Reviewerly/exHarmony huggingface dataset:
Management of Information from Surveillance Cameras at the Infrastructure Facility

CCTV cameras are integral components of any modern infrastructure facility. They are designed primarily to provide the possibility of current monitoring and temporary storage of video data. However, the video stream itself requires additional resources for processing if it is necessary to search for specific facts (events in the observation area), and is also expensive in terms of the occupied disk space. This study proposes methods for managing information from video cameras, including processing a video stream to identify objects (vehicles and people) using machine vision. An approach to analyzing the location of objects in space and recording the sequence of their movements is proposed. The methods of calculating the spatial coordinates of objects based on images from the camera are described. The description of the developed tools for joint automated processing of data from several CCTV cameras for the implementation of complex monitoring of events at the infrastructure facility is given.
Face Mask Recognition for Covid-19 Prevention

In recent years, the COVID-19 pandemic has negatively impacted all aspects of social life. Due to ease in the infected method, i.e., through small liquid particles from the mouth or the nose when people cough, sneeze, speak, sing, or breathe, the virus can quickly spread and create severe problems for people’s health. According to some research as well as World Health Organization (WHO) recommendation, one of the most economical and effective methods to prevent the spread of the pandemic is to ask people to wear the face mask in the public space. A face mask will help prevent the droplet and aerosol from person to person to reduce the risk of virus infection. This simple method can reduce up to 95% of the spread of the particles. However, this solution depends heavily on social consciousness, which is sometimes unstable. In order to improve the effectiveness of wearing face masks in public spaces, this research proposes an approach for detecting and warning a person who does not wear or misuse the face mask. The approach uses the deep learning technique that relies on GoogleNet, AlexNet, and VGG16 models. The results are synthesized by an ensemble method, i.e., the bagging technique. From the experimental results, the approach represents a more than 95% accuracy of face mask recognition.
AppFuse: An Appearance Fusion Framework for Saliency Cues

Various types of saliency cues exist, all of which can be instrumental in the foreground extraction. It brings us to an interesting problem of effectively combining them. Note that earlier works either fuse them in the spatial domain or introduce dedicated terms in the energy functions to cater to multiple cues. In contrast, this paper investigates the appearance domain and proposes a novel appearance fusion framework, which we refer to as AppFuse. It is an intuitive framework for fusing candidate appearance models into the desired one for an energy function. Thus, we do not require any alterations in the energy function anymore. Like any fusion strategy, the proposed framework also requires guidance, which we facilitate through reliability and mutual consensus phenomena. To demonstrate the efficacy, we leverage it to solve a foreground extraction problem named video co-localization, where we propose two novel concepts i) hierarchical co-saliency and ii) mask-specific proposals. Our fusion results ensure that similar objects get highlighted sufficiently to ensure localization simply by respecting our framework and different spatiotemporal constraints. Our exhaustive set of experiments using both hand-crafted and learned saliency cues reveal that our approach comfortably outperforms several competing localization methods on standard benchmark datasets.
IOHIVE: Design Requirements for a System that Supports Interactive Journaling for Beekeepers During Apiary Inspections

In this paper, we present an ongoing project that focuses on designing interactive systems and their respective interfaces for monitoring and journaling of apiculture information acquired in the actual field during apiary inspections by the beekeepers. Initially, the paper provides a brief overview of the concepts and technologies found in the domain. Next, it examines the scenarios to be used for the design of interactions related to the actual beehive inspections and desktop use in the office. The paper mainly focuses on the design requirements based on user research. It provides a review of interaction techniques that can be implemented for journaling in the workplace of the apiary, briefly outlines the infrastructure and gives a system overview at its current state of development. Finally, the paper discusses future work including, guidelines towards the development of the various system components for journaling and a preliminary evaluation plan for the case studies that will follow.
Random forests in R

Random forests are one of the most accurate supervised learning algorithms used for both classification and regression problems. Random forests can be considered as an ensemble of several decision trees, and their output is determined by the output of the majority of the decision trees making the random forest. In this chapter, we learn how to create random forest classifiers and regression models.
Management Evaluation System Based on Rao-Stirling Diversity Algorithm

