Reviewerly/exHarmony huggingface dataset:
Quantum Diffie–Hellman Extended to Dynamic Quantum Group Key Agreement for e-Healthcare Multi-Agent Systems in Smart Cities

Multi-Agent Systems can support e-Healthcare applications for improving quality of life of citizens. In this direction, we propose a healthcare system architecture named smart healthcare city. First, we divide a given city into various zones and then we propose a zonal level three-layered system architecture. Further, for effectiveness we introduce a Multi-Agent System (MAS) in this three-layered architecture. Protecting sensitive health information of citizens is a major security concern. Group key agreement (GKA) is the corner stone for securely sharing the healthcare data among the healthcare stakeholders of the city. For establishing GKA, many efficient cryptosystems are available in the classical field. However, they are yet dependent on the supposition that some computational problems are infeasible. In light of quantum mechanics, a new field emerges to share a secret key among two or more members. The unbreakable and highly secure features of key agreement based on fundamental laws of physics allow us to propose a Quantum GKA (QGKA) technique based on renowned Quantum Diffie–Hellman (QDH). In this, a node acts as a Group Controller (GC) and forms 2-party groups with remaining nodes, establishing a QDH-style shared key per each two-party. It then joins these keys into a single group key by means of a XOR-operation, acting as a usual group node. Furthermore, we extend the QGKA to Dynamic QGKA (DQGKA) by adding join and leave protocol. Our protocol performance was compared with existing QGKA protocols in terms of Qubit efficiency (QE), unitary operation (UO), unitary operation efficiency (UOE), key consistency check (KCC), security against participants attack (SAP) and satisfactory results were obtained. The security analysis of the proposed technique is based on unconditional security of QDH. Moreover, it is secured against internal and external attack. In this way, e-healthcare Multi-Agent System can be robust against future quantum-based attacks.
BERTERS: Multimodal representation learning for expert recommendation system with transformers and graph embeddings

The objective of an expert recommendation system is to trace a set of candidates' expertise and preferences, recognize their expertise patterns, and identify experts. In this paper, we introduce a multimodal classification approach for expert recommendation system (BERTERS). In our proposed system, the modalities are derived from text (articles published by candidates) and graph (their co-author connections) information. BERTERS converts text into a vector using the Bidirectional Encoder Representations from Transformer (BERT). Also, a graph Representation technique called ExEm is used to extract the features of candidates from the co-author network. Final representation of a candidate is the concatenation of these vectors and other features. Eventually, a classifier is built on the concatenation of features. This multimodal approach can be used in both the academic community and the community question answering. To verify the effectiveness of BERTERS, we analyze its performance on multi-label classification and visualization tasks.
RRGCCAN: Re-Ranking via Graph Convolution Channel Attention Network for Person Re-Identification

The classical person re-identification methods are mostly focused on employing discriminative features amongst which the distance is measured on Euclidean space, while the effort of re-ranking is constrained as the lack of the utilization of quality context representation in embedding set.In this paper, we incorporate graph models on feature subsets resorting to the initial ranking by adopting the integration of the attention mechanism into graph convolution network.On the one hand, the context information regarding embedding pairs is considered to compute feature group similarity through the aggregation operation by using graph convolution networks.On the other hand, we adopt a channel attention mechanism to enhance the contribution of relevant feature channels, further strengthening the ability of similarity pulling and dissimilarity pushing of the overall network.Experimental study shows that the proposed network structure is superior to the state-of-the-art deep neural networks on three very challenging datasets that are popular in examining person re-identification techniques.
Security Improvement of Cloud Data Using Hybrid Cryptography and Steganography

One of the significant advancements in information technology is Cloud computing, but the security issue of data storage is a big problem in the cloud environment. That is why a system is proposed in this paper for improving the security of cloud data using encryption, information concealment, and hashing functions. In the data encryption phase, we implemented hybrid encryption using the algorithm of AES symmetric encryption and the algorithm of RSA asymmetric encryption. Next, the encrypted data will be hidden in an image using LSB algorithm. In the data validation phase, we use the SHA hashing algorithm. Also, in our suggestion, we compress the data using the LZW algorithm before hiding it in the image. Thus, it allows hiding as much data as possible. By using information concealment technology and mixed encryption, we can achieve strong data security. In this paper, PSNR and SSIM values were calculated in addition to the graph to evaluate the image masking performance before and after applying the compression process. The results showed that PSNR values of stego-image are better for compressed data compared to data before compression.
Byte-Level Object Identification for Forensic Investigation of Digital Images

