Miracle-dz/UniFAR-FTUs huggingface dataset:
2 Review of Agglomerative Hierarchical Clustering Algorithms

Hierarchical methods are well known clustering technique that can be potentially very useful for various data mining tasks. A hierarchical clustering scheme produces a sequence of clusterings in which each clustering is nested into the next clustering in the sequence. Since hierarchical clustering is a greedy search algorithm based on a local search, the merging decision made early in the agglomerative process are not necessarily the right ones. One possible solution to this problem is to refine a clustering produced by the agglomerative hierarchical algorithm to potentially correct the mistakes made early in the agglomerative process. The problem of refining a clustering has many similarities with that of refining a min-cut k-way partitioning of a graph. In this paper, we explore multilevel refinement schemes for refining and improving the clusterings produced by hierarchical agglomerative clustering. This algorithm combines traditional hierarchical clustering with multilevel refinement that has been found to be very effective for computing min-cutk-way partitioning of graphs. We consider several clustering objective functions for the proposed refinement step and investigate the usefulness of these objective functions. Our experimental results demonstrate that this algorithm produces clustering solutions that are consistently and significantly better than those produced by hierarchical clustering algorithms alone. Furthermore, our algorithm has the additional advantage of being extremely fast, as it operates on a sparse similarity matrix. The amount of time required by our algorithm ranged from two second for a data set with 358 items, to 80 seconds for a data set with 9133 items on a Pentium II PC.
A disease-related rheumatoid factor autoantibody is not tolerized in a normal mouse: implications for the origins of autoantibodies in autoimmune disease

We have analyzed B cell tolerance in a rheumatoid factor (RF) transgenic mouse model. The model is based on AM14, a hybridoma, originally isolated from an autoimmune MRL/lpr mouse that has an affinity and specificity typical of disease-related RFs from this strain. AM14 binds to immunoglobulin (Ig)G2a of the "a" allotype (IgG2aa) and not to IgG2ab. Thus, by crossing the transgenes onto an IgHa (BALB/c) background or to a congenic IgHb (CB.17) background, we could study the RF-expressing B cells when they were self-specific (IgHa) or when they were not self-specific (IgHb). These features make the AM14 model unique in focusing on a true autoantibody specificity while at the same time allowing comparison of autoreactive and nonautoreactive transgenic B cells, as was possible in model autoantibody systems such as anti-hen egg lysozyme. Studies showed that AM14 RF B cells can make primary immune responses and do not downregulate sIgM, indicating that the presence of self-antigen does not induce anergy of these cells. In fact, IgHa AM14 transgenic mice have higher serum levels of transgene-encoded RF than their IgHb counterparts, suggesting that self-antigen-specific activation occurs even in the normal mouse background. Since AM14 B cells made primary responses, we had the opportunity to test for potential blocks to self- reactive cells entering the memory compartment. We did not find evidence of this, as AM14 B cells made secondary immune responses as well. These data demonstrate that a precursor of a disease-specific autoantibody can be present in the preimmune repertoire and functional even to the point of memory cell development of normal mice. Therefore, immunoregulatory mechanisms that normally prevent autoantibody production must exert their effects later in B cell development or through T cell tolerance. Conversely, the data suggest that it is not necessary to break central tolerance, even in an autoimmune mouse, to generate pathologic, disease-associated autoantibodies.
An ultra–low power ADC for distributed sensor networks

A successive approximation ADC targeted for use in distributed sensor networks is presented. The individual nodes in these sensor networks are very energy constrained. Typical use of the individual nodes will include long periods of idle time in a low power standby mode followed by a period of activity that may include sampling of the sensors, computation, and communication. The ADC reported here consumes only 3.1 µW, resulting in 31 pJ/8-bit sample at 1V supply with a maximum sampling rate slightly over 100 kS/s. The standby power consumption at 1 V supply is 41 pW. The µ-power consumption makes this one of the lowest power ADCs ever reported.
A Novel Medical Image Segmentation Method using Dynamic Programming

A novel method is proposed to segment objects in medical images whose boundaries can be described as closed curves. Based on an image with the enhanced boundary of an object of interest, the segmentation method consists of three key steps, namely, the polar transformation, dynamic programming and curve fitting. A 3D object in volumetric data can be segmented on a slice-by-slice basis by only specifying one point inside the 3D object of interest as the pole for the polar transformation. The method is also shown to be able to segment objects with very weak boundaries.
Tomlinson-Harashima Precoding in MIMO Systems: A Unified Approach to Transceiver Optimization Based on Multiplicative Schur-Convexity

