Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-model Order ICA: A Data-driven Method for Evaluating Brain Functional Network Connectivity Within and Between Multiple Spatial Scales

Background While functional connectivity is widely studied, there has been little work studying functional connectivity at different spatial scales. Likewise, the relationship of functional connectivity between spatial scales is unknown. Methods We proposed an independent component analysis (ICA) - based approach to capture information at multiple model orders (component numbers) and to evaluate functional network connectivity (FNC) both within and between model orders. We evaluated the approach by studying group differences in the context of a study of resting fMRI (rsfMRI) data collected from schizophrenia (SZ) individuals and healthy controls (HC). The predictive ability of FNC at multiple spatial scales was assessed using support vector machine (SVM)-based classification. Results In addition to consistent predictive patterns at both multiple-model orders and single model orders, unique predictive information was seen at multiple-model orders and in the interaction between model orders. We observed that the FNC between model order 25 and 50 maintained the highest predictive information between HC and SZ. Results highlighted the predictive ability of the somatomotor and visual domains both within and between model orders compared to other functional domains. Also, subcortical-somatomotor, temporal-somatomotor, and temporal-subcortical FNCs had relatively high weights in predicting SZ. Conclusions In sum, multi-model order ICA provides a more comprehensive way to study FNC, produces meaningful and interesting results which are applicable to future studies. We shared the spatial templates from this work at different model orders to provide a reference for the community, which can be leveraged in regression-based or fully automated (spatially constrained) ICA approaches. Impact Statement Multi-model order ICA provides a comprehensive way to study brain functional network connectivity within and between multiple spatial scales, highlighting findings that would have been ignored in single model order analysis. This work expands upon and adds to the relatively new literature on resting fMRI-based classification and prediction. Results highlighted the differentiating power of specific intrinsic connectivity networks on classifying brain disorders of schizophrenia patients and healthy participants, at different spatial scales. The spatial templates from this work provide a reference for the community, which can be leveraged in regression-based or fully automated ICA approaches.
Screening and Characterization of High Laccase Producing Enterobacter sp. from Devarayanadurga Forest Soil, Tumkur District

Eight laccase producing bacteria were isolated from forest soil samples using an enrichment culture technique. The bacterial isolates were grown on LB/Cu 2+ agar medium containing 10 mM guaiacol for detection of extracellular laccase enzyme. The bacterial isolates produced reddish brown coloration around the colonies was further purified and plate assayed on the same media for laccase enzyme production. In liquid culture, the highest laccase production (7.12 U/ml) was achieved on the 3 rd day of incubation at 37°C and pH 7.0. The most potent bacterial isolate-ISL6 was identified as Enterobacter sp. by morphological and biochemical characterization. The characteristic feature of the enzyme indicates that it is active in alkaline pH and has substrate specificity.
A Secure Cell-Attachment Procedure of LDACS

In Europe the Single European Sky air traffic management master plan foresees the introduction of several modern digital data links for aeronautical communications. The candidate for long-range continental communications is LDACS. LDACS is a cellular, ground-based digital communications system for flight guidance and communications related to safety and regularity of flight. Hence, the aeronautical standards for cybersecurity of the link layer and the network layer apply. In previous works, threat- and risk analyses of LDACS were conducted, a draft for an LDACS cybersecurity architecture was introduced, algorithms proposed, and the security of the Station-to-Station (STS)-based Mutual Authentication and Key Establishment (MAKE) procedure of LDACS formally verified. However, options for cipher-suites and certificate management for LDACS are still missing. This paper proposes a cell-attachment procedure, which establishes a secure LDACS communication channel between an aircraft and corresponding ground-station upon cell-entry of the aircraft, that addresses these shortcomings. It introduces a full cell-attachment protocol including ciphersuites and certificate revocation for LDACS.
DAMAS: Control-Data Isolation at Runtime through Dynamic Binary Modification

Memory corruption attacks have been a major issue in software security for over two decades and are still one of the most dangerous and widespread types of attacks nowadays. Among these attacks, control-flow hijack attacks are the most popular and powerful, enabling the attacker to execute arbitrary code inside the target process. Many approaches have been developed to mitigate such attacks and to prevent them from happening. One of these approaches is the Control-Data Isolation (CDI) that tries to prevent such attacks by removing their trigger from the code, namely indirect branches. This approach has been implemented as a compiler pass that replaces every indirect branches in the program with a table that leads the control-flow to direct hard-written branches. The drawback of this approach is that it needs the recompilation of the program. In this paper we present an approach and its implementation, DAMAS, a framework capable of deploying protections on a running software and use runtime information to optimize them during the process execution. We implemented a coarse-grain CDI protection using our framework and evaluated its impact on performance.
EmojiText: An Information Visualization Technique for Analyzing Phrases and Sentiments

