Reviewerly/exHarmony huggingface dataset:
Generation and Evaluation of Hindi Image Captions of Visual Genome

The automatic image caption generation with proper fluency and expressiveness is an emerging area of research. A lot of research has been done on image caption generation for English, but very few work has been done in the area of generating and evaluating captions in Hindi. In this paper, the problem of generation and evaluation of captions in Hindi is addressed by using a framework based on convolutional neural network (CNN) and long short-term memory (LSTM). This model maximizes the likelihood of the target caption for an input image. The framework is experimented over Hindi Visual Genome dataset. Human evaluation and pre-defined automatic evaluation metrics are used for the evaluation of generated output. The experimental results of the framework manifest that the model is generating reasonably impressive Hindi captions.
PSA: A Novel Optimization Algorithm Based on Survival Rules of Porcellio Scaber

Bio-inspired algorithms such as neural network algorithms and genetic algorithms have received a significant amount of attention in both academic and engineering societies. In this paper, based on the observation of two major survival rules of a species of woodlice, i.e., porcellio scaber, we present an algorithm called the porcellio scaber algorithm (PSA) for solving general unconstrained optimization problems, including differentiable and non-differential ones as well as the case with local optima. Numerical results based on benchmark problems are presented to validate the efficacy of PSA.
A new generalized collaborative filtering approach on sparse data by extracting high confidence relations between users

In this paper, a new collaborative filtering method is proposed based on finding similar users directly and indirectly to overcome sparsity challenge. Moreover, selecting these users through extracting dominant opinion patterns leads to tackling scalability. In this method, frequent opinions between users are extracted to be used as dominant patterns. Then, users corresponding to the same dominant pattern are considered as direct similar users. Direct similar users who have seen more items and have corresponded to more than one pattern are regarded as reference users. Each reference user mediates between users who may have no/few commonly seen items and they are considered as indirect similar users. Utilizing indirect similar user helps the method to predict opinion of query user about items, which have not been seen by any direct similar user before. Clearly, indirect users are selected based on speculation using available information about direct users’ preferences. Thus, the effect of indirect users is considered to be stricter than that of direct users on the final prediction step. Experiments conducted on MovieLens small, MovieLens 100 k, MovieLens 1 M, and Jester datasets showed that the proposed method outperforms the previously introduced methods, especially on sparse data.
Task aware optimized energy cost and carbon emission-based virtual machine placement in sustainable data centers

Management of IT services is rapidly adapting to the cloud computing environment due to optimized service delivery models. Geo distributed cloud data centers act as a backbone for providing fundamental infrastructure for cloud services delivery. Conversely, their high growing energy consumption rate is the major problem to be addressed. Cloud providers are in a hunger to identify different solutions to tackle energy management and carbon emission. In this work, a multi-cloud environment is modeled as geographically distributed data centers with varying solar power generation corresponding to its location, electricity price, carbon emission, and carbon tax. The energy management of the workload allocation algorithm is strongly dependent on the nature of the application considered. The task deadline and brownout information is used to bring in variation in task types. The renewable energy-aware workload allocation algorithm adaptive to task nature is proposed with migration policy to explore its impact on carbon emission, total energy cost, brown and renewable power consumption.
Bangla News Trend Observation using LDA Based Topic Modeling

Topic Modelling is an essential field of natural language processing (NLP) that can be considered as a type of statistical model for extracting the abstract topics that have occurred in a collection of documents. Bangla is among the most popular and used languages around the world and nowadays innumerable Bangla texts are generated through digital and social media. So the significance of extracting knowledge from these data is invaluable for various sectors. However, the number of works in this field is inadequate because of the lack of proper datasets, tools, and applications. Therefore, preparing a convenient dataset in Bangla can be a great help for topic modeling as well as for other NLP related research. In this paper, we have addressed some of those complications by creating a proper dataset. Also, we have demonstrated a method of observing the Bangla media trend by applying Latent Dirichlet Allocation (LDA) on newspaper articles. The result of our experiment suggests that the proposed method can be an admissible way of utilizing news media data to observe media trends overtime properly.
QUESTIONS IN TWO-DIMENSIONAL LOGIC

Abstract Since Kripke, philosophers have distinguished a priori true statements from necessarily true ones. A statement is a priori true if its truth can be established before experience, and necessarily true if it could not have been false according to logical or metaphysical laws. This distinction can be captured formally using two-dimensional semantics. There is a natural way to extend the notions of apriority and necessity so they can also apply to questions. Questions either can or cannot be resolved before experience, and either are or are not about necessary facts. Classical two-dimensionalism has no account of question meanings, so it has to be combined with a framework for question semantics in order to capture these observations. It is shown in [14] how two-dimensional semantics can be combined with inquisitive semantics, in which questions are analyzed in terms of information. The present paper investigates the logic of two-dimensional inquisitive semantics, and provides a complete proof system.
Graph Anomaly Detection Using Dictionary Learning

