thousands of distinctive currencies available for electronic payments. The majority of cryptocurrencies, however, experience extremely volatile price perturbations, drastically affecting investors and traders. To address this problem, this paper proposes long short-term memory approach tuned by salp swarm metaheuristics. This hybrid model has been validated on a benchmark financial dataset, and the outcomes have been compared to other cutting-edge methods. The results suggest that the proposed method outperformed the competitors, showing significant potential in time-series prediction tasks.
Comparative Analysis Of Security And Privacy Technique For Federated Learning In IOT Based Devices

The field of machine learning has been seen as a major development in the last few years. Many new algorithms and many new methods have been put forward by various researchers in this domain. Before the COVID-19 pandemic, things were done manually but after this situation, the culture of working from home has been started at almost every organization except few a necessary government organizations which include healthcare and other emergency services. Online work involves a lot of data transfer and hence it is demanded new development in machine learning and this learning emerged as one such development. Federated learning enables multiple devices to build a common machine learning model without sharing data which helps in providing better data privacy because training data are not transmitted to a central server. Federated learning is also known as collective learning where we train the algorithms across various devices with the help of decentralized data samples without the involvement of actual data. In this paper, the authors will provide various use cases, as well as a comparative study of various federated learning frameworks. This paper will provide in-depth knowledge as well as future research directions in the field of federated learning.
State of the Art of Zero-Knowledge Proofs in Blockchain

Blockchain technology promises a decentralized ecosystem for building apps with decentralized ownership and high security level. One of the most adopted blockchain ecosystems is based around Ethereum blockchain. Ethereum currently has a huge scaling issue, making its use very expensive. Different scaling solutions are proposed based on Optimistic rollups but they have a very long finality time. Zero-Knowledge (ZK) rollup (based on ZK Proof) is another way and offers high security with very fast final resolution. Problem with ZK rollup is that it is very computationally expensive, but some solutions have significant performance improvements. In this paper we presented different solutions that improve performance of ZK rollup.
Speculative Load Forwarding Attack on Modern Processors

Modern processors deliver high performance by utilizing advanced features such as out-of-order execution, branch prediction, speculative execution, and sophisticated buffer management. Unfortunately, these techniques have introduced diverse vulnerabilities including Spectre, Meltdown, and microarchitectural data sampling (MDS). Although Spectre and Meltdown can leak data via memory side channels, MDS has shown to leak data from the CPU internal buffers in Intel architectures. AMD has reported that its processors are not vulnerable to MDS/Meltdown type attacks. In this paper, we present a Meltdown/MDS type of attack to leak data from the load queue in AMD Zen family architectures. To the best of our knowledge, our approach is the first attempt in developing an attack on AMD architectures using speculative load forwarding to leak data through the load queue. Experimental evaluation demonstrates that our proposed attack is successful on multiple machines with AMD processors. We also explore a lightweight mitigation to defend against speculative load forwarding attack on modern processors.
An IoT-Based Framework and Ensemble Optimized Deep Maxout Network Model for Breast Cancer Classification

Internet of Things (IoT) plays an essential role in the area of the healthcare system. IoT devices provide information about patients in the healthcare monitoring framework. Moreover, patients can examine their health with smart devices and hence IoT is a major factor in all aspects of the health care management system. Breast cancer is a deadly cancer in women and the detection of this disease at the primary stage increases the survival rate. Due to the computational complexity associated with acquiring features, classification results generated from the existing methods are unsatisfactory and hence it is important to design a method using deep learning concepts for classifying cancer disease. An efficient and robust classification model named Student Psychology Whale Optimization-based Deep maxout network with optimization (SPWO-based Deep maxout network) classifies breast cancer disease. The advantage of using a Deep maxout network is that it effectively learns intrinsic features from the data. The weight factor of the deep learning model is updated with respect to iteration based on the fitness measure that in turn results in higher results by acquiring a minimal error value. However, the proposed model obtains outstanding accuracy, sensitivity, and specificity in terms of testing with the values of 0.931, 0.953, and 0.915 with 100 nodes.
Human object interaction detection: Design and survey

