Reviewerly/exHarmony huggingface dataset:
Fry Counting Models Based on Attention Mechanism and YOLOv4-Tiny

Accurate counting is difficult in the case of large numbers of overlapping and adhering fry. In this study, we propose a lightweight target detection counting method based on deep learning methods that can meet the deployment requirements of edge computing device for automatic fry counting while obtaining a high counting accuracy. We improve the structure of YOLOv4-tiny by embedding different attention mechanisms in the CSP blocks of the backbone network to enhance the feature extraction performance. In addition, the low efficiency of feature fusion in the original model is also addressed by adding different attention mechanisms to the neck network structure to promote the effective fusion of deep feature information with shallow feature information and improve the counting accuracy. The experimental results showed that the six models proposed in this study improved the model accuracy and recall to varying degrees compared with the original YOLOv4-tiny model, while retaining the advantages of the YOLOv4-tiny model in terms of its small number of parameters and fast inference rate. It was also shown that the CBAM(n)-YOLOv4-tiny model obtained by adding the CBAM to the neck network showed the most significant improvement, with an mean average precision (mAP) of 94.45% and a recall of 93.93%. Compared with the YOLOv4-tiny model, there were increases of 27.06% in accuracy, 30.66% in recall, 38.27% in mAP, and 28.77% in the F1-score, along with a 67.82% decrease in LAMR.
Action Recognition in Dark Videos Using Spatio-Temporal Features and Bidirectional Encoder Representations From Transformers

Several research works have been developed in the area of action recognition. Unfortunately, when these algorithms are applied to low-light or dark videos, their performances are highly affected and found to be very poor or fall rapidly. To address the issue of improving the performance of action recognition in dark or low-light videos; in this article, we have developed an efficient deep 3-D convolutional neural network based action recognition model. The proposed algorithm follows two-stages for action recognition. In the first stage, the low-light videos are enhanced using zero-reference deep curve estimation, followed by the min–max sampling algorithm. In the latter stage, we propose an action classification network to recognize the actions in the enhanced videos. In the proposed action classification network, we explored the capabilities of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R(2+1)D$</tex-math></inline-formula> for spatio-temporal feature extraction. The model's overall generalization performance depends on how well it can capture long-range temporal structure in videos, which is essential for action recognition. So we have used a graph convolutional network on the top of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R(2+1)D$</tex-math></inline-formula> as our video feature encoder, which captures long-term temporal dependencies of the extracted features. Finally, a bidirectional encoder representations from transformers is adhered to classify the actions from the 3-D features extracted from the enhanced video scenes. The effectiveness of the proposed action recognition scheme is verified on ARID V1.0 and ARID V1.5 datasets. It is observed that the proposed algorithm is able to achieve 96.60% and 99.88% as Top-1 and Top-5 accuracy, respectively, on ARID V1.0 dataset. Similarly, on ARID V1.5, the proposed algorithm is able to achieve 86.93% and 99.35% as Top-1 and Top-5 accuracies, respectively. To corroborate our findings, we have compared the results obtained by the proposed scheme with those of 15 state-of-the-art action recognition techniques.
Bringing Aggregate Programming Towards the Cloud

Aggregate Programming (AP) is a paradigm for developing applications that execute on a fully distributed network of communicating, resource-constrained, spatially-situated nodes (e.g., drones, wireless sensors, etc.). In this paper, we address running an AP application on a high-performance, centralized computer such as the ones available in a cloud environment. As a proof of concept, we present preliminary results on the computation of graph statistics for centralised data sets, by extending FCPP, a C++ library implementing AP. This: (i) opens the way to the application of the AP paradigm to problems on large centralised graph-based data structures, enabling massive parallelisation across multiple machines, dynamically joining and leaving the computation; and (ii) represents a first step towards developing collective adaptive systems where computations dynamically move across the IoT/edge/fog/cloud continuum, based on mutable conditions such as availability of resources and network infrastructures.
Few-Shot End-to-End Object Detection via Constantly Concentrated Encoding Across Heads

