Miracle-dz/UniFAR-FTUs huggingface dataset:
I-SI : Scalable Architecture of Analyzing Latent Topical-Level Information From Social Media Data

We present a general visual analytics architecture that is constructed and implemented to effectively analyze un-structured social media data on a large scale. Pipelined based on a high-performance cluster configuration, MPI processing, and interactive visual analytics interfaces, our architecture, I-SI, closely integrates data-driven analytical methods and user-centered visual analytics. It creates a coherent analysis environment for identifying event structures, geographical distributions, and key indicators of emerging events. This environment can support monitoring, analyzing, and responding to latent information extracted from social media. We have applied the I-SI architecture to collect social media data, analyze the data on a large scale and uncover the latent social phenomena. To demonstrate the efficacy and applicability of I-SI, we describe several social media use cases in multiple domains that were evaluated by experts. The use cases demonstrate that I-SI can benefit a range of users by constructing meaningful event structures and identifying precursors to critical events within a rich, evolving set of topics. Categories and Subject Descriptors (according to ACM CCS): D.2. 11 [Software Architectures]: Domain-specific architectures
Towards software autogeneration

Program generators are usually aimed for the generation of program source code. This paper introduces the idea of software source code generation and its execution on demand that we refer to as Autogeneration. Autogeneration avoids the generation of program files by using the possibility of scripting languages to evaluate program code from variables. There are several features that could be achieved by Autogeneration. Some of them are program update during its execution, optimized code without temporarily unnecessary instructions and introspection of the generation process for development purposes. An example of a web application for database content management that is implemented as an autogeneration process is presented and discussed.
Wavelength division multiplexed photonic layer on CMOS

Optical network on chip (ONoC) architectures are emerging as potential contenders to solve congestion and latency issues in future computing architectures. This paper describes WADIMOS, an EU funded research project aiming to demonstrate a complex photonic interconnect layer on CMOS. This incorporated multichannel microsources, microdetectors and various advanced wavelength routing functions directly integrated with electronic driver circuits. Design methods and system-level models compatible with an industrial NoC exploration environment were developed to enable exploration of application scenarios for such electro-photonic ICs, in an on-chip optical network context.
Applications of Switching Control in Robot Locomotion Workshop on Intelligent Control in Robotics and Automation

In this paper we discuss the application of switching control to problems in intelligent robot control. We will show a connection between research issues in robot locomotion and issues in robot gymnastics, and robot air hockey. These problems are fundamentally related to the control of redundant and underactuated robots. We discuss the general problem of switching control and relate these concepts to problems of swingup and balance of gymnastic robots and gait control in bipedal locomotion. We also discuss the relationships among hybrid control, impulsive manipulation, visually guided locomotion, and robot air hockey.
Expectations, interactions between agents and technological regimes

– We argue here two types of interactions within an organization are crucial for understanding how the making of expectations is structured. Our basic assumption is that the making of expectations is realized with different manners in a small firm which works in an entrepreneurial regime than in a large firm of the ologopolistic or routinized regime (see Winter, 1984). We follow the very revelant analysis carried out by Swann and Gilll (1993). In a young small firm with a (very) small number of individuals, in other words when the complexity is weak, the exchange of information among the members is easier than in the case of a larger firm. On the other hand, the process of decision-making is more rapid. The expectations will be flexible. And conversely for the large firms of a routinized regime, because the making expectations is based on (organizational) routines which are the mean to cope with the stronger complexity due to the large number of individual agents. The process of expectations revision will be longer and expectations more difficult to modify. The existence of two different schemes, with regards to the making of expectations, entails some consequences on the stability of growth for each of both technological regimes. We will show the technological regimes have different properties in terms of stability of growth trajectory. The way by which the expectations are made explains these differences. Classification Codes: L2, O0, A1. 1. Expectations and interactions between economic agents: a general framework Economics shares with other sciences the idea the interactions between agents produce effects and new structures. Mainstream thought, founded on general equilibrium analysis, identifies only the interactions built on the relationships in competitive markets. Interactions outside of the market, externalities, may be dealt with by the establishment of property rights. Other approaches focus mainly on the interactions outside of the market for explaining the dynamic properties of evolution. For instance, in the epidemic * Centre Walras, Universite Lyon-2, Faculte de Sciences Economiques, 16 Quai Cl. Bernard, 69365 Lyon 07, France. E-mail: lebas@univ-lyon2.fr
The Optimum Rate of Mimicry in Human-Agent Interaction

