Reviewerly/exHarmony huggingface dataset:
Machine Learning Based Automatic Classification of Customer Sentiment

Rapid growth of the use of internet has generated the enhancement of customer satisfaction as a fundamental issue in the modern era. Customer reviews can provide resourceful information for monitoring as well as enhancing customer satisfaction levels and thus business organizations can improve customer and product services to their utmost level. Sentiment analysis can manipulate these customer reviews by extracting product aspects and identifying the sentiments as positive or negative and additionally, text mining is used to switch unstructured text into structured form for this purpose. The principal goal of this paper is to perform opinion classification. Bag of Words (BOW) and Term Frequency-Inverse Document Frequency (TF-IDF) feature extraction techniques are used along with N-gram and Support Vector Machine (SVM) is regarded for review classification. Customer reviews from different restaurants of Dhaka were collected to accomplish the task. We believe that the result from the experiment can point out the effectiveness of the implemented technique of customer review analysis by saving the time as well as minimizing the difficulties in measurement of customer satisfaction.
Depth segmentation using disparity and self-adaption fringe modulation analysis

• A segmentation scheme utilizing disparity information and phase information is proposed. • Retain the valid region and accomplish the depth segmentation. • High-intensity noise and shadows are reduced. • Performances are evaluated under simulation scenes and real-world scenes. In this paper, we propose a depth segmentation method combining disparity information with fringe modulation information, which aims at improving the robustness under the noisy scene. Firstly, we investigate the continuity characteristics in the U-V disparity map and distinguishing modulation distribution in the wrapped phase images, respectively. Then, the disparity-based method is optimized to detect and classify the valid regions containing objects and their supporting surfaces. Besides, we provide an evaluation criterion to reduce the noise represented as scattered points. Furthermore, through the analysis of the different fringe modulation distributions between objects and their shadow areas, a method with adjustable thresholds is presented to suppress shadow noise. Our approach is evaluated on both simulation scenes and real-world scenes to verify the availability and robustness. Sufficient experimental results indicate that the proposed method can effectively separate the valid region into objects and detect their incomplete supporting surfaces.
StkTokens<i>: Enforcing well-bracketed control flow and stack encapsulation using linear capabilities</i>

Abstract We propose and study StkTokens: a new calling convention that provably enforces well-bracketed control flow and local state encapsulation on a capability machine. The calling convention is based on linear capabilities: a type of capabilities that are prevented from being duplicated by the hardware. In addition to designing and formalizing this new calling convention, we also contribute a new way to formalize and prove that it effectively enforces well-bracketed control flow and local state encapsulation using what we call a fully abstract overlay semantics.
QIRM: A quantum interactive retrieval model for session search

Web search has become a popular way for people to obtain information. Due to the complexity of search task, one retrieval cannot serve all of user’s information needs. Multiple interactions with the search engine are required, and session search comes into being. Currently proposed session search methods still cannot take advantage of interactive information to capture the user’s implicit information needs. Recently, quantum theory has been successively applied into information retrieval task. We find the similarities between quantum measurements and session interactions. This paper thus develops a Quantum Interactive Retrieval Model (QIRM), which involves both quantum standard measurement (QSM) and quantum weak measurement (QWM) to re-characterize the user’s cognition shifts during a session interaction. Particularly, the user’s cognition states in strong interaction and weak interaction are quantified into quantum-like representations formalized by QSM and QWM, respectively. The representations are then viewed as the user’s information needs for computing ranking score of an evaluated document. We conduct experiments on TREC 2013 and 2014 Session Track datasets. The empirical evaluation demonstrates the effectiveness of our proposed methods, which obtains very comparable performance in terms of nDCG@10 and ERR@10.
Partially black-boxed collective interpretation and its application to SOM-based convolutional neural networks

This paper aims to extend collective interpretation to networks with complicated components. The collective interpretation is used to generate an internally interpretable model independently of specific inputs and learning conditions. The internally interpretable model is obtained by network compression where multi-layers are sequentially compressed, taking into account all possible routes from inputs to outputs. The network compression is easily applied to fully connected networks, but it cannot be applied to some networks with complicated components. Thus, to make the compression possible, we black-box partially and minimally these components to be replaced by the ordinary components. For demonstrating the effectiveness of this technique, we use here a new model based on the self-organizing map (SOM). Then, we introduce the convolutional neural networks (CNN) for dealing with SOM knowledge, usually represented in two-dimensional lattices. Because our network compression cannot deal with those convolutional components, we temporarily black-box the CNN components. Fixing the other connection weights, we re-train the partially black-boxed network to obtain the simplest prototype model for interpretation. The method was applied to two well-known data sets, and we demonstrated that the present method could compress the networks to get the simplest and interpretable ones. In addition, very stable compressed weights for interpretation could be obtained for easy interpretation. The results suggest that the main mechanism of multi-layered neural networks is based on linear relations between individual inputs and targets, to which peripheral non-linear ones are added.
Using Semantic Web Technologies to Improve the Extract Transform Load Model

