Miracle-dz/UniFAR-FTUs huggingface dataset:
Accuracy-Enhanced Position-Sensing via Kalman Filter Based on Received Signal Strength

With the rapid development of today's Internet of Things technology, the integration of communication and sensing is becoming more and more important. Sensing position by the received signal strength (RSS) is a widely used technique. Its low cost and low difficulty in obtaining signals make it one of the most effective indoor sensing technologies. However, due to the inevitable issues of shadowing and path loss fadings, it remains challenging to achieve high-accuracy positioning with RSS. Therefore, this paper proposes a positioning solution based on RSS that uses Kalman filtering to improve accuracy. More precise positioning is achieved by filtering the received signal and using the data to derive a more accurate path-loss fading exponent. According to the simulation of numerical data, it is verified that this method can achieve enhanced accuracy in positioning with RSS under strongly dynamic shadowing conditions.
On the DC Microgrid Protection

The escalating utilization of electric energy derived from burning fossil fuels has given rise to several worldwide environmental and economic apprehensions. Consequently, integrating renewable energy sources into electric grids has become imperative, thus engendering problems about dependability, safety, and quality. DC microgrids provide a promising alternative to address these difficulties due to their adaptability and ability to operate in island and network-connected modes. This makes them a viable approach for integrating distributed energy systems with the main power grid. Nevertheless, due to their unique attributes, protecting DC microgrids from failures and related challenges is a formidable task. These attributes include the absence of a zero-crossing point, low cable impedance, increasing transient voltage and current, and significant capacitance. The paper contains an overview of several approaches to detecting faults, constantly powered loads, and inverters with restricted fault current capability. Implementing grounding systems is crucial in the microgrid setting, as they play an integral part in reducing fault currents and maintaining safety. The investigation dealt with the subject matter of the difficulties and approaches involved in protecting DC microgrids. The article also elucidated several configurations of protection devices and relays. Furthermore, it is essential to emphasize the need for efficient coordination among protective devices in the direct current (DC) microgrid. Developing efficient coordination among protective devices inside the direct current (DC) microgrid has become essential for guaranteeing a reliable and robust system. This coordination entails the accurate configuration of time-current properties of protection devices and the setting up communication protocols among them. Furthermore, this paper highlights the current research gaps in DC microgrid protection.
Analysis of LED Layout in Visible Light Communication Systems

Visible light communication has received widespread attention in recent years, and the design and optimization of visible light communication systems require further exploration. This paper analyzes the research significance of visible light communication, introduces the basic model of indoor visible Photosystem, introduces the three important components of the model, namely LED light source, propagation channel and receiver, and then elaborates the basic concepts and calculation ideas of the system's illuminance, signal-to-noise ratio, time extension. And pointed out the direction for further research.
Hierarchical radiosity with multiresolution meshes

The hierarchical radiosity algorithm solves for the global transfer of diffuse illumination in a scene. While its potential algorithmic complexity is superior to both previous radiosity methods and distributed ray tracing, for scenes containing detailed polygonal models, or highly tessellated curved surfaces, its time performance and memory consumption are less than ideal. My thesis is that by using hierarchies similar to those of multiresolution models, the performance of the hierarchical radiosity algorithm can be made sublinear in the number of input polygons, and thus make radiosity on scenes containing detailed models tractable. The underlying goal of my thesis work has been to make high-speed radiosity solutions possible with such scenes. To achieve this goal, a new face clustering technique for automatically partitioning polygonal models has been developed. The face clusters produced group adjacent triangles with similar normal vectors. They are used during radiosity solution to represent the light reflected by a complex object at multiple levels of detail. Also, the radiosity method is reformulated in terms of vector irradiance. Together, face clustering and the vector formulation of radiosity permit large savings. Excessively fine levels of detail are not accessed by the algorithm during the bulk of the solution phase, greatly reducing its memory requirements relative to previous methods. Consequently, the costliest steps in the simulation can be made sub-linear in scene complexity. I have developed a radiosity system incorporating these ideas, and shown that its performance is far superior to existing hierarchical radiosity algorithms, in the domain of scenes containing complex models.
An Enhanced YOLO Failure Detection Method

A novel method is proposed in this paper to detect failures in the YOLO object detection network. The proposed method is derived based on the features extracted by the YOLO network and uses a secondary neural network to predict misdetections. Subsequently, a novel Recursive Feature Elimination (RFE) based approach is proposed to make the secondary network more lightweight by selecting important features for a target class(es). Hence the computational cost is reduced with a minimum loss of accuracy. Experimental evaluation was done using a YOLO network trained on the COCO dataset considering four of the most frequently appeared classes in the dataset. The proposed failure detection method achieved an 89.79% accuracy when a single class was considered, and a 16% improvement was observed in the accuracy compared to an existing method. By using the proposed feature selection method, an 88.89% accuracy with a 56% reduction in the inference time was achieved. Feature selection was 62 times faster and achieved almost the same failure detection accuracy with a lesser number of features compared to the conventional RFE approach. Moreover, the proposed failure detection framework was evaluated by considering multiple classes together as well, and high accuracy was observed.
Dual Temporal Differencing for Real-Time Abandoned Object Detection

