Miracle-dz/UniFAR-FTUs huggingface dataset:
Smuggling as the “key to a combined market”: British American Tobacco in Lebanon

Objectives: To understand the strategy of British American Tobacco (BAT) and other transnational tobacco companies (TTCs) to gain access to the Lebanese market, which has remained relatively closed under monopoly ownership and political instability. Methods: Analysis of internal industry documents, local language secondary sources and industry publications. Results: TTCs have relied on legal and illegal channels to supply the Lebanese market since at least the 1970s. Available documents suggest smuggling has been an important component of BAT’s market entry strategy, transported in substantial quantities via middlemen for sale in Lebanon and neighbouring countries. TTCs took advantage of weak and unstable governance, resulting in uncertainty over the Regie’s legal status, and continued to supply the contraband trade despite appeals by the government to cease undermining its revenues. Since the end of the civil war in the early 1990s, continued uncertainty about the tobacco monopoly amid political instability has encouraged TTCs to seek a legal presence in the country, while continuing to achieve substantial sales through contraband. Conclusion: Evidence of the complicity of TTCs in cigarette smuggling extends to Lebanon and the Middle East where this trade has especially benefited from weak governance and chronic political instability. The regional nature of TTC strategy supports strong international cooperation under the Framework Convention on Tobacco Control to tackle the problem.
Fine-Grain Multitolerant Barrier SynchronizationSandeep

We design a multitolerant program for synchronizing the phases of concurrent processes. The tolerances of the program enable processes to (i) compute all phases correctly in the presence of faults that corrupt process state in a detectable manner, and (ii) compute only a minimum possible number of phases incorrectly before resuming correct computation in the presence of faults that corrupt process state in an undetectable manner. The program is ne-grain in the sense that each process action either updates the state of that process or involves communication with one of two neighboring processes. 1 Motivation Barrier synchronization in its general form requires that a set of processes execute a cyclic sequence of phases so that a phase is executed by each process only after all processes have completed the previous phase. This form of synchronization generalizes a variety of others, such as clock unison, phase synchronization and atomic commitment, and appears frequently in parallel, distributed, and scientiic computation applications. Often the design of barrier synchronization has to accommodate the occurrence of faults. Commonly considered examples of faults include incorrect initializations; corruption, loss, reordering, and duplication of messages; processor restarts; and performance and timing violations. While some of these fault-classes can be masked (i.e., even in their presence each phase is executed correctly), others cannot. To accommodate multiple fault-classes, not all of which can be masked, it is convenient to view the eeect of faults in each fault-class as a \corruption" of the state of some process, i.e., the state of that process prior to the fault is lost and is replaced with some other value. The state corruption view suggests that one way to accommodate all of the fault-classes is to design the set of processes to be stabilizing 2], i.e., to recover from an arbitrarily corrupted state to one from where the speciication of barrier synchronization is (re)satissed. Unfortunately, a stabilizing design allows incorrect execution of (a nite number of) phases in the presence of each fault-class before the recovery is complete, and it is thus not ideal for the fault-classes that can be masked. We therefore present in this paper barrier synchronization designs that ooer multiple levels of tolerance corresponding to multiple fault-classes, a notion which we refer to as multitolerance 3].
On Properties of the Group Membership Problem

We study fault tolerance properties of the group membership problem (GMP) in asynchronous systems. This problem requires each process to determine the processes with which it can communicate. We compare the properties of the GMP and consensus. We define failure sensitivity as the necessary property of a failure detector to enable a solution to the GMP. We demonstrate that the known consensus failure detectors — , , are fault insensitive and thus insucient to solve the GMP. In contrast we define a new reach- ability failure detector R and show it to be the weakest failure detector to solve the GMP. Furthermore, we demonstrate that R implements the consensus failure detectors. This shows that the GMP is a strictly stronger problem than consensus with respect to the failure detectors that it requires. We present our findings using the primary partition variant of the GMP. We extend them to the partitionable GMP as well as eventual GMP: a weaker variant of the GMP where a process is allowed to make finitely many mistakes in its group membership output.
The management of tuberculous meningitis.

