Reviewerly/exHarmony huggingface dataset:
Skip-YOLO: Domestic Garbage Detection Using Deep Learning Method in Complex Multi-scenes

Abstract It is of great significance to identify all types of domestic garbage quickly and intelligently to improve people's quality of life. Based on the visual analysis of feature map changes in different neural networks, a Skip-YOLO model is proposed for real-life garbage detection, targeting the problem of recognizing garbage with similar features. First, the receptive field of the model is enlarged through the large-size convolution kernel which enhanced the shallow information of images. Second, the high-dimensional features of the garbage maps are extracted by dense convolutional blocks. The sensitivity of similar features in the same type of garbage increases by strengthening the sharing of shallow low semantics and deep high semantics information. Finally, multiscale high-dimensional feature maps are integrated and routed to the YOLO layer for predicting garbage type and location. The overall detection accuracy is increased by 22.5% and the average recall rate is increased by 18.6% comparing the experimental results with the YOLOv3 analysis. In qualitative comparison, it successfully detects domestic garbage in complex multi-scenes. In addition, this approach alleviates the overfitting problem of deep residual blocks. The application case of waste sorting production line is used to further highlight the model generalization performance of the method.
Overview of path planning algorithms

Background: Path-planning algorithms are widely used in robotics, vehicles, UAVs, carrier-based aircraft towing vehicles, etc. Using these algorithms, an optimal path with safe obstacle avoidance and high efficiency can be planned. In recent years, path-planning algorithms have received more and more attention from scholars at home and abroad. Objective: In order to promote the application and development of path-planning algorithms, an in-depth analysis of the current development status of path-planning algorithms was presented. The most widely used and representative algorithms in several representative results were extracted, and the characteristics and advantages and disadvantages of each algorithm were analyzed and elaborated in detail for the readers' reference. Methods: The existing path-planning algorithms were classified, and a brief overview of each traditional algorithm was given, followed by an in-depth study of the improved algorithms to summarize the advantages and disadvantages of each type of algorithm. Finally, based on these research results, the future development trend of path-planning algorithms was projected. Results: Through the research and analysis of the path-planning algorithm, it was found that the path- planning algorithm before the improvement had the problems of an unsmooth path, low computational efficiency, a slow response time, the inability to safely avoid obstacles, etc. After the improvement and optimization of the path-planning algorithm, the performance was greatly improved. Conclusion: After research and analysis, it was found that multi-algorithm fusion had great potential for development in path-planning compared to the application and optimization of a single algorithm. In addition, further research is needed in the areas of algorithm adaptability, fusion, intelligent algorithms, and extending the range of algorithmic applications.
Performance of entanglement-assisted quantum codes with noisy ebits over asymmetric and memory channels

Entanglement-assisted quantum error correction codes (EAQECCs) play an important role in quantum communications with noise. Such a scheme can use arbitrary classical linear code to transmit qubits over noisy quantum channels by consuming some ebits between the sender (Alice) and the receiver (Bob). It is usually assumed that the preshared ebits of Bob are error free. However, noise on these ebits is unavoidable in many cases. In this work, we evaluate the performance of EAQECCs with noisy ebits over asymmetric quantum channels and quantum channels with memory by computing the exact entanglement fidelity of several EAQECCs. We consider asymmetric errors in both qubits and ebits and show that the performance of EAQECCs in entanglement fidelity gets improved for qubits and ebits over asymmetric channels. In quantum memory channels, we compute the entanglement fidelity of several EAQECCs over Markovian quantum memory channels and show that the performance of EAQECCs is lowered down by the channel memory. Furthermore, we show that the performance of EAQECCs is diverse when the error probabilities of qubits and ebits are different. In both asymmetric and memory quantum channels, we show that the performance of EAQECCs is improved largely when the error probability of ebits is reasonably smaller than that of qubits.
A Semi-Quantum Private Comparison Base on W-States

Privacy comparison is an important research topic in secure multi-party computing, widely used in e-commerce, secret ballots, and other fields. However, the development of quantum computing power poses a growing potential security threat to secure multi-party algorithms based on mathematically tricky problems, and most of the proposed quantum privacy comparison schemes could be more efficient. Therefore, based on the W-state, we offer a more efficient semi-quantum privacy comparison method. The security analysis shows that the scheme can resist third-party, measurement, and entanglement attacks. Compared with the previous work, the scheme significantly improves communication efficiency and has stronger practicability.
A formal model of Checked C1