Lately, digital data has increased a key role in providing and sharing information. Pictures and video recordings are utilized to pass on convincing messages to be utilized under a few unique situations, from propaganda to coercing. The majority of the effort in the present digital crime investigation network lies in the acquisition, retrieval, and investigation of existing data from digital machines. It is a time consuming and a humanly difficult task to collect, process and analyze each media content manually. In this paper, we provide a novel approach that solves a real-time problem for an investigator while investigating the suspect machine. Our approach acquires all image data at byte level from the suspect machine, perform fast and accurate object detection resorting to the deep learning-based algorithm and present high-level illustration of images containing suspicious object and unique objects that can be presented as evidence. Our approach aims to flag photos where suspicious objects are detected. Performance and time consumption wise, this study confirms the importance of automated object detection in digital forensics.
African buffalo algorithm: Training the probabilistic neural network to solve classification problems

Classification is used to categorize data and produce decisions for several domains. To improve the accuracy of classification, researchers have tended to hybridize the neural network with other metaheuristic algorithms in order to better exploit and explore the search space and thereby solve many different classification problems in an effective manner. The hybridization of algorithms is now commonplace and has resulted in the creation of novel methods that are more effective in comparison with those that employ a sole algorithm. Therefore, in this paper, a hybridization approach is employed to utilize the African buffalo optimization (ABO) algorithm as an optimizer to adjust the weights of the probabilistic neural network (PNN). The effectiveness of the proposed (ABO-PNN) method is investigated by applying it to several different classification problems. The efficiency of the ABO algorithm is assessed based on the PNN training results produced and its performance is compared with that of different types of optimization algorithm. The performance of the proposed algorithm in terms of classification accuracy is tested on 11 benchmark datasets. The results show that the ABO is better than the firefly algorithm (FA) in terms of both classification accuracy and convergence speed.
Understanding Citizens’ Emotional Pulse in a Smart City Using Artificial Intelligence

Over the past decade, smart city applications have gained significant attention in industrial informatics. However, little attention has been given to perceiving the emotions and perceptions of citizens who have a direct impact on smart city initiatives. In this article, we propose the use of publicly available abundant social media conversations that contain contextual information encompassing citizens' emotions and perceptions, which could be considered to provide the means to feel the “emotional pulse” of a city. We propose an automated AI-based observation framework to detect the emergence of public emotions and negativity in conversations. We evaluated the applicability of the framework using 29 928 social media conversations toward the much-debated topic of self-driving vehicles which will become increasingly relevant to smart cities. The patterns and transitions of citizens' collective emotions were modeled using the Natural Language Processing and Markov models while the negativity (toxicity) in conversations was evaluated using a deep learning based classifier. The framework could be adopted by industry leaders and government officials for smart observation of citizen opinions to improve security, communication, and policymaking.
A Dynamic Path Planning Method for Social Robots in the Home Environment

The home environment is a typical dynamic environment with moveable obstacles. The social robots working in home need to search for feasible paths in this complex dynamic environment. In this work, we propose an improved RRT algorithm to plan feasible path in home environment. The algorithm pre-builds a tree that covers the whole map and maintains the effectiveness of all nodes with branch pruning, reconnection, and regrowth process. The method forms a path by searching the nearest node in the tree and then quickly accessing the nodes near the destination. Due to the effectiveness-maintaining process, the proposed method can effectively deal with the complex dynamic environment where the destination and multiple moving obstacles change simultaneously. In addition, our method can be extended to the path-planning problem in 3D space. The simulation experiments verify the effectiveness of the algorithm.
Detail-Preserving CycleGAN-AdaIN Framework for Image-to-Ink Painting Translation

Image translation tasks based on generative models have become an important research area, such as the general framework for unsupervised image translation-CycleGAN (Cycle-Consistent Generative Adversarial Networks). A typical advantage of CycleGAN is that it can realize the training of two image sets without pairing, but there are still some problems in the preservation of semantic information and the learning of specific features. In this paper, we propose the CycleGAN-AdaIN framework based on the CycleGAN model, which can translate real photos into Chinese ink paintings. In order to retain the content of the image completely, we use one cycle consistency loss to replace two in the structure of the model. To learn the style information of the ink painting, we introduce an AdaIN (Adaptive Instance Normalization) module before the decoding process of the generation network. In addition, to correct the details of the generated image, we add the MS-SSIM (Multi-Scale-Structural Similarity Index) loss in the reconstruction loss to generate a higher quality image. Compared with the existing methods in FID, Kernel MMD, PSNR and SSIM, the experiment results show that our method can accomplish the task of transferring real photos to ink paintings and get better performance than the baseline model.
Audio Forgery Detection Techniques: Present and Past Review

