Reviewerly/exHarmony huggingface dataset:
Skeleton-based Hand Gesture Recognition for Assembly Line Operation

This research aims to develop a hand gesture recognition (HGR) by combining the OpenPose and Spatial Temporal Graph Convolution Network (ST-GCN) to classify the operator's assembly motion. By defining the hand gestures with five types of therbligs, the network model was trained to recognize the human hand gesture. Although the accuracy of recognition is 78.3% with room for improvement based on preliminary experiment results, the structure of the proposed network establishes a foundation for further improvement in future work.
Discrete Band-Limited Signal Reconstruction From Irregular Samples

Image reconstruction from discrete samples is fundamental in remote sensing. The reconstruction problem is more complicated when the sample locations are irregularly spaced and employ different aperture functions. Further, not all 2-D sampling configurations permit full image reconstruction of band-limited signals. In this article, 2-D signal reconstruction from irregular samples with variable apertures is considered using theory and examples. Exact reconstruction requires that the signal be band-limited and the sampling matrix be invertable. The results are sensitive to the sample locations and noise in the measurements. Illustrative examples are provided using simulation and actual data from the L-band Soil Moisture Active Passive (SMAP) radiometer. The general approach can be employed with other sensors.
Adaptive Graph Regularized Low–Rank Matrix Factorization With Noise and Outliers for Clustering

Clustering, which is a commonly used tool, has been applied in machine learning, data mining and so on, and has received extensive research. However, there are usually noise and outliers in the data, which will bring about significant errors in the clustering results. In this paper, a robust clustering model with adaptive graph regularization (RCAG) is proposed, on which, sparse error matrix is introduced to express sparse noise, such as impulse noise, dead line, stripes, and ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm is introduced to alleviate the sparse noise. In addition, the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> norm is also proposed mitigating the effects of outliers, and it has rotation invariance property. Therefore, our RCAG is insensitive to data noise and outliers. More importantly, the adaptive graph regularization is introduced into the RCAG to improve the clustering performance. Aiming at the optimization objective, we propose an iterative updating algorithm, named the Augmented Lagrangian Method (ALM), to update each optimization variable respectively. The convergence and time complexity of RCAG is also proved in theory. Finally, experimental results on fourteen datasets of four application scenarios, such as face image, handwriting recognition and UCI, elaborate the superiority of proposed method over seven existing classical clustering methods. The experimental results demonstrate that our approach achieves better clustering performance in ACC and Purity, which is a little less impressive in other ways.
Data mining and social networks processing method based on support vector machine and k-nearest neighbor

OBJECTIVE: With Sina Weibo data as the background, support vector machine (SVM) and k-nearest neighbor (KNN) method are used to predict and analyze the user’s micro-blog emotion and related behavior in social network, hoping to obtain rich potential business value. METHODS: First, the API interface of Sina Weibo is utilized to obtain the information of users in Sina Weibo; then, the Excel software is utilized to sort and analyze the extracted data to extract the features of micro- blogs posted by users. Second, SVM and KNN algorithms are utilized to calculate the weighted average and propose a hybrid multi-classifier-based Mixed Classifier Emotion Prediction Model (MCEPM). Finally, through the evaluation criteria, including precision (P), recall rate (R), and harmonic average (F1), the specific experimental results of SVM and KNN weight coefficients are compared with the prediction results of MCEPM. RESULTS: The prediction effect of MCEPM is associated with the weight coefficients of SVM and KNN. If the weight coefficients of SVM and KNN are 0.6 and 0.4, the prediction effect of MCEPM will be optimal. Comprehensive analysis shows that the MCEPM model can balance the prediction results of the positive and negative samples of the two classifiers. CONCLUSION: MCEPM model is superior to other algorithms in micro-blog emotion prediction, which can help enterprises analyze users’ product inclination and provide accurate customer service requirements for enterprises.
Token-Based Blockchain Solutions for Supply Chain Strategies

Distributed ledger technologies such as the blockchain technology offer an innovative solution to increase visibility and security to reduce supply chain risks. This paper proposes a solution to increase the transparency and auditability of manufactured products in collaborative networks by adopting smart contract-based virtual identities. Compared with existing approaches, this extended smart contract-based solution offers manufacturing networks the possibility of involving privacy, content updating, and portability approaches to smart contracts. As a result, the solution is suitable for the dynamic administration of complex supply chains.
Improved Skip Algorithm for Single Pattern Searching

