Reviewerly/exHarmony huggingface dataset:
MUQUT: Multi-Constraint Quantum Circuit Mapping on NISQ Computers: Invited Paper

Rapid advancement in the domain of quantum technologies have opened up researchers to the real possibility of experimenting with quantum circuits, and simulating small-scale quantum programs. Nevertheless, the quality of currently available qubits and environmental noise pose a challenge in smooth execution of the quantum circuits. Therefore, efficient design automation flows for mapping a given algorithm to the Noisy Intermediate Scale Quantum (NISQ) computer becomes of utmost importance. State-of-the-art quantum design automation tools are primarily focused on reducing logical depth, gate count and qubit counts with recent emphasis on topology-aware (nearest-neighbour compliance) mapping. In this work, we extend the technology mapping flows to simultaneously consider the topology and gate fidelity constraints while keeping logical depth and gate count as optimization objectives. We provide a comprehensive problem formulation and multi-tier approach towards solving it. The proposed automation flow is compatible with commercial quantum computers, such as IBM QX and Rigetti. Our simulation results over 10 quantum circuit benchmarks, show that the fidelity of the circuit can be improved up to 3.37 × with an average improvement of 1.87 ×.
Evaluation Techniques for Chatbot Usability: A Systematic Mapping Study

Background: The use of chatbots has increased considerably in recent years. These are used in different areas and by a wide variety of users. Due to this fact, it is essential to incorporate usability in their development. Aim: Our objective is to identify the state-of-the-art in chatbot usability and applied human–computer interaction techniques, to analyze how to evaluate chatbot usability. Method: We have conducted a systematic mapping study, by searching the main scientific databases. The search retrieved 170 references and 21 articles were retained as primary studies. Results: The works were categorized according to four criteria: usability techniques, usability characteristics, research methods and type of chatbots. Conclusions: Chatbot usability is still a very incipient field of research where the published studies are mainly surveys, usability tests, and rather informal experimental studies. Hence, it becomes necessary to perform more formal experiments to measure user experience, and exploit these results to provide usability-aware design guidelines.
Digital Blockchain Registration of Legally Significant Stages of Complex Good’s Export-Import Supplies by Business Entities of the EAEU and BRICS Jurisdictions

Purpose: The purpose of the chapter it to explore and disclose an algorithm for integrating into a single set of special legal measures and digital technologies, providing a conflict-free and mutually beneficial for the parties purchase and sale and subsequent supply to customers of complex goods (cars, airplanes, river, sea vessels). Design/Methodology/Approach: This algorithm is based on continuous online customer control of the selected product, starting from the stage of forming certain components for this product and its initial assembly on the factory conveyor, ending with supply to the address, specified by the terms of the deal. Based on a materialistic world view, the authors implement a systematic approach to the research of the problem. Findings: It is proved, that it is advisable to test the proposed legal digital algorithm on foreign trade deals, implemented by business entities of the EAEU and BRICS jurisdictions. Originality/Value: It is noted, that with a completely acceptable software and computer security and technical readiness to work in the new conditions, the legislation of both the national law-order and their integration associations, the EAEU and BRICS, lags far behind. The authors are confident that leading innovative progress will play the role of a positive impulse for the accelerated development and legal consolidation of a much-needed regulatory framework for expanding and intensifying export-import supplies of complex goods. Since modern economic growth is directly dependent on technological and information-communication tools for the implementation of public relations in this sphere of economic activity.
A Novel Mega-stable Chaotic Circuit

In recent years designing new multistable chaotic oscillators has been of noticeable interest.A multistable system is a double-edged sword which can have many benefits in some applications while in some other situations they can be even dangerous.In this paper, we introduce a new multistable two-dimensional oscillator.The forced version of this new oscillator can exhibit chaotic solutions which makes it much more exciting.Also, another scarce feature of this system is the complex basins of attraction for the infinite coexisting attractors.Some initial conditions can escape the whirlpools of nearby attractors and settle down in faraway destinations.The dynamical properties of this new system are investigated by the help of equilibria analysis, bifurcation diagram, Lyapunov exponents' spectrum, and the plot of basins of attraction.The feasibility of the proposed system is also verified through circuit implementation.
C-MHAD: Continuous Multimodal Human Action Dataset of Simultaneous Video and Inertial Sensing