Semantic Web (SW) provides new opportunities for the study and application of big data, massive ranges of data sets in varied formats from multiple sources. Related studies focus on potential SW technologies for resolving big data problems, such as structurally and semantically heterogeneous data that result from the variety of data formats (structured, semi-structured, numeric, unstructured text data, email, video, audio, stock ticker). SW offers information semantically both for people and machines to retain the vast volume of data and provide a meaningful output of unstructured data. In the current research, we implement a new semantic Extract Transform Load (ETL) model that uses SW technologies for aggregating, integrating, and representing data as linked data. First, geospatial data resources are aggregated from the internet, and then a semantic ETL model is used to store the aggregated data in a semantic model after converting it to Resource Description Framework (RDF) format for successful integration and representation. The principal contribution of this research is the synthesis, aggregation, and semantic representation of geospatial data to solve problems. A case study of city data is used to illustrate the semantic ETL model’s functionalities. The results show that the proposed model solves the structural and semantic heterogeneity problems in diverse data sources for successful data aggregation, integration, and representation.
Synthetic Network and Search Filter Algorithm in English Oral Duplicate Correction Map

Combining the communicative language competence model and the perspective of multimodal research, this research proposes a research framework for oral communicative competence under the multimodal perspective. This not only truly reflects the language communicative competence but also fully embodies the various contents required for assessment in the basic attributes of spoken language. Aiming at the feature sparseness of the user evaluation matrix, this paper proposes a feature weight assignment algorithm based on the English spoken category keyword dictionary and user search records. The algorithm is mainly based on the self-built English oral category classification dictionary and converts the user’s query vector into a user-English-speaking type vector. Through the calculation rules proposed in this paper, the target user’s preference score for a specific type of spoken English is obtained, and this score is assigned to the unrated item of the original user’s feature matrix as the initial starting score. At the same time, in order to solve the problem of insufficient user similarity calculation accuracy, a user similarity calculation algorithm based on “Synonyms Cilin Extended Edition” and search records is proposed. The algorithm introduces “Synonyms Cilin” to calculate the correlation between the semantic items, vocabulary, and query vector in the user query record to obtain the similarity between users and finally gives a user similarity calculation that integrates user ratings and query vectors method. For the task of Chinese grammar error correction, this article uses two methods of predicting the relationship between words in the corpus, Word2Vec and GloVe, to train the word vectors of different dimensions and use the word vectors to represent the text features of the experimental samples, avoiding sentences brought by word segmentation. On the basis of word vectors, the advantages and disadvantages of CNN, LSTM, and SVM models in this shared task are analyzed through experimental data. The comparative experiment shows that the method in this paper has achieved relatively good results.
AEVRNet: Adaptive exploration network with variance reduced optimization for visual tracking

For visual tracking methods based on reinforcement learning, action space determines the ability of exploration, which is crucial to model robustness. However, most trackers adopted simple strategies with action space, which will suffer local optima problem. To address this issue, a novel reinforcement learning based tracker called AEVRNet is proposed with non-convex optimization and effective action space exploration. Firstly, inspired by combinatorial upper confidence bound, we design an adaptive exploration strategy leveraging temporal and spatial knowledge to enhance effective action exploration and jump out of local optima. Secondly, we define the tracking problem as a non-convex problem and incorporate non-convex optimization in stochastic variance reduced gradient as backward propagation of our model, which can converge faster with lower loss. Thirdly, different from existing reinforcement learning based trackers using classification method to train model, we define a regression based action-reward loss function, which is more sensitive to aspects of the target states, e.g., the width and height of the target to further improve robustness. Extensive experiments on six benchmark datasets demonstrate that our proposed AEVRNet achieves favorable performance against the state-of-the-art reinforcement learning based methods.
Evaluation of self‐organizing and self‐managing heterogeneous high performance computing clouds through discrete‐time simulation