The importance of building rapport between a human and an agent is increasing with the burgeoning development of robot technology. Several recent studies have focused on the chameleon effect, using psychological concepts to investigate human-agent interaction. However, the validity of the chameleon effect in human-agent interaction is controversial. Few studies have explored the influence of individual cognitive ability and the rate of mimicry on the human-agent interaction. We explored the optimal rate of mimicry and the relationship between mimicry rate and individual empathic ability. We controlled the amount of agent mimicry and examined the effect on participants classified as high- and low-perspective takers. We found that, overall, participants preferred agents that mimicked their behavior 83% of the time. Moreover, high-, but not low-, perspective takers tended to be influenced by the mimicry rate.
Visualising Collaboration: Qualitative Analysis of an Email Visualisation Case Study

This paper reports on continuing work on visualising email collaboration (O.A. Puade and T.G. Wyeld, 2006). It reports on the re- interviewing of participants of a previous email collaboration visualisation study regarding the identification of key players. Participants were asked to comment on the finding of key player impact on the collaboration as determined by our analysis and methods. We found, while they mostly agreed with our analysis they expressed reservations regarding the methods used. This forms the ground work for yet further work in developing a real-time visualisation tool for email-mediated collaboration. The qualitative analysis case-study method used in this study helped gain a deeper understanding of the nature and characteristic of the collaboration that would otherwise have been hidden in a quantitative analysis.
SPRTN is a mammalian DNA-binding metalloprotease that resolves DNA-protein crosslinks

Ruijs-Aalfs syndrome is a segmental progeroid syndrome resulting from mutations in the SPRTN gene. Cells derived from patients with SPRTN mutations elicit genomic instability and people afflicted with this syndrome developed hepatocellular carcinoma. Here we describe the molecular mechanism by which SPRTN contributes to genome stability and normal cellular homeostasis. We show that SPRTN is a DNA-dependent mammalian protease required for resolving cytotoxic DNA-protein crosslinks (DPCs)— a function that had only been attributed to the metalloprotease Wss1 in budding yeast. We provide genetic evidence that SPRTN and Wss1 function distinctly in vivo to resolve DPCs. Upon DNA and ubiquitin binding, SPRTN can elicit proteolytic activity; cleaving DPC substrates and itself. SPRTN null cells or cells derived from patients with Ruijs-Aalfs syndrome are impaired in the resolution of covalent DPCs in vivo. Collectively, SPRTN is a mammalian protease required for resolving DNA-protein crosslinks in vivo whose function is compromised in Ruijs-Aalfs syndrome patients. DOI: http://dx.doi. org/10.7554/eLife.21491. 001
POWER EFFICIENT DATA AGGREGATION BASED ON SWARM INTELLIGENCE AND GAME THEORETIC APPROACH IN WIRELESS SENSOR NETWORK

While performing data aggregation in wireless sensor networks (WSN), significant energy is consumed not only during active communication but also during idle state. For this, an accurate estimate of energy cost and an intelligent routing technique having distributed heuristic capabilities are necessary. In this paper, we propose a power efficient data aggregation technique based on swarm intelligence and game theoretic approach for WSN. In this technique, swarm intelligence is used to perform route discovery and to select the nodes with maximum power level as controller nodes. These controller nodes always remain awake to carry out data aggregation and forwarding, while other nodes are either in sleep/awake state. In each round of data aggregation forwarding, the controller nodes are adaptively changed depending on their power levels. After the successful delivery of data to sink, game theory approach is applied to determine the energy consumed during data aggregation. By simulation results, it is observed that the proposed approach reduces the energy consumption and increase the packet delivery ratio when compared with the existing techniques.
Model Checking Boolean Programs

The reliability of software is crucial to the functioning of today’s world, which heavily depends on computer systems. Given the ever increasing complexity of software, bugs are subtle and thus hard to find with manual inspection. A more promising approach is to use a formal method such as model checking, which employs exhaustive state-space search to ensure the correctness of the software. However, existing tools typically feature two flaws: either they have insufficient capacity to handle large programs or they report many false positives. A successful approach to push the boundaries of model checking is predicate abstraction (-refinement). With this method, an abstraction of a program in a high-level programming language is constructed using predicates and represented as a Boolean program. It is analyzed by a dedicated checker to determine if an error state is reachable. Boolean program verification remains, despite the reduced state space, the bottleneck within the automated abstraction-refinement loop. This thesis introduces techniques for efficient reachability analysis of sequential and concurrent Boolean programs. We improve on known summarization algorithms for sequential Boolean programs and propose over-approximations of procedure calls. For non-recursive concurrent Boolean programs with bounded thread creation, we first introduce a transformation to a representation based on thread-state counters, which exploits the symmetry inherent in replicated programs. In a second step, we combine this representation with a method to locate thread creating cycles in order to allow an unbounded number of threads. The algorithms are implemented in BOOM, a model checking platform for Boolean programs. It is the first tool which implements a summarizing reachability engine that entirely relies on a satisfiability solver for (quantified) Boolean formulæ. The concurrent variant of BOOM is implemented using binary decision diagrams and includes partial order reduction methods. BOOM has been used in combination with the SATABS software model checker to verify safety properties of Linux device drivers. BOOM is, to the best of our knowledge, the first efficient model checker for non-recursive concurrent Boolean programs.
Fingerprint Recognition Algorithms for Partial and Full Fingerprints

