Reviewerly/exHarmony huggingface dataset:
A Hybrid Recommender System Using KNN and Clustering

Recommender Systems ([Formula: see text]) are known in the E-Commerce ([Formula: see text]) field. They are expected to suggest the accurate goods/musics/films/items to the consumers/clients/people/users. Recent Hybrid [Formula: see text]s ([Formula: see text] have made us able to deal with the most important shortages of traditional Content-based F iltering ([Formula: see text]) and Collaborative Filtering ([Formula: see text]). Cold start, scalability and sparsity are the most important challenges to [Formula: see text] recommender systems ([Formula: see text]). [Formula: see text]s combine [Formula: see text] and [Formula: see text]. While the [Formula: see text]s that are based on memory have high accuracy, they are not scalable. Contrarily, the RSs on the basis of models have low accuracy but high scalability. Thus, aiming at dealing with cold start, scalability and sparsity challenges, [Formula: see text] is proposed to use both methods and also it has been evaluated on a real benchmark. An ontology, which is automatically created by an intelligently collected wordnet, has been employed in [Formula: see text] segment of the proposed [Formula: see text]. It has been automatically created and enhanced by an additional process. The functionality of the recommended framework has been superior to the performance of the state-of-the-art methods and the traditional [Formula: see text] and [Formula: see text] embedded in our method. Using a real dataset as a benchmark, the experimentations indicate that the proposed method not only has better performance but also has more efficacy rather than the state-of-the-art methods.
A new graph feature selection approach

Feature selection (FS) is a very important pre-processing technique in machine learning and data mining. It aims to select a small subset of relevant and informative features from the original feature space that may contain many irrelevant, redundant and noisy features. Feature selection usually leads to better performance, interpretability, and lower computational cost. In the literature, FS methods are categorized into three main approaches: Filters, Wrappers, and Embedded. In this paper we introduce a new feature selection method called graph feature selection (GFS). The main steps of GFS are the following: first, we create a weighted graph where each node corresponds to each feature and the weight between two nodes is computed using a matrix of individual and pairwise score of a Decision tree classifier. Second, at each iteration, we split the graph into two random partitions having the same number of nodes, then we keep moving the worst node from one partition to another until the global modularity is converged. Third, from the final best partition, we select the best ranked features according to a new proposed variable importance criterion. The results of GFS are compared to three well-known feature selection algorithms using nine benchmarking datasets. The proposed method shows its ability and effectiveness at identifying the most informative feature subset.
Security and Privacy Issues with Virtual Private Voice Assistants

A Survey about Virtual Private Voice Assistants (VPVA) such as Alexa, Google Assistant, Apple's Siri on about how users have adopted this technology, recent advancements, Privacy concerns, Security Risks and Vulnerability associated with the device considering multiple demonstrations and connecting the scenarios with the real time incidents and analysis of security threats that are possible in each stage of Virtual Private Voice Assistant(VPVA) process, and brief demonstration on the vulnerability identified in Amazon's Alexa by Checkpoint (Cybersecurity solution company) which helped Amazon fix the issue and finally some actions from the user end, that has to be taken to minimize the privacy and security issues.
NFR‐aware prioritization of software requirements

Abstract Non‐functional requirements (NFRs) play a significant role in the software development process. However, the classical requirement prioritization methods for incremental software development, typically, consider the attributes of functional features only, often neglecting the non‐functional constraints. This might lead to catastrophic defects in the system design, as the conflicts among the NFRs are ignored. In this paper, a novel prioritization approach for functional requirements (FRs) is introduced within the incremental software development process. The proposed approach considers the conflicts among NFRs, in the prioritization process, to minimize the incon sistencies of software development. The devised NFR‐aware prioritization algorithm can be tuned according to the weights that the analyst assigns to NFR conflicts and FR‐NFR dependencies. We have assessed our prioritization approach using available requirements data sets and have compared the results in different scenarios.
Optimization of Intelligent English Pronunciation Training System Based on Android Platform

Oral English, as a language tool, is not only an important part of English learning but also an essential part. For nonnative English learners, effective and meaningful voice feedback is very important. At present, most of the traditional recognition and error correction systems for oral English training are still in the theoretical stage. At the same time, the corresponding high-end experimental prototype also has the disadvantages of large and complex system. In the speech recognition technology, the traditional speech recognition technology is not perfect in recognition ability and recognition accuracy, and it relies too much on the recognition of speech content, which is easily affected by the noise environment. Based on this, this paper will develop and design a spoken English assistant pronunciation training system based on Android smartphone platform. Based on the in-depth study and analysis of spoken English speech correction algorithm and speech feedback mechanism, this paper proposes a lip motion judgment algorithm based on ultrasonic detection, which is used to assist the traditional speech recognition algorithm in double feedback judgment. In the feedback mechanism of intelligent speech training, a double benchmark scoring mechanism is introduced to comprehensively evaluate the speech of the speech trainer and correct the speaker’s speech in time. The experimental results show that the speech accuracy of the system reaches 85%, which improves the level of oral English trainers to a certain extent.
Assessment of Eyeball Movement and Head Movement Detection Based on Reading

