Reviewerly/exHarmony huggingface dataset:
A Contrast Enhancement Model for X-Ray Mammograms Using Modified Local s-Curve Transformation Based on Multi-Objective Optimization

The biased X-ray field and the subtle difference in X-ray attenuation between normal and abnormal breast tissues prevent the biomedical sensors to generate mammograms with good quality. These are the common imperfections in the acquisition systems (sensor) of Mammography which contribute towards the degradation of mammogram image quality. This paper aims to develop a contrast enhancement model for mammograms so that the human or machine vision can distinguish easily among the variants of breast lesions and predict their severity. The proposed mammogram enhancement model exploits locally the proprieties of the sigmoidal function based on multi-objective Genetic Algorithm for improving the contrast of lesions along with a simultaneous increment in various Image Quality Assessment (IQA) parameters like: <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EME</i> , <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EC</i> , <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">AMBEn</i> and <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FSIM</i> . Herein, the multi-objective optimization problem is transformed into a single-objective optimization to find a unique solution for mammogram enhancement. An increase in these IQA metrics is indicative of better contrast, edge content, and conservation of brightness and other diagnostics information. Simulations of the proposed enhancement model are carried out on mammograms from the mini-MIAS database for benchmarking and validation of results.
Interpretable Rule-Based Fuzzy ELM and Domain Adaptation for Remote Sensing Image Classification

The size of the remote sensing (RS) data is increasing exponentially with modern technological evolution. Efficient mapping with these data using a supervised model demands ample labeled samples, and obtaining them is expensive and time-consuming. For that reason, domain adaptation (DA) methodologies that work with limited available knowledge in terms of labeled samples or trained models are highly successful. DA uses the model trained for a domain called “source” and aims to classify the data of a related domain called “target.” However, an insufficient amount of labeled samples in the target domain data set is still a bottleneck for DA models used for RS images. To address this, we have proposed an efficient DA classification model using an interpretable rule-based fuzzy extreme learning machine (IRF-ELM). These rules are derived using the maximum fuzzy membership value of features characterized by class-belonging fuzzification and two rule extraction matrices. A partially connected ELM architecture is then designed with these rules. The fuzzification process typically aligns the target domain's feature space to the source domain in the best possible ways to transfer knowledge between them. The proposed model's superiority to similar other methods is verified using different RS data sets in terms of various performance measurement indexes.
Efficient Parallel Secure Outsourcing of Modular Exponentiation to Cloud for IoT Applications

Modular exponentiation, an operation widely utilized in cryptographic protocols to transfer text and other forms of data, can also be applied to Internet-of-Things (IoT) devices with high security requirements. However, due to the high resource consumption of modular exponentiation, IoT devices can face the problem of resource insufficient. Fortunately, the secure outsourcing scheme offers a new solution for resource-constrained devices. In this article, we apply a parallel secure outsourcing scheme to provide the possibility for modular exponentiation operation, which is used in the IoT devices. After that, the task of modular exponentiation is decomposed and we introduce the scheme in more detail. In addition, based on this scheme, we designed an extension scheme for RSA, providing enhanced security for IoT devices. Finally, the analysis of experimental results based on 512-4096 b of data indicates the superiority in scalability and time consumption over the previous schemes.
PassTag: A Graphical-Textual Hybrid Fallback Authentication System

Designing a fallback authentication mechanism that is both memorable and strong is a challenging problem because of the trade-off between usability and security. Security questions are popularly used as a fallback authentication method for password recovery.However, they are prone to guessing attacks by users' acquaintances and may be hard to recall. To overcome these limitations, we present PassTag, a hybrid password scheme that takes advantage of both graphical and textual password authentication methods. PassTag combines a user-provided image and a short personalized text description of the image, imagetag, as an authentication secret.Furthermore, PassTag incorporates decoy images to make it difficult to guess the user-provided pictures. We conducted three user studies with 161 participants for up to three months to evaluate the performance of PassTag against security questions. The evaluation results demonstrate that PassTag is significantly stronger against close adversaries and highly memorable (92.6%-95.0%) after one,two, and three months, respectively. Our longitudinal study results show PassTag is a promising alternative for fallback authentication.
The Research for Recommendation System Based on Improved KNN Algorithm

In this paper, we have researched two basic tasks of recommendation system score prediction and Top-N recommendation. We have improved K nearest neighbor (IKNN) algorithm with compression and global effect. In experiments, the methods of Top-10 recommended mainly refer to the score on the basis of prediction. We recommended the items whose scores are the highest. The experimental results show that using IKNN algorithm recommendation system score predicted mean square difference (RMSE) has reduced significantly. Meanwhileit has a well recommendation precision improvement.
Aerial infrared target tracking in severe jamming using skeletal tracking technology