We present a formal model of Checked C, a dialect of C that aims to enforce spatial memory safety. Our model pays particular attention to the semantics of dynamically sized, potentially null-terminated arrays. We formalize this model in Coq, and prove that any spatial memory safety errors can be blamed on portions of the program labeled unchecked; this is a Checked C feature that supports incremental porting and backward compatibility. While our model’s operational semantics uses annotated (“fat”) pointers to enforce spatial safety, we show that such annotations can be safely erased. Using PLT Redex we formalize an executable version of our model and a compilation procedure to an untyped C-like language, as well as use randomized testing to validate that generated code faithfully simulates the original. Finally, we develop a custom random generator for well-typed and almost-well-typed terms in our Redex model, and use it to search for inconsistencies between our model and the Clang Checked C implementation. We find these steps to be a useful way to co-develop a language (Checked C is still in development) and a core model of it.
Video Clip and Its Impact on Students’ Academic Performance in Learning Creative Nonfiction

The study focused on the use of video clips as instructional material and its impact on learning Creative Non-Fiction among Humanities and Social Sciences students in Subic National High School. The researchers used an experimental research design that involved a control group and an experimental group with a total of 60 participants. The researchers utilized a researcher-made pre-test and post-test and a survey questionnaire. The pre-test and post-test results of the control group and experimental group both showed a significant increase. Results show that the experimental group achieved a very satisfactory remark after receiving treatment which is the use of video clips presentation in learning Creative Nonfiction. Moreover, students perceived that video clips with subtitles will serve as better instructional material because they help them in learning Creative Nonfiction, understand difficult concepts, keep them engaged, and motivate them to suit their learning style. The study concluded that there is a significant difference in learning Creative Nonfiction according to the students’ gender and age profile. The researchers strongly recommend that teachers should be encouraged to use video clip presentations as a supplementary tool for better academic achievement and teachers can also integrate the use of video clips in other subjects if it is applicable and suitable.
Design of A Hand Back Acupoint Massage Aid

This paper mainly develops a hand back acupoint massage guiding device, which applies OpenPose detection and image process on locating the acupoints of the hand back. To develop an assistive acupoint locating device, we use a camera to catch the photo of the hand back and then find the characteristic points of the hand back by using OpenPose recognition in the OpenCV developing environment. The positions of acupoints are calculated from combining the found hand back characteristic points and the rules of traditional Chinese medicine. Moreover, the error of the acupoint location is researched with high accuracy. In addition, according to the traditional Chinese medicine, the acupoints of the hand back are associated at the corresponding diseases. The hand massage aid APP is developed based on the hand acupoint locating system from user’s corresponding the diseases. This aid APP can give the message guidance for releasing the effect of diseases, which points out the required massage position and massage skill. Finally, the results are proved by the experiments.
Behavioral intention to use mobile health (mHealth) technologies among caregivers of cancer patients

INTRODUCTION: According to an analysis by the World Health Organization (WHO), thenumber of deaths caused by cancer has increased every year from 1990 to 2017. Mobile health(mHealth) technologies are powerful tools to improve cancer awareness by providing educationalinformation. Therefore, the aim of this study was to examine factors affecting the behaviorsamong caregivers of cancer patients in Malaysia using mHealth technologies.METHODS: The study applied the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)model as a theoretical framework, with the I-Change Model (ICM) to extend the UTAUT2. Thepopulation element in this research study focused on caregivers of cancer patients who own asmartphone capable of accessing the internet and can download mobile applications. SmartPLSwas used for model and construct testing. A total of 131 sets of valid data were collected from thetarget population through an online survey.RESULTS: Results showed Effort Expectancy (EE), Facilitating Condition (FC), Habit (HT),Awareness Factor (AWN), and Perceived Privacy Risk (PPR) were found to be significant constructs.Performance Expectancy (PE), Social Influence (SI), Hedonic Motivation (HM), and Price Value (PV)were not significant constructs.CONCLUSION: This study provided baseline data on the use of mHealth technologies to thecaregivers of cancer patients in Malaysia to reduce their caregiving burden.
Modified Finite Volume Method Study for Numerical Calculation of Lithium Battery Temperature

The report discusses the use of a modified finite volume method for calculating the temperature field of a lithium-ion battery. The modified finite volume method reduces the numerical solution of mathematical physics equations to the solution of the Cauchy problem. The method allows to reduce the complexity of the calculation by reducing the calculated volume. For the calculated volumes, the sufficiency of the reduction is monitored taking into account the specified accuracy. This prevents the violation of the Knudsen criterion. The modified finite volume method makes it possible to obtain analytical functions of the scalar temperature field. The resulting function is used to implement an algorithm for temperature control at critical points of the battery. Temperature control is carried out by measuring the temperature of the device on the surface. Mathematical models for the method under consideration are obtained, calculation results for three elements are presented, and procedures are formulated to obtain an analytical function of the temperature field.
Utilization of Augmented Reality E-Pregnant As Educational Media For Improving Prevention Behavior Caries in Pregnant Mothers

