Miracle-dz/UniFAR-FTUs huggingface dataset:
HiveRel: Towards Focused Knowledge Acquisition

In this paper we argue that focused knowledge acquisition -- knowledge acquisition in user-defined query context -- can be best achieved through presentation of relationships between search results for a given user query. We suggest that in order to expose relationships while maintaining relevance-based layout of search results, presentation of search results could benefit from using a two-dimensional layout instead of the conventional one-dimensional ranked list. We introduce HiveRel, a search system that presents search results as tiled hexagons on a map-like surface with center-out relevance ordering, in which relationships are displayed on-demand. A user study compared HiveRel to standard web search over a range of focused knowledge acquisition tasks and two different domains. The results indicate that users completed the tasks faster and with comparable quality using HiveRel, and rated it positively.
Longitudinal study of cone photoreceptors during retinal degeneration and in response to ciliary neurotrophic factor treatment.

PURPOSE To study cone photoreceptor structure and function in patients with inherited retinal degenerations treated with sustained-release ciliary neurotrophic factor (CNTF). METHODS Two patients with retinitis pigmentosa and one with Usher syndrome type 2 who participated in a phase 2 clinical trial received CNTF delivered by an encapsulated cell technology implant in one eye and sham surgery in the contralateral eye. Patients were followed longitudinally over 30 to 35 months. Adaptive optics scanning laser ophthalmoscopy (AOSLO) provided high-resolution images at baseline and at 3, 6, 12, 18, and 24 months. AOSLO measures of cone spacing and density and optical coherence tomography measures of retinal thickness were correlated with visual function, including visual acuity (VA), visual field sensitivity, and full-field electroretinography (ERG). RESULTS No significant changes in VA, visual field sensitivity, or ERG responses were observed in either eye of the three patients over 24 months. Outer retinal layers were significantly thicker in CNTF-treated eyes than in sham-treated eyes (P < 0.005). Cone spacing increased by 2.9% more per year in sham-treated eyes than in CNTF-treated eyes (P < 0.001, linear mixed model), and cone density decreased by 9.1%, or 223 cones/degree(2) more per year in sham-treated than in CNTF-treated eyes (P = 0.002, linear mixed model). CONCLUSIONS AOSLO images provided a sensitive measure of disease progression and treatment response in patients with inherited retinal degenerations. Larger studies of cone structure using high-resolution imaging techniques are urgently needed to evaluate the effect of CNTF treatment in patients with inherited retinal degenerations.
Dynamic synapses as archives of synaptic history: state‐dependent redistribution of synaptic efficacy in the rat hippocampal CA1

Plastic modifications of synaptic strength are putative mechanisms underlying information processing in the brain, including memory storage, signal integration and filtering. Here we describe a dynamic interplay between short‐term and long‐term synaptic plasticity. At rat hippocampal CA1 synapses, induction of both long‐term potentiation (LTP) and depression (LTD) was accompanied by changes in the profile of short‐term plasticity, termed redistribution of synaptic efficacy (RSE). RSE was presynaptically expressed and associated in part with a persistent alteration in hyperpolarization‐activated Ih channel activity. Already potentiated synapses were still capable of showing RSE in response to additional LTP‐triggering stimulation. Strikingly, RSE took place even after reversal of LTP or LTD, that is, the same synapse can display different levels of short‐term plasticity without changing synaptic efficacy for the initial spike in burst presynaptic firing, thereby modulating spike transmission in a firing rate‐dependent manner. Thus, the history of long‐term synaptic plasticity is registered in the form of short‐term plasticity, and RSE extends the information storage capacity of a synapse and adds another dimension of functional complexity to neuronal operations.
Modified Monthly Oceanic Rain-Rate Algorithm to Account for TRMM Boost

We provide a modified algorithm for computing Tropical Rainfall Measuring Mission (TRMM) monthly oceanic rain rates (3A11) for TRMM Microwave Imager data using the Microwave Emission Brightness Temperature Histogram (METH) technique developed by Wilheit Shin and Chiu examined changes associated with TRMM boost by adjusting the microwave brightness temperature (Tb) for the postboost data to match the preboost data. We computed a new relation between Tb and rain rate (Tb-R) for the postboost characteristics using the same radiative transfer model of Wilheit and modified the algorithm by providing two Tb-R relations for the preboost and postboost era, respectively. The modified algorithm is applied to TRMM data without Tb adjustment. Preliminary results show a significant improvement over the unmodified algorithm in terms of biases and linear trends. Their differences on the application of the METH technique to other microwave data for climate-scale rainfall are discussed.
SenseTK: a multimodal, multimedia sensor networking toolkit

