Reviewerly/exHarmony huggingface dataset:
Optimization of OCR in Detecting Research Proposal and Lecturer Community Service Documents using Thresholding Method

The development of science and technology today has changed various aspects of life, such as digitizing documents. Document digitization makes it easier to archive and search for documents ranging from book documents, ancient manuscripts, and administration. One method to extract image text into text that a computer can process is optical character recognition (OCR). However, OCR has a problem when the scanned file has noise such as blur, varying light intensity, and tilt also OCR cannot process files with the pdf extension. Therefore, image quality improvement is carried out by optimizing the thresholding method in the preprocessing process. The OCR development process uses the python programming language and the wand library to convert documents with pdf extensions into images. Optimization of thresholding in preprocessing is done by testing nine methods with the best results, namely using trunc thresholding with an accuracy of 89%. Then the pytesseract library to extracts texts in the image into editable text. The feature creation using the OCR method has been through accuracy testing using the Levenshtein distance algorithm with 95.3 % results. The performance testing with an average time of 15.2 s to produce a product ready to be integrated with the Research and Lecturer Community Service Information System (SITEDI), Faculty of Engineering, Diponegoro University and is expected to facilitate lecturers in filling out the proposal submission form.
Review on Technological Advancement and Textual Data Management Algorithms in NLP and CBIR Systems

Natural language processing (NLP) and content-based image retrieval (CBIR) systems functioned efficiently with textual input. This offers a broader framework for on-going work such as text processing applications that include spam identification, content visualization, and image retrieval for the text being queried. The preprocessing of the text handling algorithm includes part of speech tagging (POS), text encoding, and text extraction function using the word embedding encoding algorithms like Word2vec, boot strapping, hidden Markov model (HMM), and so on. The derived textual input or image attribute plays a crucial role. Following this processing, the future phase can include either of the following approach, such as content-based recovery, image recovery, or speech processing. The techniques, such as content-based retrieval, image retrieval or speech processing, each of these algorithms requires their own training period in continuation with the available technical orientations. Convolutional neural network (CNN) and recurrent neural network (RNN) play a prominent role in analyzing the input dataset when it comes to testing. Generative adversarial networks (GANs) have now launched a new era in managing NLP applications in its place. This survey provides a glimpse of various algorithms that respond to text data and return with prominent accuracy involving CBIR issues and applications for text-based image synthesis. The examination of the methodologies deployed paves way for researchers to analyze and process textual data.
(Not) Yet Another Metamodel For Traceability

Traceability helps explaining the execution and evolution of software and systems. It is a key input in many engineering tasks such as program understanding, maintenance and debugging. Several metamodels to facilitate the representation of traces and links among related artefacts have been proposed. There exists a plethora of traceability approaches that focus on distinct segments of the software and systems development. Nevertheless, we claim they lack the mechanisms to express important traceability aspects such as the quality of traces, their gradual decay, and the evidences supporting them. This affects the benefits traceability can bring to the above-mentioned tasks. This paper presents a more expressive traceability metamodel, covering all the missing dimensions in a single, but extensible and modular, design. It characterizes trace quality to consider traces as salient artifacts in system and software development and maintenance. This modularity facilitates the integration of our solution in other modeling languages or its partial adoption when only some specific traceability aspects are needed. Its extensibility facilitates its customization (e.g., in terms of the types of links and artefacts) to better cover specific domains.
Gesture-based Intention Prediction for Automatic Door Opening using Low-Resolution Thermal Sensors: A U-Net-based Deep Learning Approach

Personal health consciousness has increased amid pandemics. The implementation of automatic doors could help stop the infection. The need for an intelligent sensor emerges for automatic doors to prevent unneeded open as well as customer privacy concerns. This research proposes a novel automatic door opening mechanism using a low-resolution thermal sensor, based on which a multi-task U-Net structure network is adopted to classify hand-raising gestures. With the aid of segmentation masking, there is 74% reduction of training steps for convergence than that of mere thermal image classification while maintaining similar classification performance. On-site deployment of this approach via constantly collecting door-opening misclassification cases for model improvement will lead to practical success in the near future.
Towards Efficient Control-Flow Attestation with Software-Assisted Multi-level Execution Tracing