Abstract Recently, a new management and resource delivery model based on self‐organization and self‐management (SOSM) principles for heterogeneous cloud infrastructures was proposed. The novel architecture was designed and implemented for the provision of cloud resources for delivering high performance computing services. The cost of deployment of such framework on large‐scale cloud infrastructures is prohibitive, thus discrete‐time simulation techniques were adopted in order to evaluate its efficiency and scalability. In this work, extensive simulation experimentation is being reported and discussed, concerning four evaluation criteria, for comparing the traditional centralized resources allocation scheme to the alternative of SOSM. From the results obtained, it can be derived that SOSM can provide improved service delivery, computational efficiency, power consumption, scalability and management of resources for millions of cloud nodes, compared to the centralized resource allocation approach.
Feature selection using stochastic approximation with Barzilai and Borwein non-monotone gains

With recent emergence of machine learning problems with massive number of features, feature selection (FS) has become an ever-increasingly important tool to mitigate the effects of the so-called curse of dimensionality. FS aims to eliminate redundant and irrelevant features for models that are faster to train, easier to understand, and less prone to overfitting. This study presents a wrapper FS method based on Simultaneous Perturbation Stochastic Approximation (SPSA) with Barzilai and Borwein (BB) non-monotone gains within a pseudo-gradient descent framework wherein performance is measured via cross-validation. We illustrate that SPSA with BB gains (SPSA-BB) provides dramatic improvements in terms of the number of iterations for convergence with minimal degradation in cross-validated error performance over the current state-of-the art approach with monotone gains (SPSA-MON). In addition, SPSA-BB requires only one internal parameter and therefore it eliminates the need for careful fine-tuning of numerous other internal parameters as in SPSA-MON or comparable meta-heuristic FS methods such as genetic algorithms (GA). Our particular implementation includes gradient averaging as well as gain smoothing for better convergence properties. We present computational experiments on various public datasets with Nearest Neighbors and Naive Bayes classifiers as wrappers. We present comparisons of SPSA-BB against full set of features, SPSA-MON, as well as seven popular meta-heuristics based FS algorithms including GA and particle swarm optimization. Our results indicate that SPSA-BB converges to a good feature set in about 50 iterations on the average regardless of the number of features (whether a dozen or more than 1000 features) and its performance is quite competitive. SPSA-BB can be considered extremely fast for a wrapper method and therefore it stands as a high-performing new feature selection method that is also computationally feasible in practice.
Evaluating pre-trained models for user feedback analysis in software engineering: a study on classification of app-reviews

Automatic classification of mobile applications users’ feedback is studied for different areas of software engineering. However, supervised classification requires a lot of manually labeled data, and with introducing new classes or new platforms, new labeled data and models are required. Employing Pre-trained neural Language Models (PLMs) have found success in the Natural Language Processing field. However, their applicability has not been explored for app review classification. We evaluate using PLMs for issue classification from app reviews in multiple settings and compare them with the existing models. We set up different studies to evaluate the performance and time efficiency of PLMs compared to Prior approaches on six datasets: binary vs. multi-class, zero-shot, multi-task, and multi-resource settings. In addition, we train and study domain-specific (Custom) PLMs by incorporating app reviews in the pre-training. We report Micro and Macro Precision, Recall, and F1 scores and the time required for training and predicting with the models. Our results show that PLMs can classify the app issues with higher scores, except in multi-resource setting. On the largest dataset, results are improved by 13 and 8 micro- and macro-average F1-scores, respectively, compared to the Prior approaches. Domain-specific PLMs achieve the highest scores in all settings with less prediction time, and they benefit from pre-training with a larger number of app reviews. On the largest dataset, we obtain 98 and 92 micro- and macro-average F1-score (from 4.5 to 8.3 more F1-score compared to general pre-trained models), 71 F1-score in zero-shot setting, and 93 and 92 F1-score in multi-task and multi-resource settings, respectively, using the large domain-specific PLMs. Although prior approaches achieve high scores in some settings, PLMs are the only models that can work well in the zero-shot setting. When trained on the app review dataset, the Custom PLMs have higher performance and lower prediction times.
Counterfactual attribute-based visual explanations for classification

