Miracle-dz/UniFAR-FTUs huggingface dataset:
Brief Study on Applications of Phasor Measurement Units in Smartgrid Technology

In this review paper mostly discusses Smart grid technologies nowadays' phasor measurement units (PMUs) uses and its role in the modern power grid increases. Uses of the PMUs in a smart grid have several applications in the wide-area monitoring system to protect the power flow from long transmitting system in real-time-monitoring. In this filed, application of synchrophasor act as bidirectional functions, such as both controlling and communication technology, recognize in a specific time horizon by using GPS. Since the PMUs can handle both monitoring and communication technology from generating station to end-users and vice versa. Such devices are costly since it is practically impossible to mount PMUs on each bus, and it is preferred to use in critical buses of the network. This paper explains the advantages of PMUs inclusion in the power system network. It addresses different optimum positioning methodology used to assess the required number of PMUs to achieve maximum observability of the modern power grid and give an idea about the future scope.
Highly Z-Selective Synthesis of Disubstituted α,β-Unsaturated Cyanides and Amides Using 10-P-5 Wittig Type Reagents

Phosphoranes (10-P-5) bearing cyanomethyl, acetamide, and N,N-dimethylacetamide groups were examined for Wittig type reactions. All three reacted to give the corresponding olefins. The reaction of the cyanomethyl reagent with aldehydes gave α,β-unsaturated cyanides with high Z-selectivity in the case of aliphatic aldehydes (Z:E=94:6 to 99:1). On the other hand, the reactions of the two amide reagents with aldehydes yielded α,β-unsaturated amides with high Z-selectivity for both aromatic and aliphatic aldehydes (Z:E=99:1 to >99:<1).
Intelligent Load Balancing Techniques in Software Defined Networks: A Systematic Review

In the current era of technology, the number of users connecting to the internet has increased rapidly. If the situation is like Pandemic (COVID-19), the demand of the data services is much higher than the expectation. The major problem of this situation is load management. SDN plays an important role in this regard. Being a programmable network, it is widely admirable for its network intelligence. In this paper, we discuss SDN architecture, its components, various applications and major challenges. The unique architecture differentiates SDN from traditional network. It makes it possible to take routing decision without depending upon the underlying architecture. We also present a systematic review on the study of the different load balancing approaches, existing solutions and various load balancing techniques in SDN. With the network intelligence, SDN is able to take the load balancing decision. We categorize the load balancing techniques based on controller, server, communication path selection and cloud-based environment. Finally, we present different research challenges, existing solutions under each technique.
Interference Mitigation in HetNets to Improve the QoS Using Q-Learning

Small cells (SCs) based ultra-dense heterogeneous networks (HetNets) are one of the promising solutions for increased coverage and capacity in 5G cellular networks. However, in multi-tiered architecture, co-tier and cross-tier interferences are both performance-limiting factors. Efficient resource allocation techniques can handle interferences effectively, however, their complexity linearly increases with the density of the HetNets resulting from dynamic and unplanned deployment of SCs. Therefore, HetNets can be implemented only through an algorithm that is self-organizing and adaptive to the dynamic conditions. In this research article, a Q-Learning (QL) based adaptive resource allocation scheme is proposed and evaluated for SC-based ultra-dense HetNets. This QL scheme allocates optimal power to the small cell base station (SBS) to meet the minimum required capacity of macrocell user equipment (MUEs) and the small cell user equipment (SUEs) to provide quality of service (QoS). The proposed QL scheme not only maintains the minimum required capacities of the MUEs and SUEs but has also shown a significant improvement in the capacities of MUEs and SUEs in high interference scenarios as compared to the prior works. In the high co-tier and cross-tier interference scenario, where the state of the art schemes fail to maintain the minimum required capacity of the MUE, the proposed scheme provides a minimum MUE capacity of 2 b/s/Hz, which is twice the minimum required QoS threshold. In a similar way, the proposed solution guarantees QoS up to 16 SCs which are 37.5% more SC than the previously reported works in high interference scenario while maintaining a minimum SUE capacity of 1.5 b/s/Hz, which is 33% higher than the minimum required QoS threshold. By simultaneously mitigating co-tier and cross-tier interferences in ultra-dense HetNets, the proposed solution not only improved the minimum capacities of MUEs and SUEs but also sets a new benchmark for minimum QoS threshold.
PRADroid: Privacy Risk Assessment for Android Applications

