Reviewerly/exHarmony huggingface dataset:
Face Recognition In Low Lighting Conditions Using Fisherface Method And CLAHE Techniques

Face recognition is a biometric identification system that uses facial images as its input which is usually used in the field of human identity recognition. The accuracy of face recognition system still relies on good image quality, especially in image lighting conditions. We proposed a face recognition system that deals with facial images in low light conditions, by adding image enhancement with contrast adaptive histogram equalization (CLAHE) contrast techniques to create good quality lighting images. From the experiment conducted, we have shown that our approach improved face recognition system performed well at the brightness level of -80 with the accuracy of 76.92%.
Designing a Model to Handle Imbalance Data Classification Using SMOTE and Optimized Classifier

Classification is a supervised learning process that is used to predict target class for an input variable. In an imbalanced dataset, the total count of examples of minority class is comparatively less than the total count of majority (negative) class. Various problems accompany during classification of imbalanced data. To eliminate this problem, there is a need to balance the dataset as well to achieve higher accuracy by optimizing the parameters of a classification model. The proposed system attempts to sample the imbalance dataset into a balanced dataset by using SLS (Safe-Level Synthetic Minority Oversampling Technique) and ADASYN (Adaptive Synthetic Sampling Approach) SMOTE techniques. On this balanced data, for classification, KNN (K-Nearest Neighbors) and SVM (Support Vector Machine) classifiers are used. The system proposes to improve the performance of the classifiers using metaheuristic Cuckoo Search optimization techniques for determining hyperparameters.
Implementing Ciphertext Policy Encryption in Cloud Platform for Patients’ Health Information Based on the Attributes

The cloud computing platform has to improvise the healthcare authorities along with organization. In health industry has to develop the patients to make, organize and give out their health-related data with different users by the personal health record (PHR) administration and in addition to health insurance companies. The proposed method explains the work on outlining and implementing patient-driven, patient health information in a cloud platform in light of open-source Indivo X framework. The PHR administration level is to be facilitated about outsourcing patients’ PHR information to cloud servers with a specific end goal to upgrade its interoperability. The security saving PHR structure utilizes attribute-based encryption (ABE) and supplies the system with the goal that patients be competent to scramble their PHRs and store them on cloud servers which is half believed to such an extent that servers do not have confirmation to basic PHR data and the patients keep up full power over entrée to their PHR records, by doling out access to some approved data users, while individual users can have entry to various parts of their PHR.
A Multi-channel Convolutional Neural Network for ICD Coding

The processing of textual medical data is difficult because they are structurally free, diverse in style, and have subjective factors. Medical records are primarily designed for archiving patient clinical information and administrative healthcare tasks. They are encoded following the International Classification of Diseases (ICD) standard by medical informaticians. Such manual encoding task is error-prone because of a load of abbreviations, miswriting, and medical terms in medical records. In this paper, a multi-channel convolutional attentional network is presented to automatically predict ICD codes from clinic records. The multi-channel CNN generates the multiple representations of medical records and meets the needs of a huge number of labels. An attention mechanism is designed to address the correlation between the multiple medical records representations and corresponding codes. The results show that F1 score increases by 6.9% over the state of art. This coding task eases the burden of hospital manual coding task and improve the secondary use for clinical informatics.
Unsupervised Learning of Visual Representations by Solving Shuffled Long Video-Frames Temporal Order Prediction

There is lots of hidden information behind the sequential data and their sequences. We proposed a model for learning visual representation by solving order prediction task. We concatenated the frame pairs, instead of concatenating the feature pairs. This concatenation makes it possible to apply a 3D-CNN to extract features from the frame pairs. Also, we proposed a new grouping, which have achieved 80 percent accuracy on average. We have modified the shuffled video clips order prediction task to the shuffled frame order prediction, by selecting a frame from each clip, by random. Then this task was solved by applying our model.
Generalized figure of merit for qubit readout

