Reviewerly/exHarmony huggingface dataset:
Reusing Predicate Precision in Value Analysis

Software verification allows one to examine the reliability of software. Thereby, analyses exchange information to become more effective, more efficient, or to eliminate false results and increase trust in the analysis result. One type of information that analyses provide are precisions, which describe an analysis' degree of abstraction (tracked predicates, etc.). So far, analyses mainly reuse their own precision to reverify a changed program. In contrast, we aim to reuse the precision of a predicate analysis within a value analysis. To this end, we propose 13 options to convert a predicate precision into a precision for value analysis. All options compute precisions with various degrees of abstraction and are broadly evaluated on three applications (cooperative verification, result validation, and regression verification). Also, we compare our options against using the coarsest and finest precision as well as a state-of-the-art approach for each application. Our evaluation reveals that coarser precisions work better for proof detection, while finer precisions perform better in alarm detection. Moreover, reusing a predicate precision in value analysis can be beneficial in cooperative verification and works well for validating and reverifying programs without property violations.
Exponential extrapolation memory for tabu search

Tabu search is a well-established metaheuristic framework for solving hard combinatorial optimization problems. At its core, the method uses different forms of memory to guide a local search through the solution space so as to identify high-quality local optima while avoiding getting stuck in the vicinity of any particular local optimum. This paper examines characteristics of moves that can be exploited to make good decisions about steps that lead away from recently visited local optima and towards a new local optimum. Our approach uses a new type of adaptive memory based on a construction called exponential extrapolation. The memory operates by means of threshold inequalities that ensure selected moves will not lead to a specified number of most recently encountered local optima. Computational experiments on a set of one hundred different benchmark instances for the binary integer programming problem suggest that exponential extrapolation is a useful type of memory to incorporate into a tabu search.
Violence detection for surveillance systems using lightweight CNN models

Due to significant safety concerns, today, many places are filled with CCTV cameras, but it still uses manual security to overlook these, which leaves much space for human error, be it negligence or a dangerous situation. This may result in many anomalies such as violence, hostage, or fires. To prevent this, an intelligent and automated system has been implemented that tries to overcome this using deep learning techniques. Using Deep Learning models utilizing Convolutional Neural Networks (CNN), specifically the lightweight models - MobileNetV2 and ResNet50V2 are used to identify threat movements in the given frames over three datasets, namely UCF crime, Real life violence situations, and UBI-fights. The violence in the video is detected using frames, and accuracy is measured. A threat is detected by the system from the video frame, based on which the system for the situation generates an alert. In extreme cases, alerts are sent directly to nearby police stations and emergency services with accurate location information while also indicating the suspicious activities at an instance of time.
Open Source Handwritten Text Recognition on Medieval Manuscripts Using Mixed Models and Document-Specific Finetuning

This paper deals with the task of practical and open source Handwritten Text Recognition (HTR) on German medieval manuscripts. We report on our efforts to construct mixed recognition models which can be applied out-of-the-box without any further document-specific training but also serve as a starting point for finetuning by training a new model on a few pages of transcribed text (ground truth). To train the mixed models we collected a corpus of 35 manuscripts and ca. 12.5k text lines for two widely used handwriting styles, Gothic and Bastarda cursives. Evaluating the mixed models out-of-the-box on four unseen manuscripts resulted in an average Character Error Rate (CER) of 6.22%. After training on 2, 4 and eventually 32 pages the CER dropped to 3.27%, 2.58%, and 1.65%, respectively. While the in-domain recognition and training of models (Bastarda model to Bastarda material, Gothic to Gothic) unsurprisingly yielded the best results, finetuning out-of-domain models to unseen scripts was still shown to be superior to training from scratch. Our new mixed models have been made openly available to the community.
Locate Where You Are by Block Joint Learning Network

Unmanned aerial vehicles (UAVs) are widely applied in various fields, which is located by the global position system (GPS) in most cases. However, in the GPS-denied cases, visual localization becomes very important. In complicated environments, such as weak illumination and ground objects changed, visual localization is unstable. This letter presents a UAV visual localization model with a block joint learning network (BJN). Different from the traditional feature extraction-comparison paradigm, the proposed BJN extracts the joint features of the input images at the same time, so as to fully mine the coupling relationship between the multi-source images. Besides this, to overcome the challenges of inconsistency style changes in image matching, the saliency feature based on the attention mechanism and the traditional edge feature operator are introduced in joint feature learning. To evaluate the performance of the proposed model, the experiments on the simulated dataset and the real dataset are given. Both the results on simulated and real datasets indicate that the proposed model is effective on multi-source image matching and UAV visual localization.
(MLE$^{2}$A$^{2}$U)-Net: Image Super-Resolution via Multi-Level Edge Embedding and Aggregated Attentive Upsampler Network