In this paper, we present a novel idea for a single pattern matching in strings. The idea is named skip search (SS). The pattern is checked for presence in the given text by accessing only half the memory locations even for the worst case. The search is a modified version of the Naïve search algorithm by accessing only half the characters in the given string and by checking if those characters are the starting or ending character of the pattern. By doing this, we could obtain the skip length of the pattern size and hence reduce the search time. Since there is no preprocessing phase or no extra memory space required, the algorithm is good in terms of space complexity. The time taken for execution is compared with Naïve algorithm and Knuth–Morris–Pratt (KMP) algorithm, and the skip algorithm gave a better performance for most of the test cases. The algorithm has a big difference in execution time when the pattern is present toward the end of the text or if the pattern is not present in the text. The algorithm can be recommended for any pattern matching projects.
Universal Optimization Strategies for Object Detection Networks

With the development of deep learning technologies, object detection algorithms have made significant progress in terms of detection speed and detection performance. However, the detection speed of current detection networks still does not meet the requirements of real-world applications in some scenarios. In this paper, we propose a faster non-maximum suppression (FNMS) algorithm that reduces the processing time by a large margin while achieving the same detection precision compared with the traditional non-maximum suppression (NMS) algorithm. Moreover, an attempt is made to adopt additional lightweight network structures to improve the speed of the detection network. By combining our FNMS algorithm with other network optimization strategies, we are able to improve the detection speed of YOLO v3 on the DOTA dataset by 165%.
Collaborative Filtering Recommendation Based on Multi-Domain Semantic Fusion

Collaborative filtering based on single domains has become widely used in today's recommendation system. Nevertheless, it has two problems that need to be solved, i.e., the cold start problem and the data sparseness problem. As the result, cross-domain recommendation technology has emerged, which aims at integrating user preference characteristics from different domains. This paper proposes a collaborative filtering recommendation method based on multi-domain semantic fusion (CF-MDS). CF-MDS achieves cross-domain item similarity calculation through semantic analysis and ontology and integrates data from different domains iteratively based on domain relevance to rate users on target domain items and to produce a cross-domain user-item rating matrix. Collaborative filtering technology is then combined with multi-domain fusion recommendation algorithm. Experimental results show that the proposed method can deal effectively with the cold start problem and data sparsity problem that exist in traditional recommendation systems as well as can improve the diversity of recommendation. Compared to other cross-domain recommendation methods, the proposed method can better meet personal needs of users and also improve the accuracy of recommendation.
Event-based Access to Historical Italian War Memoirs

The progressive digitization of historical archives provides new, often domain-specific, textual resources that report on facts and events that have happened in the past; among these, memoirs are a very common type of primary source. In this article, we present an approach for extracting information from Italian historical war memoirs and turning it into structured knowledge. This is based on the semantic notions of events, participants, and roles. We evaluate quantitatively each of the key steps of our approach and provide a graph-based representation of the extracted knowledge, which allows to move between a Close and a Distant Reading of the collection.
The Old Bailey and OCR

The Proceedings of the Old Bailey is a corpus of over 180,000 page images of court records printed from April 1674 to April 1913 and presents a comprehensive challenge for Optical Character Recognition (OCR) services. The Old Bailey is an ideal benchmark for historical document OCR, representing more than two centuries of variations in documents, including spellings, formats, and printing and preservation qualities. In addition to its historical and sociological significance, the Old Bailey is filled with imperfections that reflect the reality of coping with large-scale historical data. Most importantly, the Old Bailey contains human transcriptions for each page, which can be used to help measure OCR accuracy. Since humans do make mistakes in transcriptions, the relative performance of OCR services will be more informative than their absolute performance. This paper compares three leading commercial OCR cloud services: Amazon Web Services's Textract (AWS); Microsoft Azure's Cognitive Services (Azure); and Google Cloud Platform's Vision (GCP). Benchmarking involved downloading over 180,000 images, executing the OCR, and measuring the error rate of the OCR text against the human transcriptions. Our results found that AWS had the lowest median error rate, Azure had the lowest median round trip time, and GCP had the best combination of a low error rate and a low duration.
Patch-match based detection of copy-move forgeries using rotation invariant features

