Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence as a Service for Immoral Content Detection and Eradication

Social media is referred to as active global media because of its seamless binding thanks to COVID-19. Connecting software such as Facebook, Twitter, WhatsApp, WeChat, and others come with a variety of capabilities. They are well-known for their low-cost, quick, and effective communication. Because of the seclusion and travel constraints caused by COVID-19, concerns, such as low physical involvement in many possible activities, have arisen. Depending on their information, knowledge, nature, experience, and way of behavior, various types of human beings have diverse responses to any scenario. As the number of net subscribers grows, inappropriate material has become a major concern. The world's most prestigious and trustworthy organizations are keenly interested in conducting practical research in this field. The research contributes to using Artificial Intelligence (AI) as a service (AIaaS) for preventing the spread of immoral content. As software as a service (SaaS) and infrastructure as a service (IaaS), AIaaS for immoral content detection and eradication can use effective cloud computing models to leverage this service. It is highly adaptable and dynamic. AIaaS-based immoral content detection is mostly effective for optimizing the outcomes based on big data training data samples. Immoral content is identified for semantic and sentiment evaluation, and content is divided into immoral, cyberbullying, and dislike components. The suggested paper's main issue is the polarity of immoral content that can be processed using an AI-based optimization approach to control content proliferation. To finish the class and statistical analysis, support vector machine (SVM), selection tree, and Naive Bayes classifiers are employed.
A Learning-Embedded Attributed Petri Net to Optimize Student Learning in a Serious Game

Serious games (SGs) are a practice of growing importance due to their high potential as an educational tool for augmented learning. However, little effort has been devoted to address student learning optimization in an SG from a systematic point of view. This article tackles this challenge by developing a learning-embedded attribute Petri net (LAPN) model to represent game flow and student learning decision-makings. The dynamics of learner behaviors in game are then addressed through the incorporation of learning mechanisms (i.e., reinforcement learning (RL) and random forest classification) into the Petri net model for knowledge reasoning and learning. Finally, an algorithm based on LAPN is proposed, aiming to guide learners to achieve a faster and better solution to problem-solving in game. The benefit of the proposed model and algorithm is then demonstrated in the SG Gridlock.
Scalable Scene Flow From Point Clouds in the Real World

Autonomous vehicles operate in highly dynamic environments necessitating an accurate assessment of which aspects of a scene are moving and where they are moving to.A popular approach to 3D motion estimation, termed scene flow, is to employ 3D point cloud data from consecutive LiDAR scans, although such approaches have been limited by the small size of real-world, annotated LiDAR data.In this work, we introduce a new largescale dataset for scene flow estimation derived from corresponding tracked 3D objects, which is ∼ 1, 000× larger than previous real-world datasets in terms of the number of annotated frames.We demonstrate how previous works were bounded by the amount of real LiDAR data available, suggesting that larger datasets are required to achieve state-of-the-art predictive performance.Furthermore, we show how previous heuristics such as down-sampling heavily degrade performance, motivating a new class of models that are tractable on the full point cloud.To address this issue, we introduce the FastFlow3D architecture which provides real time inference on the full point cloud.Additionally, we design human-interpretable metrics that better capture real world aspects by accounting for ego-motion and providing breakdowns per object type.We hope that this dataset may provide new opportunities for developing real world scene flow systems. Index Terms-Deep learning for
Learning and Integrating Multi-Level Matching Features for Image-Text Retrieval

In recent years, several retrieval methods for measuring the similarity between images and texts have been proposed. Despite the efficiency of most of these methods, the scalar-based cosine similarities may not be sufficiently expressive to fully capture the intricate matching pattern between the visual and textual features. In addition, the hybrid methods empirically integrate the global and local matching similarities, which results in less interpretability. This letter proposes a novel Multi-Level Matching Network (MLMN) which learns and integrates the vector-based multi-level matching features. Two vector-based matching branches are first designed to learn more powerful matching features. An interpretable matching integration strategy is also proposed, which adaptively integrate the learned matching features according to the global matching information. Moreover, the image-text retrieval is further considered as a binary classification problem, and the MLMN is trained by the binary cross-entropy loss with hardest negatives. Several experiments are performed using the MSCOCO and Flickr30K datasets. The results demonstrate that MLMN achieves a higher performance than that of the state-of-the-art methods.
An Efficient Spark-Based Hybrid Frequent Itemset Mining Algorithm for Big Data

