Reviewerly/exHarmony huggingface dataset:
Multilayer Perceptron Neural Network Supervised Learning Based Solar Radiation Prediction

Worldwide solar radiation is a basic parameter for the plan and operation of solar radiation frameworks. Long-standing records of worldwide solar radiation data are not accessible in many places because of the cost and maintenance of the measuring instruments. Sun based radiation expectation contains an incredible significance in power generation from sun based energy and makes a difference to measure photovoltaic control frameworks. With this overview, this paper enabled to predict the solar radiation using machine learning algorithms. With this context, we have utilized solar radiation dataset extracted from UCI Machine Learning. The proposed model Multilayer perceptron (MLP) based Neural Network for forecasting solar radiation are attained in four ways. Firstly, the data set is analyzed and preprocessed with Feature Scaling and missing values. Secondly, the correlation of the features is done and the relation of each features are visualized. Thirdly, the raw solar radiation data set is fitted to all the regressors and the implementation is furnished before and after scaling. Fourth, the raw data set is subjected to multilayer perceptron with various activation layers like identity, logistic, tanh and relu layers. The performance is analyzed with EVS, MAE, MSE, RScore and run time of the neural network layer. The execution is done using python language under Spyder platform with Anaconda Navigator. Experimental results show that the Gradient boost regressor have the RScore of 0.98 before and after feature scaling. The MLP regressor with TANH activation layer is tends to retain 0.99 Rscore before and after scaling.
Collaborative System for Question Answering in German Case Law Documents

Legal systems form the foundation of democratic states. Nevertheless, it is nearly impossible for individuals to extract specific information from comprehensive legal documents. We present a human-centered and AI-supported system for semantic question answering (QA) in the German legal domain. Our system is built on top of human collaboration and natural language processing (NLP)-based legal information retrieval. Laypersons and legal professionals re ceive information supporting their research and decision-making by collaborating with the system and its underlying AI methods to enable a smarter society. The internal AI is based on state-of-the-art methods evaluating complex search terms, considering words and phrases specific to German law. Subsequently, relevant documents or answers are ranked and graphically presented to the human. In ad dition to the novel system, we publish the first annotated data set for QA in the German legal domain. The experimental results indicate that our semantic QA workflow outperforms existing approaches.
Optimization of Linguistic Techniques by Extracting Opinion in Text Summarization Using Transferable Neural Network

In dense opinion mining, calculating opinion targets and expressions based on user defined texts is the core task towards constructing organized opinion review. To extract the aspect and opinion pairs, the phrase structure relations between both the terms plays an important role. The conditional adversarial domain network helps to understand the domain adaptation model’s selective information, which is influenced by the anticipating classifiers. At last, integrate the RNN with an identifier on its top for term labeling which will help in textual understanding of the final classified terms. The existing researches used manual entries by adding more number of features to have better opinion extraction but the process consumed more time. Therefore, in order to reduce time complexity for feature learning, deep learning models are proposed for extraction of sentimental across various aspects.
Decentralized Multi-agent Navigation via Knee-based Multi-objective Optimization of Potential Functions

Navigation in unknown environments while maintaining connectivity between agents is a practical research challenge with applicability to real life problem such as mapping, rescue missions, etc. In this work, a decentralized navigation to form group motion is formulated as a multi-objective optimization problem with an artificial potential function and the knee solution on the Pareto-optimal region is used to determine each agent's action. The results show reasonable performance in a static environment and in three dynamic situations (head-on collision, squeeze, and road crossing) with respect to goal reaching, collision avoidance and connectivity maintenance.
Bridging the gap between QP-based and MPC-based Reinforcement Learning

Reinforcement learning methods typically use Deep Neural Networks to approximate the value functions and policies underlying a Markov Decision Process. Unfortunately, DNN-based RL suffers from a lack of explainability of the resulting policy. In this paper, we instead approximate the policy and value functions using an optimization problem, taking the form of Quadratic Programs (QPs). We propose simple tools to promote structures in the QP, pushing it to resemble a linear MPC scheme. A generic unstructured QP offers high flexibility for learning, while a QP having the structure of an MPC scheme promotes the explainability of the resulting policy, additionally provides ways for its analysis. The tools we propose allow for continuously adjusting the trade-off between the former and the latter during learning. We illustrate the workings of our proposed method with the resulting structure using a point-mass task.
From User Requirements to Document Repository Enriched with Metadata – a Case Study

