Miracle-dz/UniFAR-FTUs huggingface dataset:
AN IMPLEMENTATION OF CHACHA20 STREAM CYPHER IN ALL-PROGRAMMABLE SoCs

School of Graduate Studies The University of Alabama in Huntsville Degree Masters of Science College/Dept. Engineering/Electrical and in Engineering Computer Engineering Name of Candidate Igor Semenov Title An implementation of ChaCha20 stream cypher in all-programmable SoCs An increased reliance on services offered online is an inherent aspect of the Information Age. Such services often handle sensitive customers’ data and therefore must ensure its confidentiality, for example, by using cryptographic algorithms. Some algorithms, such as AES, have long been used for this purpose, but they impose additional cost and/or performance overheads. However, new lightweight stream cyphers, such as ChaCha20, are emerging as a faster alternative to conventional algorithms without sacrificing the security. Development of hardware cryptographic accelerators has been widely used to reduce performance overheads of cryptographic algorithms. In the past, this approach was extremely expensive, but nowadays, widely available all programmable SoCs make it more affordable. In this thesis, we present an IP-core for ChaCha20 acceleration in a low-cost all-programmable SoC, Cyclone V. We describe the design of our core, consisting of a ChaCha20 accelerator and a custom DMA. We also present a software framework that employs multiple CPU cores or hardware accelerators for file encryption. We explore different configurations of this framework in order to find the optimal one. Our experiments show that two
Forecast of paroxysmal atrial fibrillation using a deep neural network

Atrial fibrillation (AF) is the most common heart arrhythmia. It affects between 1% and 2% of the world population over 35 years old. This disease is linked to an increased risk of stroke and heart failure. AF is a progressive disease and, at first, paroxysmal AF episodes occur, last from seconds up to a week and then stop. The disease evolves to permanent state, where the heart is always in fibrillation and can't be corrected. Forecasting paroxysmal AF episode a few seconds or minutes before its onset remains a hard challenge, but could lead to new treatment methods. For this study, we constructed a new long-term electrocardiogram (ECG) database (24 to 96 hours), composed of 10484 ECG. As a result of a careful analysis by a cardiologist, 250 AF onsets of paroxysmal AF have been detected in 140 ECG. We developed a deep neural network (DNN) model, composed of convolutional neural network (CNN) layers and bidirectional gated recurrent units (GRU) as recurrent neural network (RNN) layers. The model was trained for a supervised binary classification distinguishing between heartbeats series (RR intervals) that precede an AF onset and series distant from any AF. The model achieved an average area under the receiver operating characteristic (ROC) curve of 0.74. We evaluated the impact of heartbeat window size given as input, and the time period between the heartbeats window and the AF onset. We found that an input window of 300 heartbeats gives the best results and, not surprisingly, the closer the window is from the AF onset, the better the results. We concluded that RR intervals series contains information about the incoming AF episode, and that it can be exploited to forecast such episode.
Knowledge Graph Generation with Deep Active Learning

Creating a knowledge graph automatically from raw unstructured text has always been a job of domain expert which takes months to curate and refine. In this paper, we propose a domain-independent semi-automatic knowledge graph learning system that can be trained with less amount of data, to identify entities and relations from a large text corpus. The system performs the following tasks to extract knowledge graph from the text: (i) Named Entity Recognition (NER), and (ii) Relation Identification (Open Relation Extraction (OpenRE) and Classification). The system uses deep active learning to calculate confidence scores using maximum normalized log-probability on each prediction for both NER, and relation identification. We experimented with both LSTM and transformer based models for NER and relation identification tasks.We achieved around 88% F1 score for the NER task on OntoNotes-5.0 English data set with 40% training data set and above 83% F1 score for relation identification on TACRED dataset. The OpenRE and relation classification systems were trained on domain-specific datasets. To the best of our knowledge, we are the first to introduce a knowledge graph generation learning system with deep active learning.
The Method of Variation of Parameters

It is tempting to differentiate this expression once more and then substitute into equation (1) to determine u1 and u2. However, if we did this, the resulting expression for y ′′ p would involve second derivatives of u1 and u2, hence we would have complicated our problem. Since yp contains two unknown functions, whereas equation (1) gives only one condition for determining them, we have the freedom to impose a further constraint on u1 and u2. In order to eliminate second derivatives of u1 and u2 arising in yp we try for solutions of the form (3) satisfying the constraint
Application of Regression Techniques to Studies of Relative Growth in Crustaceans

