Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrating Temporal Difference Methods and Self-Organizing Neural Networks for Reinforcement Learning With Delayed Evaluative Feedback

This paper presents a neural architecture for learning category nodes encoding mappings across multimodal patterns involving sensory inputs, actions, and rewards. By integrating adaptive resonance theory (ART) and temporal difference (TD) methods, the proposed neural model, called TD fusion architecture for learning, cognition, and navigation (TD-FALCON), enables an autonomous agent to adapt and function in a dynamic environment with immediate as well as delayed evaluative feedback (reinforcement) signals. TD-FALCON learns the value functions of the state-action space estimated through on-policy and off-policy TD learning methods, specifically state-action-reward-state-action (SARSA) and Q-learning. The learned value functions are then used to determine the optimal actions based on an action selection policy. We have developed TD-FALCON systems using various TD learning strategies and compared their performance in terms of task completion, learning speed, as well as time and space efficiency. Experiments based on a minefield navigation task have shown that TD-FALCON systems are able to learn effectively with both immediate and delayed reinforcement and achieve a stable performance in a pace much faster than those of standard gradient-descent-based reinforcement learning systems.
To Be Optimal or Not in Test-Case Prioritization

Software testing aims to assure the quality of software under test. To improve the efficiency of software testing, especially regression testing, test-case prioritization is proposed to schedule the execution order of test cases in software testing. Among various test-case prioritization techniques, the simple additional coverage-based technique, which is a greedy strategy, achieves surprisingly competitive empirical results. To investigate how much difference there is between the order produced by the additional technique and the optimal order in terms of coverage, we conduct a study on various empirical properties of optimal coverage-based test-case prioritization. To enable us to achieve the optimal order in acceptable time for our object programs, we formulate optimal coverage-based test-case prioritization as an integer linear programming (ILP) problem. Then we conduct an empirical study for comparing the optimal technique with the simple additional coverage-based technique. From this empirical study, the optimal technique can only slightly outperform the additional coverage-based technique with no statistically significant difference in terms of coverage, and the latter significantly outperforms the former in terms of either fault detection or execution time. As the optimal technique schedules the execution order of test cases based on their structural coverage rather than detected faults, we further implement the ideal optimal test-case prioritization technique, which schedules the execution order of test cases based on their detected faults. Taking this ideal technique as the upper bound of test-case prioritization, we conduct another empirical study for comparing the optimal technique and the simple additional technique with this ideal technique. From this empirical study, both the optimal technique and the additional technique significantly outperform the ideal technique in terms of coverage, but the latter significantly outperforms the former two techniques in terms of fault detection. Our findings indicate that researchers may need take cautions in pursuing the optimal techniques in test-case prioritization with intermediate goals.
Morphology Generation for Statistical Machine Translation using Deep Learning Techniques

Morphology in unbalanced languages remains a big challenge in the context of machine translation. In this paper, we propose to de-couple machine translation from morphology generation in order to better deal with the problem. We investigate the morphology simplification with a reasonable trade-off between expected gain and generation complexity. For the Chinese-Spanish task, optimum morphological simplification is in gender and number. For this purpose, we design a new classification architecture which, compared to other standard machine learning techniques, obtains the best results. This proposed neural-based architecture consists of several layers: an embedding, a convolutional followed by a recurrent neural network and, finally, ends with sigmoid and softmax layers. We obtain classification results over 98% accuracy in gender classification, over 93% in number classification, and an overall translation improvement of 0.7 METEOR.
Blood Transfusions in Total Hip and Knee Arthroplasty: An Analysis of Outcomes

Background. Various studies have raised concern of worse outcomes in patients receiving blood transfusions perioperatively compared to those who do not. In this study we attempted to determine the proportion of perioperative complications in the orthopedic population attributable to the use of a blood transfusion. Methods. Data from 400 hospitals in the United States were used to identify patients undergoing total hip or knee arthroplasty (THA and TKA) from 2006 to 2010. Patient and health care demographics, as well as comorbidities and perioperative outcomes were compared. Multivariable logistic regression models were fitted to determine associations between transfusion, age, and comorbidities and various perioperative outcomes. Population attributable fraction (PAF) was determined to measure the proportion of outcome attributable to transfusion and other risk factors. Results. Of 530,089 patients, 18.93% received a blood transfusion during their hospitalization. Patients requiring blood transfusion were significantly older and showed a higher comorbidity burden. In addition, these patients had significantly higher rates of major complications and a longer length of hospitalization. The logistic regression models showed that transfused patients were more likely to have adverse health outcomes than nontransfused patients. However, patients who were older or had preexisting diseases carried a higher risk than use of a transfusion for these outcomes. The need for a blood transfusion explained 9.51% (95% CI 9.12–9.90) of all major complications. Conclusions. Advanced age and high comorbidity may be responsible for a higher proportion of adverse outcomes in THA and TKA patients than blood transfusions.
Composing Specifications Using Algebra Combinators