Human-Object Interaction (HOI) detection is the process of estimating the interaction between a human and an object in an image. The first attempts at HOI involved two-stage detection methods, then improved to one-stage, and finally end-to-end methods in the last year with the development of transformers. The objective of this paper is to first provide a guideline to researchers seeking to design a human object interaction detection model in addition to describing the benchmark datasets and evaluation metrics used in this task. Second, it presents a survey about the different existing methods for HOI detection and critically analyzes their design decisions through every step. Finally, we report the characteristics of future research directions and present some open issues on human-object interaction detection by presenting the limitations facing HOI detection.
PS-Mixer: A Polar-Vector and Strength-Vector Mixer Model for Multimodal Sentiment Analysis

Multimodal sentiment analysis aims to judge the sentiment of multimodal data uploaded by the Internet users on various social media platforms. On one hand, existing studies focus on the fusion mechanism of multimodal data such as text, audio and visual, but ignore the similarity of text and audio, text and visual, and the heterogeneity of audio and visual, resulting in deviation of sentiment analysis. On the other hand, multimodal data brings noise irrelevant to sentiment analysis, which affects the effectness of fusion. In this paper, we propose a Polar-Vector and Strength-Vector mixer model called PS-Mixer, which is based on MLP-Mixer, to achieve better communication between different modal data for multimodal sentiment analysis. Specifically, we design a Polar-Vector (PV) and a Strength-Vector (SV) for judging the polar and strength of sentiment separately. PV is obtained from the communication of text and visual features to decide the sentiment that is positive, negative, or neutral sentiment. SV is gained from the communication between the text and audio features to analyze the sentiment strength in the range of 0 to 3. Furthermore, we devise an MLP-Communication module (MLP-C) composed of several fully connected layers and activation functions to make the different modal features fully interact in both the horizontal and the vertical directions, which is a novel attempt to use MLP for multimodal information communication. Finally, we mix PV and SV to obtain a fusion vector to judge the sentiment state. The proposed PS-Mixer is tested on two publicly available datasets, CMU-MOSEI and CMU-MOSI, which achieves the state-of-the-art (SOTA) performance on CMU-MOSEI compared with baseline methods. The codes are available at: https://github.com/metaphysicser/PS-Mixer.
Using the nonlinear Breit-Wheeler process to test nonlinear vacuum birefringence

Quantum electrodynamics predicts that the quantum vacuum is birefringent, but due to the very small cross-section this is yet to be confirmed by experiment. Vacuum birefringence arises as the elastic part of photon-photon scattering; the inelastic part is Breit-Wheeler pair production. We outline how measurements of the photon-polarized nonlinear Breit-Wheeler process can be used to infer a measurement of all-order, nonlinear vacuum birefringence. As an example scenario, we calculate the accuracy of such a measurement for parameters anticipated at upcoming laser-particle experiments.
Quantum speed limit time: role of coherence

Abstract The minimum evolution time between multi-qubit quantum states is estimated for non-Markovian quantum channels. We consider the maximally coherent pure and mixed states as well as multi-qubit X states as initial states and discuss the impact of initial coherence and the behaviour of coherence on their speed of evolution for both dephasing and dissipative processes. The role of the non-zero value of initial coherence under information backflow conditions for the non-unital dissipative process is revealed by the flow of quantum speed limit time ( τ QSL ). The trade-off between mixedness and coherence on the speed limit time reveals the nature of the quantum process the states undergo. The complementarity effect between mixedness and coherence is more prominent in the quantum non-unital dissipation process. The parametric trajectory of speed limit time vividly depicts the difference in the evolution of pure and mixed initial states, and this could be used to distinguish between the unital and non-unital channels studied in this work. Our investigation of quantum speed limit time on multi-qubit entangled X states reveals that τ QSL can be identified as a potential dynamical witness to distinguish multi-qubit states in the course of evolution.
Blockchain in supply chain management: a multiple case study analysis on setups, contingent factors, and evolutionary patterns

Despite the hype about blockchains among supply chain management (SCM) practitioners and researchers, the technology’s adoption is still low, and confusion remains about its potential benefits for operational efficiency and effectiveness. Building on a multiple case study research, this paper clarifies current value creation opportunities enabled by the blockchain for product/material tracking and tracing. We highlight that the setup of blockchain projects depends on the presence of different drivers on customer value or efficiency and the focus towards products/components or raw materials. Based on how tracking and tracing drivers and focus influence the initial blockchain setup, contingent factors are discussed and possible evolutionary patterns are identified. These findings are elaborated in one setup matrix and three propositions. The study is one of the few to add empirical evidence to the mainly conceptual SCM blockchain literature and provides a middle-range theoretical contribution based on contingency theory. Furthermore, it offers actionable guidance for managers and policy makers about SCM blockchain adoption.
Semi-supervised cross-modal hashing via modality-specific and cross-modal graph convolutional networks

