Reviewerly/exHarmony huggingface dataset:
SFEEC: Provably Secure Signcryption-Based Big Data Security Framework for Energy-Efficient Computing Environment

Digital networks connect an increasing number of users, sensors, and devices, which communicate, access, share, and analyze data. This phenomenon of diverse data generation is recognized as Big data. While people are benefiting from the convenience added by Big data, they also meet direct threats to privacy and data security. As Big data is massive, it needs an effective security framework with energy-efficient computing environment. Signcryption technique combines both encryption and signature to present an efficient solution. To address threats to data security, chosen cipher security and chosen message security are two important attributes. This means that it is very important to design an efficient framework where we can address these issues. This article introduces an identity-based signcryption technique by combining encryption as well as signature, which provides a solution for secure and authenticated communication in the Big data environment, called SFEEC (security framework for energy-efficient computing). SFEEC fulfills the requirements of less computation and communication overheads by offering pairing-free computation at the user end. SFEEC is also proven under the “indistinguishable against chosen ciphertext” and “secure against chosen message” attacks in the standard model.
FrImCla: A Framework for Image Classification Using Traditional and Transfer Learning Techniques

Deep learning techniques are currently the state of the art approach to deal with image classification problems. Nevertheless, non-expert users might find challenging the use of these techniques due to several reasons, including the lack of enough images, the necessity of trying different models and conducting a thorough comparison of the results obtained with them, and the technical difficulties of employing different libraries, tools and special purpose hardware like GPUs. In this work, we present FrImCla, an open-source and free tool that simplifies the construction of robust models for image classification from a dataset of images, and only using the computer CPU. Given a dataset of annotated images, FrImCla automatically constructs a classification model (both for single-label and multi-label classification problems) by trying several feature extractors (based both on transfer learning and traditional computer vision methods) and machine learning algorithms, and selecting the best combination after a thorough statistical analysis. Thus, this tool can be employed by non-expert users to create accurate models from small datasets of images without requiring any special purpose hardware. In addition, in this paper, we show that FrImCla can be employed to construct accurate models that are close, or even better, to the state-of-the-art models.
Graphite

In this paper, we propose a new parallelism model denoted as MPI * X and suggest a linear algebra-based graph analytics system, namely, Graphite, which effectively employs it. MPI * X promotes thread-based partitioning to distribute computation and communication across threads on a cluster of machines, while eliminating the need for unnecessary thread synchronizations. Consequently, it contrasts with the traditional MPI + X parallelism model , which utilizes process-based partitioning to distribute data among processes as a way to scale out on a cluster of machines (the MPI part), then splits each partition into subpartitions among the threads of each process as a method to scale up within a machine (the X part). Besides adopting MPI * X, Graphite is NUMA-aware. In particular, it assigns threads to partitions in a way that exploits CPU and memory affinity, alongside leveraging faster MPI shared memory transport. Moreover, it adopts a variant of the popular GAS (Gather, Apply, and Scatter) computing model, thus decoupling the computation of partitions from the communication of partial results. Lastly, it supports thread-level asynchrony, which does not only overlap the computation with communication, but further interleaves multiple communications. We compared Graphite against GraphPad, Gemini, and LA3 graph analytics systems in an HPC environment using different graph applications. Results show that Graphite is roughly up to 3X faster than these state-of-the-art systems.
Red Blood Cell and Sickle Cell Detection from Microscopic Blood Images of Sickle Cell Anemic Patient

Sickle cell anemia (SCA) is a type of haemolytic anemia which is the most commonly inherited blood disorder. Detection of red blood cell and sickle cell from sickle cell anemic person is a very challenging task. Besides traditional visual inspection of microscopic images, various methods have been developed which are based on image processing technique for faster and more accurate diagnosis of SCA. In this paper normal and abnormal red blood cells have been detected using Niblack's thresholding technique from microscopic blood images of sickle cell anemic patient. The process involves preprocessing of microscopic blood smear and segmenting the preprocessed image using Niblack's thresholding algorithm. Then using geometrical features of blood cells a metric (form factor) is calculated to classify normal red blood cells and abnormal cells.
FELICS-AEAD: Benchmarking of Lightweight Authenticated Encryption Algorithms