Abstract In this paper, our aim is to provide human understandable intuitive factual and counterfactual explanations for the decisions of neural networks. Humans tend to reinforce their decisions by providing attributes and counterattributes. Hence, in this work, we utilize attributes as well as examples to provide explanations. In order to provide counterexplanations we make use of directed perturbations to arrive at the counterclass attribute values in doing so, we explain what is present and what is absent in the original image. We evaluate our method when images are misclassified into closer counterclasses as well as when misclassified into completely different counterclasses. We conducted experiments on both finegrained as well as coarsegrained datasets. We verified our attribute-based explanations method both quantitatively and qualitatively and showed that attributes provide discriminating and human understandable explanations for both standard as well as robust networks.
Joint Subspace and Low-Rank Coding Method for Makeup Face Recognition

Facial makeup significantly changes the perceived appearance of the face and reduces the accuracy of face recognition. To adapt to the application of smart cities, in this study, we introduce a novel joint subspace and low-rank coding method for makeup face recognition. To exploit more discriminative information of face images, we use the feature projection technology to find proper subspace and learn a discriminative dictionary in such subspace. In addition, we use a low-rank constraint in the dictionary learning. Then, we design a joint learning framework and use the iterative optimization strategy to obtain all parameters simultaneously. Experiments on real-world dataset achieve good performance and demonstrate the validity of the proposed method.
Single Image Super-Resolution with Application to Remote-Sensing Image

To improve the resolution of satellite images, many researchers are committed to machine learning and neural network-based SR methods. SR has multiple residual network frameworks in deep learning that have improved performance and can extend thousands of layers in the system. However, each layer improves accuracy by doubling the number of layers, although training thousands of layers are too expensive, the process is slow, and there are functional recovery issues. To address these issues, we propose a super-resolution wide remote sensing residual network (WRSR), in which we increase the width and reduce the depth of the residual network, due to decreasing the depth of the network our model reduced memory costs. To enhance the resolution of the single image we showed that our method improves training loss performance by performing the weight normalization instead of augmentation technology. The results of the experiment show that the method performs well in terms of quantitative indicators (PSNR) and (SSIM).
An efficient selective encryption scheme for HEVC based on hyperchaotic Lorenz system

With the wide application of video information, the protection of video information from illegal access has been widely investigated recently. An efficient selective encryption scheme for high efficiency video coding (HEVC) based on hyperchaotic Lorenz system is proposed. Firstly, the hyperchaotic Lorenz system is discretized and the generated chaotic state values are converted into chaotic pseudorandom sequences for encryption. The important syntax elements in HEVC are then selectively encrypted with the generated stream cipher. The experimental results show that the encrypted video is highly disturbed and the video information cannot be recognized. Through the analysis of objective index results, it is shown that the scheme is both efficient and security.
Web Document Encoding for Structure-Aware Keyphrase Extraction

We study keyphrase extraction (KPE) from Web documents. Our key contribution is encoding Web documents to leverage structure, such as title or anchors, by building a graph of words representing both (a) position-based proximity and (b) structural relations. We evaluate KPE performance on real-world search engine NAVER and human-annotated KPE benchmarks, and ours outperforms state-of-the-arts in both tasks.
Importance of Web Scraping in E-Commerce and E-Marketing

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Learning Methodologies to Generate Kernel-Learning-Based Image Downscaler for Arbitrary Scaling Factors

Displays and content have various resolutions and aspect ratios, requiring an image downscaler to adaptively reduce the image resolution. However, research on downscaling has garnered less attention than upscaling, including super-resolution. In practical display systems, simple interpolation, such as a bicubic filter that cannot preserve image details well, is still widely used for image downscaling rather than frame optimization-based or learning-based methods because of following reasons: frame optimization-based methods can effectively preserve image details after downscaling but are difficult to implement due to hardware costs. Learning-based methods have not been developed because defining a target downscaled image for training is difficult and training all downscaling factors is impossible. We propose a novel kernel-learning-based image downscaler to improve detail-preservation quality while supporting arbitrary downscaling factors using simple linear mapping. For this, a method to produce the ideal target downscaling result considering aliasing artifacts and detail preservation after downscaling is proposed. Then, we propose a training technique using the positional relationship between input and output pixels and a hierarchical region analysis to reproduce target images through simple kernel-based linear mapping. Lastly, a kernel-sharing technique is proposed to generate downscaling results for downscaling factors using a minimum number of trained kernels. In the simulation results, the proposed method demonstrated excellent edge preservation by improving the recall, precision, and F1 score, measuring the edge consistency between input and downscaled images, by up to 0.141, 0.079, 0.053, respectively, compared to benchmark methods. In a paired-comparison-based user study, the proposed method obtained the highest preference among benchmark methods using simple operations.