Reviewerly/exHarmony huggingface dataset:
GuardNN

This paper proposes GuardNN, a secure DNN accelerator that provides hardware-based protection for user data and model parameters even in an untrusted environment. GuardNN shows that the architecture and protection can be customized for a specific application to provide strong confidentiality and integrity guarantees with negligible overhead. The design of the GuardNN instruction set reduces the TCB to just the accelerator and allows confidentiality protection even when the instructions from a host cannot be trusted. GuardNN minimizes the overhead of memory encryption and integrity verification by customizing the off-chip memory protection for the known memory access patterns of a DNN accelerator. GuardNN is prototyped on an FPGA, demonstrating effective confidentiality protection with ~3% performance overhead for inference.
Classification of Customer Complaints: TF-IDF Approaches

Bank Rakyat Indonesia (BRI) is an Indonesian state-owned bank; the operations of the bank certainly aim to provide satisfaction to customers. This can be realized by quickly responding to the bank's response to complaints. The number of complaints received by BRI banks to date is large, but the categories of complaints are still mixed so each division of the bank has difficulties and must first filter complaints. This study aims to classify bank complaints using the Text Mining field to make it easier for BRI to provide complaints directly to each division. The classification method used is Neural Network and the data preprocessing method uses the TF -IDF method. The dataset used in the form of text is one million datasets with 5 classes, namely debit cards, credit cards, customer service, mobile banking, and loans. The results of this study indicate that the accuracy of the neural network method is better and becomes a recommendation to be implemented at BRI. This research is useful for banking in Indonesia or the service industry in managing customer relationship management.
Mobile robot path planning based on ABC-PSO algorithm

Aiming at the problem of particle swarm optimization (PSO) algorithm converging fast, easy to fall into local optimum, poor accuracy, and artificial bee colony (ABC) algorithm global and local search ability is strong but the convergence speed of the optimization process is slow, a solution is proposed. The search process and optimization process in the artificial bee colony algorithm are added to the optimized particle swarm algorithm, namely the ABC-PSO algorithm, which has good global search capabilities and fast convergence. In order to improve the efficiency and accuracy of particle search, an adaptive inertia weight method is proposed, which uses different weight values at different stages. The final results show that the algorithm in this paper can find the optimal path quickly and efficiently.
Introducing Dialogue Systems

A dialogue system is a computer program that supports spoken, text-based, or multimodal conversational interactions with humans. Generally, a distinction is made between task-oriented and non-task-oriented dialogue systems. In task-oriented dialogues the human user and the system engage in an interaction to accomplish some task. Example 1.1 is taken from a demo in which Paul Cutsinger (PC), Head of Amazon Alexa Voice Design Education, initiates a dialogue with Alexa to plan a trip and Alexa asks him a series of questions to obtain the information required to complete the task1: PC: Alexa, open plan my trip. ALEXA: Where are you planning to go? PC: I’m going to Portland. ALEXA: What city are you leaving from? PC: Seattle. ALEXA: What date are you flying out? PC: Next Thursday. ALEXA: This will be fun. You go from Seattle to Portland on April 27th, 2017.
The Devil in the Detail: Analyzing Nobuo Uematsu’s “One- Winged Angel” from Final Fantasy VII

The author discusses the musical and cultural significance of “One Winged Angel” from Final Fantasy VII (1997). Using a variety of analytical techniques, including (but not limited to) neo-Riemannian and Schenkarian perspectives to explore the cultural influences and technological restrictions that impacted the piece. The author also explores how the piece fits into the wider narrative by examining other works from the game that feed into this overall track. The author aims to showcase a number of Uematsu’s musical influences and compositional methods through these various analytical techniques.
A Bayesian Network for the Classification of Human Motion as Observed by Distributed Radar

In this article, a statistical model of human motion as observed by a network of radar sensors is presented where knowledge on the position and heading of the target provides information on the observation conditions of each sensor node.Sequences of motions are estimated from measurements of instantaneous Doppler frequency, which captures informative micromotions exhibited by the human target.A closed-form Bayesian estimation algorithm is presented that jointly estimates the state of the target and its exhibited motion class which are described by a hidden Markov model.To correct errors in the estimated motion class distribution introduced by faulty modeling assumptions, calibration of the probability distribution and measurement likelihood is performed by isotonic regression.It is shown, by
Design and Evaluation of Passive RFID-Based Music Player Textile Prototypes