In the face of an increasing attack landscape, it is necessary to cater for efficient mechanisms to verify software and device integrity for detecting run-time modifications in next-generation systems-of-systems. In this context, remote attestation is a promising defense mechanism that allows a third party, the verifier, to ensure a remote device’s configuration integrity and behavioural execution correctness. However, most of the existing families of attestation solutions suffer from the lack of software-based mechanisms for the efficient extraction of rigid control-flow information. This limits their applicability to only those cyber-physical systems equipped with additional hardware support. This paper proposes a multi-level execution tracing framework capitalizing on recent software features, namely the extended Berkeley Packet Filter and Intel Processor Trace technologies, that can efficiently capture the entire platform configuration and control-flow stacks, thus, enabling wide attestation coverage capabilities that can be applied on both resource-constrained devices and cloud services. Our goal is to enhance run-time software integrity and trustworthiness with a scalable tracing solution eliminating the need for federated infrastructure trust.
Concentrated isolation for container networks toward application-aware sandbox tailoring

Containers provide a lightweight and fine-grained isolation for computational resources such as CPUs, memory, storage, and networks, but their weak isolation raises security concerns. As a result, research and development efforts have focused on redesigning truly sandboxed containers with system call intercept and hardware virtualization techniques such as gVisor and Kata Containers. However, such fully integrated sandboxing could overwhelm the lightweight and scalable nature of the containers. In this work, we propose a partially fortified sandboxing mechanism that concentratedly fortifies the network isolation, focusing on the fact that containerized clouds and the applications running on them require different isolation levels in accordance with their unique characteristics. We describe how to efficiently implement the mechanism to fortify network isolation for containers with a para-passthrough hypervisor and report evaluation results with benchmarks and real applications. Our findings demonstrate that this fortified network isolation has good potential to tailor sandboxes for containerized PaaS/FaaS clouds.
Monocular Keypoint based Pull-ups Measurement on Strict Pull-ups Benchmark

Pull-up action is one of the critical standards and items for measuring personal physique. The traditional manual assessment method is subjective and inefficient, and the existing automatic methods require high demands for playgrounds and equipment. In this paper, we propose monocular vision-based pull-ups measurement with human keypoint estimation on our proposed strict pull-ups benchmark. Specifically, the deep neural network HRNet is employed to estimate the human keypoints frame by frame. The face keypoints are estimated with Dlib on the facial region, meanwhile, the position of the horizontal bar is estimated with Canny edge detection and Hough transform on the hand region. After keypoint smoothing and denoising with the Savitzky-Golay filter, both the valid actions and invalid actions are recognized by our proposed algorithm. On the strict pull-up video dataset we collected, this proposed method achieved 91.5% of the average counting accuracy. The dataset and code will soon be released.
A High-Performance Accelerator for Super-Resolution Processing on Embedded GPU

Recent years have witnessed impressive progress in super-resolution (SR) processing. However, its real-time inference requirement sets a challenge not only for the model design but also for the on-chip implementation. In this paper, we implement a full-stack SR acceleration framework on embedded GPU devices. The special dictionary learning algorithm used in SR models was analyzed in detail and accelerated via a novel dictionary selective strategy. Besides, the hardware programming architecture together with the model structure is analyzed to guide the optimal design of computation kernels to minimize the inference latency under the resource constraints. With these novel techniques, the communication and computation bottlenecks in the deep dictionary learning-based SR models are tackled perfectly. The experiments on the edge embedded NVIDIA NX and 2080Ti show that our method outperforms the state-of-the-art NVIDIA TensorRT significantly, and can achieve real-time performance.
A Study on Online ARIMA Algorithms applying various gradient descent optimization algorithms for Time Series Prediction

The prediction of time series data can be used in various fields, and many research for this is required. This paper has researched about applying various optimizers for online ARIMA model, which is used to make a prediction on time series data. For this work, algorithms for gradient descent like Adam, was used for updating online ARIMA model's weight for predicting the values. Our approach to finding a gradient descent algorithm that can obtain higher performance in time series prediction, the performance of the online ARIMA-based model was evaluated with the photovoltaic power generation data and gold price data. As a result, it was confirmed that there is a gradient descent-based optimization algorithm that improves the prediction performance for each time-series data.
A Collaborative Neurodynamic Optimization Algorithm Based on Boltzmann Machines for Solving the Traveling Salesman Problem

The traveling salesman problem is known to be NP-hard and has numerous areas of applications. This paper proposes a collaborative neurodynamic optimization algorithm based on Boltzmann machines for solving the traveling salesman problem. A population of Boltzmann machines is employed for local search, and their initial states are repeatedly reinitialized by using the particle swarm optimization update rule for global repositioning. The efficacy of the proposed collaborative neurodynamic optimization algorithm is substantiated on four traveling salesman problem benchmark instances.
Comparison of the Meta-Active Machine Learning Model Applied to Biological Data-Driven Experiments with Other Models