The present age is the age of technology. People are increasingly using technology in their daily activities. That is why when we go to consider people's attention to something, our head detection comes first. Detecting the head is not the only thing that ends here; the thing that is directly related to it is the eyeball movement. For example, when a student is studying, the level of attention means his concentration deeply. And the depth of this attention depends not only on the head movement but also on how many times his eyes are moving from left to right or from right to left. Because it is seen that looking at the same object at a glance does not mean that he is not paying attention, maybe he is thinking of something else or is immersed in another thought. And by using this motivation, eyeball and head movements play a vital role in the study. The system's goal is to read the video frames and determine the number of eyeballs and head movements in real time. Eyeball and head movements from left to right and right to left are counted per minute. After one minute, the previous data will be refreshed, and new data will be recorded for the next minute. Thus, the system will give us the result of each minute movement numbers, and very nicely, our system can detect eyeballs and head movements in case of reading.
Evaluation of English–Slovak Neural and Statistical Machine Translation

This study is focused on the comparison of phrase-based statistical machine translation (SMT) systems and neural machine translation (NMT) systems using automatic metrics for translation quality evaluation for the language pair of English and Slovak. As the statistical approach is the predecessor of neural machine translation, it was assumed that the neural network approach would generate results with a better quality. An experiment was performed using residuals to compare the scores of automatic metrics of the accuracy (BLEU_n) of the statistical machine translation with those of the neural machine translation. The results showed that the assumption of better neural machine translation quality regardless of the system used was confirmed. There were statistically significant differences between the SMT and NMT in favor of the NMT based on all BLEU_n scores. The neural machine translation achieved a better quality of translation of journalistic texts from English into Slovak, regardless of if it was a system trained on general texts, such as Google Translate, or specific ones, such as the European Commission’s (EC’s) tool, which was trained on a specific-domain.
Categorization of actions in soccer videos using a combination of transfer learning and Gated Recurrent Unit

Extraction of knowledge from soccer videos has enormous applications like context-based advertisement, content-based video retrieval, match summarization, and highlight extraction. Overlapping soccer actions and uncontrolled video capturing conditions make it challenging to detect action accurately. For overcoming these problems, Convolutional Neural Network and Recurrent Neural Network are used jointly to classify different lengths of soccer actions. Initially, transfer learning from pre-trained VGG network extracts characteristic spatial features. Afterwards, Gated Recurrent Unit deals with temporal dependency and solves the vanishing gradient problem. Finally, softmax layer assigns decimal probabilities to each class. Experimental results demystify the significance of the proposed architecture.
Alignment Enhancement Network for Fine-grained Visual Categorization

Fine-grained visual categorization (FGVC) aims to automatically recognize objects from different sub-ordinate categories. Despite attracting considerable attention from both academia and industry, it remains a challenging task due to subtle visual differences among different classes. Cross-layer feature aggregation and cross-image pairwise learning become prevailing in improving the performance of FGVC by extracting discriminative class-specific features. However, they are still inefficient to fully use the cross-layer information based on the simple aggregation strategy, while existing pairwise learning methods also fail to explore long-range interactions between different images. To address these problems, we propose a novel Alignment Enhancement Network (AENet), including two-level alignments, Cross-layer Alignment (CLA) and Cross-image Alignment (CIA). The CLA module exploits the cross-layer relationship between low-level spatial information and high-level semantic information, which contributes to cross-layer feature aggregation to improve the capacity of feature representation for input images. The new CIA module is further introduced to produce the aligned feature map, which can enhance the relevant information as well as suppress the irrelevant information across the whole spatial region. Our method is based on an underlying assumption that the aligned feature map should be closer to the inputs of CIA when they belong to the same category. Accordingly, we establish Semantic Affinity Loss to supervise the feature alignment within each CIA block. Experimental results on four challenging datasets show that the proposed AENet achieves the state-of-the-art results over prior arts.
Opponent learning awareness and modelling in multi-objective normal form games

