Miracle-dz/UniFAR-FTUs huggingface dataset:
Check Yourself Before You Wreck Yourself- A study of how to assess security vulnerabilities of web servers through configuration analysis

The web server is an essential component of many systems today. It has the possibility to give access to files with sensitive information and it is the backbone that enable a vast amount of applications. This makes it critical to ensure that files are only accessed and altered by intended users and that web servers are always up and running when expected. One important aspect of doing this is to ensure that the configuration of the web server does not cause security vulnerabilities. However, this is not a straightforward task as there are normally hundreds of configurations parameters and different vulnerabilities to take into account. This thesis explores security vulnerabilities related to the configuration of web servers, more specifically the web server software Apache and Nginx, and how to verify absence of security misconfiguration. The exploration consists of three major segments. First, information sources regarding security misconfiguration of Apache and Nginx are analyzed and compared. The conclusion is that there are beneficial sources but none is covering every configuration needed to avoid security misconfiguration. They could also benefit from using scoring systems to allow users to understand which security misconfigurations are the most critical. Next, tools available today that can help users verify absence of faulty configuration are examined and compared. The conclusion is that there is no tool with ready to use content fully covering every configuration needed to avoid security misconfiguration. Besides, they are, to a varied extent, not satisfactory regarding how they present rationale about and possible consequences from needed configuration and an easy to survey output. This result lead to the exploration if it is possible to use available tools to create a beneficial solution which can verify the presence of all needed configuration and at the same time educate users about why this configuration is needed and neatly present the result of this verification. This resulted in the development of new ready to use content for one of the examined tools called Chef Inspec. The purpose of the new content was to see if it was possible to cover all types of needed configuration and how Chef Inspec performed with this new content. The conclusion is that it is possible to create content covering all needed configuration, but problems arise if a user is running multiple Apache instances on the same machine. The solution is fairly satisfying but there is room for improvement of the output of Chef Inspec to facilitate the users understanding of the rational behind the suggested configuration and the survey of the result from the verification. (Less)
Perfil epidemiológico e análise microbiológica da infecção de sítio cirúrgico em pacientes humanos e animais de companhia

Surgical site infection (SSI) has been indicated as the third cause of nosocomial infection. The present study aimed to determine the epidemiological profile of SSI and its association with the risk factors. It is a transversal study done at the Sao Joao Batista Hospital of Vicosa-MG and at the Surgery Service of the Small Animals Veterinary Hospital of the Universidade Federal de Vicosa-MG, from September 2012 to February 2013. Global SSI rates were 0.7% at the human and 3.46% at the veterinary hospitals. At the veterinary hospital, SSI rates were not related to contamination potential, with clean procedures presenting the greater rates. As for the type of surgery, orthopedic ones are the most common in both hospital and also the ones presenting the greater SSI rates. Surgeries during more than 120 minutes were 15.25% of the total of procedures at the human hospital and are even less common in the veterinary, with 1.26%. Rate of SSI does not seem to be related to surgery duration in this classification. Bacteria isolated from surgical wounds were multi-resistant and the obtained data indicated that no criteria of antibiotic prophylaxis existed, mainly for clean surgeries. This scenario shows that the action of a commission to control nosocomial infection are extremely relevant in order to guarantee reliable data so that the quality of service may be evaluated and thus, promoting a decrease the risk of in post-operative complications.
Leverage side information for top‐N recommendation with latent Gaussian process

Currently, Recommender Systems (RS) have been ubiquitously applied to various online applications and obtained tremendous success due to their capability to overcome information overload; however, the available Side Information (SI), such as demographics and attributions of items, is always neglected. Actually, SI could reflect user's interests and preference, and even influence user's decision over items; therefore, it would be greatly helpful to leverage side information to improve the performance of RS, which is already partly certificated in previous research studies. Motivated by this, in this article, a novel multi‐task learning‐based recommendation system referred to as LGP‐RS is proposed, which takes utility functions mapping from the non‐linear side information to low‐rank feature space with Gaussian process (GP) priors, for users and items, respectively. In addition, Laplace approximation method is employed to approximate the posterior distribution for the utility functions, and gradient‐based method is employed to learn the hyperparameters for GP. This flexible approach is able to capture the non‐linear high‐order interactions within side information, reduce the prediction uncertainty, and provide personalized top‐N recommendation. Experimental analysis over three real‐world datasets demonstrates that LGP‐RS could deal with both explicit and implicit side information simultaneously, and significantly outperforms state‐of‐the‐art approaches.
A Commentary on the Use of Simulated Settings in Business Communication Research