Cryptographic algorithms that can simultaneously provide both encryption and authentication play an increasingly important role in modern security architectures and protocols (e.g. TLS v1.3). Dozens of authenticated encryption systems have been designed in the past five years, which has initiated a large body of research in cryptanalysis. The interest in authenticated encryption has further risen after the National Institute of Standards and Technology (NIST) announced an initiative to standardize “lightweight” authenticated ciphers and hash functions that are suitable for resource-constrained devices. However, while there already exist some cryptanalytic results on these recent designs, little is known about their performance, especially when they are executed on small 8, 16, and 32-bit microcontrollers. In this paper, we introduce an open-source benchmarking tool suite for a fair and consistent evaluation of Authenticated Encryption with Associated Data (AEAD) algorithms written in C or assembly language for 8-bit AVR, 16-bit MSP430, and 32-bit ARM Cortex-M3 platforms. The tool suite is an extension of the FELICS benchmarking framework and provides a new AEAD-specific low-level API that allows users to collect very fine-grained and detailed results for execution time, RAM consumption, and binary code size in a highly automated fashion. FELICS-AEAD comes with two pre-defined evaluation scenarios, which were developed to resemble security-critical operations commonly carried out by real IoT applications to ensure the benchmarks are meaningful in practice. We tested the AEAD tool suite using five authenticated encryption algorithms, namely AES-GCM and the CAESAR candidates ACORN, ASCON, Ketje-Jr, and NORX, and present some preliminary results.
DeLottery

In this paper, we design DeLottery, a decentralized lottery and gambling system based on block chain technology and smart contracts. Lottery is a classical form of entertainment and charity for centuries. Facing the bottleneck of the combination between lottery and information technology, we use smart contracts and blockchain in decentralized, intelligent, and secure systems for lottery industries. Moreover, we are inspired by the algorithm of RANDAO, an outstanding way of random number generation in blockchain scenario. The components and the functions of the novel system are described in details. We implement DeLottery in a blockchain network and show functioning procedure and security of the proposed lottery system.
A Survey of Popular Image and Text analysis Techniques

Image processing has made huge progress in recent times and has captured the attention of the international research community in recent times. In this survey paper the winning Convolutional Neural Network (CNN) architectures of the popular ImageNet Large Scale Visual Recognition Competition (ILSVRC) competition along with the innovations they introduced are discussed in detail. CNNs to the likes of the ZFNet, Inception V1, V2, V3 and V4 versions, ResNet versions, Inception ResNet versions, VGG versions are all covered. Also object detection is looked at as a preeminent task and popular models like RCNN, Fast RCNN, Faster RCNN and the YOLO versions along with their award winning architectures are discussed. Text classification is another major practice that is followed before making business decisions. In this survey paper section III explains the text classification techniques. preprocessing techniques such as removing stop words, stemming, lemmatization. Two variants of vectorization method such as vectorization-delta, vectorization-center. paper also explains the hyperparameter tuning for optimization of support vector machine(SVM), Logistic regression(LR), random forest(RF) and Boosted Regression Tree (BRT). Explored fine tuning parameter which enhance the performance of machine learning models.
Heterogeneous relational reasoning in knowledge graphs with reinforcement learning

Path-based relational reasoning over knowledge graphs has become increasingly popular due to a variety of downstream applications such as question answering in dialogue systems, fact prediction, and recommendation systems. In recent years, reinforcement learning (RL) based solutions for knowledge graphs have been demonstrated to be more interpretable and explainable than other deep learning models. However, the current solutions still struggle with performance issues due to incomplete state representations and large action spaces for the RL agent. We address these problems by developing HRRL (Heterogeneous Relational reasoning with Reinforcement Learning), a type-enhanced RL agent that utilizes the local heterogeneous neighborhood information for efficient path-based reasoning over knowledge graphs. HRRL improves the state representation using a graph neural network (GNN) for encoding the neighborhood information and utilizes entity type information for pruning the action space. Extensive experiments on real-world datasets show that HRRL outperforms state-of-the-art RL methods and discovers more novel paths during the training procedure, demonstrating the explorative power of our method.
Weak Constraint Gaussian Processes for optimal sensor placement

We present a Weak Constraint Gaussian Process (WCGP) model to integrate noisy inputs into the classical Gaussian Process (GP) predictive distribution. This model follows a Data Assimilation approach (i.e. by considering information provided by observed values of a noisy input in a time window). Due to the increased number of states processed from real applications and the time complexity of GP algorithms, the problem mandates a solution in a high performance computing environment. In this paper, parallelism is explored by defining the parallel WCGP model based on domain decomposition. Both a mathematical formulation of the model and a parallel algorithm are provided. We use the algorithm for an optimal sensor placement problem. Experimental results are provided for pollutant dispersion within a real urban environment.
People Identification through Facial Recognition using Deep Learning

