Reviewerly/exHarmony huggingface dataset:
Efficient three-dimensional point cloud object detection based on improved Complex-YOLO

Lidar-based 3D object detection and classification is a critical task for autonomous driving. However, inferencing from exceedingly sparse 3D data in real-time is a formidable challenge. Complex-YOLO solves the problem of point cloud disorder and sparsity by projecting it onto the bird's-eye view and realizes real-time 3D object detection based on LiDAR. However, Complex-YOLO has no object height detection, a shallow network depth, and poor small-size object detection accuracy. To address these issues, this paper has made the following improvements: (1) adds a multi-scale feature fusion network to improve the algorithm's capability to detect small-size objects; (2) uses a more advanced RepVGG as the backbone network to improve network depth and overall detection performance; and (3) adds an effective height detector to the network to improve the height detection. Through experiments, we found that our algorithm's accuracy achieved good performance on the KITTI dataset, while the detection speed and memory usage were very superior, 48FPS on RTX3070Ti and 20FPS on GTX1060, with a memory usage of 841Mib.
Morphological description and life history of the Philippine endemic Limnonectes leytensis Leyte swamp frog in Rogongon, Iligan City, Philippines

Abstract. Flores ABA, Diesmos AC, Nu?eza OM. 2022. Morphological description and life history of the Philippine endemic Limnonectes leytensis Leyte swamp frog in Rogongon, Iligan City, Philippines. Biodiversitas 23: 133-139. The Philippine endemic Limnonectes leytensis (Leyte Swamp Frog) is a widespread species, however, most studies concerning it only focus on adult stages, except for the study on Negros Island. With this, we conducted a study on the morphology and life history of L. leytensis in Rogongon, Iligan City, Philippines. We collected different tadpole stages through opportunistic sampling. Gosner developmental stages 23 to 40 were considered and measured using standard tadpole body morphometrics. Linear regression was used for modeling the relationship between the different morphometrics. Results showed that the tadpoles have a depressed body, dorsolateral eye position and has golden, light to dark brown pigmentation. Apart from this, its mouthpart showed an anteroventral shape, and the labial tooth row formula is 1/2-3, showing a single row in the upper lip and three rows in the lower lip, having the 3rd row half the length of the other two, which is different to that of the results of the past studies which reported that the labial tooth was 1/2:1-1 showing a broken gap in the first tooth row of the lower lip. The analysis revealed a positive relationship between different morphometrics to the total length (body length rs: 0.96; tail length rs: 0.99; musculature height rs: 0.90 and maximum tail height rs: 0.85) with a significance value of &lt;0.001. The data generated from this study will ignite future efforts on tadpole research in the country, given that there are differences found in the tadpoles from different regions.
Review on Facial Recognition System: Past, Present, and Future

Facial recognition is one of the most efficient mechanisms to identify an individual by using facial attributes. Artificial Intelligence has been widely used to recognize facial attributes in different circumstances. Most facial recognition systems can analyze and compare various attributes of facial patterns to verify the identity of the person efficiently. Facial Recognition is the component of a biometric identification system. There have been many Deep learning algorithms proposed to extract the facial features, like Eigen Fisher faces which extract principal components and separate one from other. In the present scenario due to pandemics, people need to wear a mask which comes as a challenge as some of the facial features are not visible. However, the application of DNN helps to identify a person with the desired accuracy. If a person is wearing a mask or eyeglasses, in this case very few facial features are visible like eyebrows forehead, and shape. Many technologies help to identify the images, based on their features, color, shape, pose variation, expression changes, 2-Dimensional images, 3-Dimensional Images, RGB, and black and white images. In this paper, we present various Feature extraction and classification techniques and also compared the accuracy level of various facial recognitions methods on different databases.
PASPP Medical Transformer for Medical Image Segmentation

Medical Transformer (MedT) has recently attracted much attention in medical segmentation as it could perform global context of the image and can work well even with small datasets. However, there are some limitations of MedT such as the big disparity between the information of the encoder and the decoder, the low resolution of input images to effectively execute, and the lack of ability to recognize contextual information in multiple scales. To address such issues, in this study, we propose an architecture that employs progressive atrous spatial pyramid pooling (PASPP) to the MedT architecture, and pointwise atrous convolution layers instead of AvgPooling layers in MedT to make robust pooling operations. In addition, we also change the convolution stem of MedT to help the model to accept a higher resolution of input with the same computational complexity. The proposed model is evaluated on two medical image segmentation datasets including the Glas and Data science bowls 2018. Experiment results show that the proposed approach outperforms other state of the arts.
Real-Time Hand Gesture Recognition Using Indian Sign Language