Although there are different views toward the use of simulations for research purposes, interactional data produced in a simulated setting (simulated data) is still common in the field of business communication research. However, little research has been conducted to investigate the extent to which simulated interactional data can be used as an alternative to authentic business communication data. In this commentary, we review the controversy over the use of simulated data and discuss some contextual factors that may influence the authenticity of simulated data. We also provide suggestions for improving the compatibility of simulated data when researching business communication.
Barriers to Software Outsourcing Partnership Formation: An Exploratory Analysis

Software Outsourcing Partnership (SOP) is a type of cooperative client-vendor relationship. SOP is an emerging strategy and is different from ordinary software development outsourcing (SDO). Usually, a fruitful outsourcing association might be converted to an outsourcing partnership. Conversely, SOP is not a risk-free business, numerous barriers associated with SOP. The overarching target of this exploratory paper is to find and analyze a list of barriers that are considered obstacles for vendors in the conversion of their surviving contractual outsourcing relationship to a partnership. Firstly, twenty-six barriers to SOP formation were identified through systematic literature review (SLR) from a sample of 106 papers and then an empirical survey was conducted with fifty experts to analyze the significance and applicability of these barriers in the SOP context. The identified barriers were further analyzed based on five variables such as decades, company size, continents, location of analysis, and perspective of the study. Ten barriers were considered as critical barriers (CBs) via SLR. Industrial experts indicate they extremely agree with five CBs. Eight CBs were equally reported on all continents. We found ten CBs common in all types of organizations. Further, twelve CBs were shared in both decades while ten CBs were found common in both academia and industry. Furthermore, four CBs were specific to clients; five were specific to vendors while ten were common to both. The association of various barriers with SOP formation is found statistically significant for twenty-five barriers with effect size (0.41 < Ø < 0.90, p < 0.05). Stakeholders in SOP should address all the listed barriers especially the critical ones to attain a partner position.
Age Related Changes in Mechanical and Thermal Injuries-A Post Mortem Study

Dating of an injury in both living and dead is an important medico-legal issue in the field of crime investigation to fix the liability. A doctor is required to date injuries specifically and individually while preparing an injury report. In this paper we study the sequential changes in mechanical and thermal injuries and to compare the findings with available standard data. All the medico-legal deaths due to injuries, brought for post-mortem examination, over a period of four months were studied. On comparing with standard data, 71% (n=32) of the abrasions were dark red instead of bright red on first day. 92% (n=13) bruises appears bluish green instead of red colour on first day and on the 5th day, the colour appears black instead of greenish colour. In cases of incised and lacerated wound margins were difficult to separate on the 2nd day, instead of separating easily. Observations of this study did not match with the sequential colour changes mentioned in the available literature. Therefore, other parameters need to be explored for dating an injury.
Which aspects of discourse relations are hard to learn? Primitive decomposition for discourse relation classification

Discourse relation classification has proven to be a hard task, with rather low performance on several corpora that notably differ on the relation set they use. We propose to decompose the task into smaller, mostly binary tasks corresponding to various primitive concepts encoded into the discourse relation definitions. More precisely, we translate the discourse relations into a set of values for attributes based on distinctions used in the mappings between discourse frameworks proposed by Sanders et al. (2018). This arguably allows for a more robust representation of discourse relations, and enables us to address usually ignored aspects of discourse relation prediction, namely multiple labels and underspecified annotations. We show experimentally which of the conceptual primitives are harder to learn from the Penn Discourse Treebank English corpus, and propose a correspondence to predict the original labels, with preliminary empirical comparisons with a direct model.
A De-Compositional Approach to Regular Expression Matching for Network Security

Regular Expression (RegEx) matching is the industry standard for Deep Packet Inspection (DPI) because RegExes are significantly more expressive than strings. To achieve high matching speed, we need to convert the RegExes to Deterministic Finite State Automata (DFA). However, DFA has the state explosion problem, that is, the number of DFA states and transitions can be exponential with the number of RegExes. Much work has addressed the DFA state explosion problem; however, none has met all the requirements of fast and automated construction, small memory image, and high matching speed. In this paper, we propose a decompositional approach, with fast and automated construction, small memory image, and high matching speed, to DFA state explosion. The first key idea is to decompose a complex RegEx that cause exponential state increases into a set of simpler RegExes that do not cause exponential state increases, where any character string that matches the complex RegEx also matches all the RegExes in the set of simpler RegExes; that is, the set of strings that match the complex RegEx is a subset of strings that match the set of simpler RegExes. The second key idea is to use a stateful post-processing engine to filter the matches that are actually the matches of the complex RegEx. Given an input string for matching, instead of using the large DFA constructed from the original complex RegEx to perform the matching, we first use the small DFA constructed from the set of simpler RegExes to perform the matching, and then, if the small DFA reports a match, we use the post-processing engine to determine whether it is a true match to the original complex RegEx. Because the pre-processing is simple, automaton construction can be automated and fast, and because most on-line processing is done by a DFA, its matching speed is close to that of a DFA alone. Our experimental results show that our decompositional approach achieves orders of magnitude faster DFA construction (in terms of seconds instead of minutes), 30 times smaller memory image, and 43% faster matching speeds, than state-of-the-art software based RegEx matching algorithms.
Measuring and improving interpretability of word embeddings using lexical resources

