Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing Secure Overlay Forwarding Systems under Intelligent DDoS Attacks

In the framework of a set of clients communicating with a critical server over the Internet, a recent approach to protect communication from Distributed Denial of Service (DDoS) attacks involves the construction of overlay systems. SOS, MAYDAY and I3 are such systems. The overlay system serves as an intermediate forwarding system between the clients and the server, which typically have ﬁxed architectures that employ a set of overlay nodes arranged in different layers that control access to the server. Although such systems perform well under simple congestion-based DDoS attacks, it is questionable whether they are resilient to intelligent DDoS attacks which aim to infer architectures of the systems to launch more efﬁcient attacks. In this paper, we deﬁne several intelligent DDoS attack models and develop an analytical approach to study the impacts of architectural design features on the system performance in terms of path availability. Our data clearly demonstrate that the system performance is indeed sensitive to the architectural features and the different features interact with each other to impact overall system performance under intelligent DDoS attacks. Our observations provide important guidelines in design of such secure overlay systems
Detecting Unauthorized Network Intrusion based on Network Traffic using Behavior Analysis Techniques

Nowadays, network intrusion detection is an essential problem because cyber-attacks are increasing in both the number and extent of the danger. Network intrusion techniques often use various methods to bypass the oversight of anomaly detection and surveillance systems. This paper proposes to use behavior analysis techniques, machine learning, and deep learning algorithms for the task of detecting network intrusions. The practical and scientific significance of our paper includes two issues: (1) Regarding the process of selecting and extracting features: instead of using typical abnormal behaviors of attacks, this study will use statistical behaviors that are easy to calculate and extract while still ensuring the effectiveness of the method; (2) Regarding the detection process, this study proposes to use the Random Forest (RF) classification algorithm, the Multilayer Perceptron (MLP) and the Convolutional Neural Network (CNN) deep learning model. The experimental results in Section IV have proven that our proposal in this paper is completely correct and reasonable. Based on the results shown in Section IV, this study has provided network surveillance systems with a number of abnormal behaviors as the basis for detecting network intrusions. Keywords—Network intrusion detection; abnormal behaviors; IDS 2018 dataset; deep learning and machine learning
Exchange Rate Risk and Asset Prices

This paper develops a continuous-time model of asset pricing for a global economy with two countries and complete asset markets. In this framework, the stochastic discount factor used to price assets will depend on the currency in which assetsreturns are denominated. Using standard continuous-time asset pricing theory, the paper develops the market price of risk equations for domestic and foreign assets denominated in the domestic currency, with currency risk as one of the risk factors driving asset returns, and demonstrates that this risk factor can be represented by the return on the carry trade. A companion paper provides strong empirical evidence for the relevance of the carry trade return as an additional risk factor for U.S. stock returns.
Monitoring of Changing Query Workloads for Online Index Recommendations

Because of the oft-cited ‘curse of dimensionality’, high-dimensional indexes do not work to efﬁ-ciently access very large multi-dimensional databases. A potential solution is to utilize index selection tools to generate multiple lower dimensional indexes. However, tools developed to perform this function are time-consuming to run and are therefore run statically at times determined by a database administrator. As such, these tools are not responsive to changes in query workload patterns, and if the sets of attributes queried evolve so that they differ from the sets of attributes in the index set, query performance will signiﬁcantly degrade. To address these issues, we introduce a parameterizable technique to statically and dynamically adjust indexes as the underlying query workload changes. We incorporate a query pattern change detection mechanism to determine when the access patterns have changed enough to warrant either the introduction of a new index, the replacement of an existing index, or the construction of an entirely new index set. By adjusting analysis parameters, we trade off analysis speed against analysis resolution. We perform experiments with a number of data sets, query sets, and parameters to show the effect that varying these characteristics has on analysis results. We also track query performance over query sets that change distribution over time, to measure the effectiveness of the query pattern change detection and dynamic index recommendation algorithm.
Achievable Throughput in Low Power Wireless Networks

Given a duty cycle, we derive bounds on the achievable capacity of random wireless networks for single channel and multiple channel cases. By modeling several state-of-the-art MAC layer protocols, we present bounds on their through-put capacity. In particular, we analyze four abstract models, sender-centric synchrony (e.g. S-MAC, SCP-MAC), receiver-centric synchrony (e.g. O-MAC), sender-centric asynchrony (e.g. X-MAC, BoX-MACs), and receiver-centric asynchrony (e.g. RI-MAC). This enables a numerical comparison on capacity and energy eﬃciency of these protocols. The results strongly suggest that receiver-centric synchronous scheme has the best performance.
BUYING INTENTIONS FACTORS AND GROWING S- COMMERCE IN KINGDOM OF SAUDI