This paper deals with the design of a multicarrier multiple-input multiple-output system which performs nonlinear Tomlinson-Harashima precoding at the transmitter and linear equalization at the receiver. The goal is the joint optimization of the processing matrices at both ends of the wireless link, assuming a constraint on the overall transmit power. We consider different design criteria which require the maximization/minimization of objective functions that depend on the mean square errors over the individual data streams. A unified framework for the solution of a great number of optimization problems is developed, based on the concept of multiplicative Schur-convexity. We show that many widely used design criteria can be easily accommodated in our framework. Our analysis is centered on two theorems which, in the case of cooperative processing among the different subcarriers, provide closed-form solutions to a number of optimization problems. On the other hand, when an independent processing is assumed at each subcarrier closed-form solutions may not exist. Nonetheless, exploiting the results of the two theorems, we show that for a variety of cost functions the optimization problem can be efficiently solved. Theoretical analysis and computer simulations are used to assess the performance of the proposed schemes and make comparisons with linear transceiver architectures. It turns out that nonlinear precoding provides better results than linear prefiltering when the cost function is multiplicatively Schur-convex. In contrast, when the cost function is multiplicatively Schur-concave the nonlinear scheme is equivalent to the linear one.
Discover, Reuse and Share Knowledge on Service Oriented Architectures

Current Semantic Web frameworks provide a complete infrastructure to manage ontologies schemes easing information retrieval with inference support. Ideally, the use of their frameworks should be transparent and decoupled, avoiding direct dependencies either on the application logic or on the ontology language. Besides there are different logic models used by ontology languages (OWL- Description Logic, OpenCyc-FOL, .) and query languages (RDQL, SPARQL, OWLQL, nRQL, etc.). These facts show integration and interoperability tasks between ontologies and applications are tedious on currently systems. This research provides a general ESB service engine design based on JBI that enables ontology query and reasoning capabilities thought an Enterprise Service Bus. An early prototype that shows how works our research ideas has been developed.
Tracking autonomous entities using rfid technology

GPS seems to be the best solution to develop outdoor location systems, but performance of these systems is not good enough to locate entities within indoor environments, mainly if accuracy and precision are required. In this article we propose a tracking indoor system based on passive RFID technology that is able to accurately locate autonomous entities, such as robots, people, etc, within a defined surface. In order to validate the proposal, we compared our system technology performance against other alternatives built on different technologies (Wi-Fi, Bluetooth, IrDA, ultrasound, etc). We also include the system evaluation and final remarks with future improvements.
Creating large numbers of game AIs by learning behavior for cooperating units

We present two improvements to the hybrid learning method for the shout-ahead architecture for units in the game Battle for Wesnoth. The shout-ahead architecture allows for units to perform decision making in two stages, first determining an action without knowledge of the intentions of other units, then, after communicating the intended action and likewise receiving the intentions of the other units, taking these intentions into account for the final decision on the next action. The decision making uses two rule sets and reinforcement learning is used to learn rule weights (that influence decision making), while evolutionary learning is used to evolve good rule sets. Our improvements add knowledge about terrain to the learning and also evaluate unit behaviors on several scenario maps to learn more general rules. The use of terrain knowledge resulted in improvements in the win percentage of evolved teams between 3 and 14 percentage points for different maps, while using several maps to learn from resulted in nearly similar win percentages on maps not learned from as on the maps learned from.
DirichletRank: Ranking Web Pages Against Link Spams

Anti-spamming has become one of the most important challenges to web search engines and attracted increasing attention in both industry and academia recently. Since most search engines now use link-based ranking algorithms, link-based spamming has become a major threaten. In this paper, we show that the popular link-based ranking algorithm PageRank, while being successfully used in the Google search engine, has a “zero-one gap” flaw, which can be potentially exploited to spam PageRank results easily. The “zero-one gap” problem arises from the current ad hoc way of computing the transition probabilities in the random surfing model. We propose a novel DirichletRank algorithm in a more principled way of computing these probabilities based on Bayesian estimation with a Dirichlet prior. DirichletRank is a variant of PageRank, but it does not have the problem of “zero-one gap” and is analytically shown to be substantially more resistant to link farm spams than PageRank. Simulation experiments using real web data show that, compared with the original PageRank, DirichletRank is significantly more robust against several typical link spams and is more stable under link perturbations, in general. Moreover, experiment results also show that DirichletRank 1 is more effective than PageRank due to its more reasonable allocation of transition probabilities. Since DirichletRank can be computed as efficiently as PageRank, it is scalable to large-scale web applications.
An Overview of Microsoft Deep QA System on Stanford WebQuestions Benchmark