In recent years, ensuring safety in the public domain has posed significant challenges, prompting extensive research into developing intelligent surveillance cameras capable of detecting suspicious items before emergencies arise. Also, it is imperative to mitigate the loss of life and property resulting from terrorist attacks within the public sphere, necessitating effective safety measures. We propose Dual Temporal Buffer Differencing (DTBD) and a Single Short Deep Convolutional Neural Network (SSDCNT) object detector to address these challenges. We built two buffers to model the background independently and uniquely segment the static foreground region. The approach can detect the candidate stationary object using contour methodology with the single shot deep convolutional neural network object detector. The candidate object is validated and identified as abandoned and attended. Our approach is unique because it is robust to various illumination changes without affecting the foreground element, and it can detect suspicious objects in any crowd or complex scenario. Its application will mitigate the risk of a potential terrorist attack in the public domain by sensing and alerting the security expert before a situation becomes an emergency. The performance of our proposed approach, the benefits, and the implementation challenges were evaluated through a publicly available dataset ABODA and PEET 2006. The result demonstrates that our proposed approach outperformed other state-of-the-art algorithms in detecting suspicious items. As a result, the approach can facilitate safety in the public domain. It will enable the security personnel to respond proactively to the suspicious item and de-escalate it before the situation becomes an emergency.
Improving Threat Object Recognition for X-Ray Baggage Screening Using Distraction Removal Techniques

X-ray security screening plays an important role in maintaining safety and security at railway stations, airports, and in the transportation system. This process involves the inspection and examination of luggage and personal belongings to detect threat items and explosive items. The complexity of luggage and baggage scanning can indeed make threat detection challenging. This paper proposes a method to improve visual search tasks in baggage security screening by utilizing automatic instance segmentation act as a diagnostic aid, drawing the screener's attention towards the threat items. This involves an inpainting method using regionfill to automatically detect and remove benign objects from the image. The average confidence score of the detected item after the regionfill approach is increased from 98% to 99.5% for threat items (gun, knife, shuriken, blade). By integrating an automatic segmentation model and inpainting methods, this work offers a promising avenue for improving the effectiveness of X-ray screening procedures in identifying potential threats.
Rule-Based Approach in Aspect-Based Sentiment Analysis of Hindi Text

Sentiment analysis is attracting the attention of both academics and companies because of the interesting research challenges it poses and the potential practical benefits it may give in a broad range of scenarios. Aspect-based sentiment analysis (ABSA) is among the most difficult problems with the Hindi language in this area. In this article, we provide a unique rulebased approach to aspect extraction from product evaluations that makes use of phrase dependency trees and common-sense information to separate both overt and covert features. In Hindi text, this method performs better than cutting-edge aspect extraction methods. The better categorizations of a considerable number of recent studies, the comparisons between those publications, and the description of the present trend in research in the rule-based ABSA of Hindi are the main help of this work. The proposed study's prime objective is to create a rule-based method for extracting aspect phrases from review sentences.
IndoJavaneseNLI: A Cross-Lingual Natural Language Inference Dataset for East Javanese “Ngoko” Register

Natural Language Inference (NLI) is a task that focuses on establishing the logical relationship between two sentences, premise sentences, and hypothesis sentences, by classifying it into “entailment”, “neutral”, and “contradiction”. The growth of NLI models has been noteworthy in the English language. However, little to no progress has been made on low-resource languages, such as Javanese. What’s more, the resulting translation from Machine Translation is blind to the cultural nuance of word choices. It tends to mix up words from various Javanese registers like “Ngoko”, “Madya”, and “Krama”. To tackle that issue, we present IndoJavaneseNLI, a cross-lingual NLI dataset for East Javanese “Ngoko” registers. Our dataset consists of the premise sentences in the Indonesian language and the hypothesis sentences in the Javanese language. This paper describes how we carefully inspect the Javanese translation, the process leading up to the building of good East Javanese “Ngoko” sentences, and its evaluation with Transformer-based models and transfer learning. We also found that XLMR yields the best result in solving NLI problems with our dataset with the accuracy of 67.56% on the fine-tuning method and 47.34% on the transfer learning method.
Application of the Random Forest Classification Algorithm to a SELDI‐TOF Proteomics Study in the Setting of a Cancer Prevention Trial

