Miracle-dz/UniFAR-FTUs huggingface dataset:
SociableSense: exploring the trade-offs of adaptive sampling and computation offloading for social sensing

The interactions and social relations among users in workplaces have been studied by many generations of social psychologists. There is evidence that groups of users that interact more in workplaces are more productive. However, it is still hard for social scientists to capture fine-grained data about phenomena of this kind and to find the right means to facilitate interaction. It is also difficult for users to keep track of their level of sociability with colleagues. While mobile phones offer a fantastic platform for harvesting long term and fine grained data, they also pose challenges: battery power is limited and needs to be traded-off for sensor reading accuracy and data transmission, while energy costs in processing computationally intensive tasks are high. In this paper, we propose SociableSense, a smart phones based platform that captures user behavior in office environments, while providing the users with a quantitative measure of their sociability and that of colleagues. We tackle the technical challenges of building such a tool: the system provides an adaptive sampling mechanism as well as models to decide whether to perform computation of tasks, such as the execution of classification and inference algorithms, locally or remotely. We perform several micro-benchmark tests to fine-tune and evaluate the performance of these mechanisms and we show that the adaptive sampling and computation distribution schemes balance trade-offs among accuracy, energy, latency, and data traffic. Finally, by means of a social psychological study with ten participants for two working weeks, we demonstrate that SociableSense fosters interactions among the participants and helps in enhancing their sociability.
Simulating the emergent behavior of complex software-intensive organizations

Organizations are composed of individuals and groups of individuals who interact with their local neighbors. These individuals and groups inherently have limited horizons both with whom they interact and in what information they can access. Because of this limited visibility no one individual can "see" the edges of the universe in which they live and in this sense the system can be considered unbounded. A major characteristic of unbounded systems is the fact that global behavior can emerge which cannot be predicted from local actions. These issues have direct relevance to the understanding of software-intensive organizations. However, performing experimental investigations to assess these organizational properties is virtually impossible. Thus we need a simulation language in which unbounded systems and emergent properties can be simulated and examined. Few, if any existing simulation languages explicitly provide the appropriate mechanisms and this has motivated the development of the Easel language, the topic of this paper.
Disclosing Documentary Archives: AR interfaces to recall missing urban scenery

This paper describes the current results of a research project whose goal is to create a mobile Application aimed at the use of the Cultural Heritage stored at the Documentary Archives of Ascoli Piceno, Italy. The application core is based on the visualization of multimedia content in Augmented Reality mode. The application shows the realistic and digitally rendered scenarios of virtual restoring vision of the places “as they were and where they were” in order to communicate the historical value of the heritage preserved in the Documentary Archives; it prioritizes the visualization of the 3D reconstructions of architectural heritage based on Archival Resources. Moreover, the 3D models become interfaces to access different associated 2D content connected generating a cascading system, structured on hierarchical information. This App, defining a technological framework mainly based on Open Source systems, aims to offer an expandable and repeatable "open data" experience of Cultural Heritage, to make available and accessible “hidden” information.
Rule-Based Method for Entity Resolution

The objective of entity resolution (ER) is to identify records referring to the same real-world entity. Traditional ER approaches identify records based on pairwise similarity comparisons, which assumes that records referring to the same entity are more similar to each other than otherwise. However, this assumption does not always hold in practice and similarity comparisons do not work well when such assumption breaks. We propose a new class of rules which could describe the complex matching conditions between records and entities. Based on this class of rules, we present the rule-based entity resolution problem and develop an on-line approach for ER. In this framework, by applying rules to each record, we identify which entity the record refers to. Additionally, we propose an effective and efficient rule discovery algorithm. We experimentally evaluated our rule-based ER algorithm on real data sets. The experimental results show that both our rule discovery algorithm and rule-based ER algorithm can achieve high performance.
Opposing Patterns of Signaling Activation in Dopamine D1 and D2 Receptor-Expressing Striatal Neurons in Response to Cocaine and Haloperidol