Tuberculous meningitis is a life threatening form of tuberculosis and the most common form of central nervous system tuberculosis. The condition is a serious cause of morbidity and mortality in developing nations, but is rare in technically advanced countries. This probably reflects the global epidemiology of tuberculosis, for out of an estimated 8 million new cases annually 7-6 million (95%) occur in the third world. ' In England and Wales in 1983 the rate of tuberculous meningitis for white patients was 0 1/ 100 000, yet was 40 times higher (a rate of 4/100 000) in patients of Indian subcontinent ethnic origin.2 The total number of cases notified in England and Wales has remained below 100 since 1982; there were 81 notifications in 1990 (JM Watson and K Fern, personal communication), predominantly in adults. Tuberculosis has emerged as an important clinical problem in patients with AIDS and its related complex,34 and extrapulmonary disease is particularly common. Central nervous system tuberculosis is an important manifestation. In one review of420 patients in the United States with AIDS or the AIDS related complex 52 developed tuberculosis, 10 having the central nervous system affected, a rate ofmore than 2000 per 100 000.5 In a population ofHIV infected patients reported from Europe Mycobacterium tuberculosis was the most common cause of meningitis, with
Interconnect scaling implications for CAD

Interconnect scaling to deep submicron processes presents many challenges to today's CAD flows. A recent analysis by D. Sylvester and K. Keutzer (1998) examined the behavior of average length wires under scaling, and controversially concluded that current CAD tools are adequate for future module-level designs. We show that average length wire scaling is sensitive to the technology assumptions, although the change in their behavior is small under all reasonable scaling assumptions. However, examining only average length wires is optimistic, since long wires are the ones that primarily cause CAD tool exceptions. In a module of fixed complexity, under both optimistic and pessimistic scaling assumptions, the number of long wires will increase slowly with scaling. More importantly, as the overall die capacity grows exponentially, the number of modules and thus the total number of wires in a design will also increase exponentially. Thus, if the design team size and per-designer workload is to remain relatively constant, future CAD tools will need to handle long wires much better than current tools to reduce the percentage of wires that require designer intervention.
Real-Time Analytics as the Killer Application for Processing-In-Memory

While Processing-In-Memory (PIM) has been widely researched for the last two decades, it was never truly adopted by the industry and remains mostly within the academic research realm. This is mainly because (1) inmemory compute engines were too slow, and (2) a realworld application that could really benefit from PIM was never identified. In recent years, the first argument became untenable, but the community still struggles to identify a posterchild application that refutes the second argument. In this position paper, we argue that real-time analytics is the killer application the community has been searching for. We show that several inherent characteristics of real-time analytics fit perfectly within the PIM paradigm, and identify Redis – a popular in-memory key-value framework – as a convenient platform to showcase PIM in this domain. Finally, we call for a targeted hardware-software co-design effort towards a PIM acceleration architecture for real-time analytics.
Training Students in Thinking Skills for Solving Social Problems: A Strategy for Helping Students Cope Constructively with School Stressors.

The project described focuses on researching approaches to ease the student's transition from elementary to middle school. The method used equips teachers to work with a set of curriculum materials built around an eight-step strategy for helping students think through everyday decisions and stressful situations. The materials target situations that children face while interacting with their peers and adjusting to school. Discussion will focus on three basic areas: (1) action-research methodology designed to develop sound curriculum materials for the fourth and fifth grades; (2) a review of empirical findings concerning the impact of stressors related to the middle school transition on students and the effects of this traiing on their ability to cope with these stressors; and (3) a description of a new ongoing experimental extention of the elementary school program through the discipline system of the middle school, the focus of this effort being to help students think through situations that are resulting in frequent school discipline episodes. Coping With School Stressors 3 Traintmg OUndinti Thinking Skills for Salving Social Problems: A Strategy for Helping Students Cope Constructively with School Stressors Today, I would like to describe an actionresearc) program that is currently being conducted in Middlesex, New Jersey, The program is conducted by the Improving Social Awareness Social Problem Solving (ISA) Project. The project began almost six years ago when school administrators from Middlesex Borough, New Jersey, and psychologists from the Community Mental Health Center of the University of Medicine and Dentistry of Rutgers Medical School and the Department of Psychology at Rutgers University joined to develop a school -based program to help reduce the stress and adjustment problems students characteristically encounter during the transition from elementary school to middle school. This multidisciplinary team came together after identifying a striking increase in persistent academic and behavioral difficulities that emerged among stndents in the sixth grade. The admission records of the CMHC also reflected this increase, as seventh grade was the modal time for referrals of children and youth for clinical services. This finding fits well with what is currently known about this period in the students' development. It is well documented that yoang adolescents' transition from elementary school to middle school cln be considered a time of "normal life crisis" (e.g., Johnson, 1980). The "crisis" is created by the wide variety of complex personal and environmental changes that occur during this time. The extent to which this inherently unstable situation demands Vtth School Obtoscori
Probabilistic Languages: A Review and Some Open Questions

