Reviewerly/exHarmony huggingface dataset:
Automatic skull-face overlay and mandible articulation in data science by AIRS-Genetic algorithm

Craniofacial superimposition (CFS) is an innovative method of identifying the missing skull of a human and has received serious attention in forensic research. Optimal overlaying of skull face is one of the fundamental issues found on forensic technology. CFS methodology generally initiates with the marking the accurate landmarking of skull and face extracted from the scanning device which is termed as ground truth data. These extracted features of skull and face will be in crisp image as 3D and 2D. Consequent of this advanced technology, CFS has upgraded to automatic level to investigate different cases. Regardless, the forensic expert invests more for investigation and it consumes large time for completing it. Most of the review study proofs that the computer-aided methods can improve the efficiency of the CFS technique can improve much better than other techniques. The introduction of computerized 3D-2D CFS in forensic research proves to be a novel one and define as most advance CFS technique in research today. This methodology can automate the operation of CFS by reducing the error on over-layered model. Due to the involvement of machine learning process in forensic field, executing the CFS problem is much easier. Coevolutionary algorithms (CEAs) are nature-inspired and very optimistic in solving complex problems by measuring the fitness of the problem. In this research work, automatic CFS is proposed for skull-face overlay and mandible articulation. Conventionally, a fuzzy set is proposed for extracting the features of skull especially the landmarking. In this work the Artificial Immune Recognition System (AIRS) model is implemented for distance calculated for crisp point of the extracted image. Due to the increase of computation time for crisp landmarks, an efficient algorithm is needed. With the point of decreasing the time of computation not by losing the accuracy the cooperative coevolutionary algorithm is proposed. Similar to the AIRS function, the Euclidean distance metrics are calculated between the AIRS set and crisp points. Hence the fitness is calculated form the algorithm for better layout of landmarking for better skull face overlying. Ultimately, the outcome of the progression is compared with the existing methodologies for measuring the level of effectiveness respectively.
Deep Learning Methods for Multi-Species Animal Re-identification and Tracking – a Survey

Technology has an important part to play in wildlife and ecosystem conservation, and can vastly reduce time and effort spent in the associated tasks. Deep learning methods for computer vision in particular show good performance on a variety of tasks; animal detection and classification using deep learning networks are widely used to assist ecological studies. A related challenge is tracking animal movement over multiple cameras. For effective animal movement tracking, it is necessary to distinguish between individuals of the same species to correctly identify an individual moving between two cameras. Such problems could potentially be solved through animal re-identification methods. In this paper, the applicability of existing animal re-identification techniques for fully automated individual animal tracking in a cross-camera setup is explored. Recent developments in animal re-identification in the context of open-set recognition of individuals, and the extension of these systems to multiple species is examined. Some of the best performing human re-identification and object tracking systems are also reviewed in view of extending ideas within them to individual animal tracking. The survey concludes by presenting common trends in re-identification methods, lists a few challenges in the domain and recommends possible solutions.
A dynamic evolutionary multi-objective virtual machine placement heuristic for cloud data centers

Minimizing the resource wastage reduces the energy cost of operating a data center, but may also lead to a considerably high resource overcommitment affecting the Quality of Service (QoS) of the running applications. The effective tradeoff between resource wastage and overcommitment is a challenging task in virtualized Clouds and depends on the allocation of virtual machines (VMs) to physical resources. We propose in this paper a multi-objective method for dynamic VM placement, which exploits live migration mechanisms to simultaneously optimize the resource wastage, overcommitment ratio and migration energy. Our optimization algorithm uses a novel evolutionary meta-heuristic based on an island population model to approximate the Pareto optimal set of VM placements with good accuracy and diversity. Simulation results using traces collected from a real Google cluster demonstrate that our method outperforms related approaches by reducing the migration energy by up to 57% with a QoS increase below 6%.
Blockchain Technology for Higher Education Sytem: A Mirror Review

The higher education system in developing countries has remained quiet unchanged for long time. Even with new and better technologies in this field has emerged tremendously; their adoption is marked extremely narrow. The connection between student and faculties is unchanged and the records are still stored and validated centrally. Block chain has introduced a fresh and novel path to decentralize the records and their exchange for gaining knowledge and skills. This paper is an attempt to understand block chain technology and its application on education. It discusses the use of block chain and the few of the many models designed to support educational bodies. The mere attempt to discuss the problem and challenges faced by block chain for successful support to education is presented in this article.
Testing the accuracy and reliability of palmar friction ridge comparisons – A black box study

