Reviewerly/exHarmony huggingface dataset:
Augmented Reality (AR) Assisted Smart Glasses: Case Study for Remote Support Between Two Distant Production Plants

Rapid developments in technology enable us to reach smart glasses and AR technology with a wide range of products and competitive prices. Hence, increase in smart glasses industrial use give an opportunity to whom work distant such as making it possible to see from each other's eyes, which increases the operational efficiency of the companies; since it is costly to make experienced staff traveled to another plant in aspects of travel cost, accommodation cost and the lack of the experienced person in his/her the main responsibility area in factories with different experiences in remote locations. This study is conducted about the application of smart glasses on one of the biggest tire manufacturing company since 2018. End to end journey of the smart glasses' remote support applications, starting with how the devices are selected and ending with an evaluation of advantages/disadvantages of the smart glasses' technology, is explained in this paper. Moreover, the study covers details and observations of staff training, new product development, and inspections, first-run test on machinery, maintenance, and audits.
Learning Policies by Learning Rules

Efficiently learning interpretable policies for complex tasks from demonstrations is a challenging problem. We present Hierarchical Inference with Logical Options (HILO), a novel learning algorithm that learns to imitate expert demonstrations by learning the rules that the expert is following. The rules are represented as linear temporal logic (LTL) formulas, which are interpretable and capable of encoding complex behaviors. Unlike previous works, which learn rules from high-level propositions, HILO learns rules by taking both propositions and low-level trajectories as input. It does this by defining a Bayesian model over LTL formulas, propositions, and low-level trajectories. The Bayesian model bridges the gap from formula to low-level trajectory by using a planner to find an optimal policy for a given LTL formula. Stochastic variational inference is then used to find a posterior distribution over formulas and policies given expert demonstrations. We show that by learning rules from both propositions and low-level states, HILO outperforms previous work on a rule-learning task and on four planning tasks while needing less data. We also validate HILO in the real world by teaching a robotic arm a complex packing task.
A Super-resolution Reconstruction Method for Single-frame Character Images Based on Wavelet Neural Network

The current single-frame character image reconstruction methods have some problems, such as the noise can not be removed effectively, the anti-interference performance is poor, and the resolution of reconstructed single frame character image is low. In order to solve these problems, this paper proposes a super-resolution reconstruction method of single frame character image based on wavelet neural network. Firstly, analyze the factors affecting image degradation and construct the image degradation model. Secondly, remove the noise in the image using the wavelet threshold denoising method. Then, the image super-resolution reconstruction is completed by wavelet neural network reflection model to improve the resolution of the image. Finally, test the reconstructed single frame character image using MATLAB software. The test results show that the proposed method can effectively remove the image’s noise, and its anti-interference performance is high and the reconstructed image’s resolution is high.
A Relation-Oriented Method for Joint Entity and Relation Extraction Based on Neural Network

Entity and relation extraction is a basic task of information extraction in natural language processing. At present, Entity and relation extraction based on artificial intelligence has been widely studied, but most methods adopt the idea of identifying the entity first and then extracting the relation, which suffers from the problems of overlapping triple and entity redundancy. We propose a Relation-Oriented method for Joint Entity and Relation Extraction (ROJER), which first extracts the relation types contained in the text through the relation extraction module, then the pre-extracted relation types are integrated into the entity recognition module, reduce the focus on irrelevant entities and avoid extracting redundant entities. Then the entity pairs corresponding to the extracted relation types are identified to tackle the overlapping triple problem and finally extract all the relation triples. Experiments on the DuIE dataset show that the F1 score of ROJER reaches 78.4%, which is 1.2% higher than the CasRel model, confirming the validity of our method.
Optimizing Deep-Learning Inference Operations

Deep neural networks (DNNs) are the foundation for many modern artificial intelligence (AI) applications [1] today. Since the breakthrough applications of DNNs for speech recognition [2] and image recognition [3], the number of applications using DNNs has exploded. The huge and increasing complexity of cutting-edge neural networks hinders the deployment and computation of deep learning on resource-limited embedded and mobile devices. Multiplicative operations are the most complex part of neural information processing and thus have been the target of many optimization methods. In this paper, we propose an idea of weight sharing and replacing multiplication computations by simpler shifts and additions, which can effectively reduce the number of multiplications in neural network computations by more than ninety percent by processing four popular neural network weights.
Adaptive Pre-Training and Collaborative Fine-Tuning: A Win-Win Strategy to Improve Review Analysis Tasks