Psychostimulants and other drugs of abuse activate extracellular signal-regulated kinase (ERK) in the striatum, through combined stimulation of dopamine D1 receptors (D1Rs) and glutamate NMDA receptors. Antipsychotic drugs activate similar signaling proteins in the striatum by blocking dopamine D2 receptors (D2Rs). However, the neurons in which these pathways are activated by psychotropic drugs are not precisely identified. We used transgenic mice, in which enhanced green fluorescent protein (EGFP) expression was driven by D1R promoter (drd1a-EGFP) or D2R promoter (drd2-EGFP). We confirmed the expression of drd1a-EGFP in striatonigral and drd2-EGFP in striatopallidal neurons. Drd2-EGFP was also expressed in cholinergic interneurons, whereas no expression of either promoter was detected in GABAergic interneurons. Acute cocaine treatment increased phosphorylation of ERK and its direct or indirect nuclear targets, mitogen- and stress-activated kinase-1 (MSK1) and histone H3, exclusively in D1R-expressing output neurons in the dorsal striatum and nucleus accumbens. Cocaine-induced expression of c-Fos and Zif268 predominated in D1R-expressing neurons but was also observed in D2R-expressing neurons. One week after repeated cocaine administration, cocaine-induced signaling responses were decreased, with the exception of enhanced ERK phosphorylation in dorsal striatum. The responses remained confined to D1R neurons. In contrast, acute haloperidol injection activated phosphorylation of ERK, MSK1, and H3 only in D2R neurons and induced c-fos and zif268 predominantly in these neurons. Our results demonstrate that cocaine and haloperidol specifically activate signaling pathways in two completely segregated populations of striatal output neurons, providing direct evidence for the selective mechanisms by which these drugs exert their long-term effects.
Are China Stock Markets Efficient after the Global Financial Crisis?

The main intention of this paper is to re-examine the martingale difference hypothesis for the two Chinese exchanges (Shanghai and Shenzhen) with new daily data. The hypothesis is tested with a new spectral method from Kuan and Lee (2004). We found that the stock prices of China do not follow the MDH except the Class A shares for Shanghai stock exchange. And also, after we divided the whole sample period into general turbulence , pre-crisis, crisis and post-crisis periods, we found that the highest inefficiency occurred during the pre-crisis period, followed by crisis, post-crisis and general turbulence period, while both the mature overseas markets and the domestic markets have shown a better efficiency in general turbulence period.
Distance Measurement by means of Radio Waves Comparison of Classical and High-Resolution Spectral Methods

Localization within buildings is a demanding task. The principles of a low cost radio technology are described, which allow to determine distance between a transponder and a receiver. Such information can be determined by performing phase measurements based on mathematical methods. Two different methods for distance determination are described and their results are compared – the classical method of radio direction finding which is similar to the Wattson-Watt process [9] with implemented Fast Fourier Transformation (FFT), and high-resolution spectral methods, similar to the Capon method [3] or respectively MUSIC (Multiple Signal Classification) process [2] – for distance determination. The high-resolution spectral method (HiRes) can particularly be used for initial positioning and to support inertial navigation.
Associations between Hyperopia and Other Vision and Refractive Error Characteristics

Purpose To investigate the association of hyperopia greater than +3.25 diopters (D) with amblyopia, strabismus, anisometropia, astigmatism, and reduced stereoacuity in preschoolers. Methods Three- to five-year-old Head Start preschoolers (N = 4040) underwent vision examination including monocular visual acuity (VA), cover testing, and cycloplegic refraction during the Vision in Preschoolers Study. Visual acuity was tested with habitual correction and was retested with full cycloplegic correction when VA was reduced below age norms in the presence of significant refractive error. Stereoacuity testing (Stereo Smile II) was performed on 2898 children during study years 2 and 3. Hyperopia was classified into three levels of severity (based on the most positive meridian on cycloplegic refraction): group 1: greater than or equal to +5.00 D, group 2: greater than +3.25 D to less than +5.00 D with interocular difference in spherical equivalent greater than or equal to 0.50 D, and group 3: greater than +3.25 D to less than +5.00 D with interocular difference in spherical equivalent less than 0.50 D. “Without” hyperopia was defined as refractive error of +3.25 D or less in the most positive meridian in both eyes. Standard definitions were applied for amblyopia, strabismus, anisometropia, and astigmatism. Results Relative to children without hyperopia, children with hyperopia greater than +3.25 D (n = 472, groups 1, 2, and 3) had a higher proportion of amblyopia (34.5 vs. 2.8%, p < 0.0001) and strabismus (17.0 vs. 2.2%, p < 0.0001). More severe levels of hyperopia were associated with higher proportions of amblyopia (51.5% in group 1 vs. 13.2% in group 3) and strabismus (32.9% in group 1 vs. 8.4% in group 3; trend p < 0.0001 for both). The presence of hyperopia greater than +3.25 D was also associated with a higher proportion of anisometropia (26.9 vs. 5.1%, p < 0.0001) and astigmatism (29.4 vs. 10.3%, p < 0.0001). Median stereoacuity of nonstrabismic, nonamblyopic children with hyperopia (n = 206) (120 arcsec) was worse than that of children without hyperopia (60 arcsec) (p < 0.0001), and more severe levels of hyperopia were associated with worse stereoacuity (480 arcsec for group 1 and 120 arcsec for groups 2 and 3, p < 0.0001). Conclusions The presence and magnitude of hyperopia among preschoolers were associated with higher proportions of amblyopia, strabismus, anisometropia, and astigmatism and with worse stereoacuity even among nonstrabismic, nonamblyopic children.
Practical approaches to the automatic verification of an ATM switch fabric using VIS