Critics and commentators have been calling for some time for black box studies in the forensic science disciplines to establish the foundational validity of those fields—that is, to establish a discipline-wide, base-rate estimate of the error rates that may be expected in each field. While the well-known FBI/Noblis black box study has answered that call for fingerprints, no research to establish similar error rates for palmar impressions has been previously undertaken. We report the results of the first large-scale black box study to establish a discipline-wide error rate estimate for palmar comparisons. The 226 latent print examiner participants returned 12,279 decisions over a dataset of 526 known ground-truth pairings. There were 12 false identification decisions made yielding a false positive error rate of 0.7%. There were also 552 false exclusion decisions made yielding a false negative error rate of 9.5%. Given their larger number, false negative error rates were further stratified by size, comparison difficulty, and area of the palm from which the mark originated. The notion of "questionable conclusions," in which the ground truth response may not be the most appropriate, is introduced and discussed in light of the data obtained in the study. Measures of examiner consistency in analysis and comparison decisions are presented along with statistical analysis of the ability of many variables, such as demographics or image quality, to predict outcomes. Two online apps are introduced that will allow the reader to fully explore the results on their own, or to explore the notions of frequentist confidence intervals and Bayesian credible intervals.
Deep Learning-Based Context-Sensitive Spelling Typing Error Correction

This study aims to solve the context-sensitive spelling error problem for English documents.There are two types of spelling errors in English: non-word spelling errors and context-sensitive spelling errors.Non-word spelling errors are simple to correct because they can only be detected by matching the words in sentences with those in a dictionary; however, context-sensitive spelling errors entail increased difficulty of correction because the relationship between the word to be corrected and the surrounding context must be known.Spelling errors are considered noise in every field that uses text information, and preprocessing via document correction is necessary to minimize this problem.Context-sensitive spelling errors include homophone errors (which arise from the incorrect use of words that sound the same but are spelled differently), typographical errors (caused by striking an incorrect key on a keyboard), grammatical errors (which occur when the user does not know the correct grammatical rules), and cross word boundary errors (which arise from incorrect spacing between words).This study focuses on typographical errors.The context-sensitive spelling error problem is solved using the deep learning method, which is not an existing statistical method.The deep learning language model-based correction approach is divided into four parts, namely, correction based on word embedding information, contextual embedding information, an auto-regressive (AR) language model, and an auto-encoding (AE) language model.In this study, the best correction performance was obtained for the AE language model-based approach, and we verified its performance through a detailed correction test.
An End-to-End Network for Remote Sensing Imagery Semantic Segmentation via Joint Pixel- and Representation-Level Domain Adaptation

It requires pixel-by-pixel annotations to obtain sufficient training data in supervised remote sensing image segmentation, which is a quite time-consuming process. In recent years, a series of domain-adaptation methods was developed for image semantic segmentation. In general, these methods are trained on the source domain and then validated on the target domain to avoid labeling new data repeatedly. However, most domain-adaptation algorithms only tried to align the source domain and the target domain in the pixel level or the representation level, while ignored their cooperation. In this letter, we propose an unsupervised domain-adaptation method by Joint Pixel and Representation level Network (JPRNet) alignment. The major novelty of the JPRNet is that it achieves joint domain adaptation in an end-to-end manner, so as to avoid the multisource problem in the remote sensing images. JPRNet is composed of two branches, each of which is a generative-adversarial network (GAN). In one branch, pixel-level domain adaptation is implemented by the style transfer with the Cycle GAN, which could transfer the source domain to a target domain. In the other branch, the representation-level domain adaptation is realized by adversarial learning between the transferred source-domain images and the target-domain images. The experimental results on the public data sets have indicated the effectiveness of the JPRNet.
Using software metrics for predicting vulnerable classes and methods in Java projects: A machine learning approach