ABSTRACT Studies of relative growth in crustaceans are often based, in whole or in part, upon the practice of log transformation of all linear morphometric data prior to the calculation of a least squares estimate linear regression for a broad size class of specimens. This and other practices may result in misinterpretation of the regression analysis. We propose that analysis of untransformed data with a Model II regression technique such as the reduced major axis is appropriate for description of relative growth in crustacean linear morphometric features, and that data for broad size-class samples should be examined as iteratively divided subsets, rather than assuming a priori that the entire sample is adequately represented by a single continuous function. A statistical test for evaluation of regression functions on the basis of randomness of residuals is provided.
Study of the Linear Elastic Fracture Mechanics of Cortical Bone as a composite material

Studying the bone structure is an increasingly appealing subject for engineers interested in understanding the properties of this material. The comprehension of the mechanical behavior of bone is advantageous, because it helps in the perception of the risk of fracture and it can also serve as a basis for the development of new and innovative composite materials. In this work, a microscopic two-dimensional characterization of a cortical bone is presented using the Abaqus software. The bone properties required for the development of this work were taken from literature relatively to bovine bones. Therefore, and in order to analyze the propagation of preexisting cracks in the material, an incremental deformation was applied to the bone in order to calculate the fracture resistance of the model. It is also studied the importance of the material properties of the osteons, cement lines and Haversian channels, in the evaluation of how these bone constituents can cause a disruption in the crack propagation. In addition, microscopic observations were made to the cortical bone of a bovine femur, in order to obtain a real insight of what happens to the cracks on a fractured surface through a three-point bending test. The results obtained clarify what makes the cortical bone in a composite material so resistant to crack propagation, this is because of the observed attraction that the crack feels for osteons lying in its path. Keywords— Cortical Bone, Abaqus, Cracks, Osteons, Cement Lines, Harversian Canals
AUDIO EVENT DETECTION AND LOCALIZATION WITH MULTITASK REGRESSION NETWORK

This technical report describes our submission to the DCASE 2020 Task 3 (Sound Event Localization and Detection (SELD)). In the submission, we propose a multitask regression model, in which both (multi-label) event detection and localization are formulated as regression problems to use the mean squared error loss homogeneously for model training. The deep learning model features a recurrent convolutional neural network (CRNN) architecture coupled with self-attention mechanism. Experiments on the development set of the challenge’s SELD task demonstrate that the proposed system outperforms the DCASE 2020 SELD baseline across all the detection and localization metrics, reducing the overall SELD error (the combined metric) approximately 10% absolute.
Noise Removal with Regular Expression in Knowledge Graphs

With more and more knowledge graphs appear in the real-world data presentation, the complexity of the knowledge graph is increasing, such as heterogeneous graphs in the biomedical area [5, 8]. Analytics on heterogeneous knowledge graphs is challenging because the types of nodes and edges are different. To be efficient, methods of extracting linear paths from knowledge graphs might fail to capture the rich semantics and topology of knowledge graphs. To be effective, methods of using the entire knowledge graph might introduce too much noise irrelevant to the topics. One of the downstream analytical tasks connecting graphs and data analysis is graph embedding, which converts the graph presentation into the numerical presentation. Among various embedding methods, randomwalk based methods are known to be an efficient way to execute such transformation [1, 10]. However, they are originally developed for homogeneous graphs [4, 9]. Without considering any limitations, the random walk will walk through any nodes including those that are irrelevant. It causes inaccurate information extraction and therefore incorrect inference from downstream data analysis. A solution to limit the node types (meta-path) was proposed to avoid such incorrect walks in citation networks [2]. Nevertheless, they assume edge type is unique between two types of nodes. Knowledge graphs in the biomedical area contain more complicated relations between entities. Toy Example. To illustrate, Figure 1 showcases a toy example involving a biomedical knowledge graph. Suppose now we need to execute a graph embedding task for the downstream data analysis. The graph embedding pipeline takes the graph as input shown in Figure 1.
The Case for Physiologically Based Biopharmaceutics Modelling (PBBM): What do Dissolution Scientists Need to Know?