This paper presents a proposal for text visualization with a focus on the representation of sentences and sentiments. It is noteworthy that traditional text visualization techniques, such as wordcloud, do not visually present relations between words, making it challenging to analyze textual data more deeply through visualization. EmojiText uses a directed graph, where the nodes represent the words and the edges represent the sequence in which the words appear in the sentences. Furthermore, the area of each node is related to the frequency of the represented word, and each node is associated with an emoji that represents the most frequent sentiment associated with the word. Finally, the edges also allow identifying the sentiment at the sentence level. The tool also includes zoom interactions, filters, details on demand, visual mapping configuration, and temporal analysis functionality through animations and multiple views for comparisons. The tool was developed in a javascript programming language using the d3.js framework and is available at http://emojitext.roberto. eti.br/.
Performance Analysis of CMOS, PTL and GDI Based Braun Multiplier for Signal Processing Applications

Power, speed and area are key design constraints in signal processing for computing applications as well as for handy electronic gadgets. Multiplier plays a significant role in energy efficient signal processing applications in digital systems. Product of large bit numbers occurs efficiently through binary multiplication. A low power and high speed Gate Diffusion Input (GDI) based Braun multiplier with improved row and column bypassing scheme is presented in this paper. From the simulated results, it is observed that, the GDI based Braun multiplier achieves energy savings more than 4% and up to 99% with CMOS and other methods and in the same way EDP savings more than 15% and up to 30% with CMOS and other methods by means of column and row bypassing techniques. From the layout, it is also analyzed that, GDI Braun multiplier is area efficient over CMOS and its other counter parts at the penalty of 9% more area in comparison with pass transistor logic (PTL) based column and row bypassing techniques.
A Comparative Analysis of Low Energy Adaptive Clustering Hierarchyand Power Efficient Gathering in Sensor Information System Progressive Conventions

A Wireless Sensor Network (WSN) is a web of sensors installed in a random fashion over an area that monitors the physical environmental conditions and parameters like pressure, temperature and moisture. The sensor nodes initially fetch the data from the immediate surroundings which is then aggregated in order to send it to the base station or the sink node. As sensors characteristically are resource-constrained in nature, reducing the energy consumed by the node becomes a challenge. The process used to transfer data from a source to what is known as a route protocol. The three types of succession channels are flat protocols, high-level and local-based protocols. Numerous studies have shown that high-speed traffic improvements improve tracking protocols in terms of deterioration and energy efficiency. Two factors namely energy efficiency and network life time play a very important role in determining the efficiency and capability of the route protocol. In this paper, we emphasize two such principles - LEACH and PEGASIS. In the LEACH protocol, the sensor nodes restructure themselves into groups called clusters and each of these structures are designated with a cluster head node. On the other hand, PEGASIS uses a technique where each individual sensor node communicates solely with its respective neighbour node and inherently. PEGASIS facilitates a chain-based communication structure where turns are taken by each node to transmit data to the base station. In this paper, comparisons between LEACH and PEGASIS protocols are based on network characteristics such as power consumption, packet delivery rate, transmission delays, passing, high percentage and dead node.
Unstructured Medical Text Classification using Machine Learning and Deep Learning Approaches

On the internet, there is a large volume of unstructured data content that includes useful information. People are having trouble in digesting, reading, and remembering the text content, as it is always changing and developing. Data mining and information extraction techniques are being used to build new automated strategies for processing unstructured text. Natural Language Processing (NLP) is a discipline of linguistics that deals with extracting most significant information from the unstructured data. NLP aids in the resolution of challenges related to text data vulnerability. Text data that is unstructured in nature is commonly available in the public domain. Among these, we have chosen medical text data, which comprises of both text and voice descriptions of the patients. Disease diagnosis, medical research, and automatic construction of disease ontology, as well as obtaining knowledge of clinical data documented in the medical literature, are all impacted by medical text classification. In this paper, we have employed a variety of machine and deep learning models to extract the summary from the text data and identified the diseases. The paper gives the comparison of the accuracy obtained w.r. t different models.
Distributed Inductance Printed Antennas

The paper considers the influence of an inductive element on the characteristics of a printed emitter. The inductive element is realized in the form of a short-circuited fragment of a coplanar waveguide. The distributed inductance of 10, 15 and 20 mm lengths were investigated in the ranges of 860-1000 MHz. The element in form of the slot allows to reduce the size of the antenna, adjust its resonant frequency and reduce interference from high-frequency sources. The results can be used in telecommunication systems and for the development of miniature antennas as a means of transmitting signals from devices to computer systems.
Towards a Visual Approach for Representing Analytical Provenance in Exploration Processes

