Reviewerly/exHarmony huggingface dataset:
Facilitating artificial intelligence supply chain analytics through finance management during the pandemic crises

The particular study is based on the concept of financial management and artificial intelligence supply chain. As all the companies and business organizations are closed for a certain time, therefore, the companies are facing financial issues. Therefore, the international business companies are facing the worst time because all the transport systems are closed and the transport system is an essential part of the international companies. In this situation, the companies have to adopt some efficient and effective strategies for the development of the financial condition of the companies. Thereafter, supply chain management is an important aspect of international companies because the companies provide their services and products to their international customers by using supply chain management. As the transports are closed the companies can use the artificial intelligence supply chain management. The usage of artificial intelligence is increasing day by day in several companies. On the other hand, the usage of artificial intelligence in supply chain management can improve the process of supply of products. Thus, the purpose of this particular research article is to analyze the importance of artificial intelligence in supply chain management during the pandemic situation. Apart from that, analyzing the analytics of artificial intelligence supply chain management through the financial management of the companies is another purpose of this particular research study. Hence, the researcher has adopted proper methods and techniques for collecting some proper and accurate data about the research topic. Therefore, the researcher has used secondary methods for collecting more knowledge and data about supply chain management and finance management. Apart from that, the researcher has used qualitative methods for analyzing all the collected data in this research study in a proper way. Therefore, all the data that are collected by using the sources of secondary methods are analyzed in this particular research study significantly.
iFitness: A Deep Learning-Based Physical Fitness Motion Detection System for Elderly People

This paper proposes a deep learning-based physical fitness motion system, named iFitness, which consists of a camera, an AI edge module, and a fitness management platform. iFitness uses a camera to capture images of the elderly when they perform physical fitness exercises. Experimental results demonstrate that iFitness can effectively recognize the fitness exercise status for elderly.
A unified deep neuro-fuzzy approach for COVID-19 twitter sentiment classification

Covid-19 braces serious mental health crisis across the world. Since a vast majority of the population exploit social media platforms such as twitter to exchange information, rapid collecting and analyzing social media data to understand personal well-being and subsequently adopting adequate measures could avoid severe socio-economic damage. Sentiment analysis on twitter data is very useful to understand and identify the mental health issues. In this research, we proposed a unified deep neuro-fuzzy approach for Covid-19 twitter sentiment classification. Fuzzy logic has been a very powerful tool for twitter data analysis where approximate semantic and syntactic analysis is more relevant because correcting spelling and grammar in tweets are merely obnoxious. We conducted the experiment on three challenging COVID-19 twitter sentiment datasets. Experimental results demonstrate that fuzzy Sugeno integral based ensembled classifiers succeed over individual base classifiers.
Deep Radio Fingerprint ResNet for Reliable Lightweight Device Identification

Nowadays, a large number of intelligent devices and smart sensors are being connected by various device identification and/or authentication protocols to satisfy various requirements of 5G services. However, how to identify devices by hardware-level radio frequency (RF) fingerprints of real mobile phones has been rarely researched. In this paper, we propose a novel deep learning (DL) based RF fingerprinting ResNet (RFFResNet) to identify different real mobile phones precisely by employing RF fingerprints hidden in wireless signals. Specifically, we quantitatively show how identification accuracy is influenced by channel conditions, noises, the scale of training data and network parameters. We also evaluate the proposed RFFResNet by using a dataset of 220GB long term evolution (LTE) simulation raw time data and a dataset of 25GB real mobile phone's raw time signals. Experiment results show that our RFFResNet can achieve about 95%-99% identification accuracy in real LTE application scenario and show great superiority compared with other existing DL model, such as ResNet18-1D, ResNet34-1D and VGG16-1D.
CHIMERA

Wearable technologies draw on a range of disciplines, including fashion, textiles, HCI, and engineering. Due to differences in methodology, wearables researchers can experience gaps or breakdowns in values, goals, and vocabulary when collaborating. This situation makes wearables development challenging, even more so when technologies are in the early stages of development and their technological and cultural potential is not fully understood. We propose a common ground to enhance the accessibility of wearables-related resources. The objective is to raise awareness and create a convergent space for researchers and developers to both access and share information across domains. We present CHIMERA, an online search interface that allows users to explore wearable technologies beyond their discipline. CHIMERA is powered by a Wearables Taxonomy and a database of research, tutorials, aesthetic approaches, concepts, and patents. To validate CHIMERA, we used a design task with multidisciplinary designers, an open-ended usability study with experts, and a usability survey with students of a wearables design class. Our findings suggest that CHIMERA assists users with different mindsets and skillsets to engage with information, expand and share knowledge when developing wearables. It forges common ground across divergent disciplines, encourages creativity, and affords the formation of inclusive, multidisciplinary perspectives in wearables development.
Personalization of Human-Robot Gestural Communication through Voice Interaction Grounding

