Reviewerly/exHarmony huggingface dataset:
Portola: A Hybrid Tree and Network Visualization Technique for Network Segmentation

Network security is critical for organizations to secure their network resources from intrusion and attacks. A security policy is a rule enforced in the network to allow or block network traffic. To write security policies, network analysts divide their networks into segments or parts with similar security needs. Segmentation makes writing security policies manageable and identifies robust security policies for the network. Visualizations can help analysts to understand the segmented network and define security policies. We contribute Portola, a hybrid tree and network visualization technique to display a segmented computer network. Portola presents an overview of the segmentation as a hierarchy and displays connections within the network. Using Portola, analysts can explore a segmented network, identify nodes and connections of interest through exploratory network analysis, and drill down on elements of interest to reason about the patterns of relationships in the network. Through this work, we also discuss the goals of network analysts who work with segmented networks and discuss the lessons learned from the user-centered iterative design of Portola.
Analysis of the Design Space for Cybersecurity Visualizations in VizSec

In this paper, we present research on the analysis of the design space for cybersecurity visualizations in VizSec. At the beginning of this research, we analyzed 17 survey papers in the field of cybersecurity visualization. Based on the analysis of the focus areas in each of these survey papers, we identified five key components of visualization design, i.e. Input Data, Security Tasks, Visual Encoding, Interactivity, and Evaluation. To show how research papers align with these components, we analyzed 60 papers published at the IEEE Symposium on Visualization for Cyber Security (VizSec) between 2016 and 2021 in the context of the five identified components. As a result, each research paper was classified into several categories derived from the selected components of the visualization design. Our contributions are: (i) an analysis of the focus areas in survey papers on cybersecurity visualization and (ii) the classification of 60 research papers in the context of the selected components of the visualization design. Finally, we highlighted the main findings of the analysis and drew conclusions.
Synthetic generation of hourly photovoltaic power forecasts using Bayesian Regularised Neural Networks

Accurate day-ahead photovoltaic (PV) power output forecasting techniques are important for grid and plant operators. In this study, a methodology for PV power forecasting is proposed based on a synthetic generation of multiple Bayesian regularised neural network (BRNN) models. Specifically, different BRNN models were developed for every hour of each day within the power production period. The final PV power production forecasting was provided by combining the outputs of all the developed models. The accuracy of the developed model was experimentally validated by deploying it to a test system installed in Cyprus. The day ahead power production forecasting performance accuracy of the proposed methodology in terms of normalised root mean square error and mean absolute percentage error was 5.80% and 4.49%, respectively.
Optimal Parameter Selection Using Explainable AI for Time-Series Anomaly Detection

Time-series anomaly detection is a technique for detecting unusual values, changes, or movements in a large amount of data arranged in time-series. It is primarily used in the fields of intrusion detection, medical diagnosis, and industrial defect damage detection and necessary to realize agents that operate intelligently and autonomously, such as changing system behavior based on detected anomalies. SALAD is a real-time time-series anomaly detection method based on deep learning. It is lightweight and determines anomaly detection threshold flexibly; however, experts need to determine an appropriate value for a parameter so that it suits any given recurrent time series, and this inhibits the realization of the agent. In this study, we propose a method to determine automatically the optimal parameter value in SALAD’s prediction model by utilizing XAI. We use SHAP, which provides interpretability to the prediction by the deep learning model. Through evaluation experiment, we demonstrate that our method is effective and provide an example of the use of XAI for time-series anomaly detection.
Diagnostic Accuracy of FNAC for Detection of Malignancy in Palpable Breast Lump and its Comparison with Tru-Cut Biopsy

