Reviewerly/exHarmony huggingface dataset:
Revolutionizing Target Detection in Intelligent Traffic Systems: YOLOv8-SnakeVision

Intelligent traffic systems represent one of the crucial domains in today’s world, aiming to enhance traffic management efficiency and road safety. However, current intelligent traffic systems still face various challenges, particularly in the realm of target detection. These challenges include adapting to complex traffic scenarios and the lack of precise detection for multiple objects. To address these issues, we propose an innovative approach known as YOLOv8-SnakeVision. This method introduces Dynamic Snake Convolution, Context Aggregation Attention Mechanisms, and the Wise-IoU strategy within the YOLOv8 framework to enhance target detection performance. Dynamic Snake Convolution assists in accurately capturing complex object shapes and features, especially in cases of target occlusion or overlap. The Context Aggregation Attention Mechanisms allow the model to better focus on critical image regions and effectively integrate information, thus improving its ability to recognize obscured targets, small objects, and complex patterns. The Wise-IoU strategy combines dynamic non-monotonic focusing mechanisms, aiming to more precisely regress target bounding boxes, particularly for low-quality examples. We validate our approach on the BDD100K and NEXET datasets. Experimental results demonstrate that YOLOv8-SnakeVision excels in various complex road traffic scenarios. It not only enhances small object detection but also strengthens the ability to recognize multiple targets. This innovative method provides robust support for the development of intelligent traffic systems and holds the promise of achieving further breakthroughs in future applications.
Digital measurement of SARS-CoV-2 transmission risk from 7 million contacts

How likely is it to become infected by SARS-CoV-2 after being exposed? Almost everyone wondered about this question during the COVID-19 pandemic. Contact-tracing apps1,2 recorded measurements of proximity3 and duration between nearby smartphones. Contacts-individuals exposed to confirmed cases-were notified according to public health policies such as the 2 m, 15 min guideline4,5, despite limited evidence supporting this threshold. Here we analysed 7 million contacts notified by the National Health Service COVID-19 app6,7 in England and Wales to infer how app measurements translated to actual transmissions. Empirical metrics and statistical modelling showed a strong relation between app-computed risk scores and actual transmission probability. Longer exposures at greater distances had risk similar to that of shorter exposures at closer distances. The probability of transmission confirmed by a reported positive test increased initially linearly with duration of exposure (1.1% per hour) and continued increasing over several days. Whereas most exposures were short (median 0.7 h, interquartile range 0.4-1.6), transmissions typically resulted from exposures lasting between 1 h and several days (median 6 h, interquartile range 1.4-28). Households accounted for about 6% of contacts but 40% of transmissions. With sufficient preparation, privacy-preserving yet precise analyses of risk that would inform public health measures, based on digital contact tracing, could be performed within weeks of the emergence of a new pathogen.
Dual-Contrastive Dual-Consistency Dual-Transformer: A Semi-Supervised Approach to Medical Image Segmentation

Medical image segmentation serves as a crucial under-pinning for a myriad of clinical applications. The advent of deep learning techniques has significantly propelled advancements in this field. However, challenges persist due to the limited availability of labelled medical imaging data and the substantial cost of data annotation. This paper introduces a novel semi-supervised learning strategy, amalgamating pseudo-labelling and contrastive learning with a consistency regularization framework. This innovative approach incorporates a modified contrastive learning strategy and a confidence-aware pseudo-labeling strategy, both of which are integrated into a dual-segmentation network ensemble learning structure. Inspired by the recent success of self-attention mechanisms, we harness the power of the Vision Transofmer(ViT) within our proposed semi-supervised framework, and conduct a comprehensive comparison among various combinations of ViT and Convolutional Neural Network(CNN) with the proposed strategy. The efficacy of our proposed method is validated using a publicly available medical image segmentation dataset, where it demonstrates state-of-the-art performance against established methods. The proposed method, all baseline methods, and dataset are available at https://github.com/ziyangwang007/CV-SSL-MIS.
AI-Powered Employee Experience

AI has become an indispensable tool for businesses seeking to enhance the employee experience. Businesses that implement it can increase employee satisfaction, enhance customer service, and decrease cost per employee served – ultimately adding significant business value. Artificial intelligence instruments can improve employee experiences across several areas: recruitment, onboarding, training, and performance management. Furthermore, this use case will profoundly impact customer experiences overall.
El Aula Inteligente: una experiencia educativa innovadora

