Miracle-dz/UniFAR-FTUs huggingface dataset:
Vulnerability Classification of Consumer-based IoT Software

This paper surveys and categorizes potential software vulnerabilities in consumer-based IoT applications. We look at the currently available reported vulnerabilities in the SmartThings platform as well as potential vulnerabilities that face IoT platforms in general. We provide a multi-step categorization that applies available guidance as well as connecting it to frameworks such as OWASP and MITRE ATT&CK to classify the vulnerabilities depending on their platform, layer, nature, class as well as the suggested mitigation.
TaiChi: A Hybrid Compression Format for Binary Sparse Matrix-Vector Multiplication on GPU

Binary Sparse Matrix-Vector Multiplication (SpMV) is a heavy computational kernel in weblink analysis, integer factorization, compressed sensing, spectral graph theory, and other domains. Testing several popular GPU-based SpMV implementations on 400 sparse matrices, we observed that data transfer to GPU memory accounts for a large part of the total computation time. The transfer of constant value 1s can be easily eliminated for binary sparse matrices. However, compressing index arrays has always been a great challenge. This paper proposes a new compression format TaiChi to further reduce index data copies and improve the performance of SpMV, especially for diagonally dominant binary sparse matrices. Input matrices are firstly partitioned into relatively dense and ultra-sparse areas. Then the dense areas are encoded inversely by marking ``0"s, while the ultra-sparse area is encoded by marking ``1"s. We also designed a new SpMV algorithm only using addition and subtraction for binary matrices based on our partition and encoding format. Evaluation results on real-world binary sparse matrices show that our hybrid encoding for binary matrix significantly reduces the data transfer and speeds up the kernel execution. It achieves the highest transfer and kernel execution speedups of 5.63x and 3.84x on GTX 1080 Ti, 3.39x and 3.91x on Tesla V100.
In vivo quantification of intracerebral GABA by single-voxel reproducible are the results?

1 H magnetic resonance spectroscopy (MRS) provides a powerful tool to measure gamma- 23 aminobutyric acid (GABA), the principle inhibitory neurotransmitter in the human brain. 24 We asked whether individual differences in MRS estimates of GABA are uniform across 25 the cortex or vary between regions. In two sessions, resting GABA concentrations in the 26 lateral prefrontal, sensorimotor, dorsal premotor, and occipital cortices were measured in 27 twenty-eight healthy individuals. GABA estimates within each region were stable across 28 weeks, with low coefficients of variation. Despite this stability, the GABA estimates 29 were not correlated between regions. In contrast, the percentage of brain tissue per 30 volume, a control measure, was correlated between the three anterior regions. These 31 results provide an interesting dissociation between an anatomical measure of individual 32 differences and a neurochemical measure. The different patterns of anatomy and GABA 33 concentrations have implications for understanding regional variation in the molecular 34 topography of the brain in health and disease.
Relationship-Based Threat Modeling

Threat modeling is a common technique for the systematic analysis of system designs to uncover security and privacy threats. Popular threat modeling techniques, however, currently only consider a very localized system context, which hinders the discovery of more complex attack scenarios that involve multiple interactions through-out a system. This may lead to the underestimation of threats that are not harmful by themselves but enable multiple other high-risk threats. Furthermore, current risk assessment approaches require stakeholders to take the system as a whole into account when providing inputs, which is tedious and error-prone. This paper introduces relationship-based threat modeling (RBTM). Using explicitly captured threat relationship knowledge, RBTM al-lows to systematically and automatically generate a threat graph which is then used as input for traceable risk calculations. This removes the need to manually take into account threat relation-ships during risk assessments and allows stakeholders to clearly identify and communicate the rationale behind the resulting risk values. The outputs of an RBTM analysis were compared to those of a manual one performed by experts to evaluate the soundness of our proposal, which also highlighted the traceability benefits.
Social news aggregations and the Bitcoin: mapping their correlation through the three dimensions of content, sentiment and time

