Reviewerly/exHarmony huggingface dataset:
Critical analysis: bat algorithm-based investigation and application on several domains

In recent years several swarm optimization algorithms, such as Bat Algorithm (BA) have emerged, which was proposed by Xin-She Yang in 2010. The idea of the algorithm was taken from the echolocation ability of bats. Purpose: The purpose of this study is to provide the reader with a full study of the Bat Algorithm, including its limitations, the fields that the algorithm has been applied, versatile optimization problems in different domains, and all the studies that assess its performance against other meta-heuristic algorithms. Approach: Bat Algorithm is given in-depth in terms of backgrounds, characteristics, limitations, it has also displayed the algorithms that hybridized with BA (K-Medoids, Back-propagation neural network, Harmony Search Algorithm, Differential Evaluation Strategies, Enhanced Particle Swarm Optimization, and Cuckoo Search Algorithm) and their theoretical results, as well as to the modifications that have been performed of the algorithm (Modified Bat Algorithm (MBA), Enhanced Bat Algorithm (EBA), Bat Algorithm with Mutation (BAM), Uninhabited Combat Aerial Vehicle-Bat algorithm with Mutation (UCAV-BAM), Nonlinear Optimization)... Findings: Shed light on the advantages and disadvantages of this algorithm through all the researches that dealt with the algorithm in addition to the fields and applications it has addressed in the hope that it will help scientists understand and develop it. Originality/value: As far as the research community knowledge, there is no comprehensive survey study conducted on this algorithm cover{\i}ng all its aspects. Keywords: Swarm Intelligence; Nature-Inspired Algorithms; Metaheuristic Algorithms; Optimization Algorithms; Bat Algorithm.
Deepfake Detection in Media Files - Audios, Images and Videos

Recent advancement in deep learning has applied to solve various complex problems ranging from big data analytic to computer vision and human-level control. One among them is the deepfake technology which becomes a real threat to privacy, democracy, and national security. Deepfake is hyper-realistic digitally manipulated videos to depict people saying and doing things that never actually happened. This technology has been used in many fields in film industries for recreating videos without re-shooting, awareness video generation such as creating voices of those who have lost theirs or updating episodes of movies without re-shooting them at very low cost. This technology has many harmful usages in social media, pornographic sites, etc. to deface peoples which largely dominate the positive side of this application of deep learning. Also, the creation and spreading of these videos are increasing rapidly along all fields of media files. Therefore, it is very much important to develop efficient tools that can automatically detect the deepfake in these videos and thus reduce the public harm caused by such videos. In the early stages of deepfake detection, traditional technologies like signal processing, image processing, lip-syncing, etc were used but this provides very little accuracy when combined with the recent technologies of deep learning. So, here a system is proposed that can automatically detect the deepfake in media files such as images, videos, and audios. This uses an image processing approaches combined with deep learning which detects the inconsistency that exists in fake media.
One Weight Bitwidth to Rule Them All

Weight quantization for deep ConvNets has shown promising results for applications such as image classification and semantic segmentation and is especially important for applications where memory storage is limited. However, when aiming for quantization without accuracy degradation, different tasks may end up with different bitwidths. This creates complexity for software and hardware support and the complexity accumulates when one considers mixed-precision quantization, in which case each layer's weights use a different bitwidth. Our key insight is that optimizing for the least bitwidth subject to no accuracy degradation is not necessarily an optimal strategy. This is because one cannot decide optimality between two bitwidths if one has smaller model size while the other has better accuracy. In this work, we take the first step to understand if some weight bitwidth is better than others by aligning all to the same model size using a width-multiplier. Under this setting, somewhat surprisingly, we show that using a single bitwidth for the whole network can achieve better accuracy compared to mixed-precision quantization targeting zero accuracy degradation when both have the same model size. In particular, our results suggest that when the number of channels becomes a target hyperparameter, a single weight bitwidth throughout the network shows superior results for model compression.
Cold Item Recommendations via Hierarchical Item2vec