This article deals with the design and assessment of a new instructional model called Intelligent Classroom (IC). The Intelligent classroom is described in the context of educational learner-centered paradigm. The instructional design is formed by 8 dimensions: Goals, Tasks, Methods, Sequence, The Teacher, The Students, Assessment and Context. The model was applied to third year baccalaureate students attending 3 private schools in Madrid. The sample consisted of 398 subjects (216 males and 182 females). The design of assessment was quasi experimental, «control group non equivalent», with two groups corresponding to traditional teaching and the Intelligent Classroom. The dependent variable studied was academic achievement, assessed by objective tests. The results show positive effects of IC in general, as well as for different levels of achievement. Some significative differences in relation with gender were found.
Strategies to promote e-health and telemedicine activities in developing countries

Logic suggests that employing information and communication technology (ICT) to deliver health care at distance would be useful to address at least some of the problems in developing countries. Governments as policy-making organizations play a pivotal role in formulating regulations in the health sector. The contribution of the government is particularly important in developing countries, where the public health system is usually the major provider of services. Reluctance to use e-health may also stem from certain prejudices. International agencies such as the World Health Organization, the United Nations, the World Bank and certain regional organizations have recognized the value of ICT in development. Policy makers must have a more open-minded attitude towards e-health. Aspects such as inertia, reluctance to change and a lack of political will are also important factors that prevent policy makers from considering e-health as an alternative for addressing health problems in developing countries.
Writing Card Games: An Early Excursion Into Software Engineering Principles

complain to some degree about the workload required for implementing any program, they want to have meaningful assignments, not busywork, handed out to them -and if the resultant deliverable is fun to use, so much the better.Providing positive modes of motivation is never easy for an instructor, so wrapping up various learning outcomes into an attractive package for the students to open is of benefit to everyone.Variations on the card game assignment have by used by the author in his first-year programming classes for the past several years, and it remains one of the favorite and more memorable assignments conducted by his students.
Learning Embedded And Real Time Systems Via Low Cost Mobile Robotics

The importance and impact of embedded and real-time computing systems on today's society far exceed that of traditional stand-alone computers; it is hard to think of new device or system that does not embed some combination of silicon-based intelligence, sensing, and communication.A parallel trend is the growth of high-level, abstract design of these systems and associated languages and CAD tools, driven by increasing processor speed, time-to-market requirements and the complexity of applications.However, many courses in embedded systems focus on low-level issues such as addressing, interrupts and interfacing.In this paper, we describe a new direction: a course with the goal of motivating students to learn the abstract concepts that underly the design of these systems via experiments that require the interaction of robots with the physical world.In this studio-format course, most conventional learning takes place outside the class, while small student teams design, build, and evaluate autonomous mobile robots in the classroom/laboratory.To keep costs down, mobile robots are created using LEGO parts and programmed in the highlevel NQC language using the Robolab RCX microcontroller module.As the semester proceeds, students tackle an array of interrelated problems that motivate the study of sensor signal processing, control, scheduling, and resource sharing.In a final project, the students tackle a distributed intelligence project in which an odometry-equipped robot communicates with a PC-based program that tracks the robot's position.To encourage adoption by other electrical engineering and computer engineering programs, a detailed description of the required resources and their cost is included.
Learning Combat in NetHack

Combat in roguelikes involves careful strategy to best match a large variety of items and abilities to a given opponent, and the significant scripting effort involved can be a major barrier to automation. This paper presents a machine learning approach for a subset of combat in the game of NetHack. We describe a custom learning approach intended to deal with the large action space typical of this genre, and show that it is able to develop and apply reasonable strategies, outperforming a simpler baseline approach. These results point towards better automation of such complex game environments, facilitating automated testing and design exploration.
LISA: Lexically Intelligent Story Assistant

This paper serves as an introduction to building an assistive tool for story writers. Our tool, Lexically Intelligent Story Assistant (or LISA), aims to assist story writers by providing real-time feedback on lexical inconsistencies in the story. LISA analyzes the narrative and builds a knowledge base, using artificial intelligence to make inferences and point out errors in the narrative. Moreover, it also allows the user to interact with the system by querying the knowledge base in natural language form. This tool shows that it is possible to create a database for a narrative and use artificial intelligence to improve authoring of stories.
Secure Multiparty Matrix Multiplication Based on Strassen-Winograd Algorithm

This paper presents the first recursive secure multiparty computation protocol for matrix multiplication, based on Strassen-Winograd algorithm. We focus on the setting in which any given player knows only one row of both input matrices and learns the corresponding row of the resulting product matrix. Neither the player initial data, nor the intermediate values, even during the recurrence part of the algorithm, are ever revealed to other players. We use a combination of partial homomorphic encryption schemes and additive masking techniques together with a novel schedule for the location and encryption layout of all intermediate computations that preserves privacy. Compared to state of the art protocols, the asymptotic communication volume and computational time is reduced from $$O(n^3)$$ to $$O(n^{2.81})$$ . This improvement in terms of communication volume arises with matrices of dimension as small as $$n=96$$ which is confirmed by experiments.
PENGARUH PENGGUNAAN MEDIA PEMBELAJARAN TERHADAP MOTIVASI BELAJAR MAHASISWA IKIP SILIWANGI BANDUNG

