Reviewerly/exHarmony huggingface dataset:
Monocular Vision based Perception System for Nighttime Driving

The perception of objects around the vehicle is important for both advanced driving assistance system (ADAS) and autonomous driving systems. However, it is a huge challenge to recognize objects in a low-light environment. In this paper, we propose a monocular vision based perception system for nighttime driving. First, the transnational nighttime driving videos are collected, which are further processed into a low-light enhancement dataset and a nighttime object detection dataset for deep learning. Then, the GAN-based EnlightenGAN is trained for enhancing the low-light image. The CNN-based YOLOX is trained and inferred to detect objects. Next, to obtain reliable pixel points on objects, dense feature points are output on the enhanced image by LoFTR, which extracts global features through the transformer. On the other hand, the deep learning-based monodepth2 is used to generate a depth map from the enhanced image. Finally, the relative distance of the object is obtained by projecting and filtering the feature points on the depth map. The experiments show that this system can robustly detect vehicles and pedestrians on the road with distance in the nighttime driving dataset, which can provide effective information for vehicles and robots in nighttime scenes.
Automatic Generation of Business Intelligence Chatbot for Organizations

Business Intelligence (BI) helps organizations in making data-driven decisions by visualizing the current and historical data. Despite the plethora of BI tools, their accessibility and usability are still issues. To alleviate these issues, organizations use Chatbots. However, due to the variety of requirements and different organizational structures, developing a BI chatbot is a complex task that requires the collaboration of technical and BI experts. To address this problem, in this paper, we propose a model-driven approach for the automatic generation of personalized BI chatbots for organizations. The approach is implemented as a framework, which includes two components. First is a modeling component that allows the user to model the business-specific chatbot. Second is an automation component that automatically generates the chatbot code. We conducted a case study to evaluate the applicability of our approach. The results are promising for developing interactive BI chatbots according to various organizational needs.
Impact of using Microsoft Teams for Teaching and Learning: A Case of WSU Butterworth Campus

This study focused on investigating the impact of using Microsoft Teams (MS Teams) for teaching and learning at WSU Butterworth because of its geographical location. In an attempt to address the objectives, the study determined the factors that need to be considered, challenges, and benefits of using MS Teams for teaching and learning at a rural university. The study adopted a quantitative research method with the use of a closed-up questionnaire. A convenience sampling technique was to select participants. Data collected was analysed using Statistical Package for Social Sciences (SPSS). The results showed that students need a conducive environment in their places of residence to experience a positive learning experience. The results showed that conditions for learning must be favorable and adequate for teaching and learning to take place. The study also found a good number of significant benefits in using MS Teams such as accessing class recordings, notes, and other resources easily which constituted a desirable out-ofclassroom support system for students.
Tell me something interesting: Clinical utility of machine learning prediction models in the ICU

In recent years, extensive resources are dedicated to the development of machine learning (ML) based clinical prediction models for intensive care unit (ICU) patients. These models are transforming patient care into a collaborative human-AI task, yet prediction of patient-related events is mostly treated as a standalone goal, without considering clinicians' roles, tasks or workflow in depth. We conducted a mixed methods study aimed at understanding clinicians' needs and expectations from such systems, informing the design of machine learning based prediction models. Our findings identify several areas of focus where clinicians' needs deviate from current practice, including desired prediction targets, timescales stemming from actionability requirements, and concerns regarding the evaluation and trust in these algorithms. Based on our findings, we suggest several design implications for ML-based prediction tools in the ICU.
Arduino nano based Smart Glasses

Smart gadgets are becoming popular these days because they help people do routine chores faster and more efficiently. This research, presents an Arduino Nano based smart glass that displays the required information on the user's one eye.
Preferences in the Willingness to Download a COVID-19 Contact Tracing App in the Netherlands and Turkey: Experimental Study

Despite the worldwide growth in using COVID-19 contact tracing apps (CTAs) and the potential benefits for citizens, governments, health care professionals, businesses, and other organizations, only a few studies have examined the factors affecting the levels of willingness to download a CTA.This study aimed to investigate individuals' preferences in the willingness to download a health app.We conducted an experimental study in 2 countries, the Netherlands (N=62) and Turkey (N=83), using 4 different vignettes (ie, data protection, manufacturer, reward, and gaming models) with different attributes. Participants were randomly assigned to 1 of the conditions within the vignettes.The results showed that data protection and gaming elements are factors that influence the willingness to download a COVID-19 CTA. More specifically, we see that data protection is an important factor explaining the willingness to download the app in Turkey, whereas including gaming elements significantly affects the willingness to download the app in the Netherlands.COVID-19 CTAs are highly promising to reduce the spread of the virus and make it easier to open up society faster, especially because they can be used quickly and share information rapidly. COVID-19 CTA developers must ensure that their apps satisfactorily and sufficiently address ethical considerations, even in times of crisis. Furthermore, integrating gaming elements in the CTA could enhance the willingness to download the CTA.
Design and Performance Analysis of Three axis Solar Tracking System

