Reviewerly/exHarmony huggingface dataset:
Physical Data Auditing for Attack Detection in Cyber-Manufacturing Systems: Blockchain for Machine Learning Process

Abstract Auditing physical data using machine learning can enhance the security in Cyber-Manufacturing System (CMS). However, the physical data processing itself is prone to cyber-attacks. Connections based on the internet in CMS opens the route for adversaries to compromise the attack detection system itself. To prevent data from malicious data injection in CMS, this paper proposes an enhanced Simple Convolutional Neural Network (SCNN) based attack detection system employing a blockchain. There are three contributions of this paper: (i) introducing a secure attack detection system using blockchain, (ii) optimizing the cost and time for CMS by training on the simulated images, and (iii) presenting a real-time attack detection system for CMS by simplifying the convolutional neural network. The paper demonstrates the effectiveness of the blockchain implementation by presenting the comparative performance analysis of the proposed attack detection system with and without blockchain implementation using an example of a simulated attack on the machine learning process.
CNN-based Skip-Gram Method for Improving Classification Accuracy of Chinese Text

Text classification is one of the fundamental techniques in natural language processing.Numerous studies are based on text classification, such as news subject classification, question answering system classification, and movie review classification.Traditional text classification methods are used to extract features and then classify them.However, traditional methods are too complex to operate, and their accuracy is not sufficiently high.Recently, convolutional neural network (CNN) based one-hot method has been proposed in text classification to solve this problem.In this paper, we propose an improved method using CNN based skip-gram method for Chinese text classification and it conducts in Sogou news corpus.Experimental results indicate that CNN with the skip-gram model performs more efficiently than CNN-based one-hot method.
A Sample‐Efficient Actor‐Critic Algorithm for Recommendation Diversification

Diversifying recommendation results gains benefits from satisfying user's existing interests as well as exploring novel information needs. Recently proposed Monte-Carlo based reinforcement learning method suffers from sample inefficiency, large variance, and even failing to perform well in large action space. We propose a novel actor-critic reinforcement learning algorithm for recommendation diversification in order to solve the above mentioned problems. The actor acts as the ranking policy, while the introduced critic predicts the expected future rewards of each candidate action. The critic target is updated by full Bellman equation and the actor network is optimized using expected gradient in the whole action space. To further stabilize and improve the performance, we also add policy-filtered critic supervision loss. Experiments on MovieLens dataset well demonstrate the effectiveness of our approach over multiple competitive methods.
REPLica: REPL instrumentation for Coq analysis

Proof engineering tools make it easier to develop and maintain large systems verified using interactive theorem provers. Developing useful proof engineering tools hinges on understanding the development processes of proof engineers. This paper breaks down one barrier to achieving that understanding: remotely collecting granular data on proof developments as they happen.
Adaptive Fusion of RGBD Data for two-stream FCN-based Level Set Tracking

In this paper, we address the contour tracking task based on a deep fully convolutional network (FCN)-driven Level Set method. Different from previous Level Set-based trackers wherein speed functions are usually modeled by handcrafted features. In our work by considering the merits of FCN on its powerful feature representation and end-to-end pixelwise prediction capabilities, we aim to integrate it into Level Set contour evolution framework. Specifically, we import RGBD data and design a two-stream FCN-based structure. An Adaptive fusion mechanism is applied to leverage these two complementary modalities. We adopt the final fusion map from the two-stream FCN output as a guidance to evolve the contour more efficiently and accurately. Experimental results on a number of video clips demonstrate the effectiveness and robustness of our proposed method, especially for tackling the challenging problems, i.e., similar appearance between target and background, and low illumination, etc.
3D Hermite Transform Optical Flow Estimation in Left Ventricle CT Sequences

Heart diseases are the most important causes of death in the world and over the years, thestudy of cardiac movement has been carried out mainly in two dimensions, however, it is important toconsider that the deformations due to the movement of the heart occur in a three-dimensional space.The 3D + t analysis allows to describe most of the motions of the heart, for example, the twistingmotion that takes place on every beat cycle that allows us identifying abnormalities of the heartwalls. Therefore, it is necessary to develop algorithms that help specialists understand the cardiacmovement. In this work, we developed a new approach to determine the cardiac movement inthree dimensions using a differential optical flow approach in which we use the steered Hermitetransform (SHT) which allows us to decompose cardiac volumes taking advantage of it as a model ofthe human vision system (HVS). Our proposal was tested in complete cardiac computed tomography(CT) volumes ( 3D + t), as well as its respective left ventricular segmentation. The robustness tonoise was tested with good results. The evaluation of the results was carried out through errors inforwarding reconstruction, from the volume at time t to time t + 1 using the optical flow obtained(interpolation errors). The parameters were tuned extensively. In the case of the 2D algorithm, theinterpolation errors and normalized interpolation errors are very close and below the values reportedin ground truth flows. In the case of the 3D algorithm, the results were compared with another similarmethod in 3D and the interpolation errors remained below 0.1. These results of interpolation errorsfor complete cardiac volumes and the left ventricle are shown graphically for clarity. Finally, a seriesof graphs are observed where the characteristic of contraction and dilation of the left ventricle isevident through the representation of the 3D optical flow.
Improving Human Development Index (HDI) by Illiteracy Eradication (Case Study in Cibadak Village)

