Reviewerly/exHarmony huggingface dataset:
Deep Sentiment Approaches for Rigorous Analysis of Social Media Content and Its Investigation

Social media has a very important contribution to human lives today.Through social media platforms people can share their information, ideas, knowledge, and activities with connecting people in the form of videos, images, texts, and audios.In the context of sharing information, incorrect information is also shared along with the correct information.In this way, unauthentic (fake news), misleading (rumors), abusing, toxic, extremist contents are also shared through social media platforms.This paper reviews the influences of social media content.In this context, vector representation of the social media sentences, word embedding models has been best applied for better accurate results.Natural language processing (NLP) and text analysis techniques are being used to extract useful information from social media content.The NLP techniques are widely used for correcting the sentences and identifying their meaning also.Currently, machine learning (Decision Tree, Random Forest, SVM, Naïve Bayes) and deep learning (LSTMs, BLSTMs, GRUs, CNNs) models are successfully being implemented to classify social media contents.In the comparative study of different works of literature and results from LSTM and CNN-LSTM deep learning model have been proved that deep learning and the word embedding model provide better accurate results for social media contents categorization.
Blueprint

Blueprint is a declarative domain-specific language for document extraction. Users describe document layout using spatial, textual, semantic, and numerical fuzzy constraints, and the language runtime extracts the field-value mappings that best satisfy the constraints in a given document. We used Blueprint to develop several document extraction solutions in a commercial setting. This approach to the extraction problem proved powerful. Concise Blueprint programs were able to generate good accuracy on a broad set of use cases. However, a major goal of our work was to build a system that non-experts, and in particular non-engineers, could use effectively, and we found that writing declarative fuzzy constraint-based extraction programs was not intuitive for many users: a large up-front learning investment was required to be effective, and debugging was often challenging. To address these issues, we developed a no-code IDE for Blueprint, called Studio, as well as program synthesis functionality for automatically generating Blueprint programs from training data, which could be created by labeling document samples in our IDE. Overall, the IDE significantly improved the Blueprint development experience and the results users were able to achieve. In this paper, we discuss the design, implementation, and deployment of Blueprint and Studio. We compare our system with a state-of-the-art deep-learning based extraction tool and show that our system can achieve comparable accuracy results, with comparable development time, for appropriately-chosen use cases, while providing better interpretability and debuggability.
Lean software startup practices and software engineering education

In the modern economy, software drives innovation and economic growth. Studies show how software increasingly influences all industry sectors. Over the past 5 decades, software engineering has also changed significantly to advance the development of various types and scales of software products. Software engineering education plays an essential role in apprising students of software technologies, processes, and practices popular in industries. Furthermore, approaches to teaching software engineering are becoming more interdisciplinary and team-centered, comparable to startup contexts. In this PhD work, I want to answer the following research questions: (1) To what extent are software engineering trends present in software engineering education research? (2) What set of common software engineering practices employed in lean software startups is transferable to the software engineering education context? (3) What is the impact of lean startup practices on software engineering students and curricula? I utilize (1) a literature review, (2) mixed-methods approaches in gathering empirical evidence, and (3) design-based research. In the first phase of the research, I pinpoint the relevance of the lean startup in software engineering education through an extensive literature review. I gather empirical evidence on lean startup practices and assess their potential transferability to software engineering education during the second research phase. I demonstrate that the lean startup is an emerging trend in software engineering education research. I demonstrate that students can acquire soft, hard, and project management skills in a more realistic context in the introduction of the growth phase of lean startup practices throughout external course activities. I expect software engineering curricula to benefit from the model and framework that I propose and validate, thus facilitating lean startup practice transfer to software engineering curricula.
Butterfly intelligent optimization algorithm based on Good Point Set and adaptive weight factor

Butterfly intelligent optimization algorithm (BOA) is an intelligent algorithm, that simulates the butterflies' smell to forage for itself and find suitable mates for mating. The existing BOA has some matters with the slow convergence speed and accuracy, and it has strong randomness of search, so this article offers a BOA based on good point set and adaptive weight factor. Firstly, the optimal point list is used to initialize species to reduce search randomness. Secondly, local search and global search capability are improved by introducing an adaptive weight factor that varies with the number of iterations. Finally, dynamic switching percent p is introduced to balance the proportion of both searches. After the data experiment, it is improved that the optimization algorithm has a better result than the original butterfly algorithm.
Quantum Computing for Applications in Data Fusion