This study introduces the design and performance of a three-axis solar tracker system. The primary objective of evolving a three-axis solar tracker is to follow the sun's location and remove shading caused by obstacles. High-rise objects, such as upcoming buildings, trees, or shading caused by the preceding row of PV modules due to the sun's changing latitudes during the winter and summer, could be obstacles. To overcome these challenges, a third axis is included to allow the height of the solar panel to be adjusted so that it is not shaded. Existing solar tracking systems attempt to generate maximum output power but are unable to eliminate 100% shading on the solar panel's surface, resulting in lower received output power. If the PV module is kept vertical to the sun's radiation, the PV system will produce the most power. With this addition to the tracker, the solar panel stays perpendicular to the rays of sunlight with this addition to the tracker, allowing it to collect the most solar radiation at sunset and sunrise, which is difficult to achieve with static solar panels and a two-axis tracker system. Test outcomes reveal that the developed system generates more output power by up to 181% and 19% as compared to the static solar panels and dual-axis (active type) solar tracker systems, respectively. Thus, the main ideology of this technology is to obtain maximum solar radiation from sunrise to sunset, regardless of the sun's changing positions (summer and winter).
Evaluation Method of English Innovative Thinking Teaching Effect Based on BP Algorithm

Aiming at the problems of poor generalization ability, low evaluation accuracy, and correlation coefficient in evaluating the teaching effect of English innovative thinking, this paper puts forward the evaluation method of English innovative thinking teaching effect based on BP algorithm, confirms the evaluation indexes of English innovative thinking teaching effect on the basis of clarifying the evaluation principles, and uses conditional information entropy to screen a large number of evaluation indexes. Establish the evaluation of English innovative thinking teaching effect of BP algorithm and finally optimize the parameters of BP algorithm through differential evolution algorithm, so as to complete the evaluation of English innovative thinking teaching effect. The experimental results show that the proposed method can effectively improve the generalization ability, evaluation accuracy, and correlation coefficient.
Evaluation of neural networks applied in forensics; handwriting verification example

There is a growing interest in the possibility of artificial neural networks’ applications in forensics. Extensive research has been published on this subject, especially in the field of handwriting examination. However, it seldom discusses forensic and legal standards, which are the most fundamental of conditions for the acceptance of artificial neural networks in forensics. From the perspective of handwriting analysis, we have exemplified and systematized general methods for an informal falsification of artificial neural networks applied to verification of offline handwritten documents’ authorship. These approaches should be generally effective against applications of neural networks in forensics, aimed to objectively expose and prove models as unreliable.
Intuitionistic Fuzzy-Based Three-Way Label Enhancement for Multi-Label Classification

Multi-label classification deals with the determination of instance-label associations for unseen instances. Although many margin-based approaches are delicately developed, the uncertainty classifications for those with smaller separation margins remain unsolved. The intuitionistic fuzzy set is an effective tool to characterize the concept of uncertainty, yet it has not been examined for multi-label cases. This paper proposed a novel model called intuitionistic fuzzy three-way label enhancement (IFTWLE) for multi-label classification. The IFTWLE combines label enhancement with an intuitionistic fuzzy set under the framework of three-way decisions. For unseen instances, we generated the pseudo-label for label uncertainty evaluation from a logical label-based model. An intuitionistic fuzzy set-based instance selection principle seamlessly bridges logical label learning and numerical label learning. The principle is hierarchically developed. At the label level, membership and non-membership functions are pair-wisely defined to measure the local uncertainty and generate candidate uncertain instances. After upgrading to the instance level, we select instances from the candidates for label enhancement, whereas they remained unchanged for the remaining. To the best of our knowledge, this is the first attempt to combine logical label learning with numerical label learning into a unified framework for minimizing classification uncertainty. Extensive experiments demonstrate that, with the selectively reconstructed label importance, IFTWLE achieves statistically superior over the state-of-the-art multi-label classification algorithms in terms of classification accuracy. The computational complexity of this algorithm is On2mk, where n, m, and k denote the unseen instances count, label count, and average label-specific feature size, respectively.
Automated Human Use Mapping of Social Infrastructure by Deep Learning Methods Applied to Smart City Camera Systems

