Reviewerly/exHarmony huggingface dataset:
Cloud storage system of teaching resources based on internet of things

In order to overcome the problems of high resource occupancy rate, poor data storage security and slow resource access speed in traditional teaching resource storage systems, a cloud storage system of teaching resources based on internet of things is proposed. The system consists of a user access module, resource management module and resource cloud storage module. The user access module is responsible for the user registration and login functions, the resource management module uses the data similarity measurement method to merge the duplicate data of teaching resources, and the resource cloud storage module encrypts the teaching resources through the AES algorithm in the internet of things. The experimental results show that the resource occupancy rate of cloud storage of teaching resources is only 30%, the security of data encryption can reach 97%, and the shortest storage speed of teaching resources is about 2.2 min.
Challenges in Coronavirus Contact-Tracing Application Implementation in Indonesia: Users' Perspective

A contact-tracing application is implemented to monitor and manage the spread of disease during pandemics. Since its launch by the government, the adoption of the PeduliLindungi, an Indonesian contact-tracing application, has remained low. PeduliLindungi users are only 34 % of smartphone users and 20% of the population in Indonesia. The effectiveness of contact-tracing apps cannot be measured without mass use and adherence to protective behavior. Approximately 80% of smartphone users, or 56% of the overall population, must use the app. Therefore, the app implementation challenges need to be researched to develop strategies to increase user adoption. This study used a qualitative methodology by conducting interviews with 17 respondents. Respondents were recruited using the purposive sampling method. This study used the NVIVO application to process and analyze data. Based on the results of data analysis using thematic coding, the challenges in contact-tracing apps implementation in Indonesia are divided into five main themes: technology limitations, lack of socialization, organizational problems, individual concerns, and socio-economic inequalities. This study contributes to the theory of challenges in contact tracing app implementation. The technical contributions and recommendations are also provided.
Human-Object Interaction Detection: A Survey of Deep Learning-Based Methods

In recent years, rapid progress has been made in detecting and identifying single object instances. In order to understand the situation in the scene, computers need to recognize how humans interact with surrounding objects. Human-object interaction (HOI) detection aims to identify a set of interactions in images or videos. It involves the positioning of interactive subjects and objects and the classification of interactive types. It is crucial to realize high-level semantic understanding of people-centered scenarios. The study of HOI detection is also conducive to promoting the research of other advanced visual tasks. In this paper, we introduce the previous works on HOI detection based on deep learning, which are raised from the two primary development trends of sequential and parallel methods. Secondly, we summarize the main challenges faced by the HOI detection task. Further, we introduce the most popular HOI detection datasets, including image and video datasets, and main metrics. Finally, we summarize the future research directions for the HOI detection task.
Graph Buddy — an interactive code dependency browsing and visualization tool

Source code comprehension of massively growing code bases is the first crucial step in today’s software development. One of the significant obstacles in reading the source code is understanding the code dependencies. To aid this process, we developed Graph Buddy, a tool integrated as a plugin for popular IDEs, which enables interactive visual code browsing as a graph of semantic code dependencies. To preserve the direct relation to the source code, visualize, and process only the right amount of details, we have developed Semantic Code Graph (SCG), an information model underlying Graph Buddy, capable of representing detailed code semantics. We implemented the SCG model extraction for the Scala and Java languages. To evaluate Graph Buddy, we conducted a user survey on a group of 10 programmers who used the tool in three programming-related tasks. The survey indicates that the tasks would be difficult to solve without the visual browsing tool. We believe that the proposed solution has a strong potential to enhance the way programmers comprehend code dependencies.
Owners of Russian Personal Book Collections in the Turkestan Region

The article is devoted to the owners of Russian personal libraries in Turkestan region at the end of the 19th – the beginning the 20th centuries, the persons who made a great contribution to creating the first public library of the European type on the territory of modern Uzbekistan. The purpose of the article is to consider the personality of the Russian personal libraries’ owners in the Turkestan region in the late 19th – early 20th centuries, as well as the history of the formation of their personal book collections, thematic content and the degree of their study. The article considers the peculiarities of the formation of Russian personal libraries, the composition and design of bibliophile collections, a variety of ownership characteristics. Russian personal book collections of the Turkestan region are of exceptional importance as a valuable source for the study of Russian book culture and socio-cultural development of the region.
EEML: Ensemble Embedded Meta-Learning