Abstract: A thorough discussion of the random forest (RF) algorithm as it relates to a SELDI‐TOF proteomics study is presented, with special emphasis on its application for cancer prevention: specifically, what makes it an efficient, yet reliable classifier, and what makes it optimal among the many available approaches. The main body of the paper treats the particulars of how to successfully apply the RF algorithm in a proteomics profiling study to construct a classifier and discover peak intensities most likely responsible for the separation between the classes.
Packaged Cost-Effective Millimeterwave Air-Filled SIW Components for Array Feed Networks

In this contribution, we investigate the implementation of air-filled substrate-integrated-waveguide (AFSIW) components based on the assembly of multiple standard manufactured single printed circuit boards (PCBs) as a cost-effective, low loss routing alternative for millimeterwave antenna arrays. To this end, a transition from grounded co-planar waveguide (GCPW) to AFSIW is designed, fabricated and measured for integration purposes with active electronics in the n257 5G band. The measured minimal insertion loss equals 0.7 dB around 27 GHz. To further validate our approach, a compact, fully shielded AFSIW filter, including the aforementioned transition, is designed and prototyped. It is shown that the component radiates less than 5% of its power, eliminating most of the spurious radiation from feed networks.
Association of CTLA-4 Gene Variants with Response to Therapy and Long-term Survival in Metastatic Melanoma Patients Treated with Ipilimumab: An Italian Melanoma Intergroup Study

Ipilimumab (IPI) blocks CTLA-4 immune checkpoint resulting in T cell activation and enhanced antitumor immunity. IPI improves overall survival (OS) in 22% of patients with metastatic melanoma (MM). We investigated the association of CTLA-4 single nucleotide variants (SNVs) with best overall response (BOR) to IPI and OS in a cohort of 173 MM patients. Patients were genotyped for six CTLA-4 SNVs (−1661A>G, −1577G>A, −658C>T, −319C>T, +49A>G, and CT60G>A). We assessed the association between SNVs and BOR through multinomial logistic regression (MLR) and the prognostic effect of SNVs on OS through Kaplan–Meier method. Both −1577G>A and CT60G>A SNVs were found significantly associated with BOR. In particular, the proportion of responders was higher in G/G genotype while that of stable patients was higher in A/A genotype. The frequency of patients experiencing progression was similar in all genotypes. MLR evidenced a strong downward trend in the probability of responsiveness/progression, in comparison to disease stability, as a function of the allele A “dose” (0, 1, or 2) in both SNVs with reductions of about 70% (G/A vs G/G) and about 95% (A/A vs G/G). Moreover, −1577G/G and CT60G/G genotypes were associated with long-term OS, the surviving patients being at 3 years 29.8 and 30.8%, respectively, as compared to 12.9 and 14.4% of surviving patients carrying −1577G/A and CT60G/A, respectively. MM patients carrying −1577G/G or CT60G/G genotypes may benefit from IPI treatment in terms of BOR and long-term OS. These CTLA-4 SNVs may serve as potential biomarkers predictive of favorable outcome in this subset of patients.
Safety of Autonomous Systems Using Reinforcement Learning: A Comprehensive Survey

The safety of autonomous systems has become an important concern in recent years. A new mechanism for developing safe and reliable autonomous systems is a very challenging task. It is also very complex to accomplish trustworthy and optimized outcomes in uncertain and dynamic environments. Safety and security issues of machines are frequently raised with the advancement of technology, for example, medical robots with smart technology can perform complex surgeries, self-driving cars with smart road assistance models can significantly reduce the number of traffic crashes, and smart autopilots can perform delicate docking makeovers. Therefore, in this paper, we studied various autonomous system safety challenges, Machine Learning (ML) methodology aspects, theory, and applications. An overview of ML categories and sub-categories that are majorly used for the safety and security of Autonomous Systems are also explored. This paper also presents numeral applications of Reinforcement Learning (RL) which helps in the safety of autonomous systems. Reinforcement Learning lacks a good literature review in this area that has been also focused.
Exploring the magic of WAND

Web search services process thousands of queries per second, and filter their answers from collections containing very large amounts of data. Fast response to queries is a critical service expectation. The well-known WAND processing strategy is one way of reducing the amount of computation necessary when executing such a query. The value of WAND has now been validated in a wide range of studies, and has become one of the key baselines against which all new top-k processing algorithms are benchmarked. However, most previous implementations of WAND-based retrieval approaches have been in the context of the BM25 Okapi similarity scoring regime. Here we measure the performance of WAND in the context of the alternative Language Model similarity score computation, and find that the dramatic efficiency gains reported in previous studies are no longer achievable. That is, when the primary goal of a retrieval system is to maximize effectiveness, WAND is relatively unhelpful in terms of attaining the secondary objective of maximizing query throughput rates. However, the BM-WAND algorithm does in fact help reducing the percentage of postings to be scored, but with additional computational overhead. We explore a variety of tradeoffs between scoring metric and processing regime and present new insight into how score-safe algorithms interact with rank scoring.
Designing a data structure for polyhedral surfaces