Many real-world multi-agent interactions consider multiple distinct criteria, i.e. the payoffs are multi-objective in nature. However, the same multi-objective payoff vector may lead to different utilities for each participant. Therefore, it is essential for an agent to learn about the behaviour of other agents in the system. In this work, we present the first study of the effects of such opponent modelling on multi-objective multi-agent interactions with nonlinear utilities. Specifically, we consider two-player multi-objective normal form games with nonlinear utility functions under the scalarised expected returns optimisation criterion. We contribute novel actor-critic and policy gradient formulations to allow reinforcement learning of mixed strategies in this setting, along with extensions that incorporate opponent policy reconstruction and learning with opponent learning awareness (i.e. learning while considering the impact of one’s policy when anticipating the opponent’s learning step). Empirical results in five different MONFGs demonstrate that opponent learning awareness and modelling can drastically alter the learning dynamics in this setting. When equilibria are present, opponent modelling can confer significant benefits on agents that implement it. When there are no Nash equilibria, opponent learning awareness and modelling allows agents to still converge to meaningful solutions that approximate equilibria.
An Econometric Perspective on Algorithmic Subsampling

Data sets that are terabytes in size are increasingly common, but computer bottlenecks often frustrate a complete analysis of the data, and diminishing returns suggest that we may not need terabytes of data to estimate a parameter or test a hypothesis. But which rows of data should we analyze, and might an arbitrary subset preserve the features of the original data? We review a line of work grounded in theoretical computer science and numerical linear algebra that finds that an algorithmically desirable sketch, which is a randomly chosen subset of the data, must preserve the eigenstructure of the data, a property known as subspace embedding. Building on this work, we study how prediction and inference can be affected by data sketching within a linear regression setup. We use statistical arguments to provide “inference-conscious” guides to the sketch size and show that an estimator that pools over different sketches can be nearly as efficient as the infeasible one using the full sample.
Monitoring drug trends in the digital environment–New methods, challenges and the opportunities provided by automated approaches

Developments in information technology have impacted on all areas of modern life and in particular facilitated the growth of globalisation in commerce and communication. Within the drugs area this means that both drugs discourse and drug markets have become increasingly digitally enabled. In response to this, new methods are being developed that attempt to research and monitor the digital environment. In this commentary we present three case studies of innovative approaches and related challenges to software-automated data mining of the digital environment: (i) an e-shop finder to detect e-shops offering new psychoactive substances, (ii) scraping of forum data from online discussion boards, (iii) automated sentiment analysis of discussions in online discussion boards. We conclude that the work presented brings opportunities in terms of leveraging data for developing a more timely and granular understanding of the various aspects of drug-use phenomena in the digital environment. In particular, combining the number of e-shops, discussion posts, and sentiments regarding particular substances could be used for ad hoc risk assessments as well as longitudinal drug monitoring and indicate “online popularity”. The main challenges of digital data mining involve data representativity and ethical considerations.
Averaged Soft Actor-Critic for Deep Reinforcement Learning

With the advent of the era of artificial intelligence, deep reinforcement learning (DRL) has achieved unprecedented success in high-dimensional and large-scale artificial intelligence tasks. However, the insecurity and instability of the DRL algorithm have an important impact on its performance. The Soft Actor-Critic (SAC) algorithm uses advanced functions to update the policy and value network to alleviate some of these problems. However, SAC still has some problems. In order to reduce the error caused by the overestimation of SAC, we propose a new SAC algorithm called Averaged-SAC. By averaging the previously learned action-state estimates, it reduces the overestimation problem of soft Q-learning, thereby contributing to a more stable training process and improving performance. We evaluate the performance of Averaged-SAC through some games in the MuJoCo environment. The experimental results show that the Averaged-SAC algorithm effectively improves the performance of the SAC algorithm and the stability of the training process.
E-Voting System Using Homomorphic Encryption Technique

Abstract The main objective of this project is to develop a safe Electronic voting System Software using Fingerprint Authentication method. i.e., e-voting system. Initially, fingerprints are collected and stored in a large database which will be created by the admins. During the elections, a valid authentication can be done through finger vein sensing. This authentication system is highly reliable and will facilitate casting of votes by the voters. In many democratic countries, elections have become a huge matter of concern. An election is seen as a system that basically creates the formal structure of a nation. It is an opportunity to elect the right leader and contribute towards the development of the nation. In addition, security and authenticity of these elections have become a major concern for the authorities. Although we use advanced electronic voting machines, the problem of rigging often arises. This e-voting software provides solution to rigging and also facilitates high authenticity and reliability. Depending on the particular implementation, e-voting may use standalone electronic voting machines (also called EVM) or computers connected to the Internet. It may encompass a range of Internet services, from basic transmission of tabulated results to full-function online voting through common connectable household devices. The data collected from the election will directly be transferred to the nearest database centers on real time basis using Zigbee systems and applications of cryptography.
Multilevel feature fusion dilated convolutional network for semantic segmentation

