Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Multi-Secret Sharing Scheme

Secret sharing schemes are very important techniques for the key management. To provide more efficient and flexible alternatives for the applications of secret sharing, this paper presents a dynamic multi-secret sharing scheme. A significant characteristic of the proposed scheme is that each participant has to keep only one master secret share which can be used to reconstruct different group secrets according to the number of threshold values. By applying successive one-way hash functions and the exclusive OR (XOR) operation, the proposed scheme is secure against the notorious conspiracy attack even though the pseudo secret shares are compromised. Further, when one of the group secrets is updated with a new one, each participant’s master secret share is still unchanged, i.e., these master secret shares are truly multi-use instead of one-time-use.
Battered women's cognitive schemata

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Phase unwrapping for multibaseline SAR interferometry using optimized data

In this paper, a new method of the multibaseline interferometric SAR phase unwrapping based on the model of the optimum data vector is introduced. The method constructs the multibaseline optimal weighted joint data (MOWJD) vector by taking advantage of the coherence information of neighboring pixels in order to adaptively coregister the SAR images and then employ the projection of the joint signal subspace onto the corresponding noise subspace to estimate the unwrapped interferometric phases (or the terrain heights). Theoretical analysis and experimental results show that the method can provide the accurate unwrapped interferometric phase even if the coregistration error is close to one pixel. The effectiveness and robustness of the technique is verified by simulated data.
An 'Enhanced' Corporate Valuation Model: Theory and Empirical Tests

In this paper, we develop an enhanced corporate valuation model based on the implied cost of equity capital (ICC). We argue that the enhanced approach extends the standard market multiples and discounted cash flow (DCF) approaches to corporate valuation. Specifically, it incorporates positive aspects of the market comparables and DCF approaches while mitigating the shortcomings of both. Unlike the traditional market comparables approach, the enhanced approach takes account of the full term structure of earnings forecasts. It does so by using the ICC calculated for the comparable companies as an “enhanced multiple” which translates the entire stream of cash flow forecasts into a value estimate. Unlike the DCF approach it does not require estimation of the cost of equity capital. As such, it avoids the complexity and uncertainty associated with estimating the cost of equity capital. In our empirical tests, we find the enhanced approach to be more accurate than either of the two traditional approaches.
RFID-assisted indoor localization and communication for first responders

An indoor localization and communication project is described that proposes to use RFID (radio-frequency identification) tags, placed in the building beforehand, as navigation waypoints for an inertial navigation system carried by a first responder. RFID devices commonly are attached to persons or to moveable objects so that the objects can be tracked by using fixed readers (special-purpose radio receivers) at different locations. In this project, we explore the "flip side" of this practice. Our concept is that detection of RFID devices in known, fixed locations by a moving reader provides a precise indication of location for tracking the person or moving object that is carrying the reader. This information can then be used to correct for any errors of an inertial tracking system.
Neuropsychological safety of nucleus accumbens deep brain stimulation for major depression: Effects of 12-month stimulation

Abstract Objectives. Deep brain stimulation (DBS) to the nucleus accumbens (NAcc-DBS) has antidepressant effects in patients suffering from treatment-resistant depression (TRD). However, limited information exists regarding the impact of NAcc-DBS on cognitive functioning. The aim of this study was to examine whether NAcc-DBS in patients with TRD has any cognitive effects. Methods. A comprehensive neuropsychological battery was administered to 10 patients with TRD before onset of bilateral NAcc-DBS and after 1 year of DBS stimulation. Neuropsychological testing covered the domains of attention, learning and memory, executive functions, visual perception, and language. Performance was analyzed at baseline and after 1 year of continuous DBS. Results. No evidence was found for cognitive decline following NAcc-DBS comparing test results after 1 year of NAcc-DBS with baseline. However, significantly improved cognitive performance on tests of attention, learning and memory, executive functions and visual perception was found. In addition, there was a general trend towards cognitive enhancement from below average to average performance. These procognitive effects were independent of the antidepressant effects of NAcc-DBS or changes in NAcc-DBS parameters. Conclusions. These results not only support cognitive safety of NAcc-DBS but also stress its beneficial role in augmenting cognitive performance in patients with TRD.
Creating Custom Taggers by Integrating Web Page Annotation and Machine Learning

We present an on-going work on a software package that integrates discriminative machine learning with the open source WebAnnotator system of Tannier (2012). The WebAnnotator system allows users to annotate web pages within their browser with custom tag sets. Meanwhile, we integrate the WebAnnotator system with a machine learning package which enables automatic tagging of new web pages. We hope the software evolves into a useful information extraction tool for motivated hobbyists who have domain expertise on their task of interest but lack machine learning or programming knowledge. This paper presents the system architecture, including the WebAnnotator-based front-end and the machine learning component. The system is available under an open source license.
Using the Constructive Genetic Algorithm for Solving the Probabilistic Maximal Covering Location-Allocation Problem