Question answering (QA) over an existing knowledge base (KB) such as Microsoft Satori or open Freebase is one of the most important natural language processing applications. There are approaches based on web-search motivated statistic techniques as well as linguistically oriented knowledge engineering. Both methods face the key challenge on how to handle diverse ways of naturally expressing predicates and entities existing in the KB. The domain independent web information extracted from the massive amount of web usage data can be used with traditional semantic parsing through a unified framework. We provide such a unified framework utilizing both statistically motivated information-theoretic embeddings and logically driven proof-theoretic decoding to significantly improve Stanford’s WebQuestions QA benchmark. In comparison to Stanford’s state of the art ParaSempre 39.9 (F1-score), our Deep QA system achieves 45.3 on the same test data and protocol.
Why Do Floral Perfumes Become Different? Region-Specific Selection on Floral Scent in a Terrestrial Orchid

Geographically structured phenotypic selection can lead to adaptive divergence. However, in flowering plants, such divergent selection has rarely been shown, and selection on floral signals is generally little understood. In this study, we measured phenotypic selection on display size, floral color, and floral scent in four lowland and four mountain populations of the nectar-rewarding terrestrial orchid Gymnadenia odoratissima in two years. We also quantified population differences in these traits and pollinator community composition. Our results show positive selection on display size and positive, negative, or absence of selection on different scent compounds and floral color. Selection on the main scent compounds was consistently stronger in the lowlands than in the mountains, and lowland plants emitted higher amounts of most of these compounds. Pollinator community composition also differed between regions, suggesting different pollinators select for differences in floral volatiles. Overall, our study is the first to document consistent regional differences in selection on floral scent, suggesting this pattern of selection is one of the evolutionary forces contributing to regional divergence in floral chemical signaling.
Understanding concurrent earcons: Applying auditory scene analysis principles to concurrent earcon recognition

Two investigations into the identification of concurrently presented, structured sounds, called earcons were carried out. One of the experiments investigated how varying the number of concurrently presented earcons affected their identification. It was found that varying the number had a significant effect on the proportion of earcons identified. Reducing the number of concurrently presented earcons lead to a general increase in the proportion of presented earcons successfully identified. The second experiment investigated how modifying the earcons and their presentation, using techniques influenced by auditory scene analysis, affected earcon identification. It was found that both modifying the earcons such that each was presented with a unique timbre, and altering their presentation such that there was a 300 ms onset-to-onset time delay between each earcon were found to significantly increase identification. Guidelines were drawn from this work to assist future interface designers when incorporating concurrently presented earcons.
Error recovery in asynchronous systems

A framework for the provision of fault tolerance in asynchronous systems is introduced. The proposal generalizes the form of simple recovery facilities supported by nested atomic actions in which the exception mechanisms only permit backward error recovery. It allows the construction of systems using both forward and backward error recovery and thus allows the exploitation of the complementary benefits of the two schemes. Backward recovery, forward recovery, and normal processing activities can occur concurrently within the organization proposed. Exception handling is generalized to provide a uniform basis for fault tolerance schemes with the atomic action structure. The generalization includes a resolution scheme for concurrently raised exceptions based on an exception tree and an abortion scheme that permits the termination of the internal atomic actions. An automatic resolution mechanism is outlined for exceptions in atomic actions which allows users to separate their recovery schemes from the details of the underlying algorithms.
SUGGEST: a Web usage mining system

During their navigation, Web users leave many records of their activity. This huge amount of data can be a useful source of knowledge. Sophisticated data mining processes are needed for this knowledge to be extracted, understood and used. In this paper, we propose a Web usage mining (WUM) system, called SUGGEST, which dynamically generates suggested links to Web pages of potential interest for a user. SUGGEST is designed to efficiently integrate the WUM process with the ordinary Web server functionalities. It can provide useful information to make the user's Web navigation easier and to optimize the Web server's performance. Two quantities are introduced in order to give a measure of the quality of our WUM system.
Libtrace: a packet capture and analysis library

This paper introduces libtrace, an open-source software library for reading and writing network packet traces. Libtrace offers performance and usability enhancements compared to other libraries that are currently used. We describe the main features of libtrace and demonstrate how the libtrace programming API enables users to easily develop portable trace analysis tools without needing to consider the details of the capture format, file compression or intermediate protocol headers. We compare the performance of libtrace against other trace processing libraries to show that libtrace offers the best compromise between development effort and program run time. As a result, we conclude that libtrace is a valuable contribution to the passive measurement community that will aid the development of better and more reliable trace analysis and network monitoring tools.
Physical activity patterns and factors that facilitate or hinder exercise among adolescents in an all-boys school