Many promising approaches to fault-tolerant quantum computation require repeated quantum nondemolition (QND) readout of binary observables such as quantum bits (qubits). A commonly used figure of merit for readout performance is the error rate for binary assignment in a single repetition. However, it is known that this figure of merit is insufficient. Indeed, real-world readout outcomes are typically analog instead of binary. Binary assignment therefore discards important information on the level of confidence in the analog outcomes. Here, a generalized figure of merit that fully captures the information contained in the analog readout outcomes is suggested. This figure of merit is the Chernoff information associated with the statistics of the analog readout outcomes in one repetition. Unlike the single-repetition error rate, the Chernoff information uniquely determines the asymptotic cumulative error rate for arbitrary readout noise. As a result, non-Gaussian readout noise common in experiments can be described by effective Gaussian noise with the same Chernoff information. Importantly, it is shown that such a universal description persists for the small number of repetitions and non-QND imperfections relevant to real experiments. Finally, the Chernoff information is used to rigorously quantify the amount of information discarded by analog-to-binary conversion. These results provide a unified framework for qubit readout and should facilitate optimization and engineering of near-term quantum devices across all platforms.
Audio Watermarking with Multiple Images as Watermarks

Nowadays, the security of information has become a burning question. Digital watermarking can be very efficient in providing security of a signal being sent. In this paper, a digital watermarking technique for ensuring security to an audio signal is presented. The proposed technique incorporates the use of discrete wavelet transform (DWT) and singular value decomposition (SVD). In the proposed technique, multi-level DWT along with the use of multiple images as watermarks ensures an increased level of security which will be evident from the experimental part of this paper. The proposed technique is developed by keeping in mind the energy density of both the host and the watermark signals and that is why good results are achieved. The proposed technique is robust as experimentally the proposed technique offers PSNR (Peak Signal to Noise Ratio) of 187.9432 dB and normalized correlation (NC) over 0.99.
Target recognition for satellite communication by employing higher‐order statistics

Summary The target recognition for satellite communication (satcom) is generally regarded as the cutting edge of electronic countermeasure research. This work is dedicated to the investigation on the theory and experiment of satellite communication target recognition on the basis of systematical analysis of satcom signal emission, propagation, and reception. The authors elaborate on the fingerprint analysis, feature extraction, and identification of satcom emitters by utilizing the nonlinearities of high‐power microwave vacuum amplifier (HPA). The mechanism of the external subtle features of satcom signal will be also discussed in detail. To acquire the qualified features that precisely represent the individual emitter, higher‐order statistics technique is introduced to implement the feature extraction, and the supervised probabilistic neural network classifier is established to execute the recognition of testing satcom samples. In testing phase, there are a total of 4000 sampling signals with BPSK modulation and variable carrier to noise ratio (CNR) originated by eight types of satcom transmitters setting for the experiment to verify the authors' viewpoints. Thanks to the fine training data set and subsequent well‐extracted features, the PNN classifier had not fail us and finally achieved satisfactory accuracy of more than 94% at CNR level of 10 dB. Those expected results will help to enhance the ability of battlefield surveillance and situational awareness that is of paramount importance in academic research and military application.
Instruction Sequence Identification and Disassembly Using Power Supply Side-Channel Analysis

Embedded systems are prone to leak information via side-channels associated with their physical internal activity, such as power consumption, timing, and faults. Leaked information can be analyzed to extract sensitive data and devices should be assessed for such vulnerabilities. Side-channel power-supply leakage from embedded devices can also provide information regarding instruction-level activity for control code executed on these devices. Methods proposed to disassemble instruction-level activity via side-channel leakage have not addressed issues related to pipelined multi-clock-cycle architectures, nor have proven robustness or reliability. The problem of detecting malicious code modifications while not obstructing the sequence of instructions being executed needs to be addressed. In this article, instruction sequences being executed on a general-purpose pipelined computing platform are identified and instructions that make up these sequences are classified based on hardware utilization. Individual instruction classification results using a fine-grained classifier is also presented. A dynamic programming algorithm was applied to detect the boundaries of instructions in a sequence with a 100 percent accuracy. A unique aspect of this technique is the use of multiple power supply pin measurements to increase precision and accuracy. To demonstrate the robustness of this technique, power leakage data from ten target FPGAs programmed with a prototype of the pipelined architecture was analyzed and classification accuracies averaging 99 percent were achieved with instructions labeled based on hardware utilization. Individual instruction classification accuracies above 90 percent were achieved using a fine-grained classifier. Classification accuracies were also verified when a target FPGA was subjected to different controlled temperatures. The classification accuracies on discrete (ASIC) pipelined-architecture microcontrollers was 97 percent.
Syntactic and semantic information extraction from NPP procedures utilizing natural language processing integrated with rules

