Reviewerly/exHarmony huggingface dataset:
Incorporating time-interval sequences in linear TV for next-item prediction

As linear TV remains a major source for media consumption, multiple stakeholders such as broadcasters and advertisers are interested in the prediction of the next programs to be watched by TV viewers. Such predictions are quite challenging given the nature of the domain, where viewing TV is not just an individual activity but is also influenced by various contextual factors. We aim to address this challenge by integrating temporal aspects of linear TV – in the form of sequences – into the next program prediction process. A user profile is built from sequences of 24-hour TV programs’ views, at intervals of 15 min. Such profiles allow us to capture viewing preferences and sequential patterns, for predicting the next program/genre/category to be viewed at any time. We conducted several experiments using naive approaches, hidden Markov models and deep learning, juggling between accuracy and interpretability of the model. The [email protected] results were extremely promising (0.836 for next category prediction with LSTM, comparing to 0.57 an 0.34 with Naive approaches, and 0.366 with hidden Markov models).
Effects of Distance Measurement Methods in K-Nearest Neighbor Algorithm to Select Indonesia Smart Card Recipient

The Smart Indonesia Program (Program Indonesia Pintar, or PIP) through the Smart Indonesia Card (Kartu Indonesia Pintar, or KIP) is the provision of educational cash assistance to school-age children (aged 6 - 21 years) who come from poor and vulnerable families. A critical step in the KIP process is to determine the eligibility of a child who is generally selected manually. In this study, the researcher wanted to use the K-Nearest Neighbor (K-NN) algorithm to classify students eligible for KIP recipients. In addition, this paper analyzes the effect of several distance measurement functions on K-NN. There are four distance functions, that is Euclidean distance, Mahalanobis distance, Manhattan distance, and Minkowski distance. In addition, it also analyzes influence of feature selection on the performance of the classification model. The result of the K-NN algorithm has been evaluated using accuracy, precision, recall, and F-1. The study found that the combination of K-NN and Mahalanobis distance function has the highest performance compared to the other three distance functions to classify high-dimensional datasets. While for fewer variables, K-NN and Manhattan distance are the best solutions with the best performance compared to the other three distance functions. The last, feature selection can improve the performance of the classification model in any experiment scenario, except for the scenario of K-NN with Mahalanobis distance function.
Tablet-to-student ratio matters: Learning performance and mental experience of collaborative inquiry

The use of virtual manipulatives (VMs) in tablets has become increasingly popular in science courses, and previous studies have indicated its educational benefits. However, the tablet-to-student ratio (TSR), which may affect students' learning, has rarely been examined. This study compares how learning in groups with different TSRs influences the learning performance and mental experience of elementary school students. Participants were 117 fifth-grade students who were randomly assigned to two groups: Group 1:1 (i.e., each student had one tablet) and Group 1:m (i.e., each group shared one tablet) to learn the topic of triboelectrification. The results demonstrated that the students with a 1:m TSR performed better than those with a 1:1 TSR in terms of group work; that students with a 1:m TSR showed a higher degree of involvement during collaborative inquiries; and that the retention test, cognitive load, and group-process satisfaction results showed no significant difference between the two conditions. The findings indicate that the positive effect of collaboration on individuals may gradually disappear and the tradeoff between TSR and time of intervention should be considered during instruction.
Status Update on Phishing Emails Awareness: Jordanian Case

Abstract—This study is a response to the rapid proliferation of high-risk phishing emails, representing one of the most dangerous cybercrimes and the primary medium for the deception of online users. This study aims to investigate the various characteristics of phishing email messages, and to measure Jordanian internet users’ awareness and knowledge about these threats. To achieve these objectives, a quantitative approach was used based on an online survey that included a total of 1531 participants. The results showed that only a few participants had the ability to distinguish illegitimate from legitimate emails, as the majority of participants could not recognize illegitimate emails by answering with “legitimate email” or “I don't know”. Similarly, only a few participants could distinguish phishing emails, with the majority being unable to recognize phishing emails by answering with “real email” or “I don't know”. Therefore, more work to increase public awareness of phishing emails is recommended at the individual and institutional levels, so as to protect users from any possible phishing email threats. This study focuses on the aspects that are related to email phishing that include techniques used to generate such phishing emails on the basis of which we will identity preventive measures to protect Jordanian users and increase their awareness of phishing emails.
Anomaly Detection of ICS Communication Using Statistical Models