As society increasingly relies on computers and digital data, the judiciary and other businesses are highly dependent on digital forensics for identification, preservation, examination and analysis of digital evidence such as digital photographs and digital documents. The wide range of powerful image editing tools that are evolving day by day helps to modify the image content, or to tamper the image with ease and does not require any expertise. The most challenging form of tampering to be detected is the copy – move forgery. The process of tampering images by copying one portion of the and pasting it in some other region of the same image is referred to as copy-move forgery. To make the copy-move forgery detection problem further complex, variations like scaling, rotation, flipping, etc. may be made over the copied region before it is pasted. Even those of us with the sharpest eyes may easily be fooled with such fake images and this is one of the major problems in courtrooms and in press publications. As such the integrity as well of authenticity of images is highly important where decision making or judgements have to be made based on the information obtained through images. Rotation- invariant features computed densely on the image and patch-match algorithm is proposed in this paper, to detect copy-move forgeries. The proposed detection algorithm is executed in three steps. Firstly, Circular Harmonic Transform based rotation invariant Zernike Moment and FMT features are computed for each pixel of interest. Secondly matching of dense features, is carried out using patch matching algorithm and finally post-processing is carried out to minimize the false positives. Experimental results demonstrate that the proposed approach is efficient in detecting copy move forgeries subjected to various types of geometric distortions.
A Blockchain Token Economy Model for Financing a Decentralized Electric Vehicle Charging Platform

Recent advances in distributed ledger technologies enable new types of decentralized governance and financing for technology platforms. In this paper, we analyze the current state-of-the-art in platform financing and propose a novel way to sustainably finance decentralized technology platforms using a blockchain-based token economy. We design and develop a token model and demonstrate its usefulness for financing the Open Charging Network, an electric vehicle charging platform governed by the Share&Charge foundation. Based on a multi-method simulation approach, we evaluate our token economy model and show, that it can provide sustainable financing for a technology platform with decentralized governance.
A Machine Learning Approach to Anaphora Resolution in Nepali Language

In this paper, we attempt a machine learning (ML) approach to Anaphora Resolution (AR) system in Nepali language. It is one of the pioneering approaches in anaphora resolution using machine learning in Nepali language, which is a resource-limited language. For this work, we have developed our own data set in the standard format available in this domain. Data has been tagged with the necessary information like Parts-of-speech (POS), Named entity, Chunking information, Gender, Number, Person, etc. We divided the data for training and testing purposes in approximately 5:1 ratio and ML classifiers are used for training and testing. Results show encouraging for further progress.
Ethereum blockchain based healthcare Industry ecosystem

Thirty-five % of the US population is affected by data breach occurred in the healthcare industry. It is a total of 114 million people affected in 2015. On average of 4 data breaches happens per week. Health care industry is losing 300 billion dollars each year in poor data integration. we can overcome this problem by bringing a decentralized system in the hospital organization. This prototype helps us to stop and prevent data breaching in the health care industry and to remove fake patient IDs in the hospital data base.
Research on Binocular Forest Fire Source Location and Ranging System

The system under OpenCV environment was studied to realize the localization and ranging of forest fire sources effectively. The forest fire was identified by using the solar-blind ultraviolet-infrared binocular detector, and a gyroscope measured the azimuth. With the switching function of the binocular sensor, the ranging binocular part of the system can be activated. The equipment that has completed camera calibration and binocular image correction uses the SGBM algorithm to perform binocular matching and obtain a disparity map. Then the three-dimensional transformation of coordinates can be completed to get the ranging result under the OpenCV environment. The system adopts ultraviolet sun-blind imaging monitoring technology to eliminate the interference of sunlight and utilizes SGBM algorithm, which has the advantages of fast processing speed and distinct processing effect. The obtained parallax map provides a reliable basis for binocular ranging; thus, it has a minor error. This system has realized the binocular forest fire source localization and ranging, with advantages of self-sensing, self-identification, self-triggering, so it has a great automatic extent. It has advanced significantly in the forest fire location and range.
Secure selective recovery protocol for multiple optically encrypted data

In this work we present a secure selective recovery protocol for multiple optically encrypted data based on sampling with orthogonal random binary masks. In this protocol, every single object is encrypted using a single arm free-space cryptosystem, filtered and multiplied by an orthogonal random binary mask, then all the encrypted, filtered and sampled objects are multiplexed. Under this protocol, an authorized user can access a single object from the multiplexed data using both the encryption key and the corresponding orthogonal random binary mask. Without knowledge of the orthogonal binary masks used in the process, neither authorized users nor attackers can recognize the amount of multiplexed data. As a demonstration of the usefulness of our proposal, we implement an optically encrypted keyboard in which each letter is recovered individually without crosstalk. We can securely deliver messages of arbitrary length by sending the encrypted keyboard, the encryption key, and the corresponding set of binary masks. We perform tests showing the resistance of the processed data to occlusion and random noise. Additionally, we discuss the potential resistance of our proposal to Dirac delta and chosen plaintext attacks. Finally, a holographic projection setup is implemented to perform a selective experimental reconstruction of the original objects. The experimental results demonstrate the viability and validity of our proposal.
Synthetic guided domain adaptive and edge aware network for crowd counting

