Reviewerly/exHarmony huggingface dataset:
Global Explanations for Multivariate time series models

Several explainable AI algorithms have been proposed to help make machine learning models more interpretable and trustworthy. However in spite of numerous methodological advancements, there is still a persistent gap between what researchers develop and what business users seek. In this work, we aim to bridge this gap for an AI system that predicts the remaining useful life of an aircraft’s engine using time series data collected from multiple sensors. We propose a novel approach to compute easily understandable explanations by fusing two explainers in sequence wherein explanations of the first explainer are explained by the second. We use this approach to build a global post-hoc model-agnostic explainer for AI models that ingest multivariate time series data. Our approach fuses a local explainer that yields feature importance weights, with a directly interpretable model that outputs global rules. Our experimental results based on the C-MAPSS open-source dataset demonstrate that the proposed two-stage explainer computes global explanations that are amenable to business users and sheds light on how the behavior of an individual and a group of sensors impacts the remaining useful life of an aircraft’s engine.
Machine versus Human Attention: A comparative study of different transfer learning models

Explainable Artificial Intelligence (AI) assists the human user to understand the decision-making process of the machine and thus builds trust and confidence in them during deployment. Though such an algorithmic explaination helps but the basic research question remains unanswered: "Whether the deep network model also generates the same attention maps as the visual attention being used by the human to perceive the task of image classification?". In this paper, the authors made an effort to find the answer to the said question with the help of an empirical study with a benchmark dataset.
Attendance System based on Blockchain and Face Recognition

Blockchain is considered one of the most innovative techniques in the recent decade, this is because it takes advantage of the trustless, distributed, and tamper-proof. Researchers have established the existence of blockchain in regions from finance to health care records. They are also devoted to extending the application in other areas. This paper proposes an attendance system integrating blockchain and face recognition techniques. The attendance system consists of two parts, the face recognition component enables the system to automatically take attendance of a participant, which reduces complicated paperwork and saves time. Then, it stores attendance information in a blockchain-based database component at a specific time, providing a reliable and tamper-proof solution where no one is entitled to edit or delete data. We introduced an attendance system that utilizes a convolutional neural network for face recognition and stores the attendance data on the blockchain. The system shows decent resilience on tamper, and saves time for automatic attendance marks.
Developing a Cyber-resilience state in Botswana's Energy Industry

- The integration of the internet in industrial control systems in the energy sector has increased the number of cyberattacks on the infrastructure, necessitating cyber resilience. Cyber resilience is the capacity to withstand and recover quickly from unforeseen and severe cyberattack interruptions. In this paper, we investigate the global energy industry's current cyber-resilience rating and Botswana's cyber-resilience developments. We will discuss the timeline of cyberattacks and the weapons used by threat actors. We also examine and recommend the Cybersecurity Capability Maturity Model (C2M2) framework in order to comprehend the current state of a cybersecurity program in Botswana and monitor its development over time.
A Cityscape Image Detail Extraction Enhancement Method for Lightweight Semantic Segmentation

Lightweight semantic segmentation is widely used in automotive driving. But the existing methods lack the ability to extract the detailed features of urban street scenes, and the semantic segmentation network structure lacks the logical relationship of interdependence. In order to improve semantic segmentation performance in automotive driving, this paper is based on BisenetV2 to propose: (1) The re-parametrization strategy to improve the ability to extract details features. (2) The SENet channel attention mechanism is adopted to explicitly establish the interdependence between feature channels. (3) Using the larger kernel in the deep layer of the network structure increases the accuracy of semantic segmentation and hardly affects the calculated amount. We tested the Cityscapes test dataset to achieve 72.23% mIoU at 2048×1024 resolution with the speed of 39.55 FPS on one NVIDIA RTX A5000 card without pre-training and accelerated implementations like TensorRT, which is 1.8% more accurate than the latest methods while almost as fast.
Maturity of information systems' security in Ethiopian banks: case of selected private banks

