Miracle-dz/UniFAR-FTUs huggingface dataset:
Querylizer: An Interactive Platform for Database Design and Text to SQL Conversion

Every day, the world becomes more data-driven. Also, an enormous amount of data generated is managed by RDBMS in the form of Tables and SQL is the language that is used to manipulate it and perform the desired operations. Not all individuals have the required knowledge of SQL language to work on the databases. This paper proposes a system that will convert natural language statements into SQL queries. Natural Language Processing, which is a component of AI, will be used by the system. NLP gives the ability to a computer program to understand human language in both spoken and written form. Moreover, the visualization of any system makes it easier to comprehend. Focusing on the Low-Code revolution presently in the industry, a visualization software for database management is proposed.
Prediction of Compression Ratio for Transform-based Lossy Compression in Time-series Datasets

As many IoT devices generate an enormous and varied amount of data that need to be processed in a very short time, storing and processing IoT big data become a huge challenge. While lossy compression can dramatically reduce data volume, finding an optimal balance between volume reduction and information loss is not an easy task. The compression ratio is within a range tolerable by the application is crucial. Motivated by this, we analyze the characteristics of data compressed and present a prediction model about the compression ratio of transformation-based lossy compression algorithms for IoT datasets collected.
Two-degree-of-freedom Robust Feedback Control of a Sliding Gate Automation

A control strategy consisting of a feedforward action and a robust feedback for a gate automation is presented, where a low-cost and non-regenerative motor drive is used. A model of the system is developed and feedback linearization is used to compensate for the highly nonlinear dynamics of the electric drive. To achieve good motion tracking performance we design a smooth reference associated with a feedforward action, based on the nominal model of the system. In addition, based on a model of the uncertainties a robust feedback controller is tuned by solving a set of linear matrix inequalities, combining the optimization of a LQR cost with some pole placement constraints. Finally, we test the proposed control strategy on an experimental device, obtaining satisfactory results.
The ZuCo benchmark on cross-subject reading task classification with EEG and eye-tracking data

We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research at the intersection between computational language processing and cognitive neuroscience. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data for the benchmark is based on the Zurich Cognitive Language Processing Corpus (ZuCo 2.0), which provides simultaneous eye-tracking and EEG signals from natural reading. The training dataset is publicly available, and we present a newly recorded hidden testset. We provide multiple solid baseline methods for this task and discuss future improvements. We release our code and provide an easy-to-use interface to evaluate new approaches with an accompanying public leaderboard: www.zuco-benchmark.com. Highlights We present a new machine learning benchmark for reading task classification with the goal of advancing EEG and eye-tracking research. We provide an interface to evaluate new approaches with an accompanying public leaderboard. The benchmark task consists of a cross-subject classification to distinguish between two reading paradigms: normal reading and task-specific reading. The data is based on the Zurich Cognitive Language Processing Corpus of simultaneous eye-tracking and EEG signals from natural reading.
PtdIns(3,4)P2, Lamellipodin, and VASP coordinate cytoskeletal remodeling during phagocytic cup formation in macrophages

Phosphoinositides are pivotal regulators of vesicular traffic and signaling during phagocytosis. Phagosome formation, the initial step of the process, is characterized by local membrane remodelling and reorganization of the actin cytoskeleton that leads to formation of the pseudopods that drive particle engulfment. Using genetically-encoded fluorescent probes we found that upon particle engagement a localized pool of PtdIns(3,4)P2 is generated by the sequential activities of class I phosphoinositide 3-kinases and phosphoinositide 5-phosphatases. Depletion of the enzymes responsible for this locally generated pool of PtdIns(3,4)P2 blocks pseudopod progression and ultimately phagocytosis. We show that the PtdIns(3,4)P2 effector Lamellipodin (Lpd) is recruited to nascent phagosomes by PtdIns(3,4)P2. Furthermore, we show that silencing of Lpd inhibits phagocytosis and produces aberrant pseudopodia with disorganized actin filaments. Lastly, vasodilator-stimulated phosphoprotein (VASP) was identified as a key actin-regulatory protein mediating phagosome formation downstream of Lpd. Mechanistically, our findings imply that a pathway involving PtdIns(3,4)P2, Lpd and VASP mediates phagocytosis at the stage of particle engulfment.
Community diversity is associated with intra-species genetic diversity and gene loss in the human gut microbiome

