Reviewerly/exHarmony huggingface dataset:
The Accuracy and Efficiency of a Reference-Based Adaptive Selection Algorithm for Comparative Judgment

Several studies have proven that comparative judgment (CJ) is a reliable and valid assessment method for a variety of competences, expert assessment, and peer assessment, and CJ is emerging as a possible approach to help maintain standards over time. For consecutive pairs of student works (representations) assessors are asked to judge which representation is better. It has been shown that random construction of pairs leads to very inefficient assessments, requiring a lot of pairwise comparisons to reach reliable results. Some adaptive selection algorithms using information from previous comparisons were proposed to increase the efficiency of CJ. These adaptive algorithms appear however to artificially inflate the reliability of CJ results through increasing the spread of the results. The current article proposes a new adaptive selection algorithm using a previously calibrated reference set. Using a reference set should eliminate the reliability inflation. In a real assessment, using reference sets of different reliability, and in a simulation study, it is proven that this adaptive selection algorithm is more efficient without reducing the accuracy of the results and without increasing the standard deviation of the assessment results. As a consequence, a reference-based adaptive selection algorithm produces high and correct reliability values in an efficient manner.
Geospatial Modelling of Solar Radiation Climate

Proper land planningPlanning and managementManagement in terms of agro-suitability of crops and adequate placement of solar photovoltaicPhotovoltaic and thermalThermal technologies require a knowledge of how the solar insolationInsolation parameter varies on spatialSpatial and temporal scales. In this chapter, we explore methodologies including satelliteSatellite remote sensing, Numerical WeatherWeather Prediction (NWP) model, and regressionRegression analysis that could enable countries to map the spatio-temporal variations in solar radiationSolar radiation. These techniques would offer researchers a route to the proper mapping of the solar resourceSolar resource potential for effectiveEffective policyPolicy decision makingDecision making.
Custom Based Obstacle Detection Using Yolo v3 for Low Flying Drones

Low flying drones are on great demand now a days due to their vast applications in Security, Surveillance, Construction sites, Emergency Response, logistics, firefighting, Precision agriculture, Traffic monitoring, Crowd monitoring etc. The greatest advantage is that there is no risk of human life! These unmanned vehicles facilitate safe and secure inspections in complicated and dangerous environments. A fully autonomous drone needs support of various sensors such as Lidars, Radars, ultrasonic sensors, Inertial Measurement Units, Global Positioning system and cameras to detect and avoid possible obstacles in the navigation path. In this paper, we are trying to find a possible solution to detect custom based obstacles using a camera as the primary sensing element. Transfer learning is performed using a YOLO v3 deep learning network.
Spear Phishing Detection

In the world today, security in the digital realm is of utmost importance. The path to achieving security begins with detecting the kind of attack and identifying patterns in the staged ways. These attacks are often termed as “phishing.” It can be described as a social engineering attack involving sophisticated attack vectors that can be used to steal sensitive information from a victim, to be more precise as per the definition of phishing. In phishing, the attacker disguises himself as a trustworthy entity, uses several tactics to win the victim’s trust, and then convinces the victim to reveal the requisite information to commit the fraud. This kind of attack can be done by sending malicious emails containing malware that can leak information when installed in the victim’s system. Such attacks can further be spread through the hacked systems to other victims. Phishing can again be classified into several types: Spear phishing, email phishing, whaling, smishing, vishing, and angler phishing. Among these, this chapter primarily concentrates on spear phishing. Spear phishing is a kind of email or social media scam that generally targets some specific individual or institution. Most of the time, the target happens to be influential. In spear phishing, the attacker gathers as much information as possible about the target to tailor the attack for that particular victim. However, many times, these attacks are found to exhibit a similar pattern. So, detecting that pattern in any email or social media post can predict any possible threat in the same. This chapter mainly focuses on finding these patterns and labeling posts as possible spear-phishing attacks using machine-learning (ML) algorithms. The classification algorithms categorize structured or unstructured data into a given number of decision classes. There are different classification algorithms, like decision tree, support vector machine, multinomial Naive Bayes, logistic regression, and K-nearest neighbors. Besides, to improve the accuracy of the classification of the data, ensemble methods are used. In machine learning, several techniques are combined to form ensemble methods to balance variance and bias or improve predictions. These ensemble methods can further be divided into sequential ensemble methods like AdaBoost, Stacking, and so on and parallel ensemble methods like Random Forest. Here, both ways have been used to have a comparative study between the accuracies of classical machine learning algorithms and the ensemble techniques. Furthermore, it is observed from our study that the ensemble approaches outperform the traditional machine learning techniques in predicting spear phishing in an email.
Blockchain-Enabled System for Interoperable Healthcare

