Reviewerly/exHarmony huggingface dataset:
From Simulation to Reality: CNN Transfer Learning for Scene Classification

In this work, we show that both fine-tune learning and cross-domain sim-to-real transfer learning from virtual to real-world environments improve the starting and final scene classification abilities of a computer vision model. A 6-class computer vision problem of scene classification is presented from both videogame environments and photographs of the real world, where both datasets have the same classes. 12 networks are trained from 2, 4, 8, , 4096 hidden interpretation neurons following a fine-tuned VGG16 Convolutional Neural Network for a dataset of virtual data gathered from the Unity game engine and for a photographic dataset gathered from an online image search engine. 12 Transfer Learning networks are then benchmarked using the trained networks on virtual data as a starting weight distribution for a neural network to classify the real-world dataset. Results show that all of the transfer networks have a higher starting accuracy pre-training, with the best showing an improvement of +48.34% image classification ability and an average increase of +38.33% for the starting abilities of all hyperparameter sets benchmarked. Of the 12 experiments, nine transfer experiments showed an improvement over non-transfer learning, two showed a slightly lower ability, and one did not change. The best accuracy overall was obtained by a transfer learning model with a layer of 64 interpretation neurons scoring 89.16% compared to the non-transfer counterpart of 88.27%. An average increase of +7.15% was observed over all experiments. The main finding is that not only can a higher final classification accuracy be achieved, but strong classification abilities prior to any training whatsoever are also encountered when transferring knowledge from simulation to real-world data, proving useful domain knowledge transfer between the datasets
LDP-Based Social Content Protection for Trending Topic Recommendation

Trending topic recommendation (TTR) has become a popular social service for social users to obtain interesting topics based on public social content. Due to sensitive privacy, the traditional differential privacy (DP) methods are used to protect social contents. However, these DP methods rely on a fully trusted third party (TTP) without considering protecting the correlations of social keywords, and do not support the privacy-preserving online social content publication. In this article, we propose a novel local DP-based TTR (LDPTR) scheme to perform high-quality online TTR services while achieving local privacy-preserving social contents. Specifically, LDPTR first clusters the social keywords with high correlations into noisy graph classes based on a graph-based LDP (GLDP) algorithm for ensuring the keyword correlation privacy. Second, LDPTR adopts a novel mechanism called ∈2-compressive sensing indistinguishability (CSI) to generate noisy social topics, thereby preventing the user-linkage attack and breaking the curse of high-dimensional local differential privacy (LDP). Then, a dynamic graph-based CSI (DGCSI) algorithm is proposed to protect the online social content privacy while ensuring data usability. Furthermore, LDPTR calculates the trending topics of interest with high burstiness by using a topic distribution similarity model-based topic burstiness (TMTB) algorithm, which achieves effective TTR services. Our LDPTR scheme satisfies ∈-LDP by detailed security analysis. Extensive experiments over two real-world data sets show that the proposed LDPTR gets high data utility while ensuring high-level privacy.
A fuzzy based high-resolution multi-view deep CNN for breast cancer diagnosis through SVM classifier on visual analysis

Breast cancer should be diagnosed as early as possible. A new approach of the diagnosis using deep learning for breast cancer and the particular process using segmentation strategies presented in this article. Medical imagery is an essential tool used for both diagnosis and treatment in many fields of medical applications. But, it takes specially trained medical specialists to read medical images and make diagnoses or treatment decisions. New practices of interpreting medical images are labour exhaustive, time-wasting, expensive, and prone to error. Using a computer-aided program which can render diagnosis and treatment decisions automatically would be more beneficial. A new computer-based detection method for the classification between compassionate and malignant mass tumours in mammography images of the breast proposed. (a) We planned to determine how to use the challenging definition, which produces severe examples that boost the segmentation of mammograms. (b) Employing well designing multi-instance learning through deep learning, we validated employing inadequately labelled data of breast cancer diagnosis using a mammogram. (c) The study is going through the Deep Lung method incorporating deep multi-dimensional automated identification and classification of the lung nodule. (d) By combining a probabilistic graphic model in deep learning, it authorizes how weakly labelled data can be used to improve the existing breast cancer identification method. This automated system involves manually defining the Region Of Interest (ROI), with the region and threshold values based on the next region. The High-Resolution Multi-View Deep Convolutional Neural Network (HRMP-DCNN) mainly developed for the extraction of function. The findings collected through the subsequent in available public databases like mammography screening information database and DDSM Curated Breast Imaging Subset. Ultimately, we’ll show the VGG that’s thousands of times quicker, and it is more reliable than earlier programmed anatomy segmentation.
Explainable Recommendation for Repeat Consumption