Nowadays, Closed Circuit Television (CCTV) monitoring systems, access control, and many other security-related applications incorporate facial recognition techniques. This disruptive tool differs from other biometric techniques, as faces can be recognized remotely. Thus, these applications can be incorporated in different institutions with the purpose of restricting access to unauthorized people, avoiding damages and losses to the property. The objective of this study is to identify people in controlled and uncontrolled environments within a university building that has suffered insecurity problems on several occasions. For this reason, it is used a convolutional neural network (CNN) architecture that was implemented on a GPU. The procedure consists of 3 phases: (i) training; (ii) learning and; (iii) classification. The CNN training stage was performed using the VGGFace2 dataset. The learning and generalization of deeply discriminatory facial embeddings of 512 bytes per face by joint supervision of softmax loss and center loss signals. The classification used a support vector machine (SVM) in several experiments with different amounts of classes. The efficiency of this approach was tested in real time on a sample of 128 students by means of quantitative metrics derived by confusion matrix. The overall accuracy in controlled and uncontrolled contexts was 96% and 71.43%, respectively. The proposed method establishes a starting point for the development of a more robust system in production environments.
ZeroLender

Since its inception a decade ago, Bitcoin and its underlying blockchain technology have been garnering interest from a large spectrum of financial institutions. Although it encompasses a currency, a payment method, and a ledger, Bitcoin as it currently stands does not support bitcoins lending. In this paper, we present a platform called ZeroLender for peer-to-peer lending in Bitcoin. Our protocol utilizes zero-knowledge proofs to achieve unlinkability between lenders and borrowers while securing payments in both directions against potential malicious behaviour of the ZeroLender as well as the lenders, and prove by simulation that our protocol is privacy-preserving. Based on our experiments, we show that the runtime and transcript size of our protocol scale linearly with respect to the number of lenders and repayments.
Sustainable Energy Consumption Modeling for Cloud Data Centers

Cloud computing technology was implemented to offers services as utility over the internet to enable number of applications for scientific and business domain. At initial of its implementation the main objective was to improve the system performance. But along with time, as hosting of applications over cloud data centers increased, to create a sustainable data center became the predominant key issue. Energy consumption influences other performance parameters such as operational cost, maintenance of cooling system etc. This paper presents challenges and architectural elements involve in energy consumption. We have considered various data center infrastructure (i.e. different resources or hardware used in computation) for modeling of energy consumption to develop sustainable data centers. In this research paper we have presented the factors involve in energy consumption and formulate them to evaluate energy consumption more precisely.
Asymptotical Locking Tomography of High-Dimensional Entanglement*

High-dimensional (HD) entanglement provides a very promising way of transcending the limitations of the two-dimensional entanglement between qubits for increasing channel capacity in many quantum protocols. In the pursuit of capitalizing on the HD entangled states, one of the central issues is to unambiguously and comprehensively quantify and reconstruct them. The full quantum state tomography is a unique solution, but it is undesirable and even impractical because the measurements increase rapidly in d 4 for a bipartite d -dimensional quantum state. Here we present a very efficient and practical tomography method—asymptotical locking tomography (ALT), which can harvest full information of bipartite d -dimensional entangled states by very few measurements less than 2 d 2 only. To showcase the validity and reasonableness of our ALT, we carry out the test with the two-photon spin-orbital angular momentum hyperentangled states in a four-dimensional subspace. Besides high-efficiency and practicality, our ALT is also universal and can be generalized into multipartite HD entanglement and other quantum systems.
Analysis and Efficient Implementations of a Class of Composited de Bruijn Sequences

