Miracle-dz/UniFAR-FTUs huggingface dataset:
SmartWrite: Extracting Chinese Lexical Grammar Patterns Using Dependency Parsing

This paper presents an interactive writing prototype system, SmartWrite , which provides Chinese word usage in the form of grammar patterns, collocations, and examples to assist language learners in writing. We propose a method of inducing common Chinese grammar patterns from a large-scale Chinese corpus. First, we try to identify grammar patterns based on the dependency relations of a given sentence parsed with existing language parsers. Then, we calculate words in a dependency relation with a target word to automatically generate the collocations. Finally, we ﬁnd good example sentences that best exem-plify each pattern. At run-time, the last content word an user typed is used for obtaining grammar patterns, collocations, and example sentences as continuous writing suggestions. Evaluation on the suggested word usage of real verbs extracted from a Web search engine log shows that the method signiﬁcantly out-performs underlying search engines, and even prevails over a commercial search engine by providing structural information of verbs.
S 2 from equine infectious anemia virus is an infectivity factor which counteracts the retroviral inhibitors SERINC 5 and SERINC 3

The lentivirus equine infectious anemia virus (EIAV) encodes the small protein S2, a pathogenic determinant that is important for virus replication and disease progression in horses. No molecular function had been linked to this accessory protein. We report that S2 can replace the activity of Negative factor (Nef) in HIV-1 infectivity, being required to antagonize the inhibitory activity of Serine incorporator (SERINC) proteins on Nef-defective HIV-1. Like Nef, S2 excludes SERINC5 from virus particles and requires an ExxxLL motif predicted to recruit the clathrin adaptor, Adaptor protein 2 (AP2). Accordingly, functional endocytic machinery is essential for S2-mediated infectivity enhancement, and S2-mediated enhancement is impaired by inhibitors of clathrin-mediated endocytosis. In addition to retargeting SERINC5 to a late endosomal compartment, S2 promotes host factor degradation. Emphasizing the similarity with Nef, we show that S2 is myristoylated, and, as is compatible with a crucial role in posttranslational modification, its N-terminal glycine is required for anti-SERINC5 activity. EIAV-derived vectors devoid of S2 are less susceptible than HIV-1 to the inhibitory effect of both human and equine SERINC5. We then identified the envelope glycoprotein of EIAV as a determinant that also modulates retroviral susceptibility to SERINC5, indicating that EIAV has a bimodal ability to counteract the host factor. S2 shares no sequence homology with other retroviral factors known to counteract SERINC5. Like the primate lentivirus Nef and the gammaretrovirus glycoGag, the accessory protein from EIAV is an example of a retroviral virulence determinant that independently evolved SERINC5-antagonizing activity. SERINC5 therefore plays a critical role in the interaction of the host with diverse retrovirus pathogens.
No Reference Prediction of Quality Metrics for H.264 Compressed Infrared Image Sequences for UAV Applications

Sequences for UAV Applications DTU Orbit (06/10/2019) No Reference Prediction of Quality Metrics for H.264 Compressed Infrared Image Sequences for UAV Applications The framework for this research work is the acquisition of Infrared (IR) images from Unmanned Aerial Vehicles (UAV). In this paper we consider the No-Reference (NR) prediction of Full Reference Quality Metrics for Infrared (IR) video sequences which are compressed and thus distorted by an H.264 codec. The proposed method works as a Bitstream Based (BB) approach and it may thus be applied on-ground. Three different types of features are first computed: codec features (based on information extracted from the bitstream), image quality features (based on BRISQUE evaluations) and Spatial and temporal perceptual information. Those features are then mapped, using a machine learning (ML) algorithm, the Support Vector Regression (SVR), to the quality scores of Full Reference (FR) quality metrics. The novelty of this work is to design a NR framework for the prediction of quality metrics by applying ML algorithm in the IR domain. A set of 5 drone energy leakage image sequences and 3 ground IR image sequences are used for evaluating the performance of the proposed method. Each of the image sequences are encoded at 4 different bitrates and the prediction of the proposed method is compared with the true FR quality metrics scores of four images metrics: PSNR, NQM, SSIM and UQI and one video metric: VQM. Results show that our technique achieves a fairly reasonable performance. The improved performance obtained in SROCC and LCC is up to 0.99 and the RMSE is reduced to as little as 0.01 between the actual FR and the estimated quality scores for the H.264 coded IR sequences.
Modeling Human Syllogistic Reasoning: The Role of "No Valid Conclusion"