Given a degraded low-resolution input image, super-resolution (SR) aims at restoring the lost textures and structures and generating high-resolution image content. Significant advances in image super-resolution have been made lately, dominated by convolutional neural networks (CNNs). The top performing CNN-based SR networks typically employ very deep models for embracing the benefits of generating spatially precise results, but at the cost of loss of long-term contextual information. Additionally, state-of-the-art (SOTA) methods generally lack in maintaining the balance between spatial details and contextual information, which is the basic requirement for exhibiting superior performance in SR task. For restoration application like SR, the overall network generally demands efficient preservation of low-frequency information and reconstruction of high-frequency details. Thus, our work presents a novel architecture with the holistic objective of maintaining spatially-precise representation by collecting contextual content and restoring multi-frequency information throughout the network. Our proposed model learns an enriched set of features, that besides combining contextual information from multiple scales simultaneously preserves the high-resolution spatial details. The core of our approach is a novel non-local and local attention (NLLA) block which focuses on (1) learning enriched features by collecting information from multiple scales, (2) simultaneously handling the different frequency information, and (3) effectively fusing the relevant low-frequency and high-frequency information by ignoring the redundant features. Additionally, for effectively mapping the low-resolution features to high-resolution, we propose a novel aggregated attentive up-sampler (AAU) block that attentively learns the weights to up-sample the refined low-resolution feature maps to high-resolution output. Extensive experiments on the benchmark SR datasets demonstrate that the proposed method achieves appealing performance, both qualitatively and quantitatively.
Fatigue and Damage Assessment of CFRP Material Using Digital Image Correlation

Fatigue testing of a Carbon Fiber Reinforced Polymer (CFRP) in tension-tension loading has been conducted. In-situ surface strain measurements were performed to examine the gradual elongation of the specimen as this relates to stiffness loss and fatigue damage. A methodology capturing the specimen at peak load has been developed, including an automated trigger mechanism that activates the camera at the desired cycle count. The material tested was a Unidirectional (UD) Non-Crimp Fabric (NCF) with carbon fibers and an epoxy matrix. The fatigue test results revealed a wide scatter in the mid-range of the high cycle fatigue region. By studying the strain in the early fatigue loading cycles and the stiffness loss over time, benchmark of the fatigue performance between different material samples could be carried out, explaining the scatter in the fatigue testing. It could be observed that the fatigue limit of the UD CFRP material in the fiber direction is in the magnitude of 80 % of the material's Ultimate Tensile Strength (UTS).
Interpolation-based reversible data hiding with blockchain for secure e-healthcare systems

With drastic development encountered in networked infrastructure, the Internet has entered into almost every sector. In this regard, a typical e-healthcare system needs effective storage and management of electronic patient record (EPR), which has to be securely shared among different clinical centers. The main challenges in achieving the security of medical data include verifying the genuineness of the data, protecting it from vulnerable attacks, and tracking the source of an attack. To figure out these problems, a novel framework called the blockchain-based authenticity of healthcare data for mediclaim (BAHDM) is proposed, by combining the interpolation-based reversible data hiding (IRDH) technique with blockchain technology. The objectives of the proposed framework are to authenticate EPR using the blockchain network and to provide higher embedding capacity without compromising the visual quality of stego medical image. At first, the EPR is embedded into medical images to generate the stego medical images. The obtained PSNR values prove that the EPR is unnoticeable to human eyes, making it invulnerable to attacks. Then, the crypto hash values of EPR-embedded stego medical images are saved in blockchain so that they can be traced and verified. Thus, the combination of reversible data hiding with blockchain offers security in terms of imperceptibility, integrity, and authenticity.
QOS-Aware IOT Services Composition: A Survey

The IoT services composition is a complex task, where the necessary services choice and the handsets in the proper order as an integrated composite service is the main challenge in a highly constrained environment. The services composition has been the subject of research in several academic works where the authors try to find the best way to combine and compose the services in an optimal way. In this paper we examine the composition mechanism of IoT services, by focusing on QoS-aware approaches, by a comparative study of some proposed approaches in the literature.
Software Fault Prediction Using Machine Learning Algorithms