Recently, convolutional neural network (CNN) has led to significant improvement in the field of computer vision, especially the improvement of the accuracy and speed of semantic segmentation tasks, which greatly improved robot scene perception. In this article, we propose a multilevel feature fusion dilated convolution network (Refine-DeepLab). By improving the space pyramid pooling structure, we propose a multiscale hybrid dilated convolution module, which captures the rich context information and effectively alleviates the contradiction between the receptive field size and the dilated convolution operation. At the same time, the high-level semantic information and low-level semantic information obtained through multi-level and multi-scale feature extraction can effectively improve the capture of global information and improve the performance of large-scale target segmentation. The encoder–decoder gradually recovers spatial information while capturing high-level semantic information, resulting in sharper object boundaries. Extensive experiments verify the effectiveness of our proposed Refine-DeepLab model, evaluate our approaches thoroughly on the PASCAL VOC 2012 data set without MS COCO data set pretraining, and achieve a state-of-art result of 81.73% mean interaction-over-union in the validate set.
Enhanced Surveillance Video Compression With Dual Reference Frames Generation

In this paper, we improve the inter coding performance of surveillance videos by simultaneously investigating the distinct characteristics of background and foreground redundancy, and introduce two novel reference frames in a complementary manner. On one hand, a block level background reference frame (BRF) is proposed to reduce the background redundancy. The proposed scheme incorporates semantic information into the compression process, and makes use of instance segmentation to facilitate the background block decision, making the generated BRF free from foreground pollution. On the other hand, in order to handle foreground redundancy, a foreground reference frame (FRF) is generated based on Surveillance Prediction Generative Adversarial Network (SP-GAN), which utilizes previous reconstructed frames, optical flow based prediction, as well as BRF to infer the foreground objects of the to-be-coded frame. We integrate the proposed scheme into HM-16.6 software and append BRF and FRF to the reference pictures list (RPS). Simulation results demonstrate considerable superiority of the proposed scheme. In particular, by adding the proposed BRF to RPS, 3% coding gains are observed compared with the state-of-the-art BRF method. When both BRF and FRF are incorporated into RPS, 5.8% gains are achieved for surveillance video coding.
A novel algorithm: fitting a spatial arc to noisy point clouds with high accuracy and reproducibility

Fitting a spatial arc to noisy point clouds with high accuracy and reproducibility is challenging, although it is important in many applications, such as precise measurement, computerized numerical control machining and robotic path planning. In optical measuring applications, an arc-shaped object is usually first scanned as point clouds by a 3D camera or multiple charge-coupled device cameras, and arc fitting is then invoked to fit these point clouds, obtaining the measuring radius and center. The accuracy of the arc-fitting algorithm plays an important role in the arc-measuring precision. In this paper, a novel algorithm is proposed to fit a spatial arc of high accuracy and reproducibility to noisy point clouds. This method combines the repeated least trimmed squares idea with the smoothing fairness function, i.e. discrete curvature, to devise the objective function, which is solved iteratively. This algorithm can successfully filter noise and fit a highly accurate arc to noisy point clouds with high reproducibility. Seven popular arc-fitting algorithms are implemented as benchmarks and both simulated and real data scanned from physical objects are tested to validate that the proposed algorithm performs best. The proposed algorithm is efficient and can be easily implemented in industrial applications.
Context-Based Multiscale Unified Network for Missing Data Reconstruction in Remote Sensing Images

Missing data reconstruction is a classical yet challenging problem in remote sensing images. Most current methods based on traditional convolutional neural network require supplementary data and can only handle one specific task. To address these limitations, we propose a novel generative adversarial network-based missing data reconstruction method in this letter, which is capable of various reconstruction tasks given only single source data as input. Two auxiliary patch-based discriminators are deployed to impose additional constraints on the local and global regions, respectively. In order to better fit the nature of remote sensing images, we introduce special convolutions and attention mechanism in a two-stage generator, thereby benefiting the tradeoff between accuracy and efficiency. Combining with perceptual and multiscale adversarial losses, the proposed model can produce coherent structure with better details. Qualitative and quantitative experiments demonstrate the uncompromising performance of the proposed model against multisource methods in generating visually plausible reconstruction results. Moreover, further exploration shows a promising way for the proposed model to utilize spatio-spectral-temporal information. The codes and models are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Oliiveralien/Inpainting-on-RSI</uri> .