Syllogistic reasoning, that is the drawing of inferences for categorical-quantified assertions, is one of the oldest branches of deductive reasoning research with a history exceeding 100 years. In syllogistic reasoning experiments, "No Valid Conclusion" (NVC) is one of the most frequently selected responses and corresponds to the logically correct conclusion for 58% of the syllogistic problem domain. To date, NVC is often neglected in computational models or just treated as a by-product of the underlying inferential mechanisms such as a last resort when the search for alternatives is exhausted. We illustrate that NVC represents a major shortcoming of current models for human syllogistic reasoning. By introducing heuristic rules for predicting NVC, we demonstrate that simple extensions of the existing models result in substantial improvements in their predictive performances. Our results emphasize the need for better NVC handling in cognitive modeling of human reasoning and provide directions for modelers on how to enhance their approaches.
School of Electrical and Electronic Engineering 2017-06-21 Chaos-based Cryptography for Cloud Computing

Cloud computing and poor security issues have quadrupled over the last six years and with the alleged presence of backdoors in common encryption ciphers, has created a need for personalising the encryption process by the client. In 2007, two Microsoft employees gave a presentation “On the Possibility of a backdoor in the NIST SP800-90 Dual Elliptic Curve Pseudo Random Number Generators” and was linked in 2013 by the New York Times with notes leaked by Edward Snowden. This confirmed backdoors were placed, allegedly, in a number of encryption systems by the National Security Agency, which if true creates an urgent need for personalising the encryption process by generating locally unbreakable one-time pad ciphers. Hybrid random binary sequences from chaotic oscillators initialised by natural noise, were exported to an online Javascript application which applies a von Neumann deskewing algorithm to improve the cryptographic strength of the encryptor. The application also provides initial statistical p-test for randomness testing. Encoding the Lenna image by XORing it with the new cipher provided another test to observe if patterns could be observed in the encoded image. Finally, the cipher was subjected to the NIST suite of statistical tests. All designs were simulated using Orcad PSpice c © V16.5.
A Viscous Vorticity Method for Propeller Tip Flows and Leading Edge Vortex

A novel numerical method solves the VIScous Vorticity Equation (VISVE) in 3D is presented in this paper. The method was proposed by Tian and Kinnas (2014) in order to model the Leading Edge Vortex (LEV) and Tip Vortex (TV) of propellers. The spatial concentration of the vorticity is exploited in the method, which is designed to be spatially compact and numerically efficient, in the meantime, capable of modeling complicated vorticity/solid boundary interaction in 2D and 3D. In this paper, the VISVE method is improved by including a potential wake model in order to handle the flow in far downstream. A block-structured gridding strategy suitable for round tip propellers is also introduced. The method is validated in the case of a 2D hydrofoil at high angle of attack against the results from Navier-Stokes (NS) simulation. The method is then applied in the cases of two single bladed propellers, one has a squared tip and another has a round tip. In the end, the method is used to simulate a real five bladed propeller at very low advance ratio.
Analisis Performansi Aplikasi Sandbox pada Sistem Operasi Windows