The connections, intersections, and fusions of knowledge between different disciplines have become more and more frequent. At the same time, this process has also been accompanied by many new views and ideas. The connections between knowledge have become more and more diversified, and the degree of connection has become more and more diversified. It’s getting deeper and deeper. The purpose of this article is to study the management evaluation system based on the Rao-Stirling diversity algorithm. Based on the study of interdisciplinary theory, this study uses a combination of literature research, citation analysis, statistical analysis, and comparative analysis to study the development of knowledge exchange in management disciplines based on the interdisciplinary research perspective. This paper also proposes the Rao-Stirling diversity algorithm based on author attributes. This algorithm considers the particularity of the author’s link relationship, so it can be used to solve the degree of interdisciplinary nature of a particular scientific individual, that is, the degree of interdisciplinary nature of the author. The experimental results show that the papers in the target paper collection belong to 9 disciplines, and the similarity value between the target discipline Government Administration and the cited discipline Public Administration is the highest.
A General Framework for Intelligent Optimization Algorithms Based on Multilevel Evolutions

Intelligent optimization is a kind of global optimization algorithms based on simulating biological intelligent behaviors such as evolution and foraging. Currently, there are numerous intelligent optimization algorithms have been proposed based on a large mount of animals' or plants' behaviors. This phenomenon shows the prosperity of this field, but bring issues about these algorithms' analysis and applications. We believe an extensive development stage has passed in the field of intelligent optimization, and more theoretical analysis and deep understanding about these algorithms become favorite. In this paper, we try to build a general framework for all population-based global optimization algorithms. This framework employs the idea of multilevel evolution, and therefore it can include not only the traditional bio-inspired evolution algorithms which often only evolute in a single level of search space, but also those population-based algorithms adopt data-driven strategies or cultural evolutions. By the help of the proposed framework, we can classify all population-based global optimization algorithms into three types, and improve the traditional algorithms. In this paper, this framework is then applied to the popular particle swarm optimization, and a modified particle swarm optimization with three-level of evolutions is proposed. Numerical results show that the modified algorithm improves the original one significantly.
Privacy-Preserving Video Fall Detection via Chaotic Compressed Sensing and GAN-Based Feature Enhancement

Currently, falling is the primary cause of injury and death of the elderly due to accidents, which seriously threatens the health and life of the elderly. Considering the phenomenon of video privacy disclosure in recent years, this article proposes a computer vision fall detection method to meet the needs of video privacy protection. The innovation here lies in compressed sensing (CS) visual privacy protection and GAN-based feature enhancement. There are three main steps, the video frame visual privacy protection with chaotic pseudo-random CS mechanism, the foreground extraction with improved low rank sparse decomposition theory, and the temporal and spatial feature enhancement and fall detection with improved-ACGAN `architecture. The experimental results on three open fall datasets show that the method can not only effectively detect the fall behavior in video, but also has high accuracy. At the same time, the overall operation speed of the algorithm increases with the increase of the number of compression layers.
The Effect of Innovation Capacity on the Relationship between Volatility and Growth

Schumpeter’s notion of creative destruction assumes that volatile growth leads to a more efficient reallocation of resources and the adoption of new technologies. However, this can only occur if such productivity-enhancing opportunities are available within society. This availability is represented by innovation capacity. I model and show empirically, that volatility affects growth differently depending on available innovation capacity. The model predicts that abundant innovation capacity can result in a positive effect of volatility on growth. I do not find empirical evidence for a positive effect. However, in line with the model, the results show that the adverse effect of volatility on growth is negated to a much higher degree when innovation capacity is high. This result emphasises the necessity to consider the current state of a society when analysing if volatility will be detrimental or harmless to growth.
ELight: Toward Efficient and Aging-Resilient Photonic In-Memory Neurocomputing

Optical phase change material (PCM) has emerged promising to enable photonic in-memory neurocomputing in optical neural network (ONN) designs. However, massive photonic tensor core (PTC) reuse is required to implement large matrix multiplication due to the limited single-core scale. The resultant large number of PCM writes during inference incurs serious dynamic energy costs and overwhelms the fragile PCM with limited write endurance, causing the severe aging issue. Moreover, the aged PCM would distort the stored value and significantly degrade the reliability of PTC. In this work, we propose a holistic solution, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ELight</monospace> , to tackle both the aging issue and the post-aging reliability issue, where a proactive aging-aware optimization framework minimizes the overall PCM write cost and a post-aging tolerance scheme overcomes the effect of aged PCM. Specifically, in the aging-aware optimization part, we propose write-aware training to encourage the similarity among weight blocks and combine it with a post-training optimization technique to reduce programming efforts by eliminating redundant writes. Next, an efficient groupwise row-based weight-PTC remapping scheme is introduced to tolerate the reprogrammability degradation due to the aged PCM. Experiments show that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ELight</monospace> can achieve over <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20 \times $ </tex-math></inline-formula> reductions in the total number of write operations and dynamic energy cost with comparable accuracy. Moreover, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ELight</monospace> can guarantee significant accuracy recovery under the aged PCM within photonic memories. With our <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ELight</monospace> , photonic in-memory neurocomputing will step forward toward practical applications in machine learning with order-of-magnitude longer lifetime, lower programming energy cost, and significant resilience against PCM aging effects.
Optimizing Visual Cues in Educational Software