This paper describes the design and implementation of a multi-modal, multimedia capable sensor networking framework called SenseTK. SenseTK allows application writers to easily construct multi-modal, multimedia sensor networks that include both traditional scalar-based sensors as well as sensors capable of recording sound and video. The distinguishing features of such systems include the need to push application processing deep within the sensor network, the need to bridge extremely low power and low computation devices, and the need to distribute and manage such systems. This paper describes the design and implementation of SenseTK and provides several diverse examples to show the flexibility and unique aspects of SenseTK. Finally, we experimentally measure several aspects of SenseTK.
HIF-1α Transgenic Bone Marrow Cells Can Promote Tissue Repair in Cases of Corticosteroid-Induced Osteonecrosis of the Femoral Head in Rabbits

Although corticosteroid-induced osteonecrosis of the femoral head (ONFH) is common, the treatment for it remains limited and largely ineffective. We examined whether implantation of hypoxia inducible factor-1α (HIF-1α) transgenic bone marrow cells (BMCs) can promote the repair of the necrotic area of corticosteroid-induced ONFH. In this study, we confirmed that HIF-1α gene transfection could enhance mRNA expression of osteogenic genes in BMCs in vitro. Alkaline phosphatase activity assay and alizarin red-S staining indicated HIF-1α transgenic BMCs had enhanced osteogenic differentiation capacity in vitro. Furthermore, enzyme linked immunosorbent assay (ELISA) for VEGF revealed HIF-1α transgenic BMCs secreted more VEGF as compared to normal BMCs. An experimental rabbit model of early-stage corticosteroid-induced ONFH was established and used for an evaluation of cytotherapy. Transplantation of HIF-1α transgenic BMCs dramatically improved the bone regeneration of the necrotic area of the femoral head. The number and volume of blood vessel were significantly increased in the necrotic area of the femoral head compared to the control groups. These results support HIF-1α transgenic BMCs have enhanced osteogenic and angiogenic activity in vitro and in vivo. Transplantation of HIF-1α transgenic BMCs can potentially promote the repair of the necrotic area of corticosteroid-induced ONFH.
Dynamic Hybrid Model for Short-Term Electricity Price Forecasting

Accurate forecasting tools are essential in the operation of electric power systems, especially in deregulated electricity markets. Electricity price forecasting is necessary for all market participants to optimize their portfolios. In this paper we propose a hybrid method approach for short-term hourly electricity price forecasting. The paper combines statistical techniques for pre-processing of data and a multi-layer (MLP) neural network for forecasting electricity price and price spike detection. Based on statistical analysis, days are arranged into several categories. Similar days are examined by correlation significance of the historical data. Factors impacting the electricity price forecasting, including historical price factors, load factors and wind production factors are discussed. A price spike index (CWI) is defined for spike detection and forecasting. Using proposed approach we created several forecasting models of diverse model complexity. The method is validated using the European Energy Exchange (EEX) electricity price data records. Finally, results are discussed with respect to price volatility, with emphasis on the price forecasting accuracy.
Analysing honeybees' division of labour in broodcare by a multi-agent model

We describe a multi-agent model of a honeybee colony and show several applications of the model that simulate experiments that have been performed with real honeybees. Our special emphasis was on the decentralized, self-organized regulation of brood nursing, which we successfully simulated: We found that brood manipulations, food-deprivation experiments and colony-size manipulations can be explained by the mechanisms we implemented into our model described here. Our agents can perform various tasks (foraging, storing, nursing). The model is spatially resolved, and contains a designated broodnest area as well as a designated honey/nectar storage area. All bees (and larvae) consume nectar/honey at a task-specific rate, allowing us to track the flow of nectar through the colony. Several kinds of stimuli, which are important for division of labour, were modelled in detail: dances, contact stimuli and chemical signals.
On k-Connectivity and Minimum Vertex Degree in Random s-Intersection Graphs

Random $s$-intersection graphs have recently received much interest in a wide range of application areas. Broadly speaking, a random $s$-intersection graph is constructed by first assigning each vertex a set of items in some random manner, and then putting an undirected edge between all pairs of vertices that share at least $s$ items (the graph is called a random intersection graph when $s=1$). A special case of particular interest is a uniform random $s$-intersection graph, where each vertex independently selects the same number of items uniformly at random from a common item pool. Another important case is a binomial random $s$-intersection graph, where each item from a pool is independently assigned to each vertex with the same probability. Both models have found numerous applications thus far including cryptanalysis, and the modeling of recommender systems, secure sensor networks, online social networks, trust networks and small-world networks (uniform random $s$-intersection graphs), as well as clustering analysis, classification, and the design of integrated circuits (binomial random $s$-intersection graphs). In this paper, for binomial/uniform random $s$-intersection graphs, we present results related to $k$-connectivity and minimum vertex degree. Specifically, we derive the asymptotically exact probabilities and zero-one laws for the following three properties: (i) $k$-vertex-connectivity, (ii) $k$-edge-connectivity and (iii) the property of minimum vertex degree being at least $k$.
Delphi Study of the Cognitive Skills of Experienced Software Developers

