Miracle-dz/UniFAR-FTUs huggingface dataset:
Measuring and Mitigating Gaps in Structural Testing

Structural code coverage is a popular test adequacy metric that measures the percentage of program structure (e.g., statement, branch, decision) executed by a test suite. While structural coverage has several benefits, previous studies suggested that code coverage is not a good indicator of a test suite's fault-detection effectiveness as coverage computation does not consider test oracle quality. In this research, we formally define the coverage gap in structural testing as the percentage of program structure that is executed but not observed by any test oracles. Our large-scale empirical study of 13 Java applications, 16K test cases and 51.6K test assertions shows that even for mature test suites, the gap can be as high as 51 percentage points (pp) and 34pp on average. Our study reveals that the coverage gap strongly and negatively correlates with a test suite's fault-detection effectiveness. To mitigate gaps, we propose a lightweight static analysis of program dependencies to produce a ranked recommendation of test focus methods that can reduce the gap and improve test suite quality. When considering 34.8K assertions in the test suite as ground truth, the recommender suggests two-thirds of the focus methods written by developers within the top five recommendations.
Model Based Decomposition of Alakananda River Basin using SAR Data

This paper aims to demonstrate the capability of a new class of model-based target decomposition (TD) technique for dual-pol synthetic aperture radar data. In order to formulate them, full quad-pol decomposition theory mappings are coupled with the Stokes vector formalism. As the region of Uttarakhand is vulnerable in terms of seismic activity and eco-biodiversity, it is necessary to monitor the development activities taking place in this area. In this regard, microwave remote sensing techniques employing Stokes parameters, is capable of characterizing ground features, in response to transmitted electromagnetic (EM) wave, as partially polarized and completely polarized wave. This is made possible by the development of a generalised model-based decomposition. From the purpose of our study, we performed classification on dual-pol datasets from Sentinel-1 that includes information about various scenarios, including agricultural land, forest cover, urban structures, and snow cover. We demonstrated that the reflected polarized signals are different from broadcasted signals due to orientation effect. Therefore, it can be used for important applications such as Land Use Land Cover (LULC) and estimation of geophysical parameter. We also sucessfully demonstrated that, depolarization is not the only factor affecting the intensity of Sentinel-1 backscattered signals for the classification of vegetation cover in the undulated terrain. This clarifies the effect of crop orientation in volume scattering due to forest canopy. Furthermore, we demonstrated the potential of coherent dual-pol frameworks in acquiring strong phase signatures over snow covered terrain, where the polarised contribution significantly affects the backscattered state of the reflected signal, resulting in elliptical polarisation. Since the decomposition techniques has rarely been tested for LULC purpose, the significant results from this study when combined with dense time series data provides excellent opportunities for classification of earth features in highly sensitive zone.
Multilinear Subspace Analysis of State Ensembles in Gas-liquid Two Phase Flow via PWUD Signal

The process states in gas-liquid two phase flow are the synthetical consequence of multiple apparent factors related to time, space, flow conditions and so on. Higher-order tensor decomposition in multilinear algebra provides a powerful framework mathematically to analyze the multifactor structure of flow state ensembles. Focusing on entangling the constitute factors or modes embedded in various states, a novel strategy about multilinear subspace analysis of state ensembles in gas-liquid two-phase flow via pulse wave ultrasonic Doppler (PWUD) signal is proposed. PWUD sensor is adopted to acquire spatiotemporal information of flow states under varying flow conditions. Based on processed PWUD signal, higher order singular value decomposition (HOSVD) is employed for tensor analysis of flow state ensembles that combine several modes, including different state categories, time, space and flow conditions. The purpose of the strategy is to perform few-shot learning on the foundation of refining apparent factors underlying the formation of flow states with a simple sensor, that is, using limited state samples as reference to further infer the states under different flow conditions. The experimental results demonstrate that multilinear representation yields improved state identification rates relative to the conventional single-factor method principal component analysis (PCA).
Machine Learning and EMI For MOSFET Aging Diagnosis

MOSFETs play a key role in static power converters, where power MOSFETs operate at high switching frequencies and are susceptible to Electromagnetic Interference (EMI) due to parasitic inductive and capacitive elements inherent in the electric circuit. Despite being unwanted, EMI can offer valuable insight into the condition of the emitting agent, as their amplitude and frequency are heavily dependent on the agent’s intrinsic characteristics. As MOSFETs age, their intrinsic characteristics change, leading to corresponding changes in the emitted EMI. Previous studies have investigated the evolution of EMI in a DC-DC converter but did not quantitatively assess the extent of degradation. In this study, we propose a machine learning-based approach for predicting failures based on EMI. We demonstrate that various regression algorithms can accurately predict MOSFET failure based on EMI, including the degree of degradation, enabling the estimation of remaining useful life (RUL). We validate the effectiveness of our approach through various simulation results.
Path Balancing for Reducing Dynamic Power Consumption in Digital Designs Containing IP-Blocks