Industrial Control System (ICS) transmits control and monitoring data between devices in an industrial environment that includes smart grids, water and gas distribution, or traffic control. Unlike traditional internet communication, ICS traffic is stable, periodical, and with regular communication patterns that can be described using statistical modeling. By observing selected features of ICS transmission, e.g., packet direction and inter-arrival times, we can create a statistical profile of the communication based on distribution of features learned from the normal ICS traffic. This paper demonstrates that using statistical modeling, we can detect various anomalies caused by irregular transmissions, device or link failures, and also cyber attacks like packet injection, scanning, or denial of service (DoS). The paper shows how a statistical model is automatically created from a training dataset. We present two types of statistical profiles: the master-oriented profile for one-to-many communication and the peer-to-peer profile that describes traffic between two ICS devices. The proposed approach is fast and easy to implement as a part of an intrusion detection system (IDS) or an anomaly detection (AD) module. The proof-of-concept is demonstrated on two industrial protocols: IEC 60870-5-104 (aka IEC 104) and IEC 61850 (Goose).
Automatic Data Augmentation by Upper Confidence Bounds for Deep Reinforcement Learning

In visual reinforcement learning (RL), various approaches succeeded to improve data efficiency. However, the approaches fail to show generalization capabilities if different colors or backgrounds are applied to its environment. The lack of generalization capabilities can hinder the use of RL in real-world environment, which contains lot of distractions and noises. In this paper, a novel automatic data augmentation method that can improve generalization capabilities of an RL agent. In the experiments, the proposed method shows better generalization capabilities than other approaches. These results provide a simple automatic data augmentation method for RL that can improve generalization capabilities.
Assessing the Potential of Solar PV Installation based on Urban Land Cover Analysis

Abstract This paper presents a technical assessment of solar photovoltaic (PV) installation potential in urban areas based on its urban land cover type, using a case study of Indonesian cities including Yogyakarta, Kupang and Tomohon. The assessment was performed using a free online application to assess the urban land cover types, i-Tree Canopy. This application can be used to identify and distinguish urban land cover types such as building rooftop, vegetation, grass, soil, road and water, which then can be used to assess the suitable area for Solar PV installation. Additionally, solar photovoltaic power output data from Global Solar Atlas is used to calculate potential energy production from PV installations in each city. The result shows that in an urbanised city such as Yogyakarta, the most suitable PV installation is in building rooftops. Meanwhile, Kupang and Tomohon have higher potential for ground-mounted PV installation in bare ground or grass. The approach and result of this study could be used for planners and policymakers to determine city-scale solar PV installation planning to maximise solar energy production. It can also be used to calculate the solar energy estimation using free online applications, which is easy to use and more accessible for stakeholders.
Automatic Modulation Classification of Radar Signal Based on Machine Learning Network

A machine learning network based on the combination of empirical parameters and convolutional neural network (CNN) is proposed to recognize the modulation types of radar emitter signals. By using the characteristics of empirical parameters, the neural network can solve the problem that a large number of samples are needed to train when only using convolutional neural network algorithm to achieve automatic classification, and can effectively reduce the training cost of convolutional neural network. The simulation results show that the classification network can effectively classify signals of different modulation types.
Image design and interaction technology based on Fourier inverse transform

Abstract As one of the main directions of applied mathematics research, inverse Fourier transform (FT) has been widely used in image speech analysis and other fields in recent decades of development. FT is the basic content of digital image processing technology. In practical analysis, image design and interaction can be realised by using time-space domain and frequency domain, which can accurately obtain image information characteristics and achieve the expected application goals. In this paper, based on the understanding of FT and inverse transform, an improved algorithm is used to lay the foundation for the realisation of image design and interactive technology.
Collision simulation of trucks and pedestrians based on Pc-Crash in the blind area of truck driving

In the process of road transportation, due to the long body of the truck, there will be a difference in the radius of the inner wheel during turning, resulting in the driver's blind spot. Once pedestrians and non-motor vehicles appear in the blind spot, traffic accidents will occur. Firstly, this paper studies the formation mechanism of truck right turn blind area, simulates the truck right turn process combined with Pc-Crash accident reconstruction software, and analyzes the difference of radius between inner wheels in the process of vehicle steering; Three collision processes at different collision positions are simulated by Pc-Crash software. By analyzing the collision process of trucks and pedestrians at different positions, and analyzing the injury degree of trucks to pedestrians, it is found that the injury degree of pedestrians at different collision positions is different, and the collision position is divided into three parts according to the injury degree of pedestrians, so as to more effectively avoid the injury of trucks to pedestrians during turning.
Interpreting Model Predictions with Constrained Perturbation and Counterfactual Instances