Social media and financial markets are ecosystems bound to interact and overlap more and more. For example, cryptocurrencies, Bitcoin in the first place, have long been among the hottest topics on social media. Here we illustrate a methodology for correlating Bitcoin price trends and social media that operates on the aggregation of news from multiple social networks as typically occurs on dedicated channels in sites such as Reddit. For this purpose, we define general laws to map the financial fluctuations of Bitcoin in a space defined on three dimensions: content volume, sentiment and time. These laws provide the foundation upon which to build effective methodologies for social media-based prediction of Bitcoin performance.
and T Lineage Potential Progenitor Populations with Clonogenic B The Blood Contains Multiple Distinct

The thymus is seeded by bone marrow-derived progenitors that circulate in the blood. Multiple cell types can be found in the thymus early after i.v. administration or in steady state, but most fail to satisfy the known characteristics of true T progenitors. Cells that do conform to classical deﬁnitions retain multilineage potential, but surprisingly, cannot make B cells. Because acquisition of the T lineage fate among noncommitted progenitors is a lengthy process, the absence of B cell potential in early thymocytes suggests that B and T lineages diverge prethymically. To test this suggestion, we screened numerous presumptive progenitor populations for T cell growth and differentiation potential, as well as for clonogenic T or B cell development. We ﬁnd that blood and marrow each contain multiple distinct subsets that display growth and differentiation potential consistent with being canonical T progenitors. Assessment of clonogenic potential further shows that although all blood and marrow populations have high T cell cloning potential, no T/non-B cells are apparent. These data suggest that either true thymic reconstitution potential derives from a small T/non-B cell subset of one of these populations, or that most of the cells deﬁned as canonical progenitors within the thymus do not, in fact, reside in the mainstream of T progenitor differentiation. The Journal of Immunology, 2007, 178: 4147–4152.
Transmission Line Effects of Long Gate-to-Gate Interconnections in Adiabatic Quantum-Flux-Parametron Logic Circuits

Adiabatic quantum-flux-parametron (AQFP) logic is an energy-efficient superconductor logic family that utilizes adiabatic switching of logic gates. At present, a pure inductance is used to model a gate-to-gate interconnection in AQFP logic circuits. However, it is necessary to consider the transmission line effects when the interconnection length becomes long to take into account the propagation delay. This study investigated the transmission line effects of a long gate-to-gate interconnection in AQFP logic circuits. We observed reflection effects in a long transmission line between AQFP gates. These effects induced errors when the interconnection length exceeded several hundred micrometers. We adopted a damping resistance to stabilize the reflection effect to realize an error-free long gate-to-gate interconnection. The circuit simulations confirmed that a damping resistance connected in parallel to the transmission line effectively stabilizes the reflection effect, enabling an interconnection length that is longer than 1 mm. We fabricated AQFP buffer chains that included a long transmission line with and without a parallel damping resistance, and measured their bit error rates. The results showed that the parallel damping resistance effectively improves the bit error rate at high frequencies.
A study of the Retinal Nerve Fiber Layer Thickness Changes in Multiple Sclerosis Using Optical Coherence Tomography in Nassiriyha city

ABSTRACT Optical coherence tomography (OCT) is a non-invasive relatively inexpensive technique that allows the quantitative cross sectional imaging of the RNFL. It has been used predominantly to investigate retinal axonal loss in glaucoma. The aim of the current study was to evaluate the retinal nerve fiber layer thickness as a structural biomarker for axonal loss in multiple sclerosis regardless the frank history of previous attack of optic neuritis. We evaluated the relationship between RNFL changes and the patients' age and duration of the disease. We also studied the correlation between the RNFL thickness and patients' clinical data as BCVA, history suggestive of optic neuritis as well as optic disc appearance by fundoscopy.
Interaction-based Ontology Modeling for User-Centric Social Networks Environments

