Miracle-dz/UniFAR-FTUs huggingface dataset:
The Top-Ten Wikipedias - A Quantitative Analysis Using Wikixray

In a few years, Wikipedia has become one of the information systems with more public (both producers and consumers) of the Internet. Its system and information architecture is relatively simple, but has proven to be capable of supporting the largest and more diverse community of collaborative authorship worldwide. In this paper, we analyze in detail this community, and the contents it is producing. Using a quantitative methodology based on the analysis of the public Wikipedia databases, we describe the main characteristics of the 10 largest language editions, and the authors that work in them. The methodology (which is almost completely automated) is generic enough to be used on the rest of the editions, providing a convenient framework to develop a complete quantitative analysis of the Wikipedia. Among other parameters, we study the evolution of the number of contributions and articles, their size, and the differences in contributions by different authors, inferring some relationships between contribution patterns and content. These relationships reflect (and in part, explain) the evolution of the different language editions so far, as well as their future trends.
Semantic Identification Attacks on Web Browsing

We introduce a Semantic Identification Attack, in which an adversary uses semantic signals about the pages visited in one browsing session to identify other browsing sessions launched by the same user. This attack allows an adver- sary to determine if two browsing sessions originate from the same user regardless of any measures taken by the user to disguise their browser or network. We use the MSNBC Anonymous Browsing data set, which contains a large set of user visits (labeled by category) to implement such an attack and show that even very coarse semantic information is enough to identify users. We discuss potential counter- measures users can take to defend against this attack.
Drugs and Disorders : From specialized resources to Web data

In this article, we focus on the may_treat predicate linking drugs and disorders. Such predicate is expressed in RDF format in the VHA National Drug File Reference Terminology (NDF-RT), a specialized medical resource. The DailyMed dataset also contains this predicate, but only in textual form: for each drug there is an indication field that links the drug’s URI to a literal that is a long description (more than 100 words on the average). We show that natural language processing (NLP) techniques can be used to further distil the indication field to extract may_treat predicates. We then move to Web exploration and show how we can apply similar NLP techniques to find may_treat predicates. The diversity in natural language expressions and the embedding of good information among noisy and often redundant data make it a challenge to exploit the Web. Still, we show that it can be used for finding NDF-RT may_treat predicates with comparable success to using DailyMed.
Routine administration of Health Related Quality of Life (HRQoL) and needs assessment instruments to improve psychological outcome – a systematic review

Background. Routine administration of Health Related Quality of Life (HRQoL) and needs assessment instruments has been advocated as part of clinical care to aid the recognition of psychosocial problems, to inform clinical decision making, to monitor therapeutic response and to facilitate patient–doctor communication. However, their adoption is not without cost and the benefit of their use is unclear. Method. A systematic review was conducted. We sought experimental studies that examined the addition of routinely administered measures of HRQoL to care in both psychiatric and non-psychiatric settings. We searched the following databases: MEDLINE, EMBASE, CINAHL, PsycLIT and Cochrane Controlled Trials Register (to 2000). Data were extracted independently and a narrative synthesis of results was presented. Results. Nine randomized and quasi-randomized studies conducted in non-psychiatric settings were found. All the instruments used included an assessment of mental well-being, with specific questions relating to depression and anxiety. The routine feedback of these instruments had little impact on the recognition of mental disorders or on longer term psychosocial functioning. While clinicians welcomed the information these instruments imparted, their results were rarely incorporated into routine clinical decision making. No studies were found that examined the value of routine assessment and feedback of HRQoL or patient needs in specialist psychiatric care settings. Conclusions. Routine HRQoL measurement is a costly exercise and there is no robust evidence to suggest that it is of benefit in improving psychosocial outcomes of patients managed in non-psychiatric settings. Major policy initiatives to increase the routine collection and use of outcome measures in psychiatric settings are unevaluated.
FAST LEARNING IN MULTILAYERED NEURAL NETWORKS BY MEANS OF HYBRID EVOLUTIONARY AND GRADIENT ALGORITHMS

This paper describes two algorithms based on cooperative evolution of internal hidden network representations and a combination of global evolutionary and local search procedures. The obtained experimental results are better in comparison with prototype methods. It is demonstrated, that the applications of pure gradient or pure genetic algorithms to the network training problem is much worse than hybrid procedures, which reasonably combine the advantages of global as well as local search.
Subjective assessment of speech-system interface usability

Methods for the evaluation of the efficiency and effectiveness of speech input / output systems are well established. However, user subjective reactions to speech interfaces may well be a more important predictor of real world success. A review of existing subjective measures of speech system usability reveals a number of limitations in the approaches taken. This paper then summarises the work we have conducted in developing a new measure for the subjective assessment of speech system interfaces (SASSI).
Accuracy of fluorescent tomography in the presence of heterogeneities:study of the normalized born ratio