Abstract [ Context ]A software vulnerability becomes harmful for software when an attacker successfully exploits the insecure code and reveals the vulnerability. A single vulnerability in code can put the entire software at risk. Therefore, maintaining software security throughout the software life cycle is an important and at the same time challenging task for development teams. This can also leave the door open for vulnerable code being evolved during successive releases. In recent years, researchers have used software metrics‐based vulnerability prediction approaches to detect vulnerable code early and ensure secure code releases. Software metrics have been employed to predict vulnerability specifically in C/C++ and Java‐based systems. However, the prediction performance of metrics at different granularity levels (class level or method level) has not been analyzed. In this paper, we focused on metrics that are specific to lower granularity levels (Java classes and methods). Based on statistical analysis, we first identified a set of class‐level metrics and a set of method‐level metrics and then employed them as features in machine learning techniques to predict vulnerable classes and methods, respectively. This paper describes a comparative study on how our selected metrics perform at different granularity levels. Such a comparative study can help the developers in choosing the appropriate metrics (at the desired level of granularity). [ Objective ] The goal of this research is to propose a set of metrics at two lower granularity levels and provide evidence for their usefulness during vulnerability prediction (which will help in maintaining secure code and ensure secure software evolution). [ Method ] For four Java‐based open source systems (including two releases of Apache Tomcat), we designed and conducted experiments based on statistical tests to propose a set of software metrics that can be used for predicting vulnerable code components (i.e., vulnerable classes and methods). Next, we used our identified metrics as features to train supervised machine learning algorithms to classify Java code as vulnerable or non‐vulnerable. [ Result ] Our study has successfully identified a set of class‐level metrics and a second set of method‐level metrics that can be useful from a vulnerability prediction standpoint. We achieved recall higher than 70% and precision higher than 75% in vulnerability prediction using our identified class‐level metrics as features of machine learning. Furthermore, method‐level metrics showed recall higher than 65% and precision higher than 80%.
A Novel and Efficient Multiple RGB Images Cipher Based on Chaotic System and Circular Shift Operations

In real time scenarios, the single image encryption schemes are not that efficient when a bunch of color images is to be encrypted. This problem should be addressed due to computational cost incurred by multiple executions of single color-image encryption scheme. The objective of this research work is to provide a new and efficient multiple color images cipher scheme. This cipher is based on chaotic system, circular shift operations and SHA-384 hash codes. First of all, an arbitrary number of RGB images are input. They are combined in a rectangular fashion to get their hash code. Then this big color-image is decomposed into its three basic components, i.e., red, green and blue. To scramble the pixels of these components, they are shifted circularly both the row-wise and column-wise. Further, XOR operation has been performed to realize the effects of diffusion in these components. Lastly, these components are joined together to get the final RGB cipher image. Apart from the 384-hash codes, a salt key of 384-bit has also been used in the proposed cipher to heighten the security effects. The SHA-384 hash codes render the required key space and plaintext sensitivity. The simulation results and the performance analyses carried out at the end clearly prove the efficiency, effectiveness, robustness and the real-world applicability of the proposed multiple RGB images cipher. Besides, the encryption throughput 7.716 Mb/s of the proposed scheme outperforms many of the existing ones.
When Do We Need the Blockchain?

The questions “When do we use the blockchain?” or “Where do we use the blockchain?” or “Why do we use the blockchain?” look simple, but these questions become complicated due to the increasing utilization of the blockchain. These days, it is being explored as a standard security solution. The blockchain has unique properties, and these are entirely different from other standard security or cryptography solutions. This article gives a summary of the uses of the blockchain, while answering the above three important questions.
Multi-scale spatial context-based semantic edge detection

The good fusion of multi-scale features obtained by Convolutional neural networks (CNNs) is key to semantic edge detection; however, obtaining fusion is challenging. This paper presents a Multi-scale Spatial Context-based deep network for Semantic Edge Detection (MSC-SED). Different from state-of-the-art methods, MSC-SED gradually fuses multi-scale low-to-high level CNN features in an end-to-end architecture. This fusion structure obtains rich multi-scale features while enhancing the details of higher-level features. Beside the overall structure, we present the following two functional modules: the Context Aggregation Module (CAM) and Location-Aware fusion Module (LAM). The CAM helps to enrich context in features at each stage, before and after fusion. The LAM helps to selectively integrate lower-level features. The proposed method outperforms state-of-the-art approaches in terms of both the edge quality and the accuracy of edge categorization on both the SBD and Cityscapes datasets.
Reinforcement learning based control of batch polymerisation processes

Control of batch polymerization has been a challenging task. In this work, we have tried to use Reinforcement Learning (RL), and Deep Reinforcement Learning (DRL) based control on addressing the existing challenges. RL is a class of machine learning wherein an agent directly interacts with the environment and learns from its experience. The RL consist of an agent who takes an action, and the action changes the state of the environment. Based on old and new state agent gets a reward, which is reinforcement for its future actions. In this work, we have implemented RL and DRL based control for batch polymerization of Polymethyl methacrylate (PMMA). In both the controllers, the input variable considered was jacket temperature, while the reactor temperature was the output variable. Both the controllers have been found to achieve the given setpoint, while the DRL controller being faster than the RL controllers. Further, RL and DRL control with risk sensitivity were also carried out to accommodate the process constraints.
A 146.52 TOPS/W Deep-Neural-Network Learning Processor with Stochastic Coarse-Fine Pruning and Adaptive Input/Output/Weight Skipping