MEASURING AND IMPROVING INTERPRETABILITY OF WORD EMBEDDINGS USING LEXICAL RESOURCES Lütfi Kerem Şenel M.S. in Electrical and Electronics Engineering Advisor: Tolga Çukur Co-Advisor: Aykut Koç August 2019 As an ubiquitous method in natural language processing, word embeddings are extensively employed to map semantic properties of words into a dense vector representations. They have become increasingly popular due to their state-of-the-art performances in many natural language processing (NLP) tasks. Word embeddings are substantially successful in capturing semantic relations among words, so a meaningful semantic structure must be present in the respective vector spaces. However, in many cases, this semantic structure is broadly and heterogeneously distributed across the embedding dimensions. In other words, vectors corresponding to the words are only meaningful relative to each other. Neither the vector nor its dimensions have any absolute meaning, making interpretation of dimensions a big challenge. We propose a statistical method to uncover the underlying latent semantic structure in the dense word embeddings. To perform our analysis, we introduce a new dataset (SEMCAT) that contains more than 6,500 words semantically grouped under 110 categories. We further propose a method to quantify the interpretability of the word embeddings that is a practical alternative to the classical word intrusion test that requires human intervention. Moreover, in order to improve the interpretability of word embeddings while leaving the original semantic learning mechanism mostly unaffected, we introduce an additive modification to the objective function of the embedding learning algorithm, GloVe, that promotes the vectors of words that are semantically related to a predefined concept to take larger values along a specified dimension. We use Roget’s Thesaurus to extract concept groups and align the words in these groups with embedding dimensions using modified objective function. By performing detailed evaluations, we show that proposed method improves interpretability drastically while preserving the semantic structure. We also demonstrate that imparting method
Simplified GNSS Fusion-based Train Positioning System and its Diagnosis

In this paper, two simple GNSS-based positioning methods are proposed and their diagnostic functions for GNSS failure are tested. Firstly, the odometer-based method, which are proposed in our previous research [1], is concretized to be implemented for general cases. This method detects faults in the GNSS solution due to satellite failure or local effects using both odometry and track geometry of the onboard system. It enables to monitor all three-dimensional solution error so that higher sensitivity for the fault detection can be achieved. Secondly, single-axis accelerometer-based approach is newly proposed. Positioning architecture of this method is designed in traveling distance domain with the configuration of single-axis accelerometer installed along the forward direction. The diagnosis of GNSS signal can be done easily in one-dimensional space. Therefore, the latter method is expected to give greater sensitivity to detect GNSS failures while maintaining a relatively simple architecture. Both methods are tested in simulation, and their abilities for detecting fault in GNSS signal are investigated
Pursuing Extreme Power Efficiency With PPCC Guided NoC DVFS

In sharp contrast to conventional performance indicative based Network-on-Chip (NoC) DVFS, where the direct relation between application performance and NoC power consumption is missing, we exploit the concept of Performance-Power Characteristic Curve (PPCC) newly proposed in the literature to approach maximum NoC power efficiency. PPCC, which defines the direct relation between application performance and NoC power consumption, consists of three distinct regions: an inertial region due to power under-provisioning, a linear region for proportional performance gain, and a saturation region due to power over-provisioning. With PPCC as a guidance, we propose <inline-formula><tex-math notation="LaTeX">$\Delta$</tex-math><alternatives><mml:math><mml:mi>Δ</mml:mi></mml:math><inline-graphic xlink:href="yao-ieq1-2949807.gif"/></alternatives></inline-formula>-DVFS, which employs a “profile-then-select” strategy to step-by-step approach maximum NoC power efficiency. <inline-formula><tex-math notation="LaTeX">$\Delta$</tex-math><alternatives><mml:math><mml:mi>Δ</mml:mi></mml:math><inline-graphic xlink:href="yao-ieq2-2949807.gif"/></alternatives></inline-formula>-DVFS is built on two observations. First, in multi-threaded applications, maximum NoC power efficiency is achieved at the boundary between the linear region and the saturation region on the PPCC. Second, PPCC stabilizes when threads repeat workloads of the same loop. This is intuitively meaningful because loop repetition stresses NoC with similar workload. Based on the observations, <inline-formula><tex-math notation="LaTeX">$\Delta$</tex-math><alternatives><mml:math><mml:mi>Δ</mml:mi></mml:math><inline-graphic xlink:href="yao-ieq3-2949807.gif"/></alternatives></inline-formula>-DVFS uses the first several loop iterations for PPCC profiling. After the profiling is done, <inline-formula><tex-math notation="LaTeX">$\Delta$</tex-math><alternatives><mml:math><mml:mi>Δ</mml:mi></mml:math><inline-graphic xlink:href="yao-ieq4-2949807.gif"/></alternatives></inline-formula>-DVFS selects and applies the optimal V/F that achieves maximum NoC power efficiency to the remaining loop iterations. To accurately and timely follow PPCC when threads proceed to different loops, <inline-formula><tex-math notation="LaTeX">$\Delta$</tex-math><alternatives><mml:math><mml:mi>Δ</mml:mi></mml:math><inline-graphic xlink:href="yao-ieq5-2949807.gif"/></alternatives></inline-formula>-DVFS utilizes an H-tree loop monitor to detect loop change among distributive threads.
I Ought to Put Down That Phone but I Phub Nevertheless: Examining the Predictors of Phubbing Behavior