The paper describes a research on collecting user requirements from international users in order to develop a document repository based on metadata, for collecting, storing, searching and analysing relevant cultural and socioeconomic documents. This research is a part of wider research within the project called MESOC that aims to propose, test and validate an innovative and original approach to measure the societal value and impact of culture, cultural policies and cultural practices. The main contributions of this paper are: a) a set of user requirements in the cultural domain collected from international users, b) a data model and database for the document repository, and c) a metadata model used to satisfy the user requirements.
Artificial vision inspection system for anomaly detection in metal stamped parts

Detecting anomalies in the production process is an important necessity in large scale industrial manufacturing, and although there are methods to detect such anomalies, these often require humans, which is not ideal where consistency is required, or make use of tailored systems to detect a specific anomaly, demanding previous knowledge of the said anomaly. Therefore, we evaluated several autoencoder networks in two cases: general defects and specific metal stamping defects. Two models are proposed, myCAE and myCAE_optuna, which obtained scores of 0.91 and 0.93, respectively, in the SSIM tests, with the latter achieving a precision above 95% in five of the seven categories of anomalies tested.
A deep learning model for detection of traffic events based on social networks publications

Nowadays, social networks are one of the biggest ways of sharing real time information. These networks, have several groups focused on sharing information about road incidents and other traffic events. The work here presented aims the creation of an AI model capable of identifying publications related to traffic events in a specific road, based on publications shared on social networks. A predictive model was obtained by training a deep learning model for the detection of publications related with road incidents with an average accuracy of 95%. The model deployed as a service is already fully functional and is operating in 24/7 while awaits a final integration with the road management system of a company where it will be used to support the Control Center team in the decision making.
Edge-to-Cloud Collaborative for QoS Guarantee of Smart Cities

With the rapid development of big data, cloud computing, mobile Internet and other new-generation information technologies, smart cities, carry more and more intelligent applications, and put forward higher requirements for data processing capacity and network service quality. The integration system of Edge-to-Cloud collaboration provides a feasible method to effectively improve network performance to adapt to the changing needs of users. However, the existing network management mechanism is difficult to adapt to the actual demand changes due to the characteristics of the edge-cloud fusion system, such as device diversity, location complexity and resource dynamics. This article to the idea of sliding mode control function of network virtualization technology and network, the study method and collaborative optimization theory as the foundation, of the application of static and dynamic models of the intelligence cities, fault prediction and fault tolerant management mechanism, task scheduling and resource allocation adaptive collaborative optimization mechanism, to ensure that the cities network real-time, reliability, and security, At the same time, combined with the smart campus platform of our school, the real-time performance and effectiveness of the network are tested and evaluated, providing a case demonstration for the intelligent application of smart cities. This project will further enrich and improve the theoretical system of end-to-end cloud collaborative optimization and provide new means and methods for intelligent cities network QoS(Quality of Service) guarantee.
Analysis of Students Perception in Reading Comprehension of English Texts at Junior High School

The purpose of this study was to identify the difficulties faced by students in reading comprehension of English texts and to identify the factors causing students' difficulties in understanding reading English texts in class VIII-B students of SMP Muhammadiyah 9 Boarding School. This research is qualitative. Data collection techniques used are tests and interviews. This study uses data analysis techniques from Miles and Huberman (1992), there are three steps of data analysis, namely data reduction, data display, and drawing conclusions. Based on the research results, students have difficulty in determining the main idea, understanding vocabulary, making conclusions, and looking for detailed information. The results of interviews regarding the factors that cause students' difficulties in understanding English reading texts are difficulties in understanding long sentences in the text, inadequate teacher intervention, and difficulties in understanding vocabulary, the home environment, and the school environment.
Analysis of Students' Speaking Skill Using Scaffolding Strategy

Based on observation made by the researcher of the eleventh grade student at SMAN 1 Porong, it discovered that students often find out difficulties while learning English, especially in speaking. These problems are faced by several factors such as unconfidently, lack of courage and seldom practiced. The implementation of scaffolding strategy in EFL learning might be an ideal solution to solve students’ poor speaking skill. This research aims to analyse students’ speaking ability after achieving the scaffolding strategy treatment. Under the descriptive quantitative method, the data were collected through students’ speaking performance. The sample comprised XI IPS 3 which consists of 34 students. The data were taken by giving speaking test in which students were required to retell their past experience after the teacher implemented scaffolding strategy. Here, the test assessed using five aspects of spoken language including pronunciation, grammar, vocabulary, fluency, and comprehension. The result of the study shows that 94 % out of 34 students have good performance in speaking.
Grayscale Text Watermarking

