Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentence based sentiment classification from online customer reviews

Sentiment analysis is the process of analyzing and classifying the rewires contents about a product, event, and place etc into positive, negative or neutral opinion. In this paper; we propose a sentence level machine learning approach for sentiment classification of online reviews. The proposed method extracts the subjective sentences from the reviews and label each sentence either positive or negative based on its word level feature using naïve Naïve Bayesian (NB) classifier. The labeled sentences create an annotated set of sentences called as BOS (Bag-of-Sentences). We train Support Vector machine (SVM) classifier on the BOS for sentences polarity classification. The contextual information in each sentence structure is taken into consideration to calculate the semantic orientation. The effectiveness of the proposed method is evaluated thought simulation. Results show that our machine learning based proposed method on average achieves accuracy of 81% and 83% with some contextual information. This method improves the sentiment classification polarity on sentence level unlike the word level lexical feature based work, by focus on sentences, this also concentrate on contextual information.
Crowdsourced Data Management: A Survey

Any important data management and analytics tasks cannot be completely addressed by automated processes. These tasks, such as entity resolution, sentiment analysis, and image recognition can be enhanced through the use of human cognitive ability. Crowdsouring platforms are an effective way to harness the capabilities of people (i.e., the crowd) to apply human computation for such tasks. Thus, crowdsourced data management has become an area of increasing interest in research and industry. We identify three important problems in crowdsourced data management. (1) Quality Control: Workers may return noisy or incorrect results so effective techniques are required to achieve high quality; (2) Cost Control: The crowd is not free, and cost control aims to reduce the monetary cost; (3) Latency Control: The human workers can be slow, particularly compared to automated computing time scales, so latency-control techniques are required. There has been significant work addressing these three factors for designing crowdsourced tasks, developing crowdsourced data manipulation operators, and optimizing plans consisting of multiple operators. In this paper, we survey and synthesize a wide spectrum of existing studies on crowdsourced data management. Based on this analysis we then outline key factors that need to be considered to improve crowdsourced data management.
Fitness Costs and Variation in Transmission Distortion Associated with the Abnormal Chromosome 10 Meiotic Drive System in Maize

The maize abnormal chromosome 10 (Ab10) meiotic drive system causes its own preferential transmission through females, yet it is found at low frequencies. Meiotic drive describes a process whereby selfish genetic elements are transmitted at levels greater than Mendelian expectations. Maize abnormal chromosome 10 (Ab10) encodes a meiotic drive system that exhibits strong preferential segregation through female gametes. We performed transmission assays on nine Ab10 chromosomes from landraces and teosinte lines and found a transmission advantage of 62–79% in heterozygotes. Despite this transmission advantage, Ab10 is present at low frequencies in natural populations, suggesting that it carries large negative fitness consequences. We measured pollen transmission, the percentage of live pollen, seed production, and seed size to estimate several of the possible fitness effects of Ab10. We found no evidence that Ab10 affects pollen transmission, i.e., Ab10 and N10 pollen are transmitted equally from heterozygous fathers. However, at the diploid (sporophyte) level, both heterozygous and homozygous Ab10-I-MMR individuals show decreased pollen viability, decreased seed set, and decreased seed weight. The observed fitness costs can nearly but not entirely account for the observed frequencies of Ab10. Sequence analysis shows a surprising amount of molecular variation among Ab10 haplotypes, suggesting that there may be other phenotypic variables that contribute to the low but stable equilibrium frequencies.
Supporting Reasoning with Different Types of Evidence in Intelligence Analysis

The aim of intelligence analysis is to make sense of information that is often conflicting or incomplete, and to weigh competing hypotheses that may explain a situation. This imposes a high cognitive load on analysts, and there are few automated tools to aid them in their task. In this paper, we present an agent-based tool to help analysts in acquiring, evaluating and interpreting information in collaboration with others. Agents assist analysts in reasoning with different types of evidence to identify what happened and why, what is credible, and how to obtain further evidence. Argumentation schemes lie at the heart of the tool, and sensemaking agents assist analysts in structuring evidence and identifying plausible hypotheses. A crowdsourcing agent is used to reason about structured information explicitly obtained from groups of contributors, and provenance is used to assess the credibility of hypotheses based on the origins of the supporting information.
Hyperacute versus Subacute Coiling of Aneurysmal Subarachnoid Hemorrhage a Short-term Outcome and Single-Center Experience, Pilot Study