A binary de Bruijn sequence is a sequence of period 2n in which every binary n-tuple occurs exactly once in each period. A de Bruijn sequence has good randomness properties, such as long period, ideal tuple distribution, and high linear complexity, and can be generated by a nonlinear feedback shift register (NLFSR). Finding an efficient NLFSR that can generate a de Bruijn sequence with a long period is a significant challenge. “Composited construction” is a technique for constructing a de Bruijn sequence of period 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n+k</sup> by an NLFSR from a de Bruijn sequence of period 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> through a composition operation repeatedly applying k times. The goal of this article is to further investigate the composited construction of de Bruijn sequences with efficient hardware implementations, and determine randomness properties such as linear complexity. Our contributions in this article are as follows. First, we present a generalized construction of composited de Bruijn sequences that is constructed by adding a combination of conjugate pairs of different lengths in the feedback function of the composited construction, which results in generating a class of de Bruijn sequences of size 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> , whereas the original composited construction can generate only two sequences. Second, we investigate the linear complexity and the correlation property of the new class of de Bruijn sequences. We prove theoretically that the linear complexity of this class of de Bruijn sequences is optimal or close to optimal. Interestingly, we also prove that the linear complexities of all the sequences of this class are equal, which strengthens Etzion's conjecture (JCTA 1985, IEEE-IT 1999) about the number of de Bruijn sequences with equal linear complexity. This is the first known construction of de Bruijn sequences of an arbitrarily long period whose linear complexities are determined theoretically. Finally, we implement our construction in hardware to demonstrate its practicality. We synthesize our implementations for a 65 nm ASIC and a Xilinx Spartan FPGA and present hardware areas, and performances of de Bruijn sequences of periods in the range of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">160</sup> to 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1056</sup> . For instance, a class of de Bruijn sequences of period 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">160</sup> (resp. 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">288</sup> ) can be implemented with an area of 3.43 (resp. 6.71) kGEs in 65 nm ASIC, and 83 (resp. 229) slices in Spartan6 FPGA.
One-Class Classification with Deep Autoencoder Neural Networks for Author Verification in Internet Relay Chat

Social networks are highly preferred to express opinions, share information, and communicate with others on arbitrary topics. However, the downside is that many cybercriminals are leveraging social networks for cyber-crime. Internet Relay Chat (IRC) is the important social networks which can grant the anonymity to users by allowing them to connect channels without sign-up process. Therefore, IRC has been the playground of hackers and anonymous users for various operations such as hacking, cracking, and carding. Hence, it is urgent to study effective methods which can identify the authors behind the IRC messages. In this paper, we design an autonomic IRC monitoring system, performing recursive deep learning for classifying threat levels of messages and develop a novel author verification approach with one-class classification with deep autoencoder neural networks. The experimental results show that our approach can successfully perform effective author verification for IRC users.
Deep Convolutional Neural Network Design Approach for 3D Object Detection for Robotic Grasping

Automation is increasing with the advent of 3D- and depth images using an infrared camera. Formerly, most of the object detection and recognition were done by a 2D-camera of Red-Green-Blue (RGB) images. Today, with the availability of economical 3D-sensors, people started investing in 3D-object detection. Over recent years, Convolutional Neural Network (CNN) has reached the epitome of image classification for different application. Explicitly in 2D-CNN, there is massive progress for object detection, but for 3D-CNN, it is still at the beginning of an era. In this proposal, RGB images and depth images will be used to classify the objects using a Structure Sensor camera. Previously, researchers mainly worked on 2D-images and received considerable accuracy, but the accuracy decreases for 3D-image classifications using 2D-techniques. The complexity of the proposed model is increased due to the partial images with variation in illumination, occlusion, and cluttered images. To overcome the complexity problem, Deep Convolutional Neural Network (DCNN) is proposed with the usage of RGB-D images, which includes one 2D-image in RGB, and the other in-depth image form. Two methods are proposed, one will be using two parallel DCNN model and another method will be using three parallel DCNN model. Afterwards, the parallel models need to be concatenated to get 3D-object detection. Public-available 3D-dataset will be used to evaluate the model, and the results of both of the models will be compared.
Lower your guards: a compositional pattern-match coverage checker

A compiler should warn if a function defined by pattern matching does not cover its inputs—that is, if there are missing or redundant patterns. Generating such warnings accurately is difficult for modern languages due to the myriad of language features that interact with pattern matching. This is especially true in Haskell, a language with a complicated pattern language that is made even more complex by extensions offered by the Glasgow Haskell Compiler (GHC). Although GHC has spent a significant amount of effort towards improving its pattern-match coverage warnings, there are still several cases where it reports inaccurate warnings. We introduce a coverage checking algorithm called Lower Your Guards, which boils down the complexities of pattern matching into guard trees . While the source language may have many exotic forms of patterns, guard trees only have three different constructs, which vastly simplifies the coverage checking process. Our algorithm is modular, allowing for new forms of source-language patterns to be handled with little changes to the overall structure of the algorithm. We have implemented the algorithm in GHC and demonstrate places where it performs better than GHC’s current coverage checker, both in accuracy and performance.