To measure the quality of human capital, The United Nations Development Program (UNDP) introduced the concept of human resource capital quality, named the Human Development Index (HDI). HDI is based on three dimensions i.e.: life expectancy at birth, literacy rate, mean years of schooling, and purchasing power parity. In this study raised the issue of the high illiteracy rate in Cibadak, Banten Province and the low expectation for old school. The situation will certainly be a barrier to the development process and economic growth through the human development index. The results showed that factors that could cause low educational status and illiteracy were grouped by researchers in research indicator variables, namely education, family, external environment, and information factors. The local government must pay more attention to this situation, because human resources are the driving wheel of the economy. The people really need care like reading and writing training assistance from the local government.
Gamified training: a new concept to improve individual soft skills

This study discusses a new concept of "gamified training" through creative-oriented leadership. This concept aims to improve individual soft skills, such as communication skills, creative intelligence and collaboration skills. Gamified training is a design of training through gamification so that it will produce an effective and targeted training. Gamification is a training policy using game pattern. The sampling technique used purposive sampling method. The 106 respondents were involved in this study. Data collection technique used questionnaires and analyzed by regression method. The results showed that there was significant positive effect between creative-oriented leadership with gamified training. Furthermore, gamified training has also been proven to improve the individual soft skills.
Quantitative Analysis of DTMF Tone Detection using DFT, FFT and Goertzel Algorithm

Dual Tone Multi-Frequency(DTMF) is used for communication through a telephone system or over the internet and is considered to be a signaling system. This system finds many applications in the telecommunication field like telephone switching centers, automated calls, in computer applications like voicemail, electronic mail, telephone banking and also for industrial applications. In this paper, we aim to compare three algorithms namely DFT, FFT, and Goertzel algorithm for detecting DTMF tones and how is the computational efficiency and accuracy of detection affected by changing the different factors. We have used MATLAB for simulation of all the algorithms and results are shown in this paper.
An Adaptive Watermark Detection Algorithm for Vector Geographic Data

With the rapid development of computer and communication techniques, copyright protection of vector geographic data has attracted considerable research attention because of the high cost of such data.A novel adaptive watermark detection algorithm is proposed for vector geographic data that can be used to qualitatively analyze the robustness of watermarks against data addition attacks.First, a watermark was embedded into the vertex coordinates based on coordinate mapping and quantization.Second, the adaptive watermark detection model, which is capable of calculating the detection threshold, false positive error (FPE) and false negative error (FNE), was established, and the characteristics of the adaptive watermark detection algorithm were analyzed.Finally, experiments were conducted on several real-world vector maps to show the usability and robustness of the proposed algorithm.
A Classification Method for E-mail Spam Using a Hybrid Approach for Feature Selection Optimization

Spam is an unwanted email that is harmful to communications around the world. Spam leads to a growing problem in a personal email, so it would be essential to detect it. Machine learning is very useful to solve this problem as it shows good results in order to learn all the requisite patterns for classification due to its adaptive existence. Nonetheless, in spam detection, there are a large number of features to attend as they play an essential role in detection efficiency. In this article, we're working on a feature selection method to e-mail spam. This approach is considered a hybrid of optimization algorithms and classifiers in machine learning. Binary Whale Optimization (BWO) and Binary Grey Wolf Optimization (BGWO) algorithms are used for feature selection and K-Nearest Neighbor (KNN) and Fuzzy K-Nearest Neighbor (FKNN) algorithms are applied as the classifiers in this research. The proposed method is tested on the SPAMBASE datasets from UCI Machine learning Repesotries and the experimental results revealed the highest accuracy of 97.61% on this dataset. The obtained results indicateed that the proposed method is suitable and capable to provide excellent performance in comparison with other methods.
Reversible 3D compression of segmented medical volumes: usability analysis for teleradiology and storage