The increase in low cost digital audio recorders, mobile applications and accelerating growth of the audio-based Internet of Things (IoT) has initiated ease in obtaining speech and audio data which will be later used for the purpose of identifying human traits, implementing voice authentication system and development of voice based embedded systems. On the other hand, the availability of free advanced mobile applications and audio editing software like Adobe, Audition CC, etc. enabling people to edit easily the meaningful content of audio recordings data for getting benefit from e-services or producing it in a courtroom for the purpose digital proof. Perhaps, most of the people do it for fun as well as the strong intention of hiding reality present. Moreover, the audio recording captured in a real-life today does not contain digital watermarking and signature content for authentication because of expensive procedure. Therefore, in recent years the researches focused more on developing active audio forgery detection techniques for copy-move and audio splicing forgeries to authenticate and verify for its genuineness. In this paper, put an effort to describe past and present developments in audio copy-move and audio splicing forgery techniques. The paper also presents an overview of audio forgeries, its classification, various post-processing operations used and available audio dataset for forgery sample preparation for benchmark testing.
RoCGAN: Robust Conditional GAN

Abstract Conditional image generation lies at the heart of computer vision and conditional generative adversarial networks (cGAN) have recently become the method of choice for this task, owing to their superior performance. The focus so far has largely been on performance improvement, with little effort in making cGANs more robust to noise. However, the regression (of the generator) might lead to arbitrarily large errors in the output, which makes cGANs unreliable for real-world applications. In this work, we introduce a novel conditional GAN model, called RoCGAN , which leverages structure in the target space of the model to address the issue. Specifically, we augment the generator with an unsupervised pathway, which promotes the outputs of the generator to span the target manifold, even in the presence of intense noise. We prove that RoCGAN share similar theoretical properties as GAN and establish with both synthetic and real data the merits of our model. We perform a thorough experimental validation on large scale datasets for natural scenes and faces and observe that our model outperforms existing cGAN architectures by a large margin. We also empirically demonstrate the performance of our approach in the face of two types of noise (adversarial and Bernoulli).
An Exploratory Study on Techniques for Quantitative Assessment of Stroke Rehabilitation Exercises

Technology-assisted systems to monitor and assess rehabilitation exercises have an opportunity of enhancing rehabilitation practices by automatically collecting patient's quantitative performance data. However, even if a complex algorithm (e.g. Neural Network) is applied, it is still challenging to develop such a system due to patients with various physical conditions. The system with a complex algorithm is limited to be a black-box system that cannot provide explanations on its predictions. To address these challenges, this paper presents a hybrid model that integrates a machine learning (ML) model with a rule-based (RB) model as an explainable artificial intelligence (AI) technique for quantitative assessment of stroke rehabilitation exercises. For evaluation, we collected therapist's knowledge on assessment as 15 rules from interviews with therapists and the dataset of three upper-limb stroke rehabilitation exercises from 15 post-stroke and 11 healthy subjects using a Kinect sensor. Experimental results show that a hybrid model can achieve comparable performance with a ML model using Neural Network, but also provide explanations on a model prediction with a RB model. The results indicate the potential of a hybrid model as an explainable AI technique to support the interpretation of a model and fine-tune a model with user-specific rules for personalization.
Job Prediction: From Deep Neural Network Models to Applications

Determining the job is suitable for a student or a person looking for work based on their job descriptions such as knowledge and skills that are difficult, as well as how employers must find ways to choose the candidates that match the job they require. In this paper, we focus on studying the job prediction using different deep neural network models including TextCNN, Bi-GRU-LSTM-CNN, and Bi-GRU-CNN with various pre-trained word embeddings on the IT job dataset. In addition, we proposed a simple and effective ensemble model combining different deep neural network models. Our experimental results illustrated that our proposed ensemble model achieved the highest result with an F1-score of 72.71%. Moreover, we analyze these experimental results to have insights about this problem to find better solutions in the future.
Fast Deep Stacked Networks based on Extreme Learning Machine applied to regression problems