In this paper we present several practical methods for formally verifying an Asynchronous Transfer Mode (ATM) network switching fabric using the Verification Interacting with Synthesis (VIS) tool. We produced Verilog RTL behavioral and netlist structural descriptions of the switch fabric at different levels of hierarchy and established several abstracted models of the fabric. Using various techniques presented in the paper, we provided a number of relevant liveness and safety properties expressible in CTL, and accomplished their verification in reasonable CPU time. Moreover, we performed equivalence checking between the structural and behavioral descriptions of each submodule of the implementation hierarchy.
Standards for Business Component Markets: An Analysis from Three Theoretical Perspectives

AbstrAct The idea of component-based software systems and markets for the exchange of components dates back to the late 1960s. However, so far no large-scale components markets can be found. The purpose of this chapter is to present a more in-depth analysis of the conditions that have to be met for the successful realization of this idea. Three perspectives are presented: first, a system-theoretic perspective; second, an economic perspective; and third, a knowledge-codification perspective of standardization. As we want to argue, the problem should be considered as an empirical question that depends to a large extent on the future technological development and its outcome, like specification techniques, software verification standards, and the performance and maturity of existing systems.
Multichannel detection of epilepsy using SVM classifier on EEG signal

Epilepsy is a chronic neurological disorder which occurs due to the recurring evoking of seizure which results due to the abnormal rhythmic discharge of electrical activities of the brain. This fluctuation in the electrical activities of the brain can be analyzed using EEG signal which provides valuable information about the physiological states of the brain. In this paper we propose an efficient mechanism algorithm based on statistical analysis using features i.e. average value, standard deviation, variance and kurtosis along with the electrical features such as least significant value, most significant value and band power. These features were used on multichannel EEG signals which provide promising results with less complexity, simplicity along with accuracy. Thus, automatic seizure detection mechanism based on multichannel EEG signal analysis using advanced signal processing techniques with the statistical and electrical features helps to reduce the physician workload.
Se p 20 06 A new tractable combinatorial decomposition

This paper introduces the umodules, a generalisation of modules for the homogeneous relations. We first present some properties of the umodule family, then show that, if the homogeneous relation fulfills some natural axioms, the umodule family has a unique decomposition tree. We show that this tree can be computed in polynomial time, under a certain size assumption. We apply this theory to a new tournament decomposition and a graph decomposition. In both cases, the decomposition tree computing time becomes linear. Moreover, we characterise the completely decomposable tournaments. Finally we give polynomial-time algorithms for the maximal umodules, the undecomposability of a relation, and its decomposition tree. We conclude with further applications of homogeneous relations.
Energy-Delay Space Exploration of Clocked Storage Elements Using Circuit Sizing

Rapid energy-delay exploration methodology based on circuit sizing as applied to clocked storage elements is presented. Circuit delay and energy are modeled using improved RC delay model of a transistor. The accuracy of the model is increased by using Logical Effort setup accounting for input signal slope and extraction of technology dependent parameters. The minimal energy-delay curve is generated by optimizing transistor sizes for minimum energy at given delay targets. Results show two orders of magnitude time improvement as compared to H-SPICE in order to generate such curves while the delay accuracy of the model used remains within 10 % as compared to H-SPICE.
Structured Querying of Web Text Data: A Technical Challenge

The Web contains a huge amount of text that is currently beyond the reach of structured access tools. This unstructured data often contains a substantial amount of implicit structure, much of which can be captured using information extraction (IE) algorithms. By combining an IE system with an appropriate data model and query language, we could enable structured access to all of the Web’s unstructured data. We propose a general-purpose query system called the extraction database, or ExDB, which supports SQL-like structured queries over Web text. We also describe the technical challenges involved, motivated in part by our experiences with an early 90M-page prototype.
Qualitative control of periodic solutions in piecewise affine models of genetic networks

Piecewise affine (PWA) systems are often used to model gene regulatory networks. In this paper we elaborate on previous work about control problems for this class of models, using also some recent results guaranteeing the existence and uniqueness of limit cycles, based solely on a discrete abstraction of the system and its interaction structure. Our aim is to control the transition graph of the PWA system to obtain an oscillatory behaviour, which is indeed of primary functional importance in numerous biological networks; we show how it is possible to control the appearance or disappearance of a unique stable limit cycle by qualitative action on the degradation rates of the PWA system, both by static and dynamic feedback, i.e. the adequate coupling of a controlling subnetwork. This is illustrated on two classical gene network modules, having the structure of mixed feedback loops.
The evolution toward exible work ow systems