Hand gesture is one of the methods used for non-verbal communication by the differently abled. Hand gesture is an integral part of sign language. There are different types of sign languages across the globe with varying progress in machine recognition. Due to a comparatively lower volume of related works, there is a potential to improve hand gesture recognition of the Indian sign language. Also, the availability of a proper, polished dataset must be taken into consideration. This work presents a novel approach for recognizing Indian sign language with continuous video sequences of the signs being considered input. With the help of a neural network, the model develops a capacity to become more efficient with subsequent usage (herein real-time data to work upon) hence encouraging more use without any compromise in performance.
Psychosomatic Study of Criminal Inclinations with Profanity on Social Media: Twitter

The World Wide Web (WWW) is a plethora of data related to individual opinion pertaining to many topics that involves personal and social issues. Using social media and various microblogging sites, users share their views and feelings, resonate their observations and decisions in their daily interaction, which can be their sentiments or opinions regarding an event or a thing. These data are on a tremendous hike and a rich source of information for any decision-making process. A mind mapping data with emotional quotient are voiced unknowingly by the users. To automate the analysis of such data, sentiment analysis has emerged. 'Twitter' being a microblogging site, limiting the number of tweets and characters in each tweet, makes the process of analysis easier. With the upsurge of useful information, there is also an increase in offensive content and obnoxious words that are sent across as messages and comments. This drives to analyze the sentiment of each user or a page. It also becomes judgmental on the abusive or criminal activity or thought behind the comment or tweet. The 'tweets' are analyzed on user sentiment and the intensity of hate content and beleaguered speech in the tweets. This helps in unceasingly monitoring a person or a page for criminal activities.
Distributed Item Recommendation Using Sentiment Analysis

The recommendation system assists in selecting the best product amongst the millions of products available on various e-commerce sites. An effective recommendation system can save the user’s time while looking for a suitable product. People are spending more time on the Internet these days, and their interests vary over time; as a result, their preference data is accumulating at a rapid pace. The recommendation system should be scalable to process the large-scale data efficiently. It should also preserve the relationship between time and user preferences. In this study, we proposed a recommender system utilizing distributed alternating least square matrix factorization that incorporates product ratings as well as user reviews. It validates user reviews using the up-vote technique and assigns a normalized rating to the reviews through the weighted rating normalization approach, which helps in improving recommendations. Since user reviews and ratings generate a large amount of data, the proposed method is scalable enough and efficiently processes this data, resulting in a better recommendation. Extensive experiments were conducted on Apache Spark to validate the results.
Enhancement of <i>K</i>-means clustering in big data based on equilibrium optimizer algorithm

Abstract Data mining’s primary clustering method has several uses, including gene analysis. A set of unlabeled data is divided into clusters using data features in a clustering study, which is an unsupervised learning problem. Data in a cluster are more comparable to one another than to those in other groups. However, the number of clusters has a direct impact on how well the K -means algorithm performs. In order to find the best solutions for these real-world optimization issues, it is necessary to use techniques that properly explore the search spaces. In this research, an enhancement of K -means clustering is proposed by applying an equilibrium optimization approach. The suggested approach adjusts the number of clusters while simultaneously choosing the best attributes to find the optimal answer. The findings establish the usefulness of the suggested method in comparison to existing algorithms in terms of intra-cluster distances and Rand index based on five datasets. Through the results shown and a comparison of the proposed method with the rest of the traditional methods, it was found that the proposal is better in terms of the internal dimension of the elements within the same cluster, as well as the Rand index. In conclusion, the suggested technique can be successfully employed for data clustering and can offer significant support.
Makerspace activities in a school setting: Top-down and bottom-up approaches for teachers to leverage pupils' making in science education

This article addresses the opportunities and challenges of turning a science, technology, engineering, and mathematics (STEM) classroom into a makerspace for hands-on experimentation with digital tools and materials in science education. In this qualitative case study, over a period of 16 weeks, video data were collected during making activities in an advanced placement science course with 19 pupils aged 12–16 years, and interviews were conducted. We combined thematic and interaction analyses of empirical data and identified three themes: 1) engagement and spontaneous concepts, 2) programming and making physical objects, and 3) subject integration. Our conceptual framework for the analyses integrated two features of the Vygotskian sociocultural theory of learning: concept development as a dialectical process of scientific and everyday concepts and the “tool and symbol” duality. Our findings show that both top-down and bottom-up approaches to integrating school subjects into a makerspace were effective but underused. We illustrate this by mapping pupils' shared understanding in a sociotechnical space, visualized as a process of “rising to the concrete”, which may require teacher's scaffolding at different levels of abstraction and use of instructional materials in different modalities.
Real-time monitoring of manual acupuncture stimulation parameters based on domain adaptive 3D hand pose estimation