Lack of interoperability and standardization of electronic health records (EHRs) are the most significant issues for effective EHR sharing. We have proposed a blockchain-enabled interoperable healthcare system that counters two problems regarding EHR context: first, how patients can have a complete view of their lifelong data to get the precise treatment and second, how doctors can get the real-time data of their patient in a standardized form which is interoperable between practitioners. The proposed solution follows the standards of openEHR to ensure the standardization of electronic records. It also offers fine-grained access permissions to the system’s stakeholders (i.e., patient, doctor, insurance company, etc.). It uses archetypes of openEHR as a reference for making the templates and then these templates are integrated to build a module in compliant standards. The proposed solution uses Proof of Authority consensus (PoA) to tackle performance issues. The proposed system ensures data interoperability and the privacy protection of sensitive medical data.
Information leakage prevention based on the separation of encrypted and compressed data

The article provides a brief overview of the existing methods of file classification in corporate networks. The necessity of discarding the file header to reduce the possibility of transmit-ting encrypted information by an internal intruder is justified. An algorithm for classifying files of 5 classes is proposed: encrypted, compressed, text, graphic and tabular. The algorithm takes into account the model of pseudo-random sequences and demonstrates an accuracy of 0.98 on the control sample of files according to the Accuracy metric and the F1-measure.
Automated medical image based anatomical point cloud generation for collaborative real-time augmented reality applications

This study aims to display the efficient generation of three-dimensional (3D) models of medical scans and the ability for physicians to utilize the models via augmented reality (AR) head mount displays. The ability to view and interact with 3D models of patients’ medical scans on head mount displays (HMD) such as the <i>Microsoft HoloLens 2</i>, opens a wide range of new possibilities for more accurate and intuitive ways for physicians to approach preoperative and intraoperative planning. Traditionally, the manual workflow for generating AR models of medical scans involves various software packages that are required to manually carry out steps such as image segmentation, mesh refinement, and file conversion<sup>1</sup>. Our web-based application automates the steps involved in generating AR models with end-to-end integration from image uploading to viewing and annotating the 3D model collaboratively on multiple AR headsets simultaneously. In addition to the main functions involving automated segmentation, interpolation, resizing, and the cropping of uploaded DICOM images, users can now automatically convert the files into a point cloud (PLY), which can be viewed and interacted with through a preview screen implemented onto the web app. Furthermore, these 3D models can be directly uploaded to an AR headset to be viewed and annotated by multiple AR headsets simultaneously using the AR app developed for this workflow.
Naïve Bayes Based Classifier for Credit Card Fraud Discovery

As financial services and operations expand, financial fraud is on the rise. Despite the use of preventative and security measures to reduce monetary fraud, criminals are constantly acquiring and developing new ways to circumvent fraud detection systems, posing a challenge to quantitative methods and predictive approaches. As a result, new methodologies must be researched and tested to leverage the insights gained from the study to assist further incorrect fraud forecasting and the establishment of fraud discovery schemes with extra measures to alleviate distrustful events. Naïve Bayes (NB) is a significant Machine Learning (ML) classifier that is not yet explored in the literature, unlike the use of common ML techniques like Decision Tree (DT), Random Forest (RF), Artificial Neural Network (ANN), and the likes. This paper, therefore, explores the use of a technique yet to be employed for credit card fraud detection (CCFD) namely Naïve Bayes. The classifier was compared using a confusion matrix for performance matrices like accuracy, precision, recall, f-measure, and ROC-AUC. It was discovered that NB outperformed most of the ML classifiers employed in state-of-the-art compared with an accuracy of 97.99%, recall of 98.02%, the precision of 99.97%, f-measure 98.98%, and FPR of 0.1971.
Corrigendum: Clifford Fuzzy Support Vector Machine for Regression and Its Application in Electric Load Forecasting of Energy System

A CORRIGENDUM ON FUNDINGThis work was supported by the National Natural Science Foundation of China under Grant 61771299 and 61771322.Missing FundingIn the original article, we neglected to include the funder National Natural Science Foundation of China, 61771299 and 61771322 to Rui Wang, Xiaoyi Xia, Yanping Li, Wenming Cao. The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.
FORTIFY: Analytical Pre-Silicon Side-Channel Characterization of Digital Designs

Power side-channel attacks are potent security threats that exploit the power consumption patterns of an electronic device to glean sensitive information ranging from secret keys and passwords to web-browsing activity. While pre-Silicon tools promise early detection of side-channel leakage at the design stage, they require several hours of simulation time. In this paper, we present an analytical framework called FORTIFY that estimates the power side-channel vulnerability of digital circuit designs at signal-level granularity, given the RTL or gate-level netlist of the design, at least 100 times faster than contemporary works. We demonstrate the correctness of FORTIFY by comparing it with a recent simulation-based side-channel leakage analysis framework. We also test its scalability by evaluating FORTIFY on an open-source System-on-Chip.
A Reconfigurable Inference Processor for Recurrent Neural Networks Based on Programmable Data Format in a Resource-Limited FPGA