The human gut microbiome contains a diversity of microbial species that varies in composition over time and across individuals. These species (and strains within species) can migrate across hosts and evolve by mutation and recombination within hosts. How the ecological process of community assembly interacts with intra-species diversity and evolutionary change is a longstanding question. Two contrasting hypotheses have been proposed based on ecological observations and theory: Diversity Begets Diversity (DBD), in which taxa tend to become more diverse in already diverse communities, and Ecological Controls (EC), in which higher community diversity impedes diversification within taxa. Previously, using 16S rRNA gene amplicon data across a range of environments, we showed a generally positive relationship between taxa diversity and community diversity at higher taxonomic levels, consistent with the predictions of DBD (Madi et al., 2020). However, this positive ‘diversity slope’ reaches a plateau at high levels of community diversity. Here we show that this general pattern holds at much finer genetic resolution, by analyzing intra-species strain and nucleotide variation in static and temporally sampled shotgun-sequenced fecal metagenomes from cohorts of healthy human hosts. We find that both intra-species polymorphism and strain number are positively correlated with community Shannon diversity. This trend is consistent with DBD, although we cannot exclude abiotic drivers of diversity. Shannon diversity is also predictive of increases in polymorphism over time scales up to ∼4-6 months, after which the diversity slope flattens and then becomes negative—consistent with DBD eventually giving way to EC. Also supporting a complex mixture of DBD and EC, the number of strains per focal species is positively associated with Shannon diversity but negatively associated with richness. Finally, we show that higher community diversity predicts gene loss in a focal species at a future time point. This observation is broadly consistent with the Black Queen Hypothesis, which posits that genes with functions provided by the community are less likely to be retained in a focal species’ genome. Together, our results show that a mixture of DBD, EC, and Black Queen may operate simultaneously in the human gut microbiome, adding to a growing body of evidence that these eco-evolutionary processes are key drivers of biodiversity and ecosystem function.
EcoAndroid: An Android Studio Plugin for Developing Energy-Efficient Java Mobile Applications

Mobile devices have become indispensable in our daily life and reducing the energy consumed by them has become essential. However, developing energy-efficient mobile applications is not a trivial task. To address this problem, we present EcoAndroid, an Android Studio plugin that automatically applies energy patterns to Java source code. It currently supports ten different cases of energy-related refactorings, divided over five energy patterns taken from the literature. We used EcoAndroid to analyze 100 Java mobile applications (≈ 1.5M LOC) and we found that 35 of the projects had a total of 95 energy code smells. EcoAndroid was able to automatically refactor all the code smells identified.
The effects of constructing robot-based storytelling system on college students' computational thinking skill and technology comprehension

Recent college students have been observed to be motivated and engaged in acquiring information and communication technology (ICT) knowledge and skills with robots and digital media. The purpose of this study is to develop students’ technology comprehension through a learning activity by constructing robot-based storytelling systems. A sample of 15 college students participated in the program. Data collected include the pre-and post-computational thinking tests (CTt) and computational thinking skill (CTs) tests for evaluating their learning effects of technology comprehension (TC) in terms of computational thinking knowledge and skills and their perceptions toward the robot-based storytelling development environment and learning activities. The results show that the learning activities were an effective approach for enhancing the students’ TC as shown in the post-test. The differences between the students’ CT and perceptions were also confirmed. The results revealed that the learning activities with the robot-based storytelling development environment could improve the students’ TC knowledge and skills, and learning perceptions.
Intramembrane client recognition potentiates the chaperone functions of calnexin

One third of the human proteome are membrane proteins. They are particularly vulnerable to misfolding, often requiring assistance by molecular chaperones. Calnexin (CNX), one of the most abundant ER chaperones, plays an important role in membrane protein biogenesis and engages clients via its sugar-binding lectin domain. Using mass spectrometric analyses, we show that Calnexin (CNX) interacts with a large number of non-glycosylated membrane proteins, suggesting additional binding modes. We find that misfolded membrane proteins are preferentially bound by CNX and that CNX uses its single transmembrane domain (TMD) for client recognition. Combining experimental and computational approaches, we systematically dissect signatures for intramembrane client recognition by CNX and identify sequence motifs within the CNX TMD region that mediate client binding. Building on this, we show that intramembrane client binding potentiates the chaperone functions of CNX. Together, this study reveals a widespread role of CNX client recognition in the lipid bilayer, which synergizes with its established lectin-based substrate binding. Molecular chaperones thus can combine different interaction modes to support the biogenesis of the diverse eukaryotic membrane proteome.
TEYSuR - Text Extraction with YOLO and Super Resolution