Background After the initial subarachnoid hemorrhage (SAH), rebleeding is the major cause of morbidity and poor outcome, which is maximal in the first 24 h. We supposed that the coiling of ruptured intracranial aneurysms within 24 h of SAH is related to the improved clinical outcome compared with coiling 24 h after aneurysmal SAH. Objective We examined whether coiling ruptured aneurysms within 24 h of aneurysmal SAH is associated with better early 24 h and late 30 days outcome. Method This prospective study was carried on 30 patients with aneurysmal SAH presenting to the Alexandria University Hospital and Insurance Main Hospital during the period from February 2013 to May 2014. They were divided into two groups: Group I (10 patients treated within 24 h of presentation) and Group II (20 patients treated after 24 h of presentation). Time interval from presentation to treatment were 78.60 and 10.60 h for subacute and hyperacute group, respectively. All patients were treated with endovascular coiling. Change between admission and immediate postoperative World Federation of Neurosurgeons classification Scale was measured for early outcome, while remote outcome was measured by modified Rankin Scale at 30 days follow-up. Results There was a clinical improvement regarding early 24 h outcome (weighted by postoperative WFNS grade) and on late 30 days outcome (weighted by Modified Rankin Scale Score) in the group managed within 24 h versus who were treated after 24 h (P = 0.049 and P = 0.024, respectively). There was a significant decrease in the incidence of clinical rebleeding detected by postoperative computed tomography (CT) of the brain in patients undergoing aneurysm treatment within 24 h (P = 0.029). Conclusion The study affirms evidence that the management of ruptured intracranial aneurysms within 24 h of SAH is associated with better immediate and short-term clinical outcome.
Blocking unsafe behaviors in control systems through static and dynamic policy enforcement

One of the most universally accepted practices in computer security is the use of security policy enforcement. Under a policy enforcement regime, users and programs can only perform actions for which they are authorized by the security policy. Unfortunately, modern control systems fail to make effective use of policy enforcement. In many cases, privilege in control systems is binary-a single password is sufficient to gain most or all privileges within the system. In this paper, we consider the benefits and challenges of enforcing security policies for code running on Programmable Logic Controllers (PLCs). We first summarize two of our previous approaches, which place no trust in the PLC to behave correctly. While these approaches show promise, especially for current PLC architectures, they are lacking in comparison to approaches based on a trustworthy PLC design. Thus, we argue that future PLCs should implement a Trusted Computing Base (TCB). Such a TCB is a small set of trusted hardware and software that is sufficient for enforcing policies directly on the PLC. We also propose a method of doing policy enforcement on PLCs supporting a small TCB, and argue that it is the simpler and more effective means of doing policy enforcement for PLCs. We conclude that future PLCs should support a small TCB.
Measurement Properties of Constraints to Sport Participation: A Psychometric Examination with Adolescents

Constraint are a major focus of theoretical and empirical study in leisure research. However, analyses of psychometric properties of constraints measures are rare. This study assessed the factor structure and invariance of a leisure constraints measure applied to sport participation among middle school adolescents (ages 11 to 15 years old) using panel data over two time periods nine months apart (N = 2,029). We compared the validity of three baseline models: a theoretical 3-factor model, a 7-factor model, and a second-order factor model. The 7-factor model exhibited a more appropriate measurement model based on fit indices and statistical comparisons. Using the 7-factor model, construct validity and invariance of the model over time (Time 1 and Time 2) was examined by gender, grade level, race/ethnicity, and socioeconomic status. The model was found to be invariant at the factor loading and intercept levels, while there was some evidence of non-invariance at further constrained levels. In general, the 7-factor model appeared to be psychometrically stable and applicable over a range of demographic subgroups, lending evidence to its use as a measure in leisure or sport participation constraint studies that involve socio-demographic comparisons. The findings suggest that while the theoretical structure may be effective in simplifying constraint categories, a more detailed specification using subfactors of the dimensions is better for measurement.
Psychoneuroimmunological factors in type 1 diabetes: characteristics and consequences