Delay type blasting caps in which the burning time of the delay fuse is at least 1.5 seconds per inch with accompanying low sensitivity to ignition, which, due to novel ignition system structure, provide for elimination of premature venting of heat and pressure required for ignition of the delay fuse and concomitant failure of the shot.
Collaborative Approaches to Complementing Qualitative Preferences of Agents for Effective Service Selection

Incomplete preferences of agents may render service selection ineffective. We address this problem by proposing a set of collaborative approaches to complementing agents' incomplete preferences in a qualitative way. For an agent, the approaches first find its similar agents, and then base the similar agents' qualitative preferences to complement this agent's missing preferences. We analyze and compare these approaches and provide experimental results to justify our arguments. We also compare our approach with the classic collaborative filtering and show the competitive advantages of our approach in service selection. Our work thus serves as an important step towards effective service selection.
Improving the Canny Edge Detector Using Automatic Programming: Improving Non-Max Suppression

In this paper, we employ automatic programming, a relatively unknown evolutionary computation strategy, to improve the non-max suppression step in the popular Canny edge detector. The new version of the algorithm has been tested on a dataset widely used to benchmark edge detection algorithms. The performance has increased by 1.9%, and a pairwise student-t comparison with the original algorithm gives a p-value of 6.45 x 10-9. We show that the changes to the algorithm have made it better at detecting weak edges, without increasing the computational complexity or changing the overall design. Previous attempts have been made to improve the filter stage of the Canny algorithm using evolutionary computation, but, to our knowledge, this is the first time it has been used to improve the non-max suppression algorithm. The fact that we have found a heuristic improvement to the algorithm with significantly better performance on a dedicated test set of natural images suggests that our method should be used as a standard part of image analysis platforms, and that our methodology could be used to improve the performance of image analysis algorithms in general.
A New Congestion Control Method for IEEE 802.16 Mesh Mode

With the increase of network service types and users’ broadband demand, broadband wireless access (BWA) has become the focus of people’s attention. The workgroup of IEEE 802 proposes the IEEE 802.16 standard to provide BWA. In IEEE802.16 mesh mode, all nodes are organized in an ad hoc fashion. Nodes use a pseudo-random function to compete for their transmission opportunities based on the scheduling information in the two-hop neighborhood. Meanwhile, they use “three-way handshaking” to achieve the allocation of data slot. In this paper, we present a novel congestion control method by adjusting the resources of MAC layer in a hop-by-hop way. A node, e.g. Base station (BS), which wants to send data to its neighbor, must detect congestion first. It will adjust the exponent of transmission holdoff time up to congestion state and send MSH-DSCH message with congestion information to its upstream node and downstream node, which also adjust the exponent or allocate minislot according to the information in the mesh message. Moreover, simulation results and analysis are given in this paper. With this method congestion has been controlled, packet loss rate and end-to-end delay have been decreased and throughput has been increased.
Internet Addiction: An Interpersonal Perspective

Internet addiction has become important as people increasingly spend time online conducting every day affairs. The excessive use of the Internet may be due to a number of different reasons and a wide variety of consequences. Therefore, the objective of this study was to understand the Internet addiction phenomenon. This quantitative study focused on the interpersonal relationship perspective, perceived online attachment motivation and perceived online relationship commitment, to explain excessive use of the Internet. A questionnaire survey to1,443 secondary school graduates found that Internet addiction was significantly related to the perceived online attachment motivation (β = .180, p < .001) and the perceived online relationship commitment (β = .366, p< .001). The model explained 25.8 percent variance of the Internet addiction measured. Implications due to the need of interpersonal relationship and the consequences of Internet addiction are discussed.
Automated community detection on social networks: useful? efficient? asking the users

In most online social networks, with the increasing number of users and content, the problem of contact filtering becomes more and more present. The recent introduction of such features in online social networks -- for instance, Circles in Google+ or Facebook Smart lists -- shows that it is a problem they are confronted to. In this paper, we explore this question through multidisciplinary aspects. First, we discuss about this issue of groups management in the context of social networks. Then, we present several techniques from the state of the art to automatically find meaningful groups of contacts in a user's contact list. Finally, we asked Facebook users to evaluate these solutions on their own Facebook network, both to compare the solutions among themselves and to assess how pertinent the best ones are according to them. The conclusions of this study is that a network analysis approach can strongly improve the efficiency of an automated detection of groups on networks, which could be used, combined with profile data extraction, to design intelligent management of groups of contacts.