Reviewerly/exHarmony huggingface dataset:
Distributed and Parallel Sparse Computing for Very Large Graph Neural Networks

Deep learning (DL) requires high-performance processing on big data. Graph Neural Networks, a challenging topic in DL using linear algebra methods, need algorithmic solutions to efficiently assign and process graph data on modern distributed and parallel machines, which are considered with mixed arithmetic and various types of tensor/matrix accelerators. Determining compression techniques for the graph’s sparse data structures is one of the key elements.Our first objective is to design and implement a reusable parallel numerical library to resolve large neural network graphs. Our design strategy is drawn on a component-based approach and targets maximum code reuse in various parallel contexts while allowing for performance optimization. The solution could be later integrated into a DL framework like MindSpore.
Deep Reinforcement Learning Based Path Planning for Mobile Robots Using Time-Sensitive Reward

In the mobile robots’ field, the global path planning task in known map scenarios is an urgent problem to be solved. Deep Reinforcement Learning (DRL), an efficient decision-making method, has been widely used to solve path-planning problems. Nonetheless, as the map size increases, the existing DRL algorithms are prone to the problem of sparse rewards. The above drawback makes the mobile robot converge slowly on the map. Even in extreme cases such as trap maps, the robot obtains the optimal convergent solution differently. For this purpose, this paper encodes the various node information in the map separately as a state description of the environment. To efficiently perform path-planning tasks for mobile robots in various complex scenarios, a time-sensitive reward function based on DRL is presented. The simulation experiments on a variety of complex environmental maps are conducted. The experimental results demonstrate the effectiveness of the proposed method. Our method ensures that the DRL algorithm is able to converges to a feasible solution quickly.
Decentralized Blockchain Based ISP Networks

This paper aims to point out the potential use, of Blockchain based decentralized computer networks in ISPs and how they promote a secure and private alternative to today’s centralized ISP networks.
Novel ABC: Aspect Based Classification of Sentiments Using Text Mining for COVID-19 Comments

Intricate text mining techniques encompass various practices like classification of text, summarization and detection of topic, extraction of concept, search and retrieval of ideal content, document clustering along with many more aspects like sentiment extraction, text conversion, natural language processing etc. These practices in turn can be used to discover some non-trivial knowledge from a pool of text-based documents. Arguments, difference in opinions and confrontations in the form of words and phrases signify the knowledge regarding an ongoing situation. Extracting sentiment from text that is gathered from online networking web-based platforms entitles the task of text mining in the field of natural language processing. This paper presents a set of steps to optimize the text mining techniques in an attempt to simplify and recognize the aspect-based sentiments behind the content obtained from social media comments.
Breast cancer detection by fusion of deep features with CNN extracted features

Breast cancer growth has become a typical factor nowadays. Physician experience of diagnosing and detecting breast cancer can be assisted by using some computerised features extraction and classification algorithms. In the recent times, breast cancer can be diagnosed by classifying tumours. In this paper, breast cancer identification and analysis is done by using machine learning statistical analysis. The proposed technique has proven to improve the exactness of foreseeing predicting cancer. The proposed method used optimised recording condition of the input image and later introduces a new interpretable feature for the identification. The simulation results are compared with conventional methods by using accuracy, sensitivity and specificity for performance assessment of the identification process.
Research on the Improvement of Teaching Ability under the Background of Teaching Ability Competition in Higher Vocational Colleges

The teaching ability competition of the national vocational college skills competition sponsored by the Ministry of Education is the most authoritative competition in which the teaching ability of teachers in vocational colleges is the competition item.The event not only comprehensively inspected the teaching level of teachers, but also paid attention to the construction of teacher teams.By continuously deepening the reform of "three educations" in vocational education, we will strive to explore an education mode combining "on-the-job courses, competition and certificates", implement the requirements of "ideological and political curriculum", and carry out online and offline mixed teaching, in order to ensure the improvement of the ability of participating teachers in teaching design concept, curriculum activity design, teaching team construction and education and teaching innovation.[1] Through the teaching ability competition, we will lead the education quality of vocational colleges, improve the teaching ability of teachers, transform the competition results into standards that can be copied and promoted, so as to reform the current classroom teaching, comprehensively improve the classroom teaching system of vocational colleges, and ensure the completion of the high-quality education goals of Vocational Colleges.
Knowledge Engineering and Data Mining

Knowledge engineering and data mining are the two biggest pillars of modern intelligent systems [...]
Quantum speed limits in arbitrary phase spaces