This study aims to know the effect of the use of learning media on the motivation of students learning IKIP Siliwangi. The research method used is descriptive qualitative, the sample of this study is a student of IKIP Siliwangi covered from several faculties and majors, including faculty of language with Indonesian and English majors, science faculty with majoring in mathematics education, faculty of science majoring in primary school teacher education, counseling guidance, paud teacher education, and out-of-school education. By limiting 30 students of IKIP Siliwangi, the instrument used is a questionnaire created through from google. The results method of this study indicates that there is a significant influence due to the fluse of learning media on the students motivation of IKIP Siliwangi.
A novel approach to improve the accuracy of the box dimension calculations: Applications to trabecular bone quality

Fractal dimension and specifically, box-counting dimension, is the main tool applied in many fields such as odontology to detect fractal patterns applied to the study of bone quality. However, the effective computation of such invariant has not been carried out accurately in literature. In this paper, we propose a novel approach to properly calculate the fractal dimension of a plane subset and illustrate it by analysing the box dimension of a trabecular bone through a computed tomography scan.
Digital cytopathology: A constant evolution (Comments on Capitanio et al. Digital cytology: A short review of technical and methodological approaches and applications)

Capitanio et al provide a succinct review of digital cytopathology technology with particularly good overviews of scanner focusing methodologies as well as explanations of deep focusing techniques. The aim of this letter to the editor is to briefly comment and expand on some of the information provided by these authors.
Workspace Modeling and Path Planning for Truss Structure Inspection by Unmanned Aircraft

Small unmanned aircraft systems can increase efficiency and reduce risk to humans during the inspection of truss-supported structures such as steel bridges. This paper describes a method to mathematically represent a truss and subsequently to plan an efficient collision-free inspection path. The algorithm checks user-defined perspectives for feasibility and redefines any infeasible perspectives. A deterministic roadmap is then generated as the complete graph over these perspectives, and some additional nodes are generated near the joints of the structure. A traveling salesman problem (TSP) is solved to find an efficient inspection path that tours all perspective points, and a local A* path planner then refines the inspection path to circumvent obstructions. The TSP is re-solved, with an updated cost based on local detours; and the process iterates until a feasible TSP solution is found. The “lazy” approach to global and local planning, for which paths are checked for feasibility after the fact and amended if necessary, ensures quick convergence to an efficient inspection path. Simulation results show the functionality of the algorithm. Comparison with a probabilistic roadmap method indicates the proposed algorithm’s efficiency.
An Optical Flow-Based Approach for Minimally Divergent Velocimetry Data Interpolation

Three-dimensional (3D) biomedical image sets are often acquired with in-plane pixel spacings that are far less than the out-of-plane spacings between images. The resultant anisotropy, which can be detrimental in many applications, can be decreased using image interpolation. Optical flow and/or other registration-based interpolators have proven useful in such interpolation roles in the past. When acquired images are comprised of signals that describe the flow velocity of fluids, additional information is available to guide the interpolation process. In this paper, we present an optical-flow based framework for image interpolation that also minimizes resultant divergence in the interpolated data.
Efficient Inference in Multi-task Cox Process Models

We generalize the log Gaussian Cox process (LGCP) framework to model multiple correlated point data jointly. The observations are treated as realizations of multiple LGCPs, whose log intensities are given by linear combinations of latent functions drawn from Gaussian process priors. The combination coefficients are also drawn from Gaussian processes and can incorporate additional dependencies. We derive closed-form expressions for the moments of the intensity functions and develop an efficient variational inference algorithm that is orders of magnitude faster than competing deterministic and stochastic approximations of multivariate LGCPs, coregionalization models, and multi-task permanental processes. Our approach outperforms these benchmarks in multiple problems, offering the current state of the art in modeling multivariate point processes.
Analysis and Improvement of an Authentication Scheme in Incremental Cryptography

Introduced in cryptography by Bellare, Goldreich and Goldwasser in 1994, incrementality is an attractive feature that enables to update efficiently a cryptographic output like a ciphertext, a signature or an authentication tag after modifying the corresponding input. This property is very valuable in large scale systems where gigabytes of data are continuously processed (e.g. in cloud storage). Adding cryptographic operations on such systems can decrease dramatically their performance and incrementality is an interesting solution to have security at a reduced cost. We focus on the so-called XOR-scheme, the first incremental authentication construction proposed by Bellare, Goldreich and Goldwasser, and the only strongly incremental scheme (i.e. incremental regarding insert and delete update operations at any position in a document). Surprisingly, we found a simple attack on this construction that breaks the basic security claimed by the authors in 1994 with only one authentication query (not necessarily chosen). Our analysis gives different ways to fix the scheme; some of these patches are discussed in this paper and we provide a security proof for one of them.
Prediction of Success or Failure for Examination using Nearest Neighbor Method to the Trend of Weekly Online Testing