Windows operating system is the most widely used platform so that have a big impact if the OS is vulnerable to attack. Examples of cases of malicious attacks that attack the Windows OS are the ”Wana Decrypt0r” virus or known by Wanna Cry. One solution that can be used is to use the Sandbox, because the anti-virus will not detect the latest virus that does not exist in the anti-virus database as a threat. On the other hand, isolation services use more resources. Memory consumption is a major concern when Windows users use Sandbox because memory consumption affects the performance of Central Processing Unit (CPU). CPU performance also affects the Input/Output (I/O) and network. In related research, no one has discussed the performance of Sandbox specifically on the Windows platform. This research is compare the performance of the parameters memory bandwidth, CPU performance, disk I/O, and network on each Sandbox to be tested. This research doing evaluate of three Sandbox applications which have been reviewed that is Sandboxie, Shade Sandbox, and Shadow Defender using multi-benchmark. Analysis shows that there is little overhead on memory performance, CPU, network, and only affect I/O process. However, all resources are generally affected by isolation or virtualization operations. The conclusion of the analysis is Shadow Defender is a Sandbox with the best performance seen from memory bandwidth and multi-threads CPU.
Force Control Algorithm for detection of Break-Through Bone Drilling

The purpose of this study is to develop a force control algorithm that detects high force during bone drilling process where the drilling process will halt and return to a safe position when high thrust force detected. The algorithm is built using Simulink model under Matlab software. The control algorithm detects excessive force and calibrates the force in tri-axial direction as the threshold value. The system is verified through an experiment drilling cow femur bone using 5 DoF CRS Catalyst-5 robots. Preliminary drilling test is conducted to observe the maximum force the robot can hold during the drilling process.This will help to increase of safety enhancement during drilling process
The SYNTAX Score and Distal Embolization during Primary Percutaneous Coronary Intervention

Introduction: Multivessel disease, as well as complex coronary anatomy characterized by high values of the SYNTAX score has a major impact on reperfusion and patients' prognostic after an acute myocardial infarction with ST segment elevation (STEMI). Distal embolization, one of the main procedural complications of primary percutaneous coronary intervention (pPCI) could explain this association. However, the relationship between the SYNTAX score and distal embolization has not been studied till now. Material and methods: A prospective clinical study was performed including all of the cases admitted with STEMI between january 1, 2011 and December 31, 2011. The SYNTAX scores were calculated and distal embolization was appreciated using the recordings of emergency coronary angiographies and pPCI procedures. Statistical analysis was performed, if the SYNTAX score values were higher or not in the cases with distal embolization. Results: One hundred and thirty-seven patients were enrolled in the study. Distal embolization was present in 45 cases (32.8%); the calculated SYNTAX score values (range: 3 to 42) were significantly higher in these patients (p=0.0035). Conclusion: Complex coronary anatomy, defined by higher SYNTAX score values, seems to be a contributing factor to distal embolization, determining a negative influence on myocardial reperfusion and subsequently on the prognosis of STEMI.
Finding the Critical Sampling Size of Big Datasets July

Big Data allied to the Internet of Things nowadays provides a powerful resource that various organizations are increasingly exploiting for applications ranging from decision support, predictive and prescriptive analytics, to knowledge and intelligence discovery. In analytics and data mining processes, it is usually desirable to have as much data as possible, though it is often more important that the data is of high quality thereby raising two of the most important problems when handling large datasets: sample and feature selection. This work addresses the sampling problem and presents a heuristic method to find the “critical sampling” of big datasets. The concept of the critical sampling size of a dataset is defined as the minimum number of examples that are required for a given data analytic task to achieve a satisfactory performance. The problem is very important in data mining, since the size of data sets directly relates to the cost of executing the data mining task. Since the problem of determining the optimal solution for the Critical Sampling Size problem is intractable, in this work a heuristic method is tested, in order to infer its capability to find practical solutions. Results have shown an apparent Critical Sampling Size for all the tested datasets, which is rather smaller than the their original sizes. Further, the proposed heuristic method provides a practical solution to find a useful critical sample for data mining tasks.
GAIT RECOGNITION BASED ON GEI & GABOR FILTER