This Examination centers around the components impacting purchasers' intentions enjoyed S-commerce in KSA. We experimentally investigated factors impacting purchaser's intentions on social media centering Facebook in non-industrial nations like KSA. Utilizing the essential parts of the TPB (Theory of Planned Behavior), we coordinated No. of Likes and Comments and Suggested Posts of Facebook in essential forerunners of social standards for example friends and families as the principle factors in impacting buying intentions of Facebook purchasers. We likewise incorporated Smartphones utilization as a significant impacting factor. With the assistance of econometric program IBM SPSS, we measurably dissected the information gathered through overviews. The consequences of this examination show that two variables leave an incredible effect on buying intentions for example Family and Utilization of Smartphones. Different discoveries of this examination underline that Facebook is liked for the shopping reason when contrasted with sites. The greater part of Purchasers like to make buys at any rate once per month. Females are effectively associated with S-commerce in KSA, when contrasted with Guys. Seeing factual outcomes, we presume that utilizing social media particularly Facebook is a gainful path for SMEs to create as the purchasers are effectively included. Uncovering the responses to the center inquiries a SME ordinarily experience prior to entering the market accessible on social media, this investigation is similarly significant for the business networks of KSA just as China.
SCG: Spotting Coordinated Groups in Social Media

Recent events have led to a burgeoning awareness on the misuse of social media sites to aﬀect political events, sway public opinion, and confuse the voters. Such serious, hostile mass manipulation has motivated a large body of works on bots/troll detection and fake news detection, which mostly focus on classifying at the user level based on the content generated by the users. In this study, we jointly analyze the connections among the users, as well as the content generated by them to Spot Coordinated Groups ( SCG ), sets of users that are likely to be organized towards impacting the general discourse. Given their tiny size (relative to the whole data), detecting these groups is computationally hard. Our proposed method detects these tiny-clusters eﬀectively and eﬃciently. We deploy our SCG method to summarize and explain the coordinated groups on Twitter around the 2019 Canadian Federal Elections, by analyzing over 60 thousand user accounts with 3.4 million followership connections, and 1.3 million unique hashtags in the content of their tweets. The users in the detected coordinated groups are over 4x more likely to get suspended, whereas the hashtags which characterize their creed are linked to misinformation campaigns.
Barriers to Health Behavior Change in People with Type 2 Diabetes: Survey Study

Background: Diabetes mellitus DM (type 2) is one of the major chronic diseases that creates burden at the population level. A prevalence of 422 million patients with diabetes worldwide is detected [1]. UAE population has an increased prevalence of DM; According to International Diabetes Federation (IDF) 17.3% of the UAE population between the ages of 20 and 79 have type 2 diabetes in 2017 [2]. UAE has been suffering with the health problems that result from obesity and sedentary lifestyle. The purpose of this study is to understand barriers to health behavior among community patients with diabetes mellitus type 2. Methods: This is a qualitative survey study of 123 adult individuals with type 2 diabetes who are above 18-years-old, who participated in a survey that explore their perception about barriers to health behavior change. The survey was distributed online and in 6 health care centers under ministry of health and prevention in the United Arab Emirates. Results: This study finds that the top barrier for behavior change is difficulty to follow healthy schedule (19.4%), workplace condition and timing (14.3%), and laziness (13%). The least barrier was lack of knowledge about healthier behaviors (5.3%). The best way to motivate behavior change was doctor’s advice during regular visit (26.3%), joining support group (18.4%), and receiving electronic tips and reminders on smartphones (15%). Conclusion: The outcomes of this study can aid directly in community programs design and implementation, health education campaigns, and health policies. The findings of this study help in understanding real barriers to initiating and sustaining positive health behavior. ReseaRch aRticle
The relationship between the rate of spread of HIV in the population and the virus evolutionary rate

HIV-1 infection is nowadays a chronic disease when before was a death sentence. HIV-1 mutates quite easily and fast so that one of the barriers that still persist in finding a cure or a vaccine is the escape and evasion mechanisms of this retrovirus. Many factors influence the outcome of an epidemic and in order to assess any relationship between rate of spread of the disease and the virus evolutionary rate an agent-based model was used. Within-host evolution was integrated in the model in the form of genetic distance to further assess the relationship with across-host rate of spread. Different scenarios, contemplating only acute, only chronic or both transmissions were simulated and the influence of super-spreaders in the epidemics outcome was evaluated. The model was validated with data from the Latvia epidemic (1987-2010). In fact results suggest that there is an inverse relationship between rate of spread of the disease and evolutionary rate. Moreover, the speed of spread is enhanced by super-spreaders and epidemics are sustained by most infections occurring in acute phase, however acute phase transmissions alone are unlikely to sustain an epidemic. Higher divergence rates are associated with longer periods of time that virus replicates within-host before transmission.
Which role for storage policies in managing grain price instability? Some insights from a thought experiment