In these times where a myriad of industries are becoming digitized and advanced, text recognition is being incorporated time and again to enhance customer satisfaction, improve accessibility and organize business processes. Conventional Deep Learning based text recognition, also termed as Optical Character Recognition (OCR) is based on learning the shapes of the characters in a language. In Text Extraction with YOLO and Super Resolution (TEYSuR) we propose a novel approach where YOLO, an object detection algorithm is used to localise and classify characters in an input image. The characters in the image are treated as individual objects by the algorithm and text is extracted with high accuracy and speed. In order to cater to input images of various font sizes the system proposes an additional Inference module which resizes images using Super Resolution and yields extremely high accuracy results. The word accuracy attained by the YOLO model was 88% and coupled with the Inference module TEYSuR attained a word accuracy of 96%. Hence, this system can be successfully used for extracting text from images with high efficiency.
The importance of environmental parameters and mixing zone in shaping estuarine microbial communities along a freshwater-marine gradient

Microbial communities are important elements in the marine environment, contributing to nutrient cycling and biogeochemical processes. Estuaries comprise environments exhibiting characteristics from freshwater to marine, leading to distinct microbial communities across this environmental gradient. Here, we examine the spatial dynamics of microbial communities in Macquarie Harbour, an estuarine system on the West coast of Tasmania, Australia. Water was sampled along the estuary to explore the structure and composition of the microbial communities using 16S/18S rRNA gene amplicon sequencing. Multivariate analyses showed environmental variables and community compositions varying along a longitudinal (river to adjacent ocean) gradient at the surface. In the harbour, differences in the microbial community were observed between surface (0-1 m) and intermediate depths (4.5-11 m depth). The results of differential abundance, network and Partial Least Square analyses suggest that Macquarie Harbour is a mixing zone, where the distributions of archaeal, bacterial and eukaryotic communities are influenced by oceanic and riverine inputs. Coupled with the natural characteristics of the Harbour, the heterotrophic component of this microbial communities inhabiting the surface and intermediate waters may play important roles in the nutrient cycle in the studied area. These results provide critical insights into the Macquarie Harbour environment and the importance of understanding the role of microbial communities for similar systems elsewhere.
GrasP: Graph-to-Sequence Learning for Automated Program Repair

Many deep learning models, for example, neural machine translation (NMT) models, have been developed for Automated Program Repair (APR). Due to the advantages of NMT model's strong generalization ability and less manual in-tervention, NMT-based methods perform well in APR. However, previous NMT-based APR approaches regard a code snippet as a sequence of tokens, which ignores the inherent structure of code. In this paper, we propose a novel end-to-end approach with Graph-to-Sequence learning, GrasP, to generate patches for buggy methods. To better represent the buggy method, we use a graph based on abstract syntax tree (AST) to represent the source code. In order to learn complex graph representation, we introduce the attention-based encoder-decoder model for graph-to-sequence learning. The empirical evaluation on the popular benchmark Defects4J shows that GrasP can generate compilable patches for 75 bugs, of which 34 patches are correct.
Localization of pallets on shelves in a warehouse using a wide-angle camera

This paper describes a method of detecting and localizing pallets on shelves using a wide-angle camera mounted on a vehicle several meters away along shelves. In previous work, we proposed the generation of reprojected images in which pallets appear to have a constant shape and size regardless of their location on the shelves and the use of the images both for detection and localization with simple edge template matching. Preliminary experiments conducted in the laboratory showed a high potential for practical usage. The present paper proposes three improvements. First, a similarity measure for binary patterns is newly added, aiming at robust pallet detection in real scenes. Second, a maximum similarity search based on the expected tangent of the ridge-shaped peak is proposed for the efficient refinement of localization. Third, effects of round corners (i.e., R-corners) of pallets are considered for higher accuracy in localization. Experiments were conducted using images taken in a real warehouse to examine the detection and localization ability of the proposed method. Despite having small areas in the original wide-angle view, pallets of interest were well detected in complex real scenes. Localization accuracy is discussed from the viewpoint of practical use.
A Joint-Entropy Approach To Time-series Classification

