Reviewerly/exHarmony huggingface dataset:
Vehicle Density Prediction in Low Quality Videos with Transformer Timeseries Prediction Model (TTPM)

Recent advancement in low-cost cameras has facilitated surveillance in various developing towns in India.The video obtained from such surveillance are of low quality.Still counting vehicles from such videos are necessity to avoid traffic congestion and allows drivers to plan their routes more precisely.On the other hand, detecting vehicles from such low quality videos are highly challenging with vision based methodologies.In this research a meticulous attempt is made to access low-quality videos to describe traffic in Salem town in India, which is mostly an un-attempted entity by most available sources.In this work profound Detection Transformer (DETR) model is used for object (vehicle) detection.Here vehicles are anticipated in a rush-hour traffic video using a set of loss functions that carry out bipartite coordinating among estimated and information acquired on real attributes.Every frame in the traffic footage has its date and time which is detected and retrieved using Tesseract Optical Character Recognition.The date and time extricated and perceived from the input image are incorporated with the length of the recognized objects acquired from the DETR model.This furnishes the vehicles report with timestamp.Transformer Timeseries Prediction Model (TTPM) is proposed to predict the density of the vehicle for future prediction, here the regular NLP layers have been removed and the encoding temporal layer has been modified.The proposed TTPM error rate outperforms the existing models with RMSE of 4.313 and MAE of 3.812.
RETRACTED: Application of blockchain technology based on big data analysis in sustainable agriculture

Abstract To improve the effect of sustainable agricultural development, this paper reports on sustainable agricultural development with the support of big data technology, combines the Internet of Things technology to apply blockchain in sustainable agricultural development, and analyzes the economic effect model. Moreover, this paper proves that the introduction of third‐party logistics in the vendor‐managed inventory strategy can effectively reduce the total cost of the supply chain. Through model analysis and model transformation, the inventory transportation integration optimization problem model is decomposed into two sub‐models: the transportation scheduling model and the replenishment delivery model. In addition, this paper designs a new solution algorithm based on the genetic algorithm and builds an intelligent blockchain model. The experimental research shows that the blockchain technology based on big data analysis proposed in this paper has certain effects.
A Vision-Based Motion Control Framework for Water Quality Monitoring Using an Unmanned Aerial Vehicle

In this paper, we present a vision-aided motion planning and control framework for the efficient monitoring and surveillance of water surfaces using an Unmanned Aerial Vehicle (UAV). The ultimate goal of the proposed strategy is to equip the UAV with the necessary autonomy and decision-making capabilities to support First Responders during emergency water contamination incidents. Toward this direction, we propose an end-to-end solution, based on which the First Responder indicates visiting and landing waypoints, while the envisioned strategy is responsible for the safe and autonomous navigation of the UAV, the refinement of the way-point locations that maximize the visible water surface area from the onboard camera, as well as the on-site refinement of the appropriate landing region in harsh environments. More specifically, we develop an efficient waypoint-tracking motion-planning scheme with guaranteed collision avoidance, a local autonomous exploration algorithm for refining the way-point location with respect to the areas visible to the drone’s camera, water, a vision-based algorithm for the on-site area selection for feasible landing and finally, a model predictive motion controller for the landing procedure. The efficacy of the proposed framework is demonstrated via a set of simulated and experimental scenarios using an octorotor UAV.
A2X: An end-to-end framework for assessing agent and environment interactions in multimodal human trajectory prediction

In recent years, human trajectory prediction (HTP) has garnered attention in computer vision literature. Although this task has much in common with the longstanding task of crowd simulation, there is little from crowd simulation that has been borrowed, especially in terms of evaluation protocols. The key difference between the two tasks is that HTP is concerned with forecasting multiple steps at a time and capturing the multimodality of real human trajectories. A majority of HTP models are trained on the same few datasets, which feature small, transient interactions between real people and little to no interaction between people and the environment. Unsurprisingly, when tested on crowd egress scenarios, these models produce erroneous trajectories that accelerate too quickly and collide too frequently, but the metrics used in HTP literature cannot convey these particular issues. To address these challenges, we propose (1) the A2X dataset, which has simulated crowd egress and complex navigation scenarios that compensate for the lack of agent-to-environment interaction in existing real datasets, (2) evaluation metrics that convey model performance with more reliability and nuance, and (3) a guideline for future data acquisition in HTP. A subset of the proposed metrics are novel multiverse metrics, which are better suited for multimodal models than existing metrics. The dataset is available at: https://mubbasir.github.io/HTP-benchmark.
Analyzing the Variability of Subjective Image Quality Ratings for Different Distortions

