Reviewerly/exHarmony huggingface dataset:
THE INFLUENCE OF STUDENTS’ LEARNING STYLES ON THE UNDERSTANDING OF READING TEXTS

This research aimed to find out the influence of students’ learning styles on their understanding of reading comprehension for students of Communication Science taking the English Course in the 2022/2023 Academic year. This research was a quantitative approach. The sample of this research was 51 students of Communication Science at USM who took the English Course. These samples were taken by using a random sampling technique. The data were collected by using questionnaires and students’ final scores on reading comprehension tests. The data were analyzed by using the regression technique with SPSS version 24.0 for Windows. The research findings show that students’ learning style gave an influence on their reading comprehension which was shown by their final scores.
Implementasi Penyisipan Pesan Teks Terenkripsi Menggunakan Kriptografi ElGamal pada Citra Digital Menggunakan Steganogarafi LSB

Message security is very important in maintaining the confidentiality of information, especially those containing important information whose contents should only be known by the entitled party. Therefore, security is needed for messages that are confidential so that messages cannot be intercepted by people who do not want them and the information content of the message becomes more secure. One way is to apply ElGamal and LSB cryptography methods to be able to provide security to text messages by inserting into digital images, the ElGamal algorithm focuses on the strength of the key in solving discrete logarithm problems. By utilizing large prime numbers and discrete logarithm problems which are quite difficult and also by applying the LSB method for text insertion, the security of the key is more guaranteed. The test results on five image objects with the .BMP extension on the application that was built were running well and correctly. The image that is inserted in the encryption process is the insertion of text in the image after it is decrypted and the image produced after the encryption and decryption process is no different from the original image and the same thing for the image size does not change
On Data Association with Possibly Unresolved Measurements

Tracking targets based on measurements provided by radar, sonar, or lidar sensors is essential to obtain situational awareness in important applications, including autonomous navigation and applied ocean sciences. A key challenge in multitarget tracking is the unknown association between the available measurements and the targets to be tracked. In particular, robust data association for closely spaced targets requires advanced methods that explicitly model unresolved measurements. Due to limited sensor resolution, the sensor produces a single measurement for two or more actual targets. If not explicitly modeled in the multitarget tracking method, unresolved measurements lead to track losses and thus, to significant tracking errors. In this paper, we propose a scalable data association method for the tracking of multiple potentially unresolved targets. A loopy belief propagation method is presented that efficiently approximates the marginal association probabilities given a set of potentially unresolved measurements. This method scales quadratically in the number of targets and linearly in the number of measurements. Our numerical results demonstrate that the computed approximate marginal association probabilities are close in $L_{1}$ distance to the true marginal association probabilities, which can only be calculated for very small tracking scenarios.
Fostering empathy in social Virtual Reality through physiologically based affective haptic feedback

We study the promotion of positive social interactions in VR by fostering empathy with other users present in the virtual scene. For this purpose, we propose using affective haptic feedback to reinforce the connection with another user through the direct perception of their physiological state. We developed a virtual meeting scenario where a human user attends a presentation with several virtual agents. Throughout the meeting, the presenting virtual agent faces various difficulties that alter her stress level. The human user directly feels her stress via two physiologically based affective haptic interfaces: a compression belt and a vibrator, simulating the breathing and the heart rate of the presenter, respectively. We conducted a user study that compared the use of such a “sympathetic” haptic rendering vs an “indifferent” one that does not communicate the presenter’s stress status, remaining constant and relaxed at all times. Results are rather contrasted and user-dependent, but they show that sympathetic haptic feedback is globally preferred and can enhance empathy and perceived connection to the presenter. The results promote the use of affective haptics in social VR applications, in which fostering positive relationships plays an important role.
Crowd-Funded Earthquake Early-Warning System

