Reviewerly/exHarmony huggingface dataset:
Kensor: Coordinated Intelligence from Co-Located Sensors

Internet of Things (IoT) is becoming more pervasive in many installations, including homes, manufacturing plants, and industrial facilities of all kinds. The data that IoT produces is a reflection of usual behavior such as daily routines and scheduled tasks, but also from unexpected behavior due to unintentional or undesirable abnormalities. Here, we focus on achieving coordinated intelligence about normal and abnormal phenomena from multiple sensors that are geographically co-located in close proximity, monitoring and controlling a set of co-located devices. Given a set of co-located sensors, we seek an intelligent approach that would automatically determine the “normal” patterns of behaviors among the correlated sensors. After normal behavior is extracted, later monitoring should detect any deviant variations over time. An example application is an entry monitoring and alert system for facilities such as nuclear reactors, where badge readers, door locks, lights, weight trackers and other co-located sensors at the entry point are collectively tracked. To address this problem, we identify the possible solution approach that can be used to solve its different variants. The implemented model is developed as a combination of rules and Markov Chain methods.
Cloud Computing Platform Services in the University Libraries for Digital Repository

The purpose of this scientific article is to provide and prepare a cloud-based architecture for automation services within the university library.Cloud computing has emerged as an effective system to support the implementation of e-learning activities in the tertiary education sector especially in this context in the university library.Academics and faculty members also experience an increase in experience related to the ease in developing and enhancing their academic activities, and therefore, the use of cloud-based services in the library is intended to help academics gain constant, efficient, fast and effective access.the latest research data content based on e-resources.This scientific article provides a holistic understanding of cloud computing and digital library systems in college libraries.This paper also focuses on opportunities from developing the challenges of implementing cloud computing in the library area.This paper explains that cloud computing technology is a promising leap in service in the library area and will be more effective, professional and help library services in services that are go-green environment.
Automatic Text Line Segmentation Directly in JPEG Compressed Document Images

JPEG is one of the popular image compression algorithms that provide efficient storage and transmission capabilities in consumer electronics, and hence it is the most preferred image format over the internet world. In the present digital and Big-data era, a huge volume of JPEG compressed document images are being archived and communicated through consumer electronics on daily basis. Though it is advantageous to have data in the compressed form on one side, however, on the other side processing with off-the-shelf methods becomes computationally expensive because it requires decompression and recompression operations. Therefore, it would be novel and efficient, if the compressed data are processed directly in their respective compressed domains of consumer electronics. In the present research paper, we propose to demonstrate this idea taking the case study of printed text line segmentation. Since, JPEG achieves compression by dividing the image into nonoverlapping 8×8 blocks in the pixel domain and using Discrete Cosine Transform (DCT); it is very likely that the partitioned 8×8 DCT blocks overlap the contents of two adjacent text-lines without leaving any clue for the line separator, thus making text-line segmentation a challenging problem. Two approaches of segmentation have been proposed here using the DC projection profile and AC coefficients of each 8×8 DCT block. The first approach is based on the strategy of partial decompression of selected DCT blocks, and the second approach is with intelligent analysis of F10 and F11 AC coefficients and without using any type of decompression. The proposed methods have been tested with variable font sizes, font style and spacing between lines, and a good performance is reported.
Confidentiality-preserving Validation of Tax Documents on the Blockchain

Information exchange between tax administrations, businesses, and auditors is key to effective tax enforcement. Therefore, organizations proposed the application of blockchain technology to interconnect the different actors and increase tax transparency. However, the lack of confidentiality measures hampers further development. Especially, businesses are concerned about the disclosure of commercially sensitive information that might threaten their competitive advantage. In this paper, we investigate how the application of zero-knowledge-proofs can contribute to solving the dilemma between transparency and confidentiality in blockchain-based tax systems. To meet this end, we provide a conceptual design of a confidentiality-preserving distributed tax ledger. Moreover, we present a prototype addressing reporting obligations in the context of value-added tax. Our evaluation shows that zero-knowledge proofs are an effective measure to trade off transparency against confidentiality. Still, their application is challenging and future research must focus on better abstractions of proving statements.
Healthcare Applications in IoT

IoT is a blessing in the field of information and technology. It is developing and deploying day by day. It is working for our betterment in the section of home, environment, retail, security, factory, industry, agriculture, education, energy, healthcare and so on. In the healthcare section, it has applications, technologies, benefits and also challenges with the four-step architecture. As the world is aging with its uprising population, there is not enough opportunity for all to get healthcare. I think Health is the most expensive property one can own. So in the absence of sufficient and quality healthcare, IoT can manufacture the best utilization of its capacity to play a vital role in creating a better world for all of us. This paper represents the basic concept of IoT in the healthcare field.
Optimal Location for Fall Detection Edge Inferencing