Design solutions for a program library are presented for combinatorial data structures in computational geometry, such as planar maps and polyhedral surfaces. Design issues considered are genericity, flcsibility, time and space efficiency, and ease-of-use. We focus on topological aspects of polyhedral surfaces. Edge-based reprew%ations for polyhedrons are evaluated with respect to the design goals. A design for polyhedral surfaces in a halfedge data structure is developed following the generic programming paradigm known from the Standard Template Library STL for C++. Connections arc shown to planar maps and face-based structures managing holes in facets.
Multi-rate power-controlled link scheduling for mesh broadband wireless access networks

The problem of multi-rate power-controlled collision-free scheduling in spatial time division multiple access (STDMA) wireless mesh networks is formulated as a mathematical program utilising cross layer information. As these mixed integer linear programs are intractable (NP-hard problems), optimal collision-free schedules can be found only for topologies consisting of a few nodes. To this end, approximation algorithms that are based on linear programming relaxation and randomised rounding are studied. The proposed framework, which aims to maximise the spatial timeslot reuse under predefined signal-to-interference noise ratio thresholds, is suitable for providing centralised scheduling in the mesh mode of the IEEE 802.16 standard. Performance aspects of the approximation algorithms under different scenarios are investigated.
A Running Controller of Humanoid Biped HRP-2LR

This article explains a control system, which stabilizes running biped robot HRP-2LR. The robot uses prescribed running pattern calculated by resolved momentum control, and a running controller stabilizes the system against disturbances. The running controller consists of posture stabilization, inverted pendulum stabilization, contact torque control, impact absorbing control, foot vertical force control and torque distribution control. Applying the proposed controller, HRP-2LR could successfully run with average speed of 0.16(m/s) repeating flight phase of 0.06 (s) and support phase of 0.3 (s).
The Flipped Classroom: A Course Redesign to Foster Learning and Engagement in a Health Professions School

Recent calls for educational reform highlight ongoing concerns about the ability of current curricula to equip aspiring health care professionals with the skills for success. Whereas a wide range of proposed solutions attempt to address apparent deficiencies in current educational models, a growing body of literature consistently points to the need to rethink the traditional in-class, lecture-based course model. One such proposal is the flipped classroom, in which content is offloaded for students to learn on their own, and class time is dedicated to engaging students in student-centered learning activities, like problem-based learning and inquiry-oriented strategies. In 2012, the authors flipped a required first-year pharmaceutics course at the University of North Carolina Eshelman School of Pharmacy. They offloaded all lectures to self-paced online videos and used class time to engage students in active learning exercises. In this article, the authors describe the philosophy and methodology used to redesign the Basic Pharmaceutics II course and outline the research they conducted to investigate the resulting outcomes. This article is intended to serve as a guide to instructors and educational programs seeking to develop, implement, and evaluate innovative and practical strategies to transform students’ learning experience. As class attendance, students’ learning, and the perceived value of this model all increased following participation in the flipped classroom, the authors conclude that this approach warrants careful consideration as educators aim to enhance learning, improve outcomes, and fully equip students to address 21st-century health care needs.
Real-Time Network Traffic Classification using Advanced Neural Embeddings

There are constantly new problems to overcome while analyzing network traffic in today's complex network systems, forcing researchers to develop new methodologies. We provide a unique technique for real-time network traffic categorization based on progressively complicated neural embeddings. Using the power of deep learning, this solution solves scalability, flexibility, and user privacy concerns while boosting the efficacy and accuracy of traffic classification. We compare our proposed technique to six current traffic classification strategies to illustrate its superiority. Our suggested method incorporates three key techniques: real-time traffic management, traffic data assessment, and the generation of neural embedding models. The act of arranging incoming network packets into flows and eliminating relevant information from those flows is known as traffic data preprocessing. The recommended methodology's remarkable accuracy of 98.5 percent is significantly more than the ordinary accuracy of 94.2 percent of conventional procedures. Furthermore, it has reduced rates of false positives (1.2% rate) and false negatives (2.3% rate) as compared to the gold standard of earlier approaches, which had rates of 6.8% and 8.9% for these two categories, respectively. Increased processing speed, scalability, adaptability, and privacy protection are just a few of the desirable characteristics exemplified by the proposed approach. Our research contributes considerably to the advancement of algorithms for categorizing network traffic, particularly in terms of real-time analysis. The suggested technique provides network administrators and security professionals with a highly precise and dependable tool for regulating and safeguarding network traffic in dynamic and changing situations.