Before 2008, we were living in a world of surpluses. In this context, public stocks were mainly viewed as a source of distortions: by supporting production prices, they were thought to generate surpluses; by being released on international markets they were assumed to depress prices. It was therefore logical to put disciplines on public stocks and it is exactly what did WTO’s Agreement on Agriculture (AoA): purchases for public stocks are included in the Aggregate Measurement of Support which is capped, often at a low level for developing countries (DCs) because they almost always had a low level of AMS spending when they signed the AoA. Because food security issues were not thought to be related to availability but only a problem of access, stocks seemed be unnecessary in the policy equation: trade and cash transfers were considered to be enough to guarantee food security in a less expensive and less distortive way than public stocks. And at that time, because of the surpluses, supporting agriculture was not in the agenda and the possible detrimental effect of price instability on agricultural investments was therefore not considered at a problem.
Modeling Dynamic 3D Caves

– A system for modeling 3D objects is presented. In this process, a new spatial data structure is introduced. At its core, the representation can be thought of as a set of slabs, each encoding a portion of the object along one axis. The goal of this work is to allow user modification of a cave environment at interactive rates, akin to existing terrain editing programs where the object is restricted to a single 2D manifold that represents a terrain surface. To achieve these rates, we use multiple fixed resolution grids that are used to provide explicit geometry for immediate mode rendering as well as simplifying the task of collision detection tests. We provide comparisons with existing cave visualization programs and techniques for dynamic models as well as examples of our technique in practice.
Capm under ambiguity

In this paper we applied the model of individual choice under ambiguity proposed by Zhang (2002) in the context of the market model of asset returns of Kwon (1985). The ambiguity is introduced via unknown volatilities of assets’ residual leading to two factor CAPM. We test this model on US stock data and find that the ambiguity factor has a significant impact on the cross section of asset expected returns. This effect seems to be economically meaningful. 1The author received financial support by the National Center of Competence in Research "Financial Valuation and Risk Management" (NCCR FINRISK), research project 2 "Conceptual Issues in Financial Risk Management".
"Tropical" Real Business Cycles? A Bayesian Exploration1

The use of frictionless small open economy models to account for business cycles in developing countries is controversial. Using Bayesian methods, we explore the role of permanent shocks to the technology trend in accounting for business cycles in these economies and we test the performance of such model against alternative models that incorporate a variety of other real impulses such as procyclical scal policies; terms of trade uctuations; and perturbations to the foreign interest rate. We compare the modelsperformance to the data from a relatively unexplored developing -and "tropical"country, Colombia. Our ndings leave us skeptics that business cycles in developing economies can be modeled with a standard neoclassical model driven solely by technology shocks as has recently been argued in other studies. Keywords: Business Cycles; Developing economies; dynamic stochastic general equilibrium models; small open economy models; Bayesian estimation. JEL classi cation: E32, F41, F47, C11. 1This paper is part of the requirements in my Ph.D. studies. I wish to thank the comments by my advisor Roberto Chang, Rodrigo Fuentes, Philipo Occhino, John Landon-Lane, Levan Mahadeva, Bruce Mizrach, Paulina Restrepo, Norman Swanson, Eugene White and other participants to the Macroeconomics Study Group at Rutgers University in January and July 2007 and the LACEA-LAMES session on Business Cycles in Bogota, Colombia on October 2007. Of course, any errors and omissions are mine alone. 2Contact: afernandez@economics.rutgers. edu.
Can Streaming SIMD Non-Temporal Instructions Beneﬁt Intra-node MPI Communication on Modern Multi-core Platforms?

Modern multi-core platforms are evolving very rapidly. Scientiﬁc applications are leveraging this growth in computing power to continually increasing the computational complexity of problems that can be solved. These applications are typically written using the Message Passing Interface (MPI), the pre-dominant parallel programming model. Currently, multi-core platforms have multiple sockets with each socket housing a multi-core processor chip. There are various levels of cache hierarchy and sharing in this platform. Cores in the same socket may have private L1 and L2 caches and share L3 cache, while cores on different sockets do not share any caches. MPI processes are placed on one process per core. These processes communicate us-ing user-level shared memory. This involves memory copies, to and from shared buffers. Thus, making performance of memory copy routines critical to overall performance. The Streaming SIMD Extensions (SSE) provide instructions that can perform streaming and non-temporal memory moves. Utilizing these instructions can improve memory bandwidth and reduce cache pollution. The impact of these instructions may depend on the levels of shared cache hierarchy between the communicating processes. In this paper, we investigate whether SSE non-temporal instructions are beneﬁcial for intra-node MPI communication. We design a framework for optimizing memory copy functions within MVAPICH2, a popular implementation of MPI on InﬁniBand. We investigate six different possible memory copy functions using different instructions and integrate them into MVAPICH2. We present performance investigation on latest multi-core architectures: AMD Barcelona, Intel Nehalem, and AMD Magny-Cours. Our investigation reveals that using non-temporal instructions for communicating processes located on different sockets can signiﬁcantly improve bandwidth at 64K Byte messages by 60% and 13% for the multi-pair bandwidth benchmark, and improve NAS benchmark and HPCC benchmark from 2% to 14% on AMD Barcelona and Intel Nehalem architectures.
ML Based Social Media Data Emotion Analyzer and Sentiment Classifier with Enriched Preprocessor