When paths between computational elements of a digital design do not have the same propagation delay, then signals at the inputs of one of these computational elements could arrive at different moments. Signals arriving early than necessary can create switching activities in the computational element if no action is taken. This leads to consuming power for useless computation. One approach to overcome this situation is to balance the length of all paths using strategies like gate resizing and/or voltage scaling. However, when the computational elements are IP Blocks (blocks from a third party), then the designer is not allowed to optimize inside the computational elements to make paths balanced; instead, buffers can be inserted in some paths while making all the paths of equal length. Inserting buffers will not change the design’s functionality since signals at the input and output of a buffer are the same. We propose an Integer Linear Programming to this problem, which allows inserting a minimal number of buffers, and more than one buffer can be inserted in any path compared to existing approaches. Also, in this paper, computational elements can have different execution delays to produce their output bits, which is not the case in published approaches. Our proposed approach solves the problem for both combinational and clocked sequential digital designs.
Development of an Optical Pressure Sensing Array: Initial Validation

Current methods to measure the strength of the pelvic floor consist of several expensive pressure sensor encapsulated in a single-use device. To expand the clinical suitability of pelvic floor pressure sensor a new class of pressure sensing elements has been explored. These elements utilise optical attenuation through deformable materials. A prototype optical sensor array has been constructed and experimentally tested. This has validated that individual sensor elements can be indexed and the predicted response measurement, with evident crosstalk and lower signal sensor strength than expected.
A Review on Security Trends and Solutions Against Cyber Threats in Industry 4.0

The Industrial Internet of Things (IIoT) has transformed traditional manufacturing and production systems by incorporating automation and IoT. This technology has increased the efficiency and timeliness of the overall manufacturing system and improved communication between products and each unit. However, it has also introduced new security challenges. The use of IoT devices in Industry 4.0 opens the gate for various cyber attacks, where attackers utilize the vulnerability of these devices to launch attacks and target smart manufacturing environments. Therefore, it is essential to develop new solutions for handling cyber attacks in Industry 4.0. Thus in this paper, we explore potential cyber threats and their effects on Industry 4.0. We discuss the available solutions for tackling cyber attacks in the IIoT domain and present identified research gaps in the state- of-the-art solutions. The paper aims to present potential impacts and directions for solutions to researchers willing to contribute to the field of attack detection in Industry 4.0.
Towards ARSPI-Net: development of an efficient hybrid deep learning framework

In this paper, we implement and experiment with a portion of our overall hybrid deep learning framework, An Affective hYbrid SPIking Neural Network [ARSPI-NET]. In order to motivate and show the usage of liquid state machines as an efficient, feature extraction framework we perform experiments on connection architecture as well as neuron model and their effect on overall classification performance. We perform our initial experimentation on the MNIST dataset and achieve a 87% classification using a liquid state machine and logistic regression classifier. Our results suggest that our framework can compare with current models in terms of accuracy, however, we outperform traditional deep learning methods in terms of energy consumption and the potential to move to energy-efficient neuromorphic platforms. In addition, our framework has the advantage of being more interpretable, as it allows us to model the spatiotemporal dynamics of signals through the usage of a liquid reservoir and an interpretable readout vector. This paper sets precedence for future experimentation and development of ARSPI-Net as a hybrid deep learning framework.
Classification Model of Grassland Degradation Based on Convolutional Neural Network

To address the bottleneck problem of the lack of automatic detection and classification of degraded grassland levels in the Qinghai-Tibet Plateau, this study utilized a low-altitude unmanned aerial vehicle to collect grassland image data in the Henan County research area of Qinghai Province, and employed data augmentation to expand the dataset, evaluating the effectiveness of this method. Three convolutional neural networks, VGG16, ResNet50, and MobileNetV1, were then used to construct static grassland plot data classification prediction models. The results indicated that MobileNetV1 was the best model when the dataset was relatively small, while ResNet50 performed best when the dataset was relatively large. The convolutional neural network-based classification prediction model can effectively diagnose degraded grassland levels in the Qinghai-Tibet Plateau.
Network Function-enabled Switch for Scalable Network Service Function Chaining