Displaying appropriate explanations for recommended items is of vital importance for improving the persuasiveness and user satisfaction of recommender systems. Although a user often consumes the same item repeatedly in some domains such as music and restaurants, existing studies have focused on generating explanations for recommending novel items. In this paper, we describe the concept of explainable recommendation for repeatedly consumed items. Because of the high proportion of repeat consumption in music listening, we suggest nine kinds of explanations for song recommendations according to three factors: personal, social, and item factors. From the results of an online survey involving 622 participants, we evaluate the usefulness of these explanations.
Introduction of ABCEP as an automatic programming method

Automatic programming is a branch of artificial intelligence that presents each solution as a mathematical formula based on heuristic mechanisms. In this study, artificial bee colony expression programming (ABCEP) is presented, which is combined simultaneously with expression programming. By using expression sharing to generate new solutions, the proposed method can minimize certain deficiencies of artificial bee colony programming, such as weak convergence and high locality. A total number of 15 real-world regression benchmark functions was used to evaluate the performance of the proposed model. For comparison purposes, successful run percentage, mean best cost, convergence performance, and run time of ABCEP were compared to those of other tested automatic programming algorithms, including artificial bee colony programming, gene expression programming, genetic programming, and quick artificial bee colony programming. A Wilcoxon signed-rank test was also done to compare the behavior of the algorithms. Additionally, the accuracy of all algorithms was then evaluated using three real-world practical benchmarks. The results indicate that the predictions generated by ABCEP are better than those obtained by other control algorithms based on successful runs, mean fitness values, and convergence rate.
Color image multiplicative noise and blur removal by saturation-value total variation

• SVTV regularization term is applied to model the target color image in HSV color space. • The fidelity term is well-adapted to multiplicative noise . • The existence and uniqueness of the minimizer of the proposed minimization problem is shown. • The convergence of an implicit scheme of the associated evolution problem is given. In this paper, we propose and develop a novel Saturation-Value Total Variation (SVTV) model for multiplicative noise and blur removal of color images. In the proposed model, SVTV regularization term is applied to model the target color image in HSV color space instead of RGB color space, and the fidelity term is well-adapted to multiplicative noise. We investigate into the existence and uniqueness of the minimizer of the proposed minimization problem. We study and show the convergence of an implicit scheme of the associated evolution problem for the numerical solution of the proposed SVTV model. Numerical examples are presented to demonstrate the performance of the proposed SVTV model is significantly better than that of other testing methods in terms of some criteria such as PSNR , SSIM and S-CIELAB color error.
Robustness Improvement of Visual Templates Matching Based on Frequency-Tuned Model in RatSLAM

This paper describes an improved brain-inspired simultaneous localization and mapping (RatSLAM) that extracts visual features from saliency maps using a frequency-tuned (FT) model. In the traditional RatSLAM algorithm, the visual template feature is organized as a one-dimensional vector whose values only depend on pixel intensity; therefore, this feature is susceptible to changes in illumination intensity. In contrast to this approach, which directly generates visual templates from raw RGB images, we propose an FT model that converts RGB images into saliency maps to obtain visual templates. The visual templates extracted from the saliency maps contain more of the feature information contained within the original images. Our experimental results demonstrate that the accuracy of loop closure detection was improved, as measured by the number of loop closures detected by our method compared with the traditional RatSLAM system. We additionally verified that the proposed FT model-based visual templates improve the robustness of familiar visual scene identification by RatSLAM.
E-learning in the present-day context: from the experience of foreign languages department, PSACEA

Abstract In the present context of COVID-19 lockdown, when pedagogies had to turn from in-person to virtual instructions, computer-mediated communication (CMC), including distance learning and e-learning, has come to the foreground to maintain the barrier-free educational environment. The three-dimensional model of the e-learning, which includes the principles of knowledge acquisition at ‘any place’, at ‘any time’, at ‘any speed’ is incomplete without the organisational culture component. To analyse what e-learning modes are used in a particular institution, to measure the efficiency of distance courses and, further, to suggest the most effective model and the ways of e-learning integration into a particular HEI according to its needs’ analysis was set up as the aim of this article. To achieve this aim, a complex of qualitative and quantitative research methods was applied. Therefore, in the first phase of our inquiry, a literature review was conducted in which we analysed the definitions of ‘distance learning’ and ‘e-learning’, traced the history of the question, and collected the main characteristics, quality parameters, and models of the distance and e-learning. On the later phases, we introduced the form ‘Analysis of the Distance Learning Tools Preferences’, disseminated among the teachers; a questionnaire ‘Distance Learning Mode of Work Satisfaction/Dissatisfaction’, disseminated among students, and ‘The Instructional Materials Motivation (IMMS)’ online survey by J.M. Keller to measure the efficiency of the distance learning courses design, also disseminated among students. The empirical data was collected at Prydniprovska State Academy of Civil Engineering and Architecture (PSACEA), the Department of Foreign Languages during March-May, 2020. Thirty instructors from the Department of Foreign Languages and twenty-three students from the first-third years of Foreign Languages studies who regularly participated in online lessons took part in the experiment. Based on the theoretical and empirical data, we got a picture of the e-learning state (in its first approximation) in our HEI, proved that the forms and questionnaires used in the experiment may serve as informative tools of quantitative measurement, drafted the format for further improvement of the e-learning in our organisation.
Multilevel fusion of multimodal deep features for porn streamer recognition in live video