In recent years, machine learning models have achieved magnificent success in many industrial applications, but most of them are black boxes. It is crucial to understand why such predictions are made in many critical areas such as medicine, financial markets, and auto driving. In this paper, we propose Coco, a novel interpretation method which can interpret any binary classifier by assigning each feature an importance value for a particular prediction. We first adopt MixUp method to generate reasonable perturbations, then apply these perturbations with constraints to obtain counterfactual instances and finally compute a comprehensive metric on these instances to estimate the importance of each feature. To demonstrate the effectiveness of Coco, we conduct extensive experiments on several datasets. The results show our method achieves better performance in identifying the most important features compared with the state-of-the-art interpretation methods, including Shap and Lime.
Research on the problem of image multi-classification

Image classification is an important research topic in the computer vision area. The classification task of small data volume, large number of classes, smaller differences between classes and larger differences within classes has always been a challenge. And this article aims to study and discuss such problems, using three methods: ResNets, EfficientNet and MoCo V2 for experiment and continuous optimization, and got the following results: The accuracy rate of ResNets classification is 0.43, of EfficientNet classification is 0.47, and of MoCo V2 classification, it is 0.62.
Quantitative Comparisons of Edge Based and Region Based Feature Detection in Digital Aerial Imagery Analysis

Digital image segmentation focuses on separation of one or more features in an image based on certain similarity or discontinuity criterion. It is inevitable for the aerial imagery to be contaminated by artifacts and noises, which causes extra blurry boundary, intensity inhomogeneity, and high complexity. Both edge based and region based feature detection are broadly applied to segmentation of digital aerial imagery. Numerous comparative results between the two have been obtained based on qualitative analysis, while conclusions are lack of convincing data-driven evidences instead. In this article, the systematic quantitative analysis approach is proposed to compare two dominating feature detection techniques using the RGB model and HSI model, from both the frequency domain and spatial domain perspectives. Three popular digital image segmentation schemes are employed for comparisons, including Canny edge detection, Gabor edge detection, and kernel fuzzy C-Means clustering. A useful set of quantitative information metrics has been introduced, such as the discrete entropy, mutual information, homogeneity, contrast, and dynamic range, in order to enhance the decision making accuracy on feature detection and ensure information integrity.
Efficient Final Exponentiation for Pairings on Several Curves Resistant to Special TNFS

Pairings on elliptic curves are exploited for pairing-based cryptography, e.g., ID-based encryption and group signature authentication. For secure cryptography, it is important to choose the curves that have resistance to a special variant of the tower number field sieve (TNFS) that is an attack for the finite fields. However, for the pairings on several curves with embedding degree <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k=\{10,11,13,14\}$</tex> resistant to the special TNFS, efficient algorithms for computing the final exponentiation constructed by the lattice-based method have not been provided. For these curves, the authors present efficient algorithms with the calculation costs in this manuscript.
Medical Image Classification Based on Information Interaction Perception Mechanism

Colorectal cancer originates from adenomatous polyps. Adenomatous polyps start out as benign, but over time they can become malignant and even lead to complications and death which will spread to adherent and surrounding organs over time, such as lymph nodes, liver, or lungs, eventually leading to complications and death. Factors such as operator's experience shortage and visual fatigue will directly affect the diagnostic accuracy of colonoscopy. To relieve the pressure on medical imaging personnel, this paper proposed a network model for colonic polyp detection using colonoscopy images. Considering the unnoticeable surface texture of colonic polyps, this paper designed a channel information interaction perception (CIIP) module. Based on this module, an information interaction perception network (IIP-Net) is proposed. In order to improve the accuracy of classification and reduce the cost of calculation, the network used three classifiers for classification: fully connected (FC) structure, global average pooling fully connected (GAP-FC) structure, and convolution global average pooling (C-GAP) structure. We evaluated the performance of IIP-Net by randomly selecting colonoscopy images from a gastroscopy database. The experimental results showed that the overall accuracy of IIP-NET54-GAP-FC module is 99.59%, and the accuracy of colonic polyp is 99.40%. By contrast, our IIP-NET54-GAP-FC performed extremely well.
Lightweight Blockchain of Things (BCoT) Architecture for Enhanced Security: A Literature Review

Both the internet of things (IoT) and distributed ledger technology (DLT), more commonly known as the blockchain, are two popular emerging technologies of this era. While blockchain offers strengthened security, along with other benefits, it requires peer-to-peer (P2P) nodes for its consensus process. On the contrary, IoT ecosystems inherently consist of many P2P nodes but it is highly critiqued for its lack of security measures. Therefore, the fusion of these complementary duos, known as the blockchain of things (BCoT), has become a recent research trend. While the fit is good and the benefits such consolidation can offer are obvious, a lot of challenges are yet to be addressed. Therefore, we have conducted a comprehensive literature review, covering 33 research articles, spanning over the last six years (2016&#x2013;2021), to report the state-of-the-art research in this domain. We have synthesised the existing literature by comparing, contrasting, resembling as well as critically evaluating them and thus, deduced the current challenges and future research directions, particularly with regards to lightweightness.
Why Do Developers Reject Refactorings in Open-Source Projects?