We studied the performance of three-dimensional fluorescence tomography of diffuse media in the presence of heterogeneities. Experimental measurements were acquired using an imaging system consisting of a parallel plate-imaging chamber and a lens coupled charge coupled device camera, which enables conventional planar imaging as well as fluorescence tomography. To simulate increasing levels of background heterogeneity, we employed phantoms made of a fluorescent tube surrounded by several absorbers in different combinations of absorption distribution. We also investigated the effect of low absorbing thin layers (such as membranes). We show that the normalized Born approach accurately retrieves the position and shape of the fluorochrome even at high background heterogeneity. We also demonstrate that the quantification is relatively insensitive to a varying degree of heterogeneity and background optical properties. Findings are further contrasted to images obtained with the standard Born expansion and with a normalized approach that divides the fluorescent field with excitation measurements through a homogeneous medium.
Implementation of NAS Parallel Benchmarks in High Performance Fortran

We present an HPF implementation of BT, SP, LU, FT, CG and MG of the NPB2.3-serial benchmark set. The implementation is based on HPF performance model of the benchmark specific primitive operations with distributed arrays. We present profiling and performance data on SGI Origin 2000 and compare the results with NPB2.3. We discuss advantages and limitations of HPF and pghpf compiler.
Integrating Formal Methods with Model-Driven Engineering

This paper presents the initial exploration work and proposal for our research to integrate formal methods with model-driven engineering. An extensive literature exists with the goal of facilitating the adoption of formal methods for educational and industrial practices, yet its adoption for teaching introductory software engineering courses and analyzing critical software systems in the industry is poor. The goal of this research is to provide an easy-to-use approach for using formal methods for industrial and academic purposes. Our approach is based on generating formal representations of static and dynamic abstractions of software expressed in a textual language, calledions of software expressed in a textual language, called Umple, which is derived from UML. To enrich the modeling experience, we adopt a pattern-based approach to specify various object-oriented and transition patterns. To maintain scalability of the dynamic aspects, we adopted a compositional approach to integrate hierarchical systems. To ensure correctness of our approach, we have adopted simulation and rigorous test-driven development methodologies. Current results have demonstrated that the constraints and generated formal methods code represent the patterns faithfully.
A connection-level call admission control using genetic algorithm for multi-class multimedia services in wireless networks

Semi-Markov Decision Process (SMDP) can be used to optimise channel utilisation with upper bounds on handoff blocking probabilities as Quality of Service constraints for call admission control in a wireless cell in a Personal Communication System (PCS). However, this method is too time consuming and therefore it fails when state space and action space are large. In this paper, we apply a genetic algorithm approach to address the situation when the SMDP approach fails. We code call admission control decisions as binary strings. The coded binary strings are fed into the genetic algorithm, and the resulting binary strings are founded to be near optimal call admission control decisions. Simulation results from the genetic algorithm are compared with the optimal solutions obtained from linear programming for the SMDP approach. The results reveal that the genetic algorithm approximates the optimal approach very well with less complexity.
Exogenous Delivery of Heat Shock Protein 70 Increases Lifespan in a Mouse Model of Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a debilitating neurodegenerative disorder that results in the progressive loss of motoneurons (MNs) in the CNS. Several survival and death mechanisms of MNs have been characterized and it has been determined that MNs do not appear to mount a complete stress response, as determined by the lack of heat shock protein 70 (Hsp70) upregulation after several stress paradigms. Hsp70 has been shown to confer neuroprotection and the insufficient availability of Hsp70 may contribute to MNs' susceptibility to death in ALS mice. In this study, recombinant human Hsp70 (rhHsp70) was intraperitoneally injected three times weekly, beginning at postnatal day 50 until endstage, to G93A mutant SOD1 (G93A SOD1) mice. The administration of rhHsp70 was effective at increasing lifespan, delaying symptom onset, preserving motor function and prolonging MN survival. Interestingly, injected rhHsp70 localized to skeletal muscle and was not readily detected in the CNS. Treatment with rhHsp70 also resulted in an increased number of innervated neuromuscular junctions compared with control tissue. Together these results suggest rhHsp70 may delay disease progression in the G93A SOD1 mouse via a yet to be identified peripheral mechanism.
Market Structure and Market Access

The authors examine an issue at the nexus of domestic competition policy and international trade, the interaction between goods trade and market power in domestic trade and distribution sectors. Theory suggests a set of linkages between service-sector competition and goods trade supported by econometrics involving imports of 22 OECD countries compared with 69 exporters. Competition in services affects the volume of goods trade. Additionally, because of interaction between tariffs and competition, the market structure of the domestic service sector becomes increasingly important as tariffs are reduced. Empirically service competition apparently matters most for exporters in smaller, poorer countries. The results also suggest that while negotiated agreements leading to cross-border services liberalization may boost goods trade as well, they may also lead to a fall in goods trade when such liberalization involves foreign direct investment leading to increased service sector concentration.
On Portfolio Choice with Savoring and Disappointment