Quantum speed limits (QSLs) provide an upper bound for the speed of evolution of quantum states in any physical process. Based on the Stratonovich-Weyl correspondence, we derive a universal QSL bound in arbitrary phase spaces that is applicable for both continuous variable systems and finite-dimensional discrete quantum systems. This QSL bound allows the determination of speed limit bounds in specific phase spaces that are tighter than those in Wigner phase space or Hilbert space under the same metric, as illustrated by several typical examples, e.g., a single-mode free field and $N$-level quantum systems in phase spaces. This QSL bound also provides an experimentally realizable way to examine the speed limit in phase spaces relevant to applications in quantum information and quantum optics.
Improving pedagogical design in higher education: untangling the learning activity complexity through students’ declared performance

&#x0D; &#x0D; &#x0D; Decision processes to improve pedagogical designs have been commonly based on the superficial comparison between learning design and students’ results. This article focuses on proposing other ways of analysing the learning experience in higher education by exploring the (1) “participant’s declared performance” (PDP) as a collection of complex data, as well as (2) the qualitative visual network analysis (VNA), as a sustainable technique, to be used for untangling the learning activity. For this purpose, a mixed-method naturalistic case study of a pre-service teacher training course in a Spanish university, run by one professor and with the participation of 58 students, is presented. Materials collected included course design documentation regarding tasks and performance roles, students’ blog posts and satisfaction questionnaires. Findings demonstrate that the joint analysis of various elements – including PDP and VNA – shows the correspondence between learning design and student’s experiences. It also highlights prominent design aspects and weaknesses and provides insights for improving the learning design in future course editions. The reflection on PDP and VNA’s use is an opportunity to provide, feasibly and sustainably, qualitative information for educators to research their pedagogical practices.&#x0D; &#x0D; &#x0D;
Storing JSON in the Database

When you work with JSON documents, you must decide if and how you will store JSON in the database. The first decision, if, needs to be made early on. Do you need the JSON document in its original format? You can make the case to do this when you are not storing all the data in relational tables, and the JSON document might contain data that could prove valuable later.
ShakeMap-EU: an update on the shakemap service in Europe

We present the status of ShakeMap-EU, an initiative initially proposed in 2018 to: (i) provide an integrated archive of ShakeMaps at the European level built on EPOS Seismology (www.epos-eu.org/tcs/seismology) services &amp; data products and modern community software; (ii) serve as a backup to authoritative ShakeMap implementations; (iii) deliver ShakeMaps for Euro-Mediterranean regions where no local capability is yet available. ShakeMap-EU products are accessible since mid-2020 at the web portal shakemapeu.ingv.it. Jointly governed by the institutions participating in the initiative, ShakeMap-EU is founded on voluntary institutional contributions and EC-funded projects. ShakeMap-EU has become a reliable European seismological service that can easily and consistently integrate authoritative models and workflows. The system is based on:&amp;#160; (a) the latest version of ShakeMap&amp;#174; (usgs.github.io/shakemap); (b) the earthquake information delivered by the EMSC (www.emsc-csem.org); (c) the earthquake shaking data distributed by ORFEUS (orfeus-eu.org/data/strong); (d) the ground motion models adopted within EFEHR (www.efehr.org) for mapping seismic hazard across Europe; (d) the official ShakeMap configurations of some of the most hazardous countries in Europe. Configuration of, and input to the system are managed via a GitHub repository that allows automatic / manual triggering and interaction by authorized users. ShakeMap-EU provides a collaboration framework and laboratory for seismological agencies to address the challenges posed by the heterogeneity of ground shaking mapping strategies across Europe and the need to promote homogenization and best practices in this domain. ShakeMap-EU is used in research projects as the test platform for novel international collaborative research: among recent examples are the ongoing enhancements towards an evolutionary hazard information system including real-time seismicity characterisation and information on earthquake-induced phenomena.
Human Activity Identification Using Artificial Intelligence

Human activity recognition is one of the popular fields of research in the last two decades. This activity recognition topic is inspired by many useful real-world applications, such as simulation, visual surveillance and understanding human behavior. Till today, there are so many research studies going on in this field. This research has become one of the important and necessary one in the area of computer vision. Any small movement or change around us is directly or indirectly related to an activity performed by a person, and also, most of the activities are captured in the form of images and videos. So, there might be some situations where we have to identify the activity from these images and videos. Understanding human behavior from images and videos gives useful information for many computer vision problems and has many applications like scene recognition and human-computer interaction. There are many actions that a person can perform in his/her day-to-day life. Those actions can be either atomic or non-atomic. The actions like sitting, standing, laying, running and walking are some examples of atomic actions. Atomic actions are also called Action Primitives. Non-atomic actions are combinations of atomic actions. There are many systems that can recognize the activity using the smartphones. But, recognizing activity from videos or images is a challenging task due to variations in lighting, viewpoint, scale etc. In this chapter, we will develop a system that recognizes some of the atomic actions from images.
Global Distributed Fibre Optic Sensing recordings of the February 2023 Turkey earthquake sequence.

