Reviewerly/exHarmony huggingface dataset:
A point and density map hybrid network for crowd counting and localization based on unmanned aerial vehicles

Crowd counting and localisation are essential tasks in crowd analysis and are vital to ensure public safety. However, these tasks via UAV bring new obstacles compared with video surveillance (e.g. viewpoint and scale variations, background clutter, and small scales). To overcome the difficulties, this research presents a novel network named PDNet. It employs the multi-task learning approach to combine the point regression and density map regression. PDNet includes a backbone to extract multi-scale features, a Dilated Feature Fusion module (DFF), a Density Map Attention module (DMA), a density map branch and a point branch. Aims of DFF is to address the difficulties of small targets and scale variations by establishing relationships between targets and their surroundings. DMA is created to address the challenges of complicated backgrounds, allowing the PDNet to focus on the target's location. In addition, the density map branch and point branch are designed for density maps regression and point regression, respectively. Experiments on the DroneCrowd dataset demonstrate that our proposed network outperforms state-of-the-art approaches in terms of localisation, L-mAP (53.85%), L-AP@10 (59.14%), L-AP@15 (63.64%), and L-AP@20 (66.21%), and we improved counting performance and significantly reduced inference time. In addition, ablation experiments are conducted to prove the modules' effectiveness.
Motorcycle and Vehicle Detection for Applications in Road Safety and Traffic Monitoring Systems

Motorcycles are becoming increasingly common in middle to low-income countries as cheaper alternatives to fourwheeled vehicles. The reliance on motorcycle-based services has also seen a substantial increase in popularity, leading to a greater proportion of motorcycles on the road. The increase in motorcycle reliance necessitates a need for motorcycle-inclusive road information generation as motorcycles are the most susceptible to fatal road crashes. We report the results of our application of the You Only Look Once (YOLOv4) algorithm to count and classify vehicles and motorcycles in traffic videos obtained by our group over a three-month period along Katipunan Avenue Southbound (KAS), Metro Manila. This has been made to run in real-time with video and is able to process a video output with its annotations and a counter for both classes. These results show that a motorcycle and vehicle detection and counting system can be feasibly considered for data-driven road safety and traffic monitoring systems.
StegYou: Model for Hiding, Retrieving and Detecting Digital Data in Images

Nowadays, people are not aware of how vulnerable they are to cyber-attacks and theft of personal and sensitive information, from stealing social media passwords to stealing a person’s full identity. People are not aware that their communication with the outside world can be intercepted and all sensitive and secret information stolen from them, while they completely rely on and believe in the implemented safety principles from various service providers. No matter how sophisticated the system is, every system is vulnerable. Therefore, in this paper, we present the StegYou model for hiding, retrieving, and detecting digital data in images. Our implementation of hiding and retrieving data is based on a custom made crypt-steganography algorithm for one or more images. By applying crypt-steganography techniques for secure data transfer between spatially-distributed individuals, the model will strive to reach the highest level of confidentiality, authenticity, and integrity of the user’s confidential and sensitive data. Also, StegYou can detect hidden data in given images with the help of machine learning methods.
CorefPM: Coreference Resolution as Linguistic Prompt Prediction and Machine Reading Comprehension

Coreference resolution is a key challenge in natural language processing and is considered as a good test of machine intelligence. Recent coreference resolution prefers to supervised methods due to its superior performance. But supervised methods suffer from data scarcity and limited generalizability. In this paper, we present CorefPM, an unsupervised approach for the coreference resolution task. We formulate the task as following steps: (1) We generate the candidate mention set by linguistic knowledge and score them by prompt prediction method.(2) We obtain another mention with its confidence score by machine reading comprehension(MRC) model.(3) We output final resolution result by comparing candidate mention and MRC mention. Experiments demonstrate that our approach significantly outperforms the baseline of few-shot and zero-shot in ZeroCLUE, although performs worse on CLUEWSC 2020 dataset than supervised learning baselines. Our experiment results suggested that linguistic knowledge is valuable and can improve the result of coreference resolution task.
Batch Active Learning for Text Classification and Sentiment Analysis