The field of Human Gait Recognition is one of the active research fields. The number of studies has increased which indicates that this area is important. The present study has many benefits as it enables us to recognize a person through his gait in front of a camera even if the gait picture is taken from a long distance with a low-quality camera. Moreover, many other biometrics require special equipment; and some of them require taking the picture from a short distance. This study aims at finding a solution with high quality and efficiency in the process of recognizing human gait. This is achieved by providing a strong training set. For this purpose, we created two training sets. The first depends on figuring out human features by using Gait Energy Image (GEI). The second set depends upon figuring out human features by using Gait Energy Image (GEI), and then filtering it by using Gabor Filter. This study was conducted as an experimental research to gain ideal parameter values for Gabor Filter that contributes to enhancing the training sets samples that leads to increasing the system performance efficiency, and we found the best values for Gabor filter parameters for our system is kernel =3 ,theta = 225 , γ=1.25, ψ=0, λ=3.5, and a bandwidth =2.8. The training and testing were conducted by using (SVM) Algorithm, the study uses the gait database (CASIA-B) to evaluate our system, Then, we used (CCR) for Performance measurement . The study reached good ratios for all views with an average of 76.50% for (GEI+GABOR FILTER) and 76.42 for (GEI). Moreover, the study achieved an ideal percentage of 78.10% for (GEI+GABOR FILTER) and 77.66 for (GEI), as compared to previous studies of the positions of normal-walking, wearing-coat, and carrying-bags at a view of 90.
OpenStack and Docker Comparison for Scientific Workflow w . r . t . Execution and Energy

Cloud computing is an emerging technology in the market to manage the data center’s infrastructure efficiently. It provides not only more flexibility, but also increases resource utilization. However, virtualization overhead reduces the performance of the job execution. Moreover, virtual machine’s specification also affects on job execution. In the scientific world, the experiment’s execution time is considerably high. Therefore, it is very important to understand the performance penalties involved in adoption of cloud computing technology for the scientific workflow. In this research, we have tested the scientific workflow on multiple virtual machines and containers with different configuration. We also captured the performance of each virtual machine and container by considering execution time and energy consumption. Our results show that high configured virtual machine gives faster results. Moreover, it also shows that virtualization technology increases energy consumption considerably. About comparison between OpenStack virtual machine and Docker container, Docker container gives faster results for CPU and memory intensive jobs. With these results, scientific community will understand the impact of adoption of cloud computing technology on scientific workflow.
CRITICAL ASSESSMENT OF CONTRIBUTION FROM INDIAN PUBLICATIONS: THE ROLE OF IN SILICO DESIGNING METHODS LEADING TO DRUGS OR DRUG-LIKE COMPOUNDS USING TEXT BASED MINING AND ASSOCIATION

Over the several decades, India is constantly challenged by communicable and non-communicable diseases which are originated either by poor lifestyle or by environmental factors. The pools of diseases are constantly posing serious threats to mankind especially among the poverty-stricken families. Scientific communities across the globe are working continuously to design drug molecules to overcome the burden of these life threaten diseases. In last three decades, many computational algorithms and tools have been developed to identify potential drug targets and their inhibitors. It is believed that computational techniques have reduced the time and money required to develop an inhibitor into drug. However, applicability and deliverability of these in silico techniques in rational drug designing are not fully evaluated. In the present study, PubMed/Medline extracted data driven analysis has been performed to highlight the influence and progress of the theoretical methods in the field of drug discovery across India and compared with the world. Drug discovery related keyword dictionary has been built and utilized to select only drug discovery related PubMed abstract. A second keyword set (related to bioinformatics tools) is used for normalized pointwise mutual information (PMI) based association analysis. Observations show that drug discovery has been an interdisciplinary research and used many tools starting with QSAR, docking, pharmacophore, Molecular Simulations etc. The publications contributed from India (2%) are similar as compared to the contribution in total world publications, suggesting large scope in future. Data coverage as represented since 1990-2015 in PubMed as indicated by number of publications associated with drug discovery is almost same in world and India (~75%). Emerging institutes/Universities are contributing since last 10 years as observed from Indian publication list. However, this method has many limitations as discussed. Keywords: PubMed; Association; nPMI; In silico techniques; Drug discovery
Amyotrophic Lateral Sclerosis : a proposed molecular model of disease development