Purpose Information systems' security is more critical than ever before since security threats are rapidly growing. Before putting in place information systems' security measures, organizations are required to determine the maturity level of their information security governance. Literature review reveals that there is no recent study on information systems' security maturity level of banks in Ethiopia. This study thus seeks to measure the existing maturity level and examine the security gaps in order to propose possible changes in Ethiopian private banking industry's information system security maturity indicators. Design/methodology/approach Four private banks are selected as a representative sample. The system security engineering capability maturity model (SSE-CMM) is used as the maturity measurement criteria, and the measurement was based on ISO/IEC 27001 information security control areas. The data for the study were gathered using a questionnaire. Findings A total of 93 valid questionnaires were gathered from 110 participants in the study. Based on the SSE-CMM maturity model assessment criteria the private banking industry's current maturity level is level 2 (repeatable but intuitive). Institutions have a pattern that is repeated when completing information security operations but its existence was not thoroughly proven and institutional inconsistency still exists. Originality/value This study seeks to measure the existing maturity level and examine the security gaps in order to propose possible changes in Ethiopian private banking industry's information system security maturity indicators. This topic has not been attempted previously in the context of Ethiopian financial sector.
Small insulator target detection based on multi‐feature fusion

Abstract The proportion of insulators in aerial power patrol images is small and the background of overhead lines is complex, often leading to incomplete and inaccurate detection of insulators. Therefore, an algorithm for detecting insulator targets based on multi‐feature fusion is developed in this study. Firstly, a dynamic threshold oriented fast and rotated brief algorithm is proposed, which uses the bag‐of‐words dictionary model to determine local shape features of the image, applies gradient weighting to the global texture feature vector extracted by the histogram of oriented gradients algorithm and performs radial gradient transformations to get the improved HOG of features. Secondly, the feature vectors are fused serially, the learning machine is trained and the parameters of the support vector machine are optimized using the quantum particle swarm optimization algorithm. Finally, the target area is pre‐divided by the selective search algorithm, and the area is classified by the learning machine. The experimental results show that the proposed feature extraction method can describe the image details more accurately than the existing methods, and the average accuracy of the feature extraction classifier can reach 93.7%, which helps to overcome the incomplete detection problem of insulator detection at the aerial work site.
Telecommunication and cyber fraud victimization among Chinese college students: An application of routine activity theory

Analyzing survey data from 1037 college students in China, a country with the world’s largest number of Internet users and the world’s largest e-commerce market, the current study demonstrates that consistent with previous research, some routine telecom/cyber activities of Chinese college students predict higher odds of being targeted for telecom/cyber fraud, but online routines do not seem to predict the odds of completed victimization resulting in a financial loss. In contrast, the perceived presence of effective formal guardianship and target suitability exert a greater influence. These findings suggest that those previously and commonly used measures of routine activity theory are better suited for explaining attempted telecom/cyber fraud victimization, whereas completed victimization is chiefly predicted by target suitability such as risky/deviant online behaviors and low self-control.
A reasoning diagram based method for fault diagnosis of railway point system

Railway Point System (RPS) is an important infrastructure in railway industry and its faults may have significant impacts on the safety and efficiency of train operations. For the fault diagnosis of RPS, most existing methods assume that sufficient samples of each failure mode are available, which may be unrealistic, especially for those modes of low occurrence frequency but with high risk. To address this issue, this work proposes a novel fault diagnosis method that only requires the power signals generated under normal RPS operations in the training stage. Specifically, the failure modes of RPS are distinguished through constructing a reasoning diagram, whose nodes are either binary logic problems or those that can be decomposed into the problems of the binary logic. Then, an unsupervised method for the signal segmentation and a fault detection method are combined to make decisions for each binary logic problem. Based on the results of decisions, the diagnostic rules are established to identify the failure modes. Finally, the data collected from multiple real-world RPSs are used for validation and the results demonstrate that the proposed method outperforms the benchmark in identifying the faults of RPSs.
Study on synchronous method of multi-sensor data acquisition in space based on Single-bus digital temperature sensor

In the study of temperature gradient measurement, the synchronization of multi-sensor array is a very important parameter. This paper proposes a distributed parallel data acquisition method based on a single bus temperature sensor to solve the problem of synchronization in measurement of space temperature gradient. The DS18B20 single bus temperature sensor is used as a single point temperature measuring device of array. ID matching is carried out firstly, then all IDs’ temperature conversion are started to keep the long process at the same period as possible. Finally, ID matching is carried out again, while temperature data are read from sensors. The experimental data show that the time consuming of distributed parallel data acquisition method for 150 temperature sensors is 3.2s, which is 70.4s shorter than the one-by-one temperature acquisition method. This proposed method improves the synchronization of data acquisition in DS18B20 sensor array. It is great significance to study the temperature field and temperature gradient model in space.
Vision-based Fall Detection: Methods and Challenges