Quantum computing promises significant improvements of computation capabilities in various fields such as machine learning and complex optimization problems. Rapid technological advancements suggest that adiabatic and gate base quantum computing may see practical applications in the near future. In this work, we adopt quantum computing paradigms to develop solvers for two well–known combinatorial optimization problems in information fusion and resource management: multi-target data association (MTDA) and weapon target assignment (WTA). These problems are NP-hard (non-)linear integer programming optimization tasks which become computationally expensive for large problem sizes. We derive the problem formulations adapted for the use in quantum algorithms and present solvers based on adiabatic quantum computing (AQC) and the Quantum Approximative Optimization Algorithm (QAOA). The feasibility of the models is demonstrated by numerical simulation and first experiments on quantum hardware.
High-Accuracy Object Detection Using Multi-view Video at Road Intersections

In this paper, we proposed algorithms that complement each other's information by multi-view video. The pixel positions of the video at the same location are correspondingly related. The two viewpoint videos are inferred using YOLO and DeepSORT, respectively, and the results of each are used against each other. We reduced the undetected rate by more than 97% with the proposed method.
Adaptive Sparse Self-attention for Object Detection

Object detection is a fundamental task for computer vision. The majority of prior methods employ global contextual information to enhance features representation. However, we argue that prior methods suffer from the superfluous features. To address the above mentioned problems, we explore the sparsity on object detection tasks in two dimensions. Specifically, a sparse spatial attention is proposed to capture global sparse long-range relationship into local features adaptively, where a learnable channel-wise mask is obtained to reduce the superfluous channels. Meanwhile, a sparse channel attention module is used to enhance the representation of each channel by introducing sparse global semantics. Experiments demonstrate that our proposed method outperforms comparative methods on the commonly used benchmark dataset, i.e., MS-COCO. The ablative experiments show that the sparsity the effectiveness in feature extraction and bounding boxes selection.
CD-SLFN: A Curiosity-Driven Online Sequential Learning Framework with Self-Adaptive Hot Cognition

In human-level artificial intelligence, curiosity can guide agents to actively explore and learn with limited prior knowledge. The existing literature has attempted to leverage curiosity in online sequential learning problems; however, they are computationally expensive in general and lack generalization capability. In this paper, we present a unified curiosity-driven learning framework to tackle online classification problems. Based on the psychological theory of human curiosity, five collative variables including novelty, uncertainty, complexity, surprisingness, and change, are quantitatively characterized to model artificial curiosity. We seamlessly integrate these collative variables into a regularized single-hidden-layer feedforward neural network (SLFN) to encourage curiosity-driven learning. As a result, the proposed curiosity-driven SLFN (CD-SLFN) can actively select the most representative data in an online sequential manner, and flexibly adapt the model complexity to prevent overfitting. Compared to other online classifiers, CD-SLFN empowered by intrinsic motivation has demonstrated superior generalization capability, especially in the early learning phase with only limited data. The experimental results on various datasets indicate that the proposed CD-SLFN can consistently outperform other SLFN baselines, with the desired learning capability through self-adaptive hot cognition.
An Energy-Efficient CNN Accelerator for Multi-object Real-Time Semantic Segmentation in Autonomous Vehicle

An energy-efficient convolutional neural network (CNN) accelerator is proposed for real-time segmentation in autonomous electric vehicle (AEV) system. The computation of semantic segmentation with high-resolution images makes it difficult for real-time operation in time-critical and resource-constrained AEV. To facilitate real-time implementation in AEV, this paper proposes two key features: 1) A compressed multi-object Depth-fused Trilateral Network (DTN) with dilated convolution and depthwise separable convolution that reduces 90% of the overall computation of baseline [1] and achieves 94.73% accuracy on KITTI Road dataset; 2) An energy-efficient CNN accelerator, which supports 5 types of CONV’s, achieving 1.33× higher throughput than the previous processor [2]. Finally, the proposed processor is designed in 28 nm CMOS technology. It consumes 65.7 mW of power and achieves 2.91 TOPS/W of energy efficiency. As a result, the system realizes 72.2 and 37 frames-per-second of semantic segmentation for road and multi-objects with high resolution.
EDI technology, an exemplary application developed on this technology and speed performance results