—Social Networks are nowadays the most relevant source of information in terms of scientiﬁc challenges and proposed computational models. This is due to the huge availability of user data, ranging from interactions, activities, and multimedia messages. The Big Data era is relatively new, and the emergence of user-and scalibility-centered solutions is particularly inﬂuenced by these novel and ever-growing data, that need to be carefully organized to remain manageable. In this contribution, we propose a novel approach to deal with social networks data representation that is able to model such complexity without affecting the ﬂexibility of who can interact within the environment, and how. In particular, we revisit the standard methodology of computational ontologies proposing a framework where objects and agents are deﬁned as compositions of atomic semantic information, avoiding preventive and static identiﬁcation of the system’s players. Our method is inspired by the work of James Gibson, who deﬁned an ecological view of the human perception based on objects’ natural affordances , in which objects spontaneously give cues about how they can be used depending on the agent who is actually interacting. The idea is that while objects and agents can potentially grow without any constraint, the spectrum of all the individual interactions can be the product of limited (and much more simple to represent) links between users and objects’ atomic semantic information. In this sense, if an agent ‘x’ acts on an object ‘y’, it means that some property of ‘x’ are activated by the action (i.e., the user embodies a speciﬁc role), and some property of ‘y’ makes the action physically possible (i.e., it allows the action to be performed). In this paper we demonstrate how an interaction-based ontology view with the use of vector spaces can reduce manual efforts while preserving control of dynamic data in social networks.
TECHNOLOGY POD-PWM BASED CAPACITOR CLAMPED MULTILEVEL INVERTER

— Multilevel inverters play a crucial part in the areas of high and medium voltage applications. Among the three main multilevel inverters used, the capacitor clamped multilevel inverter(CCMLI) has advantage with respect to voltage redundancies. This work proposes a switching pattern to improve the performance of chosen H-bridge type CCMLI over conventional CCMLI. The PWM technique used in this work is Phase Opposition Disposition PWM(PODPWM). The performance of proposed H-bridge type CCMLI is verified through MATLAB-Simulink based simulation. It has been observed that the THD is low in chosen CCMLI compared to conventional CCMLI.
Recommendation Acceptance in a Simple Adaptive Learning System

- In the following article we report the current status of our research on integrating a simple, useful adaptive learning system into a university's standard learning management system. In this context, a corresponding instructional design was implemented for a basic mathematics course with the aim of supporting students in their self-study. A rule-based tool was developed for this purpose. The theoretical basis of the design was the Cognitive Load Theory. Based on the principles of this theory, the load on the working memory of the students during learning was to be optimized by appropriate task difficulties. It was expected that the learning performance would be improved. The first results from one of our preliminary studies focusing on learning progress, activity and previous knowledge of different groups of students showed that students who actively worked with adaptive tasks benefited from the system and achieved a greater learning progress than the comparison groups. In the follow-up to this finding, new research questions have arisen for us on the basis of certain limits of the previous study. All of these questions aim to determine whether the positive learning effects can be attributed to the increase in learning activities alone or to following the recommendations or to the interaction of both. In this paper, we present the next research steps in the sense of a framework in order to find the corresponding answers.
Fuzzy sets, fuzzy logic, fuzzy methods with applications

Fuzzy Sets: Basic Notions Examples of Fuzzy Sets Operations with Fuzzy Sets t-norm-Based Operations Fuzzy Numbers and Their Arithmetic Fuzzifified Relationships: Fuzzy Relations Properties of Fuzzy Relations Fuzzy Relationships between Variables Fuzzy Programming Linguistic Variables: The Notion of a Linguistic Variable Fuzzy Control Relational Equations and Fuzzy Control Approximate Reasoning Examples for Applications of Fuzzy Controllers Measure Theory and Fuzzy Sets: Fuzzy Measures for Crisp Sets Fuzzy Measures for Fuzzy Sets Fuzziness and Probability Some Applications Fuzziness Measures Fuzzy Data Analysis: Data and Their Analysis Qualitative Data Analysis Quantitative Data Analysis Evaluation of Methods.
Optimal Medical Therapy in the Prognosis of Coronary Artery Disease