Software fault prediction (SFP) plays a vital part in quality of software. It helps in detecting the faulty constructs at an initial phases of software development life cycle (SDLC). This software fault is also known as defect, which occurs when the predicted results didn’t match the actual output. This can be referring as an error, fault, bug in a computer program. Machine learning (ML) handles with the subject of how to build or design computer programs that always increase their effectiveness at particular tasks via experiences. In field of software engineering, machine learning plays a very important role and contains different approaches like test effort prediction and cost prediction. In these prediction technique, software faults prediction (SFP) is the most prominent study in field of software engineering. In this paper, the predictions were made using machine learning algorithms such as Naive Bayes (NB), support vector machine (SVM), logistic regression (LR), random forest (RF). Feature selection method such as Spearman’s rank correlation is used for selecting highly correlated input variables. Dimensionality reduction methods such as LDA and PCA are used for reducing the dimensions. Performance and evaluation of these algorithms are carried out using accuracy, precision, and recall. Comparison is performed over four machine learning techniques with two feature selection and dimensionality reduction techniques. The results show that using Spearman’s rank correlation, SVM gives 94% of the highest accuracy compared to other algorithms. The results show that with LDA method on the dataset, NB and LR give highest accuracy of 92% over other techniques, and using PCA method, RF gives 92.8% of accuracy, which is highest among four classifiers.
Low-Light Color Imaging via Cross-Camera Synthesis

This paper presents a framework for low-light color imaging using a dual camera system that combines a high spatial resolution monochromatic (HSR-mono) image and a low spatial resolution color (LSR-color) image. We propose a cross-camera synthesis (CCS) module to learn and transfer illumination, color, and resolution attributes across paired HSR-mono and LSR-color images to recover brightness- and color-adjusted high spatial resolution color (HSR-color) images at both camera views. Jointly characterizing various attributes for final synthesis is extremely challenging because of significant domain gaps across cameras. Our proposed CCS method consists of three subtasks: reference-based illumination enhancement ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefIE</b> ), reference-based appearance transfer ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefAT</b> ), and reference-based super resolution ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RefSR</b> ), by which we can characterize, transfer, and enhance illumination, color, and resolution at both views. Each subtask is implemented using deep neural networks (DNNs) that are first trained for each subtask separately and then finetuned jointly. Experimental results show the superior qualitative and quantitative results of the proposed CCS model on both synthetic content from popular datasets and real-captured scenes. Ablation studies further verify the model generalization to various exposures and camera baselines. We will make our work accessible at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://njuvision.github.io/CCS</uri> for reproducible research.
Sentiment Analysis on COVID-19 Tweeter Dataset

Everyone is aware about coronavirus disease (in short it is COVID-19). The meaning of word COVID is defined as 'CO' stands for corona, 'VI' for virus, and 'D' for disease. It is an infectious disease caused by a newly discovered coronavirus which makes all countries on globe unstable. More than 206 countries is affected due to this COVID-19, and more than 110,00,000 people infected on the globe, and out of that more than 5,00,000 people died due to this incurable (till date no vaccination) disease. So that COVID-19 is declared pandemic. In this research, generic social media dataset related to COVID-19 is used for study and find sentiment analysis. In this article, twitter data collection, data preprocessing, and calculation of tweeter sentiment analysis were discussed in detail with respect to India and USA and whole world. Different Python libraries were discussed in this article. During 3 months, first 2 months, USA was more positive comparison to India and world. But, after lockdown in June, India is more positive compared to USA and world.
Motion-Adaptive Detection of HEVC Double Compression With the Same Coding Parameters

High Efficiency Video Coding (HEVC) double compression detection is of prime significance in video forensics. However, double compression with the same parameters and video content with high motion displacement intensity have become two main factors that limit the performance of existing algorithms. To address these issues, a novel motion-adaptive algorithm is proposed in this paper. Firstly, the analysis of GOP structure in HEVC standard and the coding process of HEVC double compression are provided. Next, sub-features composed of fluctuation intensities of intra prediction modes and unstable Prediction Units (PUs) in normal Intra-Frames (I-frames) and optical flow in adaptive I-frames are exploited in our algorithm. Each sub-feature is extracted during the process of multiple decompression. We further combine these sub-features into a 27-dimensional detection feature, which is finally fed to the Support Vector Machine (SVM) classifier. By following a separation-fusion detection strategy, the experimental result shows that the proposed algorithm outperforms the existing state-of-the-art methods and demonstrates superior robustness to various motion displacement intensities and a wide variety of coding parameter settings.
No-Reference Bitstream-Layer Model for Perceptual Quality Assessment of V-PCC Encoded Point Clouds

