Reviewerly/exHarmony huggingface dataset:
A Survey on Text Mining Tools and Techniques support early testcase prediction

Text Mining is measure in which past or recorded data is conjured utilizing various assets. The data recovery is a field where enormous measure of data is separated utilizing internet searcher to get exact and improved outcomes. As World Wide Web(WWW) gives a decent stage to assortment of data, there is need for some method to diminish time for looking through significant information and to save relentless work. Data on web by and large contains unstructured or semi organized data like content, messages, XML, HTML Pictures, MP3, Recordings and so forth likewise organizations and organizations use to save their data in text design. The new methodology called Text Mining or Information revelation is presented, which is utilized to beat issues looked by Information Retriavel(IR) for unstructured or semi organized data recovery. In this paper we present writing overview for various Content mining strategies, for example, Data Extraction, Theme following, Outline, Arrangement, Bunching, Affiliation Rule Mining (ARM) and EART with utilizations of Text Mining. Today the huge assortment of text mining devices presents the productive and compelling approach to recover fitting data from unstructured information. In this way the content mining method is turning into a significant examination zone to diminish time for looking through the unstructured data.
Collaborating The Textual Reviews Of The Merchandise and Foretelling The Rating Supported Social Sentiment

Lately, we have seen a twist of audit sites. It presents a decent opportunity to share our experience for a considerable length of time we have bought. Be that as it may, we tend to confront the information over-burdening issue. A method for mining significant information from surveys to know a client's inclinations and produce precise proposal is fundamental. Since quite a while ago settled recommender Systems (RS) considers a few variables, similar to client's buy records, item class, and geographic area. During this work, we have proposed sentiment-based rating prediction technique (RPS) to help up the expectation precision in recommender Systems. First and foremost, we examine the social user sentimental measuring approach and calculate every user’s sentiment on things/items. Furthermore, we don't exclusively consider a client's own wistful properties anyway moreover take interpersonal social sentimental influence into study. Then, at that point, we propose to consider item name, which might be deduced by the sentimental distributions of a user set that reflect clients' comprehensive analysis. Finally, we tend to intertwine 3 factors-user sentiment similarity, interpersonal social sentimental distributions of a client opinion likeness, interpersonal social sentimental influence, associate the thing's reputation relationship into our recommender system to make a talented rating prediction. Then, at that point, we arranged a presentation analysis of the 3 sentimental factors on a genuine world dataset gathered from Yelp. Our exploratory outcomes show, the sentiment will well describe user preferences, which facilitate to hike the proposal execution.
An Effective and Visualized State Detection of Metro Vehicle Door System

Metro Vehicle Door System (MVDS) is one of the most frequently used parts in rail transit. In this work, we propose an effective and visualized state detection technique of MVDS by utilizing feature extraction and matching, which is helpful to improve the safety and reliability of the door system. Five states of MVDS, the normal opening, opened, closing and closed state, as well as the anti-extrusion (also named as anti-drag) state, are to be detected in our work. In particular, we design an improved Speeded Up Robust Features (SURF) method to reduce the mismatching pairs by exploiting local space distance, which is crucial for practical usage. Furthermore, we present a scheme to detect the five states of MVDS mentioned above via our improved SURF. Finally, we conduct experiments in subjective and objective, and the resultant performance shows the effectiveness of our method.
An encoder–decoder based thermo-visible image translation for disguised and undisguised faces

Thermal cameras can capture images even in low light conditions. However, humans cannot recognize human faces in thermal images. Translation of thermal images to visible domain is one solution to the problem of face recognition in thermal images. Most of the research works have proposed Generative Adversarial Networks (GANs) based solutions for thermal to visible image translation. However, GAN is a heavy network that consumes huge amount of resource for thermal to visible image translation. In this paper, we propose an encoder–decoder architecture for thermal to visible image translation of human faces. Since our proposed architecture is not based on GANs, it is lightweight. The proposed method works well for both disguised and non-disguised thermal facial images. Standard comparison parameters such as Peak Signal-to-noise Ratio (PSNR), Structural Similarity Index (SSIM), and Multiscale Structural Similarity Index (MS-SSIM) are used to evaluate the quality of the generated visible images with respect to the ground truth. It has been found that our proposed architecture outperforms the current state-of-the-art image translator architectures namely pix2pix, Cycle-GAN, modified thermal to visible GAN and Dual GAN by a considerable margin for both disguised as well as non-disguised dataset.
Bootstrap Equilibrium and Probabilistic Speaker Representation Learning for Self-Supervised Speaker Verification