Existing public domain multi-modal datasets for human action recognition only include actions of interest that have already been segmented from action streams. These datasets cannot be used to study a more realistic action recognition scenario where actions of interest occur randomly and continuously among actions of non-interest or no actions. It is more challenging to recognize actions of interest in continuous action streams since the starts and ends of these actions are not known and need to be determined in an on-the-fly manner. Furthermore, there exists no public domain multi-modal dataset in which video and inertial data are captured simultaneously for continuous action streams. The main objective of this paper is to describe a dataset that is collected and made publicly available, named Continuous Multimodal Human Action Dataset (C-MHAD), in which video and inertial data stream are captured simultaneously in a continuous way. This dataset is then used in an example recognition technique and the results obtained indicate that the fusion of these two sensing modalities increases the F1 scores compared to using each sensing modality individually.
Probing Linguistic Features of Sentence-Level Representations in Relation Extraction

Despite the recent progress, little is known about the features captured by state-of-the-art neural relation extraction (RE) models.Common methods encode the source sentence, conditioned on the entity mentions, before classifying the relation.However, the complexity of the task makes it difficult to understand how encoder architecture and supporting linguistic knowledge affect the features learned by the encoder.We introduce 14 probing tasks targeting linguistic properties relevant to RE, and we use them to study representations learned by more than 40 different encoder architecture and linguistic feature combinations trained on two datasets, TACRED and SemEval 2010 Task 8. We find that the bias induced by the architecture and the inclusion of linguistic features are clearly expressed in the probing task performance.For example, adding contextualized word representations greatly increases performance on probing tasks with a focus on named entity and part-of-speech information, and yields better results in RE.In contrast, entity masking improves RE, but considerably lowers performance on entity type related probing tasks.
PSDS–Proficient Security Over Distributed Storage: A Method for Data Transmission in Cloud

Cloud Computing facilitates business by storing an enormous amount of data in the cloud transmitted over the Internet with seamless access to the data and no hardware compatibility limitations. However, data during transmission is vulnerable to man in middle, known plain text, chosen cipher text, related key and pollution attack.Therefore,uploading data on a single cloud may increase the risk of damage to the confidential data. Existing literature study uncovered multiple cryptography techniques such as SA-EDS, Reliable Framework for Data Administration (RFDA), Encryption and Splitting Technique (EST) to secure data storage over multi-cloud.However, existing methods are vulnerable to numerous attacks. This article emphasis on data security issues over multi cloud and proposes a Proficient Security over Distributed Storage (PSDS) method.PSDS divides the data is into two categories; normal and sensitive, further more the sensitive data is further divided into two parts. Each part is encrypted and distributed over multi-cloud whereas the normal data is uploaded on a single cloud in encrypted form. At the decryption stage, sensitive data is merged from multi-cloud. The PSDS is tested against multiple attacks and it has been concluded that it is resistant to related key attack, pollution attack, chosen ciphertext attack, and known plain text attack. Furthermore, PSDS has less computational time as compared to the STTN and RFD encryption method.
Fast Automatic Vehicle Detection in UAV Images Using Convolutional Neural Networks

Vehicle targets in unmanned aerial vehicle (UAV) images are generally small, so a significant amount of detailed information on targets may be lost after neural computing, which leads to the poor performances of the existing recognition algorithms. Based on convolutional neural networks that utilize the YOLOv3 algorithm, this article focuses on the development of a quick automatic vehicle detection method for UAV images. First, a vehicle dataset for target recognition is constructed. Then, a novel YOLOv3 vehicle detection framework is proposed according to the following characteristics: The vehicle targets in the UAV image are relatively small and dense. The average precision (AP) increased by 5.48%, from 92.01% to 97.49%, which still remains the rather high processing speed of the YOLO network. Finally, the proposed framework is tested using three datasets: COWC, VEDAI, and CAR. The experimental results demonstrate that our method had a better detection capability.
A Convolutional Neural Network for Gender Recognition Optimizing the Accuracy/Speed Tradeoff

Gender recognition has been among the most investigated problems in the last years; although several contributions have been proposed, gender recognition in unconstrained environments is still a challenging problem and a definitive solution has not been found yet. Furthermore, Deep Convolutional Neural Networks (DCNNs) achieve very interesting performance, but they typically require a huge amount of computational resources (CPU, GPU, RAM, storage), that are not always available in real systems, due to their cost or to specific application constraints (when the application needs to be installed directly on board of low-power smart cameras, e.g. for digital signage). In the latest years the Machine Learning community developed an interest towards optimizing the efficiency of Deep Learning solutions, in order to make them portable and widespread. In this work we propose a compact DCNN architecture for Gender Recognition from face images that achieves approximately state of the art accuracy at a highly reduced computational cost (almost five times). We also perform a sensitivity analysis in order to show how some changes in the architecture of the network can influence the tradeoff between accuracy and speed. In addition, we compare our optimized architecture with popular efficient CNNs on various common benchmark dataset, widely adopted in the scientific community, namely LFW, MIVIA-Gender, IMDB-WIKI and Adience, demonstrating the effectiveness of the proposed solution.
Planning to Explore via Self-Supervised World Models