Few-shot object detection (FSOD) aims to detect objects of new classes and learn effective models without exhaustive annotation. The end-to-end detection framework has been proposed to generate sparse proposals and set a stack of detection heads to improve the performance. For each proposal, the predictions at lower heads are fed into deeper heads. However, the deeper head may not concentrate on the detected objects and then degrades, resulting in inefficient training and further limiting the performance gain in few-shot scenario. In this paper, we propose a few-shot adaptation strategy, Constantly Concentrated Encoding across heads (CoCo-RCNN), for the end-to-end detectors. For each class, we gather the encodings which detect on its object instances and then train them to be discriminative to avoid degraded prediction. In addition, we embed the class-relevant encodings to the learnable proposals to facilitate the adaptation at lower heads. Extensive experimental results show that our model brought clear gain on benchmarks. Detailed ablation studies are provided to justify the selection of each component.
On the Design of Quantum Graph Convolutional Neural Network in the NISQ-Era and Beyond

The rapid growth in the size of Graph Convolutional Neural Networks (GCNs) encounters both computational- and memory-wall on classical computing platforms (e.g., CPU, GPU, FPGA, etc.). Quantum computing, on the other hand, provides extremely high parallelism for computation. Although quantum neural networks have been recently studied, the research on quantum graph neural networks is still in its infancy. The key challenge here is how to integrate both the graph topology information and the learning ability of GCNs into quantum circuits. In this work, we leverage the Givens rotations and its quantum implementation to encode graph information; in addition, we employ the widely used variational quantum circuit to bring the learnable parameters. On top of these, we present a full-quantum design of Graph Convolutional Neural Networks, namely "QuGCN", for semi-supervised learning on graph-structured data. Experiment results show our design is competitive with classical GCNs in terms of node classification accuracy on Cora sub-dataset. More importantly, we show the potential advantages that can be achieved by the proposed quantum GCN design when the number of features grows.
Digital skills development as a basis for sustainable education development

The purpose of this study was to identify methods and technological solutions which would help in attaining strong professional skills while studying online. This paper is interlinking two pieces of research “use of distance education” for “developing professional skills”. Exploratory research design is used for this research report because for this research purpose we have to look for secondary data and gain background knowledge on this topic. An exploratory research design helps a researcher in clarifying problems and hypotheses and establishing research priorities. Technology was always a significant part of education but in past years due to the high provoke of distance education different technological tools and methods are used for educating and developing professional skills. Some of the tools and methods are diagnostic assessment tools, simulation, VR-based educational software, Intelligent Tutoring Systems, and AR-based tools for distance education. There will be considered how these tools and technologies help to develop and polish professional skills while having distance education.
Enhanced Single-Shot Detector for Small Object Detection in Remote Sensing Images

Small-object detection is a challenging problem. In the last few years, the convolution neural networks methods have been achieved considerable progress. However, the current detectors struggle with effective features extraction for small-scale objects. To address this challenge, we propose image pyramid single-shot detector (IPSSD). In IPSSD, single-shot detector is adopted combined with an image pyramid network to extract semantically strong features for generating candidate regions. The proposed network can enhance the small-scale features from a feature pyramid network. We evaluated the performance of the proposed model on two public datasets and the results show the superior performance of our model compared to the other state-of-the-art object detectors.
Infinite Virtual Space Exploration Using Space Tiling and Perceivable Reset at Fixed Positions

A simultaneous walking experience in virtual and real spaces can provide a high sense of presence. However, users may face challenges when walking within a large virtual space while walking in a small and complex real space. Several methods such as Redirected Walking (RDW) and Substitutional Reality (SR) have been proposed as different approaches to this problem. However, the users must “reset” their movement direction at unpredictable moments to avoid collision in a small and complex real space when using subtle RDW that does not maintain the correspondence between virtual and real space. Contrarily, exploration through the SR has a limitation in that the VR scene is restricted to a controlled area. In this paper, we propose Reset at Fixed Positions (RFP), a method that combines RDW with the advantage of the SR and matches walkable real space with walkable virtual space. To utilize RFP, we defined Guaranteed Space Block (GSB), a unit space that constitutes a walkable virtual space. This space is obtained through the point reflection of the GSB utilizing the reset position within the GSB. RFPs can be implemented by two methods: Generating Virtual Space Using RFP (G-RFP) and Implementing Given Virtual Space Using RFP (I-RFP). G-RFP can create an infinitely large virtual space for exploration. On the other hand, I-RFP can conFigure a given virtual environment to make users walk. We observed that G-RFP provides higher presence, immersion and a higher mean distance traveled between resets compared to the existing RDW method in a complex real space through a user study. In addition, exploration through I-RFP provided a higher immersion, a comparable presence, and a similar number of resets.
Machine learning in requirements elicitation: a literature review