Learning item representations is a key building block in recommender systems research. However, representations often suffer from the cold start problem - a well-known problem in which rare items in the tail of the distribution face insufficient data yielding inadequate representations. In this work, we present a novel hybrid recommender that supports the utilization of hierarchical content-based information to mitigate the cold start problem. In particular, we assume a taxonomy of item tags in which every item is associated with several `parent' tags and the tags themselves can be associated with several `parent' tags in a hierarchical manner. Our model learns item representations that are guided by the `parent' tags of each item which allows propagating relevant information between items sharing the same hierarchy. In addition, the tags are modeled using tag representations that allow propagating information between any two tags that share a common ancestor. Due to space limitation, we focus this work on a recommendations task, however the same approach can be utilized for general representation learning e.g. language models.
Blockchain Adoption for Traceability in Food Supply Chain Networks

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A Safety Helmet and Protective Clothing Detection Method based on Improved-Yolo V 3

Safety helmet is an important protective tool for workers in the industrial production, and the protective clothing worn by workers can play a role in distinguishing other person. To solve the problem of non-contact on-line monitoring workers to wear safety helmet and protective clothing as required, this paper proposes an improved-YoloV3 method as the safety intelligent detection and identification algorithm. The large-size input layer is added for multi-scale prediction and the size of anchor boxes is adjusted to enhance the detection ability of small-size helmet and protective clothing. The improved-YoloV3 detection algorithm not only meets the real-time supervision requirements with sufficient FPS, but also achieves higher mAP at different resolutions compared with the traditional Yolo V3 algorithm.
Technical Phrase Extraction for Patent Mining: A Multi-level Approach

Recent years have witnessed a booming increase of patent applications, which provides an open chance for revealing the inner law of innovation, but in the meantime, puts forward higher requirements on patent mining techniques. Considering that patent mining highly relies on patent document analysis, this paper makes a focused study on constructing a technology portrait for each patent, i.e., to recognize technical phrases concerned in it, which can summarize and represent patents from a technology angle. To this end, we first give a clear and detailed description about technical phrases in patents based on various prior works and analyses. Then, combining characteristics of technical phrases and multi-level structures of patent documents, we develop an Unsupervised Multi-level Technical Phrase Extraction (UMTPE) model. Particularly, a novel evaluation metric called Information Retrieval Efficiency (IRE) is designed to evaluate the extracted phrases from a new perspective, which greatly supplements traditional metrics like Precision and Recall. Finally, extensive experiments on real-world patent data show the effectiveness of our UMTPE model.
Decentralized Online Voting using Blockchain and Secret Contracts

Voting is a complex process with a lot depending on it. Building an e-voting system that can guarantee anonymity, verifiability, and transparency together is a challenging task. Continuous efforts are being made to improve the voting system to achieve these properties. Recently, blockchain has hit the technology space with many promises, especially to make verifiable and transparent decentralized systems. However, a major challenge faced with blockchain-based e-voting systems is to achieve the users' anonymity while ensuring only authorized voters should be able to vote, and that too only once. To address these issues, this paper proposes a blockchain-based e-voting system with secret contracts. We have used Enigma (a secure multiparty computation platform) to design secret contracts. The proposed system meets most of the features required to conduct free and fair voting electronically.
Effectiveness of Swarm-Based Metaheuristic Algorithm in Data Classification Using Pi-Sigma Higher Order Neural Network

In this paper, Salp Swarm Algorithm (SSA) is employed in training the Higher Order Neural Network (HONN) for data classification task. In machine learning approach, to train artificial neural network is considered a difficult task which gains the attention of researchers recently. The difficulty of Artificial Neural Networks (ANNs) arises due to its nonlinearity nature and unknown set of initial parameters. Traditional training algorithms exhibit poor performance in terms of local optima avoidance and convergence rate, for which metaheuristic based optimization emerges as a suitable alternative. The performance of the proposed SSA-based HONN method has been verified by considering various classification measures over benchmark datasets chosen from UCI repository and the outcome obtained by the said method is compared with the state-of-art evolutionary algorithms. From the outcome reported, the proposed method outperforms over the recent algorithms which confirm its supremacy in terms of better exploration and exploitation capability.
The AIQ Meta-Testbed: Pragmatically Bridging Academic AI Testing and Industrial Q Needs

AI solutions seem to appear in any and all application domains. As AI becomes more pervasive, the importance of quality assurance increases. Unfortunately, there is no consensus on what artificial intelligence means and interpretations range from simple statistical analysis to sentient humanoid robots. On top of that, quality is a notoriously hard concept to pinpoint. What does this mean for AI quality? In this paper, we share our working definition and a pragmatic approach to address the corresponding quality assurance with a focus on testing. Finally, we present our ongoing work on establishing the AIQ Meta-Testbed.
Repeatable Data Hiding: Towards the Reusability of Digital Images