Live video hosted by streamers is being sought after by an increasing number of Internet users. Some streamers mix pornographic content with live video for profit and popularity, but this greatly harms the network environment. To effectively identify porn streamers, a multilevel fusion method of multimodal deep features for porn streamer recognition in live video is proposed in this paper. (1) Visual and audio features including spatial, audio, motion, and temporal context in live video are extracted by a multimodal deep network. (2) Audio-visual attention features are obtained by fusing visual and audio features at the feature level based on a multimodal attention mechanism. (3) Text features are extracted by using the bullet screen text network based on the BERT (bidirectional encoder representations from transformers) model after collecting text information from the viewers' bullet screen comments. (4) The prediction results of the audio-visual deep network and the bullet screen text network are fused at the decision level to improve the porn streamer recognition accuracy. We build a real-world dataset of porn streamers and conduct experiments and demonstrate that our method can improve the porn streamer recognition accuracy.
Handwritten Digit Classification in Bangla and Hindi Using Deep Learning

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DeepWSC: Clustering Web Services via Integrating Service Composability into Deep Semantic Features

With an growing number of web services available on the Internet, an increasing burden is imposed on the use and management of service repository. Service clustering has been employed to facilitate a wide range of service-oriented tasks, such as service discovery, selection, composition and recommendation. Conventional approaches have been proposed to cluster web services by using explicit features, including syntactic features contained in service descriptions or semantic features extracted by probabilistic topic models. However, service implicit features are ignored and have yet to be properly explored and leveraged. To this end, we propose a novel heuristics-based framework DeepWSC for web service clustering. It integrates deep semantic features extracted from service descriptions by an improved recurrent convolutional neural network and service composability features obtained from service invocation relationships by a signed graph convolutional network, to jointly generate integrated implicit features for web service clustering. Extensive experiments are conducted on 8,459 real-world web services. The experiment results demonstrate that DeepWSC outperforms state-of-the-art approaches for web service clustering in terms of multiple evaluation metrics.
Understanding Smartphone use Behavior among Distance Education Students in Completing their Coursework in English: A Mixed-Method Approach

This study focuses on the use of smartphones by the Malaysian undergraduates from the school of distance learning to complete their coursework. Using a mixed method approach, this study conducted a survey and in-depth interviews to understand students' smartphone use behavior. The study reported that students are using smartphones for academic purposes by creating opportunities for interaction, collaboration, creating content, listening, reading, viewing and browsing the web. Such activities have allowed students to consider personalized learning in completing their coursework. Also, the learning process is slowly moving from a pedagogical approach to a heutagogical approach. In sum, distance learning students are embracing smartphones to optimize the possibilities available to enhance their learning process. By understanding how distance educations students use smartphones, decisions can be made on how best to support them during their teaching and learning activities.
Image Sentiment Analysis Using Deep Learning

Determining the image sentiment is a tedious task for classification algorithms, owing to complexities in the raw images as well as the intangible nature of human sentiments. Classifying image sentiments is an evergreen research area, especially in social data analytics. In current times, it is a common practice for majority people to precise their feelings on the web by substituting text with the upload of images via a multiplicity of social media sites like Facebook, Instagram, Twitter as well as any other platform. To identify the emotions from visual cues, some visual features as well as image processing techniques are used. Several existing systems have already introduced emotion detection using machine learning techniques, but the traditional feature extraction strategies do not achieve the required accuracy on random objects. In the entire process, normalization of image, feature extraction, and feature selection are important tasks in the train module. This work articulates the newest developments in the field of image sentiment employing deep learning techniques. Also, the use of conventional machine learning techniques is compared along with deep learning algorithms. It is indicative that a combination of fast recurrent neural networks and CNN may produce high accuracy with minimum time complexity. It is noted from the survey that existing researchers reflect CNN provides around 96.50% average accuracy for sentiment classification on the flicker image dataset.
Towards Minimizing Resource Usage With QoS Guarantee in Cloud Gaming