Frequent itemset mining (FIM) is a common approach for discovering hidden frequent patterns from transactional databases used in prediction, association rules, classification, etc. Apriori is an FIM elementary algorithm with iterative nature used to find the frequent itemsets. Apriori is used to scan the dataset multiple times to generate big frequent itemsets with different cardinalities. Apriori performance descends when data gets bigger due to the multiple dataset scan to extract the frequent itemsets. Eclat is a scalable version of the Apriori algorithm that utilizes a vertical layout. The vertical layout has many advantages; it helps to solve the problem of multiple datasets scanning and has information that helps to find each itemset support. In a vertical layout, itemset support can be achieved by intersecting transaction ids (tidset/tids) and pruning irrelevant itemsets. However, when tids become too big for memory, it affects algorithms efficiency. In this paper, we introduce SHFIM (spark-based hybrid frequent itemset mining), which is a three-phase algorithm that utilizes both horizontal and vertical layout diffset instead of tidset to keep track of the differences between transaction ids rather than the intersections. Moreover, some improvements are developed to decrease the number of candidate itemsets. SHFIM is implemented and tested over the Spark framework, which utilizes the RDD (resilient distributed datasets) concept and in-memory processing that tackles MapReduce framework problem. We compared the SHFIM performance with Spark-based Eclat and dEclat algorithms for the four benchmark datasets. Experimental results proved that SHFIM outperforms Eclat and dEclat Spark-based algorithms in both dense and sparse datasets in terms of execution time.
FaaSRank: Learning to Schedule Functions in Serverless Platforms

Current serverless Function-as-a-Service (FaaS) platforms generally use simple, classic scheduling algorithms for distributing function invocations while ignoring FaaS characteristics such as rapid changes in resource utilization and the freeze-thaw life cycle. In this paper, we present FaaSRank, a function scheduler for serverless FaaS platforms based on information monitored from servers and functions. FaaSRank automatically learns scheduling policies through experience using reinforcement learning (RL) and neural networks supported by our novel Score-Rank-Select architecture. We implemented FaaSRank in Apache OpenWhisk, an open source FaaS platform, and evaluated performance against other baseline schedulers including OpenWhisk&#x0027;s default scheduler on two 13-node OpenWhisk clusters. For training and evaluation, we adapted real-world serverless workload traces provided by Microsoft Azure. For the duration of test workloads, FaaSRank sustained on average a lower number of inflight invocations 59.62 &#x0025; and 70.43 &#x0025; as measured on two clusters respectively. We also demonstrate the generalizability of FaaSRank for any workload. When trained using a composite of 50 episodes each for 10 distinct random workloads, FaaSRank reduced average function completion time by 23.05&#x0025; compared to OpenWhisk&#x0027;s default scheduler.
Shearography and its applications – a chronological review

This paper presents the activities in the field of shearography in chronological order and highlights the great potential of this holographic measurement technology. After a brief introduction, the basic theory of shearography is presented. Shear devices, phase-shift arrangements, and multiplexed shearography systems are described. Finally, the application areas where shearography has been accepted and successfully used as a tool are presented.
Multi-Pedestrian Tracking with Clusters

One of the biggest challenges in multi-pedestrian tracking arises in crowds, where missing detections can lead to wrong track-detection assignments, especially under heavy occlusion. In order to identify such situations, we cluster tracks and detections based on their overlaps and introduce different cluster states depending on the number of detections and tracks in a cluster. On the basis of this strategy, we make the following contributions. First, we propose a cluster-aware non-maximum suppression (CA-NMS) that leverages temporal information from tracks applying an increased IoU threshold in clusters with severe occlusion to reduce the number of missed detections, while at the same time limiting the number of duplicate detections. Second, for clusters with very high overlaps where detections are missing even with the CA-NMS, we utilize past track information to correct wrong assignments when missed targets are re-detected after occlusion. Furthermore, we propose a new tracking pipeline that combines the paradigms of tracking-by-detection and regression-based tracking to improve the association performance in crowded scenes. Putting all together, our tracker achieves competitive results w.r.t. the state-of-the-art on three multi-pedestrian tracking benchmarks. Our framework is analyzed with extensive ablative experiments and the impact of the proposed tracking components on the performance is evaluated.
BEmoC: A Corpus for Identifying Emotion in Bengali Texts