To accelerate learning process with few samples, meta-learning resorts to prior knowledge from previous tasks. However, the inconsistent task distribution and heterogeneity is hard to be handled through a global sharing model initialization. In this paper, based on gradient-based meta-learning, we propose an ensemble embedded meta-learning algorithm (EEML) that explicitly utilizes multi-model-ensemble to organize prior knowledge into diverse specific experts. We rely on a task embedding cluster mechanism to deliver diverse tasks to matching experts in training process and instruct how experts collaborate in test phase. As a result, the multi experts can focus on their own area of expertise and cooperate in upcoming task to solve the task heterogeneity. The experimental results show that the proposed method outperforms recent state-of-the-arts easily in few-shot learning problem, which validates the importance of differentiation and cooperation.
Blockchain Communication Platform Selection in IoT Healthcare Industry using MARCOS

The Internet of Things (IoT) healthcare industry is under tremendous pressure to simplify its secure data communication processes. Patients are beginning to consider healthcare services, such as those relating to wellness promotion, illness prevention, diagnosis, care, and recovery, as ongoing cycles. With the prevalence of chronic illnesses on the rise and public perceptions of healthcare shifting, many people increasingly see modern health services as ongoing commitments. Using data provided through the most cutting-edge technology, efficient healthcare systems should reliably provide all their patients with access to the high-quality, comprehensive medical treatment they can afford. So, this study presents a neutrosophic multicriteria decision-making (MCDM) model to optimize the selection of blockchain communication platforms in IoT healthcare applications. To identify the best blockchain platform for use in healthcare, the Measurement of Alternatives and Ranking according to the Compromise Solution (MARCOS) technique was created. The proposed model improves the efficiency, accuracy, and reliability for better Blockchain secure communication in the IoT healthcare industry.
Ghost-YOLOX: A Lightweight and Efficient Implementation of Object Detection Model

In order to solve the problems of large number of parameters and high computational complexity in current object detection models, we propose a lightweight object detection model based on YOLOX. First, we use a lightweight network GhostNet as the backbone feature extraction network, and adjust the number of channels in the output feature layers of the backbone network using depth-separable convolution, reducing the parameter quantity of the model to one-third of YOLOX-l. Then, we introduce the pyramid attention segmentation module and FReLU activation function in GhostNet to improve the backbone network’s ability to capture contextual information at different scales; The Py-PAFPN structure is also proposed by adding multi-scale pyramidal convolution to the FPN structure to efficiently fuse the image information in the output feature layer of the backbone network. Using Pascal VOC as the dataset, the experimental results show that the Ghost-YOLOX lightweight object detection model proposed in this paper has less number of parameters and computation, and achieves 89.44% accuracy, which is the best result in the comparison experiments.
Interactive learning web applications: the main stages of development and software implementation

This article discusses the importance of teaching children and adolescents additional educational programs in the field of IT technologies, allowing them to form their own individual educational trajectory, aimed at the formation of their computer literacy and obtaining new knowledge and skills. The organization of additional education in university centers of additional education is relevant for technical universities, as it allows attracting talented and motivated applicants. Accordingly, the educational process for additional development programs should take place using effective pedagogical technologies and modern teaching aids. The analysis of computer training systems made it possible to single out an electronic resource made as an interactive web application as the most effective tool. The most important stages of the development of an interactive web application for teaching information technologies on additional educational programs for children and adolescents are considered: the functionality of the application, the navigation structure of the application are presented, information on the software implementation of an interactive web application is provided. The use of interactive web applications implementing game pedagogical technology will attract and interest schoolchildren in the systematic study of programming and information technology.
TPFL: Test Input Prioritization for Deep Neural Networks Based on Fault Localization

DNN testing is a critical way to guarantee the quality of DNNs. To obtain test oracle information, DNN testing requires a huge cost to label test inputs, which greatly affects the efficiency of DNN testing. To alleviate the labeling cost problem, the paper applies the idea of the spectrum-based fault location technique to DNN testing and proposes a novel test input prioritization approach for DNNs based on fault localization (called TPFL). TPFL first performs dynamic spectrum analysis on each neuron in the DNN. TPFL then proposes a suspiciousness measure that uses the neuron spectrum to identify suspicious neurons that cause the DNN to make wrong decisions. Finally, TPFL is based on the following key insight: a test input makes the suspicious neurons fully active, it indicates that this may be a bug-revealing input, so the input should have a higher priority. To evaluate, we conduct an empirical study on 3 widely used datasets and corresponding 8 DNN models. The experimental results show that TPFL performs well in both classification and regression models and overall outperforms most existing test input prioritization techniques.
Generating a European Portuguese BERT Based Model Using Content from Arquivo.pt Archive

Building a language model from free available internet information takes several steps and challenges. This new model aims to be a BERT-based language model for European Portuguese, with no specific context. The corpus was built using a web page archive infrastructure provided by Arquivo.pt and restricted to .pt domains. This paper will describe the overall process of building the corpus and training a BERT model.
An Efficient Accelerator of Deformable 3D Convolutional Network for Video Super-Resolution