Reinforcement learning allows solving complex tasks, however, the learning tends to be task-specific and the sample efficiency remains a challenge. We present Plan2Explore, a self-supervised reinforcement learning agent that tackles both these challenges through a new approach to self-supervised exploration and fast adaptation to new tasks, which need not be known during exploration. During exploration, unlike prior methods which retrospectively compute the novelty of observations after the agent has already reached them, our agent acts efficiently by leveraging planning to seek out expected future novelty. After exploration, the agent quickly adapts to multiple downstream tasks in a zero or a few-shot manner. We evaluate on challenging control tasks from high-dimensional image inputs. Without any training supervision or task-specific interaction, Plan2Explore outperforms prior self-supervised exploration methods, and in fact, almost matches the performances oracle which has access to rewards. Videos and code at this https URL
Cybersecurity Enhancement through Blockchain Training (CEBT) – A serious game approach

Blockchain technology is increasingly finding traction in diverse areas such as finance, supply-chain management, and cloud services because of its ability to provide robust cybersecurity inherent in its system of having decentralized data storage. The rising complexity in the architecture of popular blockchain platforms create barriers to correct adoption of the technology. It becomes imperative that pedagogical tools are inducted in the blockchain ecosystem to address this perceived or real impediments for the uptake of the technology. We propose one of the first such pedagogical tool for training in blockchain using an adversarial sandbox adaptive serious game approach for students and technology professionals. We further propose use of AI to enhance NPC interactivity based on player’s responses. We plan to evaluate this serious game on a subjective metrics that is based on a game experience questionnaire.
Apriori Algorithm for the Data Mining of Global Cyberspace Security Issues for Human Participatory Based on Association Rules

This study explored the global cyberspace security issues, with the purpose of breaking the stereotype of people’s cognition of cyberspace problems, which reflects the relationship between interdependence and association. Based on the Apriori algorithm in association rules, a total of 181 strong rules were mined from 40 target websites and 56,096 web pages were associated with global cyberspace security. Moreover, this study analyzed support, confidence, promotion, leverage, and reliability to achieve comprehensive coverage of data. A total of 15,661 sites mentioned cyberspace security-related words from the total sample of 22,493 professional websites, accounting for 69.6%, while only 735 sites mentioned cyberspace security-related words from the total sample of 33,603 non-professional sites, accounting for 2%. Due to restrictions of language, the number of samples of target professional websites and non-target websites is limited. Meanwhile, the number of selections of strong rules is not satisfactory. Nowadays, the cores of global cyberspace security issues include internet sovereignty, cyberspace security, cyber attack, cyber crime, data leakage, and data protection.
Automatic identification of the number of clusters in hierarchical clustering

Hierarchical clustering is one of the most suitable tools to discover the underlying true structure of a dataset in the case of unsupervised learning where the ground truth is unknown and classical machine learning classifiers are not suitable. In many real applications, it provides a perspective on inner data structure and is preferred to partitional methods. However, determining the resulting number of clusters in hierarchical clustering requires human expertise to deduce this from the dendrogram and this represents a major challenge in making a fully automatic system such as the ones required for decision support in Industry 4.0. This research proposes a general criterion to perform the cut of a dendrogram automatically, by comparing six original criteria based on the Calinski-Harabasz index. The performance of each criterion on 95 real-life dendrograms of different topologies is evaluated against the number of classes proposed by the experts and a winner criterion is determined. This research is framed in a bigger project to build an Intelligent Decision Support system to assess the performance of 3D printers based on sensor data in real-time, although the proposed criteria can be used in other real applications of hierarchical clustering.The methodology is applied to a real-life dataset from the 3D printers and the huge reduction in CPU time is also shown by comparing the CPU time before and after this modification of the entire clustering method. It also reduces the dependability on human-expert to provide the number of clusters by inspecting the dendrogram. Further, such a process allows applying hierarchical clustering in an automatic mode in real-life industrial applications and allows the continuous monitoring of real 3D printers in production, and helps in building an Intelligent Decision Support System to detect operational modes, anomalies, and other behavioral patterns.
GaiaWorld: A Novel Blockchain System Based on Competitive PoS Consensus Mechanism