Breast cancer is the commonest malignancy in women worldwide and leading cause of cancer related deaths in women, almost half of these occurring in developing countries. Aim was to know the diagnostic accuracy of FNAC for detection of malignancy in palpable breast lump and its comparison with tru-cut biopsy Methodology: Six months following the publication of this report, researchers in the Surgical Department of Allied Hospital Faisalabad conducted this study. Starting on March 31st, 2019, and running all the way through September 30th, 2019. Allied Hospital Faisalabad's ethics council approved the study's summary, and patients who presented to the hospital's breast clinic with a palpable breast lump met the criteria for inclusion in the research. Across all patients, histopathology was performed on FNAC, tru-cut, and excisional biopsy specimens. H&amp;E staining was performed on both the tru-cut biopsy and tissue specimens from later excision procedures to examine the histological characteristics of the tumor tissue. Results: In our study, out of 190 cases, frequency of malignancy on was recorded in 28.42%(n=54), the diagnostic accuracy of Tru-cut biopsy for detection of malignancy in patients with palpable breast lump taking histopathology examination after excision biopsy (HPE) as gold standard was recorded as 80.33% sensitivity, 96.12% specificity, 90.74% positive predictive value,91.18% negative predictive value and 91.05% accuracy rate, likelihood ratio was calculated as 20.72 and diagnostic accuracy of FNAC biopsy for detection of malignancy in patients with palpable breast lump taking histopathology examination after excision biopsy (HPE) as gold standard was recorded as 70.49% sensitivity, 93.80% specificity, 84.31% positive predictive value, 87.05% negative predictive value and 86.32% accuracy rate, likelihood ratio was calculated as 11.37. Conclusion: As far as diagnostic accuracy for the diagnosis of malignancy in patients with palpable breast lumps, FNAC and Tru-cut biopsy are both good, although Tru-cut biopsy is more accurate than FNAC in terms of detecting the disease. Keywords: Palpable breast lump, malignancy, diagnosis, FNAC, Tru-cut biopsy, diagnostic accuracy
Efficient RNS Realization of High-Speed Arithmetic Multiplier with Respect to Cryptographic Computation

As in the digital era, there will always be a demand for high-speed algorithms and techniques that can accelerate computations. Arithmetic computation can be performed more quickly with the help of VLSI fabrication techniques and the residue number system (RNS). In this paper, a novel arithmetic multiplier that can be used for cryptographic applications is designed using RNS. In general, the binary numbers are converted into residues, and then, the multiplication operation takes place with residues. At the end, these residues are converted back to binary numbers using the Chinese remainder theorem (CRT). All the simulations have been implemented using Cadence Design Framework with 45 nm, and results are encouraging. It is observed that the delay savings are improved by 78%.
Diagnosis of Leukemia Based on White Blood Cell Classification

Blood diagnosis is based on a visual examination of blood smears, which is usually a time-consuming and error-prone process. In order to overcome this challenge, image processing techniques are required to assist the clinical decision-making process. Leukemia is a form of cancer, which is distinguished by the abnormal generation of immature white blood cells (WBC) known as tumors. Generally, leukemia will affect the white blood cells present in the bone marrow and/or blood. Hence, developing a prompt, safe, and accurate leukemia diagnosis is critical. White blood cells are often examined under a blood smear microscope for diagnosis purpose. On the other hand, numerous machine learning algorithms have been developed to diagnose different diseases, e.g., leukemia, and to provide a high number of misclassification error rates. The proposed research study has implemented a deep learning algorithm to classify the microscopic images for white blood count analysis. The WBC differential system is divided into two modules: detection and segmentation. The detection module initially evaluated the green bone smear pictures, detecting all WBCs in red blood cells, platelets, counts, and so on. The obtained cells are then fed into a separate module. The segmentation module was divided into two pieces. Numerous cells, including crushed cells and degraded new cells, are identified at the earliest stage of leukemia diagnosis. The WBCs may then be computed and distributed to multi-class differentiation stage by using a convolutional neural network [CNN] technique.
Structure of the Muruntau gold ore region in the Kyzyl-Kum desert (Central Asia)