The purpose of the present study was to investigate the level of physical activity (PA) in an all-boys school and, using a mixed method design, to identify factors that facilitate or hinder PA engagement. Participants (n = 382) completed the Three Day Physical Activity Recall (3DPAR) questionnaire to enable examination of the pattern of their PA and the amount of time spent on sedentary activities. To understand further the motivation behind their level of PA, 24 participants with the highest and lowest 3DPAR scores were selected for focus groups. Quantitative data of the PA levels of the all-boys sample of this study and those of a mixed sample from a study by Lee and Trost were analysed using percentage means with 95% confidence intervals, with a 2x2 analysis of variance being used for the comparisons. The focus group data were transcribed and thematic analysis was performed on the transcripts. The quantitative results revealed evidence that male students in the current sample were more active in comparison with the students from the mixed sample. The qualitative results indicated that PA participation was facilitated by factors such as intention to maintain health and fitness levels, a strong school sports culture, regular exercise with friends and family, and an individual’s exercise history. In contrast, academic workload, poor exercise history and after-school non-PA related activities were identified as factors that hindered PA participation. The findings are discussed in relation to wider school decisions and policies that have an effect on PA among adolescent students.
Students’ and teacher’s perception of task-based language teaching and the implementation in listening class (Case Study at English Education Department of IKIP-PGRI Pontianak in the Academic Year of 2015/2016)

The objectives of this study are (1) to find the students’ and the teacher’s perception about TBLT in the listening class, to identify their perspective and the practices in teaching listening by using TBLT method. (2) to discover the ways of the teacher in giving the task and the clarity of the instruction. (3) to find the ways of the students in perceiving and accomplishing the tasks in the listening class. (4) to disclose the existing problems in the implementation of TBLT in the listening class.The research method is qualitative study in which the design is case study. It was conducted towards twelve college students and one lecturer of IKIP-PGRI Pontianak in the academic year of 2015/2016. The techniques of collecting data were interview, observation, and documentation. The data were in the forms of interview transcripts, fieldnotes, and documents. The data were analysed by using ground theory to generate the theory that emerges from the reality where the theory is developed from.The findings of the study show that: (1) the students and the teacher have high understanding about TBLT. They believed that it helps them in the process of teaching and learning become more effective. The students and the teacher assumed that TBLT motivates students to be more active by using the actual use of target language. TBLT helps students understand the materials much easier where the learners as the centered in the class activities. The perception and the practices in the listening class by using TBLT method has brought to a) the learners and the teacher have good impressions toward TBLT. b) the process of teaching and learning is suitable with their needs. (2) the teacher claimed that the task has delivered clearly and optimally in the form of spoken and written by using english. (3) each student perceived and accomplished the task in different way. (4) the problems that appear such as students’ vocabulary mastery and the lack of teaching learning facilities.The implication of this study is that the students’ and the teacher’s understanding toward TBLT as well as their roles in the class influences their learning outcomes. The clarity of the instruction also plays important part in fulfilling the task. The students’ understanding toward the instruction effect them to accomplish the task successfully. Therefore, the students and the teacher have to take a part actively toward the implementation of TBLT method.Keywords: Perception, TBLT, Listening
Pulsed Melodic Processing-Using Music for natural Affective Computation and increased Processing Transparency

Pulsed Melodic Processing (PMP) is a computation protocol useable at multiple levels in data processing systems. For example at the level of spikes in an artificial spiking neural network or a pulse processing system, or at the level of exchanged messages and internal processing communication between modules in a multi-agent system or a multi-robot system. The approach utilizes musically-based pulse sets (“melodies”) for processing – capable of representing the arousal and valence of affective states. Affective processing and affective input/output is now considered to be a key tool in artificial intelligence and computing. In the designing of processing elements (e.g. bits, bytes, floats, etc), engineers have primarily focused on the processing efficiency and power. Having defined these elements, they then go on to investigate ways of making them perceivable by the user/engineer. However the extremely active and productive area of Human-Computer Interaction and the increasing complexity and pervasiveness of computation in our daily lives – supports the idea of a complementary approach in which computational efficiency and power are more balanced with understandability to the user/engineer. PMP provides the potential for a person to tap into the affective processing path to hear a sample of what is going on in that computation, as well as providing a simpler way to interface with affective input/output systems. This comes at a cost of developing new approaches to processing and interfacing PMP-based modules this cost being part of the compromise of efficiency/power versus user-transparency and interfacing. In this position paper we introduce and develop PMP; and demonstrate and examine the approach using two example applications: a military robot team simulation with an affective subsystem, and a text affective-content estimation system.