Cross-modal hashing maps heterogeneous multimedia data into Hamming space for retrieving relevant samples across modalities, which has received great research interests due to its rapid retrieval and low storage cost. In real-world applications, due to high manual annotation cost of multi-media data, we can only make use of limited number of labeled data with rich unlabeled data. In recent years, several semi-supervised cross-modal hashing (SCH) methods have been presented. However, how to fully explore and jointly utilize the modality-specific (complementarity) and modality-shared (correlation) information for retrieval has not been well studied for existing SCH works. In this paper, we propose a novel SCH approach named Modality-specific and Cross-modal Graph Convolutional Networks (MCGCN). The network architecture contains two modality-specific channels and a cross-modal channel to learn modality-specific and shared representations for each modality, respectively. Graph convolutional network (GCN) is leveraged in these three channels to explore intra-modal and inter-modal similarity, and perform semantic information propagation from labeled data to unlabeled data. Modality-specific and shared representations for each modality are fused with attention scheme. To further reduce the modality gap, a discriminative model is designed, learning to classify the modality of representations, and network training is guided by adversarial scheme. Experiments on two widely used multi-modal datasets demonstrate MCGCN outperforms state-of-the-art semi-supervised/supervised cross-modal hashing methods.
A deep learning process anomaly detection approach with representative latent features for low discriminative and insufficient abnormal data

Anomaly detection in industrial processes is vital for yield improvement and cost reduction. With the development of sensor system and information technology, industrial big data provide opportunities to detect the abnormalities of processes and raise alarms by using operating parameters. However, the slight deviations in operating parameters and the insufficient abnormal data may hinder the effectiveness of existing anomaly detection models. To cope with the above problems, a more effective process anomaly detection framework combining shallow feature fusion learning with unsupervised deep learning is constructed. Specifically, the extracted statistical features that can reflect the slight deviations of operating parameters and the original measured features are firstly concatenated to enrich the available information. Then, a combined feature selection method of SMOTE & Tomek Links and random forest is developed to further discover the abstract features closely relevant to the quality characteristics of finished products with imbalanced data. After that, an unsupervised anomaly detection method is developed, where only normal process data are available for training the stacked denoising autoencoder. The utilized autoencoder can alleviate the effect of imbalanced data as the reconstruction error would be larger when the abnormality occurs. Lastly, the anomaly discrimination criteria, which consist of the monitoring index construction and the threshold determination, are formulated to detect the state of the production process. The experimental results demonstrate that the proposed method can detect the abnormalities effectively and achieves better performance than other state-of-art anomaly detection methods in commutator spot welding of a practical motor manufacturing process.
The Impact of Physics Education Technology (PhET) Interactive Simulation-Based Learning on Motivation and Academic Achievement Among Malawian Physics Students

The study investigated the impact of PhET simulation-based learning on students' motivation and academic achievement in learning oscillations and waves among Malawian secondary students. The following research questions guided the study: (i) What were students' motivation and academic achievement levels at the beginning of the study in oscillation and waves? (ii) To what levels do PhET interactive simulation-based learning impact students' motivation and achievement in oscillations and waves? (iii) Is the change in post-test scores due to the students' characteristics in non-randomized settings or the PhET interactive simulation-based learning? A sample of 280 (44.6% females) form three secondary school students with a mean age of 17.5 (SD = 1.424) from four schools in Blantyre urban district in Malawi was used in a quasi-experimental design of non-equivalent groups. The experimental group was exposed to PhET simulation-based learning, while the conventional teaching methods were used in the control group. Pre- and post-tests were used to collect data on academic achievement, and questionnaires collected data on motivation. Independent samples t-test showed a statistical difference between the two groups on post-test of the academic achievement. Results from linear regression indicated that the differences between the two groups in the post-test were not due to students' characteristics but rather the intervention with p < 0.01. The ANCOVA test on motivation constructs showed a significant difference with a small effect size between the study groups on self-efficacy, active learning strategies, performance goals, achievement goals, learning environment stimulation, and attitudes towards learning with computer learning. The results from the study suggest that PhET simulation-based learning improved the learning of oscillations and waves. PhET simulation-based learning provides visualizations and teaching aids that help easily understand content knowledge, hence improving students' academic achievement and motivation levels.The online version contains supplementary material available at 10.1007/s10956-022-10010-3.
Navigating Robots in Dynamic Environment With Deep Reinforcement Learning