Abstract The giant gold deposit Muruntau in most works belongs to the “orogenic type”. However, in the description of the geology of the deposit, there is usually no information about the stages (deformation, metamorphism) and the role of regional processes in the formation of host and mineralized rocks of the deposit. Our fieldwork provides unique geological evidence and previously unpublished data that are not reported in any other publications to date. This article presents the results of a detailed structural study of the terrane hosting the Muruntau gold deposit on the example of four sites and the results of deep drilling of two wells. The conducted studies confirm that the lowest Muruntau megaterrane consists of multi-stage metamorphosed Neoproterozoic–Early Paleozoic sedimentary-volcanic rocks and Late Paleozoic carbonate sediments overlain by Late Carboniferous flysch and olistostrome in the top. Two main stages (D1 and D2) of rock deformation in the lower part of the megaterrane associated with the Early Paleozoic accretion and two subsequent stages (D3 and D4) associated with the Late Paleozoic accretion and orogeny are described. Thrust processes D2 and D3 caused the formation of recumbent folds of different orientation and vergence. The normal antiform (D4) extending W–E is an orogenic fold. The Muruntau gold deposit is located in a regional synmetamorphic thrust zone called the Main Muruntau Thrust (MMT), which formed during the Early Paleozoic D2 stage. The MMT is responsible for the formation of secondary rock anisotropy along the cleavage of the D2 recumbent folds and conformal lens-like body of mineralized rocks and gently sloping veins with an area of 7.0 × 2.6 km. The internal structure of the deposit retains fragments of deformed gold quartz veins into recumbent folds. These structural data make it possible to link low-grade gold mineralized zones with Early Paleozoic deformations. We suggest that rock anisotropy within the MMT controlled mineralization twice: in the Early Paleozoic as a synmetamorphic flat high-permeable zone and in the Late Paleozoic as a relatively low-permeable zone for post-magmatic fluids.
Sequence-to-Sequence CNN-BiLSTM Based Glottal Closure Instant Detection from Raw Speech

In this paper, we propose to frame glottal closure instant (GCI) detection from raw speech as a sequence-to-sequence prediction problem and to explore the potential of recurrent neural networks (RNNs) to handle this problem. We compare the RNN architecture to widely used convolutional neural networks (CNNs) and to some other machine learning-based and traditional non-learning algorithms on several publicly available databases. We show that the RNN architecture improves GCI detection. The best results were achieved for a joint CNN-BiLSTM model in which RNN is composed of bidirectional long short-term memory (BiLSTM) units and CNN layers are used to extract relevant features.
SmartMinutes—A Blockchain-Based Framework for Automated, Reliable, and Transparent Meeting Minutes Management

The aim of this research work was to investigate the applicability of smart contracts in the context of automating the process of managing meeting minutes. To this end, smartMinutes, a proof-of-concept prototype of automating meeting minutes was designed, implemented, and validated with test cases. The smartMinutes framework improves current practices related to the meeting minutes process by providing automation in areas where possible, and doing so in a transparent, flexible, reliable, and tamper-proof manner. The last feature is of paramount importance due to the fact that meeting minutes offer legal protection, as they are considered official records of the actions taken by an organisation. Additionally, smartMinutes supports meeting agendas with non-voting items as well as voting items, offering a pool of three voting schemes, executing under three different configurations. A particular configuration, the hidden mode, provides for secrecy while at the same time guaranteeing transparency.
Generational differences in viewing behaviors: an eye-tracking study

Many scholars over the past two decades have contended that constant exposure to visually-oriented technologies makes younger individuals inherently more visually skilled than previous generations. The study presented here investigates this claim by using eye tracking to examine patterns in the ways in which individuals interact with visual stimuli, specifically journalistic photographs. Study participants included 29 college students aged 18–22 (mean = 19), and 20 non-student members of the university and surrounding community, aged 40–63 (mean = 50). Eye movements were recorded using a TobiiPro x2-60 eye tracker connected to a 17-inch gaming laptop. If younger individuals and older individuals have different levels of visual ability, there should be observable differences between the eye movements of the two groups. However, the differences observed between the two groups of participants were very limited and did not point to any consistent patterns that would suggest differing levels of skill at reading images.
A novel interactive robust filter algorithm for GNSS/SINS integrated navigation