When it comes to evaluating the quality of images, individual observers show different opinions depending on the type of distortion affecting the quality of the image. While in the opinion of one observer a distortion could have a dramatic influence on the quality of the image, another observer could see the same distortion as not having an important effect on the quality of the same image. Using a subjective experiment, we aim to identify the distortions which show the largest variability among observers. For this, 22 observers evaluated the quality of 10 reference images and the 630 test images created from them (21 distortions at three levels). Our results show that the highest variability in subjective scores is linked to distortions like saturation, contrast, sharpness, quantization, some types of added noise, and radial lens distortion.
Digital Competence of Students, Postgraduates by means of Cross-cutting Digital Technologies in the Educational Process of Universities

The purpose of this study is to identify priority areas of cross-cutting digital technologies in the educational process of students and postgraduates, as well as to determine the role of digital technologies in their education. To conduct this research, we used the method of interviewing respondents from different universities of the Russian Federation on this issue. Using the survey method, it was found that 50% of universities are satisfied with the use of cross-cutting technologies. The teaching staff has been trained and plans to provide training on the use of cross-cutting technologies in the educational and scientific process of students and postgraduates. Based on the generalization and systematization of cross-cutting technologies used in engineering education, competitive advantages in their application and the increment of new competencies of students and postgraduates are also highlighted. As a result, it is proposed to distinguish between three levels of competencies, such as elementary, basic and advanced. The survey allows us to conclude that this process is developing at different speeds and universities are facing various problems that require further consideration and study in the future.
A Human-Machine Distance Control System Using Incoherent Cooperative FMCW Radar Sensors

Modern automated machinery and industrial facilities need perception systems to ensure safe operation and prevent collisions between mobile objects. A sensor system that meets the requirements for these applications is a spatially distributed cooperative radar system, because it is able to provide instantaneous information on position, velocity, and direction of movement of a target. As an example, we demonstrate a prototype of a distance control system that keeps a defined distance between a human target and a movable machine. It is based on incoherent sensors to to show the capabilities of cooperative radar units for these applications. The theory for bistatic FMCW radars and the implemented methods are presented in detail. The functionality and performance of the system is evaluated experimentally.
Relationship between Enterprise Financing Structure and Business Performance Assisted by Blockchain for Internet of Things Financing Mode

Financing structure is an important and very complex issue in the financial theory and the rights and obligations of relevant stakeholders of enterprises are also concentrated in the financing structure. Therefore, the financing structure has a significant impact on the value of enterprises. A reasonable financing structure is conducive to standardizing the behavior of enterprises and improving the value of enterprises. The change of corporate financing structure is often used as a signal to external investors about the company's future income expectations, especially because the financing structure has a certain impact on the company's performance, which makes the problem of financing structure more valued by the theoretical and financial circles. For the empirical information about company financing, this paper explores the influencing elements of the company's running overall performance assisted with the aid of the blockchain, and the net of matters provides a chain model and constructs the operating performance indicators according to the comprehensive score. We select the commercial credit financing rate, short-term loan financing rate, long-term loan financing rate, debt financing rate, equity financing rate, and endogenous financing rate. The control variables are total capital, ownership concentration, and average age of the company. The conclusion is drawn by regression analysis. Commercial savings financing rate, fairness financing fee, and endogenous financing fee are positively correlated with the working performance; short-term loans and average age of the company are negatively correlated with the operating performance; and long-term loan financing rate, bond financing rate, and equity concentration are not significantly correlated with the operating performance.
Distance Boxplot for Unsupervised Outlier Detection

Anomaly detection is one of the key step in data preprocessing. Moreover, it is also vital in decision processing with applications such as intrusion detection and spam filtering. In this paper, an unsupervised anomaly detection method based on boxplot approach is proposed. In the proposed approach, a distance metric is integrated with the classical boxplot which results in a powerful hybrid approach capable of detecting both, either a single lying outlier or a cluster of outliers. Integration of distance metric takes into account the neighborhood estimation along with the density estimation. The concept realizes the compactness of data along with the position of underlying data points. This makes it suitable even for detecting small clusters of outlying data points which can be ignored as inlier data points because of their existence in a close proximity and high density. The usefulness of the proposed approach is demonstrated by some complex synthetic datasets.
Impending Success or Failure? An Investigation of Gaze-Based User Predictions During Interaction with Ontology Visualizations