Type 1 diabetes is a serious chronic autoimmune disease that pervasively imposes itself into sufferers' lives without respite. While insulin is a treatment that aids to sustain life for those with the disease, it is not a cure, indeed presently, there is no cure. The direct and indirect physiological impact of type 1 diabetes is significant, degenerative, and ultimately for the vast majority of sufferers, life shortening. These include both acute and chronic complications that require a substantial and unceasing investment in self-care energies in order to prevent, monitor, and manage the multitude of ramifications they proffer. Additionally, the disease confers a significantly increased risk for psychopathology and neuropsychopathology. Anxiety, depression, and cognitive impairment across multiple domains, all have an increased prevalence in individuals with type 1 diabetes. There are a number of hypotheses as to why increased mind and brain disorders prevail that include factors relating to environmental, psychosocial and neurophysiological pathogenic mechanisms. While the specifics remain unclear, there is consensus that the biopsychosocial complexity of type 1 diabetes means that the pathogenic mechanisms underlying increased mind and brain comorbidities in the disease are likely to be heterogeneous and evanescent. This thesis presents a series of eleven studies that evaluate and describe the interrelationships between type 1 diabetes and a number of biopsychosocial characteristics, and their consequences. Psychosocial, psychological, cognitive, and immunoregulatory phenomena are explored throughout the thesis. A number of findings are presented across the eleven studies however, perhaps the most significant finding is that affective disorders (anxiety, depression, and comorbid anxiety-depression) are pervasively prevalent in children and adults with type 1 diabetes, and that the relationship between disordered affect and other psychosocial factors long considered ubiquitous such as increased stress and poor coping skills, differ in the disease compared to those without the condition. Moreover, the influence of affective disorders was shown to be pervasive, impacting cognitive function and inflammatory characteristics in type 1 diabetes to a far greater extent than no-type 1 diabetes controls. The influence of affective disorders was also shown to mediate the risk of diabetes-specific clinical factors such as metabolic control and complications. Ultimately, this thesis illustrates that psychopathological phenomena are a serious issue in type 1 diabetes that present a tangible impediment to general and diabetes-specific health and wellbeing. A far greater clinical investment is required to address this issue, and more research is needed to better understand the pathogenic mechanisms involved, and the consequences they produce.
Spectrogram analysis of selected tremor signals using short-time Fourier transform and continuous wavelet transform

Among a variety of spectrogram methods Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) were selected to analyse transients in non-stationary tremor signals. Depending on the properties of the tremor signal a more suitable representation of the signal is gained by CWT. Three selected broadband tremor signals from the volcanos Mt. Stromboli, Mt. Semeru and Mt. Pinatubo were analyzed using both methods. The CWT can also be used to extend the definition of coherency into a time-varying coherency spectrogram. An example is given using array data from the volcano Mt. Stromboli.
Anisotropic shock response of columnar nanocrystalline Cu

We perform molecular dynamics simulations to investigate the shock response of idealized hexagonal columnar nanocrystalline Cu, including plasticity, local shear, and spall damage during dynamic compression, release, and tension. Shock loading (one-dimensional strain) is applied along three principal directions of the columnar Cu sample, one longitudinal (along the column axis) and two transverse directions, exhibiting a strong anisotropy in the response to shock loading and release. Grain boundaries (GBs) serve as the nucleation sites for crystal plasticity and voids, due to the GB weakening effect as well as stress and shear concentrations. Stress gradients induce GB sliding which is pronounced for the transverse loading. The flow stress and GB sliding are the lowest but the spall strength is the highest, for longitudinal loading. For the grain size and loading conditions explored, void nucleation occurs at the peak shear deformation sites (GBs, and particularly triple junctions); spall damage is entirely intergranular for the transverse loading, while it may extend into grain interiors for the longitudinal loading. Crystal plasticity assists the void growth at the early stage but the growth is mainly achieved via GB separation at later stages for the transverse loading. Our simulations reveal such deformation mechanisms as GB sliding, stress, and shear concentration, GB-initiated crystal plasticity, and GB separation in nanocrystalline solids under shock wave loading.
Literature Review of Ontology in Semantic Web

Ontology is an important emerging discipline that has the huge potential to improve information organization, management and understanding. It has a crucial role to play in enabling content-based access, interoperability, communications, and providing qualitatively new levels of services on the next generation of Web transformation in the form of the Semantic Web. The issues pertaining to ontology generation, mapping and maintenance are critical key areas that need to be understood and addressed. This timely survey is presented in two parts. This first part reviews the state-of-the-art techniques and work done on semiautomatic and automatic ontology generation, as well as the problems facing these researches. The second complimentary survey is dedicated to ontology mapping and ontology evolving. Through this survey, we identified that shallow information extraction and natural language processing techniques are deployed to extract concepts or classes from free-text or semistructured data. However, relation extraction is a very complex and difficult issue to resolve and it has turned out to be the main impedance to ontology learning and applicability. Further researches are encouraged to find appropriate and efficient ways to detect or identify relations through semi-automatic automatic means.
Rethinking “Generation Me”: A Study of Cohort Effects from 1976-2006

Social commentators have argued that changes over the last decades have coalesced to create a relatively unique generation of young people. However, using large samples of U.S. high-school seniors from 1976 to 2006 (Total N = 477,380), we found little evidence of meaningful change in egotism, self-enhancement, individualism, self-esteem, locus of control, hopelessness, happiness, life satisfaction, loneliness, antisocial behavior, time spent working or watching television, political activity, the importance of religion, and the importance of social status over the last 30 years. Today's youth are less fearful of social problems than previous generations and they are also more cynical and less trusting. In addition, today's youth have higher educational expectations than previous generations. However, an inspection of effect sizes provided little evidence for strong or widespread cohort-linked changes.
Heart rate variability during sleep in snorers with and without obstructive sleep apnea.