Summarizing user reviews and classifying user sentiment are two critical tasks for modern e-commerce platforms. These two tasks can benefit each other by capturing the shared linguistic features. However, such a relationship has not been fully exploited by existing research on domain-specific contextual representations. This work explores a win-win strategy for a multi-task framework with three stages: general pre-training, adaptive pre-training, and collaborative fine-tuning. The task-adaptive continual pre-training on a language model can obtain domain-specific contextual representations, further used to improve two related tasks, sentiment classification and review summarization during the collaborative fine-tuning. Meanwhile, to effectively capture sentiment-oriented domain-specific contextual representations, we introduce a novel task-adaptive pre-training procedure, which adds a sentiment prediction task during the adaptive pre-training. Extensive experiments conducted on two adaption scenarios of a general-to-single domain and a general-to-multiple domain show that our framework outperforms state-of-the-art methods.
COVID-19++: A Citation-Aware Covid-19 Dataset for the Analysis of Research Dynamics

COVID-19 research datasets are crucial for analyzing research dynamics. Most collections of COVID-19 research items do not to include cited works and do not have annotations from a controlled vocabulary. Starting with ZB MED KE data on COVID-19, which comprises CORD-19, we assemble a new dataset that includes cited work and MeSH annotations for all records. Furthermore, we conduct experiments on the analysis of research dynamics, in which we investigate predicting links in a co-annotation graph created on the basis of the new dataset. Surprisingly, we find that simple heuristic methods are better at predicting future links than more sophisticated approaches such as graph neural networks.
Blockchain Technology: Opportunities and Challenges in Copyright Industry

This paper focuses on the application of blockchain technology in the copyright field. This distributed digital ledger technology offers a platform to record information. Blockchain technology has the potential to take over the current Digital Risk Management System (DRMS) and improve the current situation. However, the technology has its limitations and may bring future challenges in the industry.
Robust Localization for Multi-robot Formations: An Experimental Evaluation of an Extended GM-PHD Filter

This paper presents a thorough experimental evaluation of an extended Gaussian Mixture Probability Hypothesis Density filter which is able to provide state estimates for the maintenance of a multi-robot formation, even when the communication fails and the tracking data are insufficient for maintaining a stable formation. The filter incorporates, firstly, absolute poses exchanged by the robots, and secondly, the geometry of the desired formation. By combining communicated data, information about the formation, and sensory detections, the resulting algorithm preserves accuracy in the state estimates despite frequent occurrences of long-duration sensing occlusions, and provides the necessary state information when the communication is sporadic or suffers from short-term outage. Differently from our previous contributions, in which the tracking strategy has only been tested in simulation, in this paper we present the results of experiments with a real multi-robot system. The results confirm that the algorithm enables robust formation maintenance in cluttered environments, under conditions affected by sporadic communication and high measurement uncertainty.
Conversion of the Diffie-Hellman Key Exchange Algorithm Based on Elliptic Curve Equations to Elliptic Curve Equations with Private Parameters

The advantage of cryptographic systems based on elliptical curves over traditional systems is that they provide equivalent protection even when the key length used is small. This reduces the load time of the processors of the receiving and transmitting devices. But the development of computer technology leads to an increase in the stability of the cryptosystem, that is, the length of the keys. This article presents a method for converting elliptic curve equations to hidden parameter elliptic curve equations to increase stability without increasing key length.
Introduction

Geostatistical techniques are extensively used for mineral resources assessment, including grade estimation and uncertainty analysis. Support is the term used in Geostatistics to describe the size or volume of a sample. For example, suppose a grade sample obtained from diamond drill holes (DDH). In this case, the support is a function of the core’s radius and length.
Cybersecurity Curricular Guidelines

The Cybersecurity Curricular Guidelines, a joint effort of the ACM, IEEE Computer Society, AIS SIGSAC, and IFIP WG 11.8, were created to provide developers of cybersecurity curricula with guidelines for material to include. The curricular guidelines have eight knowledge areas, broken down into knowledge units and topics. Underlying cross-cutting concepts provide linkages among the knowledge areas. Disciplinary lenses enable the developer to emphasize the knowledge units appropriate to the goals of the developed curricula. Each knowledge area also includes a list of essential concepts that all curricula should cover to an appropriate depth. The guidelines can be linked to workforce frameworks and certification criteria as well as academic curricula.
CEMO : Cloud Edge Architecture Development for a Multi Object Tracking