The purpose of this article is to inform the dissolution scientist of a powerful emerging tool that provides in vivo linkage to dissolution methods. This tool is physiologically based biopharmaceutics modelling (PBBM). Dissolution scientists are mostly concerned with analytical sections of drug development, so exposure to modeling and other pharmacokinetics and biopharmaceutic concepts may be uncommon. PBBM is an in-silico model that focuses on the interactions between the in vivo physiology and the formulation and drug characteristics. The principles behind PBBM is that all mechanisms related to drug release, dissolution, and diffusion from the site of administration to the site of action can be described in a mechanistic way or semi-empirical way. The integration of in vitro dissolution data in PBBM is described, including examples of software and modeling applications. The expertise needed to use the software and appropriate training is discussed. The key inputs that are expected from the dissolution scientist include an understanding of the aqueous solubility across the physiological pH range, impact of in vivo relevant bile salts and phospholipids on solubilization, and the impact of surface pH on dissolution rate. An example of an advanced in vitro system, TNO intestinal model (TIM-1), will be discussed and its importance in establishing a biorelevant understanding of dissolution behavior. PBBM can evaluate the clinical relevance of a dissolution method and justify specifications and ultimately provide an approval advantage to the sponsor. A well-supported dissolution method that provides clinically relevant drug product specifications (CRDPS) will be viewed with favor by the regulators. Therefore, the partnering of a dissolution scientist and biopharmaceutics scientist to develop PBBM is clearly an important step forward in drug development.
Response to Review of Mottram et al : “ What is the Surface Mass Balance of Antarctica ? An Intercomparison of Regional Climate Model Estimates ”

Mottram and co-authors present an intercomparison of different regional climate models regarding their performance in simulating Antarctic Ice Sheet surface mass balance. They show that the RCMs, all forced by ERA-Interim at their boundaries, show overall satisfying (but to a varying degree) correspondence with available weather and SMB observations, and that many remaining biases are common between the different models. The integrated ice sheet SMB varies widely from model to model, but interannual variability is very similar. Overall, we think that this is an interesting paper, containing relevant and important results for the climate, SMB, and ice sheet modeling communities, and very fitting for potential publication in The Cryosphere.
Role of Tim3 in Mediating T Cell Exhaustion During Chronic Mycobacterium Tuberculosis Infection

Mycobacterium tuberculosis infection is one of the leading causes of mortality worldwide. One third of the population is estimated to be infected, however only 5-10% of those individuals can transmit the disease. While T cell immunity initially limits mycobacterium growth, it is unclear why T cell immunity fails to sterilize the infection and prevent subsequent recrudescence. One hypothesis is T cell exhaustion is mediating the failure of T cell immunity late during infection. Here we show the development of T cell exhaustion during chronic infection, and that the inhibitory receptor T cell-immunoglobulin and mucin domain containing 3 (TIM3) mediates the development of T cell exhaustion. TIM3 accumulates on the surface of T cells throughout the course of infection and there is a subsequent decrease in effector cytokine production, such as IL-2, TNFα, and IFNγ. Furthermore, antibody blockade of TIM3 restores T cell function and improves bacterial control. Our results show that TIM3 is mediating T cell exhaustion during chronic TB infection and leading to suboptimal bacterial control.
Superconducting-nanowire-single-photon- detectors in a magnetic field

Current-voltage and count rate measurements were performed on a NbN Superconducting-single-photon-detector in a magnetic field perpendicular to the meander plane. The critical current dependence on the magnetic field was found to be asymmetric with respect to the direction of the magnetic field, indicating an intrinsic asymmetry in the detector. Both the dark count rate and the photon detection rate were found to display effects that cannot be explained in terms of the hotspot model.
Enhanced Privacy and Data Protection using Natural Language Processing and Artificial Intelligence

Artificial Intelligence systems have enabled significant benefits for users and society, but whilst the data for their feeding are always increasing, a side to privacy and security leaks is offered. The severe vulnerabilities to the right to privacy obliged governments to enact specific regulations to ensure privacy preservation in any kind of transaction involving sensitive information. In the case of digital and/or physical documents comprising sensitive information, the right to privacy can be preserved by data obfuscation procedures. The capability of recognizing sensitive information for obfuscation is typically entrusted to the experience of human experts, who are over-whelmed by the ever increasing amount of documents to process. Artificial intelligence could proficiently mitigate the effort of the human officers and speed up processes. Anyway, until enough knowledge won’t be available in a machine readable format, automatic and effectively working systems can’t be developed. In this work we propose a methodology for transferring and leveraging general knowledge across specific-domain tasks. We built, from scratch, specific-domain knowledge data sets, for training artificial intelligence models supporting human experts in privacy preserving tasks. We exploited a mixture of natural language processing techniques applied to unlabeled domain-specific documents corpora for automatically obtain labeled documents, where sensitive information are recognized and tagged. We performed preliminary tests just over 10.000 documents from the healthcare and justice domains. Human experts supported us during the validation. Results we obtained, estimated in terms of precision, recall and F1-score metrics across these two domains, were promising and encouraged us to further investigations.
Th e times they are a-changin': driving forces of recent additions to the Heteroptera fauna of Austria *