Supervised learning of classifiers for text classification and sentiment analysis relies on the availability of labels that may be either difficult or expensive to obtain. A standard procedure is to add labels to the training dataset sequentially by querying an annotator until the model reaches a satisfactory performance. Active learning is a process that optimizes unlabeled data records selection for which the knowledge of the label would bring the highest discriminability of the dataset. Batch active learning is a generalization of a single instance active learning by selecting a batch of documents for labeling. This task is much more demanding because plenty of different factors come into consideration (i. e. batch size, batch evaluation, etc.). In this paper, we provide a large scale study by decomposing the existing algorithms into building blocks and systematically comparing meaningful combinations of these blocks with a subsequent evaluation on different text datasets. While each block is known (warm start weights initialization, Dropout MC, entropy sampling, etc.), many of their combinations like Bayesian strategies with agglomerative clustering are first proposed in our paper with excellent performance. Particularly, our extension of the warm start method to batch active learning is among the top performing strategies on all datasets. We studied the effect of this proposal comparing the outcomes of varying distinct factors of an active learning algorithm. Some of these factors include initialization of the algorithm, uncertainty representation, acquisition function, and batch selection strategy. Further, various combinations of these are tested on selected NLP problems with documents encoded using RoBERTa embeddings. Datasets cover context integrity (Gibberish Wackerow), fake news detection (Kaggle Fake News Detection), categorization of short texts by emotional context (Twitter Sentiment140), and sentiment classification (Amazon Reviews). Ultimately, we show that each of the active learning factors has advantages for certain datasets or experimental settings.
MPED: Quantifying Point Cloud Distortion Based on Multiscale Potential Energy Discrepancy

In this paper, we propose a new distortion quantification method for point clouds, the multiscale potential energy discrepancy (MPED). Currently, there is a lack of effective distortion quantification for a variety of point cloud perception tasks. Specifically, in human vision tasks, a distortion quantification method is used to predict human subjective scores and optimize the selection of human perception task parameters, such as dense point cloud compression and enhancement. In machine vision tasks, a distortion quantification method usually serves as loss function to guide the training of deep neural networks for unsupervised learning tasks (e.g., sparse point cloud reconstruction, completion, and upsampling). Therefore, an effective distortion quantification should be differentiable, distortion discriminable, and have low computational complexity. However, current distortion quantification cannot satisfy all three conditions. To fill this gap, we propose a new point cloud feature description method, the point potential energy (PPE), inspired by classical physics. We regard the point clouds are systems that have potential energy and the distortion can change the total potential energy. By evaluating various neighborhood sizes, the proposed MPED achieves global-local tradeoffs, capturing distortion in a multiscale fashion. We further theoretically show that classical Chamfer distance is a special case of our MPED. Extensive experiments show that the proposed MPED is superior to current methods on both human and machine perception tasks. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Qi-Yangsjtu/MPED</uri> .
Leveraging Expert Demonstration Features for Deep Reinforcement Learning in Floor Cleaning Robot Navigation

In this paper, a Deep Reinforcement Learning (DRL)-based approach for learning mobile cleaning robot navigation commands that leverage experience from expert demonstrations is presented. First, expert demonstrations of robot motion trajectories in simulation in the cleaning robot domain are collected. The relevant motion features with regard to the distance to obstacles and the heading difference towards the navigation goal are extracted. Each feature weight is optimized with respect to the collected data, and the obtained values are assumed as representing the optimal motion of the expert navigation. A reward function is created based on the feature values to train a policy with semi-supervised DRL, where an immediate reward is calculated based on the closeness to the expert navigation. The presented results show the viability of this approach with regard to robot navigation as well as the reduced training time.
Simulation and Numerical Optimization of Specific Characteristics of the Unified Range of Power Converters

In the article considers the problem of numerical modeling and optimization for the characteristics of systems such as "black box" based on algorithms for analyzing experimental interval data. The characteristics of a unified series of electric energy converters on magnetic amplifiers are chosen as an example. The problem of design the optimal experiment for obtaining data is also considered. The optimization problem is formulated in the form of an interval equation. The results of the solution of this problem are given. It is shown that the proposed approach makes it possible to optimize the mass and size characteristics of magnetic amplifiers of electrical energy converters.
PIPO: Policy Optimization with Permutation-Invariant Constraint for Distributed Multi-Robot Navigation