The need to understand effects of cross-cutting concerns defines the essence of systems-level design. Understanding the impacts of local design decisions on global requirements such as power consumption or security is mandatory for constructing correct systems. Unfortunately, domain specific models may be defined using different semantics making analysis difficulty. We define an algebra combinator that provide semantics for model composition. Given two models defined over a common abstract syntax, an algebra combinator defines a single model that embodies the composition of those specifications. Such composite models can then be used to understand the interaction of models from the original specification domains.
Towards Value-based Design Patterns for Inter-Organizational Control

We present control patterns: a framework for designing and analyzing interorganizational control mechanisms, inspired by design patterns. A control pattern is a generic solution for some recurring control problem, applicable in a certain context. The patterns are based on internal control theory from the accounting and auditing fields, and on previous work on inter-organizational controls. The application of the patterns is supported by the e 3 -control methodology, which is based on the e 3 -value business modeling tool. The patterns are applied in a case study.
Wafer-level integration of on-chip antennas and RF passives using high-resistivity polysilicon substrate technology

High-resistivity polycrystalline silicon (HRPS) wafers are utilized as low-loss substrates for three-dimensional integration of on-chip antennas and RF passive components (e.g. large inductors) in wafer-level chip-scale packages (WLCSP). Sandwiching of HRPS and silicon wafers enables to integrate large RF passives with a spacing of >150 /spl mu/m to the conductive silicon substrate containing the circuitry, while providing mechanical stability, reducing form factor and avoiding any additional RF loss. Antenna performance comparable to glass substrates and high quality factors for large spiral inductors (Q=11 at 1 GHz; 34 nH) are demonstrated. The HRPS substrates have high dielectric constant, low RF loss, high thermal conductivity, perfect thermal matching, and processing similar to single-crystalline silicon.
Experience‐dependent regulation of TrkB isoforms in rodent visual cortex

Within primary visual cortex (V1), brain‐derived neurotrophic factor (BDNF) signaling through its high‐affinity receptor TrkB is important for normal development and experience‐dependent plasticity. TrkB is expressed in several alternatively spliced isoforms, including full‐length TrkB (TrkB.FL), and several truncated isoforms (TrkB.T1, TrkB.T2, and TrkB.T4) that lack the intracellular tyrosine kinase domain. These isoforms are important components of BDNF signaling, yet little is known about the developmental or experience‐dependent regulation of their expression. Using immunohistochemistry, we found TrkB.FL and TrkB.T1 expressed in interneurons and pyramidal neurons within V1, but not in cortical astrocytes. We used real‐time PCR to quantify the changes in mRNA expression of BDNF, the four TrkB isoforms, and the low‐affinity receptor P75NTR during normal development, and in response to visual deprivation at two different ages. BDNF expression increased between postnatal days 10 (P10) and P30, and was rapidly down‐regulated by 3 days of visual deprivation during both the pre‐critical period (P14‐P17) and the critical period (P18‐P21). Over the same developmental period, expression of each TrkB isoform was regulated independently; TrkB.T1 increased, TrkB.FL and TrkB.T2 decreased, and TrkB.T4 showed transient changes. Neither brief visual deprivation nor prolonged dark‐rearing induced changes in either TrkB.FL or TrkB.T1 expression. However, TrkB.T4 expression was reduced by brief visual deprivation, whereas TrkB.T4, TrkB.T2 and P75NTR were up‐regulated by prolonged dark‐rearing into the critical period. Our data indicate that TrkB isoform expression can be selectively regulated by visual experience, and may contribute to experience‐dependent cortical plasticity. © 2009 Wiley Periodicals, Inc. Develop Neurobiol 2009
Applying genetic algorithm to select an optimal cricket team

