Miracle-dz/UniFAR-FTUs huggingface dataset:
Sentiment Classification Based on Ontology and SVM Classifier

There are a lot of text documents on the Web which contain opinions or sentiments about an object such as software reviews, product reviews, movies reviews, music reviews, and book reviews etc. Opinion mining or sentiment classification aim to extract the features on which the reviewers express their opinions and determine they are positive or negative. In this paper we proposed an ontology based combination approach to enhance the existing approaches of the sentiment classification. We also used the supervised learning techniques for classification of the sentiments in the software reviews. This paper proposed the combination of using Natural Language Processing techniques (NLP), ontology based on Formal Concept Analysis (FCA) design, and Support Vector Machine (SVM) for classifying the software reviews are positive, negative or neutral.
Advances in Medical Education and Practice Dovepress Empathy Levels among First Year Malaysian Medical Students: an Observational Study

Background: The literature indicates that medical practitioners experience declining empathy levels in clinical practice. This highlights the need to educate medical students about empathy as an attribute early in the academic curriculum. The objective of this study was to evaluate year one students' self-reported empathy levels following a 2-hour empathy workshop at a large medical school in Malaysia. Methods: Changes in empathy scores were examined using a paired repeated-measures t-test in this prospective before and after study. Results: Analyzing the matched data, there was a statistically significant difference and moderate effect size between mean empathy scores before and 5 weeks after the workshop Conclusion: The results of this observational study indicate improved mean self-reported empathy scores following an empathy workshop.
SAGEL: smart address geocoding engine for supply-chain logistics

With the recent explosion of e-commerce industry in India, the problem of address geocoding, that is, transforming textual address descriptions to geographic reference, such as latitude, longitude coordinates, has emerged as a core problem for supply chain management. Some of the major areas that rely on precise and accurate address geocoding are supply chain fulfilment, supply chain analytics and logistics. In this paper, we present some of the challenges faced in practice while building an address geocoding engine as a core capability at Flipkart. We discuss the unique challenges of building a geocoding engine for a rapidly developing country like India, such as, fuzzy region boundaries, dynamic topography and lack of convention in spellings of toponyms, to name a few. We motivate the need for building a reliable and precise address geocoding system from a business perspective and argue why some of the commercially available solutions do not suffice for our requirements. SAGEL has evolved through 3 cycles of solution prototypes and pilot experiments. We describe the learnings from each of these phases and how we incorporated them to get to the first production-ready version. We describe how we store and index map data on a SolrCloud cluster of Apache Solr, an open-source search platform, and the core algorithm for geocoding which works post-retrieval in order to determine the best matches among a set of candidate results. We give a brief description of the system architecture and provide accuracy results of our geocoding engine by measuring deviations of geocoded customer addresses across India, from verified latitude, longitude coordinates of those addresses, for a sizeable address set. We also measure and report our system's ability to geocode up to different region levels, like city, locality or building. We compare our results with those of the geocoding service provided by Google against a set of addresses for which we have verified latitude-longitude coordinates and show that our geocoding engine is almost as accurate as Google's, while having a higher coverage.
Correlation of perceived social support and size of social network with quality of life dimension in cancer patients

Background: Cancer represents a stressful life with negative consequences requiring social support for patients. This study describes the correlation between perceived social support and network with quality of life in cancer patients. Material and Methods: A descriptive-co relational design with continuos sampling method was performed and 220 patients in Firoozgar & Hazrat Rasool hospital and Emam Khomeini cancer Institute. Data obtained through social support questionnaires (SSQ) and the Ferrans and Powers quality of life index- cancer version (QLI-CV) and analyzed using SPSS version 13, Pearson correlation, Onaway Anova and Kruskal- Wallis tests. Results: The mean scores for perceived social support and quality of life were (136.44 and 18.22 respectively) which most of them received moderate to high social support from their meddle-size social network (4.17). Also, statistically, significant relations were found between age and marital status and perceived social support with the quality of life and diagnosis duration, type of treatment and economical condition (P<0.001) while there was not any relation between the quality of life and type of cancer. A significant positive correlation was found between perceived social support and quality of life (r=0.446, p<0.001) especially the family dimensions and size of social network(r=0.200, p<0.001), also quality of life and the size of social network(r=0.246, p<0.05). Conclusions : The results of this study give insights into the importance of perceived social support for promotion of the quality of life in cancer patients. Oncology nurse are recommended to apply findings to enhance the social networks of cancer patients and give them appropriate support. So that, further researches are recommended in this issue.
Timebands Framework – 2011