Service Function Chaining (SFC) enriches the network functionalities to fulfill the increasing demand for value-added services. By leveraging software-defined network (SDN) and network functions virtualization (NFV) for SFC, it becomes possible to meet the demand fluctuation and construct a dynamic SFC. However, a naive implementation of SFC in SDN-NFV requires complicated packet-headers modifications for processing status identification, multiple packet matching for traffic steering and excessive network-traffic generation for packets forwarding to many NFs. These operations result in lower system performance, scalability, and agility. To achieve better scalability and agility of SFC, this paper proposes a network function-enabled switch with the support of a many-field packet classification (MFPC) engine. The experimental results show that the matching speed to steer network traffic with many-field ruleset is improved by 4 times with our MFPC algorithm. Moreover, the proposed system is able to improve system bandwidth 5 times better compared to the native solution and maintain the context switching latency with the proposed SFC implementation using SDN and NFV. Finally, the performance of a network function is improved by 8 times by leveraging a graphics processing unit (GPU) as a parallel computation platform.
Numerical Investigation of Silicon-Embedded Microchannel Structures Containing Different Shaped Interior Flow-Splitting Walls and Micro Pin-Fins for Hotspot Mitigation of GaN High-Power Devices

The significance of cutting-edge thermal management technologies has been growing apace as a continuing trend in miniaturization of the gallium nitride-based wide band-gap devices poses major challenges in heat extraction. Bringing the coolant in close proximity to gate fingers is a promising method to handle concentrated heat in a small footprint. Therefore, the implementation of die-embedded microfluidic structures may substantially enhance hotspot mitigation. To prevent such devices from overheating, a variety of die-embedded microfluidic cooling structures with interior flow-splitting walls and different combinations of micro pin-fins and sidewall ribs are proposed. Proposed microfluidic structures are benchmarked for the operating condition where the heat flux of each gate finger is approximately 18 kilowatts per square centimeter. Results indicate that microchannels with straight interior flow-splitting walls offer more than two times higher local Nusselt numbers than traditional straight microchannel heat sinks. Furthermore, compared to microchannels with straight interior walls, integration of wavy interior flow-splitting walls inside substrate-embedded microfluidic structures may provide up to 43% improvement in local Nusselt numbers at high Reynolds numbers. Nevertheless, it should also be considered that wavy details increase friction factor accompanied by pressure drop due to hindered fluid flow. Moreover, based on the operating conditions in this study, elliptical micro pin-fin structures outperform their circular and diamond-shaped counterparts in overall performance. The findings presented in this paper will be particularly beneficial for researchers aiming to employ suitable microfluidic structures for hotspot mitigation.
A Multi-Hop-Aware User To Edge-Server Association Game

Nowadays, services and applications are becoming more latency-sensitive and resource-hungry. Due to their high computational complexity, they can not always be processed locally in user equipment, and have to be offloaded to a distant powerful server. Instead of resorting to remote Cloud servers with high latency and traffic bottlenecks, service providers could map their users to Multi-Access Edge Computing (MEC) servers that can run computation-intensive tasks nearby. This mapping of users to MEC distributed servers is known as the Edge User Allocation (EUA) problem, and has been widely studied in the literature from the perspective of service providers. However, users in previous works can only be allocated to a server if they are in its coverage. In reality, it may be optimal to allocate a user to a distant server (e.g., two hops away from the user) if the latency threshold and system cost are both respected. This work presents the first attempt to tackle the multi-hop aware EUA problem. We consider the static EUA problem where users have a simultaneous-batch arrival pattern, and detail the added complexity compared to the original EUA setting. Afterwards, we propose a game theory-based distributed approach for allocating users to edge servers. We finally conduct a series of experiments to evaluate the performance of our approach against other baseline approaches. The results illustrate the potential benefits of allowing multi-hop allocations in providing better overall system cost to service providers.
Enhancing Prostate Cancer Diagnosis with Multi-class Classification of CT Scan Images

This work intends to develop a multi-class classification system for prostate cancer prediction utilising CT scan data in order to enhance prostate cancer diagnosis and treatment planning, and hence patient outcomes. To begin, a preprocessing phase in this research eliminated noise and artefacts from the CT images. Following that, we extracted features using a hybrid approach by mixing deep learning with classic image processing techniques. Following the retrieval of features, they were used to train a convolutional neural network (CNN) multi-class classification model. The technique's efficiency was evaluated experimentally using a collection of CT images from males diagnosed with prostate cancer. The results demonstrated that the proposed multi-class classification approach was very accurate in predicting the existence of prostate cancer as well as the stage of the disease. In conclusion, our work shows that multi-class classification models may be used to predict prostate cancer using CT scan pictures. The described strategy has the potential to significantly increase both the precision with which prostate cancer is identified and the efficacy with which treatment strategies are devised.
Enhancing Crop Health by Identifying Leaf Disease with High Accuracy Using CNN