This article proposes a repeatable data hiding framework for digital images, in which the distortion caused by data hiding is invariable no matter how many times the embedding operation is executed. As a result, the usability of images can be always guaranteed. To achieve repeatable data hiding, we deduce the theoretical modification probabilities of the elements in cover image (an image used for data hiding). Then we design an embedding framework to make the practical modification probabilities of cover elements equal to the deduced ones. When additional data is embedded into cover image with the deduced probabilities, the modification-trace of current embedding is replaced by the latter embedding. That means multiple embeddings do not produce new modification-trace, so that the distortion caused by data hiding is invariable. Therefore, the repeatability of data hiding can be guaranteed. Finally, we describe some applications of repeatable data hiding, e.g., logistics management, authentication database management, and steganography, to show the practicability of our framework.
Would you please like my tweet?! An artificially intelligent, generative probabilistic, and econometric based system design for popularity-driven tweet content generation

An understudied area in the field of social media research is the design of decision support systems that can aid the manager by way of automated message component generation. Recent advances in this form of artificial intelligence has been suggested to allow content creators and managers to transcend their tasks from creation towards editing, thus overcoming a common problem: the tyranny of the blank screen. In this research, we address this topic by proposing a novel system design that will suggest engagement-driven message features as well as automatically generate critical and fully written unique Tweet message components for the goal of maximizing the probability of relatively high engagement levels. Our multi-methods design relies on the use of econometrics, machine learning, and Bayesian statistics, all of which are widely used in the emerging fields of Business and Marketing Analytics. Our system design is intended to analyze Tweet messages for the purpose of generating the most critical components and structure of Tweets. We propose econometric models to judge the quality of written Tweets by way of engagement-level prediction, as well as a generative probability model for the auto-generation of Tweet messages. Testing of our design demonstrates the need to take into account the contextual, semantic, and syntactic features of messages, while controlling for individual user characteristics, so that generated Tweet components and structure maximizes the potential engagement levels.
Combination Evaluation Method of Fuzzy C-Mean Clustering Validity Based on Hybrid Weighted Strategy

Clustering validity function is an index used to judge the accuracy of clustering results. At present, most studies on clustering validity are based on single clustering validity function. Research shows that no clustering validity function can handle any data and always perform better than other indexes. Therefore, a hybrid weighted combination evaluation method based on fuzzy C-means (FCM) clustering validity functions was proposed. The weighting method combines expert weighting with information entropy weighting to improve the subjective factor influence of expert weighting and the shortcoming of information entropy weighting in the value judgment of each clustering validity function. Four clustering validity function combination methods of linear, exponential, logarithm and proportion was studied. Finally, the proposed fuzzy clustering validity evaluation method is verified by experiments on artificial data sets and UCI data sets. The experimental results show that the proposed fuzzy clustering validity evaluation method can overcome the shortcoming of single clustering validity function, and can get the optimal clustering number more accurately for different data sets.
A Comparative Survey on Blockchain Based Self Sovereign Identity System

The Internet has changed business, education, healthcare, banking etc. and it is the main part of technological evolution. Internet provides us a connecting world to perform our day to day life activities easily. Internet is designed in such a way that it can uniquely identify machine, not a person, on the network hence there is need to design a system that can perform entity identification on the Internet. Currently on Internet, service providers provide identity of a user with user name and password and store this information on a centralized server. These servers become honey pot for hackers to steal user's personal identity information and service provider can utilize user identity information using data mining, artificial intelligence for economic benefits. Aim of Self sovereign identity system is to provide decentralized, user centric identity system which is controlled by identity owner that can be developed along with distributed ledger technology i.e. blockchain. In this paper, we intend to make an exhaustive study on different blockchain based self sovereign identity implementations (such as Sovrin, Uport, EverID, LifeID, Sora, SelfKey) along with its architectural components and discuss about use case of self sovereign identity.
Incorporating Similarity Measures to Optimize Graph Convolutional Neural Networks for Product Recommendation