Sentiment Analysis or opinion mining is NLP's method to computationally identify and categorize user opinions expressed in textual data. Mainly it is used to determine the user's opinions, emotions, appraisals, or judgments towards a specific event, topic, product, etc. is positive, negative, or neutral. In this approach, a huge amount of digital data generated online from blogs and social media websites is gathered and analyzed to discover the insights and help make business decisions. Social media is web-based applications that are designed and developed to allow people to share digital content in real-time quickly and efficiently. Many people define social media as apps on their Smartphone or tablet, but the truth is, this communication tool started with computers. It became an essential and inseparable part of human life. Most business uses social media to market products, promote brands, and connect to current customers and foster new business. Online social media data is pervasive. It allows people to post their opinions and sentiments about products, events, and other people in the form of short text messages. For example, Twitter is an online social networking service where users post and interact with short messages, called "tweets. " Hence, currently, social media has become a prospective source for businesses to discover people's sentiments and opinions about a particular event or product. This paper focuses on the development of a Multinomial Naive Bayes Based social media data emotion analyzer and sentiment classifier. This paper also explains various enriched methods used in pre-processing techniques. This paper also focuses on various Machine Learning Techniques and steps to use the text classifier and different types of language models.
RAIL: Predictable, Low Tail Latency for NVMe Flash

Flash-based storage is replacing disk for an increasing number of data center applications, providing orders of magnitude higher throughput and lower average latency. However, applications also require predictable storage latency. Existing Flash devices fail to provide low tail read latency in the presence of write operations. We propose two novel techniques to address SSD read tail latency, including Redundant Array of Independent LUNs (RAIL) which avoids serialization of reads behind user writes as well as latency-aware hot-cold separation (HC) which improves write throughput while maintaining low tail latency. RAIL leverages the internal parallelism of modern Flash devices and allocates data and parity pages to avoid reads getting stuck behind writes. We implement RAIL in the Linux Kernel as part of the LightNVM Flash translation layer and show that it can reduce read tail latency by 7× at the 99.99th percentile, while reducing relative bandwidth by only 33%.
Pyrolysis of Liulin Coal Simulated by GPU-Based ReaxFF MD with Cheminformatics Analysis

In this study, the first GPU-enabled ReaxFF MD program with significantly improved performance, surpassing CPU implementations, was employed to explore the initial chemical mechanisms and product distributions in pyrolysis of Liulin coal, a bituminous coal from Shanxi, PRC. The largest coal model ever used in simulation via ReaxFF MD, the Liulin coal molecular model consisting of 28 351 atoms was constructed based on a combination of experiments and classical coal models. The ReaxFF MD simulations at temperatures of 1000-2600 K were performed for 250 Ps to investigate the temperature effects on the product profile and the initial chemical reactions of the Liulin coal model pyrolysis. The generation rates of C-14-C-40 compounds and gas tend to equilibrate within 150-250 ps, indicating that the simulation should allow most of the thermal decomposition reactions complete and the simulated product profiles are reasonable for understanding the chemical reactions of the Liulin coal pyrolysis. The product (gas, tar, and char) evolution tendencies with time and temperature observed in the simulations are fairly in agreement with the experimental tendency reported in the literature. In particular, the evolution trends of three representative products (naphthalene, methyl-naphthalene and dimethyl-naphthalene) with temperature are very consistent with Py-GC/MS experiments. The detailed chemical reactions of the pyrolysis simulation have been generated using VARMD (Visualization and Analysis of Reactive Molecular Dynamics), which was newly created to examine the complexity of the chemical reaction network in ReaxFF MD simulation. The generation and consumption of HO center dot and H3C. radicals with time and temperature are reasonable and consistent both with the evolution of H2O and CH4, and with the detailed chemical reactions obtained as well. The amount of six-membered ring structures was observed to decrease with time and temperature, because of their conversion into 5-membered rings or 7-9-membered rings or even-larger-membered ring structures that will further open and decompose into small fragments. This work demonstrates a new methodology for investigating coal pyrolysis mechanism by combining GPU-enabled high-performance computing with cheminformatics analysis in ReaxFF MD.