Visualization techniques are useful tools to explore data by enabling the discovery of meaningful patterns and causal relationships. The discovery process is often exploratory and requires multiple views to support analyzing different or complementary perspectives to the data. In this context, analytic provenance shows great potential to understand users’ reasoning process through the study of their interactions on multiple view systems. In this paper, we present an approach based on the concept of chained views to support the incremental exploration of large, multidimensional datasets. Our goal is to provide visual representation of provenance information to enable users to retrace their analytical actions and to discover alternative exploratory paths without loosing information on previous analyses. We demonstrate that our implementation of the approach, MGExplorer (Multidimensional Graph Explorer), allows users to explore different perspectives to a dataset by modifying the input graph topology, choosing visualization techniques, arranging the visualization space in meaningful ways to the ongoing analysis and retracing their analytical actions. MGExplorer combines multiple visualization techniques and visual querying while representing provenance information as segments connecting views, which each supports selection operations that help define subsets of the current dataset to be explored by a different view. We demonstrate the usage of the tool through a study case where we explore co-authorship data. We assess the approach through performance metrics, temporal ordering of tasks, number of physical actions, and amount of information to be recalled inbetween actions applied to the chosen visual exploration scenarios using chained views.
Autonomous Heavy-Duty Mobile Machinery: A Multidisciplinary Collaborative Challenge

Heavy-duty mobile machines (HDMMs) are a wide range of machinery used in diverse and critical application areas which are currently facing several issues like skilled labor shortages, poor safety records, and harsh work environments. Consequently, efforts are underway to increase automation in HDMMs for increased productivity and safety, eventually transitioning to operator-less autonomous HDMMs to address skilled labor shortages. However, HDMMs are complex machines requiring continuous physical and cognitive inputs from human operators. Thus, developing autonomous HDMMs is a huge challenge, with current research and developments fragmented into several independent research domains. Furthermore, autonomous HDMM technologies are a stack of several technologies requiring a convergence of diverse competencies from the different domains. Through this study, we provide an overview of the HDMM industry and use the bounded rationality concept to propose multidisciplinary collaborations for new developments in autonomous HDMMs. Furthermore, we apply the transaction cost economics framework to highlight the conceptual challenges and implications of these collaborations. Therefore, we bring together several domains of the HDMM industry to introduce autonomous HDMMs as a general and unified approach. The collaborative challenges and potentials are mapped out between the following topics: mechanical systems, AI methods, software systems, sensors, connectivity, simulations and process optimization, business cases, organization theories, and finally, regulatory frameworks. In doing so, we highlight the need for new and multidisciplinary perspectives that should be considered by academic and industrial practitioners working on the development and deployment of autonomous HDMMs.
Adaptive Arrays Based on Real-Valued Arithmetic Linearly-Constrained IQRD RLS Adaptive Filtering Algorithms

This paper presents computational procedures for implementation of multi-beam adaptive antenna arrays with digital beamforming. The weights of the arrays are calculated using the Linearly Constrained Inverse QR Decomposition Recursive Least Squares adaptive filtering algorithms. Two versions of the algorithms are presented: with and without square roots arithmetic operations. The algorithms are fitted to the antenna arrays with odd symmetry. This symmetry allows to develop the adaptive filtering algorithms with most of the computations in real-valued arithmetic. This saves the number of the arithmetic operations required per algorithm iteration. Besides, this implementation decreases the transient response duration and improves the steady-state interference cancellation for about 3 dB in terms of the depths of the radiation pattern gaps towards the interference sources. The efficiency of the proposed solution is demonstrated via simulation of interference cancellation using a rectangular flat adaptive antenna array.
High-Precision Demodulation Method for Surface Acoustic Wave Strain Sensor Based on Hybrid Changing Area Niche Genetic Algorithm

The echo signal energy of surface acoustic wave (SAW) strain sensor is weak, resulting in low demodulation accuracy. In order to improve the measurement result, a SAW sensor demodulation method by combining hybrid changing area niche genetic algorithm and MUSIC algorithm was proposed. The proposed method was based on the MUSIC algorithm to construct the power spectrum function of SAW echo signal. A hybrid changing area niche genetic algorithm (CANGA) and particle swarm optimization (PSO) algorithm was developed to improve the spectrum peak search accuracy and reduce the amount calculations of MUSIC algorithm. The power spectrum function of the MUSIC algorithm was used as the fitness function of the hybrid algorithm. The echo frequency was estimated by the variable value corresponding to the optimal solution. The proposed hybrid PSO-CANGA has the characteristics of strong global search ability and fast convergence speed to improve the performance of MUSIC algorithm. The numerical comparisons were performed. The results showed that the hybrid PSO-CANGA gives fastest convergence and the highest estimation accuracy compared to original PSO and NGA. In addition, compared with traditional spectrum estimation algorithms, the proposed algorithm had a minimum estimation error with standard deviation of 0.27KHz. The proposed demodulation method was used to SAW strain system. The nonlinearity of the strain measurement was improved, with a nonlinearity of 0.55%. Simulation and experiment results verified the effectiveness of the proposed algorithm for the demodulation of the SAW strain sensor system. It helps to improve the accuracy of SAW strain measurement.
Study of Fake News Detection using Machine Learning and Deep Learning Classification Methods