A leading cause of physical injury sustained by elderly persons is the event of unintentionally falling onto a hard surface. Approximately 32-42% of those 70 and over fall at least once each year, and those who live in assisted living facilities fall with greater frequency per year than those who live in residential communities. Delay between the time of fall and the time of medical attention can exacerbate injury if the fall resulted in concussion, traumatic brain injury, or bone fracture. Several implementations of mobile, wireless, wearable, low-power fall detection sensors (FDS) have become commercially available. These devices are typically worn around the neck as a pendant, or on the wrist, as a watch is worn. Based on features collected from IMU sensors placed at sixteen body locations, and used to train four different machine learning models, our findings show the optimal placement for an FDS on the body is in front of the shinbone.
Real-time Operation of Silicon Photonic Neurons

In this paper, we use standard silicon-photonic components in order to implement a neuromorphic circuit with two neurons. The network exhibits reconfigurable weights and nonlinear transfer functions, enabling high-bandwidth analog signal processing tasks.
Few-Shot Graph Classification with Model Agnostic Meta-Learning.

Graph classification aims to extract accurate information from graph-structured data for classification and is becoming more and more important in graph learning community. Although Graph Neural Networks (GNNs) have been successfully applied to graph classification tasks, most of them overlook the scarcity of labeled graph data in many applications. For example, in bioinformatics, obtaining protein graph labels usually needs laborious experiments. Recently, few-shot learning has been explored to alleviate this problem with only given a few labeled graph samples of test classes. The shared sub-structures between training classes and test classes are essential in few-shot graph classification. Exiting methods assume that the test classes belong to the same set of super-classes clustered from training classes. However, according to our observations, the label spaces of training classes and test classes usually do not overlap in real-world scenario. As a result, the existing methods don't well capture the local structures of unseen test classes. To overcome the limitation, in this paper, we propose a direct method to capture the sub-structures with well initialized meta-learner within a few adaptation steps. More specifically, (1) we propose a novel framework consisting of a graph meta-learner, which uses GNNs based modules for fast adaptation on graph data, and a step controller for the robustness and generalization of meta-learner; (2) we provide quantitative analysis for the framework and give a graph-dependent upper bound of the generalization error based on our framework; (3) the extensive experiments on real-world datasets demonstrate that our framework gets state-of-the-art results on several few-shot graph classification tasks compared to baselines.
STBS-Stega: Coverless text steganography based on state transition-binary sequence

Information-hiding technology has recently developed into an area of significant interest in the field of information security. As one of the primary carriers in steganography, it is difficult to hide information in texts because there is insufficient information redundancy. Traditional text steganography methods are generally not robust or secure. Based on the Markov chain model, a new text steganography approach is proposed that focuses on transition probability, one of the most important concepts of the Markov chain model. We created a state transition-binary sequence diagrams based on the aforementioned concepts and used them to guide the generation of new texts with embedded secret information. Compared to other related works, the proposed method exploits the use of the transition probability in the process of steganographic text generation. The associated developed algorithm also encrypts the serial number of the state transition-binary sequence diagram needed by the receiver to extract the information, which further enhances the security of the steganography information. Experiments were designed to evaluate the proposed model. The results revealed that the model had higher concealment and hidden capacity compared to previous methods.
Dynamic Graph Regularization and Label Relaxation-Based Sparse Matrix Regression for Two-Dimensional Feature Selection

Sparse matrix regression (SMR) is a two-dimensional supervised feature selection method that can directly select the features on matrix data. It uses several couples of left and right regression vectors for each classifier and integrates them in formulating the regression function. However, SMR does not consider the local geometry of image samples, and it assumes that the training samples should exactly fit a linear model or a strict binary label matrix by left and right regression matrices. In order to enlarge margins between different classes and preserve the intrinsic geometry structure of samples in the transformed space, we will propose dynamic graph regularization and label relaxation-based SMR (abbreviated as DGRLR-SMR) method for two-dimensional supervised feature selection. First, the label relaxation SMR is established by relaxing the strict binary label matrix into a slack variable matrix via a nonnegative label relaxation matrix by the $\varepsilon $ -dragging technique. Second, we construct a dynamic graph matrix learning model, rather than using the heat kernel function to obtain a fixed graph matrix, to capture the discriminative information and the local manifold structure of the image samples. Therefore, the proposed model not only enlarges margins between different classes, but also obtains a sparse transformation matrix and avoids the problem of over-fitting. An optimization algorithm is devised to solve this model, and it has closed-form solutions in each iteration so that it can be implemented easily in real application. Extensive experiments on several data sets demonstrate the superiority of our method.
Psychological virtual reality in a computer interactive environment