To solve the problem of Kalman filter (KF) performance degradation in unmanned aerial vehicle (UAV) applications, a novel interactive robust filter algorithm for GNSS/SINS integrated navigation is proposed in this paper. The strong tracking Kalman filter (STKF) is robust to uncertain system noise but is ineffective to abnormal measurement information. Based on the same performance index function with STKF, a measurement noise covariance matrix adaptive Kalman filter algorithm (MAKF) is presented, but it is ineffective under uncertain system noise. Furthermore, the interactive robust filter algorithm based on STKF and MAKF (IF-STKF-MAKF) is proposed, given the complementary characteristics of the above two filter algorithms. The STKF and MAKF operate in parallel based on the same system model. The filter probability of each filter is updated according to the likelihood function to perform output fusion and input interaction. The simulation and experiment results demonstrate that the IF-STKF-MAKF is effective and can achieve high estimation accuracy under both system noise anomalies and measurement information anomalies. In the vehicle experiment, the position accuracy of the proposed IF-STKF-MAKF method has been improved by more than 30% compared with KF, STKF, and MAKF. This method can also be extended to land vehicles, mobile robots, etc.
Differential-Aided Preimage Attacks On Round-Reduced Keccak

Abstract At FSE 2008, Leurent introduced the preimage attack on MD4 by exploiting differential trails. In this paper, we apply the differential-aided preimage attack to Keccak with the message modification techniques. Instead of directly finding the preimage, we exploit differential characteristics to modify the messages, so that the differences of their hashing values and the changes of given target can be controlled. By adding some constraints, a trail can be used to change one bit at a time and reduce the time complexity by a factor of 2. When the number of rounds increases, we introduce two-stage modification techniques to satisfy part of constraints as well. In order to solve other constraints, we also combine the linear-structure technique and accordingly give a preimage attack on 5-round Keccak[$r=1440,c=160,l=80$].
Key role of neuronal diversity in structured reservoir computing

Chaotic time series have been captured by reservoir computing models composed of a recurrent neural network whose output weights are trained in a supervised manner. These models, however, are typically limited to randomly connected networks of homogeneous units. Here, we propose a new class of structured reservoir models that incorporates a diversity of cell types and their known connections. In a first version of the model, the reservoir was composed of mean-rate units separated into pyramidal, parvalbumin, and somatostatin cells. Stability analysis of this model revealed two distinct dynamical regimes, namely, (i) an inhibition-stabilized network (ISN) where strong recurrent excitation is balanced by strong inhibition and (ii) a non-ISN network with weak excitation. These results were extended to a leaky integrate-and-fire model that captured different cell types along with their network architecture. ISN and non-ISN reservoir networks were trained to relay and generate a chaotic Lorenz attractor. Despite their increased performance, ISN networks operate in a regime of activity near the limits of stability where external perturbations yield a rapid divergence in output. The proposed framework of structured reservoir computing opens avenues for exploring how neural microcircuits can balance performance and stability when representing time series through distinct dynamical regimes.
Epistemic envy in the keyboard warrior: A Bionian analysis

In this paper, we outline an object relations approach to the understanding of the factors underlying the behavior exhibited by a specific type of “uncivil” social media user, the keyboard warrior. Analyzing ten key features of the keyboard warrior’s behavior, we lay out the hypothesis that it is underlaid by an object relation of the type that Bion conceptualised as “minus K”: namely, an envious relation to the breast as the original provider not only of food but also of knowledge. We conclude by discussing the crisis of constructive dialogue that is ongoing at various levels in contemporary Western society and culture.
Ignoring Internal Utilities in High-Utility Itemset Mining