An efficient inference processor for recurrent neural networks is designed and implemented in an FPGA. The proposed processor is designed to be reconfigurable for various models and perform every vector operation consistently utilizing a single array of multiply-accumulate units with the aim of achieving a high resource efficiency. The data format is programmable per operand. The resource and energy efficiency are 1.89MOP/LUT and 263.95GOP/J, respectively, in Intel Cyclone-V FPGA. The functionality has been verified successfully under a fully-integrated inference system.
Osteosarcoma Classification using Convolutional Neural Network

A biopsy is the final procedure of the diagnosis process and functions to distinguish the primary and secondary osteosarcomas (OS). The problem comes when many pathologists lead to misdiagnosis in the differentiation of various specimens of neoplasms due to its morphological diversity. This study aims to classify the cell viability of the osteosarcoma’s dataset with hematoxylin and eosin (H&E) stained. To increase efficiency and precision, Matlab is used to construct a convolutional neural network (CNN). The CNN architecture included six convolution layers, max-pooling layers, and fully connected layers for feature extraction. Data augmentation is used to boost performance. The classification process using the CNN architecture produces an average accuracy of 95.37 percent. The accuracy of each class, namely non-tumor is 92.8 percent, non-viable is 98 percent, and viable is 95.239 percent. To conclude, the CNN technique for classifying the OS works in high accuracy numbers. This study can significantly obtain better results compared with the manual process and effectively help the pathologist works.
GMilk: A Framework for Mobile and Web Application for Breast Milk Services in the Philippines

Breast milk contains antibodies that protect infants from diseases and could prevent infant death. However, not all mothers can produce enough breast milk for their infants, which lead them to acquire it from milk banks or peer-to-peer milk sharing practices. But informal milk sharing or having no official platform for this process and lack of knowledge about donations may expose infants to bacteria and diseases. To facilitate milk donation and requests, this study presents a framework for mobile and web applications where donors and requestors can easily search the nearest milk recipients, request and donate breast milk with a validation process to address the gaps. With this, possible risks coming from donor milk can be eliminated. Also, this study will help donors and requestors learn more about the essential information on milk sharing and other mothering-related info. Furthermore, this proposed application will serve as a platform for mothers to connect those in need and those blessed to produce breast milk.
Hanse Kogge Kiosk Terminal for Game-Based Learning at Deutsches Schifffahrtsmuseum

Deutsches Schifffahrtsmuseum (DSM), known as the German Maritime Museum in Bremerhaven, collaborating with Hochschule (University of Applied Science) Bremerhaven was developing digital game-based learning (DGBL). The DGBL&#x0027;s theme is to introduce Hanse Kogge as a cultural heritage boat from the mediaeval era. The target group of this game is museum visitors, principally younger learners. This game is packaged in a terminal kiosk in the form of a miniature wooden boat. The game controller consists of Rudder, which functions to direct the boat orientation, and a capstan that functions to catch the wind as the power to move the boat. This research departs from DSM as an actual client, which provides several requirements to 6 International master students as their Master Project. This project developed from user needs to a boat shape design with a morphological approach by referring to the literature study. The final result of this research is a 2D game packaged in terminal kiosks, which are placed on the table. This research is examined successfully after going through usability testing with a good score.
Multi-Agent Reinforcement Learning via Directed Exploration Method

In reinforcement learning, as a result of sparse reward feedback, it is difficult for agents to learn effective strategy in complex environment. Therefore, this paper proposes a multi-agent reinforcement learning algorithm called Exploration MATD3, which uses directed exploration method to improve the performance of multiple agents in environment with sparse rewards. The Exploration MATD3 algorithm is based on multi-agent twin delayed deep deterministic policy gradient (MA TD3), and uses reward shaping to drive agents to explore unknown states. Reward is combined of extrinsic reward and intrinsic reward. The extrinsic reward is original reward from environment, and intrinsic reward is calculated by the k-nearest neighbor states and random network distillation in each episode to reflect the novelty of new states. Evaluation is on Atari 2600 multiplayer games, and results verify that the proposed algorithm has better performance than MATD3 in environment with sparse rewards.
Treed-Gaussian Processes with Support Vector Machines as Nodes For Nonstationary Bayesian Optimization