Android has become the most popular mobile operating system all over the world. Due to its openness, users can install applications freely. At the same time, users are increasingly storing personal privacy information in their mobile phones, which has led to Android becoming the main target of malicious applications, and privacy leaks have occurred from time to time. Although the Android operating system provides the permissions to restrict the ability of applications to access sensitive resources, users do not pay much attention to the granting of permissions, which makes malicious applications available. Therefore, it is of great significance to make risk assessments for applications before installation. In this paper we propose a privacy risk assessment framework called PRADroid for Android applications. Through likelihood assessment based on permissions and severity assessment based on information flow analysis, a risk matrix is finally generated to score the application privacy risk. We evaluate PRADroid on 2000 Android applications, and the experimental results show that PRADroid can score reasonably and effectively.
Sample Contribution Pattern Based Big Data Mining Optimization Algorithms

As is the case in many big data mining scenarios with a large scale of samples, the heavy computation cost hinders the application of machine learning, which has to iteratively compute by passing over the whole dataset without considering the roles of different samples in training computation. However, we argue that most of the samples dominating computation resources contribute little to the gradient-based model update, particularly when the model is close to convergence. We define this observation as the Sample Contribution Pattern (SCP) in machine learning. This paper proposes two approaches to exploit SCP by detecting gradient characteristics and triggering the reuse of outdated gradients. In particular, this paper reports research results in (1) the definition and description of SCP to reveal an intrinsic gradient contribution pattern of different samples; (2) a novel SCP-based optimizing algorithm (SCPOA) that outperforms alternative tested algorithms in terms of computation overhead; (3) a variant of SCPOA that incorporates discarding-recovering mechanisms to carefully tradeoff between model accuracy and computation cost; (4) the implementation and evaluation of two algorithms based on popular distributed big data mining platforms running typical sample-sets; (5) intuitive convergence proof of the algorithms. Our experimental results illustrate that the proposed approaches can significantly reduce the computation cost with competitive accuracy.
Artificial Bee Colony Optimization Based Non-Intrusive Appliances Load Monitoring Technique in a Smart Home

Recent advances of energy management system in a smart home can lead to load monitoring of electrical appliances for energy saving and reduction of electricity bill. Thus, smart metering technology is getting widely implemented in several electricity distribution networks. Most of the existing research are concentrated towards individual power consumption of different household loads using some machine learning algorithm, which can increase computational burden of the processor. In this article, an improved method for estimation of the individual appliance current is carried out from a group of connected consumer electronics loads. The proposed method consists of two steps, first is to collect and store the current data of individual appliances with varying load for on line application. The second step is to estimate the individual load current using the stored data. In the proposed method, a search-based optimization, i.e., Artificial Bee Colony (ABC) algorithm is used for the estimation of individual electrical load. Suitable simulations and experimental studies are carried out on a practical household system to demonstrate the suitability of the proposed methodology.
Automatic Test Case Generation for Vulnerability Analysis of Galois Field Arithmetic Circuits

The research work proposes a framework for checking the correctness of Galois field arithmetic operations in digital circuits. The authors propose to automatically generate the test cases from the user input, avoiding reliance upon predesigned test cases, comprising Galois field-width and respective choice of irreducible polynomial. We do this through the use of polynomial arithmetic to verify the circuits. To the best of author's knowledge, though extensive work has been carried out in optimising the performance of arithmetic operations in Galois field, there exist no testbench to evaluate the efficacy of hardware circuits incorporating this concept. By automating the process of generating test cases, the work can be scaled to test circuits of arbitrarily large field widths, thus providing a flexible architecture that guarantees correctness of the underlying design under test. We present simulation results for Galois field polynomials of width $\mathbf{GF}(2^{2})$), $\mathbf{GF}(2^{4})$ and $\mathbf{GF}(2^{8})$. This work can be applied to test and prevent intentional tampering of data bit stream and safeguarding it against malicious activities, especially in applications such as cryptography that heavily relies on Galois field arithmetic.
An Accurate Time-Based MPPT Circuit With Two-Period Tracking Algorithm and Convergence Range Averaging Technique for IoT Applications

This article proposes a highly efficient Time-Based Maximum-Power-Point-Tracking (TB-MPPT) integrated-circuit. Conventional TB-MPPT circuit shows unstable status (oscillation) near the Maximum Power Point (MPP), which causes the degradation of the overall power tracking efficiency. To overcome this critical issue, the proposed circuit separates the tracking operation into two periods, and it utilizes convergence range averaging technique with an adaptive perturbation step according to each control signal to accurately extract the current power level without oscillation problem. This work is fabricated in 180 nm CMOS process to demonstrate advantages of the proposed scheme. With 0.47V 12mW Photovoltaic (PV) cell, the measurement results show 94.2% of MPPT efficiency and 91.6% of power conversion efficiency (PCE). It occupies silicon chip area of $1.69~\text {mm}^{2}$ .
Investigating genre distinctions through discourse distance and discourse network