This study examined the effects of software graphical user interface (GUI) visual cues in educational software on user performance. It specifically studied the effectiveness of three distinct and commonly used visual cues -- bolded text, buttons, and arrows to guide a software application user through a series of tasks. The study attempted to prove the hypothesis that specific visual cues in educational software applications could decrease task time. The study population consisted of a group of 134 post-secondary undergraduate students in Honolulu, Hawaiʻi, that engaged in a web-based educational software simulation which recorded response times when prompted by each of the three distinct visual cues. The web-based simulation consisted of six steps. Each step consisted of a simple question and the appearance of a new visual cue to lead the participant to the next step only after selecting the correct answer. Each step of the experiment was automatically timed and recorded in milliseconds from the moment the participant selected the correct answer until the moment they clicked on the visual cue to proceed to the next step. Slower response times indicated that during the first two steps, participants were still scanning the screen for the visual cue after they selected the correct answer. Of the three cues studied on the first two steps of the simulation, the Arrows and Bolded text were clearly the most quickly recognized cues among participants, while the response times for the Button cue were significantly slower. However, in the last four steps of the simulation, no visual cue could be identified as the leader in participant response times. This would indicate that, since the visual cues were consistently in the same position, the participants acclimated to the position of the cue. At this point, there was no notable differences in response times among the different cues. This study suggests that using arrows and/or bolded text in educational software are better choices for visual cues than buttons. It also suggests that keeping visual cues for common functions in a consistent location is optimal.
Controllable Model Compression for Roadside Camera Depth Estimation

In the Cooperative Intelligent Transportation System (C-ITS) paradigm, vehicles could communicate with roadside units to augment their traffic knowledge. Smart roadside units could provide second-order information (e.g., vehicle count) from raw first-order data (e.g., visual feed, point clouds), and this “smart” feature is usually provided using deep neural network models. However, implementing these useful models implies a cost for computational complexity that could hinder the future deployment of smart roadside units needed for sustainability in transportation systems. In this paper, we propose to use model compression on deep image processing models to promote its feasibility for usage in smart sensors. We formulated a controllable convolutional model compression (CCMC) algorithm that can perform filter-wise evolutionary pruning on image processing networks, along with a predefined compression ratio. CCMC is applicable for image processing networks, which have multiple possible traffic data sources (e.g., road camera surveillance). Furthermore, CCMC has a definable target compression ratio that is useful for controlling the trade-off between resource consumption and output performance. We tested our proposed method on depth estimation, which is useful for scene understanding and mapping the locations of objects in the 3D space. Our experiments show that the pruned model has minimal performance discrepancy from the original one, supporting the sustainability features needed for intelligent transportation systems.
UBDM: Utility-Based Potential Pattern Mining over Uncertain Data Using Spark Framework

In practical scenarios, an entity presence can depend on existence probability instead of binary situations of present or absent. This is certainly relevant for information taken in an experimental setting or with instruments, devices, and faulty methods. High-utility patterns mining (HUPM) is a collection of approaches for detecting patterns in transaction records that take into account both object count and profitability. HUPM algorithms, on the other hand, can only handle accurate data, despite the fact that extensive data obtained in real-world applications via experimental observations or sensors are frequently uncertain. To uncover interesting patterns in an inherent uncertain collection, potential high-utility pattern mining (PHUPM) is developed. This paper proposes a Spark-based potential interesting pattern mining solution to work with large amounts of uncertain data. The suggested technique effectively discovers patterns using the probability-utility-list structure. One of our highest priorities is to improve execution time while increasing parallelization and distribution of all workloads. In-depth test findings on both real and simulated databases reveal that the proposed method performs well in a Spark framework with large data collections.
Reclamation of extraordinary utility items in the multi-level catalogue