Anomaly detection in networked signals often boils down to identifying an underlying graph structure on which the abnormal occurrence rests on. We investigate the problem of learning graph structure representations using adaptations of dictionary learning aimed at encoding connectivity patterns. In particular, we adapt dictionary learning strategies to the specificity of network topologies and propose new methods that impose Laplacian structure on the dictionaries themselves. In one adaptation we focus on classifying topologies by working directly on the graph Laplacian and cast the learning problem to accommodate its 2D structure. We tackle the same problem by learning dictionaries which consist of vectorized atomic Laplacians, and provide a block coordinate descent scheme to solve the new dictionary learning formulation. Imposing Laplacian structure on the dictionaries is also proposed in an adaptation of the Single Block Orthogonal learning method. Results on synthetic graph datasets comprising different graph topologies confirm the potential of dictionaries to directly represent graph structure information.
A Hybrid GRU-CNN Feature Extraction Technique for Speaker Identification

Speaker identification with diversified voice clip across the globe is a crucial and challenging task, specially extracting vigorous and discriminative features. In this paper, we demonstrated an end-to-end speaker identification pipeline introducing a hybrid Gated Recurrent Unit (GRU) and Convolutional Neural Network (CNN) feature extraction technique. At first, the voice clip is converted to a spectrogram, then processed with the GRU and CNN model, a part of it is again transformed with residual CNN model optimizing the subspace loss to extract best and substantial feature vector. Later, a statistical based feature selection method is applied to combine and select most significant features. To validate the proposed GRU-CNN feature extractor, we have examined it in a large-scale voxcelb dataset from comprising of 6000 real world speakers with multiple voices. Finally, a comparative analysis with state-of-art feature extraction techniques is applied with a promising outcome of 91.08% accuracy along with 93.51% and 94.74% precision and recall values respectively.
On Chamfer Distances on the Square and Body-Centered Cubic Grids: An Operational Research Approach

Linear programming is used to solve optimization problems. Thus, finding a shortest path in a grid is a good target to apply linear programming. In this paper, specific bipartite grids, the square and the body-centered cubic grids are studied. The former is represented as a “diagonal square grid” having points with pairs of either even or pairs of odd coordinates (highlighting the bipartite feature). Therefore, a straightforward generalization of the representation describes the body-centered cubic grid in 3D. We use chamfer paths and chamfer distances in these grids; therefore, weights for the steps between the closest neighbors and steps between the closest same type points are fixed, and depending on the weights, various paths could be the shortest one. The vectors of the various neighbors form a basis if they are independent, and their number is the same as the dimension of the space studied. Depending on the relation of the weights, various bases could give the optimal solution and various steps are used in the shortest paths. This operational research approach determines the optimal paths as basic feasible solutions of a linear programming problem. A directed graph is given containing the feasible bases as nodes and arcs with conditions on the used weights such that the simplex method may step from one feasible basis to another one. Thus, the optimal bases can be determined, and they are summarized in two theorems. If the optimal solution is not integer, then the Gomory cut is applied and the integer optimal solution is reached after only one Gomory iteration. Chamfer distances are frequently used in image processing and analysis as well as graphics-related subjects. The body-centered cubic grid, which is well-known in solid state physics, material science, and crystallography, has various applications in imaging and graphics since less samples are needed to represent the signal in the same quality than on the cubic grid. Moreover, the body-centered cubic grid has also a topological advantage over the cubic grid, namely, the neighbor Voronoi cells always share a full face.
Adaptive Gabor Filtering using Grey Wolf Optimization for Enhancement of Brain MRI

During last few decades, brain tumor becomes the 10th most dominant origin of fatality among the men, women and children. Regular screening, early detection and proper treatment arrangements are very effective in lessening the mortality rate. Poor visibility of medical images misguides the radiologist to detect the prognosis of cancer. In this view, present study proposes a fast and automated image enhancement approach by introducing a novel aspects of contrast enhancement of brain MRI by addressing grey wolf optimization (GWO) based Gabor filtering (GF). GWO hierarchically searches the optimum parameters of GF efficiently in terms of the proposed fitness function. The filtered image is able to capture high frequency intensity regions (edges/curvatures). Dynamic contrast of filtered image is corrected by automated power-law transformation (PLT). Performances of the proposed methodology are analyzed qualitatively and quantitatively in compare to other conventional image enhancement techniques. Since the proposed approach is able to improve the visual clarity of suspicious region of brain MRI so it performs superior to other techniques. The result of the proposed method is enterprising with very low computational time and accuracy of 95.1%.
Plankton Image Classification Using Deep Convolutional Neural Networks with Second-order Features