Abstract The notion of genre has been widely explored using quantitative methods from both lexical and syntactical perspectives. However, discourse structure has rarely been used to examine genre. Mostly concerned with the interrelation of discourse units, discourse structure can play a crucial role in genre analysis. Nevertheless, few quantitative studies have explored genre distinctions from a discourse structure perspective. Here, we use two English discourse corpora (RST-DT and GUM) to investigate discourse structure from a novel viewpoint. The RST-DT is divided into four small subcorpora distinguished according to genre, and another corpus (GUM) containing seven genres are used for cross-verification. An RST (rhetorical structure theory) tree is converted into dependency representations by taking information from RST annotations to calculate the discourse distance through a process similar to that used to calculate syntactic dependency distance. Moreover, the data on dependency representations deriving from the two corpora are readily convertible into network data. Afterwards, we examine different genres in the two corpora by combining discourse distance and discourse network. The two methods are mutually complementary in comprehensively revealing the distinctiveness of various genres. Accordingly, we propose an effective quantitative method for assessing genre differences using discourse distance and discourse network. This quantitative study can help us better understand the nature of genre.
Feedback-based Channel Gain Complement and Cluster-based Quantization for Physical Layer Key Generation

Physical layer key generation is an important handshaking step for establishing secure wireless communication in wireless networks. Existing methods based on fine-grained Channel State Information (CSI) have seen significant limitations. Reciprocity, the theoretical basis of physical layer key generation, is sensitive to factors such as noise, interference and hardware level variations. Feedback-based Channel Gain Complement is designed to conquer the imperfect reciprocity between two communication parties in actual environments. In addition, we have found that the adjacent subcarriers are strongly correlated. Quantizing all 30 subcarriers without discrimination will reduce the randomness and strength of the secret key while discarding some carriers will lose important information. We propose a cluster-based quantization method, which balances between the generation speed and consistency in initial key bits. Evaluation results in static and mobile scenarios have shown that under the sampling rate of 5 packet/sec, Bit Generation Rate can reach 20∼ 26 bps, and the generated key sequence can pass the NIST standard randomness test.
Defense Against on-Chip Trojans Enabling Traffic Analysis Attacks

Interconnection networks for multi/many-core processors or server systems are the backbone of the system as they enable data communication among the processing cores, caches, memory and other peripherals. Given the criticality of the interconnects, the system can be severely subverted if the interconnection is compromised. The threat of Hardware Trojans (HTs) penetrating complex hardware systems such as multi/many-core processors are increasing due to the increasing presence of third party players in a System-on-chip (SoC) design. Even by deploying naïve HTs, an adversary can exploit the Network-on-Chip (NoC) backbone of the processor and get access to communication patterns in the system. This information, if leaked to an attacker, can reveal important insights regarding the application suites running on the system; thereby compromising the user privacy and paving the way for more severe attacks on the entire system. In this paper, we demonstrate that one or more HTs embedded in the NoC of a multi/many-core processor is capable of leaking sensitive information regarding traffic patterns to an external malicious attacker; who, in turn, can analyze the HT payload data with machine learning techniques to infer the applications running on the processor. Furthermore, to protect against such attacks, we propose a Simulated Annealing-based randomized routing algorithm in the system. The proposed defense is capable of obfuscating the attacker's data processing capabilities to infer the user profiles successfully. Our experimental results demonstrate that the proposed randomized routing algorithm could reduce the accuracy of identifying user profiles by the attacker from >98% to <15% in multi/many-core systems.
Attention Mechanism and Feature Correction Fusion Model for Facial Expression Recognition

The automatic recognition of facial expressions is challenging for many reasons, and there is still scope for research in the control of sample quality, the construction of effective feature extraction model and multi feature fusion. This study proposes a facial expression recognition method that combines transfer learning, attention mechanism, and feature fusion technology. In this method, various features are extracted from several pre-trained deep learning networks; the recognition weight of each sample is then learned via a quality attention network. This weight can be used to distinguish the quality of samples and suppress the contribution of low-quality samples in the training process. Finally, for a variety of features, this study proposes a fusion method based on feature clustering and correction. The experimental results indicate that the proposed method can significantly improve the accuracy of expression recognition and achieve state-of-the-art recognition accuracy for RAF-DB and FER2013+ datasets.
Malware Detection & Classification using Machine Learning

In today’s internet world, malware is still the most harmful threat to the internet users. The new malware developed are distinct from conventional one, more dynamic in design and usually inherits the properties from two or more malware types, these type of malware are called polymorphic. Polymorphic malware is a form of malware which constantly modifies its recognisable features to fool detection using traditional signature-based models. Behavior-based identification of ransomware tests not just the file’s identity, but also the operation it intends to take after some time span or at specific time. Now everyone wanted to get the behavioural pattern that can be derived from static analysis or dynamic analysis, with these pattern various machine learning models can be used to predict whether it is a malware or not, or identify its family of malware. In this work, behavior-based detection methods are address and how these various machine learning techniques are used to develop behavior-based malware detection and classification methods.
Performance Analysis of Chronic Kidney Disease through Machine Learning Approaches

