Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards a Flexible Bayesian and Deontic Logic of Testing Descriptive and Prescriptive Rules

Research on the Wason selection task (WST) has raised fundamental doubts about the rationality of human hypothesis testing and added to the development of both domain-specific and domain-general theories of reasoning. This work proposes a rational but domain-specific synthesis aimed at integrating converging lines of research in the WST debate. For this synthesis two realms are distinguished, that of testing descriptive rules (hypotheses) and that of testing prescriptive rules (prescripts). For both realms, accounts are proposed that have normative aspects, but also domain-specific aspects. For the testing of descriptive hypotheses, a flexible Bayesian logic is developed, which is opposed to the falsificationist research program and builds on previous Bayesian accounts (on Oaksford and Chater, 1994, 2003, in particular). However, instead of advocating a universal Bayesian model a knowledge-based account is pursued which may explain the negative results of previous experiments. Additionally, the Bayesian treatment of conditionals is extended to other logical connectors. For the testing of conditional prescripts, a flexible deontic logic is proposed which draws more fully on the deontic logic of prohibitions, obligations and permissions than previous accounts. Moreover, this is combined with a goal-based, but systematic, mechanism of cooperator and cheater detection. Twelve experiments largely support the predictions of the proposed account. In two experiments the different strategies for testing descriptive or prescriptive rules were investigated. In seven experiments, positive support for the Bayesian account was obtained by actively introducing the preconditions of the models. Additionally, different Bayesian models of a conditional were distinguished and first evidence for a Bayesian logic of different connectors was obtained. In three experiments, the deontic logic of checking prescripts and its interaction with the goals of cheater or cooperator detection (including double foci) was investigated. The results cannot be explained by other current theories of the selection task, such as mental model theory, social contract theory, or pragmatic resoning theory, but do at least necessitate substantial extensions of these theories. In contrast, the results support the flexible Bayesian and deontic logic of testing descriptive and prescriptive rules.
Functional Verification of Clock Domain Crossing in Register Transfer Level

Numerous million-transistor systems running with multiple asynchronous clocks at frequency as high as multiple gigahertz have been made possible by diminishing component geometries and more complicated designs. Multiple interfaces are available on SoC systems, some of which employ standards with wildly divergent clock frequencies. Many contemporary serial interfaces are by nature asynchronous with the rest of the chip. To address the issue of clock skew across big processors, significant SoC sub-blocks are increasingly being designed to operate on separate clocks. Partition-based implementation and verification have typically been the primary emphasis of design approaches. These divisions are frequently based on clock domains. The cross-clock domain crossing (CDC) signals present a special and difficult verification difficulty. The verification of clock domain crossings cannot be done using conventional functional simulation. Although correct clock domain implementation and verification have received little attention, static timing analysis (STA) is a crucial component of the timing confinement strategy. Conventional techniques offer an impromptu partial verification that is laborious, error-prone, and manual. Designs may contain functional flaws that are not discovered until the end of the design cycle or worse still, during post-silicon testing, if the sources of potential mistakes are not addressed and confirmed early on. At this point, correcting mistakes would be quite expensive. Chips are reportedly “dead in the water” in some large system households as a result of CDC issues.
Detection of Lung Tumor using an efficient Quadratic Discriminant Analysis Model

Deep Learning has paved a way for finding solutions to numerous problems across the globe. It is one of the techniques being used widely in all the application being used worldwide. Healthcare is a field where various new innovations need to be done in order to diagnosis various new health related issues. In today's modern era, the increase in new medications is required at the most. One such application is the diagnosis of lung tumor. Tumors are the cells that do not grow as normally as other cells in the body and leads to many other side effects and if diagnosed lately could also lead to death. Hence, to diagnosis it very early need to new medications and techniques are required. Numerous types are tumors are present and they tend to occur in various parts of a human body. Sometimes they are very easily identified whereas in some cases they get detected only after a long time. Detection of tumor in very early stages is curable and hence it is very important that it needs to be identified very early. In this research, we present a model that uses the Deep Learning approach in order to identify tumors in the lungs at extremely early stages. Through there are numerous researchers working on the same domain, we have enhanced our model with the use of the Quadratic Discriminant Analysis. To detect the tumor in lung, the model initially detects and segments the tumor cells. Once the cells are segmented, the model also classifies if the tumor cells are malignant or benign. The findings illustrate how the suggested model differs from the conventional approaches in a number of ways. The comparative research was carried out, and the data reveal that the model that was presented is better to the other in terms of the amount of time it needs, clocking in at an average of 2.16 seconds.
Mobile Aerial Base Stations for Ultra-Reliable and Energy-Efficient Downlink Communications

Mobile aerial base stations (BSs) for ultra-reliable device-centric downlink communication are proposed in this paper. BSs are carried on unmanned aerial vehicles (UAVs) that travel and transmit data to devices when they are hovering as close to the scheduled device as possible. The performance of the proposed system is compared to a network-centric downlink scheme in which stationary UAV-BSs communicate with the devices. The transmission success probability is derived using stochastic geometry, with the model taking into account the aggregate interference, trajectory, height, antenna directivity, air-to-ground channel, and others. Owing to its ultra-reliability, the device-centric scheme outperforms the network-centric one in terms of the downlink energy efficiency from the UAV perspective in addition to offering constant performance for all devices regardless of their locations.
A Survey on Mapping of Urban Green Spaces within Remote Sensing Data Using Machine Learning & Deep Learning Techniques

For environmental protection, urban planning, monitoring, and management of the urban ecosystem, mapping urban green spaces is a crucial undertaking. A vital source of information for United Nations Sustainable Development Goal 11.7 could come from urban green space mapping. The standard method of mapping urban green spaces requires field measurements and takes a lot of time. It is also important to update urban green space maps periodically because urban green spaces can change quickly over time due to development. With the advent of high-resolution satellite sensors like Sentinel-1 and Sentinel-2, a large number of remote sensing images may be gathered, providing quick and precise information over urban areas. This work intends to offer a new perspective on how crowd sourced geospatial big data and remote sensing may be combined to enhance the mapping of urban green spaces, including time optimization and accurate information through machine learning and deep learning. For the revitalization of cities, this data will be valuable. Remote sensing imagery data can be classified using machine learning techniques like Support vector machines (SVM), Random forests (RF), and Naive Bayes (NB). In deep learning techniques such as Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), K Nearest Neighbor (KNN), Generative Adversarial Networks (GAN), and Recurrent Neural Networks (RNN) can be used to classify remote sensing images.
Smart Mobility for Sustainable Development Goals: Enablers and Barriers

Smart mobility is a wide umbrella to different systems and services to meet various end-user needs such as safety, seamless accessibility, reliability, and affordability without compromising the collective good of the society and the environment in terms of reduced congestion, reduced emission, and sustainability. Emerging smart mobility systems and services can play instrumental roles in achieving several Sustainable Development Goals (SDGs) adopted by UN Member States. This paper sheds light on the roles of smart mobility as an enabler to sustainable development in developed and developing countries. The paper also discusses a few barriers that need to be properly handled to achieve the full potential of smart mobility technology as a key enabler to achieve SDGs.
Image Segmentation on Gabor Filtered images using Projective Transformation

In recent years, image segmentation has been applied on various images for obtaining various results. They are being deployed in numerous applications. There are lot of techniques adopted to make the image distortion free. Methods are also applied when the original image needs to be extracted from the image containing the background. Projective transformation is one of the techniques where we can apply phase correlation among the images and segment the part of the image that is needed for the next processing step. In this proposed work, we have deployed a model where an input image is given. The image is then undergone into various scaling techniques. Use of active contours and graph cut methods are embedded to obtain the segmented image. The key role of this model is to apply the Gabor filters and perform projective transformation among the image to segment the original part of the image from its background. The results are proposed by giving an output in a binary form. The work runs for a maximum of 100 iterations and for that the relaxation factor is taken as 0.5.
Development, Implementation and Control of Active Compliance on Servo Motors in Robotics Applications

Servo motors provide numerous benefits in robotic applications. They are small, powerful and easy to program. This paper aims to design an electronic driver for a DC servo motor for compliance usage to the human robot interaction and obstacles avoidance. The General and main aim is to Design an electronic driver for DC servo motor for compliance usage for the human robot interaction (HRI) and obstacles interaction. Firstly the main objective is to model a servo motor and to develop a full functioning standing alone software to provide compliance control. The driver must be capable of controlling the output torque (Force control Active Compliance) of the motor by setting a desired torque. Also Implementing a C library for the driver to make the end user-to-user experience able to attach the driver for any compliance robotic application.
An Analysis on the Techniques for Water Quality Prediction from Remotely Sensed data

Water Quality plays an important role in every aspect of human life as it is a major source of metabolism in every living organism. The prediction of Water Quality is very important considering the changes in the ecological and topological changes in the environment. This paper presents a systematic review of Prediction of water Quality by Remote Sensing approaches along with retrieval measures of different parameters to make the concentration of water at its purest form that is fit for consumption. The data sources are classified as Multispectral and Hyperspectral which can be achieved through satellite and airborne imagery and upon analysis and manipulation of this data, the features can be extracted and different parameters can be retrieved from water bodies. A study of this review can help in improvement in analysis by providing an insight for different approaches in Artificial Intelligence and Machine Learning Techniques for Water Quality Prediction.
Effects of ESRGAN in Sugar Apple Ripeness Detection

In using the CNN algorithm, detecting and classifying objects will output classification errors in images throughout its usage. Presently, using SRGAN was not tested whether it has an effect on the use of CNN in determining sugar apple ripeness. The researchers used Enhanced SRGAN to improve the quality of sugar apple images taken. Images taken from sugar apples were compared to images stored in the dataset of the device using CNN, which then tells the ripeness stage of the sugar apple image. The same set of images captured is enhanced using ESRGAN to compare if there will be an effect on the results of the system using CNN. The researchers saw that better resolution and quality of the images could produce better results based on the data collected. Images without ESRGAN saw an accuracy of 84.00% and with a confidence of 49.57%. Enhanced images using ESRGAN produced more promising results with an accuracy of 92.00% and a confidence of 52.61% compared to normal images.
MQTT Broker Performance Comparison between AWS, Microsoft Azure and Google Cloud Platform

The rapid development of a new communication technology called the Internet of Things (IoT) has caused this technology to dominate the current technology market. The easy access of IoT to various devices has led to the development of new applications that generate several data from different objects. In its implementation, IoT requires a protocol that has high communication performance, fast response and light size. MQTT is the right protocol for IoT because this protocol is designed for machine-to-machine communication and can send data quickly and with low bandwidth. MQTT consists of three main components, namely Publisher, Broker and Subscriber. Publishers are IoT devices that periodically send sensor data to data subscription applications known as Subscribers. While MQTT Broker is an entity that collects data sent by publishers and then forwards it to subscribers. In previous studies, many studies discussed the comparison of the performance of the HTTP Protocol in Cloud Services. This research discusses the performance of existing MQTT Brokers on AWS, Microsoft Azure and Google Cloud Platform (GCP) cloud services. It was found that Azure is good for using the Average Throughput Subscriber parameter at each QoS level and the Average Throughput Publisher at QoS 1 and 2. Meanwhile, GCP is good for using the Average throughput publisher at QoS level 0 and receives the most messages at QoS levels 1 and 2.
Network Change Detection Based on Random Walk in Latent Space

The detection of network changes over time is based on identifying deviations of the network structure. The challenge mainly lies in designing a good summary or descriptor of the network structure for facilitating the measure of deviations. In particular, a network may have a huge number of nodes and edges. Moreover, there can exist complicated dependences among edges, e.g., the existence of some edges may be because of others. Therefore, it is non-trivial to measure the contribution of each node and each edge to the deviation of the entire network structure. Existing descriptors are designed to have factors less than the number of nodes and edges. They also model edge dependences, but can only achieve partial modeling. In this paper, we propose a novel type of descriptor. We first obtain node coordinates or positions in a latent space where nodes connected by edges have close positions by network embedding. Node positions are low-dimensional. More importantly, node positions can fully model edge dependences. We then design the descriptor based on random walk on the node positions. We conducted extensive experiments on synthetic datasets and three real-world datasets to demonstrate the effectiveness of our proposed change detection framework with the descriptor.
A Review of Deep Learning Methods for Automated Clinical Coding

Clinical coding is an administrative function in hospitals, which involves the transformation of clinical notes into structured codes that can be analyzed statistically. Coding data has a number of benefits, including speeding up the administration process of insurance companies and hospitals, improving global data sharing, and facilitating statistical analysis and forecasting. Current healthcare practice involves an individual as a clinical coder interpreting information about an aspect of patient care and assigning standardised codes. Thus, the current manual coding process is labor-intensive, time-consuming, and error-prone. Computer-assisted coding has emerged in the healthcare industry in recent years. AI-based systems together with expert-led services can help reduce labor costs, facilitate the administration process, and provide more informed and efficient healthcare. Consequently, researchers are increasingly interested in the use of deep neural networks to automate the process of clinical coding. The objective of this brief literature review is to summarize and describe the characteristics of the International Classification of Diseases (ICD), list the commonly used ICD-coded datasets, discuss the state-of-the-art deep learning models for ICD coding and the effect of injecting ICD ontology into these models, and present the interpretability mechanism that have been developed and implemented for clinical coding.
A Review on the analysis of Behavioral pattern of Students during pandemic COVID-19

The widespread of (covid-19) has become the major reason for many physical illnesses in addition to psychological encounters to the whole world. The psychological challenges brought in due to the Covid-19 pandemic have resulted in decrease in the learning curve of students to a very large extent risking the academic ability of students due to psychological/mental health. Hence it is a challenge to identify valid cues for disorientation in the learning ability of the student at the right time and to suggest necessary support and guidance. This paper aims to describe about the work done so far and analyzes the future challenges to be addressed based on the learning curve of a student and gives an insight of how a student can be identified to be psychologically disturbed.
Advanced Analysis of Electro-Oculographic Signal using Deep Neural Networks for Safety Purposes in Automation and Production Systems

This article deals with the problematics of obtaining and processing the EOG signals. This research describes the ways in which it is possible to measure and evaluate the measured bio-electrical signals of brain and muscle activity in more detail. The work also describes the analysis of the current state of this problematics and individual models of artificial intelligence are also described in the work - artificial neural networks that can be used for such advanced analysis. The experimental part of this work is performed in the MATLAB environment using the Deep Neural Network Toolbox. The experimental part of the work is focused on the analysis and processing of bio-electrical activity eye-motor muscles in the area of eye-tracking with the help of the OLIMEX EEG-SMT device. The information provided in this research can be used for improving safety and reliability and elimination of potential risks connected to an epilepsy disease and epilepsy seizures in automated production systems.
A Study of Dependency Between Students’ In-Class Emotions and Academic Performance

To enhance students’ academic performance, it’s crucial to identify knowledge gaps early. Educators can then provide support, such as remedial materials, to help students better understand course content. This paper explores the correlation between in-class emotions and assignment performance as a means of predicting knowledge gaps. Results showed that emotions at the beginning of a knowledge point are significantly related to assignment performance. This suggests that it is possible to identify knowledge gaps through in-class emotions, rather than waiting for assignment results. This could inspire future tech advancements in academic learning.
A Survey on Music Genre Classification Using Multimodal Information Processing and Retrieval

Musical descriptors play a very crucial role in automatic music genre classification. With the increase in digitized music, query and retrieval of musical data has always been subjective and controversial. More often, a musical clip is often spread across the genres, making the classification process very challenging. This paper presents a contemporary survey of the recent advances made in the classification of music genre using different machine learning and deep learning algorithms. Specifically, it looks into the recent developments in the area of genre classification based on audio based descriptors, image based descriptors, and other descriptors which include album cover art, brain signals, lyrics and musical instrument digital interface data.
A Correlation Graph Based Approach for Personalized and Compatible Web APIs Recommendation in Mobile APP Development

Using Web APIs registered in service sharing communities for mobile APP development can not only reduce development period and cost, but also fully reuse state-of-the-art research outcomes in broad domain so as to ensure up-to-date APP development and applications. However, the big volume of available APIs in Web communities as well as their differences make it difficult for APIs selection considering compatibility, preferred partial APIs and expected APIs functions which are often of high variety. Accordingly, how to recommend a set of functional-satisfactory and compatibility-optimal APIs based on the APP developer's multiple function expectation and pre-chosen partial APIs is on demand as a significant challenge for successful APP development. To address this challenge, we first construct a Web APIs correlation graph that incorporates functional descriptions and compatibility information of Web APIs, and then propose a correlation graph-based approach for personalized and compatible Web APIs recommendation in mobile APP development. Finally, through extensive experiments on a real dataset crawled from Web APIs websites, we prove the feasibility of our proposed recommendation approach.
A Lightweight Controller for Autonomous Following of a Target Platform for Drones

Drones or Unmanned Aerial Vehicles (UAVs) are providing interminable opportunities to capture high-quality video feeds that were previously impossible and have transformed the digital era. Many applications today require computer vision (CV) and machine learning (ML) techniques to extract the useful information captured from the onboard camera, and send it to an embedded controller that can make independent decisions. For instance, maneuvering the drone to follow a target platform by using only the onboard camera feed is critical in target tracking. Therefore, in this paper, we exploit the applicability of a low-computational embedded tracking controller to follow a target platform e.g. a car or pedestrian, and thus, react in real-time, adjusting the drone steering angles and velocity. We developed a system that enables drones to follow a target platform autonomously without requiring continuous human intervention on an embedded state-of-the-art STM32 Nucleo board. The system includes a lightweight controller that controls the drone's movement and enables it to track and follow a target platform accurately. To validate the performance of our embedded controller, we performed a number of experiments in an open-source AirSim simulator. The tracking controller footprint and memory consumption was less than 3%, and was able to reliably track and trail the target platform in different environmental conditions.