This paper proposes a method for plankton image classification, which uses deep convolutional neural networks with second-order pooling layers. By stacking layers of convolution and nonlinearity, convolutional neural networks could learn various feature representations. However, most works concentrate only on deeper or wider architecture design, ignoring the exploring of higher-order features in pooling layers. Instead of the most commonly used first-order statistics, in this paper we explore the performance of deep convolutional networks on plankton image classification using second-order features, achieving a top-1 accuracy of 75.82% on PlanktonSet 1.0, which has 2.24% gains than ResNet-101.
Modified ZM-SPECK: A Low Complexity and Low Memory Wavelet Image Coder for VS/IoT Nodes

The zero-memory set partitioned embedded block (ZM-SPECK) algorithm is a memory efficient block-based image coder used to encode the wavelet transformed images. It is a listless form of the SPECK algorithm and has no provision for saving the significance information for various coefficients/sets. Since it performs significance testing of sets recursively at each bit-plane, therefore it is computationally complex. Due to this limitation, it is difficult to implement it on low resource sensor/IoT nodes. In this paper, its low complexity version named Modified Zero Memory Set Partitioned Embedded Block (MZM-SPECK) is proposed which reduces the recursive significance test of sets. The simulation results indicate that the proposed algorithm surpasses other wavelet-based coders with regard to memory and computational complexity while maintaining the excellent coding efficiency.
Application of simulated annealing particle swarm optimization in complex three-dimensional path planning

Abstract Particle Swarm Optimization (PSO) has achieved good results in UAV path planning, but there is still the phenomenon of abandoning the global optimal path and choosing the local optimal one. In order to improve the ability of particle swarm in path planning, a simulated annealing particle swarm algorithm is proposed. First, tent reverse learning is used to initialize the population so that the algorithm is evenly distributed in space. Then annealing operation is performed after iteration once, which has better local path judgment ability and avoids the phenomenon of local optimum to some extent, so as to find a more satisfactory path. Simulated annealing particle swarms can find a clear and satisfactory path with high stability through the complex three-dimensional path planning simulation of UAV.
A Performance Comparison of Euclidean, Manhattan and Minkowski Distances in K-Means Clustering

The Indonesian police department has a role in maintaining security and law enforcement under the Republic of Indonesia Law Number 2 of 2002. In this study, data on the crime rate in the Bontang City area has been analyzed. It becomes the basis for the Police in anticipating various crimes. The K-Means algorithm is used for data analysis. Based on the test results, there are three levels of crime: high, medium, and low. According to the analysis, the high crime rate in the Bontang City area is special case theft and vehicle theft. Furthermore, it becomes the police program to maintain personal and vehicle safety.
Key Roles of Crypto-Exchanges in Generating Arbitrage Opportunities

The evolving crypto-currency market is seen as dynamic, segmented, and inefficient, coupled with a lack of regulatory oversight, which together becomes conducive to observing the arbitrage. In this context, a crypto-network is designed using bid/ask data among 20 crypto-exchanges over a 2-year period. The graph theory technique is employed to describe the network and, more importantly, to determine the key roles of crypto-exchanges in generating arbitrage opportunities by estimating relevant network centrality measures. Based on the proposed arbitrage ratio, Gatecoin, Coinfloor, and Bitsane are estimated as the best exchanges to initiate arbitrage, while EXMO and DSX are the best places to close it. Furthermore, by means of canonical correlation analysis, we revealed that higher volatility and the decreasing price of dominating crypto-currencies and CRIX index signal bring about a more likely arbitrage appearance in the market. The findings of research include pre-tax and after-tax arbitrage opportunities.
THE DIGITAL IDENTITIES IN THE CONTEXT OF BLOCKCHAIN AND ARTIFICIAL INTELLIGENCE

Digital identity is an identity which involves from records and cultural capital, personal profiles created by interactions between individuals, commented “ I connect therefore I am” with expression, celebrity tag of Descartes “ I think therefore I am” by updating with social media platforms, in YouTube videos. A digital identity is comprised in time by users of technologies such as e-mail, text messaging, social media and so on electronic communication tools. Digital identity is defined in nine dimensions as digital access, digital law, digital communication, digital commerce, digital health, digital security, digital rights and digital responsibilities, digital literacy and digital ethic. The aim of this article is to reveal importance of digital identity concept and elements of digital identities and to explore roles of blockchain and artificial intelligence in development process of digital identities. This article is written as the theoretically. Therefore, in study has been used descriptive analysis method. Consequently, blockchain contributes increasing security measures of digital identity. In case artificial intelligence contributes development of digital identity with face recognition systems and algorithms. Moreover, digital identity got more secure thanks to blockchain systems while digital identities is defined more quickly and easily by means of developed technologies with artificial intelligence
Detecting Localized Adversarial Examples: A Generic Approach using Critical Region Analysis