Using trends of estimated abilities in terms of item response theory for online testing, we can predict success/failure for term-end examinations for each student at early stages in courses. We applied the newly developed nearest neighbor method for determining the similarity of learning skills in the trends of estimated abilities, resulting in better prediction accuracy for success or failure. This paper shows that the use of the learning analytics incorporating trends for abilities is effective. ROC curve and recall precision curve are also utilized in the proposed method.
A Hybrid Clustering Approach for Bag-of-Words Image Categorization

The Bag-of-Words (BoW) model is a well-known image categorization technique. However, in conventional BoW, neither the vocabulary size nor the visual words can be determined automatically. To overcome these problems, a hybrid clustering approach that combines improved hierarchical clustering with a K-means algorithm is proposed. We present a cluster validity index for the hierarchical clustering algorithm to adaptively determine when the algorithm should terminate and the optimal number of clusters. Furthermore, we improve the max-min distance method to optimize the initial cluster centers. The optimal number of clusters and initial cluster centers are fed into K-means, and finally the vocabulary size and visual words are obtained. The proposed approach is extensively evaluated on two visual datasets. The experimental results show that the proposed method outperforms the conventional BoW model in terms of categorization and demonstrate the feasibility and effectiveness of our approach.
Temporal Epistemic Gossip Problems

Gossip problems are planning problems where several agents have to share information (‘secrets’) by means of phone calls between two agents. In epistemic gossip problems the goal can be to achieve higher-order knowledge, i.e., knowledge about other agents’ knowledge; to that end, in a call agents communicate not only secrets, but also agents’ knowledge of secrets, agents’ knowledge about other agents’ knowledge about secrets, etc. Temporal epistemic gossip problems moreover impose constraints on the times of calls. These constraints are of two kinds: either they stipulate that a call between two agents must necessarily be made at some time point, or they stipulate that a call can be made within some possible (set of) interval(s). In the non-temporal version, calls between two agents are either always possible or always impossible. We investigate the complexity of the plan existence problem in this general setting. Concerning the upper bound, we prove that it is in NP in the general case, and that it is in P when the problem is non-temporal and the goal is a positive epistemic formula. As for the lower bound, we prove NP-completeness for two fragments: problems with possibly negative goals even in the non-temporal case, and problems with temporal constraints even if the goal is a set of positive atoms.
A framework for inducing artificial changes in optimization problems

Environmental changes are traditionally considered intrinsic in evolutionary dynamic optimization. However, by ignoring that changes can instead be induced, we are ignoring that environmental changes can be eventually beneficial. To investigate the impact of artificial changes on the optimization speed up, we propose a framework for inducing artificial changes in any pseudo-Boolean or continuous optimization in this paper. Seven types of changes can be induced. Knowing when and how the changes occur allows us to design new strategies for evolutionary algorithms. Through computational experiments and illustrative examples, the impact of introducing changes in the optimization process is investigated. Experimental results indicate that changing the environments according to the proposed framework can lead to higher speed up, but not for all problems and change types. The best performance was obtained by change types that introduce plateaus and/or modify the gradient of regions of the fitness landscape around the current best solution. By doing this, the evolutionary dynamics is modified, eventually allowing the population to escape faster from local optima and reach new zones of the fitness landscape. Given a pseudo-Boolean or continuous optimization static problem, the proposed framework can be used to dynamically change the problem to speed up the optimization.
Scheduling in Queueing Systems and Networks Using ANFIS

This paper is concerned with a scheduling problem in many real-world systems where the customers must be waiting for a service known as queueing system. Classical queueing systems are handled using probabilistic theories, mostly based on asymptotic theory and/or samples analysis. We address a situation where neither enough statistical data exists, nor asymptotic behavior can be applied to. This way, we propose to use an Adaptive Neuro-Fuzzy Inference System (ANFIS) method to infer scheduling rules of a queueing problem, based on uncertain data. We use the utilization ratio and the work in process (WIP) of a queue to train an ANFIS network to finally obtain the estimated cycle time of all tasks. Multiple tasks and rework are considered into the problem, so it cannot be easily modeled using classical probability theory. The experiment results through simulation analysis show an improvement of our ANFIS method in the performance measures compared with traditional scheduling policies.