In this paper, the problem of entropy-based classification of time-series into stochastic, chaotic, and periodic is addressed, followed by proposing an alternative joint-entropy approach to time series classification. These data-driven methods describe the behavior of a signal, using the association of the entropy of a time-series with emergence and self-organization, as complex systems characteristics. First, we deduce that certain groups of entropies, namely Fuzzy entropy, and Distribution entropy, share more similarities with emergence, while permutation and dispersion entropies could be associated with self-organization. Then, we utilize these resemblances to propose a joint-entropy alternative approach, in which one of the specific entropies is presented for each characteristic. Further, in simulations, we evaluated the performance of our proposed approach, comparing with single entropy methods, using different classifiers and decision boundaries. The results reveal an excellent performance of 98% accuracy for simultaneous utilization of the Distribution and Permutation entropies as the input features of Random Forest classifier, while this value is at best 89% for when only a single entropy is fed to the classifier.
Marginal Contribution of Active and Reactive Power in Allocation of Distribution Losses

This paper presents a new method for the allocation of losses to consumers in distribution systems (DSs) under the reform structure of the electricity market. The proposed method adopts a branch-oriented approach and follows the principle of an electrical circuit to determine the power flow in the distribution branches. It identifies the marginal contributions of active and reactive power of loads in system losses corresponding to their power consumption at nodes. Apart from traditional marginal loss coefficient (MLC) methods, however, the proposed method recovers entire system losses without a step of reconciliation. The average marginal contribution of both active and reactive power makes the proposed method more effective and adoptable, especially for the radial structure of DSs. In order to test the proposed method, 33-bus DS is being considered. The results of the proposed method are compared and validated with those of the power summation and branch decomposition methods of loss allocation.
Implementation of a Person Following Robot in ROS-gazebo platform

This research work involves the implementation of a person follower robot in ROS_Gazebo platform As part of this research work, we designed a mobile robot (for simulation)that can follow human beings. It assists people carrying heavyweights to their desired locations by following them. The entire simulation work is carried out with the help of ROS2 (Robot Operating System Version2). We used Gazebo and RVIZ as simulation tools to verify the outputs. MakeHuman and Blender software's assisted in creating an animated person and verify the rendering in the gazebo. The mobile robot created makes use of a Depth camera (Kinect) to detect a person and Lidars to calculate the distance between the robot and person. YOLOv3 algorithm has been used for image recognition. With this mobile robot, we simulated with multiple persons in gazebo software to make sure the robot is following only one particular person. The Kinect sensor uses object detection algorithms to identify and follow a particular person.
Detailed Performance Analysis of SPV Array Configurations under Partial Shading Conditions

The primary objective of this work is to present a detailed performance analysis of various 2×2 solar photovoltaic (SPV) array configurations, i.e., series (S), parallel (P), series-parallel (SP), and total-cross-tied (TCT) under different shading conditions. It may be noted that a good amount of literature already exists on this problem. Various performance parameters such as peak power output, the voltage at maximum power point (MPP), current at MPP, mismatch losses, relative power losses, and the fill factor have been reported. But, a quick survey reveals that all these parameters, though very important, have not been investigated together, leading to a complete performance investigation. This very fact has been the primary motivation to take up this work. In addition, a systematic procedure is developed for designing and developing various interconnection schemes to thoroughly analyze most of the performance parameters for different shading conditions. This work is expected to be very useful for beginners, particularly those who want further research in this area. The presented investigation revealed the following. The series configuration offers lower current and power outputs under partial shading conditions (PSC) while having multiple peak power-voltage (P-V) curves leading to maximum power point tracking (MPPT) problem. Further, the parallel configuration has a single peak in the P- V curve leading to better performance than other configurations under PSC. However, it also suffers from lower voltage output. The SP configuration offered better performance than the above two schemes under PSC. The best configuration has been the TCT yielding moderate current and voltage outputs at MPP, higher power output at MPP, lower relative power losses, lower mismatch losses, and improved fill factor. Therefore, the TCT configuration has better performance than other interconnection schemes under PSC.
A Novel Defect Classification Scheme Based on Convolutional Autoencoder with Skip Connection in Semiconductor Manufacturing

The semiconductor process cannot avoid defects due to its complex and diverse processes. In particular, wafer can be said to be the core of semiconductor manufacturing because they are directly related to the productivity of semiconductors. Therefore, detecting and classifying defects on wafers can help engineers address the root cause of defects and improve yield. In this paper, we propose a convolutional autoencoder using skip connection for wafer map defect classification. First, the encoder and decoder are designed by constructing a convolutional block. And connect the symmetrical blocks with skip connection. Finally, the training data of the classifier is encoded using the weights of the learned encoder. The loss of the model was successfully reduced with skip connection, and improved performance was obtained by reusing the encoder.