High-utility itemset mining discovers a set of items that are sold together and have utility values higher than a given minimum utility threshold. The utilities of these itemsets are calculated by considering their internal and external utility values, which correspond, respectively, to the quantity sold of each item in each transaction and profit units. Therefore, internal and external utilities have symmetric effects on deciding whether an itemset is high-utility. The symmetric contributions of both utilities cause two major related challenges. First, itemsets with low external utility values can easily exceed the minimum utility threshold if they are sold extensively. In this case, such itemsets can be found more efficiently using frequent itemset mining. Second, a large number of high-utility itemsets are generated, which can result in interesting or important high-utility itemsets that are overlooked. This study presents an asymmetric approach in which the internal utility values are ignored when finding high-utility itemsets with high external utility values. The experimental results of two real datasets reveal that the external utility values have fundamental effects on the high-utility itemsets. The results of this study also show that this effect tends to increase for high values of the minimum utility threshold. Moreover, the proposed approach reduces the execution time.
Analisis Pengetahuan Kepala Madrasah Tentang Sistem Informasi Dan Iklim Kerja Untuk Membentuk Efektivitas Kepemimpinan Di MTS Swasta Yaspen Muslim Pematang Tengah

&#x0D; &#x0D; &#x0D; &#x0D; The formulation of the problem in this study is: How is the effectiveness of the madrasa principal's leadership in managing information systems and work climates at Yaspen Muslim Islamic Private MTs Pematang Tengah. Another problem that can be found is that the creation of a good work climate in the madrasa environment has not been fulfilled. The principal applies an authoritarian system in leading the school so that a conducive school climate is not created. Leadership does not work effectively in madrasas. The impact is certainly very large for the education management that is carried out. The goals to be achieved will be difficult to achieve optimally because the existing systems are not well developed with one another. Innate will tend to be uncomfortable at work and the results will definitely not be effective. For this reason, it is necessary to build a good working relationship between the principal and the teacher. The conclusions in this study are that: An analysis of the madrasa principal's knowledge of information systems and work climate has a significant effect on the effectiveness of the madrasa principal's leadership. This is because if the principal does not have an understanding of information systems and the work climate, it can hinder the formation of leadership effectiveness. So, based on the results of observations and interviews conducted with resource persons, it can be concluded that the head of the madrasa has made a maximum contribution in shaping leadership effectiveness through knowledge of information systems and a good work climate.&#x0D; &#x0D; &#x0D; &#x0D;
SolarRadnet: A novel variant input scoring optimized recurrent neural network for solar irradiance prediction

Solar irradiance prediction is an essential one in providing renewable energy proficiently. The solar irradiance plays a major role in solar power system, solar thermal system, and photovoltaic grid-connected system, owing to uncertainty and variability. Conventional data analysis approaches are complex for demonstrating superior generalization. Therefore, the resource planners are flexible in accommodating these uncertainties while executing planning. To enhance the performance of solar irradiance forecasting, a new Variant Input Scoring Optimized Recurrent Neural Network (VIS-ORNN) is developed. The suggested approach includes two stages that are data collection and three stage simulation. At first, the data are gathered from the various meteorological standard dataset. Then, the prediction begins with feeding data directly to the ORNN. Here, the parameters of RNN are optimized with the help of Adaptive Escaping Energy-based Harris Hawks Coyote Optimization (AEE-HHCO) algorithm. Thus, the first score prediction is obtained. In the second phase, the first order statistical features act as an input, and it is given to the same ORNN, in which the second score is determined. In the third phase, the deep features are extracted by Convolutional Neural Network (CNN) that is subjected to the same ORNN for attaining the score. Finally, the final simulation is determined by taking the average of three prediction models. From the experimental results, while taking the MAE, the suggested AEE-HHCO-ORNN method has correspondingly secured 34.3% enhanced than PSO-ORNN, 7.7% enhanced than WOA-ORNN, 21.7% enhanced than COA-ORNN and 26.5% enhanced than HHO-ORNN. Thus, the simulation outcomes reveal that the offered method ensures maximum accuracy while validating with other baseline methodologies.