Nowadays, deformable 3D convolutional network (D3D-ConvNet) has been widely used in the field of video super-resolution (VSR), due to the powerful capabilities of multiple frame alignment and spatiotemporal information extraction. However, the expensive computational complexity and irregular memory accesses caused by the deformable 3D convolution (D3D-Conv) hinder the deployment of D3D-ConvNet on edge devices. To tackle these issues, an algorithm and hardware co-optimization framework is proposed to accelerate D3D-ConvNet for VSR on field-programmable gate array (FPGA) in this paper. Firstly, at the algorithm level, a tile decoupling computing strategy is introduced to execute the computations at the tile-level granularity. Thus, the memory requirements caused by the deformed receptive field and large input resolution can be reduced significantly. Moreover, the image restoration performance can be maintained sufficiently. Secondly, based on the proposed strategy, the computing modules together with a ping-pong transposition storage scheme are designed to accelerate D3D-Conv operations, avoiding irregular memory access patterns. Thirdly, an overall hardware architecture and a memory-efficient dataflow are further developed to accelerate deformable convolutional layers. Experimental results demonstrate that the proposed design provides more promising visual perceptions than prior FPGA-based VSR methods. Meanwhile, our design surpasses existing hardware implementations for deformable convolutions significantly in terms of throughput and processing speed.
Analysis of Deep Learning Models for Anomaly Detection in Time Series IoT Sensor Data

The anomaly detection in Internet of Things (IoT) sensor data has become an important research area because of the possibility of noise and unavailability of labels in the sensors readings. The conventional machine learning algorithms cannot detect the anomalies when there is high correlation between the data points of the sensor data. Further, the volume and velocity of the data generated by the sensors in the IoT also a reason that the conventional statistical and machine learning algorithms fails to detect the anomalies. In recent years, the Deep Learning (DL) is gaining significant attention in the anomaly detection research due to the property of unsupervised learning of the high volume data and high detection accuracy of abnormalities. To this end, this paper proposed to study three DL models such as Autoencoders, Long Short Term Memory (LSTM) Autoencoder, and LSTM Recurrent Neural Networks (LSTM-RNN) for detecting anomalies in time series IoT sensor data. Simulations have been conducted using the Intel Berkeley Research Labs (IBRL) Sensor data to evaluate the performance. The results reveal which method performed better in terms of detection accuracy and training time.
CostDCNet: Cost Volume Based Depth Completion for a Single RGB-D Image

Successful depth completion from a single RGB-D image requires both extracting plentiful 2D and 3D features and merging these heterogeneous features appropriately. We propose a novel depth completion framework, CostDCNet, based on the cost volume-based depth estimation approach that has been successfully employed for multi-view stereo (MVS). The key to high-quality depth map estimation in the approach is constructing an accurate cost volume. To produce a quality cost volume tailored to single-view depth completion, we present a simple but effective architecture that can fully exploit the 3D information, three options to make an RGB-D feature volume, and per-plane pixel shuffle for efficient volume upsampling. Our CostDCNet framework consists of lightweight deep neural networks ( $$\sim $$ 1.8M parameters), running in real time ( $$\sim $$ 30 ms). Nevertheless, thanks to our simple but effective design, CostDCNet demonstrates depth completion results comparable to or better than the state-of-the-art methods.
Towards Efficient Discovery of Partial Periodic Patterns in Columnar Temporal Databases

Finding partial periodic patterns in temporal databases is a challenging problem of great importance in many real-world applications. Most previous studies focused on finding these patterns in row temporal databases. To the best of our knowledge, there exists no study that aims to find partial periodic patterns in columnar temporal databases. One cannot ignore the importance of the knowledge that exists in very large columnar temporal databases. It is because real-world big data is widely stored in columnar temporal databases. With this motivation, this paper proposes an efficient algorithm, Partial Periodic Pattern-Equivalence Class Transformation (3P-ECLAT), to find desired patterns in a columnar temporal database. Experimental results on synthetic and real-world databases demonstrate that 3P-ECLAT is not only memory and runtime efficient but also highly scalable. Finally, we present the usefulness of 3P-ECLAT with a case study on air pollution analytics.
Programmable Photonic Circuits powered by Silicon Photonic MEMS Technology

Programmable photonic chips allow flexible reconfiguration of on-chip optical connections, controlled through electronics and software. We will present the recent progress of such complex photonic circuits powered by silicon photonic MEMS actuators.
High Fidelity 12-Mode Quantum Photonic Processor Operating at InGaAs Quantum Dot Wavelength