As various infrared countermeasures have been developed, aerial infrared target tracking is becoming increasingly challenging. In this paper, we develop an algorithm that can reliably track fighter in the case of severe infrared jamming. Since most parts of the target are obscured by a decoy, we propose an algorithm that can track a fighter by detecting its visible components rather than the whole target. Inspired by the human skeletal model of the Microsoft Kinect sensor, a skeletal model of a fighter comprising three components is designed. Then, a detection method based on Cascade R-CNN is used to detect those components. Situation assessment is made based on the detection result. As a result, the position of the target can be estimated through the detection result and skeletal model. We evaluated this proposed approach on an infrared image dataset against six state-of-the-art trackers. The experimental results demonstrate that our algorithm can reliably track a fighter in severe infrared jamming while running at a fast speed of 11.2 FPS.
What risk? I don't understand. An Empirical Study on Users' Understanding of the Terms Used in Security Texts

Users receive a multitude of security information in written articles, e.g., newspapers, security blogs, and training materials. However, prior research suggests that these delivery methods, including security awareness campaigns, mostly fail to increase people's knowledge about cyber threats. It seems that users find such information challenging to absorb and understand. Yet, to raise users' security awareness and understanding, it is essential to ensure the users comprehend the provided information so that they can apply the advice it contains in practice. We conducted a subjective study to measure the level of users' understanding of security texts. We find that 61% of the terms security experts used in their writings are hard for the public to understand, even for people with some IT backgrounds. We also observe that 88% of security texts have at least one such term. Moreover, we notice that existing dictionaries, including the online ones (e.g., Google Dictionary), cover no more than 35% of the terms found in security texts. To improve users' ability to understand security texts, we developed a framework to build a user-oriented security-centric dictionary from multiple sources. To evaluate the effectiveness of the dictionary, we developed a tool as a service to detect technical terms and explain their meanings to the user in pop-ups. The results of a subjective study to measure the tool's performance showed that it could increase users' ability to understand security articles by 30%.
Physicochemical Characterization of Mining Waste from the Betare-Oya Gold Area (East Cameroon) and an Adsorption Test by Sabga Smectite (North-West Cameroon)

This paper, firstly, characterizes the mining waste of the Betare-Oya gold area (East Cameroon). Surface waters are sampled and characterized physically (pH, electrical conductivity, turbidity, and suspended solids) and chemically by the determination of trace metals (Cu, Zn, Ni, Mn, Fe, Cr, As, and Pb). The concentrations of heavy metals in sediment samples are determined by X-ray Fluorescence Spectrometry (XRF) analysis. The statistical analysis tool is used to determine the existing correlation between the different physicochemical parameters. Secondly, an adsorption test for heavy metals determined is carried out on smectic clay of Sabga (North-West Cameroon) at ambient temperature. The results of physicochemical characterization of water samples reveal that waters of this gold mining zone are slightly acidic to neutral (6.3 pH < 7.1), mineralized (18.54 EC < 43 μs.cm-1), turbid (60 SS < 237.67 NTU), and polluted by suspended solids (50.5 < SS < 666.6 mg L-1).The determination of heavy metals in water samples shows that manganese, iron, arsenic, and lead concentrations are above the World Health Organization (WHO) limits. This indicates pollution. High concentrations of heavy metals as Cu, Ni, Mn, Fe, and Cr are observed in sediment samples. The correlation analysis indicates high correlations between turbidity and suspended solids; cadmium and lead in water samples; and copper and iron in sediment samples. The adsorption test reveals that the maximum percentage removal is 60, 89, 55, -89.74, and 100% for Cu, Zn, Mn, Fe, and Pb, respectively, after adding 0.5 g of Sabga smectic clay. This clay has a good potential adsorption of lead, zinc, manganese, and copper. However, the operating conditions favored desorption of iron.
Redundant Robot Control Using Multi Agent Reinforcement Learning

Robot control in task-space <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> needs the inverse kinematics and Jacobian matrix. They are not available for redundant robots, because there are so many degrees-of-freedom (DOF). Intelligent learning methods, such as neural networks (NN) and reinforcement learning (RL) can learn them. However, NN needs big data and RL is not suitable for multilink robots as the redundant robots. In this paper, we propose a full cooperative multi-agent reinforcement learning (MARL) to solve the above problems. Each joint of the robot is regarded as one agent. Although the dimension of the learning space is very large, the full cooperative MARL uses the kinematic learning and avoids the function approximators in large learning space. The experimental results show that our MARL is much more better compared with the classic methods such as, Jacobian-based methods and neural networks. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Task-space (or Cartesian space) is defined by the position and orientation of the end effector of a robot. Joint-space is defined by angular displacements of each joint of a robot.
POSTER: Content-Agnostic Identification of Cryptojacking in Network Traffic