Developments in information and communication technologies accelerate digital integration and transformations in the logistics sector, as in all sectors. Electronic data interchange (EDI), one of the first of these digital transformations, is the automatic exchange of predefined and structured data for business purposes between two or more person/organization information systems [1]. EDI contributes positively to performance by providing fast, accurate and reliable transfer of data and files between relevant parties in an international standard structure. EDI technology has an important role in the modernization and higher performance of the logistics sector. In addition, the realization of EDI integration with Industry 4.0 technologies such as the Internet of Things (IoT), big data, block chain, artificial intelligence (AI), smart manufacturing and industrial robots will make a more positive contribution to this performance.In this study, EDI technology used in electronic data and information exchange, a sample EDI application developed for a logistics company with the help of current information technologies and speed performance results obtained by using this application are shared.
UX Needs Cards – A Pragmatic Tool to Support Experience Design Through Psychological Needs

The psychological needs-driven UX approach is a well-explored area in UX research and a powerful framework for the design of optimal experiences with systems and products. However, the transfer from research to practice is slow and this approach is not yet widely used by practitioners. As card-based methods have been shown to support designers in both the generation of ideas and the evaluation of their designs, we created the UX needs cards as a pragmatic tool able to support a needs-driven UX process. We present the iterative development of the card-set and its associated techniques and report on three use cases, demonstrating the effectiveness of this tool for user research, idea generation and UX evaluation. Our empirical findings suggest that the UX needs cards are a valuable tool able to support design practice, being easily understood by lay users and a source of inspiration for designers. Acting as a tangible translation of a research framework, the UX needs cards promote theory-driven design strategies and provide researchers, designers, and educators with a tool to clearly communicate the framework of psychological needs.
PriTAEC: Privacy-Preserving Task Assignment Based on Oblivious Transfer and Edge Computing in VANET

Spatial crowdsourcing, e.g., Vehicular Ad-Hoc Network (VANET)-based spatial crowdsourcing, is a new distributed computing paradigm, in which task assignments highly rely on the wisdom of the crowdsourcing platform. However, with the increase in user data leakage incidents, the existing task assignment methods are no longer sufficient to meet the privacy requirements of users. In the existing VANET-based spatial crowdsourcing, task assignments are usually performed by a trusted third party based on the real locations of tasks and drivers (task performers), which may lead to the leakage of the users' locations. Furthermore, the drivers usually prefer to query the nearest tasks to them in a geometric range, at this point sending query requests to remote crowdsourcing servers increases unnecessary response delays. To assign tasks securely and efficiently, we propose a privacy-preserving task assignment scheme based on OT and edge computing (PriTAEC), which is the first to apply Oblivious Transfer (OT) and edge computing to preserve the location privacy of VANET-based spatial crowdsourcing. In the scheme, we first utilize Hilbert Curve and Bloom Filter to implement location range queries. Then, we use geohash location encoding and Oblivious Transfer to achieve fine-grained location matching. In particular, we design a task assignment algorithm with an offline-online phase to improve the efficiency of task assignments. Finally, we prove the security of the scheme and evaluate its performance, which shows our scheme is secure and efficient.
SASIC: Stereo Image Compression with Latent Shifts and Stereo Attention

We propose a learned method for stereo image compression that leverages the similarity of the left and right images in a stereo pair due to overlapping fields of view. The left image is compressed by a learned compression method based on an autoencoder with a hyperprior entropy model. The right image uses this information from the previously encoded left image in both the encoding and decoding stages. In particular, for the right image, we encode only the residual of its latent representation to the optimally shifted latent of the left image. On top of that, we also employ a stereo attention module to connect left and right images during decoding. The performance of the proposed method is evaluated on two benchmark stereo image datasets (Cityscapes and InStereo2K) and outperforms previous stereo image compression methods while being significantly smaller in model size.
Rank-Label Anonymization for the Privacy-Preserving Publication of a Hypergraph Structure

Social networks are often published in the form of a simple graph. The simple graph representation of a social graph shows the dyadic relationship among the social entities whereas it is unable to efficiently represent the relationship among more than two entities, such as the relationship found in the social groups. This type of relationship is called super-dyadic relationship, and it can be effectively represented by a hypergraph model. This work proposes an anonymization scheme called rank-label anonymization for the privacy-preserving publication of a hypergraph structure. Here, an attack model called rank-label attack is proposed, and an anonymization solution is provided to counter this attack. The percentage of disclosure risk shows that the rank-label attack is stronger than the existing rank attack. We propose a method based on sequential clustering to achieve rank-label anonymization called sequential rank-label anonymization (SA). Another algorithm called greedy rank-label anonymization (GA) is also proposed. The quality of the anonymization solution reported by SA and GA is compared with the help of normalized anonymization cost ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NCost</i> ). Results show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">NCost</i> reported by SA is less than that of GA for both Adult and MAG-10 datasets. In Adult dataset, approximately 58% and 62% reduction in the average execution time of GA and SA are obtained than that of a general-purpose computing system due to the use of a high-performance computing system. In MAG-10 dataset, this average reduction percentage is reported to be 56% for GA and 53% for SA. The time complexity of SA is found to be O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> ) whereas it is O(n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) in case of GA.
Conditional Domain Confusion Networks for Cross-Domain Detection