The birth of blockchain has promoted the development of electronic currencies such as Bitcoin and Ethereum. Blockchain builds a financial system based on cryptology instead of credit, which allows parties to complete the tran... | Find, read and cite all the research you need on Tech Science Press
Information Security and Cybersecurity Management: A Case Study with SMEs in Portugal

Information security plays a key role in enterprises management, as it deals with the confidentiality, privacy, integrity, and availability of one of their most valuable resources: data and information. Small and Medium-sized enterprises (SME) are seen as a blind spot in information security and cybersecurity management, which is mainly due to their size, regional and familiar scope, and financial resources. This paper presents an information security and cybersecurity management project, in which a methodology based on the well-known ISO-27001:2013 standard was designed and implemented in fifty SMEs that were located in the center region of Portugal. The project was conducted by a business association located at the center of Portugal and mainly participated by SMEs. The Polytechnic of Leiria and an IT auditing/consulting team were the other two entities that participated on the project. The characterisation of the participating enterprises, the ISO-27001:2013 based methodology developed and implemented in SMEs, as well as the results obtained in this case study, are depicted and analysed in the paper. The attained results show a clear benefit to the audited and intervened SMEs, being mainly attested by the increasing of their information security management robustness and collaborators’ cyberawareness.
Flipped Classroom and Aronson´s Puzzle: a Combined Evaluation in the Master´s Degree in Preuniversitary Teaching

This article presents a double evaluation carried out in the subject Complements for disciplinary training II: Computing, corresponding to the Master's degree teacher training in secondary education, baccalaureate, vocational training and languages taught by the Universidad Rey Juan Carlos. The students of the subject had to learn how to prepare simple web pages, using HTML, CSS and JavaScript programming languages. To this end, the flipped classroom technique was used to present the necessary contents, combined with the adaptation of Aronson's cooperative learning puzzle technique, used to carry out a group practice that reflected the knowledge acquired. It is worth mentioning, as a complement to the two techniques used, the use of an adapted assessment rubric, which was provided to the students at the beginning of the teaching block. The evaluation was carried out during two consecutive academic years, 2018/2019 and 2019/2020. There were important differences between the two studies: in the first study, the students' previous self-assigned level was much higher (2.8 points as opposed to 1.4 points on a scale of 1 to 5). The other difference, even more relevant, was that in the second year all teaching was done at home, in a non-attendance format, on a mandatory basis, due to the period of confinement decreed by the state of alarm at that moment, because of the pandemic caused by the SARS-CoV-2 virus, popularly known as coronavirus. At the end of the experience, the students expressed their satisfaction with the learning acquired and with the tasks performed, in both cases. The techniques used were well-appreciated, in the first year more than in the second, and especially flipped classroom. The scores obtained were, in addition, always very relevant.
Human Action Recognition Based on Transfer Learning Approach

Human action recognition techniques have gained significant attention among next-generation technologies due to their specific features and high capability to inspect video sequences to understand human actions. As a result, many fields have benefited from human action recognition techniques. Deep learning techniques played a primary role in many approaches to human action recognition. The new era of learning is spreading by transfer learning. Accordingly, this study's main objective is to propose a framework with three main phases for human action recognition. The phases are pre-training, preprocessing, and recognition. This framework presents a set of novel techniques that are three-fold as follows, (i) in the pre-training phase, a standard convolutional neural network is trained on a generic dataset to adjust weights; (ii) to perform the recognition process, this pre-trained model is then applied to the target dataset; and (iii) the recognition phase exploits convolutional neural network and long short-term memory to apply five different architectures. Three architectures are stand-alone and single-stream, while the other two are combinations between the first three in two-stream style. Experimental results show that the first three architectures recorded accuracies of 83.24%, 90.72%, and 90.85%, respectively. The last two architectures achieved accuracies of 93.48% and 94.87%, respectively. Moreover, The recorded results outperform other state-of-the-art models in the same field.
Noisy-Labeled NER with Confidence Estimation

Recent studies in deep learning have shown significant progress in named entity recognition (NER).Most existing works assume clean data annotation, yet a fundamental challenge in real-world scenarios is the large amount of noise from a variety of sources (e.g., pseudo, weak, or distant annotations).This work studies NER under a noisy labeled setting with calibrated confidence estimation.Based on empirical observations of different training dynamics of noisy and clean labels, we propose strategies for estimating confidence scores based on local and global independence assumptions.We partially marginalize out labels of low confidence with a CRF model.We further propose a calibration method for confidence scores based on the structure of entity labels.We integrate our approach into a self-training framework for boosting performance.Experiments in general noisy settings with four languages and distantly labeled settings demonstrate the effectiveness of our method 1 .