Miracle-dz/UniFAR-FTUs huggingface dataset:
Reinforcing Data Integrity in Renewable Hybrid AC-DC Microgrids from Social-Economic Perspectives

The microgrid (MG) is a complicated cyber-physical system that operates based on interactions between physical processes and computational components, which make it vulnerable to varied cyber-attacks. In this article, the impact of data integrity attack (DIA) has been considered, as one of the most dangerous cyber threats to MGs, on the steady-state operation of hybrid microgrids (HMGs). Additionally, a novel method based on the sequential hypothesis testing approach is proposed to detect DIA on the renewable energy sources’ metering infrastructure and improve the data security within the HMGs. The proposed method generates a binary sample, which is used to compute a test statistic that is further used against two thresholds to decide among three alternatives. The performance of the suggested method is examined using an IEEE standard test system. The results illustrated the acceptable performance of the proposed methodology in detection of DIAs. In addition, to evaluate the effect of DIA on the operation of the HMGs, DIAs with different severities are launched on the measured power generation of renewable energy resources like wind turbines. The results showed that a successful DIA on renewable units can severely affect the operation of electric grids and cause serious damage.
Sampling May Not Always Increase Detector Performance: A Study on Collecting Training Examples

In recent years, the research of computer vision is popular. However, the image data that can be used for computer vision training is very limited, so it is necessary to find an effective method to expand the datasets based on the existing image data. In this paper, we study methods to collect more training data from existing datasets and compare detectors’ performance trained with datasets generated by different methods. One method is to perform sampling-based on statistical properties of feature descriptors. For every feature, the underlying assumption is that a probability density function (PDF) exists, such PDF is approximated with existing training examples and new training examples are sampled from the approximated PDF. The other method is simply to expand the existing datasets by flipping each training example along its symmetric axis. Locally Adaptive Regression Kernel (LARK) feature is used in this paper because it is robust against illumination changes and noise. Our experimental results demonstrate that an expanded training dataset is not always preferable, even if the expanded dataset includes all original training data.
A Legal Question Answering System Based on BERT 

With the development of artificial intelligence technology, intelligent question-answering systems in general fields have been widely accepted by people. However, the development of intelligent question-answering systems in limited areas is not very satisfactory. Moreover, due to the diversification of Chinese expressions, matching user input problems with prior problems is very important. This paper proposes a scheme to obtain the problem vector representation based on the BERT model. In addition, the Milvus vector search engine is used in this paper, which can not only provide store vector representation information but also calculate vector similarity. Finally, we return the answer through the database. When the threshold value of our proposed scheme is 0.2, the recall rate reaches 86%, and the mismatch rate reaches 84%. The results verify that the system has relatively good performance.
An Overview of Indian Spoken Language Recognition from Machine Learning Perspective

Automatic spoken language identification (LID) is a very important research field in the era of multilingual voice-command-based human-computer interaction (HCI). A front-end LID module helps to improve the performance of many speech-based applications in the multilingual scenario. India is a populous country with diverse cultures and languages. The majority of the Indian population needs to use their respective native languages for verbal interaction with machines. Therefore, the development of efficient Indian spoken language recognition systems is useful for adapting smart technologies in every section of Indian society. The field of Indian LID has started gaining momentum in the last two decades, mainly due to the development of several standard multilingual speech corpora for the Indian languages. Even though significant research progress has already been made in this field, to the best of our knowledge, there are not many attempts to analytically review them collectively. In this work, we have conducted one of the very first attempts to present a comprehensive review of the Indian spoken language recognition research field. In-depth analysis has been presented to emphasize the unique challenges of low-resource and mutual influences for developing LID systems in the Indian contexts. Several essential aspects of the Indian LID research, such as the detailed description of the available speech corpora, the major research contributions, including the earlier attempts based on statistical modeling to the recent approaches based on different neural network architectures, and the future research trends are discussed. This review work will help assess the state of the present Indian LID research by any active researcher or any research enthusiasts from related fields.
Association of clonal hematopoiesis mutations with clinical outcomes: A systematic review and meta‐analysis