We appreciate the comments by Dr. Mahmoud and colleagues about our study. They argued that heterogeneity for major bleeding may have confounded our analysis due to an imbalanced use of radial access and glycoprotein IIb/IIIa inhibitors (GPI) between the unfractionated heparin (UFH) and bivalirudin groups. First, as we noted in our original paper (1), the heterogeneity appeared to originate from the singlecenter study (i.e., How Effective Are Antithrombotic Therapies in Primary Percutaneous Coronary Intervention [HEAT PPCI]). Once this study was removed from our sensitivity analysis, no significant heterogeneity (p 1⁄4 0.123) remained among the 4 remaining (multicenter) trials. It is well known that a singlecenter study is typically an outlier when combined with multicenter trials in a meta-analysis (2). Therefore, it is strongly recommended that meta-analysts perform sensitivity analyses to demonstrate how their conclusions might be affected by the exclusion of single-center trials (2). Second, the primary focus of our paper was to evaluate the effect of prolonged bivalirudin infusion on major bleeding and acute stent thrombosis. Prolonged bivalirudin infusion at the full percutaneous coronary intervention (PCI) dose was used only in the MATRIX (Minimizing Adverse Hemorrhagic Events by Transradial Access Site and Systemic Implementation of Angiox), EUROMAX (European Ambulance Acute Coronary Syndrome Angiography Trial), and BRIGHT (Bivalirudin in Acute Myocardial Infarction vs Heparin and GPI Plus Heparin Trial) trials. No heterogeneity was found among these trials (Q 1⁄4 3.1; df 1⁄4 3; p 1⁄4 0.374; I 1⁄4 3) for the prolonged infusion subgroup analysis. Radial access was used in the same number of patients in both the bivalirudin and UFH groups in these trials. In addition, the MATRIX trial investigators found no evidence of interaction between access route and allocation to the bivalirudin or UFH groups for major bleeding (p for interaction $0.64) (3). In the EUROMAX trial, bivalirudin was associated with a significantly lower rate of major bleeding (odds ratio: 0.44; 95% confidence interval: 0.24 to 0.82), even when compared with UFH without routine GPI (4). Similarly, in the BRIGHT trial, bleeding (1 of the primary endpoints) was reduced with bivalirudin compared with UFH alone and UFH with GPI (4.1%, 7.5%, and 12.3%, respectively; p < 0.001). Furthermore, during sensitivity analysis, removing the BRIGHT-UFHþGPI data did not change our summary result for major bleeding (risk ratio: 0.31; 95% CI: 0.15 to 0.63; p 1⁄4 0.001). Finally, current guidelines recommend a 70 to 100 U/kg bolus of UFH without GPI or a 50 to 70 U/kg bolus of UFH with GPI for primary PCI, which were used in these trials. Thus, it is very unlikely that our conclusion about the effect of prolonged bivalirudin infusion on major bleeding was confounded by these factors. However, as we noted in our original paper, access to patientlevel data would enable multivariate analysis, which would markedly strengthen this analysis.
Empowering and interacting with statistical produsers: a practical example with Eurostat data as a service

Since policy advice is becoming increasingly supported by data resources [1], public organisations are leveraging the use of these resources to inform decisions. In this context, supporting data analytics, which aims eventually at extracting valuable information from data to use in intelligent ways by means of advanced statistical and computational techniques [2], offers big opportunities – and challenges – for enhanced insight and decision-making [3]. Data analytics draws not only on existing and new sources of ever-growing data, it builds also on new methodologies and emerging Information and Communication Technologies tools, and advances thanks to innovative initiatives. In particular, with increased availability of open data, new developments in open technologies and recent breakthroughs in data science, it is believed that data analytics can help improve current governance processes by enabling policy-driven data-informed evidence-based decision-making and potentially reduce the bias, costs and risks of policy decisions [4][5]. Eventually, it seems very logical, and appealing, to complement the statistical offices' toolbox with data analytics tools. Still, the development and deployment of such tools will need sound judgment as an abundance of data and computing power does not automatically guarantee good decision-making.
On the band gap of CuAlO2 delafossite