Currently, many methods that could estimate the effects of conditions on a given biological target require either strong modelling assumptions or separate screens. Traditionally, many conditions and targets, without doing all possible experiments, could be achieved by driven experimentation or several mathematical methods, especially conversational machine learning methods. However, these methods still could not avoid and replace manual labels completely. This paper presented a meta-active machine learning method to resolve this problem. This project has used nine traditional machine learning methods to compare their accuracy and running time. In addition, this paper analyzes the meta-active machine learning method (MAML) compared with a classical screening method and progressive experiments. The obtained results show that applying this method yields the best experimental results on the current dataset.
Study of the influence of Arabic mother tongue on the English language using a hybrid artificial intelligence method

Accent recognition refers to the problem of inferring the native language of a speaker from his foreign-accented speech. Differences in accent are due to both articulation and prosodic characteristics. The automatic identification of foreign accents is valuable for different speech systems, such as speech recognition, speaker identification or voice conversion. This paper aims to identify the native languages of non-native English speakers from different countries in the Arabic region: the researchers choose Saudi Arabia to represent the eastern Arabic region in Asia, Egypt to represent the eastern Arabic region in Africa and Tunisia to represent the western Arabic region in Africa. In this research, reinforcement learning (RL), a sub-branch of machine learning, and artificial neural network will be used as an intelligent method to classify and predict the speech. The aim is to train a neural network to automatically detect speech accents. Then the researchers develop a hybrid multi-agent RL algorithm that takes advantage from the multi-agent communication and cooperative agents on the language detection process. Hence, the aim is to help sociolinguists and discourse analysts. As for the Saudi context, this study will be very useful on resolving e-learning issues such as linguistic problems of students.
Automatic generation of a large dictionary with concreteness/abstractness ratings based on a small human dictionary

Concrete/abstract words are used in a growing number of psychological and neurophysiological research. For a few languages, large dictionaries have been created manually. This is a very time-consuming and costly process. To generate large high-quality dictionaries of concrete/abstract words automatically one needs extrapolating the expert assessments obtained on smaller samples. The research question that arises is how small such samples should be to do a good enough extrapolation. In this paper, we present a method for automatic ranking concreteness of words and propose an approach to significantly decrease amount of expert assessment. The method has been evaluated on a large test set for English. The quality of the constructed dictionaries is comparable to the expert ones. The correlation between predicted and expert ratings is higher comparing to the state-of-the-art methods.
Circuit depth reduction algorithm for QUBO and Ising models in gate-model quantum computers

Gate-model quantum computers, including IBM and IonQ, have gained considerable attention as backbones for implementing near-term quantum computer architectures and quantum devices. Their performances rely on circuit depths, whose minimization ensures optimal results and reduced noises. We develop an algorithm which rearranges the terms in the quadratics of the QUBO model and the Ising model, when implementing the Quantum Approximate Optimization Algorithm for the Hamiltonian of a linear system. We test the algorithm on IBM quantum computer and IBM simulator to verify the effectiveness of our algorithm for minimizing circuit depths.
Analysis of common problems in industrial wastewater treatment and countermeasures

Abstract With the development of industry, the industrial wastewater produced by all walks of life become more and more, which cannot be overestimated, studies on the technology of industrial wastewater treatment are conducive to the development of industrial wastewater treatment. The paper analyzes in detail the treatment methods of industrial wastewater, including physical method, chemical method and biological method, The application of the three methods is relatively simple, and several methods have been merged. Moreover, the current industrial wastewater treatment in the paper industry, printing and dyeing industry, pharmaceutical industry is analyzed, the results show that industrial wastewater treatment in the pharmaceutical industry is difficult. In addition, the role of Aquamats ecological technology in industrial wastewater treatment is analyzed. In addition, this paper analyzes and discusses the status quo of industrial wastewater treatment and the main existing problems, and proposes countermeasures to further improve the treatment level of industrial wastewater.
The Implementation of Video DES Encryption Engine in FPGA

In DVB and VOD system, the DES and Triple DES(3DES) algorithm are still be widely used for MPEG-2 packets encryption during the video traffic transmission.
Fall warning machine with image processing via the Internet of things