An urgent need to develop accurate biometric recognition system is expressed by governmental agencies at the local, state, and federal levels, as well as by private commercial companies. Fingerprinting is the most practical and widely used biometric technique. The pattern of ridges and valleys of each fingerprint is unique. The minutiae based algorithm is widely used for fingerprint authentication. One of the significant parts of this algorithm is the classification of fingerprints which allows minimizing significantly the number of fingerprints referenced for each identification procedure. However, the minutiae algorithm has some serious drawbacks. If the core of a fingerprint is not visible, then identification cannot proceed. Yet in some cases, partial fingerprints need to be identified. We recently developed a novel contactless line scanner for recognition of fingerprint pattern that converts a three dimensional object like a finger into a two dimensional image with minimal distortion. This novel imaging technique based on a line by line scanned image required the development of a new recognition algorithm. In this study, we propose two new algorithms. The first algorithm, called the spaced frequency transformation algorithm (SFTA), is based on taking the fast Fourier transform of the images. The second algorithm, called the line scan algorithm (LSA), was developed to compare partial fingerprints and reduce the time taken to compare full fingerprints. A combination of SFTA and LSA provides a very efficient recognition technique.
Training Effects on Immune Function in Judoists

Background: It has been reported that high intensity long term training in elite athletes may increase risk of immune function. Objectives: This study is to examine training effects on immunoglobulin and changes of physiological stress and physical fitness level induced by increased cold stress during 12-week winter off-season training in elite Judoists. Patients and Methods: Twenty-nine male participants (20 ± 1 years) were assigned to only Judo training (CG, n = 9), resistance training combined with Judo training (RJ, n = 10), and interval training combined with Judo training (IJ, n = 10). Blood samples collected at rest, immediately after all-out exercise, and 30-minute recovery period were analyzed for testing IgA, IgG, and IgM, albumin and catecholamine levels. Results: VO2max and anaerobic mean power in IJ (P < 0.05) and anaerobic power in RJ (P < 0.05) were significantly increased after 12-week training compared to CG. There was no significant interaction effect (group × period) in albumin after 12-week training; however, there was a significant interaction effect (group × period) in epinephrine after 12-week training (F (4, 52) = 3.216, P = 0.002) and immediately after all-out exercise and at 30-minute recovery (F (2, 26) = 14.564, P = 0.008). There was significantly higher changes in epinephrine of RJ compared to IJ at 30-minute recovery (P = 0.045). There was a significant interaction effect (group × period) in norepinephrine after 12-week training (F (4, 52) = 8.141, P < 0.0001), at rest and immediately after all-out exercise (F (2, 26) = 9.570, P = 0.001), and immediately after all-out exercise and at 30-minute recovery (F (2, 26) = 8.862, P = 0.001). Conclusions: Winter off-season training of IJ increased physical fitness level as well as physical stress induced by overtraining. Along with increased physical stress, all groups showed reduced trend of IgA; however, there was no group difference based on different training methods.
Domain Level Personalization Technique

We propose a novel technique for web search personalization that exploits the clustering of the results of web searches. Our approach is based on an automatic characterization of the user search history through the collection of semantic domains and web sources chosen by the user. The semantic domains are the terms extracted directly from the clusters contents which describe the main topics covered by the involved documents. The web sources are the web roots of the urls of the documents. The idea is that a user submits a query to a general purpose search engine and then selects clusters or documents from the resulting list. In the rst case we can assume that, for that particular query, the user is interested in the topics covered by the selected clusters. We use these information to construct a user prole by assigning the clusters semantic domains to the query submitted by the user. In the second case we keep trace of the relevance and reliability of web sources of the selected documents, by assigning them to the submitted query.
Semantic parsing using word confusion networks with conditional random fields

A challenge in large vocabulary spoken language understanding (SLU) is robustness to automatic speech recognition (ASR) errors. The state of the art approaches for semantic parsing rely on using discriminative sequence classification methods, such as conditional random fields (CRFs). Most dialog systems employ a cascaded approach where the best hypotheses from the ASR system are fed into the following SLU system. In our previous work, we have proposed the use of lattices towards joint recognition and parsing. In this paper, extending this idea, we propose to exploit word confusion networks (WCNs), compiled from ASR lattices for both CRF modeling and decoding. WCNs provide a compact representation of multiple aligned ASR hypotheses, without compromising recognition accuracy. For slot filling, we show significant semantic parsing performance improvements using WCNs compared to ASR 1-best output, approximating the oracle path performance. Index Terms: conditional random field, semantic parsing, word confusion network, natural language understanding
Facilitating Peer Learning and Knowledge Sharing in STEM Courses via Pattern Based Graph Visualization