Background DICOM standard does not have modules that provide the possibilities of two‐dimensional Presentation States to three‐dimensional (3D). Once the final 3D rendering is obtained, only video/image exporting or snapshots can be used. To increase the utility of 3D Presentation States in clinical practice and teleradiology, the storing and transferring the segmentation results, obtained after tedious procedures, can be very effective. Purpose To propose a strategy for preserving interaction and mobility of visualizations for teleradiology by storing and transferring only binary segmented data, which is effectively compressed by modern adaptive and context‐based reversible methods. Material and Methods A diverse set of segmented data, which include four abdominal organs (liver, spleen, right, and left kidneys) from 20 T1‐DUAL and 20 T2‐SPIR MRI, liver from 20 CT, and abdominal aorta with aneurysms (AAA) from 19 computed tomography‐angiography datasets, are collected. Each organ is segmented manually by expert physicians, and binary volumes are created. The well‐established reversible binary compression methods PNG, JPEG‐LS, JPEG‐XR, CCITT‐G4, LZW, JBIG2, and ZIP are applied to medical datasets. Recently proposed context‐based (3D‐RLE) and adaptive (ABIC) algorithms are also employed. The performance assessment has been presented in terms of the compression ratio that is a universal compression metric. Results Reversible compression of binary volumes results with substantial decreases in file size such as 254 to 2.14 MB for CT‐AAA, 56.7 to 0.3 MB for CT‐liver. Moreover, compared to the performance of well‐established methods (i.e., mean 76.14%), CR is observed to be increased significantly for all segmented organs from both CT and MRI datasets when ABIC (95.49%) and 3D‐RLE (94.98%) are utilized. The hypothesis is that morphological coherence of scanning procedure and adaptation between the segmented organs, that is, bi‐level images, contributes to compression performance. Although the performance of well‐established techniques is satisfactory, the sensitivity of ABIC to modality type and the advantage of 3D‐RLE when the spatial coherence between the adjacent slices are high results with up to 10 times more CR performance. Conclusion Adaptive and context‐based compression strategies allow effective storage and transfer of segmented binary data, which can be used to re‐produce visualizations for better teleradiology practices preserving all interaction mechanisms.
Digital and programmable economy applications: A smart cities congestion case by fuzzy sets1

Currently, cities are facing great challenges such as the population growing, citizen wellbeing, externalities management or environmental deterioration. The search for solutions are making significant inroads into the incorporation of ICT in them and subsequent large-scale digitalization such as p rogrammable economy (PE) applications, offering the possibility to develop new approaches over these issues, in particular which related to sustainability management. Operating under a fuzzy numbers methodology and FIS (Fuzzy Inference System), the present exploratory work shows a new approach to city urban congestion management by deploying PE applications, which include some disruptive inputs such as the Internet of value, blockchain/DLT (distributed ledger technology), smarts contracts, digital assets and the monetization, all of this combined with the human motivation.
PERBANDINGAN ALGORITMA C4.5 DAN NAÏVE BAYES UNTUK MENENTUKAN KELAYAKAN PENERIMA BANTUAN PROGRAM KELUARGA HARAPAN

PKH is a program that provides conditional cash transfer to very poor households where they have been determined as PKH participants with certain conditions.Sometimes, this assistance is not in its target.Therefore, it is becoming a problem in this Government empowerment program.This problem arises due to ineffective data verification in the selection of citizens who are eligible to receive PKH assistance.Therefore, it is necessary to analyze PKH for determining the feasibility of a PKH Program problem.Through the results of the PKH analysis, it could be seen whether or not the citizens were feasible to get assistance.Based on this problem, comparative data mining classification methods were used to find out which algorithm is good for predicting citizenship feasibility.There were two algorithms used, namely the C4.5 algorithm and Naïve Bayes.After testing with the two algorithms using RapidMiner tools, the results showed that C4.5 algorithm produced an accuracy value of 91.25% and AUC value of 0.930 with a diagnosis level of Excellent Classification.While the Naïve Bayes algorithm produced an accuracy value of 87.11% and AUC value amounted to 0.923 with a diagnosis level of Excellent Classification.In conclusion, C4.5 algorithm is a good algorithm to be applied to the feasibility of PKH.
Complex-Valued Neural Networks for Noncoherent Demodulation