In this paper, we propose a method that detects cryptojacking activities by analyzing content-agnostic network traffic flows. Our method first distinguishes crypto-mining activities by profiling the traffic with fast Fourier transform at each time window. It then generates the variation vectors between adjacent time windows and leverages a recurrent neural network to identify the cryptojacking patterns. Compared with the existing approaches, this method is privacy-preserving and can identify both browser-based and malware-based cryptojacking activities. Additionally, this method is easy to deploy. It can monitor all the devices within a network by accessing packet headers from the gateway router.
Cryptographic Algorithms to Secure Networks - A Technical Survey on Research Perspectives

Cryptology - the art of secret writing is a branch of science with the fundamental objective as “To enable people to communicate securely over a public insecure channel”. Cryptology guarantees secure communication. This paper is to make a survey paper on the evolution of cryptographic systems and the cryptanalysis that lead to the failure of each system over time from the classical cryptosystems till modern cryptosystem. The paper also lights on the new research scopes for budding scholars. The survey takes into account the discovery, the acceptance of the system by the society, the drawbacks thus lead to the discovery of the next cryptosystem. This paper outlines a good idea on the earlier cryptosystems and also gives a spark for new research fields that are yet to be touched and discovered.
WA VIS: A Web-Based Augmented Reality Text Data Visual Analysis Tool

When text visualization comes into the field of MAR (Mobile Augmented Reality), some problems go with it. Firstly, the natural object in the real scene holds abundant text information. Trying to recognize the natural object as a visual element rather than a marker, make text data intuitively understand. But MAR has been restricted by mobile computing speed and capability. Until now, the limitation makes text visualization difficult to meet the users' increasing needs in NFT (Natural Feature Tracking). Tools have been needed to recognize natural object to obtain considerable text information; even worse, being limited by a small screen of mobile, two-dimensional text visualization cannot intuitively and comprehensively present all kinds of text information, and interaction methods in text visualization are too insufficient to meet users' needs. The main technical contribution to the work is a natural feature recognition algorithm. The work is based on WebAR (Web-based AR) and an inverse BOVW (Bag of View Words) model. Transforming the feature points matching problem into a tree search problem. Compared with conventional BOVW, inverse BOVW drastically reduces the consumption of space and time; WA VIS shortens the time cost of others ranged from 8.5% to 13.7% with accuracy in 95%. And also, WebGL (Web Graphics Library) enriches interaction methods of text visualization in three-dimensional space with MAR. The test results verified that the proposed method can effectively improve the recognition speed and accuracy of the natural image in MAR for text visualization, enrich interaction in text visualization with MAR.
2D Human Pose Estimation from Monocular Images: A Survey

Human pose estimation is a computer vision problem that tries to estimate human body joints location and decide how they are connected to each other. It has been long studied and is still a frontier research field nowadays. In this paper, a comprehensive survey of human pose estimation from 2D monocular images is given, including from the classical representative works to the most recent deep-learning-based methods. The goal of the paper is to let the reader get a brief understanding on how human pose estimation methods work and see how these methods have developed, how they different from each other but in the same time share some common ideas. This paper inherits one of the most admitted way to categorize human pose estimation methods by dividing them into top-down and bottom-up methods, pipelines and some of the milestone works are introduced, comparison and discussion among ideas and methods are made. There are also new methods that jump out of the restriction of purely top- down or bottom-up, this paper includes them as well in later sections. Then this paper collects some datasets that is used frequently, and ways of error measurement are given. Finally, overall discussion is made including unsolved problems and currently challenging tasks.
Autonomous Airborne Multi-Rotor UAS Delivery System

Within current combat environments, there is a demand for rapid and extremely precise re-supply missions. Typical combat airdrops require long periods of planning and can produce a large signature in an operating environment which relies on stealth for various mission sets. Team Hermes, made up of four members from the West Point graduating class of 2019, offers a new re-supply method to answer this demand. The design will allow for the delivery of a quadcopter carrying 1.5 pounds of cargo within a 5-meter radius of an impact point on the ground. Each quadcopter is first transported via a wooden dispenser which is linked to the Air Force's Joint Precision Airdrop System (JPADS). JPADS is executed with a C-130. The dispenser payload is loaded into the back of the aircraft, and upon command, is dropped to route toward the impact point. JPADS descends and the dispenser releases the drones once it reaches a target altitude and proximity. The team worked through an extensive design process and developed a system capable of achieving the mission with autonomy. Through calculated testing procedures, Team Hermes achieved success and proved the capability to autonomously deliver the microlight payload to within 5 meters of a waypoint on the ground.
HOMER: Cryptography based Currency Detection System for Visually Impaired People