As part of a global distributed acoustic sensing (DAS) campaign, multiple DAS interrogators (from academia and industry) recorded simultaneously from 1st till 28th February 2023 in different regions of the globe. The objective is to define if and how a global monitoring system based on DAS could perform for teleseismic event record and analysis. Each participant uploaded triggered data window from earthquakes with magnitude larger than 5, as defined by global seismological networks, to a central storage location. Data was pre-processed following common filtering parameters (spatial and temporal sampling). Bottle-necks in data format, storage, and legal issues are identified and reviewed to pose the basis for a common DAS data archive strategy. In this study, we present a selection of DAS records of the Turkey earthquake sequence, from borehole, surface, on-land, submarine telecommunication or dedicated cables all over the globe. They comprise a few kilometers long railroad track (Switzerland), an 0.8 km long deployed cable in the Limmat river, near Z&amp;#252;rich (Switzerland), a 1 km deployed cable at Mt. Zugspitze in the Alps (Germany/Austria), a 21 km telecom cable in the forest around Potsdam (Germany), a 17 km telecom cable surface geothermal field (north Iceland), a 0.2 km borehole at Etna volcano (Italy), a telecom cable in the city of Istanbul (Turkey), a 25 km telecom cable in Melbourne (Australia), in the inner city line in Graz (Austria), in the city of Seattle, WA (USA), a submarine cable in the North Sea, a submarine cable connecting Ny &amp;#197;lesund and Longyearbyen at Svalbard (Norway), a 0.8 km dedicated fibre in a quick clay area in Norway, amongst many others. We show that signals from the two destructive earthquakes in Turkey were recorded all over the globe. We discuss the signal quality and their potential use to study teleseism signals. We analyze recorded strain amplitudes according to the different array geometries and the differing sensitivities to wave types (body, surface waves, possibly others) and deployment conditions. When available, comparison with other sensors located in the same place is performed. Finally, we analyze the influence of local geological conditions due to the passing large amplitudes waves. With the increasing availability, reduced cost and improved simplicity of DAS systems and the wide spread existing fibre optic networks, we believe fibre-optic sensing will play an ever-increasing role in the global seismic monitoring.
Emozionarsi in mille e più fraseologismi… Alcune riflessioni sulla desemanizzazione delle unità fraseologiche con i nomi delle parti del corpo come componente in ambito emozionale

The article aims to investigate the problem of desemanticizing of phrasemes containing names of body parts, and at the same time referring to the emotional sphere.Within the three main research areas (face, heart and body as a whole), and based on three types of semes (spatial, physical and functional), the analysis allows to determine the participation of individual sems in the process of motivating the indicated phraseological relationships.
Open Science Training in TRIPLE

<ns3:p>This case study focuses on the online training activities on Open Science delivered within the H2020 project Transforming Research through Innovative Practices for Linked Interdisciplinary Exploration (TRIPLE, Grant Agreement 863420). The project is dedicated to building a discovery platform for the Social Sciences and Humanities (SSH) and is committed to promoting and supporting the uptake of Open Science within research practices.</ns3:p><ns3:p> In order to address SSH research and training communities’ needs for enhanced competencies on Open Science and for stronger support in the Findable, Accessible, Interoperable, Reusable (FAIR) management of digital training materials, two reusable outputs were produced. The work carried out is presented as a novel approach to tackle the issues related to FAIRifying research and training practices and to create training resources whose reusability and relevance reaches beyond the project lifetime and framework. The case study presents the methods by which the results were produced so as to encourage and enable their future adaptation and reuse.</ns3:p><ns3:p> The TRIPLE Open Science training series (result 1) targets SSH researchers, research support personnel and infrastructure developers in need of practical tools and specific skills to integrate Open Science practices in their workflows. The training series provides 12 competence-oriented online training events in Open Access whose training materials are available as Open Educational Resources (OER).</ns3:p><ns3:p> The TRIPLE Training Toolkit (result 2) targets training organisers and research performing organisations who wish to design and manage training events as OERs and increase the impact of their training following good practice. The Toolkit is an easily reproducible workflow designed to help trainers minimise the time they spend in managing training events following FAIR practice. The workflow follows a FAIR-by-design method to address the frequent findability and reusability issues related to the management of digital training resources<ns3:bold>.</ns3:bold></ns3:p>
Efficient Construction of Involutory Linear Combinations of Anticommuting Pauli Generators for Large-Scale Iterative Qubit Coupled Cluster Calculations