Data mining and machine learning play a vital role in health care and also medical information and detection, Now a day machine learning techniques use awareness of some major health risks such as diabetic prediction, brain tumor detection, covid 19 detections, and many more. The kidney is the most important organ of our body and if it has any problem then the impact is more dangerous to our body. Chronic kidney disease (CKD), otherwise referred to as renal disease. CKD requires disorders that damage and reduce the capacity of our kidneys to keep us healthy. So, it is required to be concerned about kidney disease to our very primary stage. We take a few attributes to measure our analysis about chronic kidney disease and this attribute is one of the major occurrences of chronic kidney disease. Therefore 8 machine learning classifier are used to measure analysis using weka tools namely: Logistic Regression(LG), Naive Bayes(NB), Multilayer Perceptron(MLP), Stochastic Gradient Descent(SGD), Adaptive Boosting(Adaboost), Bagging, Decision Tree(DT), Random Forest(RF) classifier are used. We feature extraction of all attributes using principal component analysis(PCA). We gain the highest accuracy from the Random Forest(RF) and it is 99 % and ROC(receiver operating characteristic) curve value is also highest from other algorithms.
Modular Framework and Instances of Pixel-Based Video Quality Models for UHD-1/4K

The popularity of video on-demand streaming services increased tremendously over the last years. Most services use http-based adaptive video streaming methods. Today’s movies and TV shows are typically recorded in UHD-1/4K and streamed using settings attuned to the end-device and current network conditions. Video quality prediction models can be used to perform an extensive analysis of video codec settings to ensure high quality. Hence, we present a framework for the development of pixel-based video quality models. We instantiate four different model variants (hyfr, hyfu, fume and nofu) for short-term video quality estimation targeting various use cases. Our models range from a no-reference video quality model to a full-reference model including hybrid model extensions that incorporate client accessible meta-data. All models share a similar architecture and the same core features, depending on their mode of operation. Besides traditional mean opinion score prediction, we tackle quality estimation as a classification and multi-output regression problem. Our performance evaluation is based on the publicly available AVT-VQDB-UHD-1 dataset. We further evaluate the introduced center-cropping approach to speed up calculations. Our analysis shows that our hybrid full-reference model (hyfr) performs best, e.g. 0.92 PCC for MOS prediction, followed by the hybrid no-reference model (hyfu), full-reference model (fume) and no-reference model (nofu). We further show that our models outperform popular state-of-the-art models. The introduced features and machine-learning pipeline are publicly available for use by the community for further research and extension.
Epigenetic Markers Associated with Schistosomiasis

Summary It is important to consider the use of the epigenome as source of complementary data for genome knowledge, which is suitable for the diagnosis of schistosomiasis. Usually, a laboratory diagnosis of schistosomiasis is performed by means of 1. Egg detection in the stool or urine by microscopy remains with limited sensitivity; 2. Immunological screening, in which positivity persists after treatment, and 3. Molecular appraisals prevail over the disadvantages of the currently used methods. In this sense, molecular methodologies are being developed based on epigenetic biomarkers, aiming to improve the diagnosis of the disease and clinical treatment as early as possible to prevent the occurrence of serious liver damage.
The interfacial factor in the heat‐induced conversion of chlorophyll to pheophytin in green leaves

The rates of the conversion of chlorophyll to pheophytin in the green leaves of common vegetables and other plants at different temperatures in buffered electrolyte systems were studied. Kinetic measurements showed that the conversion only occurred above a threshold temperature of 50–60 °C and was most probably a pseudo-first order reaction. Rates were found to be proportional to √[H+]. The dependence of the rate of conversion upon species, temperature and electrolyte type can be explained on the basis of a heat-induced metamorphosis of the chloroplasts to a smectic mesophase which occludes the chlorophyll. The rate of the reaction then depends on the transfer of hydronium ions across the interface and can be largely controlled by modifying the interfacial potential.
Redetermination of 1,13‐diphenyl‐2,4,6,8,10,12‐hexaoxatridecane at 161 K

The title compound, C19H24O6, crystallizes with two half-molecules per asymmetric unit; each molecule has a crystallographic twofold axis passing through the central CH2 group. The two mol­ecules have different orientations of the terminal benzyl groups. The C—O bond lengths in the central section of each polyoxymethyl­ene helix are almost constant. The average C—O bond length, corrected for librational motion, is 1.421 A. The mol­ecules are connected into layers by inter­molecular C—H⋯O and C—H⋯π(phen­yl) inter­actions. The structure was previously reported by Noe, Miculka & Bats [(1994), Angew. Chem. Int. Ed. Engl. 33, 1476–1478].