Emotion classification in text has growing interest among NLP experts due to the enormous availability of people's emotions and its emergence on various Web 2.0 applications/services. Emotion classification in the Bengali texts is also gradually being considered as an important task for sports, e-commerce, entertainments, and security applications. However, It is a very critical task to develop an automatic emotion classification system for low-resource languages such as, Bengali. Scarcity of resources and deficiency of benchmark corpora make the task more complicated. Thus, the development of a benchmark corpus is the prerequisite to develop an emotion classifier for Bengali texts. This paper describes the development of an emotional corpus (hereafter called 'BEmoC') for classifying six emotions in Bengali texts. The corpus development process consists of four key steps: data crawling, pre-processing, labelling, and verification. A total of 7000 texts are labelled into six basic emotion categories such as anger, fear, surprise, sadness, joy, and disgust, respectively. Dataset evaluation with 0.969 Cohen's κ score indicates the close agreement between the corpus annotators and the expert. The analysis of evaluation also represents that the distribution of emotion words obeys Zipf's law. Moreover, the results of BEmoC analysis shown in terms of coding reliability, emotion density, and most frequent emotion words, respectively.
Secure Collaborative Augmented Reality Framework for Biomedical Informatics

Augmented reality is currently of interest in biomedical health informatics. At the same time, several challenges have appeared, in particular with the rapid progress of smart sensor technologies, and medical artificial intelligence. This yields the necessity of new needs in biomedical health informatics. Collaborative learning and privacy are just some of the challenges of augmented reality technology in biomedical health informatics. This paper introduces a novel secure collaborative augmented reality framework for biomedical health informatics-based applications. Distributed deep learning is performed across a multi-agent system platform. The privacy strategy is then developed for ensuring better communications of the different intelligent agents in the system. In this research work, a system of multiple agents is created for the simulation of the collective behaviours of the smart components of biomedical health informatics. Augmented reality is also incorporated for better visualization of medical patterns. A novel privacy strategy based on blockchain is investigated for ensuring the confidentiality of the learning process. Experiments are conducted on real use cases of the biomedical segmentation process. Our strong experimental analysis reveals the strength of the proposed framework when directly compared to state-of-the-art biomedical health informatics solutions.
Active Model Learning of Stochastic Reactive Systems

Black-box systems are inherently hard to verify. Many verification techniques, like model checking, require formal models as a basis. However, such models often do not exist, or they might be outdated. Active automata learning helps to address this issue by offering to automatically infer formal models from system interactions. Hence, automata learning has been receiving much attention in the verification community in recent years. This led to various efficiency improvements, paving the way towards industrial applications. Most research, however, has been focusing on deterministic systems. Here, we present an approach to efficiently learn models of stochastic reactive systems. Our approach adapts \(L^*\)-based learning for Markov decision processes, which we improve and extend to stochastic Mealy machines. Our evaluation demonstrates that we can reduce learning costs by a factor of up to 8.7 in comparison to previous work.
CONAN: Contrastive Fusion Networks for Multi-view Clustering

With the development of big data, deep learning has made remarkable progress on multi-view clustering. Multi-view fusion is a crucial technique for the model obtaining a common representation. However, existing literature adopts shallow fusion strategies, such as weighted-sum fusion and concatenating fusion, which fail to capture complex information from multiple views. In this paper, we propose a novel fusion technique, entitled contrastive fusion, which can extract consistent representations from multiple views and maintain the characteristic of view-specific representations. Specifically, we study multi-view alignment from an information bottleneck perspective and introduce an intermediate variable to align each view-specific representation. Furthermore, we leverage a single-view clustering method as a predictive task to ensure the contrastive fusion is working. We integrate all components into an unified framework called CONtrAstive fusion Network (CONAN). Experiment results on five multi-view datasets demonstrate that CONAN outperforms state-of-the-art methods. Our source code will be available soon at https://github.com/guanzhou-ke/conan.
On the Real-World Effectiveness of Static Bug Detectors at Finding Null Pointer Exceptions

Static bug detectors aim at helping developers to automatically find and prevent bugs. In this experience paper, we study the effectiveness of static bug detectors at identifying Null Pointer Dereferences or Null Pointer Exceptions (NPEs). NPEs pervade all programming domains from systems to web development. Specifically, our study measures the effectiveness of five Java static bug detectors: CheckerFramework, ERADICATE, INFER, NULLAWAY, and SPOTBUGS. We conduct our study on 102 real-world and reproducible NPEs from 42 open-source projects found in the BUGSWARM and DEFECTS4J datasets. We apply two known methods to determine whether a bug is found by a given tool, and introduce two new methods that leverage stack trace and code coverage information. Additionally, we provide a categorization of the tool’s capabilities and the bug characteristics to better understand the strengths and weaknesses of the tools. Overall, the tools under study only find 30 out of 102 bugs (29.4%), with the majority found by ERADICATE. Based on our observations, we identify and discuss opportunities to make the tools more effective and useful.
Scalable recommendation systems based on finding similar items and sequences