Procedures play a key role in ensuring safe operation at nuclear power plants (NPPs). Development and maintenance of a large number of procedures reflecting the best knowledge available in all relevant areas is a complex job. This paper introduces a newly developed methodology and the implemented software, called iExtractor, for the extraction of syntactic and semantic information from NPP procedures utilizing natural language processing (NLP)-based technologies. The steps of the iExtractor integrated with sets of rules and an ontology for NPPs are described in detail with examples. Case study results of the iExtractor applied to selected procedures of a U.S. commercial NPP are also introduced. It is shown that the iExtractor can provide overall comprehension of the analyzed procedures and indicate parts of procedures that need improvement. The rich information extracted from procedures could be further utilized as a basis for their enhanced management.
Implementation of Automatic First Arrival Picking On P-Wave Seismic Signal Using Logistic Regression Method

The development of automation technology is currently very fast and helps human work, one of them is used by the Badan Meteorologi, Klimatologi dan Geotisika (BMKG) to detect earthquakes. Automatic First Arrival Picking is a system that can detect primary waves at the first arrival or P-Wave that occurs in an earthquake seismic signal. This study aims to create an Automatic First Arrival Picking system and test the performance of the Logistic Regression method to classify this Automatic First Arrival Picking system in detecting primary waves at the first arrival or P-Wave. In this Automatic First Arrival Picking study, data samples taken on the IRIS (Incorporated Research Institutions for Seismology) website with 100 earthquake events taken from the three closest stations with magnitude 5-8 SR. Data samples will be processed using four Feature Extraction: Recursive STA/LTA, Classic STA/LTA, Carl STA/ LTA and Delayed STA/LTA.Furthermore, the results of Feature Extraction that will be used as a dataset will be classified by the Logistic Regression method. From the test results of the Automatic First Arrival Picking system it is known that several parameters can produce the best system performance, that is 50 seconds for time windowing, 55%: 45% for a ratio training and testing, and a value of 100 for Inverse of Regularization. The results of the research conducted using the Logistic Regression method to detect P-waves in the Automatic First Arrival Picking system with a calibration scheme that carried out that obtained accuracy of 83%, Precision by 75%, Recall of 64% and F1-Score of 67%.
From Knowledge Transmission to Knowledge Construction: A Step towards Human-Like Active Learning

Machines usually employ a guess-and-check strategy to analyze data: they take the data, make a guess, check the answer, adjust it with regard to the correct one if necessary, and try again on a new data set. An active learning environment guarantees better performance while training on less, but carefully chosen, data which reduces the costs of both annotating and analyzing large data sets. This issue becomes even more critical for deep learning applications. Human-like active learning integrates a variety of strategies and instructional models chosen by a teacher to contribute to learners’ knowledge, while machine active learning strategies lack versatile tools for shifting the focus of instruction away from knowledge transmission to learners’ knowledge construction. We approach this gap by considering an active learning environment in an educational setting. We propose a new strategy that measures the information capacity of data using the information function from the four-parameter logistic item response theory (4PL IRT). We compared the proposed strategy with the most common active learning strategies—Least Confidence and Entropy Sampling. The results of computational experiments showed that the Information Capacity strategy shares similar behavior but provides a more flexible framework for building transparent knowledge models in deep learning.
A Case Study Using CBR-Insight to Visualize Source Code Quality

Creating and maintaining high-quality source code is especially important for critical systems such as those made for NASA and the DoD, and for software product lines where long-lived, reusable modules are intended to be shared by multiple systems. CBR-Insight is an automated code assessment tool developed for the US Air Force, and released as open source on GitHub, to provide an objective and understandable measure of software quality. CBRI-Insight supports the ability of technical and non-technical decision makers to verify that a project's software implementation follows through on promises around developing and sustaining reliable and maintainable software while managing technical debt. The primary contributions of this work include advancing the state of the art in assessing software code quality, presenting a method to communicate code quality to decision makers, and examining a case study where these methods are applied to develop software in the aerospace domain.
Efficient Detection of Byzantine Attacks in Federated Learning Using Last Layer Biases