For large-scale multi-agent systems (MAS), ensuring the safety and effectiveness of navigation in complicated scenarios is a challenging task. With the agent scale increasing, most existing centralized methods lose their magic for the lack of scalability, and the popular decentralized approaches are hampered by high latency and computing requirements. In this research, we offer PIPO, a novel policy optimization algorithm for decentralized MAS navigation with permutation-invariant constraints. To conduct navigation and avoid un-necessary exploration in the early episodes, we first defined a guide-policy. Then, we introduce the permutation invariant property in decentralized multi-agent systems and leverage the graph convolution network to produce the same output under shuffled observations. Our approach can be easily scaled to an arbitrary number of agents and used in large-scale systems for its decentralized training and execution. We also provide extensive experiments to demonstrate that our PIPO significantly outperforms the baselines of multi-agent reinforcement learning algorithms and other leading methods in variant scenarios.
Nationality Detection Using Deep Learning

A new intelligent monitoring model is developed by us for determining gender and nationality from frontal picture candidates utilizing the face area based on deep learning. Face recognition is influenced by a number of characteristics, including picture quality, illumination, rotation angle, blockage, and facial expression. As a result, we must first recognize an input image before converting it to a genuine input. Nationality is the most well distinguishing characteristic that is applied in every nation, and it is also important to secure authentication. Image detection is crucial in this case. Then, we can determine the facial shape, gender, and nationality of the candidate image. In the end, we return the result based on the distance comparison using the use of a library to measure the sample. There were significant discrepancies across photographs while measuring samples based on their gender and facial features. The photos used in the input must be the same as those used in the output. Picture of a frontal face with clean lighting and no blemishes at every angle of rotation. The model may be used by ordinary people, models, celebrities, actors, and others. In the end, computer can tell nationality by looking at a picture of a person’s face.
Prototypical Contrastive Learning and Adaptive Interest Selection for Candidate Generation in Recommendations

Deep Candidate Generation plays an important role in large-scale recommender systems. It takes user history behaviors as inputs and learns user and item latent embeddings for candidate generation. In the literature, conventional methods suffer from two problems. First, a user has multiple embeddings to reflect various interests, and such number is fixed. However, taking into account different levels of user activeness, a fixed number of interest embeddings is sub-optimal. For example, for less active users, they may need fewer embeddings to represent their interests compared to active users. Second, the negative samples are often generated by strategies with unobserved supervision, and similar items could have different labels. Such a problem is termed as class collision. In this paper, we aim to advance the typical two-tower DNN candidate generation model. Specifically, an Adaptive Interest Selection Layer is designed to learn the number of user embeddings adaptively in an end-to-end way, according to the level of their activeness. Furthermore, we propose a Prototypical Contrastive Learning Module to tackle the class collision problem introduced by negative sampling. Extensive experimental evaluations show that the proposed scheme remarkably outperforms competitive baselines on multiple benchmarks.
A Biased Sampling Method for Imbalanced Personalized Ranking

Pairwise ranking models have been widely used to address recommendation problems. The basic idea is to learn the rank of users' preferred items through separating items into positive samples if user-item interactions exist, and negative samples otherwise. Due to the limited number of observable interactions, pairwise ranking models face serious class-imbalance issues. Our theoretical analysis shows that current sampling-based methods cause the vertex-level imbalance problem, which makes the norm of learned item embeddings towards infinite after a certain training iterations, and consequently results in vanishing gradient and affects the model inference results. We thus propose an efficient <u>Vi</u>tal <u>N</u>egative <u>S</u>ampler (VINS) to alleviate the class-imbalance issue for pairwise ranking model, in particular for deep learning models optimized by gradient methods. The core of VINS is a bias sampler with reject probability that will tend to accept a negative candidate with a larger degree weight than the given positive item. Evaluation results on several real datasets demonstrate that the proposed sampling method speeds up the training procedure 30% to 50% for ranking models ranging from shallow to deep, while maintaining and even improving the quality of ranking results in top-N item recommendations.
Is It Enough Just Looking at the Title?

In a news recommender system, a user tends to click on a news article if she is interested in its topic understood by looking at its title. Such a behavior is possible since, when viewing the title, humans naturally think of the contextual meaning of each title word by leveraging their own background knowledge. Motivated by this, we propose a novel personalized news recommendation framework CAST (Context-aware Attention network with a Selection module for Title word representation), which is capable of enriching title words by leveraging body text that fully provides the whole content of a given article as the context. Through extensive experiments, we demonstrate (1) the effectiveness of core modules in CAST, (2) the superiority of CAST over 9 state-of-the-art news recommendation methods, and (3) the interpretability with CAST.
Deep locally linear embedding network