We revisit the model proposed by Gollier and Muermann [Gollier C, Muermann A 2010 Optimal choice and beliefs with ex ante savoring and ex post disappointment. Management Sci. 568:1272--1284; hereafter, GM]. In the GM model, for a given lottery, agents form anticipated expected payoffs and the set of possible anticipations is assumed to be exogenously fixed. We propose sets of possible anticipations that are endogenously determined. This permits us to compare and evaluate in a consistent manner lotteries with different supports and to revisit the portfolio choice problem. We obtain new conclusions and interesting insights. Our extended model can rationalize a variety of empirically observed puzzles such as a positive demand for assets with negative expected returns, preference for skewed returns, and underdiversification of portfolios. This paper was accepted by Rakesh Sarin, decision analysis.
The Likely Range of Climate Sensitivity

The IPCC suggests a “likely range” for the equilibrium climate sensitivity of atmospheric carbon dioxide. At the recommended upper limit of this likely range, the calculated temperature rise (using the quasi-steady approximation) is found to be substantially higher than the observed historical data. Theoretically, the quasi-steady response is indeed expected to be higher than the actual (time dependent) response—the difference should be proportional to a (thermal inertia) time lag parameter τ . Estimates of the value of τ—based on currently available observational data—suggest that the upper limit for equilibrium climate sensitivity is significantly smaller than that suggested by the IPCC.
A Comparison of Univariate, Multivariate, Bilinear Autoregressive, and Bandpower Features for Brain-Computer Interfaces

Introduction The signal processing toolchain in a brain-computer interface (BCI) consists of several components that influence the overall performance of the system. In general, raw electroencephalographic (EEG) signals are first preprocessed with temporal or spatial filters. Next, features are extracted before the final classification stage. After that, a control signal can be derived. The aim of this offline analysis was to assess the influence of different feature types derived from autoregressive (AR) models on the overall performance of a BCI (measured by classification accuracy). We hypothesized that multivariate AR (MVAR) and bilinear AR (BLAR) parameters could yield higher classification accuracies because they contain more information about the underlying signals. In contrast to univariate AR (UVAR) parameters, MVAR parameters also describe the relationships between single channels, and BLAR parameters can model certain nonlinear signals. Methods We used data set 2A from the BCI Competition 2008 [1]. Nine subjects took part in two sessions on different days. The cue-based paradigm involved four different motor imagery tasks (left hand, right hand, foot, tongue). A session consisted of 6 runs with 48 trials each (12 for each of the four classes). We used signals from three bipolar electrodes C3, Cz, and C4. From those three EEG channels, we extracted different features: (1) univariate AR (UVAR) parameters for each channel,
Tuberous Sclerosis Complex Activity Is Required to Control Neuronal Stress Responses in an mTOR-Dependent Manner

Tuberous sclerosis complex (TSC) is a neurogenetic disorder caused by loss-of-function mutations in either the TSC1 or TSC2 genes and frequently results in prominent CNS manifestations, including epilepsy, mental retardation, and autism spectrum disorder. The TSC1/TSC2 protein complex plays a major role in controlling the Ser/Thr kinase mammalian target of rapamycin (mTOR), which is a master regulator of protein synthesis and cell growth. In this study, we show that endoplasmic reticulum (ER) stress regulates TSC1/TSC2 complex to limit mTOR activity. In addition, Tsc2-deficient rat hippocampal neurons and brain lysates from a Tsc1-deficient mouse model demonstrate both elevated ER and oxidative stress. In Tsc2-deficient neurons, the expression of stress markers such as CHOP and HO-1 is increased, and this increase is completely reversed by the mTOR inhibitor rapamycin both in vitro and in vivo. Neurons lacking a functional TSC1/TSC2 complex have increased vulnerability to ER stress-induced cell death via the activation of the mitochondrial death pathway. Importantly, knockdown of CHOP reduces oxidative stress and apoptosis in Tsc2-deficient neurons. These observations indicate that ER stress modulates mTOR activity through the TSC protein complex and that ER stress is elevated in cells lacking this complex. They also suggest that some of the neuronal dysfunction and neurocognitive deficits seen in TSC patients may be attributable to ER and oxidative stress and therefore potentially responsive to agents moderating these pathways.
Developing an annotated corpus for Gıkuyu using language-independent machine learning techniques