Complex real-time systems must integrate physical processes with digital control, human operation and organisational structures. New scientific foundations are required for specifying, designing and implementing these systems. One key challenge is to cope with the wide range of time scales and dynamics inherent in such systems. To exploit the unique properties of time, with the aim of producing more dependable computer-based systems, a timeband framework has been developed. In this paper we review this framework and report on modifications, extensions and applications that have been undertaken in funded research that completed in 2011.
Improved Data-Aware Task Dispatching for Batch Queuing Systems

This paper describes a data-aware task dispatching strategy called Improved Data-Aware Task Dispatching (IDAD). This approach exploits the high-performance of local file access in non-uniform storage-access (NUSA) file systems and is based on our previous work, Data-Aware Dispatch (DAD). In IDAD, the method of calculating data placement is revised, and the CPU factor is removed, as it has no major impact on performance but significantly reduces the difficulty for tweaking parameter.We evaluated our approach in comparison with DAD and the stock FIFO Torque scheduler using BLAST benchmarks. We observed makespan reductions of 10.40% and 35.05% compared with DAD and stock FIFO schedulers, respectively.
The Development of Causal Reasoning

Research on the development of causal reasoning has broadly focused on accomplishing two goals: understanding the origins of causal reasoning, and examining how causal reasoning changes with development. This chapter reviews evidence and theory that aim to fulfill both of these objectives. In the first section, it focuses on the research that explores the possible precedents for recognizing causal events in the world, reviewing evidence for three distinct mechanisms in early causal reasoning: physical launching events, agents and their actions, and covariation information. The second portion of the chapter examines the question of how older children learn about specific causal relationships. It focuses on the role of patterns of statistical evidence in guiding learning about causal structure, suggesting that even very young children leverage strong inductive biases with patterns of data to inform their inferences about causal events, and discussing ways in which children’s spontaneous play supports causal learning.
Gene prediction by multiple syntenic alignment

Summary Given the increasing number of available genomic sequences, one now faces the task of identifying their functional parts, like the protein coding regions. The gene prediction problem can be addressed in several ways. One of the most promising methods makes use of similarity information between the genomic DNA and previously annotated sequences (proteins, cDNAs and ESTs). Recently, given the huge amount of newly sequenced genomes, new similarity-based methods are being successfully applied in the task of gene prediction. The so-called comparative-based methods lie in the similarities shared by regions of two evolutionary related genomic sequences. Despite the number of different gene prediction approaches in the literature, this problem remains challenging. In this paper we present a new comparative-based approach to the gene prediction problem. It is based on a syntenic alignment of three or more genomic sequences. With syntenic alignment we mean an alignment that is constructed taking into account the fact that the involved sequences include conserved regions intervened by unconserved ones. We have implemented the proposed algorithm in a computer program and confirm the validity of the approach on a benchmark including triples of human, mouse and rat genomic sequences.
Machine learning for drug design, molecular machines and evolvable artificial cells

An artificial cell is a complex chemical system with many components fabricated and assembled in the laboratory. The molecular components can be designed to interlock in a variety of different way to achieve the emergence of minimal life [1][2]. One experimental design is composed of three modules or sub-systems: lipid vesicles, a metabolic system and a cell free expression system. Due to the high number of molecular species and their non-trivial interactions in an artificial cell any prediction of the emerging properties in this high dimensional space of compositions is extremely difficult. Previously we have developed and used a machine learning process Evo-DoE (Evolutionary Design of Experiments) coupled with a robotic workstation for liquid handling to optimize a liposomal drug formulation [3] as well as a cell free expression system for the synthesis of the GFP (green fluorescent protein in vitro) [4]. In addition we have results of vesicle fusion providing a protocol to design a life-cycle for evolvable artificial cells. Now we propose how our technologies could be used to optimize artificial cells.
Decentralized approaches for self-adaptation in agent organizations

Self-organizing multi-agent systems provide a suitable paradigm for developing autonomic computing systems that manage themselves. Towards this goal, we demonstrate a robust, decentralized approach for structural adaptation in explicitly modeled problem solving agent organizations. Based on self-organization principles, our method enables the autonomous agents to modify their structural relations to achieve a better allocation of tasks in a simulated task-solving environment. Specifically, the agents reason about when and how to adapt using only their history of interactions as guidance. We empirically show that, in a wide range of closed, open, static, and dynamic scenarios, the performance of organizations using our method is close (70–90%) to that of an idealized centralized allocation method and is considerably better (10–60%) than the current state-of-the-art decentralized approaches.
Tolerance of Pseudomonas aeruginosa to Melaleuca alternifolia (tea tree) oil is associated with the outer membrane and energy-dependent cellular processes.