In this paper we develop a gestural communication perception system for a social robot companion that is able to autonomously learn novel gestures on-the-fly. The system constantly tracks human gestural activities with a camera and evaluates the performed gestures under an open-set assumption. This allows for the identification of unknown gestures. Once detected, the system stores motion sequences of the novel gesture class and employs a dialogue interaction with the human to automatically label the unknown gesture. Subsequently, the gestural model is updated, grounding the unknown gesture through dialog interaction. In our experiment, we evaluate a neural network with varying threshold values for the open gesture recognition with unknown detection. Results show that the general classifier reaches an accuracy of more than 83%, and an f1-score of 0.79 in an open-ended scenario. The method is furthermore tested in a first in-lab interaction setting, which shows the system usability and its potential for future personalized human-robot gestural communication.
Online social transparency in enterprise information systems: a risk assessment method

Abstract Teleworking refers to the utilization of information and communication technologies for work done outside the workplace. The Covid-19 crisis led to increased utilisation of social networking tools within enterprises, especially when working remotely. The aim of their use is often to improve situational awareness, coordination, and collaboration amongst employees. Online social transparency, typically done through social networks or enterprise social software, refers to the voluntary sharing of personal and contextual information such as those relating to their own and team status, intentions, motivation, capabilities, goal priorities besides updates on the physical and social context, with other colleagues. An ad-hoc practice of social transparency can introduce risks such as information overload, social loafing and peer pressure. Despite recognising its adverse effects, there is a lack of systematic methods that identify and assess the risks of online social transparency. In this paper, we present a method to identify and evaluate these within enterprises. We present the method’s workflow, stakeholders, the novel artefacts and techniques devised to use and the outcomes to produce. We evaluate our proposed method by applying it in a real organisational context and assess applicability, efficiency, and effectiveness in identifying risks and supporting managers in risk assessment. The results showed that the method gives a framework of thinking and analysis and helps recognize and identify risks in a specialized manner.
Higher-rate relativistic quantum key distribution

One of the major problems in the field of quantum key distribution (QKD) is the low key rates at which the systems operate. The reasons for this are the processes used to ensure the key distribution itself: sifting, parameter estimation, key reconciliation, and privacy amplification. So, this reduction in the rate of communication is inherent to all existing quantum key distribution schemes. This paper is concerned with proposing a solution to mitigate the rate reduction of the so-called relativistic QKD. To mitigate the reduction, we introduce a modified relativistic QKD protocol, which is based on Mach-Zehnder interferometer being used as a probabilistic basis selection system (basis misalignment occurs between the parties in approximately half of the transferred qubits). The interferometric scheme allows the participating parties to correlate the mutual unbiased bases (MUBs) chosen by them. In this regard, a qubit could be used to transfer more than one bit of information. To be precise, by implementing the proposed interferometric scheme into a relativistic QKD protocol, a qubit is able to transfer two bits of information. This results in achieving a protocol, which is characterized with a greater rate of communication, two times greater than the usual rate. The modified protocol is proven to be secure against intercept-resend and collective attacks.
How to measure gender bias in machine translation: Real-world oriented machine translators, multiple reference points

In this paper, as a case study, we present a systematic study of gender bias in machine translation with Google Translate. We translated sentences containing names of occupations from Hungarian, a language with gender-neutral pronouns, into English. Our aim was to present a fair measure for bias by comparing the translations to an optimal non-biased translator. When assessing bias, we used the following reference points: (1) the distribution of men and women among occupations in both the source and the target language countries, as well as (2) the results of a Hungarian survey that examined if certain jobs are generally perceived as feminine or masculine. We also studied how expanding sentences with adjectives referring to occupations effect the gender of the translated pronouns. As a result, we found bias against both genders, but biased results against women are much more frequent. Translations are closer to our perception of occupations than to objective occupational statistics. Finally, occupations have a greater effect on translation than adjectives.
Bringing Blockchain Technology in Innovating Industries: A Systematic Review