No-reference bitstream-layer models for point cloud quality assessment (PCQA) use the information extracted from a bitstream for real-time and nonintrusive quality monitoring. We propose a no-reference bitstream-layer model for the perceptual quality assessment of video-based point cloud compression (V-PCC) encoded point clouds. First, we study the relationship between the perceptual coding distortion and the texture quantization parameter (TQP) when geometry encoding is lossless. The results indicate that the perceptual coding distortion depends on the texture complexity (TC). Next, we estimate TC using TQP and the texture bitrate per pixel (TBPP), both of which are extracted from the compressed bitstream without resorting to complete decoding. This allows us to build a texture distortion model as a function of TQP and TBPP. By combining this texture distortion model with a geometry distortion model that depends on the geometry quantization parameter (GQP), we obtain an overall no-reference bitstream-layer PCQA model that we call bitstreamPCQ. Experimental results show that the proposed model markedly outperforms existing models in terms of widely used performance criteria, including the Pearson linear correlation coefficient (PLCC), the Spearman rank order correlation coefficient (SRCC) and the root mean square error (RMSE).
RuVa: A Runtime Software Variability Algorithm

Context-aware and smart systems that require runtime reconfiguration to cope with changes in the environment increasingly demand variability management mechanisms that can address runtime concerns. In recent years, we have witnessed new dynamic variability solutions using dynamic software product line (DSPL) approaches. However, while few solutions proposed so far have addressed the need to add, change and remove variants dynamically, none of them provide a way to check the constraints between features at runtime. Because all SAT solvers perform variability constraint checking in off-line mode, we suggest in this ongoing research paper the integration of RuVa, a runtime variability algorithm, with the FaMa tool suite to check feature constraints dynamically before a new feature is added or an existing feature is removed. This research suggests a novel approach to modifying the variability model of context-aware systems dynamically and check the feature constraints on the fly.We integrate our solution with a SAT solver that can be invoked at runtime by a cyber-physical system.We validate the effectiveness and performance of the proposed algorithm using simulations.We also provide a proof-of-concept for updating the configuration of a robot’s variability model based on contextual changes.
Ontological Reference Model for Piloting Procedures

Knowledge modeling in the context of an intelligent educational system remains a key research issue. Indeed, intelligent learning systems need a high-quality knowledge formalization of the domain theory related to the context. Ontology-based languages allow a detailed semantic description of knowledge that can support the intelligent system in understanding the domain theory. In this paper, we present an ontology-based approach for modeling complex procedural knowledge related to aircraft piloting procedures. We have developed a reference model for piloting tasks with a strong semantic grounding to the domain theory. The decomposition of a reference procedure in a task ontology with links to the domain ontology for supporting the execution that is highly related to the context will be detailed. This work is a preliminary step to an intelligent tutoring system aims at assisting pilots and at providing useful feedback during piloting task.
A System Design of Virtual Reality Enabled Chinese Ancient Books for Enhancing Reading Promotion and Culture Dissemination

Chinese ancient books (CABs) possess precious values in the aspects of Chinese literature, cultural relics, history, and arts. However, ordinary modern Chinese readers have language and cultural barriers to understanding CABs, which leads to the reader group of CABs being limited in the Chinese literature and historical research field. To enhance CABs reading promotion and Chinese culture dissemination, this paper aims to explore the method of using cultural tourism resources to illustrate CABs text in a virtual reality (VR) environment. It specifically illustrates text elements and narrative storylines contained in CABs to reduce readers’ difficulties in CABs’ reading comprehension, improve their reading interest, achieve the purpose of CABs reading promotion from a minority group to mass readers. This paper systematically analyzes the relevant VR cultural heritage applications and develops a VR CAB prototype based on one case. It contributes a VR CAB design method and verifies the feasibility and value of the VR CAB prototype by using illustration resources from LAMs and cultural tourism fields.
Multiscale One-Class Classification Network for Machine Health Monitoring

Currently, most machine health monitoring methods require labeled abnormal or faulty samples to build a supervised classification model. However, it is difficult and expensive to collect enough faulty samples for accurate and reliable fault detection and diagnosis. To address this issue, we formulate machine health monitoring as a one-class classification problem and propose a new unsupervised health monitoring model named multiscale one-class classification network (MOCN) only relying on normal data without any label information. It consists of two sequential learning modules: a multiscale feature representation learning module to extract effective features from raw one-dimensional vibration signals and a one-classifier training module to build a hypersphere model to contain all normal samples with a minimum radius. Unlike traditional two-step one-class classification methods, both modules in our proposed MOCN are jointly optimized, and therefore it can simultaneously learn compact representations and train a deep one-class classifier. Our proposed MOCN method is evaluated through experiments on three datasets, and experimental results have proved the superiority of our proposed MOCN model compared with several existing methods.