Pregnant women are one of the groups that are susceptible to dental and oral diseases. This is because during pregnancy there is an increase in the amount of the hormones estrogen and progesterone so that the acidity in the mouth increases which can increase the risk of caries. This condition causes babies to be born with low weight (LBW) and premature due to lack of nutrition. Thus, to reduce the magnitude of the incidence of caries in pregnant women, a learning media is needed. The technology in education that is currently developing is augmented reality. To produce an augmented reality application model of E-Pregnant as an appropriate and effective educational media in improving the behavior of pregnant women towards the prevention of dental caries and decreasing the OHIS score. The type of research used is Research and Development and the research design used is Quasy experiment Pretest-Posttest group design. Variables in this study: Knowledge, attitudes, actions and OHIS scores carried out for 21 days by opening the application 3 times a week and uploading photos 2 times a day. The research subjects were in 2 groups: 24 interventions using an e-pregnant caries application and 24 controls using a pocket book. The Augmented Reality E-Pregnant Caries model is feasible as a media for dental caries prevention education with 90% expert validation results. The results of the unpaired test stated that the effective application of increasing knowledge (Δ3.58), attitude (Δ15.63), action (Δ5.75), and also reduced the OHIS score (Δ0.60) compared to the control group. The Augmented Reality E-Pregnant Caries model is proven to be feasible as a caries prevention, education media and its application is effective as an effort to improve caries prevention behavior and reduce OHIS scores in pregnant women compared to the control group.
Media Pembelajaran Multimedia Interaktif Menggunakan Metode MDLC Pada Zoom Meetings

Multimedia is a means of disseminating information so that multimedia can be used as an interactive learning medium. This research is based on the results of previously written studies which aim to create a learning media from the discussion results of previous people. Data collection uses the MDLC (Multimedia Development Life Circle) method. The results of this research are to successfully create interactive multimedia learning media using the MDLC (Multimedia Development Life Circle) method as an interactive learning media facility using Zoom Meeting as the meeting medium. This learning media has been tested. Tests in research use tests that focus on the results of visualization displays and effectiveness in conveying information.
Machine Learning Performance at the Edge: When to Offload an Inference Task

Machine Learning (ML) techniques play a crucial role in extracting valuable insights from the large amounts of data massively collected through networked sensing systems. Given the increased capabilities of user devices and the growing demand for inference in mobile sensing applications, we are witnessing a paradigm shift where inference is executed at the end devices instead of burdening the network and cloud infrastructures. This paper investigates the performance of inference execution at the network edge and at end-devices, when using both a full and a pruned model. While pruning reduces model size, thus making the model amenable for execution at an end-device and decreasing communication footprint, trade-offs in time complexity, potential accuracy loss, and energy consumption must be accounted for. We tackle such trade-offs through extensive experiments under various ML models, edge load conditions, and pruning factors. Our results show that executing a pruned model provides time and energy (on the device side) savings up to 40% and 53%, respectively, w.r.t. the full model. Also, executing inference at the end-device may lead to 60% faster decision-making compared to inference execution at a highly loaded edge.
Optimizing word sense disambiguation for Hindi language using extended Lesk and conceptual density

Word Sense Disambiguation (WSD) is a crucial task in natural language processing, aiming to determine the correct sense of a word in a given context. Word sense disambiguation (WSD) is an indispensable task in natural language processing for language understanding like question answering, information retrieval and machine translation system. Word sense disambiguation technique is to find most relevant sense of ambiguous word. In simple way WSD is a technique which clubs a word which has multiple meaning. Word Sense Disambiguation is an open challenging problem for Natural Language Processing. We people can understand the sense and meaning of word in different context. But for system to decide the best and correct meaning from different word meaning system needs to be trained. This paper provides a extended Lesk and Conceptual Density (CD) approach together to remove word sense ambiguity. The density of overlapped word from context and sense bag is calculated. BLUE is used to evaluate the score. On the basis of score, system is able to judge the correct sense of word. The proposed approach not only addresses the challenges specific to Hindi language but also offers a general framework that can be applied to other morphologically rich languages.
RLT-CPABE: Revocable Location and Time Aware Ciphertext Policy Attribute-Based Encryption