OBJECTIVES The essential oil of Melaleuca alternifolia (tea tree oil) and its components have antimicrobial activity against a wide range of Gram-positive and Gram-negative bacteria, fungi and viruses. The mechanism(s) by which Pseudomonas aeruginosa NCTC 10662 maintains a decreased susceptibility to tea tree oil and components was investigated. RESULTS Ethylene diamine tetraacetic acid enhanced the antimicrobial activity of tea tree oil and terpinen-4-ol against stationary phase P. aeruginosa while polymyxin B nonapeptide enhanced the activity of tea tree oil and gamma-terpinene. Pre-treatment with the protonophore carbonyl cyanide m-chlorophenylhydrazone increased the susceptibility of exponential phase cells to sub-inhibitory concentrations of tea tree oil, terpinen-4-ol and gamma-terpinene, indicating that intrinsic tolerance to tea tree oil and components is substantially energy dependent. CONCLUSIONS Increased tolerance to tea tree oil in P. aeruginosa is directly related to the barrier and energy functions of the outer membrane, and may involve efflux systems.
Model driven design for flexure-based Microrobots

This paper presents a non-linear, dynamic model of the flexure-based transmission in the Harvard Ambulatory Microrobot (HAMR). The model is derived from first principles and has led to a more comprehensive understanding of the components in this transmission. In particular, an empirical model of the dynamic properties of the compliant Kapton flexures is developed and verified against theoretical results from beam and vibration theory. Furthermore, the fabrication of the piezoelectric bending actuators that drive the transmission is improved to match theoretical performance predictions. The transmission model is validated against experimental data taken on HAMR for the quasi-static (1-10 Hz) operating mode, and is used to redesign the transmission for improved performance in this regime. The model based redesign results in a 266% increase in the work done by the foot when compared to a previous version of HAMR. This leads to a payload capacity of 2.9g, which is ~ 2× the robot's mass and a 114% increase. Finally, the model is validated in the dynamic regime (40-150 Hz) and the merits of a second order linear approximation are discussed.
The contrast effect in reading general conditionals

To adjudicate between deterministic and probabilistic accounts of the meaning of conditionals, we examined the influence of context on the reading of general conditionals. Context was varied with the contrast context, where participants judged uncertain conditionals after certain conditionals, and the control context, where participants judged only uncertain conditionals. Experiment 1 had participants to judge whether a set of truth table cases was possible for the conditional. Experiment 2 had participants to judge whether the conditional was true for a set of truth table cases. The findings are as follows. Possibility and truth judgments showed a similar response pattern. The reading of general conditionals varied with conditional contexts. The predominant reading was deterministic in the contrast context but was probabilistic in the control context. Conditional contexts yielded a significant contrast effect. Meanwhile, conditional probability P(q|p) made a smaller difference to the acceptance rate in the contrast context than in the control context. The overall pattern is beyond both the deterministic and probabilistic accounts. Alternatively, we propose a dynamic-threshold account for the relative reading of general conditionals.
A cooperative distributed Hyper-Heuristic framework for scheduling

In recent years, hyper-heuristics have emerged as a new search methodology that is motivated by the goal of increasing the level of generality of meta-heuristics. In this paper, we aim at investigating the role of cooperative decision making in the selection process of low level heuristics. We propose a novel cooperative distributed hyper-heuristic framework. The cooperative distributed hyper-heuristic framework is an agent-based system composed of a Hyper-Heuristic Agent (HHA) and a number of Low Level Heuristic Agents (LLHA). The HHA is in charge of the selection of the low level heuristic to apply at a decision point in the search process. The LLHAs search synchronously through the same solution space, starting from the same solution and using different low level heuristics. We conducted several experiments, using the permutation flow shop benchmark instances, to investigate the performance of the developed cooperative distributed hyper-heuristic approaches.
Teaching Conversation Skills to Individuals with Severe Disabilities with a Communication Book Adaptation. Instructional Handbook.