Manual acupuncture (MA) is a widely used type of therapy method in the world, its treatment result and clinical safety are highly related to the selection of stimulation parameters (needling amplitude and frequency) of acupuncturists. However, to date, there is no stimulation parameter measurement solution that can be conveniently used in the clinic. Thus, there is an urgent need to develop a single camera-based real-time monitoring system for MA operation. This monitoring system is expected to give the result of both the amplitude and frequency of MA. Considering that constructing the labeled MA monitoring dataset is laborious and time-consuming and that there is a large amount of unlabeled data, we propose an adaptive orientation-based domain adaptation framework to alleviate the domain shift and achieve better performance. Moreover, we contribute a benchmark that contains 20 videos of on-body MA operation and 30 videos of on-simulator MA operation with 3D coordinates to facilitate the future development of real-time MA monitoring. Extensive experiments on the proposed benchmark demonstrate the superiority of the proposed methods on both movement estimation and frequency estimation of hand acupuncture. The application prospects of this framework for the clinical work of MA included the investigation of dose–effect relationship of MA, enhancement of its operation safety, etc. Our data is publicly available at https://github.com/SHUTCM-tcme/SUTCM-AM.
Social networks in Iranian academic libraries: complementary or antagonistic tools?

Purpose The present study aimed to investigate the impact of social networks on the use of academic libraries by university students. Design/methodology/approach The method used in the present study was a survey. The statistical population included 461 university students. The data collection tool was a questionnaire. The result of the Cronbach test was equal to 0.726 indicating the acceptable reliability of the questionnaire. For data analysis, descriptive statistical methods and inferential statistical methods using SPSS 21 software were employed. Findings The findings showed that 243 of the participants used social networks for 4–6 h a day, 192 students never used university libraries and 229 used the university library only once in a month. Communication with friends was also reported to be one of the main goals in using social networks. The results of regression analysis also indicated that four predictor variables including information retrieval, social influence, trust and attractiveness of social networking environment were statistically able to explain the variance of reluctance to use university libraries. Originality/value The present study is one of the few studies that has examined the negative impact of social networks on visiting university libraries.
A novel temporal moment retrieval model for apron surveillance video

This study addresses the problem of retrieving a specific moment from an untrimmed apron surveillance video through a query sentence. Existing methods cannot tackle it effectively by using sliding windows over the entire video, which suffers from exhaustively enumerated candidates. Here temporal and spatial characters of events are abstracted to corresponding heuristic rules in first-order predicate logic. Temporal characters are exploited to skip unimportant frames while potential time boundaries are deduced from spatial characters. Meanwhile, an object-detection model is designed to obtain spatial characters of potential time boundaries, which is compared with pre-defined heuristic rules for detection. If the obtained spatial characters do not match the heuristic rules, a multiple-time-scales searching module is devised to localize the boundaries. This method is evaluated on TACoS and Apron Surveillance Video (ASV) dataset. Results show that it outperforms the state-of-the-art methods on TACoS and accurately retrieves target events on ASV in one minute.
Fast image visibility enhancement based on active polarization and color constancy for operation in turbid water

Vehicles operating in a water medium sometimes encounter harsh conditions with high turbidity and low scene illumination, making it challenging to obtain reliable target information through optical devices. Although many post-processing solutions were proposed, they are not applicable to continuous vehicle operations. Inspired by the advanced polarimetric hardware technology, a joint fast algorithm was developed in this study to address the above problems. Backscatter attenuation and direct signal attenuation were solved separately by utilizing the revised underwater polarimetric image formation model. A fast local adaptive Wiener filtering method was used to improve the backscatter estimation by reducing the additive noise. Further, the image was recovered using the fast local space average color method. By using a low-pass filter guided by the color constancy theory, the problems of nonuniform illumination caused by artificial light and direct signal attenuation were both addressed. The results of testing on images from laboratory experiments showed improved visibility and realistic chromatic rendition.
Register Allocation for Compressed ISAs in LLVM

We present an adaptation to the LLVM greedy register allocator to improve code density for compressed RISC ISAs.
SQLite Encryption Method for Embedded Databases Based on Chaos Algorithm