In this paper we discuss how standard genetic algorithm (SGA) could be applied to get an optimal cricket team from a set of 50 national level players. Since a cricket team needs to be more flexible, balanced and diverse, we defined fitness function to check the optimality of the team, and how the optimality changes with the change in gene composition of chromosome. The proposed method takes into account several important factors that affect a team combination like: No. of pacers and spinners, composition of left hander and right hander, partnership records etc. Starting from a random initial population, the normal SGA operations (selection, reproduction and mutation) to get the next population set and the process is iteratedtill an optimal team is produced or a fixed number of times. Theproposed system is made very generic and it can be applied for any game by just modifying the fitness function.
Quality of Data in mediation systems

Quality of Data (QoD) became a major concern which more and more considers necessary to evolve the satisfaction of final users. In this work, we are approaching a methodology which consists of classifying and listing factors impacting Data Quality in Data Integration Systems, and more literally these which influence mediation systems and which will constitute our long-term research axis. Moreover, we shall propose in this work a contribution in the form of an attempt to define main factors impacting Quality of Data.
AZD2014 Radiosensitizes Oral Squamous Cell Carcinoma by Inhibiting AKT/mTOR Axis and Inducing G1/G2/M Cell Cycle Arrest

Background Oral squamous cell carcinoma (OSCC) is one of the most common malignant neoplasms in Taiwan. Activation of the mTOR signaling pathway has been linked to decreased radiation responsiveness in human oral cancer, thus it limits efficacy of radiotherapy. To address this question, we investigated the effect of AZD2014, a novel small molecular ATP-competitive inhibitor of mTORC1 and mTORC2 kinase, as a radiosensitizer in primary OSCC and OSCC-derived cell line models. Methods We isolated primary tumor cells from OSCC tissues and cell lines. AZD2014 was administered with and without ionizing radiation. The radiosensitizing effect of AZD2014 were then assessed using cell viability assays, clonogenic survival assays, and cell cycle analyses. Western blotting was used to detect protein expression. Results Combination treatment with AZD2014 and irradiation resulted in significant reduction in OSCC cell line and primary OSCC cell colony formation due to the enhanced inhibition of AKT and both mTORC1 and mTORC2 activity. Pre-treatment with AZD2014 in irradiated oral cancer cells induced tumor cell cycle arrest at the G1 and G2/M phases, which led to disruption of cyclin D1-CDK4 and cyclin B1-CDC2 complexes. Moreover, AZD2014 synergized with radiation to promote both apoptosis and autophagy by increasing caspase-3 and LC3 in primary OSCC cells. Conclusions These findings suggest that in irradiated OSCC cells, co-treatment with AZD2014, which targets mTORC1 and mTORC2 blockade, is an effective radiosensitizing strategy for oral squamous cell carcinoma.
A (hopefully) Unbiased Universal Environment Class for Measuring Intelligence of Biological and Artificial Systems

The measurement of intelligence is usually associated with the performance over a selection of tasks or environments. The most general approach in this line is called Universal Intelligence, which assigns a probability to each possible environment according to several constructs derived from Kolmogorov complexity. In this context, new testing paradigms are being defined in order to devise intelligence tests which are anytime and universal: valid for both artificial intelligent systems and biological systems, of any intelligence degree and of any speed. In this paper, we address one of the pieces in this puzzle: the definition of a general, unbiased, universal class of environments such that they are appropriate for intelligence tests. By appropriate we mean that the environments are discriminative and that they can be feasibly built, in such a way that the environments can be automatically generated and their complexity can be computed.
Achieving Approximate Soft Clustering in Data Streams

In recent years, data streaming has gained prominence due to advances in technologies that enable many applications to generate continuous flows of data. This increases the need to develop algorithms that are able to efficiently process data streams. Additionally, real-time requirements and evolving nature of data streams make stream mining problems, including clustering, challenging research problems. In this paper, we propose a one-pass streaming soft clustering (membership of a point in a cluster is described by a distribution) algorithm which approximates the "soft" version of the k-means objective function. Soft clustering has applications in various aspects of databases and machine learning including density estimation and learning mixture models. We first achieve a simple pseudo-approximation in terms of the "hard" k-means algorithm, where the algorithm is allowed to output more than $k$ centers. We convert this batch algorithm to a streaming one (using an extension of the k-means++ algorithm recently proposed) in the "cash register" model. We also extend this algorithm when the clustering is done over a moving window in the data stream.
Compelled to Speak: Addressing student reticence in a university EFL classroom