Context-free languages are commonly used to describe the structure of programming ~ languages. However many interesting problems involve not just a language's structure but also the actual usage of the language. Adding a notion of probability to ordinary grammars gives rise to probabilistic context-free grammars. Interesting in their own right because of some pretty theorems, probabilistic context-free languages can be apldied to the analysis of progrAmmiug languages, automatic parsers, and error correctors. A complete outline of the theory is presented with examples. Some open questions are posed.
Sentiment analysis of social media content using N-Gram graphs

Sentiment Analysis over Social Media facilitates the extraction of useful conclusions about the average public opinion on a variety of topics, but poses serious technical challenges. This is because of the sparse, noisy, multilingual content that is posted on-line by Social Media users. In this paper, we introduce a novel method for capturing textual patterns that inherently supports this challenging type of content. In essence, it creates a graph whose nodes correspond to the character n-grams of a document, while its weighted edges denote the average distance between them. Multiple documents of the same polarity can be aggregated into a polarity class graph, which can be compared with individual documents in order to identify the category of their sentiment. To evaluate our approach, we conducted large scale experiments on a real-world data set stemming from a snapshot of Twitter activity. The outcomes of our evaluation indicate significant improvements over other the methods typically used in this context, not only with respect to effectiveness, but also to efficiency.
Intelligent model for measuring learners' satisfaction towards new contents

Learner satisfaction is an important factor for a successful e-Learning. It allows learners to continue learning and provide their conveniences to facilitate the knowledge. That is why, the main purpose of this study is to enhance learning services in online environments, in order to keep learners' motivation and track their cognition' evolution. Namely, identify their satisfaction toward new inserted contents. In this paper, we present a learning model that detects satisfaction' degree of learners at two levels; during and after completing the course. During learning by using satisfaction agent (SA) that collects data and calculates satisfaction level towards each content' unit. This process allows to integrate improvement if we have a low levels of satisfaction. And after completing course in order to evaluate the evolution of learners' knowledge, and test the effectiveness of the methods and tools used in.
A review on abstractive summarization methods

Text summarization is the process of extracting sal ient information from the source text and to presen t that information to the user in the form of summary. It is very difficult for human beings to manually summarize large documents of text . Automatic abstractive summarization provides the r equired solution but it is a challenging task because it requires de eper analysis of text. In this paper, a survey on a bstractive text summarization methods has been presented. Abstractive summarization methods are classified into t wo categories i.e. structured based approach and seman tic based approach. The main idea behind these methods has been discussed. Besides the main idea, the strengths and weaknesses of each method have al so been highlighted. Some open research issues in abst ractive summarization have been identified and will address for future research. Finally, it is conclud ed from the literature studies that most of the a bstractive summarization methods produces highly coherent, cohesive, information rich and less redundant summary.
The Lived Experiences of Children in Care with the Going Home Process in a Chinese Context: An Exploratory Study

Results: The findings indicated that the children’s lived experience with the going home process was a path of stabilization, which meant that their sense of control over their lives was increasing from the first to the second to the third wave of data collection. The children’s stories were progressive, with their narrations characterized by advancement. Three themes, namely “uncertainty”, “restoration” and “challenge”, emerged at three points in time, with distinctive concerns and feelings occurring in each stage.
Ordering Heuristics for Arc Consistency Algorithms

Arc consistency algorithms are used in solving constraint satisfaction problems and are important in constraint logic programming languages. Search order heuristics for arc consistency algorithms signiicantly enhance the ef-ciency of their implementation. In this paper we propose and evaluate several ordering heuristics. Care is taken with experimental design , involving random problems, and statistical evaluation of results. A heuristic is identi-ed which yields about 50% savings on average, using the standard measure of consistency pair checks, with reasonable heuristic computation cost.
Integrated modeling of the major events in the MHC class I antigen processing pathway