Music has always been an important way of expressing ourselves. Creating music from bodily interaction has gained much attention: Various gestures, touches, hand and foot movements are used to make music. The traditional identification and sensing technology-passive ultra-high frequency (UHF) radio frequency identification (RFID) technology-can be turned into a battery-free music player for creating music with a simple touch or gesture. Firstly, the technical feasibility of the created RFID system was evaluated by a simple on/off test. Next, this paper introduces two music player textile prototypes: The Touch Prototype can be activated by touching, while the Block Prototype can be activated by blocking, unblocking, or hovering over a specific spot on the created music player textile. Finally, the study gears towards the evaluation of the system in-use. Both developed prototypes showed a high input detection rate (95&#x2013;100%) based on preliminary testing. The Block Prototype was found to be more flexible for the user, as the user does not need to be as precise in the movement as in the Touch Prototype. However, as the Touch Prototype allows separating the RFID IC and the antenna, it provides flexibility for the placement of the music player textile, for example when used as part of clothing.
How Blockchain and Artificial Intelligence Will Effect the Cloud-Based Accounting Information Systems?

Digitalization reduces unnecessary workload by accelerating and diversifying business processes and enables it to focus on more useful areas. In this context, the transformation of accounting in the digital age aims to ensure efficiency in accounting transactions with real-time accounting information system integration. In terms of real-time accounting, it is important to benefit from the power of new technologies by connecting all current technologies to every stage of financial accounting processes and to establish a solid integrated system. The contribution of every new technology to the process is inevitable within the scope of cloud-based accounting information systems. In order to find an answer to the question of how artificial intelligence and blockchain technologies will affect cloud-based accounting systems, a detailed literature review has been conducted and discussed conceptually. In this context, the study focuses on the advantages of cloud-based accounting systems unlike traditional accounting systems, the effectiveness of blockchain and artificial intelligence technologies in accounting processes, and the synergy of blockchain and artificial intelligence. Today, accounting basic functions have been significantly integrated into artificial intelligence technology. Decentralized artificial intelligence emerging as a combination of artificial intelligence and blockchain allows the processing of reliable, digitally signed, and secure shared data that is stored on a decentralized and distributed blockchain without trusted third parties or intermediaries. The basic understanding of this decentralized, reliable, and secrecy system is based on the reliability and credibility of information. Central data storage can be highly sensitive in terms of security and privacy when it contains personal and private data about users, operations, and financial information. Artificial intelligence applications can expose the capacity and scaling issues of the centralized infrastructure that needs to process, transform, and store big data sets. Blockchain-based decentralized storage infrastructure will simplify cryptographically secure data storage across participatory networks. Thus, technology integration will offer benefits such as enhanced data security, collective decisions making, decentralized intelligence, and high efficiency. Multi-user accounting processes involving stakeholders such as business management, regulators, financial institutions, or government are inherently inefficient by reason for the multilateral authorization of business transactions. The integration of artificial intelligence and blockchain will enable automatic and rapid verification of data-asset-value transfers between different stakeholders. Thus, it is clear that the stakeholders involved in the process (financial advisors, auditors, public and fiscal authorities, shareholders, creditors) will also provide practical solutions to all their needs.
Design of Integrated Artificial Intelligence Techniques for Video Surveillance on IoT Enabled Wireless Multimedia Sensor Networks

The recent advancements in the Internet of Things (IoT) and Wireless Multimedia Sensor Networks (WMSN) made high-speed multimedia streaming, data processing, and essential analytics processes with minimal delay.Multimedia sensors used in WMSN-based surveillance applications are beneficial helpful in attaining accurate and elaborate details.However, it has become essential to design an effective and lightweight solution for data traffic management in WMSN owing to the massive quantities of data, generated by multimedia sensors.The development of Artificial Intelligence (AI) and Machine Learning (ML) techniques can be leveraged to investigate, collect, store, and process multimedia streaming data for decision-making in real-time scenarios.In this aspect, the current study develops an Integrated AI technique for Video Surveillance in IoT-enabled WMSN, called IAIVS-WMSN.The proposed IAIVS-WMSN technique aims to design a practical scheme for object detection and data transmission in WMSN.The proposed IAIVS-WMSN approach encompasses three stages: object detection, image compression, and clustering.The Mask Regional Convolutional Neural Network (Mask RCNN) technique is primarily utilized for object detection in the target region.Besides, Neighbourhood Correlation Sequence-based Image Compression (NCSIC) technique is applied to reduce data transmission.Finally, Artificial Flora Algorithm (AFA)-based clustering technique is designed for the election of Cluster Heads (CHs) and construction clusters.The design of object detection with compression and clustering techniques for WMSN shows the novelty of the work.These three processes' designs enable one to accomplish effective data transmission in IoT-enabled WMSN.The researchers conducted multiple simulations to highlight the supreme performance of the IAIVS-WMSN approach.The simulation outcomes inferred the enhanced performance of the IAIVS-WMSN algorithm to the existing approaches.
Machine learning-based approach for designing and implementing a collaborative fraud detection model through CDR and traffic analysis