Utility mining is the central area of data mining that reveals a data set with high-use items (HUIs). To date, several techniques to extract HUIs in a single component database have been projected. According to the literature survey, multilevel data sets do not have utility mining algorithms. HUIs of a multilevel data set is the primary goal of this work. This paper proposes to find HUIs in a multilevel dataset with a unique utility mining algorithm called MUMA (Multilevel Utility Mining Algorithm). To supply the valuable data of both the item set, MUMA utilizes a parse tree known as MUMT, a multi-layered efficiency mining tree. This tree produces candidate item sets that reduce the method's space complexity. The research has also suggested a reinforced tree-based algorithm called MU UP Mining, Multilateral Utility Mining, MU UP, and Lexicographic Tree (MU LG). The study also proposed an enriched tree-based algorithm. These experiments are done using various datasets such as transaction datasets, weblog data, and synthetic datasets, with all the suggested algorithms. Each dataset evaluated performance factors such as performance time, reminiscence space, the number of potentially high utilities, and the number of HUIs collected on each level.
Joint Dynamic Grouping and Gradient Coding for Time-Critical Distributed Machine Learning in Heterogeneous Edge Networks

In edge networks, distributed computing resources have been widely utilized to collaboratively perform a machine learning task by multiple nodes. However, the model training time in heterogeneous edge networks is becoming longer because of excessive computation and delay caused by slow nodes, namely, stragglers. The parameter server even abandons stragglers which fail to return the outcome within a reasonable deadline, called straggler dropout, decreasing the model accuracy. To optimize the computation cost and maintain the model accuracy, we focus on mitigating the heavy computation of stragglers and preventing straggler dropout. Therefore, we propose a novel scheme named dynamic grouping and heterogeneity-aware gradient coding (DGH-GC) to tolerate stragglers by employing dynamic grouping and gradient coding. DGH-GC evenly distributes stragglers in each group and encodes gradients based on their computation capacity to prevent them drop out. However, DGH-GC exacerbates the communication burden by making data duplication to tolerate stragglers. Relying on the scheme, we further propose an algorithm called DGH-(GC)2 to compress transferred gradients in both upstream communication and downstream communication. Experimental evaluations prove that DGH-(GC) outperforms all state-of-the-art methods and DGH-(GC)2 further speeds up the convergence time of the trained model and saves about 26% average iteration time compared to the DGH-(GC).
Utilizing Deep Belief Network for Ensuring Privacy-Preserved Access Control of Data

Deep belief network is an unsupervised deep learning methodology of neural networks and has gained increasing attention during recent years. In contrast to conventional learning methods, deep learning is powerful methodology that is greatly influencing cloud environments for model training purposes where collaborative learning between different data sources exists. It is utilized for wide application domains varying from health care to pattern or speech recognition. However, for the healthcare data domains, the privacy and security issues are the major concerns due to the presence of sensitive information regarding patient’s medical data during collaborative learning phase. This paper aims at presenting an effective privacy-preserved access control solution, called PPAC, for the collaborative healthcare data. The overarching aim of improving privacy of health data is managed during three proposed phases, namely user-registration, control setup, and validation. Encryption and hashing functions are utilized for controlled access in collaborative cloud framework. In addition, the deep belief network algorithm of deep learning is used for the classification of cloud user as either genuine or attacker. This paper further elaborated the performance analysis of PPAC to reveal its effectiveness on performance metrics, such as precision, recall, and F-measure. The experiment results reveal that the proposed PPAC method provided best precision, recall, and F-measure of 0.9427, 0.945, and 0.9438, respectively.
The Principle and Implementation of Sentiment Analysis System

The sentiment analysis system is one of the most classic applications in natural language processing and enduring. The development of the mobile Internet has greatly increased people's participation, and everyone can make their own comments on social media platforms such as Weibo. Through public opinion mining and emotional analysis of text information, a rich potential value of information can be obtained. However, in the face of a large number of comment data, how is it more convenient for public opinion workers to see the whole picture and take timely measures? This is the practical problem to be addressed in this article. The main work of this article is to build a public opinion emotional analysis system about Jiangsu Police Institute, to realize the emotional analysis of the comments related to Jiangsu Police Institute in Weibo and post bar. Then related workers are able to screen out the comments of negative emotions. The experimental dataset in this article is a training dataset composed of positive and negative reviews selected from JD commodity reviews, and the test data are random reviews selected from the microblog of Jiangsu Police Institute. This article details the processing of the Chinese comment text dataset. Because Chinese text is involved, the data is first partitioned using the jieba participle. After the data pre-processing, we input the processed data into the Word2Vec model, and set the relevant parameters according to the formatting rules of the word2vec, so that the dataset of text can be quantized to facilitate code learning.