Designing and developing innovative visualizations to assist humans in the process of generating and understanding complex semantic data has become an important element in supporting effective human-ontology interaction, as visual cues are likely to provide clarity, promote insight, and amplify cognition. While recent research has indicated potential benefits of applying novel adaptive technologies, typical ontology visualization techniques have traditionally followed a one-size-fits-all approach that often ignores an individual user's preferences, abilities, and visual needs. In an effort to realize adaptive ontology visualization, this paper presents a potential solution to predict a user's likely success and failure in real time, and prior to task completion, by applying established machine learning models on eye gaze generated during an interactive session. These predictions are envisioned to inform future adaptive ontology visualizations that could potentially adjust its visual cues or recommend alternative visualizations in real time to improve individual user success. This paper presents findings from a series of experiments to demonstrate the feasibility of gaze-based success and failure predictions in real time that can be achieved with a number of off-the-shelf classifiers without the need of expert configurations in the presence of mixed user backgrounds and task domains across two commonly used fundamental ontology visualization techniques.
Composites: A Tangible Interaction Paradigm for Visual Data Analysis in Design Practice

Conventional tools for visual analytics emphasize a linear production workflow and lack organic "work surfaces." A better surface would simultaneously support collaborative visualization construction, data and design exploration, and reasoning. To facilitate data-driven design within existing design tools such as card sorting, we introduce Composites, a tangible, augmented reality interface for constructing visualizations on large surfaces. In response to the placement of physical sticky-notes, Composites projects visualizations and data onto large surfaces. Our spatial grammar allows the designer to flexibly construct visualizations through the use of the notes. Similar to affinity-diagramming, the designer can "connect" the physical notes to data, operations, and visualizations which can then be re-arranged based on creative needs. We develop mechanisms (sticky interactions, visual hinting, etc.) to provide guiding feedback to the end-user. By leveraging low-cost technology, Composites extends a working surface to support a broad range of workflows without limiting creative design thinking.
Iris Flower Classification by using Random Forest in AWS

"Order is an Artificial Intelligence (AI) strategy for predicting crowd enrolment for information events. Neural organisations are presented to improve on the issue of arrangement. This report focuses on IRIS plant orders that use the neutral network. The sepal and petal width of the IRIS plant are used to divide the class of the IRIS plant in the characterization of the IRIS plant. Using the examples of sepal length and petal length, the unclear information can be disguised all the more naturally in the coming years. Fake neural organisations are effectively used for events such as design grouping, work timing, future work advancement, and work recollection. The current study aims to identify the types of iris flowers by using a dataset that was prepared in advance by other biologists to study the flower types using data mining techniques and neural network classifiers. The real issue in this study is figuring out how to create a new way to classify iris flowers and identify their type in order to study their behaviour and help biologists with new machine learning (ML) techniques. This type of problem is known as a classification problem. This can be solved in the proposed work by using a supervised machine learning classification algorithm, the Random Forest- Classification Algorithm (Iris Datasets).The experimental results show that the base error rate was 0.01067, the preparation time was 0.691 ms (milliseconds), and the number of stowed neurons was four.
A Hybrid Particle Swarm Optimizer for Curriculum Sequencing Problem

Curriculum sequencing problem is crucial to e-learning system, which is a NP-hard optimization problem and commonly solved by swarm intelligence. As a form of swarm intelligence, particle swarm optimization (PSO) is widely used in various kinds of optimization problems. However, PSO is found ineffective in complex optimization problems. The main reason is that PSO is ineffective in diversity preservation, leading to high risks to be trapped by the local optima. To solve this problem, a novel hybrid PSO algorithm is proposed in this study. First, a competitive-genetic crossover strategy is proposed for PSO to balance the convergence and diversity. Second, an adaptive polynomial mutation is introduced in PSO to further improve its diversity preservation ability. Furthermore, a curriculum scheduling model is proposed, where several constraints are taken into considerations to ensure the practicability of the curriculum sequencing. The numerical comparison experiments show that the proposed algorithm is effective in solving function optimization in comparison to several popular PSO variants; furthermore, for the optimization of the designed curriculum sequencing problem, the proposed algorithm shows significant advantages over the compared algorithms with respect to the degree of the satisfaction of the objectives, i.e., 20, 14, and 5 percentages higher, respectively.
Research on Mobile Ideological and Political Teaching in Universities Based on the Android Platform