Rational design of epitope‐driven vaccines is a key goal of immunoinformatics. Typically, candidate selection relies on the prediction of MHC–peptide binding only, as this is known to be the most selective step in the MHC class I antigen processing pathway. However, proteasomal cleavage and transport by the transporter associated with antigen processing (TAP) are essential steps in antigen processing as well. While prediction methods exist for the individual steps, no method has yet offered an integrated prediction of all three major processing events. Here we present WAPP, a method combining prediction of proteasomal cleavage, TAP transport, and MHC binding into a single prediction system. The proteasomal cleavage site prediction employs a new matrix‐based method that is based on experimentally verified proteasomal cleavage sites. Support vector regression is used for predicting peptides transported by TAP. MHC binding is the last step in the antigen processing pathway and was predicted using a support vector machine method, SVMHC. The individual methods are combined in a filtering approach mimicking the natural processing pathway. WAPP thus predicts peptides that are cleaved by the proteasome at the C terminus, transported by TAP, and show significant affinity to MHC class I molecules. This results in a decrease in false positive rates compared to MHC binding prediction alone. Compared to prediction of MHC binding only, we report an increased overall accuracy and a lower rate of false positive predictions for the HLA‐A*0201, HLA‐B*2705, HLA‐A*01, and HLA‐A*03 alleles using WAPP. The method is available online through our prediction server at http://www‐bs.informatik. uni‐tuebingen.de/WAPP.
Learning the Ontological Positions of Natural Language Objects

This thesis endeavors to solve a text classification (TC) problem of a real-world system, New Brunswick Opportunities Network (NBON), an online tendering system that helps the vendors and the purchasing agents to provide and obtain information about business opportunities. The solution mainly involves techniques in the areas of machine learning and natural language processing (NLP). We use a Näıve Bayes classifier, a simple and effective machine learning approach for TC tasks, to automatically classify the tenders of the NBON system. We implement three smoothing algorithms for the Näıve Bayes classifier, namely, no-match, Laplace correction, Lidstone’s law of succession, and we show that the difference between the accuracies obtained for the three algorithms is negligible. We show that the effectiveness of the Näıve Bayes classifier is better than that of three other TC techniques that are equally simple, namely, Strong Predictors (a modification of Term Frequency), TF-IDF (Term Frequency Inverse Document Frequency), and WIDF (Weighted Inverse Document Frequency). NLP tools such as stop lists and stemmers are adopted for the text operations on the historic NBON data that is used to train the classifiers. We experiment with variations of such tools and show that NLP techniques do not have much impact on the effectiveness of a classifier.
Application of a methodology to characterize rootkits retrieved from honeynets

Techniques and methods currently exist to detect if a certain type of rootkit has exploited a computer systems. However, these current techniques and methods can only indicate that a system has been exploited by a rootkit. We are currently developing a methodology to indicate if a rootkit is previously known or if it is a modified or entirely new rootkit. We present in this paper an application of our methodology against a previously unseen rootkit that was collected from the Georgia Tech Honeynet. We conduct our analysis process against this rootkit and are able to identify specific characteristics for subsequent detections of this rootkit. This ability will provide system administrators, researchers, and security personnel with the information necessary in order to take the best possible recovery actions. This may also help to detect and fingerprint additional instances and prevent further security instances involving rootkits.
Behavioral Service Graphs: A Big Data Approach for Prompt Investigation of Internet-Wide Infections

The task of generating network-based evidence to support network forensic investigation is becoming increasingly prominent. Undoubtedly, such evidence is significantly imperative as it not only can be used to diagnose and respond to various network-related issues (i.e., performance bottlenecks, routing issues, etc.) but more importantly, can be leveraged to infer and further investigate network security intrusions and infections. In this context, this paper proposes a proactive approach that aims at generating accurate and actionable network-based evidence related to groups of compromised network machines. The approach is envisioned to guide investigators to promptly pinpoint such malicious groups for possible immediate mitigation as well as empowering network and digital forensic specialists to further examine those machines using auxiliary collected data or extracted digital artifacts. On one hand, the promptness of the approach is successfully achieved by monitoring and correlating perceived probing activities, which are typically the very first signs of an infection or misdemeanors. On the other hand, the generated evidence is accurate as it is based on an anomaly inference that fuses big data behavioral analytics in conjunction with formal graph theoretical concepts. We evaluate the proposed approach as a global capability in a security operations center. The empirical evaluations, which employ 80 GB of real darknet traffic, indeed demonstrates the accuracy, effectiveness and simplicity of the generated network-based evidence.
Consistency conditions for multi-object distributed operations

The traditional distributed shared memory (DSM) model provides atomicity at levels of read and write on single objects. Therefore, multi-object operations such as double compare and swap, and atomic m-register assignment cannot be efficiently expressed in this model. We extend the traditional DSM model to allow operations to span multiple objects. We show that memory consistency conditions such as sequential consistency and linearizability can be extended to this general model. We also provide algorithms to implement these consistency conditions in a distributed system.