During the past approximate 25 years 84 Heteroptera species were published for the fi rst time for Austria. Th ese species account for 9.4 % of the 896 Heteroptera species currently known to occur in Austria and means that species were added at a rate of more than 3 per year, including previously overlooked species. Two thirds of the species (68 %) were overlooked for taxonomic reasons or because of their cryptic appearance or biology; the remaining species (32 %) are considered new arrivals. Excluding single records, 18 species (21 %) are considered established new arrivals. Th is is a rate of 0.72 species
A Developer Recommendation Method Based on Code Quality

During the development cycle of a project, it is common for software requirements and functionality to change and for code errors to occur. To deal with these unforeseen changes, the artifact known as change request, which is a formal proposal to alter a system, is used. Its assignment is an important step in the development process. Projects can receive a very high number of requests daily, which makes the automation of this process compelling. This work proposes a method for assigning unresolved requests, based on developer’s profiles. The proposed method consists of three steps. The first step is to extract code quality metrics, commit data and previously resolved requests, in order to model developers through the mining of repositories. The second step concerns with the selection of the profile of potential developers through the application of natural language processing and information retrieval techniques. And finally, in the third step the appropriate developers are selected based on the quality of their code and the impact of their commits. Results from experimental evaluation show that the method is able to recommend more developers with a positive impact on the repository quality if compared to the iMacPro method.
T OKEN P AIRING TO I MPROVE N EURAL P ROGRAM S YNTHESIS M ODELS

In neural program synthesis (NPS), a network is trained to output or aid in the output of code that satisﬁes a given program speciﬁcation. In our work, we make modiﬁcations upon the simple sequence-to-sequence (Seq2Seq) LSTM model. Extending the most successful techniques from previous works, we guide a beam search with an encoder-decoder scheme augmented with attention mechanisms and a specialized syntax layer. But one of the withstanding difﬁculties of NPS is the implicit tree structure of programs
Structural Modeling of Plant Secondary MetabolitesCharacterized by an Inhibitory Potential of Insect Enzymes

Structural Modeling of Plant Secondary Metabolites Characterized by an Inhibitory Potential of Insect Enzymes Management of pests in the world is facing economic and environmental challenges posed by the majority of chemical pesticides. The identification of new effective insecticides is essential to fight against these parasites. Inhibition of insect enzymes has been opined as a possible mode of action of various plant secondary metabolites. However, it is necessary to gain knowledge about the binding mode of these metabolites to the target region so as to facilitate an understanding of the evolution of novel molecules for pest management.
The Effect of the ECB’s Forward Guidance on Interest Rate Forecasts

This study analyzes the impact of forward guidance (FG) by the ECB on the forecast error of financial markets participants regarding the interest rate level and the slope of the yield curve. We refer to OIS (overnight index swap) forwards as the relevant forecasts and purge the prediction error of several macroeconomic and financial variables to gain a pure representation of the exogenous forecast error. To isolate the effect of FG, this study refers to the absolute deviation of forecasts from actual rates and further controls for variables representing unconventional monetary policies. We find that the introduction of FG improved interest rate predictability for shorter maturities while the substantial decline of long-term interest rates has caught markets by surprise. Hence, the ECB’s intended reduction of refinancing rates at the longer end of the interest rate curve came at the cost of lower predictability of the slope of the yield curve.
Bypassing PatchGuard on Windows x64

: The Windows kernel that runs on the x64 platform has introduced a new feature, nicknamed PatchGuard, that is intended to prevent both malicious software and third-party vendors from modifying certain critical operating sys-tem structures. These structures include things like speciﬁc system images, the SSDT, the IDT, the GDT, and certain critical processor MSRs. This feature is intended to ensure kernel stability by preventing uncondoned behavior, such as hooking. However, it also has the side eﬀect of preventing legitimate products from working properly. For that reason, this paper will serve as an in-depth analysis of PatchGuard’s inner workings with an eye toward techniques that can be used to bypass it. Possible solutions will also be proposed for the bypass techniques that are suggested. Thanks : The
: A survey of personalized driver assistance systems

—The ﬁeld of advanced driver assistance systems (ADAS) has matured towards more and more complex assistance functions, applied with wider scope and a strongly increasing user base due to wider market penetration. To deal with such a large variety of usage conditions and patterns, personalization methods have been developed to ensure optimal user experience. In this paper we review current approaches in the literature that demonstrate an adaptation to the drivers’ preferences, driving styles, skills and driving patterns. We discuss the general assumptions on which personalization in the automotive context is based, the general design of personalized ADAS, the current approaches with their practical realization and point out open issues in the design and implementation of a personalized driving experience. Based on this analysis we propose a general conceptual framework to personalization in ADAS. It suggests a modular decomposition for the next generation of personalized ADAS and HMI which can be expected to continuously adapt in interaction with the driver.