This paper reports on an action research investigation of a classroom activity called stand-up, designed to address student reluctance to express ideas and engage in whole-class discussion. It is set within a tertiary EFL context in Japan where students are typical reticent. To promote more active participation, students were asked to stand and volunteer an answer before they could sit down. Student opinions about the activity were collected in anonymous surveys, and data were tabulated and analysed using qualitative procedures. It was found that, though there was some misgiving and anxiety, students were strongly supportive of the activity, in part for what many saw as a needed push toward engagement, in part because participation was seen as fairly distributed and concentration was strengthened. Although the findings reflect only one teacher's approach to solving a problem of interaction, the study carries broader implications, suggesting that the L2 classroom can be a site of cultural transformation, leading to new patterns of interaction and ways of expression.
Joint learning hash codes and distance metric for visual tracking

In this paper, we propose a novel tracking algorithm based on joint learning hash codes and distance metric. We formulate the visual tracking as an Approximate Nearest Neighbor (ANN) searching process in which hashing methods have achieved promising performances. But most existing hashing methods rely on an affinity or similarity matrix measured by simple Euclidean distance. To obtain more robust hash codes for tracking, we utilize distance metric learning method to measure the similarity. We propose a joint learning hash codes and distance metric algorithm for visual tracking and a fast solution is developed to solve these two problems simultaneously by cross gradient descent. Then we use the learnt hash function to encode the templates and candidates and conduct the ANN searching. Extensive experiments on various challenging sequences show that the proposed algorithm performs favorably against the state-of-the-art methods.
Constraint Restrictiveness versus Local and Global Consistency

Constraint networks are a simple representation and reasoning framework with diverse applications. In this paper, we identify two new complementary properties on the restrictiveness of the constraints in a network| constraint tightness and constraint looseness|and we show their usefulness for estimating the level of local consistency needed to ensure global consistency, and for estimating the level of local consistency present in a network. In particular, we present a su cient condition, based on constraint tightness and the level of local consistency, that guarantees that a solution can be found in a backtrack-free manner. The condition can be useful in applications where a knowledge base will be queried over and over and the preprocessing costs can be amortized over many queries. We also present a su cient condition for local consistency, based on constraint looseness, that is straightforward and inexpensive to determine. The condition can be used to estimate the level of local consistency of a network. This in turn can be used in deciding whether it would be useful to preprocess the network before a backtracking search, and in deciding which local consistency conditions, if any, still need to be enforced if we want to ensure that a solution can be found in a backtrack-free manner. Two de nitions of local consistency are employed in characterizing the conditions: the traditional variable-based notion and a new de nition of local consistency called relational consistency. New algorithms for enforcing relational consistency are introduced and analyzed1 . Some of the results reported in this paper have previously appeared at KR-94 [32] and AAAI-94 [31].
Robust H-Infinity Filtering on Stochastic Uncertain Systems

This paper is concerned with the problem of robust Hinfin filtering on stochastic uncertain systems subject to parameter uncertainties in both state matrix and output matrix of the state space model. The parameter uncertainty is assumed to be time- varying norm-bounded The aim is to design a filter by auxiliary systems, which ensures both the robustly stochastic stability and prescribed level of Hinfin performance for the filtering error dynamics for all admissible uncertainties. The derivation process is simplified by introducing auxiliary systems and the sufficient condition and necessary condition are obtained for the existence of Hinfin filter. During the study, the main results were expressed as LMIs by employing various matrix techniques. Using LMI toolbox of Matlab software, it is very convenient to obtain the appropriate filter. Finally, a numerical example shows that the method is effective and feasible.
Teachers being creative : technology in drama?

The importance of creativity in the teaching of Drama in secondary schools has long been accepted (Davis 2010) and during the last 20 years, the importance of technology in the teaching of any school subject has grown. This thesis explores the journey of two Drama teachers as they attempted to develop their creative use of technology. My findings contribute to increased understanding of the challenges in developing the creative use of technology in Drama teaching. The key research question focussed on whether the participants were able to use technology creatively. The research used a naturalistic method of enquiry, based in two similar schools in the same county. An action research cycle took place over three phases during one academic year. The data collection methods used were video recordings of the Year 7 lessons focussing on the teacher with both participants blogging about their experiences. The data was analyzed using Template analysis and Critical Incident analysis. The research challenges the view that creativity has a definitive meaning, rather that creativity is a fluid set of concepts that can be applied in a variety of styles in the classroom. The main findings of the research reveal the evolution of the creative attitudes of the participants as their technology use developed. This research contributes to the theoretical knowledge of the concept of creativity in education and its practical applications in the classroom. The study reveals the creative journey of both participants, the divergence of these journeys and the potential to enhance the creative experience for teachers in our schools. It also challenges the belief that creativity in the classroom can be generated using a pre-determined set of approaches.