Due to increase of video surveillance situation, advance of autonomous driving technology, and development of artificial neural network, the multi-object tracking (MOT) has been attracted attention in the computer vision community. Moreover, the importance of multi-input processing and real-time analysis is increasing with the need for fast processing of many videos. Modern multi-object trackers use sequential processing to input continuous frames of video and derive tracking trajectories for all objects mainly on a single server. When performing deep learning with high computation on a single server, latency inevitably occurs. The latency is the main reason that the tracker cannot meet the real-time requirements. Removing the number of operations to reduce latency will immediately lead to poor performance of tracker. Cloud edge computing is the best way to meet the real-time distributed requirements because it can solve the data transmission delay problem of traditional cloud computing and effectively cooperate between edge devices. In this paper, we propose a new system structure called Cloud Edge Multi Object (CEMO) tracker for developing deep learning-based cloud-edge real-time video analysis applications. CEMO tracker is a container-based microservice structure that divides large application functions into small, independent units, and is a flexible architecture based on Kubernetes, a container orchestration platform. CEMO tracker, which can efficiently perform operations on simultaneous input, integrate the results and show them to users, is expected to solve multiple objects tracking problems efficiently through distributed cloud edge computing technology.
Enhancing Project Based Learning with Unsupervised Learning of Project Reflections

Natural Language Processing (NLP) is an area of research and application that uses computers to analyze human text. It has seen wide adoption within several industries but few studies have investigated it for use in evaluating the effectiveness of educational interventions and pedagogies. Pedagogies such as Project based learning (PBL) centers on learners solving an authentic problem or challenge which leads to knowledge creation and higher engagement. PBL also lends itself well in plugging the gap between what is taught in classrooms and applying the knowledge gained to the real working environment. In this study, we seek to investigate how we can use NLP techniques to uncover insights into and enhance our PBL process. Both topic modelling and sentiment analysis techniques are applied to analyze final year students’ reflections written as part of their final year project module. We described the entire process from text cleansing, pre-processing, modelling to visualization and evaluated the use of Latent Dirichlet Allocation and Attention Based Aspect Extraction for topic modelling. The results or visualizations which we derived from the topic and sentiment models showed that we can both effectively infer the key topics as reflected by our learners and extract actionable insights on the PBL process.
Action Recognition for Anomaly Detection Using Transfer Learning and Deep Architectures

Sánchez, Francisco LuqueTabik, SihamHerrera, FranciscoAutomatic video surveillance is an emerging research area, where a huge number of publications are appearing everyday. However, there is still much room for improvements, since results of state-of-the-art are still limited. The mainstream approach to the problem using deep models consists in transfer learning from action recognition and weakly supervised fine-tuning. Nonetheless, there does not exist a systematic study of the importance of the different steps in the process. In this work, our aim is to identify the key aspects of those models and assess the importance of the different pieces that compose the final solution.
Mesozoic gold deposits of Eastern Zabaykalye: relationship to magmatism, isotopic composition

Abstract It is noted that the formation of Mesozoic gold mineralization in Eastern Zabaykalye is associated with magmatic formations of the Amudzhikansky (J 2-3), Shakhtaminsky (J 2-3 ), and Sokhondinsky (J 1 ) complexes formed during collisional and post–collisional processes. The nature of these magmatic formations has both mantle ( 87 Sr/ 86 Sr &lt; 0.0706) and crustal components ( 87 Sr/ 86 Sr &lt; 0.0706).Calculation of isotopic composition of oxygen from the ore veins of Aleksandrovsky and Lyubavinsky gold deposits revealed that fluids of magmatic nature were involved in their formation. These data indicate a magmatic source of the Mesozoic gold mineralization in Eastern Zabaykalye.
Player Rating Correlation Prediction Using Machine Learning

Predicting the effect of the game and players performance can benefit the team to move step towards the victory. In any gaming system, rating of the players plays an important role. It is essential to find the relationship between the ratings of the players where we can select a best player from different good players. In our paper, we find the relationship between the ratings of the player based on absolute dispute resolution by using gradient descent and stochastic gradient descent technique. We executed for 100 epochs and at the end, best fit line showed that the player having least absolute dispute resolution value. We evaluated the model using stochastic gradient descent and gradient descent and observed that stochastic gradient descent will get best fit line than the gradient descent.
Convolutional Neural Network (CNN)-Based Signature Verification via Cloud-Enabled Raspberry Pi System