Clonal hematopoiesis (CH) mutations are common among individuals without known hematologic disease. CH mutations have been associated with numerous adverse clinical outcomes across many different studies. We systematically reviewed the available literature for clinical outcomes associated with CH mutations in patients without hematologic disease. We searched PubMed, EMBASE, and Scopus for eligible studies. Three investigators independently extracted the data, and each study was verified by a second author. Risk of bias was assessed using the Newcastle‐Ottawa Scale. We identified 32 studies with 56 cohorts that examine the association between CH mutations and clinical outcomes. We conducted meta‐analyses comparing outcomes among individuals with and without detectable CH mutations. We conducted meta‐analyses for cardiovascular diseases (nine studies; HR = 1.61, 95% CI = 1.26–2.07, p = .0002), hematologic malignancies (seven studies; HR = 5.59, 95% CI = 3.31–9.45, p < .0001), therapy‐related myeloid neoplasms (four studies; HR = 7.55, 95% CI = 4.3–13.57, p < .001), and death (nine studies; HR = 1.34, 95% CI = 1.2–1.5, p < .0001). The cardiovascular disease analysis was further stratified by variant allele fraction (VAF) and gene, which showed a statistically significant association only with a VAF of ≥ 10% (HR = 1.42, 95% CI = 1.24–1.62, p < .0001), as well as statistically significant associations for each gene examined with the largest magnitude of effect found for CH mutations in JAK2 (HR = 3.5, 95% CI = 1.84–6.68, p < .0001). Analysis of the association of CH mutations with hematologic malignancy demonstrated a numeric stepwise increase in risk with increasing VAF thresholds. This analysis strongly supports the association of CH mutations with a clinically meaningful increased risk of adverse clinical outcomes among individuals without hematologic disease, particularly with increasing VAF thresholds.
OPTN/SRTR 2020 Annual Data Report: Liver

This year was marked by the COVID‐19 pandemic, which altered transplant program activity and affected waitlist and transplant outcomes. Still, 8906 liver transplants were performed, an all‐time high, across 142 centers in the United States, and pretransplant as well as graft and patient survival metrics, continued to improve. Living donation activity decreased after several years of growth. As of June 30, 2020, 98989 liver transplant recipients were alive with a functioning graft, and in the context of increasing liver transplant volume, the size of both the adult and pediatric liver transplant waitlists have decreased. On February 4, 2020, shortly before the pandemic began, a new liver distribution policy based on acuity circles was implemented, replacing donor service area‐ and region‐based boundaries. A policy change to direct pediatric livers to pediatric recipients led to an increase in deceased donor transplant rates and a decrease in pretransplant mortality rate among children, although the absolute number of pediatric transplants did not increase in 2020. Among adults, alcohol‐associated liver disease became the predominant indication for liver transplant in 2020. After implementation of the National Liver Review Board and lower waitlist priority for most exception cases in 2019, fewer liver transplants were being performed via exception points, and the transplant rate between those with and without hepatocellular carcinoma has equalized. Women continue to experience higher pretransplant mortality and lower rates of liver transplant than men.
Research on Football Team Ability Based on Random Forest and Ranking Method

The outcome of a football match covers many skills between individual ability and team strategy. This paper is an in-depth analysis of football team strategy. Firstly, the passing network between players is established, and the passing network is used to determine the formation of players. Next, it is determined that the performance indicators that reflect successful team cooperation, including the diversity of game types and the coordination of players, and then determine other team-level processes. Finally, create a model according to the determined performance indicators and team process, and use the model to analyze the structure, configuration, and dynamic characteristics of the team operation. In the experimental stage, through the observation and analysis of team cooperation mode, this paper puts forward the optimization structure strategy to achieve greater efficiency, and helps the coach improve the success rate of the team through network analysis. In this problem, according to all the matches of the four FIFA world cups from 2002 to 2014, we compared three different modeling methods for predicting the score of football matches: the Poisson regression model, random forest and the ranking method. The first two methods are based on the covariate information of the team, while the latter method estimates sufficient capability parameters, which can best reflect the strength of the current team. In this comparison, the prediction method with the best performance on the training data finally becomes the ranking method and random forest. By combining the random forest with the team ability parameters of the ranking method as additional covariates, the prediction ability can be greatly improved.
Tunneling Emitter Bipolar Transistor as a Characterization Tool for Dielectrics and their Interfaces

The metal insulator semiconductor and metal insulator metal structures are of interest for transistor technology and resistive switching based memory. We propose the tunneling emitter bipolar transistor as a complementary characterization tool of both structures. Using this technique one can distinguish between electron and hole injection through the insulator and detect the presence of recombination centers at the dielectric-semiconductor interface. In addition, the base-collector p-n junction adjacent to the dielectric is a sensitive detector for material damage. We have examined two dielectric materials: Al2O3 and Si3N4 using a tunneling emitter bipolar transistor based upon the InP/GaInAs material system. The main conclusion drawn from the experiments is that the dominant transport mechanism through the insulators is filamentary defect assisted transport. Thermal treatment of both materials significantly reduced the interface recombination velocity.
Road Extraction from High-Resolution Satellite Images using Deep Learning Model