In this paper, we propose self-supervised speaker representation learning strategies, which comprise of a bootstrap equilibrium speaker representation learning in the front-end and an uncertainty-aware probabilistic speaker embedding training in the back-end. In the front-end stage, we learn the speaker representations via the bootstrap training scheme with the uniformity regularization term. In the back-end stage, the probabilistic speaker embeddings are estimated by maximizing the mutual likelihood score between the speech samples belonging to the same speaker, which provide not only speaker representations but also data uncertainty. Experimental results show that the proposed bootstrap equilibrium training strategy can effectively help learn the speaker representations and outperforms the conventional methods based on contrastive learning. Also, we demonstrate that the integrated two-stage framework further improves the speaker verification performance on the VoxCeleb1 test set in terms of EER and MinDCF.
Integrating Throttle into a Reinforcement Learning Controller for a Perched Landing of a Variable Sweep Wing UAV

This paper details the training, and simulation and experimental results for controlling a perching manoeuvre of a variable sweep uncrewed air vehicle (UAV) using reinforcement learning (RL). This builds on previous work by the authors by allowing the RL controller to determine the throttle cut-off point during the initial phase of the manoeuvre. This additional control allows the system to show significantly improved performance over the baseline models. In simulation the addition of throttle showed a 5x improvement in performance under a 8 m/s headwind. Similar improvements were seen in challenging real-world conditions with significant headwinds. Further testing of the trained agents under varying real-world flight conditions would help confirm initial results. A further degree of throttle control may also improve performance but will require a significantly improved numerical model.
Well-known Open-Source Cloud Computing Platforms

Cloud computing is an adaptive model that provides resources to end-users. This model moves many organizations to develop open-source cloud computing solutions. This article surveys popular open-source cloud computing platforms such as Eucalyptus, OpenNebula, Nimbus, Xen Cloud, and AbiCloud. These well-known cloud platforms can help researchers to choose the appropriate platform for their environments. For more illustration, a comparative study is given according to their architecture.
Platform Independent Browser Forensic Tool for Advanced Analysis of Artifacts and Case Management

A web browser is a major attack vector which cyber-criminals utilize to land in an environment. The evidence related to the malicious browsing activities can be found in the host which gives valuable information related to the case. These digital footprints involve history, cookies, bookmarks, saved credentials and downloads etc. This paper presents a sophisticated tool aiding the conventional manual investigation process from evidence collection to the final v e rdict b y a u tomating h u man dependent functions, resulting a fast and unbiased analysis of browser forensic artifacts. This tool states its unique value over the existing tools by working operating systems independently, collecting all browsing evidence including deleted artifacts and encrypted saved credentials, automatically analysing the reputation of the extracted evidence, integrating evidence collected from different web browsers into a single timeline, and correlating the adjacent distrustful events inside and outside the host. Eventually, this tool calculates a browsing reputation scorecard and creates a profile for the host, condensing the findings g a thered t h roughout the investigation. The paper presents another important methodology to predict the future browsing reputation score based on the past browsing patterns. Furthermore, multiple cases management feature and dashboard provide a concise overview of overall findings to the forensic investigator.
Trustworthy Knowledge Graph Population From Texts for Domain Query Answering

Obtaining answers to domain-specific questions over large-scale unstructured (text) data is an important component of data analytics in many application domains. As manual question answering does not scale to large text corpora, it is common to use information extraction (IE) to preprocess the texts of interest prior to posing the questions. This is often done by transforming text corpora into the knowledge-graph (KG) triple format that is suitable for efficient processing of the user questions in graph-oriented data-intensive systems.In a number of real-life scenarios, trustworthiness of the answers obtained from domain-specific texts is vital for downstream decision making. In this paper we focus on one critical aspect of trustworthiness, which concerns aligning with the given domain vocabularies (ontologies) those KG triples that are obtained from the source texts via IE solutions. To address this problem, we introduce a scalable domain-independent text-to-KG approach that adapts to specific domains by using domain ontologies, without having to consult external triple repositories. Our IE solution builds on the power of neural-based learning models and leverages feature engineering to distinguish ontology-aligned data from generic data in the source texts. Our experimental results indicate that the proposed approach could be more dependable than a state-of-the-art IE baseline in constructing KGs that are suitable for trustworthy domain question answering on text data.
Learning Decision Rules or Learning Decision Models?