Smartphones are ubiquitous and frequently used in copresent interactions. This behavior is often seen as inappropriate and thus has been termed phubbing, compromising the words “phone” and “snubbing. ” Although being a worldwide phenomenon, little is known about what predicts phubbing behavior in the first place. Drawing on injunctive norms (i.e., what ought to be done), the study’s aim was to shed light on the relationship between mobile phone norms (MPN) and phubbing behavior. Furthermore, the role of being permanently online and permanently connected (POPC) and fear of missing out (FOMO), reflecting approach and avoidance orientations, respectively, as additional predictors and moderators was investigated. As expected, the findings of an online survey (N = 278) supported the assumption that MPN were negatively related to phubbing behavior. Moreover, results showed that both FOMO and POPC were significantly positively connected to phubbing behavior but did not play significant moderating roles concerning the norm–phubbing relationship.
Fork Path: Batching ORAM Requests to Remove Redundant Memory Accesses

Outsourcing data to a third-party cloud provider has become quite common with the increasing use of cloud computing. This brings convenience, as well as the concern for data security and privacy. It is believed that data encryption alone is often not enough to protect users’ privacy from the cloud provider. According to previous work, the sequence of storage locations accessed by the client can leak up to 90% of the sensitive information, even with data encrypted. In this context, Oblivious RAM (ORAM) is proposed. ORAM algorithms allow the client to hide its access pattern from the service provider while introducing a lot of extra operations. Among all the prototypes, Path ORAM is one of the most promising designs. However, there are still redundant memory accesses that can be removed without harming the security of traditional ORAM as we observed. We came up with three optimization techniques, including path merging, ORAM request scheduling, and merging aware caching. We also propose a prefetching technique to further decreasing the access overhead. Moreover, we also illustrate the compatibility of Fork Path and some state-of-the-art Path ORAM optimizations. Compared to traditional Path ORAM approaches, our Fork Path ORAM can reduce overall performance overhead and power consumption of memory system by 65% and 44%, while the design overhead is trivial.
EFFECT OF GENTAMICIN ON SPECIFIC IMMUNE RESPONSE IN RABBITS

Objective: To study the effect of gentamicin on humoral and cell mediated immune system in rabbits. Methods: Rabbits were treated with gentamicin at 4 mg/kg and 8mg/kg body weight doses for 5 days along with single intramuscular injection of 0.5 ml of Brucella plain killed antigen and humoral and cellular immune responses were measured. Results: There was no significant variation in the humoral and cellular immune response between the antigen control and the groups given gentamicin along with antigen. Conclusion: Gentamicin at the two dose levels used for 5 days, in rabbits, did not affect the specific immune response.
The impact of computer-mediated contexts on interaction pattern of ESL learners in collaborative writing

ABSTRACT This case study examined dynamic interactions of two small teams of English as Second Language students when they performed two collaborative argument writing tasks in asynchronous and synchronous web-based contexts. The two teams exhibited different interaction patterns when switching between contexts. These patterns were identified in terms of ‘equality’ and ‘mutuality’ by analysing language functions and writing change functions during the process of collaborative writing. The dynamic feature of peer interactions in different contexts was explained. Participants’ interviews and reflection papers, along with online discourse, revealed that contexts affect interactions through a set of different factors. The findings may help explain the variations of collaboration in online writing contexts, and provide insights into how the contexts should be considered in the design of web-based collaborative writing activities in L2 classes.