In an increasingly connected world, where information is easily spread through multiple channels and platforms, digital watermarking has been broadly investigated in authorship attribution and intellectual property protection of digital content. However, text contents pose many challenges due to a low capacity to embed a watermark. In this paper, we propose a new structural watermarking method for small pieces of text that may allow to hide upwards of 15 bits of watermark per character manipulating the underlying font grayscale values. The proposed method ensures length preservation and is robust to the copy and paste activities. Moreover, the method is able to embed a password-based watermark returning visually indistinguishable watermarked text.
Meta-stasis of the Internet

This paper offers a brief history of the information age in order to demonstrate how the loss of user control and the increase in certain forms of automation have metastasized into imminent and ongoing threats to social order and the democratic way of life.
Experimental Evaluation of Low Code development, Java Swing and JavaScript programming

Low Code is a technology that has been gaining popularity over the years, due to its potential and simplicity. But so far there has not been an experimental evaluation with other programming methods. This paper aims to introduce Low Code development technology and compare it with Java Swing programming and manual development with HTML (HyperText Markup Language), CSS (Cascading Style Sheet), and JavaScript. These technologies are compared using the following metrics: development time, execution time, and the number of written code lines. In this evaluation, two applications are implemented, a simple calculator, and a text editor, developed in all technologies. It is concluded that it is faster to develop applications in Low Code but in terms of execution time, these are usually slower. Although the Low Code development is still at a somewhat embryonic stage which leads to some bugs and errors, Low Code development is better in general than Java Swing programming, and somewhat similar to manual programming with HTML, CSS, and JavaScript. Another benefit is that Low Code generates HTML and CSS automatically.
Ultra-high system detection efficiency superconducting nanowire single-photon detectors for quantum photonics and life sciences

Ultra-high system detection efficiency ( SDE) s uperconducting nanowire single-photon detectors are demonstrated for a broad range of wavelengths, from UV to mid-infrared, opening novel possibilities in the fields of quantum photonics, neuroimaging and astronomy.
160 Gb/s true physical random bit generation using broadband, high-entropy semiconductor laser chaos

This study investigates broad-band, high-entropy chaos generation using a semiconductor laser subject to intensity-modulated optical injection. The proposed chaotic source can be used as a 2-bit true random bit generator with a rate of 160Gb/s.
Parallel intermodal four-wave mixing for high-dimensional photon-pair entanglement

We present a proposal for photon-pair generation through intermodal four-wave mixing with high-dimensional entanglement in transverse mode. Based on this, we suggest a simple fiber design capable of generating four-dimensional Bell states.
Terahertz Image Super-Resolution Reconstruction Using Unpaired Real-World Images

Terahertz imaging technology has been widely used in applications of security detection, defect detection and biomedicine. The in- sufficient resolution is still the main defect of this technology so far. The use of deep neural network for super-resolution reconstruction of image is a mainstream enhancement of resolution methods. Aiming at solving the shortcomings of existing methods to obtaining training sets and the design of networks, this paper proposes to use real- world terahertz images as low-resolution images and real-world optical images as ground truth to organize unpaired training sets, also a cycle adversarial super-resolution network is designed for the characteristics of unpaired training sets and different picture styles. In this paper we trained the network using training sets organized with the terahertz images which was collected through the Internet, and the images suitable for training in the Office-Home Datasets. The obtained results with real terahertz images shown that through the proposed network the resolution of terahertz images improved, which proves the feasibility of this network.
Secure Image Retrieval Based on Deep CNN Features in Cloud Computing

In cloud computing era, data owners become increasingly motivated to outsource their images from local sites to the commercial public cloud for great flexibility and economic savings. For the consideration of users' privacy, sensitive images have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Image CNN features are once considered confidential for image retrieval in cloud computing. However, recent studies have shown that these features contain ambiguous semantic information and can be reconstructed to images. We design a computable encryption scheme based on vector and matrix calculations for the risk of CNN feature-based image retrieval. The scheme uses SIFT and CNN features to pre-build a feature-based index to provide feature-related information about each encrypted image and then chooses the efficient image similarity measure as the pruning tool to carry out the retrieval procedure. Security analysis and performance evaluation show that the scheme is INI-CCA. The experimental results show that the proposed method has achieved excellent results on content-based image retrieval.
MemInspect2: OS-Independent Memory Forensics for IoT Devices in Cybercrime Investigations