Reconfigurable photonic processors are crucial for photonic quantum computing. We report a low-loss, high-fidelity and universal 12-mode photonic processor at a wavelength of 940 nm, which is compatible with InGaAs quantum dot light sources.
Retrieval-Augmented Response Generation for Knowledge-Grounded Conversation in the Wild

Users on the internet usually have conversations on interesting facts or topics along with diverse knowledge from the web. However, most existing knowledge-grounded conversation models consider only a single document regarding the topic of a conversation. The recently proposed retrieval-augmented models generate a response based on multiple documents; however, they ignore the given topic and use only the local context of the conversation. To this end, we introduce a novel retrieval-augmented response generation model that retrieves an appropriate range of documents relevant to both the topic and local context of a conversation and uses them for generating a knowledge-grounded response. Our model first accepts both topic words extracted from the whole conversation and the tokens before the response to yield multiple representations. It then chooses representations of the first N token and ones of keywords from the conversation and document encoders and compares the two groups of representation from the conversation with those groups of the document, respectively. For training, we introduce a new data-weighting scheme to encourage the model to produce knowledge-grounded responses without ground truth knowledge. Both automatic and human evaluation results with a large-scale dataset show that our models can generate more knowledgeable, diverse, and relevant responses compared to the state-of-the-art models.
“Cut out the middleman” – Automating Business Processes with Blockchains and Smart Contracts

Competence in data management is fundamental for the survival of companies. In this paper we go beyond topics like data integration, data quality, data governance or data wrangling. The notion is to outline the conceptual approaches of blockchain technology and smart contracts as an innovative approach to sharing data and automating contractual processes between interested parties. Cryptocurrencies - with bitcoin the first and most prominent one – have become highly relevant on the financial markets. The underlying technology for crypto money follows a distributed ledger concept called blockchain. However, blockchain technology today amounts to much more than storing virtual money, having gained new ground with the capability to implement so-called smart contracts, i.e., software code as part of the chain. In this discussion we outline the concept of distributed ledgers and blockchain and outline fundamentals of smart contracts. Finally, proof of concepts and first applications exemplify the options that companies now have to enhance their business.
Scaling Blockchains and the Case for Ethereum

This chapter provides a high-level introduction to scaling solutions for blockchains, with a special focus on Ethereum 2.0. Current blockchain capacity is a hurdle for the widespread adoption of Web3 and cryptocurrencies. First, we discuss the considerations and pitfalls of blockchain scaling strategies. We then explore the design landscape—layer-1 and layer-2 solutions—and discuss concepts in each category, namely sharding, rollups, and sidechains.
Research on the construction of smart care question answering system based on knowledge graph

With the deepening of aging in China, the demand of intelligent care system for the elderly is increasing day by day. The rapid development of information technology and artificial intelligence technology have gradually promoted the transformation of traditional care services from artificial to intelligent. Based on knowledge graph technology, this study built a knowledge graph model for elderly chronic disease smart care. The specific process includes ontology design, knowledge extraction, knowledge graph construction and question answering system design. The uniqueness of the knowledge graph in this paper is that it uses specific information about the elderly and data from professional care books. It is well guaranteed in practicality and reliability.With the help of the knowledge map constructed in this paper, primary care staff can effectively query high-quality and customized chronic disease care knowledge.
Supervised rigid image registration with CNN for MR brain images

Image registration is an important and fundamental topic in medical image analysis applications. The objective of image registration is to get spatial transformation for the moving image to align with a reference image. In addition to conventional image registration methods, deep learning methods, which have gained popularity recently, give successful results by applying them to registration problems as well as many other computer vision problems. In this study, spatial transformation parameter estimation has been performed by utilizing a supervised deep learning method. The rigid method has been used as the spatial transformation model. A supervised convolutional neural network (CNN) estimates the rigid transformation parameters that are used for warping moving images to a fixed image. The most important contribution of this study is to perform supervised rigid image registration with the CNN regression model for MR brain images. In the study, it has been tried to determine the parameters that give the best results for the problem by trying different parameters.
Flexible model-driven runtime monitoring support for cyber-physical systems

Providing adequate runtime monitoring is critical for ensuring safe operation and for enabling self-adaptive behavior of Cyber-Physical Systems. This requires identifying runtime properties of interest, creating Probes to instrument the system, and defining constraints to be checked at runtime. Implementing and setting up a monitoring framework for a system is typically a challenging task, and most existing approaches lack support for the automated generation and setup of monitors. GRuM significantly eases the task of creating monitors and maintaining them throughout the lifetime of the system by automatically generating runtime models and providing support for updating and adapting them when needed.