In the present paper, a qualitative research of the cognitive skills of experienced software developers is presented. The data for the research was gathered using the Delphi method. The re- spondents were 11 software developers who have worked at least five years after their graduation. Two questionnaire rounds were conducted. In the first round, the respondents mentioned altogether 32 different skills. In the second round, 10 of the respondents answered and evaluated the difficulty of these 32 skills (e.g., does the skill efficiently differentiate experts from novices). The results are divided into two categories: composition and comprehension. Approximately 40% of the skills were classified into the category "comprehension. " For each skill, the evaluated degree of diffi- culty of the skill is presented. In the category comprehension, skills related to comprehension of a program or a problem as a whole were evaluated as the most difficult.
Apprehension in Learning English

The explosion of technology in the modern years has paved way for global interaction in English. Learning English has become mandatory and fashion of the day. Meanwhile it has become a frightful experience to learners. They are put in an uncomfortable emotional state in which they sense powerless and experience stress in using English. Learners’ self perceptions of the mastery of English, communication apprehension felt towards teachers and peers, tests in an unfamiliar language, teachers’ questions and corrections in the classroom environment, decline in the regular usage of English for communication and the negative attitudes towards English courses may be some of the reasons for the stimulation of the anxiety in learning English. Now, the process of learning English has become simpler because of the umpteen numbers of online and offline resources. Though learning English has become easy, English is still atypical to many ordinary people. The learners have to overcome the threats that loom over its existence. One of the best methods to overcome the language anxiety is to THINK IN ENGLISH.
Career Cruising Impact on the Self Efficacy of Deciding Majors.

The purpose of this study was to analyze the impact of Career Cruising on selfefficacy of deciding majors in a university setting. The use of the self-assessment instrument, Career Cruising), was used with measuring the career-decision making selfefficacy in a pre and post-test with deciding majors. The independent variables are the Career Cruising©, self-assessment instrument, gender, age, ethnic background, year in college, GPA, generation of education, and involvement. The dependent variables are the levels of selfefficacy in the five subscales of pre and post-test results. Those subscales include Accurate Self-appraisal, Gathering Occupational Information, Goal Selection, Planning for the Future, and Problem Solving. The study involved 73 deciding majors through the Office of Academic Advising at the University of Northern Iowa. The theoretical framework used was academic advising, student development theory, self-efficacy and career decision making self-efficacy. The instrument was the Career Decision Self-EfficacyShort Form (CDSE-SF) used in a pre and posttest methodology. The treatment of a data was analyzed using a paired t-test and independent t-test to measure any differences in mean scores. The results of the study indicated a slight increase in self-efficacy for students who took Career Cruising© combined with academic advising. Academic advisors including discovery majors like recreation may consider using Career Cruising© when advising. The results of this study have continued to build on the body of knowledge associated with deciding majors, Career Cruising©, and career decision self-efficacy. Further research on the topic of Career Cruising© and deciding majors should be conducted.
Hardness and tractability of detecting connected communities

We say that there is a community structure in a graph when the nodes of the graph can be partitioned into groups (communities) such that each group is internally more densely connected than with the rest of the graph. However, the challenge seems to specify what is to be dense, and what is relatively more connected (there seems to exist a similar situation to what is a cluster in unsupervised learning). Recently, Olsen [13] provided a general definition that is significantly more generic that others. We make two observations regarding such definition. First, we show that finding a community structure with k connected equal size communities is NP-complete. Then, we show that this problem can be solved efficiently on trees. Finally, we observed that every tree has a 2-community structure. This result is based on a reduction from an extremely popular heuristic (the Girvan-Neumann algorithm [12]) for detecting communities in large networks.
Detecting Unknown Worms Using Randomness Check

From the appearance of CodeRed and SQL Slammer worm, we have learned that the early detection of worm epidemics is important to reduce the damage caused by their outbreak One prominent characteristic of Internet worms is to choose next targets randomly by using a random generator In this paper, we propose a new worm detection mechanism by checking the random distribution of destination addresses Our mechanism generates the traffic matrix and checks the value of rank of it to detect the spreading of Internet worms From the fact that a random binary matrix holds a high value of rank, ADUR (Anomaly Detection Using Randomness check) is proposed for detecting unknown worms based on the rank of the traffic matrix From the experiments on various environments, we show that the ADUR mechanism effectively detects the spread of new worms in an early stage, even when there is only one host infected in a monitoring network.
Acculturation Experiences of the Ethiopian Diaspora in USA: The Case of Las Vegas, Nevada