Fraud in telecommunications networks is a constantly growing phenomenon that causes enormous financial losses for both the individual user and the telecommunications operators. We can denote many researchers who have proposed various approaches to provide a solution to this problem, but still need to be improve to ensure the efficiency. Detecting fraud is difficult and, it's no surprise that many frauds schemes have serious limitations. Different types of fraud may require different systems, each with different procedures, parameter adjustments, database interfaces, and case management tools and capabilities. This article uses the K-Means algorithm to handle fraud detection based on Call Detail Record (CDR) and traffic analysis in a telecommunication industry. Our algorithm consists to compare traffic and CDR generated in the network and check if there is abnormal behavior and if yes, our model is used to confirm if users suspecting of fraud are really fraudster or not. To build our model we used real word CDR data collected in November 2021. Our model associates the Differential Privacy model to encrypt users' personal information, and the k-means algorithm to group users into different clusters. Those clusters represent non fraud users having similar characteristics based on criteria used to build the model. Users having abnormal behavior that can be assimilated to fraudsters are those who are far from the different clusters center. Thanks to a representation in a plan, we better visualize user’s behavior. We validated our model by evaluating our segmentation method. The interpretation of the results shows sufficiently that our approach allows to obtain better results. Our approach can be used by all telecommunications operator to reduce the impact of fraud on internet services.
9th Workshop on Cloud Technologies in Education: Report

This is an introductory text to a collection of selected papers from the 9th Workshop on Cloud Technologies in Education (CTE 2021) which held in Kryvyi Rih, Ukraine, on the December 17, 2021. It consists of short introduction, papers' review and some observations about the event and its future.
A machine learning model for predicting, diagnosing, and mitigating health disparities in hospital readmission

The management of hyperglycemia in hospitalized patients has a significant impact on both morbidity and mortality. Therefore, it is important to predict the need for diabetic patients to be hospitalized. However, using standard machine learning approaches to make these predictions may result in health disparities caused by biases in the data related to social determinants (such as race, age, and gender). These biases must be removed early in the data collection process, before they enter the system and are reinforced by model predictions, resulting in biases in the model’s decisions. In this paper, we propose a machine learning pipeline capable of making predictions as well as detecting and mitigating biases in the data and model predictions. This pipeline analyses the clinical data and determines whether biases exist in the data, if so, it removes those biases before making predictions. We evaluate the performance of the proposed method on a clinical dataset using accuracy and fairness measures. The findings of the results show that when we mitigate biases early during the data ingestion, we get fairer predictions.
Adapting visualizations and interfaces to the user

Abstract Adaptive visualization and interfaces pervade our everyday tasks to improve interaction from the point of view of user performance and experience. This approach allows using several user inputs, whether physiological, behavioral, qualitative, or multimodal combinations, to enhance the interaction. Due to the multitude of approaches, we outline the current research trends of inputs used to adapt visualizations and user interfaces. Moreover, we discuss methodological approaches used in mixed reality, physiological computing, visual analytics, and proficiency-aware systems. With this work, we provide an overview of the current research in adaptive systems.
Research on motivational, creative, communicative and organizational components of future educators’ readiness to use innovative technologies

Introduction. The authors investigate the problem of future educators’ readiness to use innovative technologies in the preschool education system. The purpose of the article is to identify and describe the components of future educators’ readiness to use innovative technologies in the preschool education system, as well as to evaluate the development of motivational, creative, communicative and organizational components of perspective educators’ readiness to use innovative technologies in the preschool education system. Materials and Methods. The study followed the system-activity approach. Within the framework of the methodology of the system-activity approach, the following methods were used: analysis of regulatory legal documents and scholarly literature, systematization and generalization of research data, studying activities of the participants of the educational process and vocational placements, etc. Empirical data were collected using questionnaires developed by the authors and methods of psychological and educational research. Methods of mathematical statistics and visualization of obtained results were used for data processing. The research was conducted at the Northern (Arctic) Federal University named after M.V. Lomonosov in Arkhangelsk (the Russian Federation). The research sample consisted of 2nd, 3rd, 4th, and 5th year full-time and part-time undergraduate students (n = 212) pursuing degree programmes in “Preschool education and psychology”, and "Preschool and primary education" at the Higher School of Pedagogy, Psychology and Physical Education. Results. The authors clarified the concept of future educators’ readiness to use innovative technologies in the preschool education system, identified and described the leading components of their readiness to use innovative technologies. Data analysis allowed to propose methods of measuring motivational, creative, communicative and organizational components of future educators’ readiness to use innovative technologies in the preschool education system. Moreover, the authors evaluated the development of motivational, creative, communicative and organizational components of the future educators’ readiness to use innovative technologies in the preschool education system. As a part of the research, the need for purposeful formation of educators’ readiness to use innovative technologies has been proved. Conclusions. The article summarizes the conclusions about the need to develop motivational, creative, communicative and organizational components of future educators’ readiness to use innovative technologies in the preschool education system. Empirical data on the level of the studied components are summarized.
Computer Network Security Management of Data Encryption Technology