The mainstream digital technology uses significantly changes how industry protect their records due to new security and vulnerability challenges. Leveraging cryptography, blockchain technology enables large agile value chains, transaction transparency, and flexible integration with many digital technologies like Internet-of-Things and cloud computing. The capabilities of blockchain technology further empower secure transferable wide-range collaboration between industry allies and public sectors. Recognized by its potentials in driving innovation in industries, blockchain is expected to play significant roles in industrial transformation. This paper reviews the significant developments of blockchain applications in information systems of many industries. The review aims to provide insights on the advances of blockchain industrial applications including use cases, classifications, and emerging trends. We conducted a systematic review on published blockchain works from high-quality sources using formulated review questions, selection criteria, and processes. Summarizations of blockchain applications across many industries are presented including use cases, objectives, and application examples. Moreover, we classified and characterized the innovative features of presented blockchain applications based on the application’s distinct characteristics. Subsequently, summarization of emerging trends in blockchain applications based on recent industry use cases and implemented features are discussed. This paper highlights the consequential advances of blockchain in industries that eventually lead to research opportunities for future undertaking.
Mirror Mirror On The Wall - What Are Cybersecurity Educational Games Offering Overall: A Research Study and Gap Analysis

Cybersecurity educational games are capable of meeting a variety of goals, including learning of fundamental cybersecurity concepts, exposure to cybersecurity literacy, basic awareness, experiential learning plus situational awareness, and being a medium for K-12 outreach, college level learning plus corporate training. Existing literature shows that there have been several prior research surveys focused on analyzing cybersecurity educational games from various viewpoints. However, to our knowledge, there has been no previous work that has analyzed cybersecurity educational games in terms of their alignment with the current benchmarks in academic and industry standards that include the cybersecurity curriculum plus assessment guidelines in higher education, the K-12 standardized cybersecurity concepts, and the cybersecurity job-related functions. In an effort to address this research gap, we use the CSEC2017 curricular guidelines, the Cybersecurity Assessment Tools (CATS) model, the National Security Agency (NSA) GenCyber concepts and the National Initiative for Cybersecurity Education (NICE) framework for performing a unique analysis of a list of popular cybersecurity educational games, plus a few Capture the Flag (CTF) instances. The list of cyber educational games, which we study, comprises of Anti-Phishing Phil, Cyber Awareness Challenge, Cyber CIEGE, Cyber Protect, Nsteens, NOVA Labs, What.Hack, PASDJO, CyberStart Go, OnGuardOnline, Safe Online Surfing, and Interland. This non-traditional survey-based research work demonstrates a novel, multi-faceted approach for analyzing popular opensource cybersecurity educational games in terms of their alignment with the standard academic and industry benchmarks. Our survey results include conceptual mapping of these cyber educational games to (1) the CSEC2017 curricular knowledge areas (Data Security, Software Security, Component Security, Connection Security, System Security, Human Security, Organizational Security and Societal Security), (2) the CATS concept inventory topics, including the Cybersecurity Concept Inventory (CCI), which pertains to basic first year college concepts, and the Cybersecurity Curriculum Assessment (CCA), which contains core concepts and learning outcomes for a college graduate, (3) the NSA GenCyber concepts (Defense in Depth, Confidentiality, Integrity, Availability, Think Like an Adversary and Keep It Simple), and (4) NICE framework-based specialized skill sets (Analyze, Collect & Operate, Investigate, Operate & Maintain, Oversee & Govern, Protect & Defend and Securely Provision). Our research provides a first-of its kind study and a user-friendly analysis of open-source cybersecurity educational games that can serve as an insightful reference for cybersecurity educators and other audiences for using these games. Additionally, in this full research paper, we exhibit how the results of our study can be used for performing an overall gap analysis with cybersecurity educational games in terms which benchmarks they cover and which they do not.
Multi-Scale Aggregation with Self-Attention Network for Modeling Electrical Motor Dynamics