In this position paper, we discuss the reasons for the lack of success of rule learning, as witnessed by the quasi-absence of commercial applications, and what can be done to revive the domain and possibly to kick-start the kind of explosive development that statistical Machine Learning and Neural Networks have experienced over the past 15 years. The root cause of the problem is well-known, and it is not the rule learning algorithms themselves: if the representation language to which a rule learning algorithm has access – often only the representation model of the input data – is not appropriate to represent the decision rules, the algorithm has no way to generate a decision ruleset that generalizes well. Feature generation and other methods that have been proposed to augment the data representation language are useful, but we argue that the focus should be on discovering the conceptual model that underly the decision. We claim that this amounts to discovering the structure of decisions, that is, to learn decision models. We outline some potentially fruitful research directions, and how this topic is central to neuro-symbolic learning.
Region-and-Attention Network for Semantic Segmentation

In this article, we address the task of semantic segmentation by aggregating the information of feature regions at different scales and modeling the dependency between regions by combining the attention mechanism. Instead of using global pooling to bundle information from each channel into a single value in the past, we propose a Region-and-Attention Network (RANet) to pay attention to local features and their global dependence at the regional level. Specifically, we use different pooling rates for the high-level output to obtain multi-scale information aggregates, and regard the multi-channel sequence contained in each pixel position in the pooling result as the global information integration of the corresponding region before the feature layer pooling. Unlike SENet, we perform a separate channel attention transformation for the regional information represented by each pixel position, and use one-dimensional convolution instead of multi-layer perceptrons. The final segmentation result is enhanced region-level prediction by fusing RANet outputs at different pooling scales. The proposed approach achieves advanced segmentation performance on two challenging common segmentation datasets, Cityscapes and Pascal VOC.
Mathematical Modelling of Quantum Kernel Method for Biomedical Data Analysis

This study presents a novel method to detect the medical application based on Quantum Computing (QC) and a few Machine Learning (ML) systems. QC has a primary advantage i.e., it uses the impact of quantum parallelism to provide the consequences of prime factorization issue in a matter of seconds. So, this model is suggested for medical application only by recent researchers. A novel strategy i.e., Quantum Kernel Method (QKM) is proposed in this paper for data prediction. In this QKM process, Linear Tunicate Swarm Algorithm (LTSA), the optimization technique is used to calculate the loss function initially and is aimed at medical data. The output of optimization is either 0 or 1 i.e., odd or even in QC. From this output value, the data is identified according to the class. Meanwhile, the method also reduces time, saves cost and improves the efficiency by feature selection process i.e., Filter method. After the features are extracted, QKM is deployed as a classification model, while the loss function is minimized by LTSA. The motivation of the minimal objective is to remain faster. However, some computations can be performed more efficiently by the proposed model. In testing, the test data was evaluated by minimal loss function. The outcomes were assessed in terms of accuracy, computational time, and so on. For this, databases like Lymphography, Dermatology, and Arrhythmia were used.
Image Dehazing Based on Colour Ellipsoid Prior and Low-Light Image Enhancement

The images in hazy environment are not clearly visible due to atmospheric light scattering. Hence, image dehazing is required to reduce the haze effect. In this paper, a colour ellipsoid prior-based model is utilized for image dehazing. After dehazing, the output appears dark and with less contrast. These images experience with low brightness and may downgrade the performance for computer vision systems. The LIME (low-light image enhancement) is implemented in our proposed work, to avoid the over-dark and less-contrast outcome. The image enhancement scheme is implemented for the dehazed image using LIME. For a dehazed image, the Illumination Map (IM) is computed and it is further refined by using structure prior. Through the refined IM, the dehazed image can be enhanced effectively. For the proposed method, the experimental results show better qualitative and quantitative results when compared to the state-of-the-art methods.
Classify Arabic Text using Vector Space Models

The researchers of this study chose 242 Arabic abstract doucments. Computer science and information systems are mentioned in all of these abstracts. The researchers created an Arabic-specific autonomous information retrieval system, the system was written in the C# NET programming language and its compatible with IBM/PCs and other microcomputers. For this corpus, The researchers used an automatic indexing strategy. The system was created using the Vector Space Model (VSM). In this model, the researcher take all measurements and utilize the Cosine, Dice, Jaccard, and Inner Product Similarity measures. Using the Vector Space Model, the researchers compared the retrieval results. In Arabic documents, the researchers discovered that the retrieval result for cosine is better than the retrieval result for other measures.
A Real-Time Target Tracking Algorithm Based on Improved Kernel Correlation Filter

At present, target tracking technology is a popular computer vision technology, which is of great significance in people's production and life. But there are still some important and difficult problems in the field of target tracking, such as background similarity, target occlusion, motion blur and so on. Although the target tracking method based on deep learning has good robustness, its off-line training process costs a lot of time and is not flexible enough to use. In this paper, starting from the background requirements of shipboard platform sea target tracking, the algorithm is improved based on kernel correlation filtering algorithm, and a set of high robust real-time target tracking algorithm based on online learning method is proposed.
Object Goal Visual Navigation Using Semantic Spatial Relationships