With the continuous promotion of high and new technologies such as computers and big data, humans enter a new period-information age. Mass data and information can be transmitted and exchanged online through the network. In recent years, research on image encryption based on optical information processing technology is becoming more and more favored by researchers. In this regard, this study will focus on the research of encryption algorithms in optical images and realize encryption through fractional Fourier transform and Arnold transform. This article first introduces the research background of the subject and analyzes the current situation of the subject at home and abroad and then proposes the theoretical guidance basis for the subject research, which covers the fractional Fourier transform, Arnold transform, computational hologram, and picture evaluation index system. Finally, MATLAB simulation and performance analysis are carried out for fractional Fourier transform, Arnold transform, and holographic encryption technology. The results shows that the improved algorithm in this study performs better in the encryption and decryption process.
Ultra-short-term prediction method of PV power output based on the CNN–LSTM hybrid learning model driven by EWT

This study proposes a new method for ultra-short-term prediction of photovoltaic (PV) power output using a convolutional neural network (CNN) and long short-term memory (LSTM) hybrid model driven by empirical wavelet transform (EWT) to address the intermittent and stochastic nature of PV power generation. Given the differences in the spatial and temporal distribution of features between PV sample data and meteorological conditions, a hybrid learning model for multibranch feature extraction was designed. First, the frequency band of PV output data was adaptively selected using EWT and decomposed into the amplitude modulation–frequency modulation single components with frequencies ranging from low to high. Second, data reconstruction was performed on the obtained power components to exploit the extraction ability of the two-dimensional CNN model for short-term local invariance and periodic features. Third, the combined one-dimensional CNN–LSTM model was used for the sample daily meteorological conditions to extract their spatiotemporal features, and the LSTM model was used to learn the correlation between the power data features and the predicted daily weather conditions and to obtain the corresponding component prediction results. Finally, the prediction results of each component were reconstructed to achieve the ultra-short-term prediction. Using Hangzhou Dianzi University's PV microgrid system as an example, the training and testing sets were randomly selected based on different seasons and weather. The results show that this method outperforms traditional learning models in terms of overall prediction performance. The proposed method of a hybrid deep learning model will provide a novel approach for ultra-short-term prediction of PV output.
StretchAR

Over the past decade, augmented reality (AR) developers have explored a variety of approaches to allow users to interact with the information displayed on smart glasses and head-mounted displays (HMDs). Current interaction modalities such as mid-air gestures, voice commands, or hand-held controllers provide a limited range of interactions with the virtual content. Additionally, these modalities can also be exhausting, uncomfortable, obtrusive, and socially awkward. There is a need to introduce comfortable interaction techniques for smart glasses and HMDS without the need for visual attention. This paper presents StretchAR, wearable straps that exploit touch and stretch as input modalities to interact with the virtual content displayed on smart glasses. StretchAR straps are thin, lightweight, and can be attached to existing garments to enhance users' interactions in AR. StretchAR straps can withstand strains up to 190% while remaining sensitive to touch inputs. The strap allows the effective combination of these inputs as a mode of interaction with the content displayed through AR widgets, maps, menus, social media, and Internet of Things (IoT) devices. Furthermore, we conducted a user study with 15 participants to determine the potential implications of the use of StretchAR as input modalities when placed on four different body locations (head, chest, forearm, and wrist). This study reveals that StretchAR can be used as an efficient and convenient input modality for smart glasses with a 96% accuracy. Additionally, we provide a collection of 28 interactions enabled by the simultaneous touch-stretch capabilities of StretchAR. Finally, we facilitate recommendation guidelines for the design, fabrication, placement, and possible applications of StretchAR as an interaction modality for AR content displayed on smart glasses.