One of the most complex and intriguing biological problems is perhaps the development of neurodegeneration. Neurodegenerative diseases are many, and many seem to be their causes: their etiologies are generally of different nature, making their study difficult and highly specialized. Lately, however, one process in particular, neuroinflammation, has surfaced as a strong common feature in a variety of neurodegenerative diseases (Alzheimer’s, Amyotrophic Lateral Sclerosis, Frontal Temporal Lobar Dementia, Multiple Sclerosis, Parkinson’s, Rett’s Syndrome, and more) suggesting that this is either a mechanism through which our brain responds to neural dysfunction or is directly responsible for the initiation of various pathologies of the nervous system. Nonetheless, its contribution to disease is still very unclear. In light of this, I and Dr. LaRocca set out to investigate the role of the innate immune response in an in vitro amyotrophic lateral sclerosis model: TDP-43 knockdown primary rat astrocytes. Here, I describe the activation of the intracellular innate immune response in such astrocytes upon the silencing of TDP-43. I used RNA-Seq data (generated and analyzed by Dr. LaRocca), immunofluorescence staining, and quantitative real time-PCR (qRT-PCR) to detect the differential expression of innate immune response markers, and fluorescence in-situ hybridization to investigate the expression and localization of repetitive elements. We have identified specific immune markers that are reliably overexpressed in our ALS model, increased expression of repetitive elements, and an increased accumulation of cytoplasmic dsRNA. These iii
Interest Dynamics with Social Constraints and Exogenous Drivers

An important dynamic among interacting knowledge workers is the transition from cognitive convergence (a positive group phenomenon) to collapse (which can lead to overlooking critical information). This paper extends previous studies of this subject in two ways. 1) We place agents in distinct social groups and vary within-group affinity. 2) We provide exogenous drivers of interest. We exhibit a metastable configuration of this system with three phases, and show how to distinguish convergence from collapse. Then we use this metric to explore the system’s dynamics, over the space defined by social affinity and precision of queries, and under a range of different functions for the influence that an interaction partner has on an agent.
INTEGRATION OF SOLAR AND PV BATTERY WITH ADVANCED CONTROL STRATEGY OF A THREE-LEVEL NPC INVERTER 1

In this paper proposes the design and analysis of the proposed configuration and the theoretical framework of the propose modulation technique. A novel configuration of a three-level neutral-pointclamped (NPC) inverter that can integrate solar photovoltaic (PV) with battery storage in a gridconnected system proposed in this paper. The strength of the proposed topology lies in a novel extended unbalance three-level vector modulation technique than can generate the correct ac voltage under unbalanced dc voltage conditions. In order to control the power deliver between the solar PV, battery, and grid, which simultaneously provides maximum power point tracking (MPPT) operation for the solar PV with new control algorithm for the proposed system is also presented. The effectiveness of the proposed methodology is investigated by the simulation of several scenarios, including battery charging and discharging with different levels of solar irradiation. By using the simulation results we can analyze the proposed method.
New genetic operators in the Fly algorithm : application to medical PET image reconstruction

This paper presents an evolutionary approach for image reconstruction in positron emission tomography (PET). Our reconstruction method is based on a cooperative coevolution strategy (also called Parisian evolution): the “fly algorithm”. Each fly is a 3D point that mimics a positron emitter. The flies’ position is progressively optimised using evolutionary computing to closely match the data measured by the imaging system. The performance of each fly is assessed using a “marginal evaluation” based on the positive or negative contribution of this fly to the performance of the population. Using this property, we propose a “thresholded-selection” method to replace the classical tournament method. A mitosis operator is also proposed. It is triggered to automatically increase the population size when the number of flies with negative fitness becomes too low.