Objectives: analysis of the correlation between concepts “cyberspace”, “computer virtual reality”, “computer interactive environment”, “interactive experience”; wording of the author’s definition of the term “psychological virtual reality”; identification of its functions and features. Background. Computer interactive environments are becoming the main platforms for the professional and personal self-realization of a modern person, the social activity of youth is shifting to the Internet space, models and forms of interpersonal interaction and communication are changing, new psychological constructs are formed that reflect virtual world and provide interaction with it. Psychological virtual reality acts as an intermediary between a person as a personality and a computer interactive environment as a form of cyberspace representation in modern human life, and the psychological constructs generated in it determine the formation of new socio-psychological behavior patterns. Methodology: systemic and subjective approaches, comparative analysis method. Conclusions. The creation of computer interactive environments has opened a new stage in the formation of a new interactive human experience as a system of information technology and socio-psychological competencies. There is a need for a systematic study of the conceptual-categorical apparatus that is used to describe computer interactive environments. Psychological virtual reality is a component of computer interactive environment, performing number of important functions of optimizing human interaction with computer environment. The analysis of the "man—computer environment" system gives reason to distinguish between nine basic psychotypes of social subjects which are participants of virtual reality. The typology developed in the article can be recommended for use as a methodological basis in the development of socio-psychological diagnostic tools, in the practice of socio-psychological counseling and social work.
Boosting Factual Correctness of Abstractive Summarization

A commonly observed problem with abstractive summarization is the distortion or fabrication of factual information in the article. This inconsistency between summary and original text has led to various concerns over its applicability. In this paper, we firstly propose a Fact-Aware Summarization model, FASum, which extracts factual relations from the article and integrates this knowledge into the decoding process via neural graph computation. Then, we propose a Factual Corrector model, FC, that can modify abstractive summaries generated by any model to improve factual correctness. Empirical results show that FASum generates summaries with significantly higher factual correctness compared with state-of-the-art abstractive summarization systems, both under an independently trained factual correctness evaluator and human evaluation. And FC improves the factual correctness of summaries generated by various models via only modifying several entity tokens.
Multimedia Image Compression Method Based on Biorthogonal Wavelet and Edge Intelligent Analysis

At present, network image communication is still restricted by channel coding, image and multimedia transmission and other key technologies. Therefore, the transmission process needs to convert the image signal into a digital signal, and then use the relevant band compression technology to reduce these signals to narrow the occupied frequency band, that is, to reduce the amount of information to be transmitted in synchronous transmission. Wavelet transform is a powerful tool for image compression because of its low entropy, multi-resolution, decorrelation and flexible base selection. In this paper, a method of multimedia image compression based on biorthogonal wavelet packet is proposed, which includes the establishment of linear phase biorthogonal wavelet basis, the selection of 3 or 4 levels of wavelet decomposition and reconstruction stage, and the combination of improved band division and preservation strategy. Finally, a compression test is performed based on the selected wavelet basis function and the optimal decomposition and reconstruction layers of the standard test image, which enables to obtain a more ideal compression ratio.
Experimental Research of English Teaching Management Model Based on Personalized Multimedia Technology

The further integration of information technology and subject teaching is the megatrends of teaching reform nowadays.With the help of information technology, teachers, learners and teaching environment have been effectively integrated; the enthusiasm of teachers and students in learning has been greatly improved.However, the actual effect of foreign language learning has not been substantially improved.Instead, they have become optional outside the classroom.How to use the existing equipment and resources to build a personalized learning environment acceptable to learners and can improve the results of learning is a problem that our English teachers should consider.In view of the rationality of constructivism and contextual cognitive theory in supporting individualized classroom learning, combining with the above theory, based on MMFU (Multimedia for You), this paper attempts to realize it in classroom learning through the application case analysis of foreign language learning with the method of materialization and humanization.
Motion Feedback Design for Video Frame Interpolation