Earthquake early warning systems has been proven to save countless lives in Japan, Mexico, and Chile, where earthquake warnings are often broadcast live on TV up to a minute before residents experience shaking. Unfortunately, traditional early warning systems require extensive capital investment. The high cost of traditional earthquake early-warning systems and limited budgets prevent earthquake-prone developing countries like the Philippines, Indonesia, Afghanistan, India, Burma, Ghana, Nigeria, Columbia, Venezuela, and Bolivia from building traditional earthquake warning systems. This project describes repurposing old Android smartphones into affordable dedicated seismometers to detect tremors. These smartphones have become disposable items and are continuously “upgraded“ and replaced. Yet every one of these devices includes everything needed to act as a dedicated seismometer: Wi-Fi capability, GPS, and an accelerometer. The software developed for this project converts these smartphones into dedicated seismometers and uses existing web technologies for telemetry services. This system would also trigger alerts to all devices that has the software installed whenever a tremor is detected, effectively making each seismic detection station double as an earthquake early-warning alarm. A large network of these seismic detection stations will effectively create an affordable earthquake early-warning system that can be rapidly implemented at an extremely low cost. It would provide developing nations an affordable life-saving alternative to expensive traditional earthquake early-warning systems. This solution is cheap, keeps old smartphones from landfills, and will save lives. (Abstract)
Multi-user Searchable Attribute Based Encryption for Outsourced Big Data

Petabytes of data are generated every minute, and this creates a corresponding demand for servers which ingest, process, store, and maintain this data. The scale of data has become sufficiently large that normal file access and processing present different challenges than in prior decades. In the Web 2.0 era, data owners commonly stored and shared data using on-premise servers. In the past decade, cloud computing has quickly become the dominant hosting paradigm, as it offers data owners the benefits of cost-savings, flexible scaling, and ease of administration. However, cloud computing presents a myriad of security challenges. Attribute based encryption has been key to tackling these challenges. We present a method to allow users to maintain their own secure data on a shared system, while eliminating the time penalty and single point-of-failure associated with traditional authorization techniques. This is implemented as a Representational State Transfer API, removing the burden of managing auxiliary files or encryption libraries when uploading documents. This system also supports data encrypted at rest, removing concerns of administrator access to unencrypted contents in storage. We leverage key policy attribute-based encryption (KP-ABE) to store this data, search its contents, and receive/decrypt this data. We further demonstrate that KP-ABE can be implemented as part of a distributed or centralized authorization method. We compare KP-ABE against AES cryptography, and present system metrics from its execution on representative data.
$\delta{-}$ GLMB Filter Based on Multiple Model Multiple Hypothesis Tracking

The kinematics of maneuvering multiple targets are generally unknown and time-varying. Using only a single frame of data, the conventional δ-Generalized Labeled Multi-Bernoulli (GLMB) filter has large tracking errors. Although the Multiple Hypothesis Tracking (MHT) algorithm utilizes multi-frame information, the amount of calculation will increase sharply with the number of targets and the complexity of environment. To address this issue, this paper incorporates the Multiple Model (MM) and MHT algorithm into δ-GLMB filter. MM is used to generate multiple hypothesis of δ-GLMB target components, and these hypotheses are enriched by association with measurements data on the level of multiple target component. In this way, multi- hypothesis information of each component is transmitted among successive multiple frames. Due to the use of multi-frame information, the performance of δ-GLMB filter for maneuvering targets in complex scenarios is effectively improved. At the same time, the computational complexity can be reduced by forming multiple hypothesis on the level of target components. Simulation results show that the proposed MM-MHT -δ-GLMB algorithm can effectively track targets in multi-target complex motion scenario and has better multi-target tracking accuracy than the conventional single model δ-GLMB filter.
Autonomous Voice Recognition Wheelchair Control System

Autonomous wheelchairs are essential for improving the mobility of individuals with disabilities or physical challenges. Advances in computer and wireless communication technologies have paved the way for the design of smart wheelchairs to match the needs of disabled people. This research paper introduces the design and implementation of an electric wheelchair controller using voice recognition. This model is based on a voice recognition algorithm for classifying the essential commands to drive the wheelchair. A pre-designed adaptive neuro-fuzzy controller has been used to generate the required control signals for activating the motors of the wheelchair. This controller alters data gathered from obstacle avoidance sensors and a voice recognition classifier. Deep learning algorithms have made many technological developments and influences modern daily lives. After testing our developed deep learning algorithm, the overall classification accuracy for distinguishing between eight voice commands was 98 %.
Inter-Intra Camera Identity Learning for Person Re-Identification with Training in Single Camera