Networking the development of computational resources for African languages can be greatly advanced if researchers aim to develop tools that are to a large extent language-independent and therefore reusable for other languages. In this paper we describe a particular case study, namely the development of an annotated corpus of Gı̃kũyũ, using language-independent machine learning techniques. The general aim of our work on Gı̃kũyũ is two-fold: on the one hand we wish to digitally preserve this resource-scarce language, while on the other hand it serves as a feasibility study of using language-independent machine learning techniques for linguistic annotation of corpora. To this end we investigate established annotation induction techniques like unsupervised learning and knowledge transfer. These methods can provide interesting perspectives for the linguistic description of many other resource-scarce languages.
TRANSITION-BASED PARSING

Transition-based models for dependency parsing use a factorization defined in terms of a transition system, or abstract state machine. In this lecture, I will introduce the arc-eager and arcstandard transition systems for dependency parsing (§1) and discuss two different approaches to learning and decoding with these models: greedy classifier-based parsing (§2) and beam search and structured learning (§3). Finally, I will discuss different techniques for non-projective transition-based parsing (§4).
Channel Estimation Techniques in MIMO-OFDM LTE Systems

There is an increasing demand for high data transmission rates with the evolution of the very large scale integration (VLSI) technology. The multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) systems are used to fulfill these requirements because of their unique properties such as high spectral efficiency, high data rate and resistance towards multipath propagation. MIMO-OFDM systems are finding their applications in the modern wireless communication systems like IEEE 802.11n, 4G and LTE. They also offer reliable communication with the increased coverage area. The bottleneck to the MIMO-OFDM systems is the estimation of the channel state information (CSI). This can be estimated with the help of any one of the Training Based, Semiblind and Blind Channel estimation algorithms. This paper presents various channel estimation algorithms, optimization techniques and their effective utilization in MIMO-OFDM for modern wireless LTE systems.
Exploring the complex context of Canadian Indigenous maternal child-health through maternity experiences: the role of social determinants of health.

BACKGROUND: The marginalization of Indigenous women in Canada has resulted in adverse pregnancy and infant health outcomes. While the epidemiological research focuses on statistical evidence, it fails to address the context and underlying causes, including social determinants of health. Despite clear evidence on how social determinants influence health, there is limited research on Indigenous women’s perspectives and experiences. Indigenous women’s narratives during pregnancy, birth and the early months of parenthood are critical to understand the underlying causes and proposed solutions. This research demonstrates how Indigenous women’s maternity experiences are embedded within their historical, social and cultural experiences, thus explaining the importance of addressing contexts related to social determinants of health. METHODS: Through an Indigenous and decolonized lens, maternity narratives from ten in-depth interviews were conducted with Indigenous birth mothers in British Columbia, Canada. Thematic content analysis findings contextualize Indigenous maternity experiences within proximal, intermediate and distal determinants of health. RESULTS: The proximal determinates include barriers to education, employment, income, food (in)security and a lack of safe and affordable housing and homelessness due to urban migration and violent relationships. Intermediate experiences included barriers to accessing maternity healthcare, including geographic barriers and experiences of racism. Distal determinants including traumatic narratives related to immediate and intergenerational impacts of colonialism, including the Indian residential school system and foster care, which has impacted mental health and addictions. CONCLUSION: This research contributes to expanding research on culturally safe and improved maternal-child health, healthcare and maternity research, as well as highlighting the need to address and alleviate adverse social determinants.
Mutual vulnerability and intergenerational healing: Black women HBCU students writing memoir

ABSTRACT This qualitative phenomenological study was designed to gain insight into the unique experiences of six Black women students who were writing creative non-fiction toward the goal of self-definition in a Black feminist learning environment at a Historically Black College/University (HBCU). Interviews were triangulated against participants’ personal writing, revealing obstacles of racism, sexism, internalized oppressions, and an initial difficulty with sharing personal experiences. Participants wrote about overcoming these obstacles through survival and success strategies like practicing mutual vulnerability with their classmates and teacher, and practicing healing transformation through intergenerational healing (re-gifting their new awareness to the next generation). Findings revealed underexplored reciprocities in Black feminist pedagogical delivery and engagement, which may advance culturally specific expressive writing and research methods, and offer culturally specific methods to advance the healing of multigenerational traumas that impact Black women students.
The Effect of Heat Treatment Methods on Different Rates Boron Added AISI 4140 Improve Steel

AISI 4140 Improved steel has the many area such as agricultural machine parts, automobile, construction, machine tools, aircraft parts, axles, crankshafts, gears. In this study, AISI 4140 steel was melted at 1640 oC and was added 17 and 32 ppm boron. After the casting process, the boron free and boron added steels were rolled and heated for 30 min at 980 oC then cooled in ice water, normally water, at room temperature, and furnace. Then heat treated the specimens were examined by optical microscopy. Also, Micro hardness measurements are performed and the results are discussed in comparison with the literature.