This paper introduces a feedback-based approach to interpolate video frames involving small and fast-moving objects. Unlike the existing feedforward-based methods that estimate optical flow and synthesize in-between frames sequentially, we introduce a motion-oriented component that adds a feedback block to the existing multi-scale autoencoder pipeline, which feedbacks information of small objects shared between architectures of two different scales. We show that feeding this additional information enables more robust detection of optical flow caused by small objects in fast motion. Using experiments on various datasets, we show that the feedback mechanism allows our method to achieve state-of-the-art results, both qualitatively and quantitatively.
GraphMDL: Graph Pattern Selection Based on Minimum Description Length

Many graph pattern mining algorithms have been designed to identify recurring structures in graphs. The main drawback of these approaches is that they often extract too many patterns for human analysis. Recently, pattern mining methods using the Minimum Description Length (MDL) principle have been proposed to select a characteristic subset of patterns from transactional, sequential and relational data. In this paper, we propose an MDL-based approach for selecting a characteristic subset of patterns on labeled graphs. A key notion in this paper is the introduction of ports to encode connections between pattern occurrences without any loss of information. Experiments show that the number of patterns is drastically reduced. The selected patterns have complex shapes and are representative of the data.
A Survey on Edge Benchmarking

Edge computing is the next Internet frontier that will leverage computing resources located near users, sensors, and data stores for delivering more responsive services. Thus, it is envisioned that a large-scale, geographically dispersed and resource-rich distributed system will emerge and become the backbone of the future Internet. However, given the loosely coupled nature of these complex systems, their operational conditions are expected to significantly change over time. In this context, the performance of these systems will need to be captured rapidly, referred to as benchmarking, for application deployment, resource orchestration, and adaptive decision-making. Edge benchmarking is a nascent research avenue that has started gaining momentum over the last five years. This article firstly examines articles published over the last three decades to trace the history of benchmarking from tightly coupled to loosely coupled systems. Then it systematically classifies research to identify the system under test, techniques analyzed, quality metrics, and benchmark runtime in edge benchmarking.
Residual Learning of Transfer-learned AlexNet for Image Denoising

In today’s scenarios, deep learning has fascinated all researchers from numerous arenas who developed ways to achieve obligatory outcomes. In deep learning, transfer learning is undergoing deep study, because the study helps to practice a pre-trained network for our own tasks. A novel, transfer-learned AlexNet-based residual learning for Gaussian noise reduction is presented in this paper. The method can remove any level of Gaussian noise without having information about the noise variance in both gray scale and color images. Therefore, our technique is blind Gaussian image denoising that learns a residual image by eradicating the clean image from the transfer-learned AlexNet, and removes noise by identifying the difference from the input image. Experimental results with the proposed scheme are compared against a Gaussian denoiser for image denoising in terms of peak signal-to-noise ratio (PSNR) and visual perception. The results have revealed that our residual learning using transfer-learned AlexNet attains promising denoising results.
Training Data Extraction and Object Detection in Surveillance Scenario

Police and various security services use video analysis for securing public space, mass events, and when investigating criminal activity. Due to a huge amount of data supplied to surveillance systems, some automatic data processing is a necessity. In one typical scenario, an operator marks an object in an image frame and searches for all occurrences of the object in other frames or even image sequences. This problem is hard in general. Algorithms supporting this scenario must reconcile several seemingly contradicting factors: training and detection speed, detection reliability, and learning from small data sets. In the system proposed here, we use a two-stage detector. The first region proposal stage is based on a Cascade Classifier while the second classification stage is based either on a Support Vector Machines (SVMs) or Convolutional Neural Networks (CNNs). The proposed configuration ensures both speed and detection reliability. In addition to this, an object tracking and background-foreground separation algorithm is used, supported by the GrabCut algorithm and a sample synthesis procedure, in order to collect rich training data for the detector. Experiments show that the system is effective, useful, and applicable to practical surveillance tasks.
PLANNING AND EVALUATION DURING EDUCATIONAL DISRUPTION: LESSONS LEARNED FROM COVID-19 PANDEMIC FOR TREATMENT OF EMERGENCIES IN EDUCATION

The aim of this paper is to formulate a proposal for responding to emergencies in education, either at the level of the education system at a national or regional dimension (macro level) or at the level of an organization (meso level). The impetus of this proposal stemmed from the crisis caused by the COVID-19 pandemic in education systems and educational/training organizations worldwide, which has resulted in the disruption of educational function. It should be clarified in advance that this proposal does not concern crises of the education system itself, but situations where a major crisis at another level of public life creates a secondary crisis in education, an emergency in which education cannot fulfill its function. Article visualizations: