Miracle-dz/UniFAR-FTUs huggingface dataset:
Extensional and Non-extensional Functions as Processes

Following Milner’s seminal paper, the representation of functions as processes has received considerable attention. For pure λ-calculus, the process representations yield (at best) non-extensional λ-theories (i.e., β rule holds, whereas η does not). In the paper, we study how to obtain extensional representations, and how to move between extensional and non-extensional representations. Using Internal π, Iπ (a subset of the π-calculus in which all outputs are bound), we develop a refinement of Milner’s original encoding of functions as processes that is parametric on certain abstract components called wires. These are, intuitively, processes whose task is to connect two end-point channels. We show that when a few algebraic properties of wires hold, the encoding yields a λ-theory. Exploiting the symmetries and dualities of Iπ, we isolate three main classes of wires. The first two have a sequential behaviour and are dual of each other; the third has a parallel behaviour and is the dual of itself. We show the adoption of the parallel wires yields an extensional λ-theory; in fact, it yields an equality that coincides with that of Böhm trees with infinite η. In contrast, the other two classes of wires yield non-extensional λ-theories whose equalities are those of the Lévy-Longo and Böhm trees.
Electrocardiographic Signal Quality Assessment Without Morphology Analysis

The electrocardiogram (ECG) is the primary screening method of the cardiologist and is fundamental to understand the electrical activity of the heart. Signal interference sources that are non-issues in medical recordings become significant sources of noise in wearable devices recordings using dry electrodes. It is crucial to develop methods to assess recording quality in order to minimize unreliable data and provide cleaner raw recordings to algorithms such as machine learning. In this paper a methodology for classification of the most common signal distortion sources affecting dry electrodes ECG recordings is presented; classification is not reliant on absolute signal analysis and ECG morphology, making it suitable for applications where the system cannot directly analyze the ECG due to regulatory restrictions. The methodology was successfully validated with a commonly used dataset - Computing in Cardiology Challenge 2011 - as well as with data obtained in real driving conditions using the CardioWheel system [1].
Prototype of a medical device for measuring intravaginal forces

Pelvic organ prolapse (POP) is a disease that progressively affects women, creating a growing demand for the development of new devices and materials capable of diagnosing and treating the problem more quickly and effectively. The device currently undergoing validation is composed of a speculum equipped with multiple sensors, accoupled into the outer faces of the speculum blades, with the purpose to gauge the strength or muscular tension of the vaginal walls. The values measured by the device were of the same order of magnitude between ex vivo and in vivo study, values that vary between 0,15 and 1,33 N and tend to increase with the increment of the speculum opening. The device that has been developed has the capability to assess variations in the distribution of force in different spatial locations. The device has consistently provided reliable measurements, motivating us to continue improving and validating it.
Automated Placement of In-Network ACL Rules

Automatically deploying distributed Access Control Lists (ACLs) in a software-defined network can ensure their internal services and hosts connectivity, security and reliability. ACLs are often deployed in a switch using Ternary ContentAddressable Memory (TCAM). Since TCAM memory is often too limited to store a large ACL, one has to split the lists and distribute the parts on several switches in such a way that every packet travelling from a source to a destination undergoes the required match-action rules. In this paper, we develop and compare three algorithms based on graph theory and Reinforcement Learning (RL) techniques to automatically distribute ACLs across networks switches, while minimizing their TCAM memory occupancy. We compare the three algorithms on several network topologies to evaluate their efficiency in terms of memory occupancy.
Experimental Hydraulic Performance Investigation of Fan Enclosure and Competing Effects

Air cooling has been widely adopted in electronic cooling. The fan as the airflow driver is the critical component in the air-cooling approach. Therefore, a detailed understanding of fan performance is essential to cooling design. In this paper, fan performance is experimentally investigated in two sections: single-fan investigation and multi-fan investigation. The single fan investigation section tests fan performance for various fan packages, including with or without front and back finger guards, fan cards, etc. The multi-fan section investigates the competing effect of fans working together in parallel with various designed distances between fans. The flow rate reduction due to the enclosure installed could be more than 7% at a lower fan speed. Hex opening shape finger guard on the front would have around a 2% flow rate increase compared to the downstream location. The fan competing effect only exists at lower fan speed and could increase the flow rate to 4% when fan spacing increases to 20 mm. This detailed fan performance investigation could contribute to individual fan design optimization and the scenario with multiple fans running simultaneously.
Semi supervised segmentation of thyroid based on ultrasound images with wavelet and boundaries features

This paper describes a method of semi-supervised segmentation of thyroid based on ultrasound images with wavelet and boundaries features. Discrete Wavelet Transform and Inverse Discrete Wavelet Transform were used to extract image frequency features and semantic information, while Convexity Loss Function and Active Contours Loss Function were used to evaluate boundaries features, which can improve the segmentation accuracy. 3168 thyroid ultrasound images have been segmented using the proposed method. DICE is 93.39%, IOU is 87.69%, PA is 96.65% and Convexity is 91.17%. It can be seen from contrast experiments that the method proposed in this paper can improve DICE by 5.56%, IOU by 8.95%, PA by 4.66% and Convexity by 2.43%.
Prevention of Space Charge Accumulation and Space Charge Induced Breakdown Using Electrets

In this study, we numerically and experimentally demonstrate the validity of incorporating electrets for space charge induced dielectric breakdown prevention. Numerical models that represent our experiments have been built and simulated in COMSOL Multiphysics software. The simulation results clearly exhibit the prevention of space charge injection and accumulation that can be achieved via electrets. In the experimental validation, first, we fabricate electrets using the polyvinylidene fluoride (PVDF) by triode corona charging and measure the charge embedded in the electrets through an electrostatic voltmeter. Second, we measure the breakdown voltage of a regular dielectric and a regular dielectric coated with an electret film under DC voltage. The difference in the breakdown voltage of the two cases will demonstrate the efficacy of electrets as the prevention mechanism of space charge injection and accumulation.
Singular Value Decomposition (SVD) based Thermal Resistance Matrix (TRM) Size Reduction for Fast Optimization of On-Die Floorplan and Temperature Sensor Placement

Thermal-aware design with limited resources essentially requires the accurate Thermal Resistance Matrix (TRM) that represents the relationship between temperature and power with adequate heat transfer structure that include package. The TRM has been widely used for temperature sensor placement and floor-plan optimization in board and on-die design. However, the large size of TRM usually causes the computational burden by consuming a lot of time for design optimization. In this paper, the systematic scheme that reduces the TRM size is being proposed. By using Singular Value Decomposition (SVD), it is presented that the size of TRM can be reduced with K dominant singular values, which can save computational resources to achieve a steady-state temperature map. Thermal resistance matrix, R, can be defined as a rectangular matrix of M x N, where M and N represent monitoring points and power, respectively. The size of [R] can be reduced using [R‘] derived by SVD. While M x N multiplications are required for the original TRM, the reduced matrix achieved from this procedure requires (N+M) x K multiplications when K is the dominant singular values. Since K is usually much smaller than N, (N+M) x K multiplications required by the modified TRM, [R‘], are also much smaller than a number of N x M multiplications required by original [R]. By using this method, several case studies are presented to demonstrate that the proposed systematic scheme is effective and useful for time-efficient optimization of on-die floor-plan and temperature placement when designing System on-Chip (SoC) that include multiple heterogeneous cores.
SmallRace: Static Race Detection for Dynamic Languages - A Case on Smalltalk

Smalltalk, one of the first object-oriented programming languages, has had a tremendous influence on the evolution of computer technology. Due to the simplicity and productivity provided by the language, Smalltalk is still in active use today by many companies with large legacy codebases and with new code written every day. A crucial problem in Smalltalk programming is the race condition. Like in any other parallel language, debugging race conditions is inherently challenging, but in Smalltalk, it is even more challenging due to its dynamic nature. Being a purely dynamically-typed language, Smalltalk allows assigning any object to any variable without type restrictions, and allows forking new threads to execute arbitrary anonymous code blocks passed as objects. In Smalltalk, race conditions can be introduced easily, but are difficult to prevent at runtime. We present SmallRace, a novel static race detection framework designed for multithreaded dynamic languages, with a focus on Smalltalk. A key component of SmallRace is SmallIR, a subset of LLVM IR, in which all variables are declared with the same type-a generic pointer 18✶. This allows SmallRace to design an effective interprocedural thread-sensitive pointer analysis to infer the concrete types of dynamic variables. SmallRace automatically translates Smalltalk source code into SmallIR, supports most of the modern Smalltalk syntax in Visual Works, and generates actionable race reports with detailed debugging information. Importantly, SmallRace has been used to analyze a production codebase in a large company with over a million lines of code, and it has found tens of complex race conditions in the production code.
Running Virtual Services for the Intelligent Edge: A Review

In the world of technology, there are several emerging technologies that are driving research towards making the Intelligent Edge suitable for various application growth. Some of these technologies include Artificial Intelligence, the Internet of Things, Virtualization, and Edge Computing. Virtualization is a popular choice as it provides adaptable development of services. Meanwhile, Artificial Intelligence offers promising data manipulation opportunities, and Edge Computing provides lower latency and higher data privacy to users. This paper focuses on the architecture for virtual service deployment in the Intelligent Edge, with a particular emphasis on significant concepts such as Virtualization, Network Function Virtualization, Artificial Intelligence, and the Intelligent Edge. The paper also discusses various modifications for virtual services, such as the Round Robin Algorithm, Minimum-Minimum algorithm, Multiheuristic Algorithm, and Particle Swarm Optimization Algorithm. These algorithms are compared based on performance indicators such as makespan, standard deviation, degree of imbalance, and processing speed. While the results vary from one algorithm to another, they suggest that there is always room for improvement. Furthermore, the paper presents future opportunities for virtual service deployment in Intelligent Edge Computing.
SCREEN TASK EXPERIMENTS FOR EEG SIGNALS BASED ON SSVEP BRAIN COMPUTER INTERFACE.

Development BCI system based stay state visual evoked potential (SSVEP), require establish the characteristics of the stimuli presented the user for optimal development of the extraction of signal characteristics; for it is necessary to determine the stimulation system and the evidence perform for detecting events. There are many types of stimulators that can be used to evoke the SSVEP: monitors include cathode ray tube (CRT) and liquid crystal display (LCD) or an array of light emitting diode (LED). This paper aims to show the different tests and methodologies that have been presented in different studies for generating visual stimuli in a short period of time
Investigation of temperature effects on the ageing of paper-oil insulation under repetitive transient voltages

In this work, the effects of temperature over a range of 20 – 100 °C on paper-oil insulation under repetitive transient voltages have been analyzed. The ageing of the paper-oil insulation is measured based on the dielectric frequency response, partial discharge inception voltage, and intensity of discharges. The Luminol™ TRi oil, considering its oxidation stability and negative index for gassing has been selected as test oil to evaluate its applicability as an alternative liquid insulation for transformers facing continuous high-frequency repetitive transient voltages. The results help in understanding the ageing response of turn-to-turn insulation in wind-turbine step-up transformers subjected to repetitive transient voltages at different operating temperatures.
Utilizing Neural Networks to Resolve Individual Bats and Improve Automated Counts

Accurate population counts are essential for understanding the status of species and for researchers studying various phenomena including monitoring the relationship between environmental stresses and the spread of disease within populations. Both small roosts and large colonies of bats provide challenges when attempting to determine an accurate population count. Recently, there have been a number of new video analysis software applications, that are available on the internet, which can be used to provide population counts. When software-based counts are compared with manual counts, the software provides counts that are substantially less labor intensive, determined substantially more quickly, and have the potential to be more accurate. This short paper discusses the use of neural networks to determine the number of bats that there are in a region when multiple bats may overlap. The work discussed in this manuscript demonstrates that the counts of multiple overlapping bats can be improved using trained neural networks. This is a critical improvement for providing accurate counts in high density videos. This manuscript contains the biological motivations, and a brief overview of how artificial intelligence is being implemented. The results discussed compare the accuracy values of neural networks for a few case studies including cross-comparisons of data trained on different video types and for different animals which can have accuracy values above 90 % for comparable video types. Finally, the generation and use of synthetic images, to increase the amount of data in a training set, is also discussed, which resulted in a trained neural network that produced an accuracy value of 80% on 12 unbiased categories.
Development of Sorrow Analysis Dataset for Speech Depression Prediction

Computers can get insight into the user's mental state, including depression prediction, by analyzing speech signals. Numerous uses exist, ranging from customer service to depression-related suicide prevention. In this study, we proposed a novel depression detection method based on deep learning. Deep neural network variants, 1D-CNN, 2D-CNN, and BiLSTM, were utilized. This research developed a new speech depression dataset, namely the Sorrow Analysis Dataset. It is an English depression audio dataset of 64 recordings of depressed and non-depressed individuals. Results showed that of the various architectures tested, 1D-CNN was found to produce the highest average accuracy of 97% with 5-fold validation.
Detection of Pneumonia Cases from X-ray Chest Images using Deep Learning Based on Transfer Learning CNN and Hyperparameter Optimization

Pneumonia is a viral infection affecting many people, especially in underdeveloped and impoverished nations where contaminated, crowded, and unhygienic living conditions are common and inadequate healthcare infrastructures. Recognizing pneumonia immediately is a challenging step that can increase survival odds and allow for early-stage treatment. The successful construction of prediction models makes use of the artificial intelligence discipline of deep learning. There are many approaches to identifying pneumonia, including CT scans, pulse oximetry, and many others, but X-ray tomography is the most popular method. However, reviewing chest X-rays (CXR) is difficult and vulnerable to subjectivity variations. Using x-ray chest images, this study suggests a novel deep learning-based architecture for the quick diagnosis of covid-19 and pneumonia cases. As our basic model, we use the CNN transfer learning models VGG16, ResNet50, and InceptionV3. To adjust the hyperparameters of our model, we use random search optimization approach.
Lightweight and Reliable Deep Learning Model for Plant Disease Detection

With the continued penetration of smartphones among the current populace, tasks that were previously assigned to humans can now be done by our mobile devices with better efficiency. One such use case is found in the field of agriculture, from simple tasks like transferring farming news to meticulous ones like reading soil moisture, smartphones are on a clear path to becoming indispensable companions to our farmers. Contributing to nearly 20% of India's GDP (as of 2021), agriculture and its allied activities remain a relevant and powerful area of research, one such research is on the application of smartphones in the detection of crop plant diseases. One of the largest challenges faced by the agricultural industry is that of the onslaught of plant diseases and pests, It has been estimated that around 30-40% of global crop production is lost due to disease and pest attacks, so, rapid identification of crop diseases still remains a prevalent task in many parts of the world. With the advent of better and more efficient machine learning architectures, we are now ever so closer to the future of mobile crop disease diagnosis. Although, due to the limited processing powers of smartphones, especially in the budget category, image classification via more demanding, yet accurate deep learning models still seems to be impractical. In this paper, we research and propose the use of the EfficientNet family of deep learning models for the task of detecting crop plant diseases in a mobile setting, receiving an all-time high accuracy of 98.64% with significantly fewer model parameters than the other currently available deep learning models, we believe we are on a clear path towards a more practical mobile crop disease diagnosis.
Identifying Key Classes for Initial Software Comprehension: Can We Do It Better?

Key classes are excellent starting points for developers, especially newcomers, to comprehend an unknown software system. Though many unsupervised key class identification approaches have been proposed in the literature by representing software as class dependency networks (aka software networks) and using some network metrics (e.g., h-index, a-index, and coreness), they are never aware of the field where the nodes exist and the effect of the field on the importance of the nodes in it. According to the classic field theory in physics, every material particle is in a field through which they exert an impact on other particles in the field via non-contact interactions (e.g., electromagnetic force, gravity, and nuclear force). Similarly, every node in a software network might also exist in a field, which might affect the importance of class nodes in it. In this paper, we propose an approach, iFit, to identify key classes in object-oriented software systems. First, we represent software as a CSNWD (Weighted Directed Class-level Software Network) to capture the topological structure of software, including classes, their couplings, and the direction and strength of couplings. Second, we assume that the nodes in the CSNWD exist in a gravitation-like field and propose a new metric, CG (Cumulative Gravitation-like importance), to measure the importance of classes. CG is inspired by Newton's gravitational formula and uses the PageRank value computed by a biased-PageRank algorithm as the masses of classes. Finally, classes in the system are sorted in descending order according to their CG values, and a cutoff is utilized, that is, the top-ranked classes are recommended as key classes. The experiments were performed on a data set composed of six open-source Java systems from the literature. The results show that iFit is superior to the baseline approaches on 93.75% of the total cases, and is scalable to large-scale software systems. Besides, we find that iFit is neutral to the weighting mechanisms used to assign the weights for different coupling types in the CSNWD, that is, when applying iFit to identify key classes, we can use any one of the weighting mechanisms.
Automatic classification and permittivity estimation of glycerin solutions using a dielectric resonator sensor and Machine Learning techniques

This paper presents the application of a dielectric resonator sensor to characterize glycerin solutions. Air and nine different concentrations were measured within a relative permittivity range from 1 to 78.3. Principal Component Analysis (PCA) and Support Vector Machine (SVM) were used to perform automatic classification with an 100% accuracy and the regression of both concentration and permittivity with a RMSE of 0.34% and 0.287 respectively.
An Analysis Of Anomaly Detection Techniques for IoT Devices: A Review

There is an increasing demand for effective intrusion detection systems to protect the privacy and security of IoT devices and the data they collect and transmit. IoT devices are susceptible to a broad spectrum of security risks, such as unauthorised access, assaults, and breaches, which can put in danger the network's integrity, confidentiality, and availability. This study investigates the various methodologies used in IoT device anomaly detection. The paper discusses possible solutions, such as using lightweight algorithms, distributed intrusion detection systems, and adaptive security mechanisms. Some of the frameworks reviewed in this paper are BFA-PDBSCAN, B-Stacking, Two stream neural network and Hybrid (Anomaly-based +Specification-based) detection. Effective anomaly detection in IoT devices requires a multi-layered security approach that incorporates various intrusion detection techniques and best practises in order to protect the confidentiality and anonymity of IoT devices or the collected and transmitted data.