The target-driven visual navigation is a popular learning-based method and has been successfully applied to a wide range of applications. However, it has some disadvantages, including being ineffective at adapting to unseen environments. In this paper, a navigation method based on Semantic Spatial Relationships (SSR) is proposed and is shown to have more reliable performance when dealing with novel conditions. The construction of joint semantic hierarchical feature vector allows for learning implicit relationship between current observation and target objects, which benefits from construction of prior knowledge graph and semantic space. This differs from the traditional target driven methods, which integrate the visual input vector directly into the reinforcement learning path planning module. Moreover, the proposed method takes both local and global features of observed image into consideration and is thus less conservative and more robust in regards to random scenes. An additional analysis indicates that the proposed SSR performs well on classical metrics. The effectiveness of the proposed SSR model is demonstrated comparing with state-of-the-art methods in unknown scenes.
Logical Characterizations of Fuzzy Simulations

We provide and prove logical characterizations of fuzzy simulations between fuzzy Kripke models that use a general t-norm-based semantics. We also extend the results for fuzzy Kripke models defined over a general residuated lattice. Our logical characterizations of fuzzy simulations are formulated w.r.t. positive existential fragments of fuzzy propositional dynamic logic without tests and concern fuzzy preservation of positive existential modal formulas under fuzzy simulations as well as the Hennessy-Milner property of fuzzy simulations.
Blockchain Based Student Information Management System

Information management system is in general a tedious and an important one. Among the various system, student information management system is important as here the data is sensitive and needs regular updating until they complete their degree. Along with the recent data, the history is also necessary to keep track of the course completion. In literature, there are various method proposed for the same. Independent of the technology, security and privacy of the data is important. One of the major issues with the student information is the space and the duration for which it must be maintained. Student information’s must be maintained atleast for 5 years. This occupies for more space meanwhile the security of the data also becomes an important factor when it gets stored in cloud storage. In this paper, a Blockchain based student information management is proposed. The system contains a web interface in which the faculties enter the marks. These marks get stored in the Blockchain with the help of smart contract.
Role of Lithium-Ion Batteries in Increasing the Operational Safety of Offshore Service Vessels

Abstract For ensuring the operational safety of offshore service vessels (OSVs) during critical operations, marine classification agencies recommend operating redundant diesel generators (DGs) as a spinning reserve for dynamic positioning (DP) systems. In view of the reduced fuel efficiency and increased emissions of DG sets when operated at low loads, lithium-ion (Li-ion) batteries are preferred as a green choice to serve the class-recommended DP power backup for 12 min. Based on the on-demand reliability analysis and Li-ion battery failure models, it is identified that an OSV with a 1.6-MW‐capacity DP system requires 538 kWh of Li-ion battery power to reliably replace a spinning DG set in serving power backup for 12 min. Based on the IEC61508/11 health, safety, and environment framework, the methodology to identify the OSV risk under various operational conditions and the battery system minimum maintenance interval requirements to meet various safety integrity levels are described.
A Survey of Story-Generation Apps and Online Systems between 2017 and 2020

Automatic story generation has been one of the greatest dreams of human beings. Since Tale-Spin (1976), people have done a lot of work in this field. They have proposed various concepts on how to improve story generation techniques and they devise quite a few apps and systems to facilitate story creation. This paper focuses on available story generation apps and online systems released or renewed in the past three years. Most them are about character generation and plot generation. This survey describes and analyses the advantages and shortcomings of them. It is shown that automatic story generation is still at its beginning stage, but developers may get much inspiration and help from natural language processing technology.
FlagDetSeg: Multi-Nation Flag Detection and Segmentation in the Wild

We present a simple and effective flag detection approach for multi-nation flag instance segmentation in-the-wild based on data augmentation and Mask-RCNN PointRend. To the best of our knowledge, this is the first multi-nation flag detection work incorporating recent deep object detection with code and dataset that will be released for public use. Flag images with binary segmentation are collected from public domain including the Open Image V6 and annotated for up to 225 countries. Additional flag images are generated from template flag images with cropping, warping, masking, and color adaption to hallucinate realistic-looking flag images for training and testing. Data augmentation is performed by fusing and transforming the segmented flags on top of natural image backgrounds to synthesize new images. To cope with the large variability of flags with the lack of authentic annotated flags, we combine the trained binary Mask-RCNN segmentation weights with the new multi-nation classifier for fine-tuning. For evaluation, the proposed model is compared with other popular detectors and instance segmentation methods including YOLACT++. Results show the efficacy of the proposed approach.