Modeling induction motor dynamics is a crucial problem in the industry. The previous works mainly model the dynamics based on the physical model assumption and state equation. However, due to the complex internal structure of motors, the traditional methods cannot estimate dynamics precisely. To address this issue, we adopt a deep learning-based approach that takes the time-series motor data measured from the sensor to estimate the dynamics without making any assumptions about the motor’s interior. In this paper, we propose a multi-scale feature aggregation with self-attention network (MASNet) to deal with modeling motor dynamics. First, our model extracts multi-scale features from motor signals by various convolutional kernel sizes. Then, the proposed adaptive feature aggregation module fuses different receptive signals effectively. To further refine these high-level motor features, two-stream components, containing Bidirectional LSTM and self-attention module, are applied to improve motor contextual information. Moreover, we present a novel temporal relative loss to enhance consecutive signal consistency, which improves the performance of modeling dynamics. To deploy the service in the real-world scenario, our network is very lightweight and reduces the number of parameters from 0.62M to 0.09M (around 85% reduction) but outperforms state-of-the-art algorithms by extensive experiments on simulation and real-world motor datasets.
Ontology Versioning Driven by Instance Evolution in the τOWL Framework

Like other components of Semantic Web-based applications, ontologies are evolving over time to reflect changes in the real world. Several of these applications require keeping a full-fledged history of ontology changes so that both ontology instance versions and their corresponding ontology schema versions are maintained. Updates to an ontology instance could be non-conservative that is leading to a new ontology instance version no longer conforming to the current ontology schema version. If, for some reasons, a non-conservative update has to be executed, in spite of its consequence, it requires the production of a new ontology schema version to which the new ontology instance version is conformant so that the new ontology version produced by the update is globally consistent. In this paper, we first propose an approach that supports ontology schema changes which are triggered by non-conservative updates to ontology instances and, thus, gives rise to an ontology schema versioning driven by instance updates. Note that in an engineering perspective, such an approach can be used as an incremental ontology construction method driven by the modification of instance data, whose exact structure may not be completely known at the initial design time. After that, we apply our proposal to the already established [Formula: see text]OWL (Temporal OWL 2) framework, which allows defining and evolving temporal OWL 2 ontologies in an environment that supports temporal versioning of both ontology instances and ontology schemas, by extending it to also support the management of non-conservative updates to ontology instance versions. Last, we show the feasibility of our approach by dealing with its implementation within a new release of the [Formula: see text] OWL-Manager tool.
Analysis Water Quality of Springs on the East Slope of Mount Sumbing, Central Java, Indonesia for Sanitation Hygiene Purposes Based on the Physical and Chemical Properties

Abstract The eastern slope of Mount Sumbing has many springs used as a source of raw water for hygienic sanitation purposes. However, there has been no scientific research showing the quality of springs. Therefore the purpose of this study is to analyze the quality of springs on the eastern slopes of Mount Sumbing for community sanitation hygiene purposes based on water physical and chemical properties. Water samples were taken from several points in the study area and observed for physical parameters (TDS, smell, taste, and temperature) and chemical parameters (acidity, nitrate, fluoride, and sulfates). The results showed that from 30 samples of springs, only one spring fulfilled the Indonesia Minister of Health Regulation No. 32 in 2017. The spring located at STA 51 Kaliloro, Krajan, Kaliangkrik, Magelang Regency. There was a spring classified as deep geothermal groundwater and the others classified as shallow groundwater. Based on the acidity of water, most of the springs on the eastern slopes of Mount Sumbing could be used as hygiene sanitation by treating the water acidity and regulating the purposes of springs.
Multi-Agent Technology for Assessing the Availability of Information at the Software Development Initial Stages

The development of multi-agent technology for assessing the availability of information at the software development initial stages is an actual task, the solution of which is the purpose of this study. The paper presents the structure and functioning of multi-agent technology for assessing the availability of information at the software development initial stages, which is suitable for working with requirements specifications in modern conditions, when software requirements often change, - due to the speed and ease of specifications&#x0027; analysis. Both the developer and the customer can always check the availability of information in the updated requirements specification, without spending a lot of time and effort.
MISA: Online Defense of Trojaned Models using Misattributions

Recent studies have shown that neural networks are vulnerable to Trojan attacks, where a network is trained to respond to specially crafted trigger patterns in the inputs in specific and potentially malicious ways. This paper proposes MISA, a new online approach to detect Trojan triggers for neural networks at inference time. Our approach is based on a novel notion called misattributions, which captures the anomalous manifestation of a Trojan activation in the feature space. Given an input image and the corresponding output prediction, our algorithm first computes the model's attribution on different features. It then statistically analyzes these attributions to ascertain the presence of a Trojan trigger. Across a set of benchmarks, we show that our method can effectively detect Trojan triggers for a wide variety of trigger patterns, including several recent ones for which there are no known defenses. Our method achieves 96% AUC for detecting images that include a Trojan trigger without any assumptions on the trigger pattern.