Abstract A growing trend in requirements elicitation is the use of machine learning (ML) techniques to automate the cumbersome requirement handling process. This literature review summarizes and analyzes studies that incorporate ML and natural language processing (NLP) into demand elicitation. We answer the following research questions: (1) What requirement elicitation activities are supported by ML? (2) What data sources are used to build ML-based requirement solutions? (3) What technologies, algorithms, and tools are used to build ML-based requirement elicitation? (4) How to construct an ML-based requirements elicitation method? (5) What are the available tools to support ML-based requirements elicitation methodology? Keywords derived from these research questions led to 975 records initially retrieved from 7 scientific search engines. Finally, 86 articles were selected for inclusion in the review. As the primary research finding, we identified 15 ML-based requirement elicitation tasks and classified them into four categories. Twelve different data sources for building a data-driven model are identified and classified in this literature review. In addition, we categorized the techniques for constructing ML-based requirement elicitation methods into five parts, which are Data Cleansing and Preprocessing , Textual Feature Extraction, Learning, Evaluation, and Tools . More specifically, 3 categories of preprocessing methods, 3 different feature extraction strategies, 12 different families of learning methods, 2 different evaluation strategies, and various off-the-shelf publicly available tools were identified. Furthermore, we discussed the limitations of the current studies and proposed eight potential directions for future research.
SkinningNet: Two-Stream Graph Convolutional Neural Network for Skinning Prediction of Synthetic Characters

This work presents SkinningNet, an end-to-end Two-Stream Graph Neural Network architecture that computes skinning weights from an input mesh and its associated skeleton, without making any assumptions on shape class and structure of the provided mesh. Whereas previous meth-ods pre-compute handcrafted features that relate the mesh and the skeleton or assume a fixed topology of the skeleton, the proposed method extracts this information in an end-to-end learnable fashion by jointly learning the best relationship between mesh vertices and skeleton joints. The proposed method exploits the benefits of the novel Multi-Aggregator Graph Convolution that combines the results of different aggregators during the summarizing step of the Message-Passing scheme, helping the operation to general-ize for unseen topologies. Experimental results demonstrate the effectiveness of the contributions of our novel architecture, with SkinningNet outperforming current state-of-the-art alternatives.
Mining idioms in the wild

Existing code repositories contain numerous instances of code patterns that are idiomatic ways of accomplishing a particular programming task. Sometimes, the programming language in use supports specific operators or APIs that can express the same idiomatic imperative code much more succinctly. However, those code patterns linger in repositories because the developers may be unaware of the new APIs or have not gotten around to them. Detection of idiomatic code can also point to the need for new APIs.
Blockchain and 6G Networks: A Use Case for Cost-Efficient Inter-Provider Smart Contracts

6G is expected to play a crucial role in future communication evolution, which will address the current challenges 5G faces. Blockchain and Distributed Ledger Technologies (DLT) have gained significant attention due to their decentralization, transparency, security, and emerging smart contract-based applications. In telecom sectors, the mismatch between the requirements of increasing use-cases and 5G network capabilities steered the demand for decentralized blockchain-based 6G architecture. Smart contracts can automate an inter-provider agreement that allows stakeholders or actors of a platform or a marketplace to transact and exchange services while maintaining trust. The characteristics of DLT are critical in a future-generation network where separate operators control various administrative domains and resource providers share resources (virtual network functions, services, or slices). This study aims to provide a framework for a smart contract-based inter-provider agreement leveraging IOTA Tangle, IPFS, and chainlink to minimize the overall cost of use-cases of 6G networks (such as inter-provider agreements). The proposed framework is emulated on the testnet to analyze the results. Moreover, the potential advantages of the proposed solutions are demonstrated through emulation results. When establishing the smart contract using IOTA, the transaction cost drops since IOTA supports fee-less transactions. In addition, the overall cost of the storage by using IPFS is roughly reduced by more than 80%. Furthermore, we reduce the overall data access cost by approximately 50% using chainlink in our approach.
An Empirical Assessment of Security and Privacy Risks of Web-Based Chatbots