Considering the failure of fully exploring the transferability of samples, domain adaptation (DA) is still troubled by the problem of the disturbed distribution of the target domain. In this article, a conditional domain confusion network (CDCN) is proposed, which commits to exploit the minimum class confusion (MCC) to achieve unsupervised cross-domain detection. To utilize the transferability of samples to better respond to domain shift, we first propose a transfer disturbance factor under the guidance of class confusion to constrain the classifier, designed to eliminate the impact of hard-to-transfer samples on the model. Moreover, an adaptive auxiliary component is embedded before the classifier, aiming at improving the generalization performance of the model, which adjusts the intra- and interclass distances of samples by uniting the classification loss to better fit the classifier. Extensive experiments are performed on multiple transfer tasks, which demonstrate the superiority of our proposed CDCN over the state-of-the-art DA strategies.
Vision and Knowledge Bimodal Fine-grained Recognition Based on Hierarchical Structured Knowledge Extraction

In the field of fine-grained recognition, the most important method is to use visual information as the most important modality to complete the task. In order to prevent the model from being greatly affected by the data of a single modality, and to improve the accuracy and robustness and security, existing methods fused information such as text, voice or knowledge graphs on a single modality basis. In view of the limitations of the previous methods in the way of integrating knowledge modalities, this paper optimizes the existing knowledge extraction methods for bird datasets, and proposes a hierarchical and structured knowledge extraction method, so that the model can be classified when only knowledge modalities are used. The accuracy reaches 48.69%, which improves the accuracy by about 10% compared with previous methods. After multimodal fusion of knowledge extracted by this method and visual modalities, about 2% improvement was achieved on the knowledge-visual fusion framework KGRF, and the results proved the effectiveness of this method.
BanglaHandwritten: A Comparative Study among Single, Numeral, Vowel Modifier, And Compound Characters Recognition Using CNN

The difficulty of handwritten character identification varies by language, owing to differences in shapes, lines, numbers, and size of characters. There are several studies for the identification of handwritten characters accessible for English in comparison with other significant languages like Bangla. In their recognition procedures, existing technologies use multiple techniques such as classification tools and feature extraction. CNN has recently been shown to be proficient in handwritten character recognition in English. A Handwritten Bangla character identification system based on CNN has been examined in this research. Using CNN, the suggested approach for feature, labelling and normalizing the handwritten character of images, as well as categorizing different characters. It doesn’t use a feature extraction approach like previous research in the field. This research used almost 4,50,000 unique handwritten characters in a variety of styles. The recommended model has been proved to have a high recognition accuracy level and outperforms some of the most widely used methods already in use. In this research, identify the Bangla handwritten character and digits with the use of 189 classes consisting of 50 fundamental characters, 119 compound characters, 10 numerals, and 10 modifiers. The accuracy rate of basic characters is 84.62%, numerals 94%, modifiers 96.46%, compound characters 77.60% using created new model.
Credit Card Fraud Detection Using Hybrid Learning

Online banking allows a user to conduct financial transactions through the internet from any place of our convenience without physically visiting the bank. It facilitates almost all basic banking transactions and online payments efficiently and instantly. However, at the same time, internet use for money transfers is quite risky and prone to various kinds of intrusive actions. Online banking fraud is a type of malicious action or theft committed using internet technology to illegally steal money from a bank account. The distribution of attribute value is imbalanced and non-symmetric. Another challenge is that we cannot detect fraud if the user performs a low number of transactions. Due to the lack of labeled datasets, such a security analysis system must be able to work in an unsupervised or semi-supervised fashion. The main objectives of the current research are to develop an effective Banking Fraud Detection System to achieve the following points: (a) reduce skewness in the dataset, (b) accurately detect fraudulent transactions and reduce the misclassification rate. In this research, we have proposed a semi-supervised online banking fraud analysis and decision support system for online banking transactions. Tukey’s method and LSTM techniques are used to handle imbalanced fraud datasets and for classification analysis.