Data Extraction has always been one of the major interests of several companies and government agencies for obtaining information through satellite images. The Data extraction in the form of road extraction from satellite images has played a very important part in navigation, military, national security, agriculture, and understanding the geology of the earth’s surface. We are introducing ReLU as the classification function, with the soft-max function. In this paper, we present an automated model for road extraction from a high-resolution satellite image. Here, we are implementing Rectified Linear Unit through Segmentation for obtaining roads out of satellite images with pre-processing, training, and validation of input data. And re-define the quality aspect of satellite images.
A Systematic Survey on recent Deep Learning trends in Intrusion Detection System

With the recent growth in the use of communication networks along with data processing systems, there has also been an increasing trend of growing complexity in the volume and variety of data flowing over the internet. Proportionally, the volume and complexity of malicious activities for gaining access to important data has also increased manifolds and thus has become a global headache. Recently, deep learning-based solutions have been found to be quite effective in dealing with intrusion detection and related areas. For the purpose of analyzing deep learning as a key solution to intrusion detection, this paper tries to investigate various deep learning-based approaches for detecting intrusions along with describing their main contributions and capabilities. The growing intensity of attacks, along with the capability of the attackers, existing methods may get bypassed and it is relevant that new techniques are developed. Therefore, in parallel to the ongoing research, a comprehensive analysis of various methods also becomes important. By presenting an expository study of numerous modern methods, this paper provides a strong foundation for the future research work in this domain.
Redefining Connectivity With 5G Networks: A Special Focus on Highland Areas

With the advent of 5G networks, the world of cellular communication has had a massive transformation. The introduction of such technologically advanced networks leads ultimately to an incessant increase in the number of users and generates an unimaginable amount of data traffic. Technologies like ultra-dense networks, D2D communication, massive MIMO, fixed wireless access promise a great future ahead for upcoming wireless networks. This also opens doors to research and testing for proposing feasible solutions to explore the untapped potential of 5G in the most remote and a high terrain area as well with the use of path loss models.
AMC backed Hysteresis Shaped Monopole Antenna for Wi-Fi, WiMAX, and IoT Applications

This paper describes a coplanar waveguide wide band antenna with a reverse hysteresis curve shaped artificial magnetic conductor with three operational bands and enhanced gain. The main antenna consists of a coplanar waveguide feed and stepped rectangular patch with a triangular cut at the middle. Design consists of both main antenna and artificial magnetic conductor, where artificial magnetic conductor is placed below the main antenna which enhances the gain. The gain enhancement is the major objective of this analysis. The main antenna works at 3.4GHz-5.86GHz, 10.28GHz-11.86GHz & 13.69GHz-14.28GHz. After placing the artificial magnetic conductor, it works at 3.19GHz-6.05GHz, 10.27GHz- 11.77GHz & 13.79GHz-14.36GHz. The gain after placing the artificial magnetic conductor is greater or equal to 1.2 times of the main antenna. The bandwidth has been increased lesser frequencies. Total analysis of main antenna in both the cases of artificial magnetic conductor is described in this paper. The antenna proposed works for different applications such as Wi-Fi, WI-Max, and IOT.
Reinforcement-Learning-based IDS for 6LoWPAN

The Routing Protocol for low power Lossy networks (RPL) is a critical operational component of low power wireless personal area networks using IPv6 (6LoWPANs). In this paper we propose a Reinforcement Learning (RL) based IDS to detect various attacks on RPL in 6LoWPANs, including several un-addressed by current research. The proposed scheme can also detect previously unseen attacks and the presence of mobile intruders. The scheme is well suited to the resource constrained environments of our target networks.
Wide Power Dynamic Range CMOS RF-DC Rectifier for RF Energy Harvesting System: A Review

This article investigates the latest research outcomes on the wide power dynamic range (PDR) CMOS RF-DC rectifier for on-chip radio frequency energy harvesting (RFEH) system as a viable approach to extend the high power conversion efficiency (PCE) operating range which is limited by the varying nature of the available far-field RF power. The significance of enhancing the rectifier’s PDR is that it offers a more reliable operation of RFEH in real-life applications. Therefore, this review seeks to navigate the research and development focus of the RFEH system towards extending the PDR of the CMOS RF-DC rectifier by providing analysis of the effect of state-of-the-art PDR extension techniques and their design tradeoffs. The review encapsulates the transmission of the RF power, a brief overview of the RFEH front-end circuit, and a comprehensive review of the CMOS rectifier design focusing on PDR. At the end of this article, we discuss the future design aspects to address the limitation of the RFEH system. Recent research shows that extending the rectifier’s PDR will enhance the overall performance of the RFEH system.
The neural code for “face cells” is not face-specific