Deep neural networks (DNNs) have been applied in a wide range of applications, e.g., face recognition and image classification; however, they are vulnerable to adversarial examples. By adding a small amount of imperceptible perturbations, an attacker can easily manipulate the outputs of a DNN. Particularly, the localized adversarial examples only perturb a small and contiguous region of the target object, so that they are robust and effective in both digital and physical worlds. Although the localized adversarial examples have more severe real-world impacts than traditional pixel attacks, they have not been well addressed in the literature. In this paper, we propose a generic defense system called TaintRadar to accurately detect localized adversarial examples via analyzing critical regions that have been manipulated by attackers. The main idea is that when removing critical regions from input images, the ranking changes of adversarial labels will be larger than those of benign labels. Compared with existing defense solutions, TaintRadar can effectively capture sophisticated localized partial attacks, e.g., the eye-glasses attack, while not requiring additional training or fine-tuning of the original model's structure. Comprehensive experiments have been conducted in both digital and physical worlds to verify the effectiveness and robustness of our defense.
Federated Deep Payload Classification for Industrial Internet with Cloud-Edge Architecture

Payload classification is a kind of powerful deep packet inspection model built on the raw payloads of network traffic, and hence can remove the need of any configuration assumptions for network management and intrusion detection. While in the emerging industrial Internet, a majority of local industry owners are not willing to share their private payloads that possibly contain sensitive information and thus cause the classification model not always well trained due to the lack of sufficient training samples. In this paper, we address this privacy concern and propose a federated learning model for industrial payload classification. In particular, we consider a cloud-edge architecture for the industrial Internet topology, and assemble federated learning process by cloud-edge collaboration: each data owner has his own edge server for learning a local classification model and the industrial cloud takes the responsibility for aggregating local models to a federated one. We adopt a gradient-based deep convolutional neural network model as our local classifier and use the method of weighted gradient averaging for model aggregation. By this way, the data owners can avoid to disclose their private payload for model training, but instead share their local model's gradients to keep the federated model able to learn local samples indirectly. At the end, we have conducted a large set of experiments with real-world industrial Internet traffic datasets, and have successfully confirmed the effectiveness of the proposed federated model for payload classification with privacy-preserved.
Detepet Mobile Application for Pet Tracking

We have often heard of cases of loss of animals in the world, the owners of these animals are very troubled in looking for lost animals, so often when the pet is lost, the owner leaves it or makes the news of the competition for who can find it. We strive to help these pet owners easily detect the presence of pets they have through a mobile application that we make, namely Detepet that can track all the pets you have, so you are not afraid of losing your pet. In this case, the user should register their pet and the tool GPS necklace will be attached to the pet and the pet will be under monitoring of the systems.
High-Performance Server-Based Live Streaming Transmission Optimization for Sports Events in Smart Cities

Smart cities allow cities to run more efficiently and have been approved by a lot of cities. During the process of building smart cities, a large amount of data is generated. Particularly, live sports events have been regarded as the inalienable part of smart cities. However, with the improvement in the quality of life, people tend to obtain better watching experience in terms of sports events. For such purpose, this paper proposes the live streaming transmission optimization method based on high-performance server, called HPTO, including two main modules, that is, high-performance server optimization and transmission optimization. Specifically, for the server optimization, this paper devises a distributed storage strategy to avoid producing the internal disk fragments and improve the writing efficiency of sports videos. For the transmission optimization, this paper devises a deep-learning-based video compression strategy to save the storage space of server and accelerate the transmission of sports videos. In addition, this paper makes simulation experiments based on PyCharm. The experimental results show that HPTO has higher storage efficiency, smaller transmission time, and lower packet loss rate than benchmarks, which indicates that the proposed two aspects of optimization strategies (server optimization and transmission optimization) are efficient.
Neuroevolution in deep neural networks

A variety of methods have been applied to the architectural configuration and learning or training of artificial deep neural networks (DNNs). These methods play a crucial role in the success or failure of the DNNs for most problems. Evolutionary Algorithms are gaining momentum as a computationally feasible method for the automated optimisation of DNNs. Neuroevolution is a term that describes these processes. This newsletter article summarises the full version available at https://arxiv.org/abs/2006.05415.