Fall accidents cause great harm to the elderly. Fall detection becomes an important research topic as the elderly population grows. In the research field of signal processing, many fall detection methods have been proposed, such as fall detection based on audio signal processing, radar, pressure sensor, acceleration sensor or computer vision. This paper briefly reviews vision-based fall detection, summarizes the advantages and disadvantages of recent methods, and discusses future topics.
Method of Developing Hand Gesture Sets for Vehicle Entertainment and Ambiance Controls

Along with the development of autonomous and semi-autonomous driving, how people spend their time in vehicles will also change and these changes may require new methods of interacting with services in the vehicle such as entertainment, ambiance, and air conditioning. Based on these anticipated needs, this study investigates free air hand gesture control as a method of developing a set of hand gestures for control. The objective of this research is to model how people express commands to machine interfaces with free air hand gestures in order to develop consensus gesture sets. To collect the gesture data, participants perform hand gestures following 18 types of experiment tasks, and the gestures are recorded by 3 cameras. K-means++ clustering of the landmark coordinate data for every 5 frames extracts common gesture sequences for each task. The clustered sequences are then used to develop consensus gesture sets are developed for each task.
R2-B2: A Metric of Synthesized Image’s Photorealism by Regression Analysis based on Recognized Objects’ Bounding Box

In recent years, a lot of researches on AI (Artificial Intelligence) for Image Synthesis and Image Generation have been being conducted actively, and state of the art GANs (Generative Adversarial Networks) for text-to-image have been able to generate precise images with high photorealism for a text-based user query (but also no-good images). However, it is pointed out that the precision of all images generated for a query has been not always enough high. Therefore, for practical usages, they are required to be re-ranked and/or filtered based on some sort of metric(s). This paper proposes a novel metric, R2-B2 (RR-BB), on photorealism, especially “size balance” (i.e., balance between in-image objects’ size), of a manually or automatically synthesized image by Regression analysis based on multiple Recognized objects’ Bounding Box, i.e., the position $(x, y)$ and size (width, height, or area) of objects recognized in the image.
How Similar is Image Cognition in Humans and Computer Visions?

The development of deep learning for image cognition is remarkable. Computer Vision Models (CVMs) such as CNN, Vision Transformer (ViT), and CLIP, which were pretrained on a huge amount of training data, were released. We studied environmental cognition using photos. Photos were quantified by many participants manually measuring the inter-phot visual similarity (IVS). We had three types of photosets:, 200 student life photos, 242 townscapes, and 100 garden landscapes. We measured how similar the inter-photo CVM similarity is with the IVS by using MDS coordinates. In the garden landscapes, the first axis of CVM MDS was very similar to that of IVS, whereas the second axis was not similar. In Kawagoe townscapes and the student life photos, there were several clusters in IVS MDS. These clusters got spread in CVM MDS. In Kawagoe townscapes, the clusters appeared overspread. In the student life photos, both MDSs were similar, and CVM MDS looked better in fine classification. Representation seemed also involved in the differences in MDS between IVS and CVM.
A Hotel Recommendation Method based on Hotel Feature Analysis using Surrounding Facilities

In this paper, we propose a method for recommending hotels to users’ destinations by analyzing the features of the hotels, such as RATING, VALUE, COST, and ATTRIBUTE, from facilities surrounding the hotels for supporting hotel selection. We then calculate the scores for four different attributes: shopping, eating, entertainment, and service, based on each hotel’s features, and recommend the hotels to the users based on the hotels’ scores according to their needs. Finally, we verified the relevance between hotel attributes using Spearman’s rank correlation coefficient, and we discussed our proposed recommendation method using the NDCG to measure the accuracy of the proposed hotel rankings. As a result, we confirmed that the area types, scales, and locations could affect the correlation between hotel attributes; and the scales and locations could affect the accuracy of our proposed method.
Human Recognition from High-altitude UAV Camera Images by AI based Body Region Detection