The traditional ideological and political teaching is very old-fashioned, the teaching content is very fixed, and the teaching methods are very old. Based on the analysis of the problems existing in the traditional teaching mode, this study proposes a cloud service architecture plan with cloud, network, and terminal as the core design ideas., clarifies the goals and problems to be solved, and then conducts functional requirements analysis. In the system design stage, the whole system is firstly designed as a whole, and then the business process in the teaching system is analyzed, the redundant business in the business process is found, and the process is reorganized. The mobile learning system based on the Android platform meets the existing teaching needs and can be expanded to a certain extent. It can achieve good results in teaching application and make certain contributions to the construction of educational informatization, improving learning efficiency and enhancing students’ autonomous learning ability.
<i>Jelou pipol</i>

Abstract This study explores the characteristics of the language used in Twitch, one of the most popular streaming platforms worldwide, as an example of computer-mediated communication (CMC). Some of the most salient traits used to describe CMC are found in Twitch chat messages, as the paper will show, confirming that the communicative interaction that takes place among users of this platform matches the features of CMC described in the academic literature. Additionally, the synchronous and multimodal nature of this platform, as well as the pragmatic implications of the use of subscriber-exclusive emotes are peculiarities that must be considered for a comprehensive description of the language that takes place on Twitch. Lastly, in the case of the videogame chats in Spanish studied, the paper introduces as a key factor in the description of this language the characteristics of the lexicon: its foreign origin, its neological nature and its high level of terminological specialisation.
Evaluation of a new spectrum sensing technique for Internet of Things: An AI approach

In the last decade we observed a great demand for wireless sensor applications as the connectivity of objects related to the Internet of Things concept increased. The growing number of wireless sensors leads to more spectrum demand and eventually to collisions due to overcrowding, causing a decrease in their performance level. Thus, to avoid collisions, detailed knowledge of the radio spectrum is required such as the degree of spectrum occupancy and the radio modulations used. This paper presents an analysis of the impact of different radio signal representations (I/Q coordinates, polar coordinates, and Fast Fourier Transform) on the performance level of machine learning algorithms in spectrum sensing classification. Our results shown that machine learning algorithms achieve a higher classification accuracy when the FFT representation of the radio signal is used, with a classification accuracy of 98.6%. When using the time series, the I/Q representation of the radio signal obtained an accuracy of 68.6% on the test dataset meanwhile the polar coordinates achieved an accuracy of 90%, respectively.
Search Space Adaptation for Differentiable Neural Architecture Search in Image Classification

As deep neural networks achieve unprecedented performance in various tasks, neural architecture search (NAS), a research field for designing neural network architectures with automated processes, is actively underway. More recently, differentiable NAS has a great impact by reducing the search cost to the level of training a single network. Besides, the search space that defines candidate architectures to be searched directly affects the performance of the final architecture. In this paper, we propose an adaptation scheme of the search space by introducing a search scope. The effectiveness of proposed method is demonstrated with ProxylessNAS for the image classification task. Furthermore, we visualize the trajectory of architecture parameter updates and provide insights to improve the architecture search.
Discovery of AI/ML Supply Chain Vulnerabilities within Automotive Cyber-Physical Systems

Steady advancement in Artificial Intelligence (AI) development over recent years has caused AI systems to become more readily adopted across industry and military use-cases globally. As powerful as these algorithms are, there are still gaping questions regarding their security and reliability. Beyond adversarial machine learning, software supply chain vulnerabilities and model backdoor injection exploits are emerging as potential threats to the physical safety of AI reliant CPS such as autonomous vehicles. In this work in progress paper, we introduce the concept of AI supply chain vulnerabilities with a provided proof of concept autonomous exploitation framework. We investigate the viability of algorithm backdoors and software third party library dependencies for applicability into modern AI attack kill chains. We leverage an autonomous vehicle case study for demonstrating the applicability of our offensive methodologies within a realistic AI CPS operating environment.