Abstract The rapid growth in the airline industry, which started in 2009, continued until the COVID‐19 era, with the annual number of passengers almost doubling in 10 years. This situation has led to increased competition between airline companies, whose profitability has decreased considerably. They aimed to increase their profitability by making services like seat selection, excess baggage, Wi‐Fi access optional under the name of ancillary services. To the best of our knowledge, there is no recommendation system for recommending ancillary services for airline companies. Also, to the best of our knowledge, there is no testing framework to compare recommendation algorithms considering their scalabilities and running times. In this paper, we propose a framework based on Lambda architecture for recommendation systems that run on a big data processing platform. The proposed method utilizes association rule and sequential pattern mining algorithms that are designed for big data processing platforms. To facilitate testing of the proposed method, we implement a prototype application. We conduct an experimental study on the prototype to investigate the performance of the proposed methodology using accuracy, scalability, and latency related performance metrics. The results indicate that the proposed method proves to be useful and has negligible processing overheads.
Improving classification algorithm on education dataset using hyperparameter tuning

In this paper, researchers propose a classification method for any institution’s campus placement possibility using Placement Data Full Class for campus recruitment dataset. Researchers attempt to study the supervised learning classification algorithms such Logistic Regression, Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), Gaussian Naive Bayes, Decision Tree, Random Forest, Gradient Boosting, and Linear Discriminant Analysis (LDA). Hyperparameter optimization also used to optimize the supervised algorithms for better results. Experimental results have found that by using hyperparameter tuning in Linear Discriminant Analysis (LDA), it can increase the accuracy performance results, and also given a better result compared to other algorithms.
Security Threat and Vulnerability Assessment and Measurement in Secure Software Development

Security is critical to the success of software, particularly in today's fast-paced, technology-driven environment. It ensures that data, code, and services maintain their CIA (Confidentiality, Integrity, and Availability). This is only possible if security is taken into account at all stages of the SDLC (Software Development Life Cycle). Various approaches to software quality have been developed, such as CMMI (Capability maturity model integration). However, there exists no explicit solution for incorporating security into all phases of SDLC. One of the major causes of pervasive vulnerabilities is a failure to prioritize security. Even the most proactive companies use the “patch and penetrate” strategy, in which security is accessed once the job is completed. Increased cost, time overrun, not integrating testing and input in SDLC, usage of third-party tools and components, and lack of knowledge are all reasons for not paying attention to the security angle during the SDLC, despite the fact that secure software development is essential for business continuity and survival in today's ICT world. There is a need to implement best practices in SDLC to address security at all levels. To fill this gap, we have provided a detailed overview of secure software development practices while taking care of project costs and deadlines. We proposed a secure SDLC framework based on the identified practices, which integrates the best security practices in various SDLC phases. A mathematical model is used to validate the proposed framework. A case study and findings show that the proposed system aids in the integration of security best practices into the overall SDLC, resulting in more secure applications.
Geochemical and Hydrothermal Alteration Patterns of the Abrisham-Rud Porphyry Copper District, Semnan Province, Iran

In this study, the zonality method has been used to separate geochemical anomalies and to calculate erosional levels in the regional scale for porphyry-Cu deposit, Abrisham-Rud (Semnan province, East of Iran). In geochemical maps of multiplicative haloes, the co-existence of both the supra-ore elements and sub-ore elements local maxima implied blind mineralization in the northwest of the study area. Moreover, considering the calculated zonality indices and two previously presented geochemical models, E and NW of the study have been introduced as ZDM and BM, respectively. For comparison, the geological layer has been created by combining rock units, faults, and alterations utilizing the K-nearest neighbor (KNN) algorithm. The rock units and faults have been identified from the geological map; moreover, alterations have been detected by using remote sensing and ASTER images. In the geological layer map related to E of the study area, many parts have been detected as high potential areas; in addition, both geochemical and geological layer maps only confirmed each other at the south of this area and suggested this part as high potential mineralization. Therefore, high potential areas in the geological layer map could be related to the mineralization or not. Due to the incapability of the geological layer in identifying erosional levels, mineralogy investigation could be used to recognize this level; however, because of the high cost, mineralogy is not recommended for application on a regional scale. The findings demonstrated that the zonality method has successfully distinguished geochemical anomalies including BM and ZDM without dependent on alteration and was able to predict erosional levels. Therefore, this method is more powerful than the geological layer.