Research on communication instruction for students with severe intellectual disabilities is reviewed, focusing on the pragmatic elements involved in accomplishing a successful communicatiVe exchange. A federally funded project, called the "Conversation and Social Competence Research Project," is then described. The project sought to develop an effective conversati,-1 training program that would provide students with severe disabil_ties with the skills and adaptations they need to initiate communicative social interactions and maintain reciprocal turntaking. The project conducted four studies, which investigated: the relationship between conversation skills and inappropriate social interaction behaviors; interacting with peers through conversation turntaking with a communication book adaptation; generalized effects of conversation skill training; and using a communication book adap1:in at home with informed family members. Results of these studies led to the development of a conversation training program for elementary to high-school age students. Program components include generation of topic menus to guide the content of each participant's conversation; development of individualized communication books (containing photographs and drawings of objects, places, actions, and people associated with the topic menu items) to serve as the conversation medium; and training on a specific "turntaking" structure. Appendices contain pages from conversation books, a Partner Training Script, a description of conversation training, and a conversation data sheet. (Includes 39 references) (JDD) NJ, , U.E. DEPARTMENT OF EDUCATION Office of EducatoonaIfteseerth and ImPeorrIldot EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) r This dOcernstm nes been nlOrOducbd Welted frOm Ill. pereOn or croanizstion originating it r Min*, changes Moue toren made lo trnprpee reproduction giddily Points of wow of opinions stated in this docurnent do not neCettleatily r5Orsonnt orficrat OERI positeon or poircy Teaching Conversation Skills to Individuals with Severe Disabilities with a Communication Book Adaptation Instructional Handbook
State-of-the-art Piezoelectric Transformer technology

To date, piezoelectric transformers (PTs) have been extensively commercialised in step-up applications, such as cold cathode fluorescent lamp backlighting for LCDs. As step-down PT technology matures, PT-based converters are set to replace conventional transformers and power converters in a wide variety of applications that require relatively low power levels, low cost, high efficiency, high power density, small size and weight, low EMI, and high reliability. This paper examines the various types of piezoelectric transformer and provides an overview of the materials technology, converter designs, and control methods involved with their use.
An efficient and robust combined clustering technique for mining in large spatial databases

Mining knowledge from large amounts of spatial data is known as spatial data mining. It becomes a highly demanding field because huge amounts of spatial data have been collected in various applications ranging from geo-spatial data to bio-medical knowledge. The amount of spatial data being collected is increasing exponentially. So, it far exceeded human's ability to analyze. Recently, clustering has been recognized as a primary data mining method for knowledge discovery in spatial database. The database can be clustered in many ways depending on the clustering algorithm employed, parameter settings used, and other factors. Multiple clusterings can be combined so that the final partitioning of data provides better clustering. Applying cluster combinations by using neural networks can yield dramatic improvements in generalization performance. Another problem with most clustering algorithms is that the user must input the desired number of clusters. Quite often the optimal number of clusters is not known prior to execution. The main objective of this paper is to propose an efficient robust combined clustering technique using neural networks for large image databases that does not require a priori knowledge of the proper number of clusters. It only requires the user to provide a maximum number of clusters. Results on real databases are given to show that the proposed robust combined clustering technique can (i) improve quality and robustness, and (ii) enable distributed clustering.
Goal-Oriented Approach to Self-Managing Systems Design

Abstract. This paper is intended to identify issues in design methodology of sophisticated self-managing systems. Whereas self-managing systems are intended to achieve increasingly sophisticated business-level objectives, current agentoriented design methodologies begin requirements gathering and analysis at much lower levels. This leaves a hazardous gap in the system analysis phase of selfmanaging system design. This paper proposes desiderata for design methodology that include requirements gathering from high-level abstract business goals. It proposes structured and systematic means of decomposing and mapping high-level business objectives down to the finer requirements necessary to agent-oriented methodologies. It explores the possibility of applying experience gained from goaloriented software engineering techniques as well as methodologies in integrated management of networks and services. Finally, this paper proposes an evolving high-level notation of design patterns to model self-managing systems in an integrated way.
OX40 Facilitates Control of a Persistent Virus Infection

During acute viral infections, clearance of the pathogen is followed by the contraction of the anti-viral T cell compartment. In contrast, T cell responses need to be maintained over a longer period of time during chronic viral infections in order to control viral replication and to avoid viral spreading. Much is known about inhibitory signals such as through PD-1 that limit T cell activity during chronic viral infection, but little is known about the stimulatory signals that allow maintenance of anti-viral T cells. Here, we show that the co-stimulatory molecule OX40 (CD134) is critically required in the context of persistent LCMV clone 13 infection. Anti-viral T cells express high levels of OX40 in the presence of their cognate antigen and T cells lacking the OX40 receptor fail to accumulate sufficiently. Moreover, the emergence of T cell dependent germinal center responses and LCMV-specific antibodies are severely impaired. Consequently, OX40-deficient mice fail to control LCMV clone 13 infection over time, highlighting the importance of this signaling pathway during persistent viral infection.