In recent years, plant diseases have become a top concern for farmers and agriculturalists. In this paper, seven plant species like apple, tomato, potato, grape, bell pepper, corn, peach leaf diseases have been found in healthy classes as well. The system uses a pre-trained CNN to extract features from an image of the plant leaf as its input. The dataset for the study is a collection of images of various diseased plant leaves. Back propagation and stochastic gradient descent are used to train many layers of the CNN architecture-convolutional, pooling, and fully connected were applied in this study. The extracted traits are then classified using a fully connected layer to determine whether the leaf is afflicted with disease. When compared to K-Means, SVM, RBF, MLP, BPNN, and other similar algorithms which have an accuracy of less than 90%, the CNN algorithm is one of the most accurate. For the implementation of the leaf disease detection, we make use of Python software. Farmers can use the proposed system to predict disease in the early stage and can give diagnosis to plant diseases, allowing for prompt treatment and control measures.
Diagnostic and Prognostic Health Management of Electric Vehicle Powertrains: An Empirical Methodology for Induction Motor Analysis

The growing interest in electric vehicles has led to an increased focus on the development of efficient and reliable electric motors. To ensure reliable operation, it is essential to incorporate on-board diagnostic and prognostic tools that can detect and predict potential failures. This paper proposes an approach to diagnose and predict the health condition of induction motors used in electric vehicle powertrain applications using machine learning techniques. The proposed approach utilizes vibration signals collected from accelerometers attached to the motor and employs decision forest and decision tree algorithms to classify the health condition of the motor. The study aims to identify the most significant features of the vibration signals and evaluate the effectiveness of the proposed approach in diagnosing and predicting the health of the motor. The models are trained on full extracted features and selected features using Principal Component Analysis (PCA) and Correlation Analysis (CA) to improve the classification performance. The experimental results demonstrate that the combination of selected features using PCA with the Decision Forest (DF) algorithm achieves the best classification performance for the simulated motor fault conditions. This suggests that machine learning techniques can be effective in diagnosing and predicting the health of induction motors used in electric vehicle powertrain applications.
An Efficient Rainfall Prediction Model Using Deep Learning Method

Rainfall is a crucial aspect of the Earth's natural cycle and it is necessary for various activities such as agriculture, water supply and hydroelectric power generation. However excessive rainfall can lead to floods, landslides and other destructive consequences, while insufficient rainfall can cause droughts and water shortages. Therefore accurate estimation of rainfall is essential to manage and mitigate the impacts of rainfall. In this study, the dataset is collected from the NASA Power database [22] to predict the annual rainfall in Mangalore(Karnataka), India. The data is collected from January 1, 2003 to February 04, 2023 using NASA POWER API. The study used four models MLP[15], LSTM, BiLSTM, CNN to predict the daily average precipitation that contributes to the annual rainfall. The input parameters considered for the prediction are maximum monthly temperature, minimum monthly temperature, humidity, atmospheric pressure and wind speed[9]. The model's performance is measured using mean squared error (MSE) and mean absolute error (MAE) of the predicted values on training and testing ratio 80:20. CNN(Convolutional Neural Network) model outperforms and gives the MSE and MAE for the CNN(Convolutional Neural Network) model are 0.0041 and 0.0456 respectively.
RCGraph - A Tool to Integrate Readme and Commits through Temporal Knowledge Graphs

Readme files and commit logs carry important and useful project information, corresponding to project dependencies, project functionalities, additions, deletions, and so on. These two artifacts have been analysed separately to obtain project specific information corresponding to contribution guidelines, bug prediction and localisation. Linking the readme files with associated commits and further querying the linked data could help in assessing time stamp specific changes made to the readme files. Utilizing knowledge graph representation of data is observed to largely support querying and integration and extraction of data from heterogeneous sources. To this end, we present a tool to generate readme specific temporal knowledge graph, as a first step towards integrating readme files and commit logs. As commits contain temporal information of the changes, we see that overlaying this information over the corresponding text in readme files could help in arriving at a temporal knowledge graph (TKG). We present a case study of querying the TKG for one repository and further evaluate the tool on 10 repositories spanning across 10 programming languages on GitHub. For demo video, visit - https://youtu.be/4YOCDngf4bY. For website of the tool, visit - https://akhilasrimanasa.github. io/rcgraph/
Weak Gravity Conjecture and Black Holes in $N = 2$ Supergravity

In a previous paper \cite{Cottrell:2016bty}, we used the entropy function formalism to argue that loops of charged particles can give unexpected contributions to the entropy of extremal black holes. Here, we show that similar results hold for loops of BPS particles in $\mathcal{N}=2$ supergravity, potentially connecting these results to familiar string setups. However, we will argue that string theory avoids these corrections altogether with the help of the Weak Gravity Conjecture. Finally, we discuss the relationship between these results and the semi-classical analysis of Antonov \cite{antonov,1989ApJS}, who showed that sub-extremal particles suffer a `gravo-thermal catastrophe' in the Newtonian limit.