Significant development in digital technology has contributed to the emergence of the Internet of Things (IoT). Machines can now interact between each other through the Internet without any human intervention. The rapid integration of the IoT has revolutionised many industries and expanded their capacity to operate more autonomously and data-centrically. The presence of vast amount of interrelated computing device promotes the usage and capability of machine learning, which is the ability of a system to acquire its own knowledge by extracting patterns from raw data. Machine learning has been applied in many areas to increase productivity and security and enhance overall efficiency for the end user. One of them is to aid in verifying signatures to prevent fraudulent. A normal signature verification process is usually done electronically and visually, and it requires a tedious procedure. This is due to the high intrapersonal variability nature of a signature. To facilitate this process, we propose to integrate machine learning and the IoT in a portable scale to perform high accuracy verification system. This model uses a pre-trained convolutional neural network (CNN) on a Raspberry Pi. The CNN will analyse pixels from a signature image taken by the Pi camera to recognise abnormalities and differences and to identify false signature. Other than requiring a secure digital authentication to operate, it also informs the user immediately on the app execution and image being scanned via a cloud-based system. The system is expected to provide on-the-spot signature verification and minimise any logistic issues that stem from faulty signature to an organisation.
Attention-guided Convolutional Neural Network for Lightweight JPEG Compression Artifacts Removal

JPEG compression artifacts seriously affect the viewing experience. While previous studies mainly focused on the deep convolutional networks for compression artifacts removal, of which the model size and inference speed limit their application prospects. In order to solve the above problems, this paper proposed two methods that can improve the training performance of the compact convolution network without slowing down its inference speed. Firstly, a fully explainable attention loss is designed to guide the network for training, which is calculated by local entropy to accurately locate compression artifacts. Secondly, Fully Expanded Block (FEB) is proposed to replace the convolutional layer in compact network, which can be contracted back to a normal convolutional layer after the training process is completed. Extensive experiments demonstrate that the proposed method outperforms the existing lightweight methods in terms of performance and inference speed.
Documents Scoring and Querying for Business Matching using Elasticsearch

A collaborative platform is established to connect businesses that are embracing the Fourth Industrial Revolution (Industry 4.0). The initiatives establish a central repository system that enables businesses to participate in ongoing practices through the use of intelligent and modern technologies. To expedite the process, business entities must connect to one another based on their respective business needs and offerings. A business matching engine serves business entities that meet the Industry 4.0 criteria and makes recommendations to business entities seeking solutions from other businesses. The businesses recommendations are ranked based on documents scoring, in which its accuracy deviates when business requirements are updated and, more importantly, when newly inserted documents are considered. Thus, the article discusses the constraints associated with recommending business entities and the matching query problem, and proposes two-way index scoring as a solution.
Experiments on the Generalization of Machine Learning Algorithms

The inductive programming system WILLIAM is applied to machine learning tasks, in particular, centralization, outlier detection, linear regression, linear classification and decision tree classification. These examples appear as a special case of WILLIAM’s general operation of trying to compress data without any special tuning.
Parts-of-Speech Tagger for Gujarati Language using Long-short-Term-Memory

Parts-of-Speech (POS) tagging is a crucial step to process the natural languages. It is a state-of-art method of providing the lexicon category such as noun, verb, adjective, etc. to each word that best suits the context of the sentence in which it is used. Being a part of pre-processing makes this task an important step in linguistics and semantics. Gujarati is an Indian language widely spoken in Asia and across the world. Part-of-Speech tagging can be used in word sense disambiguation, Information retrieval, machine translation and parsing. In this paper, we proposed Long-short-Term-Memory (LSTM) based Part-of-Speech tagger for Gujarati language. With our proposed approach, this paper envisions achieving accuracy of 95.34% and 96% precision with the help of this novel & efficient gradient based method.
Comparative Analysis of Techniques Used in Book-based Recommender System

Recommender system has become a very important tool to accelerate businesses’ growth by recommending suitable products to customers. However, in general public, their basic knowledge regarding recommender system still needs to be improved. Besides, the understanding on types of recommender systems and which type of recommender system performed the best need to be studied more in depth. Hence, this paper aims to study the existing techniques in recommender systems and evaluate their accuracy on predicting an item's rating. The chosen techniques will be implemented in the prototype using Python. With the help of graphical user interface, the result can be visualized in better manner. The results obtained from this paper will be able to prove or deny existing theory made on the techniques. Besides, commercial company would be able to have an insight on which type of recommender system that should be implemented.