With the ever-growing amount of online data and information, recommender systems are becoming overwhelmingly popular as an adequate approach for overcoming the challenge of information overload. Artificial Intelligence (AI) and Deep Learning (DL) have accumulated significant interest in many research areas, and recommender systems are one of them. In this paper, a Graph Convolutional Neural Network (GCNN)-based approach was used for online product recommendation. Graph-based methods have undergone substantial consideration for several recommendation tasks, with effective results. However, handling the computational complexities and training large datasets remain a challenge for such a model. Even though they are useful, the excessive measure of the model’s boundaries obstructs their applications in real-world recommender frameworks to a great extent. The recursive way of generating neighbor node embeddings for each node in the graph makes it more challenging to train a deep and large GCNN model. Therefore, we propose a model that incorporates measures of similarity between two different nodes, and these similarity measures help us to sample the neighbors beforehand. We estimate the similarity based on their interaction probability distribution with other nodes. We use KL divergence on different probability distributions to find the distance between them. This way, we set a threshold criterion for neighbor selection and generate other clusters. These clusters are then converted to subgraphs and are used as input for the proposed GCNN model. This approach simplifies the task of neighbor sampling for GCNN, and hence, we can observe a significant improvement in the computational complexity of the GCNN model. Finally, we compared the results with those for the previously proposed OpGCN model, basic GCNN model, and other traditional approaches such as collaborative filtering and probabilistic matrix factorization. The experiments showed that the complexity and computational time were decreased by estimating the similarity among nodes and sampling the nodes before training.
Recurrent Context Aggregation Network for Single Image Dehazing

Existing learning-based dehazing methods are prone to cause excessive dehazing and failure to dense haze, mainly because that the global features of hazy images are not fully utilized, while the local features of hazy images are not enough discriminative. In this letter, we propose a Recurrent Context Aggregation Network (RCAN) to effectively dehaze images and restore color fidelity. In RCAN, an efficient and generic module, called Context Aggression Block (CAB), is designed to improve the feature representation by taking advantage of both global and local features, which are complementary for robust dehazing because that local features can capture different levels of haze, and global features can focus on textures and object edges of a whole image. In addition, RCAN adopts a deep recurrent mechanism to improve the dehazing performance without introducing additional network parameters. Extensive experimental results on both synthetic and real-world datasets show that the proposed RCAN performs better than other state-of-the-art dehazing methods.
Text Detection and Classification from Low Quality Natural Images

Detection of textual data from scene text images is a very thought-provoking issue in the field of computer graphics and visualization. This challenge is even more complicated when edge intelligent devices are involved in the process. The low-quality image having challenges such as blur, low resolution, and contrast make it more difficult for text detection and classification. Therefore, such exigent aspect is considered in the study. The technology proposed is comprised of three main contributions. (a) After synthetic blurring, the blurred image is preprocessed, and then the deblurring process is applied to recover the image. (b) Subsequently, the standard maximal stable extreme regions (MSER) technique is applied to localize and detect text. Soon after, K-Means is applied to get three different clusters of the query image to separate foreground and background and also incorporate character level grouping. (c) Finally, the segmented text is classified into textual and non-textual regions using a novel convolutional neural network (CNN) framework. The purpose of this task is to overcome the false positives. For evaluation of proposed technique, results are obtained on three mainstream datasets, including SVT, IIIT5K and ICDAR 2003. The achieved classification results of 90.3% for SVT dataset, 95.8% for IIIT5K dataset, and 94.0% for the ICDAR 2003 dataset, respectively. It shows the preeminence of the proposed methodology that it works fine for good model learning. Finally, the proposed methodology is compared with previous benchmark text-detection techniques to validate its contribution.
Cyclists or avatars: is virtual cycling filling a short-term void during COVID-19 lockdown?

Indoor cycling has been a trend during the COVID-19 pandemic due to the lockdowns and social distancing measures imposed by most countries. Virtual cycling (eCycling) has grown exponentially, and its popularity among amateur and professional cyclists is shown by the fact that they regularly participate in virtual competitions in which people from all over the world interact via streaming. Although eCycling is derived from a traditional sport and adapted to a virtual platform, its future success once real competitions are reinstated has been seriously questioned. This virtual cycling modality has been openly criticized for the potential differences when competing and that it does not represent the true essence of cycling. These questions and the lack of clear guidelines for its competition suggest that eCycling has to go a long way before being considered a sport. This manuscript aims to present some challenges and opportunities regarding how virtual racing could be sustainable over time once the COVID-19 lockdown is over and demonstrate that eCycling was not just filling a short-term void left by real cycling during this pandemic.