Deep learning techniques are commonly used to process large amounts of data, and good results are obtained in many applications. Those methods, however, can lead to long training times. An alternative to simultaneously tune all parameters of a large network is to stack smaller modules, improving the model efficiency. However, methods such as Deep Stacked Network (DSN) have some problems that increase its training time and memory usage. To deal with these problems, Fast DSN (FDSN) was proposed, where the modules are trained using an Extreme Learning Machine (ELM) variant. Nonetheless, to speed-up the FDSN training, the ELM random feature mapping is shared among the modules, which can impact the network performance if the weights are not properly chosen. In this paper, we focus on the weight initialization of FDSN in order to improve its performance. We also propose FKDSN, a kernel-based variant of FDSN, besides discussing the theoretical complexity of the methods. We evaluate three different initialization approaches on ELM-trained neural networks over 50 public real-world regression datasets. Our experiments show that FDSN when combined with a more complex initialization method achieves similar results to ELM algorithms applied to large SLFNs, besides having a shorter training time and memory usage, implying that it can be suitable to be used on systems with restrict resources, such as Internet of Things devices. FKDSN also obtained similar results and training time to the large SLFNs, requiring less memory.
Dynamic and Internal Longest Common Substring

Abstract Given two strings S and T , each of length at most n , the longest common substring (LCS) problem is to find a longest substring common to S and T . This is a classical problem in computer science with an $$\mathcal {O}(n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> -time solution. In the fully dynamic setting, edit operations are allowed in either of the two strings, and the problem is to find an LCS after each edit. We present the first solution to the fully dynamic LCS problem requiring sublinear time in n per edit operation. In particular, we show how to find an LCS after each edit operation in $$\tilde{\mathcal {O}}(n^{2/3})$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>O</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mrow> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mrow> <mml:mn>2</mml:mn> <mml:mo>/</mml:mo> <mml:mn>3</mml:mn> </mml:mrow> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> time, after $$\tilde{\mathcal {O}}(n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>O</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> -time and space preprocessing. This line of research has been recently initiated in a somewhat restricted dynamic variant by Amir et al. [SPIRE 2017]. More specifically, the authors presented an $$\tilde{\mathcal {O}}(n)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>O</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mrow> <mml:mo>(</mml:mo> <mml:mi>n</mml:mi> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> -sized data structure that returns an LCS of the two strings after a single edit operation (that is reverted afterwards) in $$\tilde{\mathcal {O}}(1)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mover> <mml:mi>O</mml:mi> <mml:mo>~</mml:mo> </mml:mover> <mml:mrow> <mml:mo>(</mml:mo> <mml:mn>1</mml:mn> <mml:mo>)</mml:mo> </mml:mrow> </mml:mrow> </mml:math> time. At CPM 2018, three papers (Abedin et al., Funakoshi et al., and Urabe et al.) studied analogously restricted dynamic variants of problems on strings; specifically, computing the longest palindrome and the Lyndon factorization of a string after a single edit operation. We develop dynamic sublinear-time algorithms for both of these problems as well. We also consider internal LCS queries, that is, queries in which we are to return an LCS of a pair of substrings of S and T . We show that answering such queries is hard in general and propose efficient data structures for several restricted cases.
Google Play Content Scraping and Knowledge Engineering using Natural Language Processing Techniques with the Analysis of User Reviews

Abstract To maintain the competitive edge and evaluating the needs of the quality app is in the mobile application market. The user’s feedback on these applications plays an essential role in the mobile application development industry. The rapid growth of web technology gave people an opportunity to interact and express their review, rate and share their feedback about applications. In this paper we have scrapped 506259 of user reviews and applications rate from Google Play Store from 14 different categories. The statistical information was measured in the results using different of common machine learning algorithms such as the Logistic Regression, Random Forest Classifier, and Multinomial Naïve Bayes. Different parameters including the accuracy, precision, recall, and F1 score were used to evaluate Bigram, Trigram, and N-gram, and the statistical result of these algorithms was compared. The analysis of each algorithm, one by one, is performed, and the result has been evaluated. It is concluded that logistic regression is the best algorithm for review analysis of the Google Play Store applications. The results have been checked scientifically, and it is found that the accuracy of the logistic regression algorithm for analyzing different reviews based on three classes, i.e., positive, negative, and neutral.