Ciphertext policy attribute-based encryption provides fine-grained access control on the data stored in data centres in a dynamic and ubiquitous cloud environment. It enables data owners to create access policies by designating attribute constraints and embedding security policies in the ciphertext that allows the data owners to have access control on data. However, one of the major limitations of existing Ciphertext Policy Attribute Based Encryption (CP-ABE) is that accessibility to data is dependent on static user attributes. There are various applications which require access implementation based on the user’s contextual information such as time and location. In this paper, we propose a Revocable Location and Time Aware Ciphertext Policy Attribute-Based Encryption (RLT-CPABE) which enhances data security by integrating location and time into the encryption and decryption process. Our scheme employs a single range derivation function to implement time range comparison and re-encryption technique to embed the current time in the ciphertext. Geo-hashes of the locations are used as attributes to support location constraints. RLT-CPABE implements time and location-driven encryption and decryption while satisfying user revocation. In addition, we have introduced fog computing services for efficient implementation, where partial decryption operations, revocation and computing towards time and location verification are outsourced to fog nodes. This reduces the computational load on resource-constrained user devices. The security and performance analysis shows the efficacy of our cryptosystem for practical applications.
Handling qualitative conditional preference queries in SPARQL: possibilistic logic approach

Purpose The purpose of this paper is to address the need for new approaches in locating items that closely match user preference criteria due to the rise in data volume of knowledge bases resulting from Open Data initiatives. Specifically, the paper focuses on evaluating SPARQL qualitative preference queries over user preferences in SPARQL. Design/methodology/approach The paper outlines a novel approach for handling SPARQL preference queries by representing preferences through symbolic weights using the possibilistic logic (PL) framework. This approach allows for the management of symbolic weights without relying on numerical values, using a partial ordering system instead. The paper compares this approach with numerous other approaches, including those based on skylines, fuzzy sets and conditional preference networks. Findings The paper highlights the advantages of the proposed approach, which enables the representation of preference criteria through symbolic weights and qualitative considerations. This approach offers a more intuitive way to convey preferences and manage rankings. Originality/value The paper demonstrates the usefulness and originality of the proposed SPARQL language in the PL framework. The approach extends SPARQL by incorporating symbolic weights and qualitative preferences.
Bluff: A Multi-Robot Dispersion Based on Hybrid Reciprocal Velocity Obstacles to Solve the Blind Man’s Buff Problem

This chapter introduces Bluff, a hybrid reciprocal velocity obstacles-based approach to disperse multi-robot systems avoiding collisions with the obstacles, the cohort robots, and the seeker robots and test it to solve the Blind Man’s Buff problem for robots. The environment is clustered and bounded with multiple seekers, a multi-robot system and obstacles that are both dynamic and static. Compared to some of the traditional approaches, Bluff incorporates a decentralized approach where the robots are not in contact with each other. It makes use of hybrid reciprocal velocity obstacles approach that takes into account the unsavory factions of both velocity obstacles and reciprocal velocity obstacles methods. This makes it even more challenging to solve the problem. However, this is offset by the fact that the robots’ ultimate trajectory is oscillation-free, more natural, and collision-free. The results have been tested using robotic operating system as the middleware and Gazebo as the simulation environment. Pioneer 3-AT has been used as the research robot for the purpose because of its ease of movement and adaptability.
Replication-Based Query Management for Resource Allocation Using Hadoop and MapReduce over Big Data

We live in an age where everything around us is being created. Data generation rates are so scary, creating pressure to implement costly and straightforward data storage and recovery processes. MapReduce model functionality is used for creating a cluster parallel, distributed algorithm, and large datasets. The MapReduce strategy from Hadoop helps develop a community of non-commercial use to offer a new algorithm for resolving such problems for commercial applications as expected from this working algorithm with insights as a result of disproportionate or discriminatory Hadoop cluster results. Expected results are obtained in the work and the exam conducted under this job; many of them are scheduled to set schedules, match matrices' data positions, clustering before determining to click, and accurate mapping and internal reliability to be closed together to avoid running and execution times. Mapper output and proponents have been implemented, and the map has been used to reduce the function. The execution input key/value pair and output key/value pair have been set. This paper focuses on evaluating this technique for the efficient retrieval of large volumes of data. The technique allows for capabilities to inform a massive database of information, from storage and indexing techniques to the distribution of queries, scalability, and performance in heterogeneous environments. The results show that the proposed work reduces the data processing time by 30%.