The usage of unmanned aerial vehicles (UAVs) has rapidly increased during the recent past. The application areas for UAVs include search and rescue operations, tracking humans for surveillance, disaster management etc. In most such situations, the UAV’s ability to automatically recognize humans, body regions, and other objects is crucial. Yet visibility issues may exist in UAV images resulting in challenges for object detection. In the case of recognizing humans from the high-altitude UAV camera images, conventional methods mostly recognize humans, based on the features of the entire body. Such methods are less effective when the parts of the body appear in the images, especially during high-altitude flights. In this paper, we develop a framework for human recognition by detecting the human body regions with YOLOv5 and Haar cascade classifier. The body part detection is done using YOLOv5, and then the output is forwarded to Haar cascade classifiers to classify body regions such as head, upper body, lower body. Human availability is confirmed, if the classified body part belongs to one of above body regions. We conducted experiments using the VisDrone data set. Preliminary results show promise on the effectiveness of the method.
Learning from the Past: Regularization by Validation

Traditional deep model optimization methods discard the training weights can which contain information about the loss landscape that could guide further model optimization. In this paper, we show that a supervisor neural network could be used to predict the validation performance of another target neural network (student) through its training weights. Then based on this behavior, we propose a weight-loss pair-based training framework called REVAL to help decrease overfitting and increase the performance of the student model by using a supervisor model to learn the trajectories of weight updates of the student model. We conduct experiments on the MNIST, CIFAR10, and CIFAR-100 datasets with naive neural networks and show that we can improve the model classification performance with such simple network structures and training trajectories. Our code and models are available at https://github.con zhangenzhi/ rebyval.
Human-robot interaction environment to enhance the sense of presence in remote sports watching

The recent coronavirus disease 2019 (COVID-19) outbreak has helped increase the popularity of online video communication and meeting platforms as alternatives to face-to-face interactions. Such a trend has also triggered the emergence of remote cheering systems, hinting at the possibility that people could enjoy watching sports games in virtual environments even from their homes in the near future. However, reproducing a sense of presence similar to the atmosphere felt by fans in stadiums or event venues is a major challenge within virtual environments. Thus, our idea is to embed groups of cheerful robots in virtual environments, thereby creating a sense of unity and mimicking emotion spread among fans. In this study, we built a virtual cheering environment and embedded a game-event driven behavior model that enables a group of robots to display various emotions through nonverbal reactions according to the game flow. Then, we conducted a preliminary evaluation of the proposed system, where the participants and a group of robots were placed in a virtual cheering environment to watch a baseball game. The obtained results hinted at the meaningfulness of the proposed approach. Nevertheless, further work is necessary to achieve a sufficient sense of presence and validate the effectiveness of our proposed environment.
Effect of Using Continuous Integration (CI) and Continuous Delivery (CD) Deployment in DevOps to reduce the Gap between Developer and Operation

This paper focuses on how to use continuous integration (CI) and continuous Delivery (CD) methodology in DevOps to reduce the developer-operator gap. It also, shows how CI can be a CD bridge. The paper review DevOps and analyze strategies, methodologies, issues, and processes identified for the adoption and implementation of continuing practices. The result of our case studies shows the benefits, and advantages of using CI/CD in software development. Furthermore, this paper presents DevOps as a new model for reducing the gaps between development (Dev) and operations (ops). The Azure tool is used as DevOps CI/CD for the empowerment of continuous delivery of software to enable rapid and frequent releases, this enables rapid responses to changing customer requirements and thus it may be a decisive competitive advantage. This paper also measures the effectiveness of using CI/CD for reducing the time and effort in software development. We also, focus on the DevOps initiative to benefit of CI/CD and to effect of enhances flexibility in delivering the program with the expected quality on time to determine the areas that bridge the gap between Continuous Integration for Continuous Delivery. The methodology used in this paper by exploring and tracking a project developed by the company using the Azure software development tool. The experiment includes a project performance and evaluation.
Current Trends in Internet of Things Forensics

Digital forensics is essential when performing in-depth crime investigations and evidence extraction, especially in the field of the Internet of Things, where there is a ton of information every second boosted with latest and smartest technological devices. However, the enormous growth of data and the nature of its complexity could constrain the data examination process since traditional data acquisition techniques are not applicable nowadays. Therefore, if the knowledge gap between digital forensics and the Internet of Things is not bridged, investigators will jeopardize the loss of a possible rich source of evidence that otherwise could act as a lead in solving open cases. The work aims to introduce examples of employing the latest Internet of Things forensics approaches as a panacea in this regard. The paper covers a variety of articles presenting the new Blockchain, fog, and video-based applications that can aid in easing the process of digital forensics investigation with a focus on the Internet of Things. The results of the review indicated that the above current trends are very promising procedures in the field of Internet of Things digital forensics and need to be explored and applied more actively.