Changes in sympathetic and vagal tone may be the substrate for the development of cardiac arrhythmias in patients with obstructive sleep apnea (OSA). The cardiovascular responses in the traditional autonomic tests show great interindividual and intraindividual variations. During sleep there are repetitive modifications of heart rate (HR) that are not influenced by psychologic factors or the patient's cooperation. For this reason, we evaluated HR modifications in relation to spontaneous body movements (BM) and sleep apneas during nonrapid eye movement (NREM) and rapid eye movement (REM) sleep in habitual snorers with normal and pathologic respiratory disturbance index (RDI). From 132 consecutive patients referred to our sleep center for habitual snoring and/or daytime somnolence, we selected 35 male patients younger than 60 years without clinical evidence of autonomic dysfunction. They were divided into three groups: group A (RDI < 10); group B (RDI > 10 and < 20); and group C (RDI > 20). No significant difference was found among the three groups in the HR variability related to BM. In the evaluation of bradytachyarrhythmias related to apneic events of 20 to 30 s, we found a significant difference between group A and the other two groups. In patients with RDI > 10, a reduced HR variability related to a reduced sympathetic tone in the post-apnea phase was observed. Some authors suggested that an HR increase during the post-apnea period can be used as an index of "brainstem arousal. " Our results seem to indicate a reduced apnea-related "arousability" in patients with RDI > 10. This finding might be one of the factors contributing to the worsening of OSA.
Coordinated motion control of a robot arm and a positioning table with arrangement of multiple goals

The minimum-time motion coordination is an important subject in robotics. In this study, the arrangement of several goals, which is treated as a traveling salesman problem (TSP), is incorporated to this subject. Although TSP has been studied in most previous works; but, solving a TSP that takes into account collision occurrences has not received much attention. This instance arises when a robot arm has to plan a sequence of reaching goals with other moving objects and/or other robot arms. If goals are also moving, then the problem becomes more complex since the end configuration of robot arm is undefined when reaching a goal. In this study, in particular, a 6-DOF robot arm has to reach several goals found in an object while a 1-axis positioning table simultaneously positions the object; thereby changing the goal locations and collision occurrences are inevitable. For the purpose of this study, the TSP is solved effectively with motion coordination and collision avoidance. The collision-free configurations of a robot arm when reaching goals are solved through motion coordination. Collision is avoided by exploiting the redundancy of the system. The above-mentioned solution is verified through a simulation utilizing an object with various numbers of goals and their positions, and is proven effective.
Journal of Autonomous Agents and Multiagent Systems (jaamas) Manuscript No. Sequentially Optimal Repeated Coalition Formation under Uncertainty

Coalition formation is a central problem in multiagent systems research, but most models assume common knowledge of agent types. In practice, however, agents are often unsure of the types or capabilities of their potential partners, but gain information about these capabilities through repeated interaction. In this paper, we propose a novel Bayesian, model-based reinforcement learning framework for this problem, assuming that coalitions are formed (and tasks undertaken) repeatedly. Our model allows agents to refine their beliefs about the types of others as they interact within a coalition. The model also allows agents to make explicit tradeoffs between exploration (forming " new " coalitions to learn more about the types of new potential partners) and exploitation (relying on partners about which more is known), using value of information to define optimal exploration policies. Our framework effectively integrates decision making during repeated coalition formation under type uncertainty with Bayesian reinforcement learning techniques. Specifically, we present several learning algorithms to approximate the optimal Bayesian solution to the repeated coalition formation and type-learning problem, providing tractable means to ensure good sequential performance. We evaluate our algorithms in a variety of settings, showing that one method in particular exhibits consistently good performance in practice. We also demonstrate the ability of our model to facilitate knowledge transfer across different dynamic tasks.
Predictive performance modeling of virtualized storage systems using optimized statistical regression techniques

Modern virtualized environments are key for reducing the operating costs of data centers. By enabling the sharing of physical resources, virtualization promises increased resource efficiency with decreased administration costs. With the increasing popularity of I/O-intensive applications, however, the virtualized storage used in such environments can quickly become a bottleneck and lead to performance and scalability issues. Performance modeling and evaluation techniques applied prior to system deployment help to avoid such issues. In current practice, however, virtualized storage and its performance-influencing factors are often neglected or treated as a black-box. In this paper, we present a measurement-based performance prediction approach for virtualized storage systems based on optimized statistical regression techniques. We first propose a general heuristic search algorithm to optimize the parameters of regression techniques. Then, we apply our optimization approach and create performance models using four regression techniques. Finally, we present an in-depth evaluation of our approach in a real-world representative environment based on IBM System z and IBM DS8700 server hardware. Using our optimized techniques, we effectively create performance models with less than 7% prediction error in the most typical scenario. Furthermore, our optimization approach reduces the prediction error by up to 74%.