With the emergence of the smart city, there is a growing need for scalable methods that sense how humans interact and use infrastructure in order to model social behaviors relevant to designing sustainable and resilient built environments. Cyber-physical system (CPS) frameworks used to monitor and automate infrastructure systems in smart cities can be extended to sense people to better understand how they use infrastructure systems including social infrastructure (e.g., parks, markets). This paper adopts convolutional neural network (CNN) architectures to automate the detection and spatiotemporal mapping of people using camera data to form a cyber-physical-social system (CPSS) for smart cities. The Mask region based convolutional neural network (R-CNN) detector was adopted and tailored to identify and segment human subjects in real time using camera images with an average speed of 7 frames per second. The Mask R-CNN framework was trained end to end using the Objects in Public Open Spaces (OPOS) image data set that includes classified segmentations of people in public spaces. A two-dimensional/three-dimensional (2D-3D) lifting algorithm based on a monocular camera calibration model was also employed to accurately position detected people in space. Finally, a Hungarian assignment algorithm based on association metrics extracted from detected people was used to assign people to spatiotemporal trajectories. To demonstrate the proposed framework, this study used the Detroit riverfront parks to study how people utilize community parks, which are a form of social infrastructure. The Mask R-CNN detector is proven precise in detecting and classifying the behavior of people in parks with mean average precision well above 85% for all class types defined in the OPOS library. The framework is also shown to be effective in spatially mapping the various uses of park furnishings, leading to better management of parks.
BA-CNN: Bat Algorithm-Based Convolutional Neural Network Algorithm for Ambulance Vehicle Routing in Smart Cities

This article proposes an ambulance vehicle routing approach in smart cities. The approach is based on the bat algorithm and convolutional neural network (BA-CNN). It aims to take transfer the patients confidentially, accurately, and quickly. The type of CNN used in this research is a residual network (ResNet). The node method is responsible for creating the city map. In the beginning, information about the accident place is received by the control station and forwarded to both the hospital and the ambulance. The driver feeds the data that contain the ambulance vehicle’s node position and the accident location to the BA-CNN vehicle routing algorithm. The algorithm then obtains the shortest path to reach the location of the accident by the driver. When the vehicle arrives at the accident location, the driver updates the algorithm with hospital and accident positions. Then, the shortest path (which leads to the fast reach time) to the hospital is calculated. The bat algorithm provides offline data for a possible combination of different source and destination coordinates. The offline data are then trained by utilizing a neural network. The neural network is used for finding the shortest routes between source and destination. The performance evaluation of the BA-CNN algorithm is based on the following metrics: end-to-end delay (EED), throughput, and packet delivery fraction (PDF). This BA-CNN is compared with counterparts, including three different existing methods such as TBM, TVR, and SAODV. The experiments demonstrate that the PDF of our method is 0.90 for 10 malicious nodes, which is higher than in the TBM, TVR, and SAODV.
Analysis of Post-Quantum Cryptography for Internet of Things

Cryptographic algorithms are crucial for secure communication in the Internet of Things (IoT). Quantum computers threaten public key cryptography algorithms like RSA (Rivest, Shamir, Adleman) and Diffie-Hellman (DH) key exchange schemes, which are widely in use today. As cryptographic algorithms are the foundation of secure internet communication, the development of Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC) is essential to protect transactions over the internet. However, solutions like Quantum Key Distribution require heavy infrastructure. So, it is not feasible for the Internet of Things with fewer resources in computing power and form factor. Post-Quantum Cryptography is helpful on these devices. But, it needs further developments and improvements. This paper analyzes cryptography algorithms and protocols of IoT with different categories. Integrated PQC of National Institute of Standards and Technology (NIST) third level finalists on Raspberry Pi 4 (RPI4) board with Linux operating system. The performance of Post-Quantum Cryptography schemes for IoT has been evaluated. Finally, a Post-Quantum Cryptography scheme has been suggested for the Internet of Things based on the performance analysis.
Raspberry Pi Processor-based i-Gloves for Mute Community and Home Automation System

Communications plays the major role for sharing our thoughts and make others to understand our point of view. But the major difficulty faced by the deaf and dumb people is speaking and hearing illness. Dumb people use sign language to convey their thoughts but most of the people aren&#x0027;t know about the sign language which make them more difficult in sharing their thoughts. This research helps the deaf and dumb people to convey their thoughts without any difficulties. The aim of the research is to develop a cost efficient system to help the dumb people. In this project the sign language is converted into a text and voice over mechanism. The sign language gesture is analyzed using the flex sensor and then it gets converted into a text and voice. The output is displayed using the speaker. The data processing will be done using the processor. The gesture recognition will follow the principle of Hall Effect. This system helps the deaf and dumb people to get a chance to grow in their respective carrier and extended to home automation system.
Quantum-Safe Group Key Establishment Protocol from Lattice Trapdoors