Web-based chatbots provide website owners with the benefits of increased sales, immediate response to their customers, and insight into customer behaviour. While Web-based chatbots are getting popular, they have not received much scrutiny from security researchers. The benefits to owners come at the cost of users' privacy and security. Vulnerabilities, such as tracking cookies and third-party domains, can be hidden in the chatbot's iFrame script. This paper presents a large-scale analysis of five Web-based chatbots among the top 1-million Alexa websites. Through our crawler tool, we identify the presence of chatbots in these 1-million websites. We discover that 13,392 out of the top 1- million Alexa websites (1.58%) use one of the five analysed chatbots. Our analysis reveals that the top 300k Alexa ranking websites are dominated by Intercom chatbots that embed the least number of third-party domains. LiveChat chatbots dominate the remaining websites and embed the highest samples of third-party domains. We also find that 721 (5.38%) web-based chatbots use insecure protocols to transfer users' chats in plain text. Furthermore, some chatbots heavily rely on cookies for tracking and advertisement purposes. More than two-thirds (68.92%) of the identified cookies in chatbot iFrames are used for ads and tracking users. Our results show that, despite the promises for privacy, security, and anonymity given by most websites, millions of users may unknowingly be subject to poor security guarantees by chatbot service providers.
An Overview and Comparison of XAI Methods for Object Detection in Computer Tomography

Modern hardware and software developments in the medical field generate massive amounts of data that clinicians need to analyze. Many solutions based on deep learning have been introduced to support the diagnostic process. Nonetheless, the transparency and reasoning of such systems are important for medical practices that limit the application of artificial intelligence techniques that work in 'black box' scenarios. The purpose of this paper is to present algorithms that allow interpretation of the complex structure of models used in object detection. Based on the ablation study results, a detailed analysis of the advantages and disadvantages of the chosen methods has been provided. Infidelity and consistency metrics were used to assess the algorithms of explanation.
FPoR: Fair proof-of-reputation consensus for blockchain

Current consensus protocols for permissionless blockchain cannot balance security, performance and centralization issues. In the paper, we present a reputation based consensus, FPoR, which combines reputation, committee based consensus, PBFT, reward and penalization mechanisms. FPoR can balance scalability, security and decentralization, and promote fairness, increase participation and strengthen security. Experiment results show that FPoR is with high performance and scalability, which can be used for permissionless blockchain. FPoR can also be extended to permissioned blockchain applications.
The geology and geophysics of Lake Tarawera, New Zealand: Implications for sublacustrine geothermal activity

The Okataina Volcanic Centre is a large caldera system in the Taupo Volcanic Zone, New Zealand, which includes the Haroharo caldera, where the geothermally active Lake Tarawera is located at its southwestern boundary. Here, we show the results of high-resolution bathymetric mapping of the lake floor combined with gravity, magnetic and heat-flow surveys carried out over the lake, constrained by geochemical sampling. Our combined data elucidate the detailed geometry of the SW structural margin of the Haroharo caldera bisecting Lake Tarawera, and the relationship with the regional faults related with the Taupo Volcanic Zone rift. In this context, the spatial distribution of heat-flow in Lake Tarawera appears controlled by fluid-focusing permeable zones associated with the structural lineaments and by peripheral, gravity-driven circulation of meteoric water associated with cooling of highly permeable rhyolite domes around the lakeshore. The structural boundary of the Haroharo caldera provides first-order control of heat-flow in the lake, with WNW striking normal faults to a lesser degree. An estimated total conductive output of ∼5.3 MW escaping through the floor of Lake Tarawera is refracted by the thick layer of sediments, providing an impermeable layer to the convection of fluids, which in turn diminishes heat output compared to other lakes in this region. Our results contribute to modelling of hydrothermal circulation at the Okataina Volcanic Centre, and when integrated with data from the other lakes and the terrestrial geothermal output, it will contribute to determine the global geothermal output at this caldera system. The relationships to eruptive history and structural setting are the key to modelling similar geothermal system of other silicic calderas worldwide.
Immersive virtual reality application for intelligent manufacturing: Applications and art design

Intelligent manufacturing (IM), sometimes referred to as smart manufacturing (SM), is the use of real-time data analysis, machine learning, and artificial intelligence (AI) in the production process to achieve the aforementioned efficiencies. Human-machine interaction technology has recently been a hot issue in smart manufacturing. The unique interactivity of virtual reality (VR) innovations makes it possible to create a virtual world and allow users to communicate with that environment, providing users with an interface to be immersed in the digital world of the smart factory. And virtual reality technology aims to stimulate the imagination and creativity of creators to the maximum extent possible for reconstructing the natural world in a virtual environment, generating new emotions, and transcending time and space in the familiar and unfamiliar virtual world. Recent years have seen a great leap in the development of intelligent manufacturing and virtual reality technologies, yet little research has been done to combine the two popular trends. To fill this gap, this paper specifically employs Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines to conduct a systematic review of the applications of virtual reality in smart manufacturing. Moreover, the practical challenges and the possible future direction will also be covered.