With the widespread use of embedded systems, chaos is a nonlinear system with certainty and complexity. It is an important topic in the field of information security at present, and it is an effective way to apply to embedded systems. It has great practical value in theory and in practice. This research mainly focuses on the encryption technology of SQLite embedded database and proposes an improved sparrow algorithm (Logistic Chaos Sparrow Search Algorithm, LCSSA) based on Logistic Chaos Map. It shows that the security level of SQLite in web development is higher than that of conventional Access. The population is initialized by the logistic chaotic mapping method, which improves the quality of the initial solution, increases the diversity of the population, and reduces the risk of premature maturity of the algorithm. The initial value <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>y</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> </math> determines the encryption method of the nonlinear function. Taking the integer variable (int) as an example, the value range is -231~231. It can be seen that the key space is sufficient to prevent various conventional attacks. When the key is the wrong key, decryption will not yield any data. It can be found that encryption and decryption are very sensitive to the key, which is also determined by the sensitivity of chaotic encryption system to the initial value. The benchmark function compares the performance of the improved algorithm with the algorithm before the improvement and compares it with the SSA. The LCSSA has better convergence performance, higher accuracy, and better stability.
Low-complexity systolic array structure for field multiplication in resource-constrained IoT nodes

Security and privacy issues with Internet of Things (IoT) network make it difficult to use IoT technology. Cryptographic protocols can be put in place on IoT edge nodes to address security flaws. Since the edge nodes have few resources, it is challenging to implement these protocols on them. The key operation in these protocols is finite-field multiplication, and how well it is carried out has a big effect on how well they perform. Therefore, we present in this work a novel irreducible All-One Polynomial (AOP)-based low-complexity bit-serial systolic implementation for multiplication in the binary-extended field. The shown multiplier structure features regular cell architectures and local communication connections between the cells, making it more appropriate for VLSI implementation. When compared to competing multipliers, the complexity analysis of the suggested multiplier reveals a significant savings in area and area-time product. As a result, it is more ideal for cryptographic systems that set additional constraints on area.
An improved gradient neural network for solving periodic Sylvester matrix equations

The gradient neural network (GNN) method is a novel approach to solving matrices. Based on this method, this paper improves the gradient neural network (IGNN) model with a better effect. The convergence speed is increased by replacing the Xi−1(k) matrix in the original gradient neural network with the current matrix Xi−1(k+1). According to theoretical study, IGNN is globally convergent to the solution of periodic matrix equations. The computer simulation results further confirm the feasibility and effectiveness of IGNN.
Patho-Genomics Fused Database Schema and Optimization for Automatic Pathology Workflow

Complete database design, implementation, optimization as well as testing is obtained in order to collect and fuse all the morphological, morphometrical, genomic metadata during the whole, automatic, digital pathology workflow without any human interaction. The subsequent purpose of the fused database is to evaluate texture based (external) and biochemical (internal) features of tumor, cancer collectively. Hence, finding correlation between them might make us able to predict and make diagnose at an earlier stage.
Application of a New Hybrid Machine Learning (Fuzzy-PSO) for Detection of Breast’s Tumor

Breast cancer is the second leading cause of death after lung cancer. The only possible way to save patients' lives is early diagnosis of the disease; Because if this disease is diagnosed in the early stages and with a high level of accuracy, the chance of survival increases. Different fuzzy-based soft computing techniques have been proposed. In this research, the proposed fuzzy hybrid algorithm - particle swarm has been used to detect the type of breast tumors based on the analysis of features in mammography images. The proposed method in this study, the fuzzy hybrid algorithm - the proposed particle swarm algorithm, has a remarkable performance of 94.58% in breast cancer diagnosis. The results obtained from this study can be used for timely diagnosis and providing effective treatments for breast cancer.
Training YOLO v4 on DOTA dataset

The paper focuses on satellite image based object detection. Satellite images-based object detection is being used for an increasingly wide range of tasks. For object detection deep neural networks are used. The training set was created from the DOTA dataset by splitting and scaling the images. As neural network, in this research YOLO architecture is used, more specifically the Yolov4 and Yolov4-tyni configurations. The test set was created from satellite-based images on different scales. The dataset for testing was managed and labelled in CVAT annotation program. In the paper we try to synthesize the results achieved during the training and testing of the mentioned neural networks. Objects are very diverse in size, the recognition rate during testing is greatly influenced by how much the image size is proportional to the size of the images used during training. The images in the testing dataset were scaled to a different size. We tried to evaluate how much the scale of the images and size of the object affect the rate of recognition.
Capsule Networks - With reconstruction to explainability

Artificial intelligence is the mainstream solution to various problems, and thanks to developments in hardware, the performance of the models is increasing, but with it, the complexity is increasing as well. This creates the need to explain how and why the models are deciding. This paper presents a novel approach of using Capsule networks in the field of explainability. Using reconstruction, capsules can explain in more detail some aspects of their decision. The paper concludes with possible future research on this topic.