An energy efficient Deep-Neural-Network (DNN) learning processor is proposed for on-chip learning and iterative weight pruning (WP). This work has three key features: 1) stochastic coarse-fine pruning reduced computation workload by 99.7% compared with previous WP algorithm while maintaining high weight sparsity, 2) adaptive input/output/weight skipping (AIOWS) achieved 30.1× higher throughput than previous DNN learning processor [1] for not only the inference but also learning, 3) weight memory shared pruning unit removed on-chip weight memory access for WP. As a result, this work shows 146.52 TOPS/W energy efficiency, which is 5.79× higher than the state-of-the-art [1].
A New Fuzzy Cluster Validity Index for Hyperellipsoid or Hyperspherical Shape Close Clusters With Distant Centroids

Determining the correct number of clusters is essential for efficient clustering and cluster validity indices are widely used for the same. Generally, the effectiveness of a cluster validity index relies on two factors: first, separation, defined by the distance between a pair of cluster centroids or a pair of data points belonging to different clusters and second, compactness, which is determined in terms of the distance between a data point and a centroid or between a pair of data points belonging to the same cluster. However, the existing cluster validity indices for centroid-based clustering are unreliable when the clusters are too close, but corresponding centroids are distant. To mitigate this, a new cluster validity index, Saraswat-and-Mittal index, has been proposed in this article for hyperellipsoid or hyperspherical shape close clusters with distant centroids, generated by fuzzy c-means. The proposed index computes compactness in terms of the distance between data points and corresponding centroids, whereas the distance between data points of disjoint clusters defines separation. These parameters benefit the proposed index in the analysis of close clusters with distinct centroids efficiently. The performance of the proposed index is validated against ten state-of-the-art cluster validity indices on artificial, UCI, and image datasets, clustered by the fuzzy c-means.
On the use of a scanpath predictor and convolutional neural network for blind image quality assessment

Image quality assessment is an important field in computer vision, since it has a great impact on related tasks. To meet these needs, a plethora of metrics has been developed. In this paper, we propose an efficient method that estimates the quality of 2D images without access to the pristine image. This metric is modeled based on the relevant patches selected by saliency information and a convolution neural network. To exploit the saliency information, only the more perceptually relevant patches that impact subjective judgment more, are considered. To this end, we first compute the saliency map of the distorted image. Then, a scanpath predictor that aims to mimic the visual behavior is employed as patch selector. Finally, a CNN model is used to predict the quality score through the extracted patches. To the best of our knowledge this is the first study to associate a scanpath prediction method and CNN to assess the quality of 2D images. Four CNN models were compared (AlexNet, VGG16, VGG19 and ResNet50) and the performance of the best CNN was compared to the state-of-the-art on four datasets. Experimental results demonstrated the efficiency of the proposed approach and its generalization capacity.
Ontologies and Data Management: A Brief Survey

Information systems have to deal with an increasing amount of data that is heterogeneous, unstructured, or incomplete. In order to align and complete data, systems may rely on taxonomies and background knowledge that are provided in the form of an ontology. This survey gives an overview of research work on the use of ontologies for accessing incomplete and/or heterogeneous data.
Tropical Tensor Network for Ground States of Spin Glasses

We present a unified exact tensor network approach to compute the ground state energy, identify the optimal configuration, and count the number of solutions for spin glasses. The method is based on tensor networks with the tropical algebra defined on the semiring of $(\mathbb{R}\ensuremath{\cup}{\ensuremath{-}\ensuremath{\infty}},\ensuremath{\bigoplus},\ensuremath{\bigodot})$. Contracting the tropical tensor network gives the ground state energy; differentiating through the tensor network contraction gives the ground state configuration; mixing the tropical algebra and the ordinary algebra counts the ground state degeneracy. The approach brings together the concepts from graphical models, tensor networks, differentiable programming, and quantum circuit simulation, and easily utilizes the computational power of graphical processing units (GPUs). For applications, we compute the exact ground state energy of Ising spin glasses on square lattice up to 1024 spins, on cubic lattice up to 216 spins, and on three regular random graphs up to 220 spins, on a single GPU; we obtain exact ground state energy of $\ifmmode\pm\else\textpm\fi{}J$ Ising spin glass on the chimera graph of D-Wave quantum annealer of 512 qubits in less than 100 s and investigate the exact value of the residual entropy of $\ifmmode\pm\else\textpm\fi{}J$ spin glasses on the chimera graph; finally, we investigate ground-state energy and entropy of three-state Potts glasses on square lattices up to size $18\ifmmode\times\else\texttimes\fi{}18$. Our approach provides baselines and benchmarks for exact algorithms for spin glasses and combinatorial optimization problems, and for evaluating heuristic algorithms and mean-field theories.