Group communication enables Internet of Things (IoT) devices to communicate in an efficient and fast manner. In most instances, a group message needs to be encrypted using a cryptographic key that only devices in the group know. In this paper, we address the problem of establishing such a key using a lattice-based one-way function, which can easily be inverted using a suitably designed lattice trapdoor. Using the notion of a bad/good basis, we present a new method of coupling multiple private keys into a single public key, which is then used for encrypting a group message. The protocol has the apparent advantage of having a conjectured resistance against potential quantum-computer-based attacks. All functions-key establishment, session key update, node addition, encryption, and decryption-are effected in constant time, using simple linear-algebra operations, making the protocol suitable for resource-constrained IoT networks. We show how a cryptographic session group key can be constructed on the fly by a user with legitimate credentials, making node-capture-type attacks impractical. The protocol also incorporates a mechanism for node addition and session-key generation in a forward- and backward-secrecy-preserving manner.
Designing technologies to support everyday mindfulness beyond meditation for mental well-being

Mindfulness is a powerful tool to help individuals increase their awareness of the present moment non-judgmentally, thereby supporting mental well-being. While numerous mindfulness technologies and resources are available for teaching novices to do meditation, there is little research on supporting people to apply the awareness they have learned in mindfulness meditation to their daily activities. Taking a research-though-design approach, my dissertation work will explore the design of mindfulness technologies that go beyond supporting meditation. I aim to combine semi-structured interviews and co-design workshops with mindfulness practitioners to understand the characteristics of daily mindfulness experiences and the design space of technologies to support this practice. I also plan to apply an autobiographical design approach to obtain a nuanced understanding of the lived experience of embedded mindfulness in daily life. This dissertation work has the potential to inform the design of mindfulness technology to be better situated in one's daily activities and support meaningful mindfulness journeys.
Narrating by Doing: A Bridging Concept for Understanding and Informing the Design of Tangible Interfaces for Storytelling

Abstract We present and discuss the concept ‘narrating by doing’ as the process of creating narratives by performing different embodied actions with tangible interfaces for storytelling. We characterize it as a ‘bridging concept’ that can facilitate exchange between theory and design, informing research and design of TUIs for storytelling targeting young children. The concept builds on theories of cognition, learning and narration, specifically drawing upon the following perspectives: Constructionism, Socio-Constructivism, Embodied Cognition, Narratology and The Narrative Practice Hypothesis. Building upon these theoretical foundations, we identify and discuss four ‘design articulations’ (i.e. important parameters that express the qualities of the concept), namely communicative situation, narrative function of the tangible objects, collaborative and embodied actions and the narrator’s position. Finally, we add evidence to the concept and discuss its productiveness by presenting a set of considerations to inform the design of tangible interfaces for storytelling.
Interactive error correcting codes over binary erasure channels resilient to &gt; ½ adversarial corruption

An error correcting code (ECC) allows a sender to send a message to a receiver such that even if a constant fraction of the communicated bits are corrupted, the receiver can still learn the message correctly. Due to their importance and fundamental nature, ECC's have been extensively studied, one of the main goals being to maximize the fraction of errors that the ECC is resilient to.
Understanding the Practices of Global Censorship through Accurate, End-to-End Measurements

It is challenging to conduct a large scale Internet censorship measurement, as it involves triggering censors through artificial requests and identifying abnormalities from corresponding responses. Due to the lack of ground truth on the expected responses from legitimate services, previous studies typically require a heavy, unscalable manual inspection to identify false positives while still leaving false negatives undetected. In this paper, we propose Disguiser, a novel framework that enables end-to-end measurement to accurately detect the censorship activities and reveal the censor deployment without manual efforts. The core of Disguiser is a control server that replies with a static payload to provide the ground truth of server responses. As such, we send requests from various types of vantage points across the world to our control server, and the censorship activities can be recognized if a vantage point receives a different response. In particular, we design and conduct a cache test to pre-exclude the vantage points that could be interfered by cache proxies along the network path. Then we perform application traceroute towards our control server to explore censors' behaviors and their deployment. With Disguiser, we conduct 58 million measurements from vantage points in 177 countries. We observe 292 thousand censorship activities that block DNS, HTTP, or HTTPS requests inside 122 countries, achieving a 106 false positive rate and zero false negative rate. Furthermore, Disguiser reveals the censor deployment in 13 countries.