Federated learning (FL) is an alternative to centralized machine learning (ML) that builds a model across multiple decentralized edge devices (a.k.a. workers) that own the training data. This has two advantages: i) the data used for training are not uploaded to the server and ii) the server can distribute the training load across the workers instead of using its own resources. However, due to the distributed nature of FL, the server has no control over the behaviors of the workers. Malicious workers can, therefore, orchestrate different kinds of attacks against FL. Byzantine attacks are amongst the most common and straightforward attacks on FL. They try to prevent FL models from converging by uploading random updates. Several techniques have been proposed to detect such kind of attacks, but they usually entail a high cost for the server. This hampers one of the main benefits of FL, which is load reduction. In this work, we propose a highly efficient approach to detect workers that try to perform Byzantine FL attacks. In particular, we analyze the last layer biases of deep learning (DL) models on the server side to detect malicious workers. We evaluate our approach with two deep learning models on the MNIST and CIFAR-10 data sets. Experimental results show that our approach significantly outperforms current methods in runtime while providing similar attack detection accuracy.
Analysis of Audio Steganography combined with Cryptography for RC4 and 3DES Encryption

As most of the real time applications make use of digital data, security of the data resources become more important and essential. Different aspects to be considered while dealing with such digital data are preserving authenticity and make sure that the data is not modified in transit and no one else other than intended user can read this data. To fulfil these requirements, many techniques are available to protect this data. Cryptography is to jumble the original information so that it cannot be viewed by any unauthorized users and steganography is used to hide the original information. Depending on the cover medium used, steganography has many types such as image, audio, and video. Audio steganography deals with the noise, echo, samples etc present in the audio. LSB, Echo hiding, Phase coding, Tone insertion are some of the types of audio steganography techniques. These techniques have their respective pros and cons. The main intention of the presented technique is to re-conciliate cryptography with steganography and deploys a better protection.
Kernel-Level Rootkits Features to Train Learning Models Against Namespace Attacks on Containers

The container-based cloud computing service is increasingly adopted by many service providers for its efficiency and flexibility. Containers isolated by namespaces share OS kernel. When the kernel-level rootkits exploit vulnerabilities existing in kernel, the namespace can be invalidated leading to critical security incidents. Even though many traditional approaches have been made to detect kernel-level rootkits, it is hard to detect new attacks conducted in the new environment such as container-based cloud computing system. In this paper, we show some possible attack scenarios by kernel-level rootkits exploiting kernel namespaces and suggest key features that can be used to train machine learning and neural network models.
Software maintainability prediction of open source datasets using least squares support vector machines

(2020). Software maintainability prediction of open source datasets using least squares support vector machines. Journal of Statistics and Management Systems: Vol. 23, Intelligent Decision Making using Best Practices of Big Data Technologies (Part-I), pp. 1011-1021.
R-SiamNet: ROI-Align Pooling Baesd Siamese Network for Object Tracking

Recently, deep Siamese network-based trackers have achieved superior performance in the visual tracking community. Siamese trackers formulate the visual tracking problem as learning the similarity metric by cross-correlation between the target template and the search region. However, previous Siamese trackers are still susceptible to the distractors, mainly due to two reasons: (1)template region contains non-target information; (2)insufficient distractor learning in template frame. In this paper, we comprehensively combine the ROI align pooling with the Siamese Network (named R-SiamNet). Due to accurate region pooling operator, we present simple yet effective multi-distractors learning for template, which imposes the discriminative of feature embedding. The experimental results show that our R-SiamNet outperforms the state-of-the-art trackers on VOT2016 [1], VOT2018 [2] and OTB100 [3] datasets.
Video Summarization via Cluster-Based Object Tracking and Type-Based Synopsis

In this paper, we construct a trajectory-based system for the synopsis of surveillance video stream. The proposed approach first applies a cluster-based tracking method to extract foreground object from input videos, then extracts the abnormal object and classifies them into different motion patterns. Finally a type-based synopsis scheme is proposed to properly gather the moving object of different pattern types into limited time endurance. As a consequence, this system would be helpful for accurate and fast surveillance videos analysis.
Design and performance evaluation of reversible network covert channels

Covert channels nested within network traffic are important tools for allowing malware to act unnoticed or to stealthily exchange and exfiltrate information. Thus, understanding how to detect or mitigate their utilization is of paramount importance, especially to counteract the rise of increasingly sophisticated threats. In this perspective, the literature proposed various approaches, including distributed wardens, which can be used to collect traffic in different portions of the network and compare the samples to check for discrepancies revealing hidden communications. However, the use of some form of reversibility, i.e., being able to restore the exploited network carrier to its original form before the injection, can challenge such a detection scheme. Therefore, in this work we introduce and evaluate the performances of different techniques used to endow network covert channels with reversibility. Results indicate the feasibility of achieving reversibility but the used protocol plays a major role.