Traditional person re-identification (re-ID) methods generally rely on inter-camera person images to smooth the domain disparities between cameras. However, collecting and annotating a large number of inter-camera identities is extremely difficult and time-consuming, and this makes it hard to deploy person re-ID systems in new locations. To tackle this challenge, this paper studies the single-camera-training (SCT) setting where every person in the training set only appears in one camera. In this work, we design a novel inter-intra camera identity learning (I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CIL) framework to effectively address the SCT person re-ID. Specifically, (i) we design a Dual-Branch Identity Learning (DBIL) network consisting of inter-camera and intra-camera learning branches to learn person ID discriminative information. The former learns camera-irrelevant feature representations by constraining the distance of inter-camera negative sample pairs closer than the distance of intra-camera negative sample pairs. The latter focuses on pulling the distance of intra-camera positive sample pairs closer and pushing the distance of intra-camera negative sample pairs further, partially alleviating weak ID discrimination caused by the lack of inter-camera annotations. (ii) We design a Mixed-Sampling Joint Learning (MSJL) strategy, which is capable to capture inter- and intra-camera samples and independently accomplish the inter- and intra-camera learning tasks at the same time, avoiding the mutual interference between the two tasks. Extensive experiments on two public SCT datasets prove the superiority of the proposed approach.
Current Cybersecurity Challenges of Applying Blockchain in Healthcare

The most significant problems faced in electronic healthcare are data protection, sharing, and interoperability. These problems may be reliable by using Blockchain. With blockchain, transparency and trust can be established in transactional systems by using a peer-to-peer (P2P) distributed ledger technology. This technology enhances security, data exchange, interoperability, integrity, and real-time updating and access when correctly implemented. Blockchain regulates accessibility to the database, and transfers of rights among individuals based on certain situations and it facilitates access to the information of user profiles, the user will have full access to his information and control how his data will be shared. A blockchain would also securely store access control policies, and only the users could change them. This creates an environment of transparency and allows the user to make all decisions as to what data is collected and how the data is shared. However, the most difficult challenges for blockchain healthcare are the data backup and compliance with regulation. There are national and international privacy laws such as HIPAA, EU's General Data Protection Regulation, and the GDPR-like California Consumer Privacy Act. Decentralization in blockchain makes it impossible to have a full data backup.
Discriminative Gradient Adjustment with Coupled Knowledge Distillation for Class Incremental Learning

Class Incremental Learning (CIL) is a promising approach to addressing the catastrophic forgetting problem when learning for new categories. Though recent works based on dynamic architectures achieve convincing performance, data imbalance caused by limited size of memory and compression of the increasingly growing network are challenges to be solved. In this paper, we propose the novel Discriminative Gradient Adjustment (DGA) and Coupled Knowledge Distillation strategy (CKD) for these two challengs. The DGA mitigates the data imbalance problem by designing the loss function with a static global balance factor and a ground-truth-based dynamic factor. The CKD fully utilizes intermediate layers of the dual-branch models by feature-level distillation with moving-average weight updating for network compression. Extensive experiments on CIFAR100 and ImageNet100 datasets demonstrate the superiority of our method for CIL.
Conservative Data Reduction for Covariance Matrices Using Elementwise Event Triggers

Decentralized data fusion algorithms are fundamentally built on the exchange of estimates and covariance matrices between the individual components. This leads to a high volume of data, mainly caused by the covariance matrices, which can be problematic, especially in environments with limited bandwidth. In order to guarantee the proper functioning of decentralized estimation algorithms, data reduction methods for covariance matrices must ensure that the reduced matrices are conservative, i.e., do not underestimate the actual uncertainty. Motivated by these considerations, this paper presents an elementwise event-triggered method for the data-reduced transmission of covariance matrices that takes into account the aforementioned condition concerning uncertainty. For this purpose, several event triggers are proposed and, based on the event data and diagonal dominance, upper bounds for the actual covariance matrices are derived. An investigation of the data reduction and its influence on the estimation results is performed in a decentralized tracking scenario. The results show that substantial data reduction is possible with only minor losses in estimation quality.
One and one make eleven: An interpretable neural network for image recognition