Recent years have seen great progress in many deep learning-based super-resolution methods that learn global responses through the non-local strategy. However, improper linear fitting tends to produce unsatisfactory results with notable artifacts. To relieve the improper linear fitting and reuse the hierarchical features, we propose an end-to-end locally linear embedding super-resolution network, termed as LLE-Net, based on the assumption that the local geometric relationship in the low-resolution manifold space also exists in high-level feature manifold space. The proposed LLE-Net includes two major components: 1) the local linear embedding block (LLEB) and 2) the hierarchical non-local block (HNB). In particular, LLEB searches sparse and similar feature maps and embeds the geometric relationship into the high-level feature space, allowing more attention to be paid to objects’ textural details. To promote the representation ability, the proposed HNB is able to explore layer- and pixel-level interdependencies. We conduct extensive experiments to evaluate the superiority of LLE-Net via two groups of experiments: (1) super-resolution tasks on two satellite image datasets and a satellite video image dataset, and (2) the subsequent high-level image processing tasks (i.e., satellite semantic segmentation). The proposed method achieves an 0.7 dB improvement in PSNR value on the Draper dataset and 0.07% segmentation accuracy improvement. The source code of the proposed LLE-Net is publicly available.
LEGO application for a microelectronics powered upper limb exoskeleton.

Abstract In general, the main aim of education through models is based on stimulating young people's ability to think logically and use their creativity to develop practical problem-solving skills. Over time, the standard educational process has identified the need to find and use new ways of transmitting basic information in the teaching of students and this way has been quantified by the phrase - serious games. This approach allows a faster and more efficient access of users (pupils-students) to the information bases and a correct and beneficial transmission of their theoretical and practical training. Thus, in the first part of the paper a number of aspects related to educational resources based on Lego pieces and an analysis of the opportunity of their use in the processes of instruction, learning and creation are presented. In the second part of the paper, an analysis of the different variants of Lego pieces assemblies used for the development of exoskeleton shapes or for upper limb training is carried out, as well as for the study of the possibility of obtaining versatile and customized variants. In the third part of the paper, the experimental model of a microelectronically actuated upper limb exoskeleton is presented, useful in training procedures for the rehabilitation of elbow joint and wrist movements. In the final part of the paper, observations obtained from the construction of the system and conclusions regarding the use of such a demonstration-educational model in practical problem-solving skills development actions are presented.
Knowledge Management in Projects Using Agile Methodologies

With the introduction of agile project management methodologies, knowledge management plays an important role. This article examines the features of using knowledge management tools in companies with agile project management approach. The purpose of the work is to determine the most and least popular tools used in companies of various industry areas, the application features, as well as to make recommendations for the implementation of these tools. The work employs the methods as a survey and an interview.
On the importance of feedback for categorization: Revisiting category learning experiments using an adaptive filter model.

Associative accounts of category learning have been, for the most part, abandoned in favor of cognitive explanations (e.g., similarity, explicit rules). In the current work, we implement an Adaptive Linear Filter (ALF) closely related to the Rescorla and Wagner learning rule, and use it to tackle three learning tasks that pose challenges to an associative view of category learning. Across three computational simulations, we show that the ALF is in fact able to make the predictions that seemed problematic. Notably, in our simulations we use exactly the same model and specifications, attesting to the generality of our account. We discuss the consequences of our findings for the category learning literature. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
A hybrid clustering method based on k-means algorithm

We propose a novel hybrid algorithm that effectively combines K-means clustering and hierarchical and uses triangle inequality to accelerate the clustering speed. The HTK clustering algorithm can produce the same results as the standard K-means clustering algorithm. The proposed algorithm is superior to the standard K-means clustering algorithm in terms of running time and memory usage, thus improving the clustering speed and time complexity of the algorithm. The proposed clustering methods are tested on sci-kit learn datasets, and they are more favorable than the random restart K-means algorithm.
Contactless Hospitality Technology in Post-COVID-19 Era: Future Research Clusters

The global travel, tourism and hospitality sector is facing the effect of pandemic COVID-19 outbreak. Health and the safe of travelers’ issue has become the critical focus and crucial in hospitality recovery plan. The sudden COVID-19 outbreak has led to accelerated adoption of technology for both travelers and hospitality service providers. This chapter aims to explore type of contactless technology used in hospitality industry and proposed a research cluster for future research agenda. Among the technology discuss in this chapter are Virtual Reality, Chatbots, Robotic, contactless payment, voice search, mobile check-in, recognition technology and many more.