False or misleading information, created deliberately to misinform and deceive the reader, is referred to as Fake News. As the most extensively utilized network for disseminating information, social media has become a highly popular platform for individuals to propagate incorrect information. Fake news can be pernicious when it comes to specific individuals and their opinions and perceptions, influencing and misleading their actions. Several initiatives have been taken in the past to reduce the spread of fake news and detect inaccurate information by devoting a significant amount of time and effort. In this paper, we have compared the performance of various Machine Learning and Deep Learning models for Fake News Detection. For our study, we used four datasets. From our experimentation, we realized that Random Forest and Bag of Words on the FARN Dataset outperformed the rest with an accuracy of 98.8%. In addition, we discovered that TF-IDF outperforms other feature extraction methods.
Investigation of Adsorption Enthalpy of Prolin on the Surface of Graphene with and without Si: A DFT Study

The project is comparing three types of calculation derived graphene. To evaluate the effect of silicon element to Thermochemistry parameters of absorption of prolin in these derivatives. These derivatives of graphene carbon prolin connection made, the difference is only the position of prolin (named GPC1 , GPC2 , GPC3). But in other Derivations first put silicon instead of carbon position, then is added to the silicon(named GPSi1 , GPSi2 , GPSi3).
Speech Enhancement Techniques and its Implementation

Speech enhancement aims to improve quality and intelligibility. Quality refers to the amount of noise free in speech and intelligibility refers to the percentage number of words understand in the sentence. Speech enhancement involves noise estimation as crucial part. Noise estimation approaches and their implementation using MATLAB is discussed in this work. Basic noise estimation includes estimation of noise from first frames and then speech presence and absence based approaches are discussed. Also the statistical based approaches for speech enhancement plays important role in speech enhancement. In this work traditional statistical estimation methods and their implementation using MATLAB is discussed. In this work different databases (clean, noisy speech samples) available for speech enhancement are discussed. To evaluate the speech enhancement algorithms the objective and subjective performance measures available in literature are discussed and the sources for MATLAB implementation is discussed. The performance of speech enhancement methods is compared with help of Signal to Noise Ratio (SNR), Segmental SNR (Seg SNR), Perceptual Evaluation of Speech Quality (PESQ). Also the subjective performance is analyzed using spectrograms.
Dual Battery Powered Drive System Using an Open-End Winding Brushless DC Motor

Dual inverter-fed open-end winding (OEW) motor drive is one of the new promising solutions, which has several advantages over standard three-phase drives such as multilevel voltage output, lower DC link voltage, higher efficiency and better fault tolerant capability. In this paper, such an approach has been used for brushless DC motor (BLDCM) drive system, which has significant advantages for low-power electric vehicles. At the first, a possibility of forming of basic vectors in the structure of OEW BLDCM drive is shown for two cases of discrete switching of armature windings with 120-degree and 180-degree switches conductivity. Then, for a simpler 120-degree commutation mode, a control system was developed, which is being characterized by a change in the switching of the two inverters in different modes of the electric drive system operation. In order to increase energy efficiency, a PWM is applied only to one of the inverters. In addition, a simple strategy of charge equalization for two batteries has been developed. The effectiveness of the proposed solutions is confirmed by computer simulation in the Matlab/Simulink.
Role of Complexing Agent in Chemical Bath Deposition of Thin Films: A Review

Background: An economical and simple chemical bath deposition technique for depositing various types of thin films has been reported by many researchers. Objective: In this work, the main objective is to investigate the role of complexing agent in the chemical bath deposited thin films. Results: As we know, complexing agent is one of the crucial chemical additives in preparation of thin films. Therefore, the influence of the complexing agent on the structural, optical, surface morphological and electrical properties of films was highlighted. The obtained films were characterized using various tools such as SEM, AFM, XRD, EDX and UV-Visible spectrophotometer. Conclusion: The experimental findings indicated that the films prepared in the presence of complexing agent were good quality thin films.