The fall detection system in the elderly is an imperative system in trend of increasing the number of the elderly population, but many of the systems that are presented are not available in all areas. Wearing a device with the person being tracked makes routine activities less difficult. This research presents another way to solve this problem by using the principles of image processing. All processing systems will run only on the processing device. And send the report only the result of falling out in order to certify that the image data Will not be published in this regard, the test was conducted to study the effects. The perspective of installing a camera or even the effect of wearing clothing is similar to the color of the background during a fall.
Approaches to Creating a Multi-agent Architecture in the Industrial Internet of Things Systems

The issues of the application of "Internet of Things" technologies for digitalization in the industrial sphere are considered. The logical structure of the concept of an "Internet-Thing" and its interaction with the Virtual World and Physical World is described. The relevance of the tasks of creating a technology for designing "Industrial Internet of Things" systems is justified. Approaches to creating a multi-agent software architecture in "Industrial Internet of Things" systems are proposed.
On Ethically-Sensitive User Story Engineering

The increasingly anthropomorphic, at times even autocratic, nature of software, exhibited during routine activities such as decision-making, question-answering, or recommending, has only contributed to the enduring issue of software ethics. This paper, after providing an understanding of the unique nature of software and that of ethicality, models ethicality as a meta-quality attribute and proposes an ethically-sensitive, standards-based, technology-and-tool-independent, applicable to software-as-a-product-or-service, semi-formal framework, comprising interrelated conceptual meta-models that provide an understanding of ethicality, user story environment, and user story process. It describes an approach of integrating ethicality naturally and systematically in the user story process, illustrates this approach by means of representative examples from a variety of application domains, and highlights the associated challenges in doing so. It also presents the results of a preliminary survey of students and professionals on their knowledge and experience of ethics in (agile) software projects. Finally, it outlines directions of research, and provides recommendations for those in academia and industry, which have broad implications for ethically-sensitive (agile) requirements engineering education and (agile) software testing.
Multi-task learning with Attention : Constructing auxiliary tasks for learning to learn

With the development of deep learning in various fields, a deep learning method that can optimize multiple target tasks at the same time, that is, deep multi-task learning (MTL), has attracted more and more attention. We aim to find a multitask learning method to optimize a single goal. For image classification tasks, it is difficult to find multiple tasks with task relevance. We use an unsupervised clustering algorithm to construct multiple related auxiliary tasks in the dataset to solve this problem in order to achieve a kind of data enhancement. The purpose is to improve the accuracy of the main task. While these newly constructed auxiliary tasks may exhibit semantic features that are not relevant to the main task, in order to reduce the impact of such non-ideal auxiliary tasks on the main task, we use a multi-task learning based on learning to learn (MTL-LTL) approach with a spatially dependent attention function embedded in an underlying joint model with hard parameter sharing that allows our model to have pixelated modeling capabilities. In addition, we also use the method of learning to learn to randomly sample multiple auxiliary tasks. It can train these tasks on the shared hidden layer, and at the same time minimize the loss of the main task, and ensure that the optimization direction leads to the improvement of the main task. In order to verify the classification performance of the model, three image dataset are used for experimental analysis. The experimental results show that the model proposed in this paper is better than the current popular benchmark methods and can effectively improve the accuracy of image classification.
Dynamic Virtual Machine Allocation to Minimize Task Migrations in Cloud

Cloud Clients (CC) in a virtualized Cloud environment have no assurance of the quality on the services which are being offered by the Cloud providers, particularly in context with the deadline of the tasks submitted by the CC. The Service Level Agreements (SLA) is mutually agreed upon by the CC and the Cloud provider which involves the requirement of resources from the CC including CPU, memory and bandwidth. The other services namely the deadline time for the task, response time are also included in the SLA. The allocation algorithms for allocating Virtual Machines (VM) to the tasks have a major role to play in determining the quality of services provided to the CC. The major problem with current allocation algorithms includes the decisions as to when the tasks need to be migrated from one VM to another, large waiting time for the task execution and not meeting the deadline threshold. This paper addresses dynamic allocation issue for allocating VM to tasks. The proposed approach of allocating VM to task is split into two phases. In the first phase, an algorithm is proposed for dynamic allocation of VM to tasks. In the second phase, another algorithm is proposed for determining whether the migration of task is needed or not. The proposed approach significantly reduced the waiting time, completion time and idle time. The proposed algorithm results are compared with Max-Min and Min-Min approaches and the results shows 15 percent reduction in waiting time, 11 percent reduction in completion time and 13 percent reduction in idle time of VMs compared to the Min-Min approach and 12 percent reduction in waiting time, 10 percent reduction in completion time and 11 percent reduction in idle time of VMs compared to Max-Min approach.