A large family of black box methods rely on surrogates of the unknown, possibly non linear non convex reward function. While it is common to assume stationarity of the reward, many real-world problems satisfy this assumption only locally, hindering the spread application of such methods. This paper proposes a novel nonstationary regression model combining Decision Trees and Support Vector Machine (SVM) classification for a hierarchical non-axis-aligned partition of the input space. Gaussian Process (GP) regression is performed within each identified subregion. The resulting nonstationary regression model is the Treed Gaussian process with Support Vector Machine (SVMTGP), and we investigate the sampling efficiency from using our a model within a Bayesian optimization (BO) context. Empirically, we show how the resulting algorithm, SVMTGP-BO never underperforms BO when this is applied to an homogeneous Gaussian process, while it shows always better performance compared to the homogeneous model with nonlinear functions with complex landscapes.
Formation of the Energy-Efficient Platform of Hi-Tech Development of Renewable Power

The possibilities of reducing the conflict of interests of the efficiency and innovativeness of high-tech development of renewable energy in the conditions of the formed platform have been substantiated. A model for organizing joint activities of energy facilities in the virtual space of the platform has been developed. The model allows combining digital and analog control methods using the Date Science toolkit. The transition from the conditions of separate functioning of objects and self-organization to regulated development in the complex has been substantiated. The platform takes into account the impact of the resources of science and education in projects of high-tech development of renewable energy facilities. The uncertainty of high-tech-logical development in the platform is taken into account in terms of the quality indicators of management of the coordination of interaction between objects in assessing the entropy of synergy of resource integration processes. A regulator of the synergy of methods and projects in the Center for Regulation of High-Tech Development of the Complex is proposed to increase energy efficiency and reduce uncertainty.
A Simple Data Privacy and Security Protection Algorithm Based on Federated Learning

Abstract Although federated learning can break the data island and enhance privacy, it is not absolutely secure. External eavesdroppers can intercept the model when clients upload their models to the server, which might be used to infer the data information. A natural solution is to add noise to the uploaded model. However, this solution has its drawback, because the added noise cannot be eliminated when the models are aggregated at the server, which leads to poor performance. In order to tackle this problem, we propose a simple but effective algorithm, FedNoise. In the vanilla federated learning algorithm, a gradually decreasing learning rate is applied at the client side. While in our algorithm, we divide the parameter update into client-side and server-side. The stochastic gradient descent update with constant learning rate is performed at the client side and a gradually decreasing learning rate is performed at the server. Our algorithm can not only ensure the data security, but also maintain comparable system performance. Simulation results validate our conclusion.
Reply to Bretscher: Structures for directing mass attention

Bretscher’s letter in PNAS (1) argues that structures for diversifying the types of research that are feasible could be sufficient to jolt fields free of ossified canon. In particular, he proposes creating a grant track with panels composed of experts in neighboring fields alongside the existing funding process with in-field panel members. Initiatives such as these foster the development of more novel, less-canonical ideas by adding to the opportunity structure (what research is feasible or convenient) within a field. Providing researchers funding and time to engage in extracanonical investigations may ameliorate one of the problems we listed … [↵][1]1Email: johan.chu{at}kellogg.northwestern.edu. [1]: #xref-corresp-1-1
Using Decision Tree Algorithms in Detecting Spam Emails Written in Malay: A Comparison Study

Emails have become the most economical and fastest communication forms. However, during the past few years, the increment of email users has dramatically increased spam emails. Various anti-spam techniques have been developed to minimize if not eliminate the spam problem. In this paper, we study the disparity in the effectiveness of using different decision tree algorithms in email classification and combat spam problems. For that, we have chosen Universiti Utara Malaysia emails as a case study. To achieve the best possible classification accuracy, we compared all chosen algorithms’ performance, which are Random Forest, LMT, Decision Stump, J48, Random Tree, and REP Tree. The experimental results showed that the Decision Stump algorithm is more effective to be used in classifying the emails, and the F-measures, Precision, and recall score for the Decision Stump algorithm are higher than the other comparison algorithms.
LightBlue: Nurture Your Personal Chatbot

Chatbot has long been an important research topic in artificial intelligence and attracts lots of attention recently. Despite significant advancements in language ability, the interactions between users and chatbots are rather generic, short-term, and transnational. It has always been challenging to develop truly personal chatbots and even more difficult to establish longterm, affective connections. This paper first brings up “nurture” as a new interaction mode with chatbots. We introduce the nurture framework and accordingly design the learning algorithm and nurture functions. Then we present LightBlue – a platform that allows non-professionals to nurture personal chatbots from scratch. Experiments on both closed- and open-domain tasks validate the proposed framework and demonstrate a promising method for facilitating long-term interaction between users and chatbots.