Cloud gaming has been very popular recently, but providing satisfactory gaming experiences to players at a modest cost is still challenging. Colocating several games onto one server could improve server utilization. However, prior work regarding colocating games either ignores the performance interference between games or uses simple performance model to charaterize it, which may make inefficient game colocation decisions and cause QoS violations. In this article, we address the resource allocation issues for colocating games in cloud gaming. We first propose a novel machine learning-based performance model, which is able to capture the complex relationship among the performance interference, the contention features of colocated games and resource partition. Guided by the performance model, we then propose efficient and effective algorithms for two resource allocation scenarios in cloud gaming. We evaluate the proposed solutions through extensive experiments using a large number of real popular games. The results show that our performance model is able to identify whether a colocated game satisfies QoS requirement within an average error of 5 percent, which significantly outperforms the alternatives. Our resource allocation algorithms are able to increase the resource utilization by up to 60 percent compared to the state-of-the-art solutions.
Authenticated Range Query Using SGX for Blockchain Light Clients

Due to limited computing and storage resources, light clients and full nodes coexist in a typical blockchain system. Any query from light clients must be forwarded to full nodes for execution, and light clients verify the integrity of query results returned. Since existing authenticated query based on Authenticated Data Structure (ADS) suffers from significant network, storage and computing overheads by virtue of Verification Objects (VO), an alternative way turns to Trust Execution Environment (TEE), with which light clients have no need to receive or verify any VO. However, state-of-the-art TEE cannot deal with large-scale application conveniently due to limited secure memory space (i.e, the size of enclave in Intel SGX is only 128MB). Hence, we organize data hierarchically in both trusted (enclave) and untrusted memory and only buffer hot data in enclave to reduce page swapping overhead between two kinds of memory. Security analysis and empirical study validate the effectiveness of our proposed solutions.
An automated resource management framework for minimizing SLA violations and negotiation in collaborative cloud

Collaborative Cloud computing is an emergent and encouraging computational prototype for sharing on-demand cloud resources in a multi-cloud environment. In a collaborative cloud environment, we make use of unused computational resources within an interconnected network to provide seamless service to the customers. SLA (Service Level Agreement) means a legal agreement signed among the consumer and the cloud service provider to guarantee a better quality of service. Nowadays due to more user request, suppliers of cloud service are not able to deliver guaranteed Quality of Service (QoS) to the customers leading to SLA violation. Existing SLA- based resource allocation models in today's cloud market consider an agreement for each QoS parameters independently and follow fixed pricing models, which results in lacking an end-to-end approach for the client task that requires composing multiple services and inefficient utilization of computing resources, which has direct negative effect on performance and cost. In this paper, we have designed a framework model for the effective dynamic provision of resources considering various categories of SLAs laterally with their penalty. We used Hybrid Cuckoo Search algorithm for scheduling of user requests in collaborative cloud computing environment. The proposed framework services the customer requests from its interconnected cloud servers and has an SLA Negotiation policy to minimize the penalty cost and maximize customer satisfaction by reducing SLA violations.
Understanding Object Detection Through an Adversarial Lens

Deep neural networks based object detection models have revolutionized computer vision and fueled the development of a wide range of visual recognition applications. However, recent studies have revealed that deep object detectors can be compromised under adversarial attacks, causing a victim detector to detect no object, fake objects, or mislabeled objects. With object detection being used pervasively in many security-critical applications, such as autonomous vehicles and smart cities, we argue that a holistic approach for an in-depth understanding of adversarial attacks and vulnerabilities of deep object detection systems is of utmost importance for the research community to develop robust defense mechanisms. This paper presents a framework for analyzing and evaluating vulnerabilities of the state-of-the-art object detectors under an adversarial lens, aiming to analyze and demystify the attack strategies, adverse effects, and costs, as well as the cross-model and cross-resolution transferability of attacks. Using a set of quantitative metrics, extensive experiments are performed on six representative deep object detectors from three popular families (YOLOv3, SSD, and Faster R-CNN) with two benchmark datasets (PASCAL VOC and MS COCO). We demonstrate that the proposed framework can serve as a methodical benchmark for analyzing adversarial behaviors and risks in real-time object detection systems. We conjecture that this framework can also serve as a tool to assess the security risks and the adversarial robustness of deep object detectors to be deployed in real-world applications.