Although non-interpretable (black-box) deep learning models are well known for their accuracy, interpretable deep learning models should be used for high stake decisions, such as: healthcare. In this paper, we present a novel technique of combining the existing state-of-the-art models, and using them as a base model to build an interpretable deep learning model Comb-ProtoPNet. In contrast to usual technique of combining the logits of two (or more) algorithms to form an ensemble algorithm, we combine the algorithms themselves. Our proposed interpretable model applies a prototype layer on top of the convolutional layers of an ensemble base model. We trained and tested our algorithm over the dataset of chest CT-scan images of COVID-19 patients, pneumonia patients and normal people. The use of a certain combination of blocks from two different state-of-the-art models (statistically) significantly improved the accuracy compared to the individual use of the state-of-the-art models as the base models, and this is where the part of the title “One and one make eleven” comes from.
Automation of Customer Support System (Chatbot) to Solve Web Based Financial and Payment Application Service

One of the most important features of any online service is the quality of its customer care. However, with the development of NLP tools, businesses are considering automated chatbot solutions to keep up with the increasing demand for their products and services. In view of this, the chatbot was developed using AIML java interpreter library Program AB which helps match input and output predefined in the AIML file. AIML (Artificial Intelligence Markup Language) was used to preprocess and train the bot using ready-made AIML file for FAQ questions. Also, vaadin was used to build a web UI to interact with the trained AIML bot. Finally, a google script was written to translate from any language to English for the bot to understand and send the response in the preferred language of the user. Findings showed that the response time of the bot is dependent of the network, as the design gave a score of 70%, 80%, 90% and 90% for load testing, stability, reliability testing and usability testing, respectively. Also, the bot is compatible with different operating systems, both for forward compatibility and backward compatibility having a score of 95%. The bot was able to answer customer questions, enquiries and complaints and the response time of the bot depends on the strength of the network since it is web based. Hence, the system provided a simple, cheaper, and durable customer financial and payment application service. Since chatbots cannot answer all questions, businesses that decide to implement them should ensure that they have enough protections in place against attacks and that routine requests are standardised to ensure optimal performance.
Penggunaan Media Pembelajaran dengan Benda Konkret untuk Meningkatkan Kemampuan Berhitung pada Peserta Didik Kelas II

Learning outcomes are described as written statements of what a learner is expected to know, understand and/or be able to do at the end of a period of learning", which means that learning outcomes are statements about what students are expected to know at the end of learning Learning media in education is something that absolutely must be used in achieving learning goals. Today's reluctance to use media is widely felt by teachers for complicated and troublesome reasons. Concrete media is the easiest tool to use, because we don't need to make any preparations other than using it right away. What is meant by real objects as media are tools for conveying information in the form of real or original objects or objects that have not undergone significant changes. Based on some of the definitions above, it can be concluded that these concrete objects are actual objects, objects or media that help students' real experiences.
Remote sensing and GIS based approach to evaluate the impact of stone quarrying and crushing activities on land resources

Remote sensing and GIS based approach to evaluate the impact Remote sensing and GIS based approach to evaluate the impact of stone quarrying and crushing activities on land resources of stone quarrying and crushing activities on land resources
<scp>Bi‐RRT</scp>*: An Improved Bidirectional <scp>RRT</scp>* Path Planner for Robot in <scp>Two‐Dimensional</scp> Space

Path planning is one of the fundamental issues in the research field of robot. In the last decades, Rapidly‐exploring Random Tree star (RRT*) was one of the chosen path planner for robot with its probabilistically complete. In this paper, an improved bidirectional RRT* path planner for robot is proposed (Bi‐RRT*), which has planned path with safety and smoothly guarantee. The main contributions of this paper include, firstly, combining the backtracking idea of the Quick‐RRT algorithm with the greedy search strategy of the RRT‐Connect algorithm, which can reduce the path cost and improve the operation efficiency of the algorithm. Second, in order to further reduce the running time of the algorithm, a new collision detection algorithm based on cross‐product is proposed to replace the conventional collision detection strategy. To ensure the quality of the path, two post‐processing strategies, path optimization strategy and path smoothing strategy, are proposed. Path optimization strategy is based on triangle inequality, and two optimization methods are proposed to effectively reduce the path cost. The path smoothing strategy based on the Bezier curve improves the continuity of a smooth arc and is better applied to path smoothing. Simulation results for both virtual and real environments show the advantages of combining Quick‐RRT and RRT‐Connect and verify the effectiveness of the proposed collision detection algorithm. Compared with similar algorithms, the proposed Bi‐RRT algorithm has higher operational efficiency, smaller path cost, smoother planned paths, and improved path quality. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.