In the age of rapid development of the Internet of Things (IoT) world, more and more cybersecurity incidents have emerged in many IoT devices and systems. Therefore, the need for cybercrime investigation, especially for IoT devices, has become more imperative than ever. Memory forensics, the approach that inspects the memory dump to understand the current state or behavior of the attacked machine, contributes an important position in digital forensics and incident response for IoT systems. However, memory forensics encounter various challenges, including virtual address space (VAS) reconstruction or extracting kernel data structure in a given memory image. Most current tools and approaches leverage the knowledge of the operating system or propose heuristics to evade the commission of rebuilding VAS. In this research, we present our novel methodology to reconstruct the VAS for the memory images by using the paging mechanism of the Central Processing Unit (CPU), primarily for the ARM architectures (32 and 64 bit), one of the most popular microprocessors in the IoT world. In addition, with the support of VAS, we extract the typical kernel data structure like the process linked list. Finally, we build a MemInspect2 toolset that gathers all algorithms, and we also test the tool in many standard OS kernels like Linux and BSD.
A Study of Language Landscape Adaptation in Smartphone Apps

With the popularity of smart terminal devices, information services and applications based on smart devices have become a way of daily life for people. Due to the weak digital technology capability and lack of information literacy of the elderly group, coupled with the limited digital information reception channels specifically for the elderly, digital information products neglect to adapt to the cognitive characteristics and operational capabilities of the elderly group from content, expression form, multimodal use, to operation, i.e. insufficient age-appropriateness. This paper adopts a case study approach to examine the functions and modalities of the linguistic landscape of smartphone apps. The study finds that the function of the linguistic landscape of smartphone apps is affected by various modalities such as font size, colour and placement, thus providing a research reference for the construction of ageing-friendly smartphone apps.
Face Swapping for Film and Television Video based on FaceNet and Local Translation Warp

Face swapping, a study of editing the face identity attributes, has received more attention in recent years due to the breakthrough progress of deep learning. Due to its application and technical value, this paper attempts to swap faces in a selected video clip. To begin with, we use the deep learning framework facenet pytorch to build a MTCNN model for face detection, next obtain the face feature vector based on FaceNet, and compare the Euclidean distance for target face recognition. Then use the InsightFace library to locate the 106 facial landmarks of the target person. Lastly, select the appropriate facial landmarks on the two-dimensional image to realize the pixel-based local translation operation, so that the face contour changes.
Emerging Computing Systems Utilizing Electro-Magnetic Near-Field Connectivity

This paper introduces technology directions of next-generation emerging computer systems utilizing electro-magnetic near-field connectivity around IC chips. We will discuss the potential advantages of this technology in demonstrations of 1) high-bandwidth low-cost wireless interconnections for vertically-stacked 3D ICs, wireless power and data links for non-contact ICs, and electro-magnetic disturbance sensors for secure IC against physical attacks.
Film Analysis from the Perspective of Cinemeitrics Based on OpenCV and Deep Learning

As a new method of film research, Cinemeitrics uses a systematic and digital way to measure and analyze film style, It focuses more on the measurability of objects and the accuracy of results. As an artistic medium, film has become a popular recording tool for creating historical documents. It is more and more actively getting rid of the shackles of text archives and playing an increasingly important role in recording the times, writing history. So this paper takes the historical film “The Founding of a Republic” as an example, uses image processing tools such as OpenCV to quantitatively analyze the film. The main work includes: shot segmentation, key frame extraction, subtitles extraction, object recognition and face tracking, providing data can be used to analyze film’s style, rhythm and emotion.
Research on Text Generation of Medical Intelligent Question and Answer Based on Bi-LSTM and Neural Network Technology

Text generation is a popular research direction in the field of natural language processing as well as artificial intelligence, especially in the medical field. Text generation technology plays an extremely important role in medical intelligent question and answer systems. In previous Q&A systems, the display of generated dialogues is usually limited to Q&A and is difficult to adapt to vertical domains with strong characteristics. In this paper, to solve the task of specialized Q&A in medical field and provide users with comprehensive answers. We propose a TensorFlow architecture-based approach to medical text generation that uses sequential models in Keras to transform the task into a classification problem by treating the text generation problem as a prediction problem. By training and experimenting with the model on a large-scale Chinese medical question-and-answer dataset, the results show that our model has a good fit with applications in this specific domain. The model can aggregate medical domain knowledge, extract useful treatment information and generate medical knowledge text. At the same time, through research, it is found that splitting questions into phrases and then inputting them into the model can effectively solve the problem of repeated sentences in the generated answers. Since the model we proposed can answer patients' questions well and accurately in most cases, this research provides an improvement direction for exploring medical intelligent question answering and text generation.