Noncoherent demodulation is an attractive choice for many wireless communication systems. It requires minimal protocol overhead for carrier synchronization, and it is robust to radio impairments commonly found in low-cost transceivers. Machine learning techniques, such as neural networks and deep learning, offer additional benefits for these systems. Practical communication systems often include nonlinearities, non-stationarity, and non-Gaussian noise, which complicate mathematical derivation of optimum demodulators. Learning approaches can optimize demodulator performance directly from simulated or measured radio data, which is often plentiful in the design and verification of today's integrated transceivers. This paper examines several candidate neural network topologies for use in noncoherent demodulation and provides a mathematical framework for their comparison. Each is based on a complex-valued feature detection layer, which may be characterized as coherent or noncoherent, followed by one or more real-valued classification layers. Backpropagation equations for the noncoherent feature layer include a synchronization term that facilitates training with noncoherent input data. The coherent layer does not synchronize training data, however a noncoherent demodulator can still be constructed by increasing the coherent layer capacity and adding a max pooling layer to marginalize the unknown signal phase. A frequency classification example highlights the differences between the topologies and confirms that optimum noncoherent demodulation can be learned in the presence of AWGN and random phase offsets. The topologies considered here are suitable for noncoherent demodulation of power-efficient modulations such as FSK and ASK, which are typical in today's short-range wireless communication systems. It is hoped that such topologies will lead to a future common architecture that can support the wide range of modulation formats in this space.
AIS-Ethics as an Ethical Domain: A Response to Guragai, Hunt, Neri and Taylor (2017) and Dillard and Yuthas (2002)

In this paper, I analyze the claim that AIS-ethics is a distinct subset of the broader fields of accounting and ethical studies.Guragai Hunt, Neri and Taylor (2017), building on Dillard and Yuthas (2002), call for the creation of a subset of business ethics with the intent to not only apply existing ethical concepts to AIS activities, but to establish "AIS-ethics" as a distinct area of practice and research in its own right.Their objective is for AIS-ethics to play the same role in AIS that Bioethics does in medicine.In order to analyze this argument, I analyze the examples provided in the literature on the impact of accounting information systems on ethics.My analysis indicates that there is a need to be more specific as to where exactly ethical issues arise in AIS, and most critical of all, why they do so.Too often, the existence of ethical problems in AIS is presumed to be so self-evident that no further explanation is needed-or provided-about what those ethical issues are, or what the circumstances are that give rise to them.Moreover, the behavior of a decision-maker in an AIS context is sometimes attributed to ethical considerations when a more detailed analysis indicates that the underlying cause of that behavior is either economic and/or not directly impacted by the presence of the AIS system.To remedy this lack of clarity as to what is an ethical problem in AIS, I argue that a necessary condition for individuals to consider that they face a decision with ethical consequences is that they perceive that there is a conflict between their sense of morality and the other sources of guidance relevant to making that decision.
An exploration of ontology-based EMR data abstraction for diabetic kidney disease prediction.

Diabetic Kidney Disease (DKD) is a critical and morbid complication of diabetes and the leading cause of chronic kidney disease in the developed world. Electronic medical records (EMRs) hold promise for supporting clinical decision-making with its nationwide adoption as well as rich information characterizing patients' health care experience. However, few retrospective studies have fully utilized the EMR data to model DKD risk. This study examines the effectiveness of an unbiased data driven approach in identifying potential DKD patients in 6 months prior to onset by utilizing EMR on a broader spectrum. Meanwhile, we evaluate how different levels of data granularity of Medications and Diagnoses observations would affect prediction performance and knowledge discovery. The experimental results suggest that different data granularity may not necessarily influence the prediction accuracy, but it would dramatically change the internal structure of the predictive models.
Institutional Drivers of Internet Voting Adoption in Ghana: A Qualitative Exploratory Studies

Internet voting system adoption in elections can bring enormous benefits to an electoral process. Though few countries have adopted it for their legally binding elections, others have discontinued its use because of perceived vulnerabilities. But it has been argued that the benefits the internet voting system provide outweigh the perceived vulnerabilities. The main purpose of this study is to examine the drivers of the internet voting system from the organizational context. The study is purely qualitative using semi-structured interviews. The interview participants were top management of EC staff, political parties’ executives and experts who were purposively selected, and thematic analysis was used to extract patterns from the data collected. The main themes that emerged from the thematic analysis include pressure from political parties, pressure from the government, legal framework, financial readiness of EC, EC top management support, convenience, accuracy, and increase voter turnouts. The discussion of the findings and the implication of the results were discussed in the study.
WeSAL: Applying Active Supervision to Find High-quality Labels at Industrial Scale

Obtaining hand-labeled training data is one of the most tedious and expensive parts of the machine learning pipeline.Previous approaches, such as active learning aim at optimizing user engagement to acquire accurate labels.Other methods utilize weak supervision to generate low-quality labels at scale.In this paper, we propose a new hybrid method named WeSAL that incorporates Weak Supervision sources with Active Learning to keep humans in the loop.The method aims to generate large-scale training labels while enhancing its quality by involving domain experience.To evaluate WeSAL, we compare it with two-state-of-the-art labeling techniques, Active Learning, and Data Programming.The experiments use five publicly available datasets and a real-world dataset of 1.5M records provided by our industrial partner, IBM.The results indicate that WeSAL can generate large-scale, high-quality labels while reducing the labeling cost by up to 68% compared to active learning.