Research findings indicate that migration from homeland is commonly associated with different kinds of acculturation problems. Other evidences indicate that migration is an opportunity for better life particularly when life at home is seriously negotiated. The third view holds that the outcome of migration is rather a function of the personal journey of the immigrant per se as well as the accommodative characteristics of the host country. Along these lines of arguments, the objective of this study was to assess the acculturation experiences of the Ethiopian Diaspora in Las Vegas. A total of 118 participants were drawn to constitute the sample from among the Ethiopian diaspora living in Las Vegas at the time of data collection. Both quantitative (questionnaire) and qualitative (interview) tools were used to generate data. The questionnaire constituted the ‘Acculturation Strategy Scale’ (Berry,2006), ‘Self-Evaluated Language Proficiency Scale’ (Marin and Gamba, 1996),‘Acculturative Stress Scale’(Mena et al. cited in Tafoya, 2011) and ‘Coping Scale’(Carver, 1997). Interview guide was developed and held with some immigrants to explore their acculturation experiences. Descriptive statistics, one sample t-test, independent t-test, correlation analysis and analysis of variance (ANOVA) were successively employed to analyse the quantitative data. Major findings indicated that Ethiopian immigrants in Las Vegas were well integrated into the American culture, had high level of language proficiency, weren’t vulnerable to acculturative stress and employed positive coping to dealing with stressful experiences and migration related difficulties. It was recommended that establishing and strengthening an Ethiopian Community in Las Vegas may even boost these acculturation experiences further. It was also recommended to take lessons from Las Vegas and scale up support strategies to Ethiopian diaspora in other parts of the Globe. Keywords: Acculturation experience, acculturation strategy, acculturative stress, coping, Ethiopian Diaspora, language proficiency, migration
Comparative serum proteome expression of the steroid-induced femoral head osteonecrosis in adults

Steroid-induced osteonecrosis of the femoral head (SONFH) is a disabling, aseptic and ischemic disease that develops following steroid therapy. The pathogenesis of SONFH is unclear, so the early diagnosis and treatment for this disease is yet to be established. The purpose of the present study was to identify potential biomarkers for SONFH. The differential expression of serum proteins from patients with SONFH and healthy volunteers was analyzed by the proteomics method. The protein samples were labeled and subjected to isoelectric focusing and two-dimensional gel electrophoresis. The resultant protein spots were matched and quantified by an imaging analysis system. The differentially-expressed protein spots were subjected to in-gel trypsin digestion followed by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Significantly lower levels of complement component 3 (C3), C4, inter-α-trypsin inhibitor heavy chain H4 and α-2-macroglobulin were found in the serum of patients with SONFH. These proteins are reported to be actively involved in intravascular coagulation, apoptosis and reactive oxygen species imbalance, indicating that multiple pathological reactions occur in SONFH and these proteins may serve as potential biomarkers for the diagnosis of SONFH.
Mining Data from Interactions with a Motivational-Aware Tutoring System Using Data Visualization.

Tutoring systems are a common tool for delivering educational content and recent advances in this field include the detection of and reaction to learners' motivation. A data set derived from interactions in a tutoring system and its motivationally-aware variant provided opportunities to discover patterns of behavior in connection with motivational feedback. The data collected consists of individual log files capturing the behavior of the learner during his/her interaction with the system. To mine this data, techniques were employed to discover patterns of interest when motivational scaffolding was provided by the tutoring system. A graph was constructed to visualize these patterns and to identify significant transitions derived from dyads of actions. This is a first step towards analyzing behaviors when motivational scaffolding is provided in a tutoring system. Work for the future consists of investigating the patterns' impact on learning with the motivationally-aware tutoring system.
Understanding and Using Factor Scores: Considerations for the Applied Researcher

Following an exploratory factor analysis, factor scores may be computed and used in subsequent analyses. Factor scores are composite variables which provide information about an individual’s placement on the factor(s). This article discusses popular methods to create factor scores under two different classes: refined and non-refined. Strengths and considerations of the various methods, and for using factor scores in general, are discussed. Exploratory factor analysis (EFA) has been used as an analytical tool in educational research. The methods may be used with novel or exploratory research scenarios as a precursor to latent variable modeling or confirmatory factor analyses (CFA) (Schumaker & Lomax, 2004). However, in many research situations, EFA is used as the focal methodology. Practitioners may use EFA for a variety of purposes such as reducing a large number of items from a questionnaire or survey instrument to a smaller number of components, uncovering latent dimensions underlying a data set, or examining which items have the strongest association with a given factor. Once a researcher has used EFA and has identified the number of factors or components underlying a data set, he/she may wish to use the information about the factors in subsequent analyses (Gorsuch, 1983). For example, researchers may want to identify an individual’s placement or ranking on the factor(s), use the information with hypothesis tests to determine how factor scores differ between groups, or to incorporate factor information as part of a regression or predictive analysis. To use EFA information in follow-up studies, the researcher must create scores to represent each individual’s placement on the factor(s) identified from the EFA. These