In this letter, we discuss the electronic structure of copper aluminate (CuAlO2) on the basis of absorption measurements at low temperature and under high pressure in single crystals and thin films, combined with ab initio electronic structure calculations. The indirect character of the fundamental transition could be clearly established through the photon energy dependence of the absorption edge as measured in single crystals, yielding a band gap (plus a phonon) of 2.99±0.01eV at room temperature. An indirect excitonic structure is also observed at low temperature. The lowest energy direct allowed transition occurs near 3.53±0.01eV and is identified through its low-energy tail in single crystals and its excitonic peak in thin films. Comparing our results with ab initio calculations available in the literature, we conclude that CuGaO2 and CuInO2 can hardly be considered as wide gap semiconductors, even if they are transparent in thin films.
The Boy Who Cried Bear: Multi-Model Approach for Predicting Market State

: Enormous amounts of wealth are changing hands at every moment in the financial markets. Conse-quently, an accurate forecast of the future is a valuable piece of information for any investor. While one might generally believe that one cannot accurately predict the price of an individual security, being able to predict the “state” of the market would unambiguously give investors an advantage over others. The idea of predicting the market state has been widely accepted and discussed by academics and professionals. In this paper we review the popular definitions of Bull, Bear and Neutral market states and create a method to predict the “state” of the market by by combining an unsupervised learning method (HMM) with a supervised learning method (XG-boost). We then deploy this hybrid model with a basic trading strategy on the Russell 3000 index and analyze its performance. Our aim is not to find the most profitable trading strategy, but to demonstrate its potential to be used as part of a risk management strategy.
GAN-based Defect Image Generation for Imbalanced Defect Classification of OLED panels

Image classiﬁcation based on neural networks has been widely explored in machine learning and most research have focused on developing more efﬁcient and accurate network models for given image dataset mostly over natural scene. However, industrial image data have different features with natural scene images in shape of target objects, background patterns, and color. Additionally, data imbalance is one of the most challenging problems to degrade classiﬁcation accuracy for industrial images. This paper proposes a novel GAN-based image generation method to improve classiﬁcation accuracy for defect images of OLED panels. We validate our method can synthetically generate defect images of OLED panels and classiﬁcation accuracy can be improved by training minor classes with the generated defect images.
Crosslayer network outage classification using machine learning

Network failures are common, difficult to troubleshoot, and small operators with limited resources need better tools for troubleshooting. In this paper, we analyse two years of outages from a small global network for high-quality services. Then, we develop a machine learning model for outage classification that can be set up with little effort and low risk. We use passive Bidirectional Forwarding Detection (BFD) data to classify Layer2 problems and add active packet loss data to classify other problems. The Layer2 problems were classified with a 99% accuracy and the other problems with 40%--100% accuracy. This is a significant improvement when we observe that only 35% of the customer cases we studied received any Reason for Outage (RFO) response from the Customer Support Centre.
Impaired Neuronal Communication Syndrome (INCS) as Novel Neurological Side Effect to Botulinum Toxin Type A Therapy with 16 Case Reports

Based on the data of the 16 cases we report here, prior experience with side effects to botulinum, and the published literature, we define a novel syndrome as an adverse reaction to the toxin therapy which we named: Impaired Neuronal Communication Syndrome (INCS). We define INCS as a disease of toxin- induced impairment of neuronal connection and impaired mediators balance in the central, peripheral and/or autonomic nervous system with, most of the time, no identifiable anatomical cell damage. This syndrome differs from the reported in the literature autoimmune adverse reactions to the toxin, affecting the central (encephalitis) or the peripheral nervous system (inflammatory polyneuropathies, brachial plexitis etc). The major difference is that, in toxin-induced autoimmune syndromes, it is more likely to have positive findings in laboratory tests and imaging or on examination while, in INCS the majority of the patients have normal laboratory and imaging tests and neurological examination. This suggests that, in such cases, the clinical features and the proximity of the symptoms’ occurrence to the toxin therapy are the main and often the only tools to achieve a correct diagnosis. We identify that the side effects from botulinum toxin therapy can be severe, persistent, and disabling. We suggest an algorithm for safer toxin use. Impaired Neuronal Communication Syndrome; LD50: Lethal Dose of the toxin that kills 50% of the mice population; SNAP-25: Synaptosomal Associated Protein - 25.