Crowd counting is an important surveillance application and receives significant attention from the computer vision community. Most of the current methods treat crowd counting by density map estimation and use the Fully Convolution Network (FCN) for prediction. The mainstream framework is to predict density maps and use the sum up the density maps to get the number of people. In such methods, the main drawback is the poor local quality of the dense part and the sparse part of an image. As we investigated, it is due to the lack of an efficient method to learn the heads' structure information. To address the above problem, in this paper, we propose a domain adaptive model called synthetic guided learning that learns features' structure from synthetic data. We also propose a multi-scale edge-aware loss for improving the boundary clearness of the estimated density map. Our experimental results show, learning from the structure information effectively improves the density maps' estimation quality and promotes the counting accuracy. Comprehensive experiments and comparisons with state-of-the-art methods on four publicly available data sets demonstrate the superiority of our proposed method. We provide a reference implementation of this technique at https://github.com/MRJTM/SGEANet.
Designing IT Service Management at Indonesia Internet Domain Names Registry Association's Helpdesk Function

Indonesia Internet Domain Names Registry Association (PANDI) hosts helpdesk function as a part of .id domain management. It is a one-stop facility for all registrants and the public to submit questions and complaints regarding .id domain names. Previously, helpdesk function had poor performance since its business processes were manual and slow. This research aimed to design Information Technology Service Management (ITSM) implementation as guidelines for PANDI helpdesk. It demonstrated case study research using qualitative approach by involving helpdesk officers, and c-level in depth interview. This research relied on ITIL V3 2011 framework to generate ITSM design: service catalog, Service Level Agreement, and Standard Operating Procedures. It resulted in tracking status, priority label, reporting feature, and service categories can be added on the application side. Those artifacts guided helpdesk to improve the quality of services provided by PANDI. They consisted of a service catalog that includes 11 Customer Facing Service (CFS) and Resource Facing Service (RFS), five SLAs for helpdesk business process services, and three SOPs for account changes request service.
Underwater Image Detection using Laplacian and Gaussian Technique

In submerged picture handling got consider consideration because of its difficult nature and its significance. The nature of submerged picture is more regrettable than that of pictures shot noticeable all around and pictures typically seem foggy and cloudy. In this task a Laplacian and Gaussian pyramid process are proposed for submerged picture upgrade. Further the improved picture is accommodated histogram leveling. Picture combination strategy, differentiate weight examination, standardization and distinctive technique to improve the debased submerged picture lastly intertwine the above outcome pictures by straightforward weighted mixing.
360$$^\circ $$ User-Generated Videos: Current Research and Future Trends

The 360\(^\circ \) video, also known as immersive or spherical video, allows the observer to have a 360\(^\circ \) view and an immersive experience of the surroundings. Each direction in this video is recorded at the same time either by an omni-direction camera or by an assembly of cameras synchronized together. The viewing perspectives are controlled by the viewer during playbacks. This article gives an overview of the existing research areas and methods in the user-generated 360\(^\circ \) videos for streaming, transcoding, viewport-based projections, video standardization, and summarization. This survey also provides an analysis of the experience estimation in 360\(^\circ \) videos. The study of multiple quality evaluation criteria is also reviewed. Moreover, 360\(^\circ \) video user experience studies are also focused on this survey. The merits and demerits of each technique are investigated in depth.
Logic Programming and Machine Ethics

Transparency is a key requirement for ethical machines. Verified ethical behavior is not enough to establish justified trust in autonomous intelligent agents: it needs to be supported by the ability to explain decisions. Logic Programming (LP) has a great potential for developing such perspective ethical systems, as in fact logic rules are easily comprehensible by humans. Furthermore, LP is able to model causality, which is crucial for ethical decision making.
Music Style Transfer with Vocals Based on CycleGAN

Abstract In recent years, with the development of generative adversarial networks (GAN), the application of generative adversarial networks has gradually matured. An important application area for generating adversarial networks is called neural style transfer. In recent years, neural style transfer has played a major role in the field of image applications. However, it performed poorly in the music field. In addition, algorithms in the field of music style transfer have poor effect on the style transfer of music with vocals. Therefore, this paper extracts the CQT features and Mel spectrogram features of music, and then uses CycleGAN to transfer the styles of the CQT features and Mel spectrogram mapping pictures, and finally realizes the style transfer of music. On the classifier we trained, the average style transfer rate of music that meets our requirements reached 94.07%.