Currency is of great importance in everyday life and is the lifeline of every transaction. Even with the vast utilization of cards and other electronic payments, paper currency is still extensively used because of its convenience. There are around 10 different types of notes in Indian currency, and every banknote is different from other in dimensions. For instance, colour and pattern, size of the note, and so on. Most of the people practice the usage of Master Cards and Electronic payment and where the money is accepted for ordinary exchanges because of its accessibility. Visually impaired people undergo the difficulty of differentiating the currency papers. Currency Recognition System (CRS) may be effective for the blind and visually impaired people during the process of currency trade. In the proposed method a Currency Recognition System based on Accelerated- KAZE(AKAZE) algorithm is suggested, Accelerated-KAZE (AKAZE) algorithm is based around the concept of nonlinear diffusion filtering. It aims to provide an accurate and efficient alternative to Oriented FAST and rotated BRIEF(ORB). The proposed system is applied to Indian paper currencies. Originally, some of the preprocessing procedures are conducted on the input paper currency image. Then, colour mapping of the input image is done to distinguish colour components from depth. After this, the ROI (Region of Interest) is the essence of the input image. The AKAZE Algorithm is used to find key points and descriptors of the input image. Finally. the detection of currency is done by using brute force matcher technique, it matches descriptors & keypoints of the input image with every descriptor stored in dataset using Hamming Distance Calculation. In a real-world situation, the proposed system playa a vital role in identifying currency images with greater accuracy of 90 % which is achieved and determined. This proposed system aims to make the life of visually impaired people easier.
FSA: A Fine-Grained Systolic Accelerator for Sparse CNNs

Sparsity, as an intrinsic property of convolutional neural networks (CNNs), has been widely employed for hardware acceleration, and many customized accelerators tailored for sparse weights or activations have been proposed in these years. However, the irregular sparse patterns introduced by both weights and activations are much more challenging for efficient computation. For example, due to the issues of access contention, workload imbalance, and tile fragmentation, the state-of-the-art sparse accelerator SCNN fails to fully leverage the benefits of sparsity, leading to nonoptimal results for both speedup and energy efficiency. In this article, we propose an efficient sparse CNN accelerator for both weights and activations, namely finegrained systolic accelerator (FSA), which jointly optimizes both hardware dataflow and software partitioning and scheduling strategy. Specifically, to deal with the access contentions problem, we present a fine-grained systolic dataflow, in which the activations move rhythmically along the horizontal processing element array while the weights are fed into the array in a fine-grained order. We then propose a hybrid network partitioning strategy that sets different partitioning strategies for different layers to balance the workload and alleviate the fragmentation problem caused by both sparse weights and activations. Finally, we present a scheduling search strategy to find the optimized schedules for neural networks, which can further improve energy efficiency. Extensive evaluations show that the proposed FSA consistently outperforms SCNN over AlexNet, VGGNet, GoogLeNet, and ResNet with an average speedup of 1.74× and up to 13.86× energy efficiency.
Lightweight and DPA-Resistant Post-Quantum Cryptoprocessor based on Binary Ring-LWE

With the exponential growth in the internet of things (IoT) applications such as smart city and e-health, more embedded devices and smart nodes are connected to the network. In order to provide security for such resource-constrained devices, different cryptographic schemes such as public key encryption (PKE) are required. However, considering the high complexity and vulnerability of classic PKE schemes against quantum attacks, it is necessary to consider other possible options. Recently, lattice-based cryptography and especially learning with errors (LWE) have gained high attention due to resistance against quantum attacks and relatively low-complexity operations. During the past decade, some of the variants of Ring-LWE cryptosystem have been implemented efficiently on both hardware and software and are shown to be practical on different resource-constrained and embedded devices in IoT. Besides efficiency, every practical implementation of any cryptosystem must also be analyzed against side-channel attacks. In this paper, we propose masking countermeasure against differential power analysis (DPA) attack on lightweight implementations of binary Ring-LWE on hardware. Our experimental results indicate that proposed DPA-secure implementations have lower than 14% performance overhead and are still practical on resource-constrained devices. Moreover, the FPGA implementation results indicate more than 99% and 81% improvement for speed and efficiency, respectively compared to previous work.