‘Face cells’ are visual neurons that respond more to faces than other objects. Clustered together in inferotemporal cortex, they are thought to carry out face processing specifically and are thus studied using faces almost exclusively. Analyzing neural responses in and around macaque face patches to hundreds of objects, we found graded response profiles for non-faces that were predictive of the degree of face selectivity and provided information on face-cell tuning that could not be characterized with actual faces. This relationship between non-face and face responses was not predicted by color and simple shape properties, but by information encoded in deep neural networks trained on general object classification rather than face identification. These findings contradict the long-standing assumption that face cells owe their category selectivity to face-specific features, instead providing evidence for the notion that category-selective neurons are best understood as tuning directions in an integrated, domain-general object space.
GenErode: a bioinformatics pipeline to investigate genome erosion in endangered and extinct species

Many wild species have suffered drastic population size declines over the past centuries, which have led to ‘genomic erosion’ processes characterized by reduced genetic diversity, increased inbreeding, and accumulation of harmful mutations. Yet, genomic erosion estimates of modern-day populations often lack concordance with dwindling population sizes and conservation status of threatened species. One way to directly quantify the genomic consequences of population declines is to compare genome-wide data from pre-decline museum samples and modern samples. However, doing so requires computational data processing and analysis tools specifically adapted to comparative analyses of degraded, ancient or historical, DNA data with modern DNA data as well as personnel trained to perform such analyses. Here, we present a highly flexible, scalable, and modular pipeline to compare patterns of genomic erosion using samples from disparate time periods. The GenErode pipeline uses state-of-the-art bioinformatics tools to simultaneously process whole-genome re-sequencing data from ancient/historical and modern samples, and to produce comparable estimates of several genomic erosion indices. No programming knowledge is required to run the pipeline and all bioinformatic steps are well-documented, making the pipeline accessible to users with different backgrounds. GenErode is written in Snakemake and Python3 and uses Conda and Singularity containers to achieve reproducibility on high-performance compute clusters. The source code is freely available on GitHub (https://github.com/NBISweden/GenErode). GenErode is a user-friendly and reproducible pipeline that enables the standardization of genomic erosion indices from temporally sampled whole genome re-sequencing data.
Impaired peri-olfactory cerebrospinal fluid clearance mediated cognitive decline after dyssomnia

Animal experiments have demonstrated the dependency of CSF clearance function on age and sleep, which partially underlay the cognitive decline in the elderly. However, evidence is lacking in humans mainly due to limited method to assess CSF clearance function. We aimed to image CSF clearance pathways in human brain by dynamic MRI with intrathecal contrast agent as a CSF tracer. We performed T1-weighted and T2-fluid attenuated inversion recovery imaging with equal sequence parameters before and at multiple time points after intrathecal injection of contrast agent to visualize the putative meningeal lymphatic pathway, peri-olfactory nerve pathway and peri-optic nerve pathway. We defined the CSF clearance function as the percentage change in signal unit ratio of critical locations in these pathways from baseline to 39 hours after intrathecal injection. CSF clearance through the putative meningeal lymphatic and peri-neural pathways were clearly visualized in all 85 patients. The CSF clearance function of meningeal lymphatic and peri-neural pathways were reduced with aging (all P < 0.05). The CSF clearance function through peri-olfactory nerve pathway was positive correlated with sleep quality and cognitive function (both P < 0.05). Moreover, the CSF clearance function through peri-olfactory nerve pathway mediated the association of sleep quality with cognitive function (percent change in β [bootstrap 95% confidence interval]: 35% [−0.220, −0.003]). The study shows promise for dynamic MRI with intrathecal injection of contrast agent as a method to assess CSF clearance function through putative peri-neural pathways, and interprets the impaired clearance through putative peri-olfactory nerve pathway may explain the cognitive decline in patients with dyssomnia.
Generation and Maturation of Human iPSC-derived Cardiac Organoids in Long Term Culture

Cardiovascular diseases remain the leading cause of death worldwide; hence there is an increasing focus on developing physiologically relevant in vitro cardiovascular tissue models suitable for studying personalized medicine and pre-clinical tests. Despite recent advances, models that reproduce both tissue complexity and maturation are still limited. We have established a scaffold-free protocol to generate multicellular, beating and self-organized human cardiac organoids (hCO) in vitro from hiPSCs that can be cultured for long term. This is achieved by differentiation of hiPSC in 2D monolayer culture towards cardiovascular lineage, followed by further aggregation on low-attachment culture dishes in 3D. The generated human cardiac organoids (hCOs) containing multiple cell types that physiologically compose the heart, gradually self-organize and beat without external stimuli for more than 50 days. We have shown that 3D hCOs display improved cardiac specification, survival and maturation as compared to standard monolayer cardiac differentiation. We also confirmed the functionality of hCOs by their response to cardioactive drugs in long term culture. Furthermore, we demonstrated that hCOs can be used to study chemotherapy-induced cardiotoxicity. This study could help to develop more physiologically-relevant cardiac tissue models, and represent a powerful platform for future translational research in cardiovascular biology.