Refactoring operations are behavior-preserving changes aimed at improving source code quality. While refactoring is largely considered a good practice, refactoring proposals in pull requests are often rejected after the code review. Understanding the reasons behind the rejection of refactoring contributions can shed light on how such contributions can be improved, essentially benefiting software quality. This article reports a study in which we manually coded rejection reasons inferred from 330 refactoring-related pull requests from 207 open-source Java projects. We surveyed 267 developers to assess their perceived prevalence of these identified rejection reasons, further complementing the reasons. Our study resulted in a comprehensive taxonomy consisting of 26 refactoring-related rejection reasons and 21 process-related rejection reasons. The taxonomy, accompanied with representative examples and highlighted implications, provides developers with valuable insights on how to ponder and polish their refactoring contributions, and indicates a number of directions researchers can pursue toward better refactoring recommenders.
MultiSense: Cross Labelling and Learning Human Activities Using Multimodal Sensing Data

One of the major challenges for fully enjoying the power of machine learning is the need for the high-quality labelled data. To tap-in the gold-mine of data generated by IoT devices with unprecedented volume and value, we discover and leverage the hidden connections among the multimodal data collected by various sensing devices. Different modal data can complement and learn from each other, but it is challenging to fuse multimodal data without knowing their perception (and thus the correct labels). In this work, we propose MultiSense, a paradigm for automatically mining potential perception, cross labelling each modal data, and then improving the learning models for recognizing human activity accurately. We design innovative solutions for segmenting, aligning, and fusing multimodal data from different sensors. We implement our framework and conduct comprehensive evaluations on a rich set of data. Our results demonstrate that MultiSense significantly improves the data usability and the power of the learning models. With 9 diverse activities performed by users, our framework automatically labels multimodal sensing data generated by five different sensing mechanisms (video, smart watch, smartphone, audio, and wireless-channel) with an average accuracy 98.5%, while for each single-modal data model, the accuracy is 95.2%, 92.1%, 71.1%, 91.5%, and 34.8% respectively. Furthermore, it enables the existing models to learn unknown activities from other modalities and thus greatly improves the activity recognition accuracy.
Trainable Diffusion Network Based on Morphological Laplacian

This paper presents a deep network for image processing based on diffusion equation and morphological filtering. The diffusion equation has been applied to image processing, and is unwrapped to the trainable network, of which parameters are trained by the back-propagation. In this paper, we introduce the morphological Laplacian that is defined by morphological filters to the diffusion network. Since the morphological filtering can be implemented without multiplications, almost computations of the network are min and max operations and addition. Due to this property, the proposed network can be implemented with low-precision unsigned integer arithmetic. We apply the proposed morphological diffusion network to the Gaussain denoising and image completion. Denosing examples show the proposed network obtains denosing results comparable to BM3D [1] with the number of parameters that is about 1/15 of full scale deep convolutional neural networks [2]. Image completion examples show that the result of the proposed method is superior to the convolutional neural network in terms of PSNR with less number of the parameters.
Research on minimizing VR motion sickness in VRChat

In the VR social game - VRChat, VR motion sickness, as known as Cybersickness(CS), becomes a serious negative factor that degrades user experiences in VRChat. Current solutions that have been utilized to solve CS either degrade user experience in game or contain risk factors. Therefore, better CS solving methods should be applied in this field. The paper first investigates the influencing factors and inducement mechanisms of CS; analyze two classical theories - Postural instability theory and Sensory conflict theory, which explains motion sickness problems, and settle the exact inducement of CS in VRChat. Then the paper analyzes the better CS solutions according to the inadequacy of current solutions. Three promising solutions found are tricking vestibular organs, infinite space and the BCI system. After research, each of the three better solutions presented by the paper is considered to also contain disadvantages. The side effect of tricking vestibular organs requires further study; large omnidirectional treadmills are too expensive; BCI systems and EEG algorithms need improvements in accuracy. In conclusion, current solutions are not perfect and require further study in allusion to the VRChat. In the plateau of the productivity of VR technology, as technology and market size advance, there will certainly be more satisfying solutions to solve CS problems. The author also believes that peripheral devices such as VR treadmills will have better development prospects due to auxiliary features.