High quality education in Science, Technology, Engineering and Math (STEM) majors expects not only the acquisition of comprehensive domain knowledge, but also the mastery of skills to solve open ended and even ill-defined problems. Problem-based Learning (PBL) is usually adopted to achieve such goals. However, PBL requires sustained and indepth faculty involvement, hence making PBL not scalable. Also, existing knowledge discovery techniques do not facilitate the capture and reuse of solutions to recurring problems. To address these challenges, we present MicroBrowser, an interactive Q&A system augmented with 2D discussion visualizations and pattern based expertise sharing interfaces. MicroBrowser allows learners to browse and navigate important discussions in PBL based on topic similarity encoded by node proximity in a knowledge graph. MicroBrowser also provides a set of pattern based expertise sharing interfaces to allow both learners and instructors to highlight, share and reuse major findings in PBL. Through a 32subject study, we found MicroBrowser to be effective at facilitating knowledge discovery. Moreover, students understood and were able to use design patterns to complete open ended
The 1997 American Diabetes Association Criteria Versus the 1985 World Health Organization Criteria for the Diagnosis of Abnormal Glucose Tolerance: Poor agreement in the Hoorn Study

OBJECTIVE Recently, the American Diabetes Association (ADA) introduced new diagnostic criteria. These new criteria are based on fasting plasma glucose levels, avoiding the burdensome oral glucose tolerance test (OGTT). We compared the 1997 ADA criteria with the 1985 World Health Organization (WHO) criteria with respect to the prevalence of diabetes and the cardiovascular risk profile in the population of the Hoorn Study. RESEARCH DESIGN AND METHODS The Hoorn Study is a population-based survey of 2,484 men and women, aged 50–75 years. An OGTT was performed and cardiovascular risk factors were determined in 2,378 subjects without known diabetes. Subjects were categorized according to both sets of diagnostic criteria. RESULTS Although the prevalence of diabetes was similar for both sets of criteria, 47 of 120 (39.2%) subjects who were diagnosed with diabetes according to the 1997 ADA criteria were not classified as having diabetes when using the 1985 WHO criteria. Similarly, of 285 subjects diagnosed with impaired fasting glucose by the 1997 ADA criteria, 195 (68.4%) were classified as having normal glucose tolerance by the 1985 WHO criteria. The overall agreement was poor (K 0.33; 95% CI 0.28−0.38). Subjects who were diagnosed as having diabetes by either set of criteria had an adverse cardiovascular risk profile, which was between the cardiovascular risk profiles of concordant normal and concordant diabetic subjects. CONCLUSIONS In this study, both sets of criteria diagnosed a similar number of diabetic subjects, but many of the subjects shifted between glucose intolerance categories. With either set of criteria, a considerable number of subjects at risk of developing diabetes and subjects carrying an increased risk of cardiovascular disease, as reflected by an adverse cardiovascular risk profile, will be missed.
Genotet: An Interactive Web-based Visual Exploration Framework to Support Validation of Gene Regulatory Networks

Elucidation of transcriptional regulatory networks (TRNs) is a fundamental goal in biology, and one of the most important components of TRNs are transcription factors (TFs), proteins that specifically bind to gene promoter and enhancer regions to alter target gene expression patterns. Advances in genomic technologies as well as advances in computational biology have led to multiple large regulatory network models (directed networks) each with a large corpus of supporting data and gene-annotation. There are multiple possible biological motivations for exploring large regulatory network models, including: validating TF-target gene relationships, figuring out co-regulation patterns, and exploring the coordination of cell processes in response to changes in cell state or environment. Here we focus on queries aimed at validating regulatory network models, and on coordinating visualization of primary data and directed weighted gene regulatory networks. The large size of both the network models and the primary data can make such coordinated queries cumbersome with existing tools and, in particular, inhibits the sharing of results between collaborators. In this work, we develop and demonstrate a web-based framework for coordinating visualization and exploration of expression data (RNA-seq, microarray), network models and gene-binding data (ChIP-seq). Using specialized data structures and multiple coordinated views, we design an efficient querying model to support interactive analysis of the data. Finally, we show the effectiveness of our framework through case studies for the mouse immune system (a dataset focused on a subset of key cellular functions) and a model bacteria (a small genome with high data-completeness).