In the fight against COVID-19, many robots replace human employees in various tasks that involve a risk of infection. Among these tasks, the fundamental problem of navigating robots among crowds, named robot crowd navigation, remains open and challenging. Therefore, we propose HGAT-DRL, a heterogeneous GAT-based deep reinforcement learning algorithm. This algorithm encodes the constrained human-robot-coexisting environment in a heterogeneous graph consisting of four types of nodes. It also constructs an interactive agent-level representation for objects surrounding the robot, and incorporates the kinodynamic constraints from the non-holonomic motion model into the deep reinforcement learning (DRL) framework. Simulation results show that our proposed algorithm achieves a success rate of 92%, at least 6% higher than four baseline algorithms. Furthermore, the hardware experiment on a Fetch robot demonstrates our algorithm’s successful and convenient migration to real robots.
Knowledge representation and reuse model of civil aircraft structural maintenance cases

Maintenance cases outside the Structural Repair Manual (OSRM) often occur in civil aircraft structural maintenance (CASM) work. At this time, maintenance personnel needs to learn from historical cases to formulate new maintenance plans. Since CASM cases are diverse, empirical, and heterogeneous, we propose a knowledge representation and reuse model for CASM cases. Firstly, the CASM cases domain ontology is established using Web Ontology Language (OWL), and the case similarity index system is proposed. Then, the proposed global similarity measure integrates three local similarity measures related to different attributes. In particular, considering the engineering significance of attributes, a semantic similarity measure based on ontology structure is proposed. Next, the historical case with the highest similarity with the target case is retrieved under the condition of similarity threshold. Finally, using CASM cases from actual engineering scenes, it is verified that the model has higher retrieval accuracy than traditional methods. Our study shows that case-based decisions have essential reference significance for designing OSRM structural maintenance plans.
Twitter Sentiment Analysis Using Enhanced BERT

With the fast improvement of Internet social platforms, consumer evaluations have emerged as a critical foundation for clients to recognize merchandise and make decisions. New means of communication, such as microblogging, have evolved in the last decade, and a lot of information is provided by tweets and short messages, that are used to predict feelings of the users and their perception of what is going on around the globe. The most prominent sentiment in the tweet can be recognized by sentiment analysis techniques. Sentiment analysis is the method of extracting and recognizing the user's evaluations of products and models and has various approaches using machine learning algorithms to classify the emotion behind that text. This paper investigates the usage of Enhanced BERT models to recognize the sentiment behind the tweet. BERT or bi-directional encoder representations from transformers was designed to help computers understand the meaning of ambiguous language in text, by using the surrounding text to understand the context in which that text could have been written. For a successful evaluation using Enhanced BERT, the Kaggle SMILE dataset is considered and will be tested for emotions such as happiness, sadness, etc., and categorized as such. Experiments display that this version of the model achieves an accuracy of 0.96.
Towards General Purpose Vision Systems: An End-to-End Task-Agnostic Vision-Language Architecture

Computer vision systems today are primarily N-purpose systems, designed and trained for a predefined set of tasks. Adapting such systems to new tasks is challenging and often requires nontrivial modifications to the network architecture (e.g. adding new output heads) or training process (e.g. adding new losses). To reduce the time and expertise required to develop new applications, we would like to create general purpose vision systems that can learn and perform a range of tasks without any modification to the architecture or learning process. In this paper, we propose GPV-1, a task-agnostic vision-language architecture that can learn and perform tasks that involve receiving an image and producing text and/or bounding boxes, including classification, localization, visual question answering, captioning, and more. We also propose evaluations of generality of architecture, skill-concept <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> For this work, we define concepts, skills and tasks as follows: Concepts - nouns (e.g. car, person, dog), Skills - operations that we wish to perform on the given inputs (e.g. classification, object detection, image captioning), Tasks - predefined combinations of a set of skills performed on a set of concepts (e.g. ImageNet classification task involves the skill of image classification across 1000 concepts). transfer, and learning efficiency that may informfuture work on general purpose vision. Our experiments indicate GPV-1 is effective at multiple tasks, reuses some concept knowledge across tasks, can perform the Referring Expressions task zero-shot, and further improves upon the zero-shot performance using a few training samples.