We present an efficient method for construction of a fully anticommutative set of Pauli generators (elements of the Pauli group) from a commutative set of operators that are composed exclusively from Pauli x̂ operators (purely X generators) and sorted by an associated numerical measure, such as absolute energy gradients. Our approach uses the Gauss–Jordan elimination applied to a binary matrix that encodes the set of X generators to bring it to the reduced row-echelon form, followed by the construction of an anticommutative system in a standard basis by means of a modified Jordan–Wigner transformation and returning to the original basis. The algorithm complexity is linear in the size of the X set and quadratic in the number of qubits. The resulting anticommutative sets are used to construct the qubit coupled cluster Ansatz with involutory linear combinations of anticommuting Paulis (QCC-ILCAP) proposed in J. Chem. Theory Comput. 2021, 17 (1), 66–78. We applied the iterative qubit coupled cluster method with the QCC-ILCAP Ansatz to calculations of ground-state potential energy curves for symmetric stretching of the water molecule (36 qubits) and dissociation of N2 (56 qubits).
TEACHING DIGITAL IN PANDEMIC TIMES: RECALIBRATING THE CLASSROOM FOR WEB 3.0 STUDENTS

In post pandemic times when looking forward to the normality is the focus of everyone's attention, research areas abound in papers spinning around the same topic- COVID-19. From disinformation to a mired economy, to the effects on global mobility, education, health, social and political aspects, all papers observe, analyze, count and draw conclusions on what COVID-19 meant for the whole world. Studies vary from a national level approach to the international, continental and global, with a specialist or generalist approach, from analyses on figures from economy and tourism to lexical analyses on occurrences of news topics or frequently used words, to measure society in terms of psychological unrest and confidence, after an invariable introductory section about the context and time COVID-19 first started and where it initiated, to stages of the virus spread and sectors affected. Looked at on the long term, education along with general and mental health are among the most frequently brought under lenses mainly due to the long term isolation consequences on social evolution. In this context, the present paper aims at highlighting the need for a recalibration of the educational process, to tune in with a virtual area, to accommodate a differently represented type of student and a variety of pedagogical approaches to adapt the syllabus and tailor activities to facilitate learning in an environment which has been recently re-purposed to fit and last, to answer and to teach. The universal tool-bag is extremely generous, the technological support products are abundant in apps designed for educational purposes, while the key to set it all in motion is creativity, attention to students' needs, a proper selection of the pedagogical resorts and of the content, in a close combination with specificities of online communication in such contexts. A comparison will be drawn between two apps used for formal synchronous learning, three types of students, and four components in class management, all having one goal, the knowledge transfer.
CLASSIFICATION OF DOG BREEDS FROM SPORTING GROUPS USING CONVOLUTIONAL NEURAL NETWORK

The use of convolutional neural networks has been applied to various applications. Such as image clas-sification, object detection and recognition, and others. One of the most popular uses for neural networks is image classification. Image classification mainly identifies and categorizes images according to the specified group. One application is to distinguish between one type of dog to another. Classification of dog breeds has its challenges because several kinds of dogs have similar physical characteristics, espe-cially those that belong to the same group. This study explains how to develop a dog breed classification system from a sporting group using a residual neural network (ResNet). The system's goal is to make it simpler for people to identify the dog breed. Five types of dog breeds were used, which were obtained from the Tsinghua Dogs dataset. In its implementation, two variants of CNN are used to be compared, ResNet 50 and ResNet 101, using the same configuration. Based on the research results, ResNet 101 shows better macro-average f1-score results while maintaining high accuracy. The ResNet 50 produces an f1-score of 84%, while ResNet 101 makes an f1-score of 86%.
Micromotion-synchronized pulsed Doppler cooling of trapped ions

We propose and demonstrate a method for Doppler cooling trapped-ion crystals where the distribution of micromotion amplitudes may be large and uneven. The technique uses pulses of Doppler cooling light synchronized with the trap rf that selectively target ions when their velocity is near a node, leading to more uniform cooling across a crystal by a single tone of cooling light. We lay out a theoretical framework that describes where this technique is practical and provides a simple experimental demonstration.
Intelligent Edge Computing for IoT: Enhancing Security and Privacy

Edge computing is a distributed computing paradigm that involves processing data at or near the edge of the internet of things (IoT) network, instead of centralized server. This makes the cyber-attacks increasingly sophisticated, and traditional security measures become no longer sufficient to protect against them. Concurrently, privacy concerns arise when sensitive data is involved in Edge computing applications containing confidential information. In this paper, we propose a privacy-preserved federated learning (FL) approach for cyber-attack detection in edge based IoT ecosystem. A novel lightweight convolutional Transformer network (LCT) network is designed to precisely identify cyber-attacks though learning attack patterns from IoT traffics in local edge devices, where model is personalized though fine-tuning. The privacy of model and data is preserved in our system via incorporating differential privacy and secure aggregation during the cooperative training process on edge devices. We evaluate our proposed approach on a real-world dataset of network traffic (NSL-KDD) containing various types of attacks, and the experimental results show that our personalized FL approach outperforms traditional FL in terms of detection accuracy. We also show that our approach is effective in handling non-stationary data and adapting to changes in the network environment.