The Maximal Covering Location Problem (MCLP) maximizes the population that has a facility within a maximum travel distance or time. Numerous extensions have been proposed to enhance its applicability, like the probabilistic model for the maximum covering location-allocation with constraint in waiting time or queue length for congested systems, with one or more servers per service center. In this paper we present one solution procedure for that probabilistic model, considering one server per center, using the Constructive Genetic Algorithm. The results of tests on the solution procedure are presented.
Information Distance-Based Subvector Clustering for ASR Parameter Quantization

In this letter, we propose a new data-driven subvector clustering technique for parameter quantization in automatic speech recognition (ASR). Previous methods such as Greedy-n m-let and maximum clique partition have been proven to be effective. However, the former yields subvectors of equal sizes while the latter cannot determine the number of subvectors. In our method, we define a new object function based on information distance (ID) and optimize this using the cross-entropy (CE) method to overcome both of the aforementioned limitations. We compare the ASR performances using the Resource Management (RM) and Wall Street Journal (WSJ0) speech recognition tasks and show that the proposed technique performs better than previous heuristic techniques.
PerSoN-Vis: Visualizing Personal Social Networks (Ego Networks)

Recently, we witness a high usage of social media platforms for different purposes, from contacting with friends and colleagues to raising voice for different causes. Many times, on such social media platforms people are also interested to see and analyze the connections not only with their connected people but also the connections between these connected people (called the ego network of a user). Hereby they want to find out different behavioral patterns and interesting findings. In this work, we introduce our developed visualization tool, called PerSoN-Vis (Personal Social Network Visualizer). It visualizes intuitively people ego-networks and enables them to explore interactively their social contacts and the relations between these contacts. Further, it allows them to filter the resulting visualization based on several collected features. A preliminary evaluation study shows that participants performed much better in our PerSoN-Vis tool compared to Socialab social-network visualization tool.
Research on Transaction Web Service Selection Algorithm in WSC

Web service composition (WSC) needs transactional support to guarantee its consistent and reliable execution. To improve the validity of service selection, a transaction service composition model (TSM) is proposed. The model associates the quality of service (QoS) with dependencies between transactional states and time. Based on the model, a parallel algorithmłłtransaction QoS selection algorithm (TQSA) is developed, which combines genetic algorithm (GA) and service compensation computing, and can be applied to dynamic service composition due to its mechanism of selection and evaluation performing at the same time. To improve the success of composition, an algorithm I-TQSA is further developed by optimizing evolution strategy. Experiment results show that TQSA resolves the QoS-aware selection NP-hard complete problem, and I-TQSA further improves the effectiveness of selection and reduces composition time.
System Z: A Natural Ordering of Defaults with Tractable Applications to Nonmonotonic Reasoning

Recent progress towards unifying the probabilistic and preferential models semantics for non-monotonic reasoning has led to a remarkable observation: Any consistent system of default rules imposes an unambiguous and natural ordering on these rules which, to emphasize its simple and basic character, we term "Z-ordering. " This ordering can be used with various levels of refinement, to prioritize conflicting arguments, to rank the degree of abnormality of states of the world, and to define plausible consequence relationships. This paper defines the Z-ordering, briefly mentions its semantical origins, and illustrates two simple entailment relationships induced by the ordering. Two extensions are then described, maximum-entropy and conditional entailment, which trade in computational simplicity for semantic refinements.
Multimode Radiative beta Cephei and delta Cephei Models

As a by-product of β Cephei and δ Cephei radiative model surveys we have found interesting cases of multimode pulsation. In case of β Cephei stars, we found two multimode domains with two or three modes being involved. The origin of the multimode pulsation can be traced to one of the two different mechanisms: either to the non-resonant coupling of the two excited modes (double-mode pulsation) or to the 2 ω 1 ω 0 + ω 2 parametric resonance (triple-mode pulsation). In case of δ Cephei models, a triple-mode pulsation domain, connected with the 2:1 resonance, 2 ω 0 ω 2 , is found. These models are of theoretical interest only, since they do not obey a proper mass-luminosity relation.
When Are Technologies Disruptive: A Demand-Based View of the Emergence of Competition

By identifying the possibility that technologies with inferior performance can displace established incumbents, the notion of disruptive technologies, pioneered by Christensen (1997), has had a profound effect on the way in which scholars and managers approach technology competition. While the phenomenon of disruptive technologies has been well documented, the underlying theoretical drivers of technology disruption are less well understood. This article identifies the demand conditions that enable disruptive dynamics. By examining how consumers evaluate technology and how this evaluation changes as performance improves, it offers new theoretical insight into the impact of the structure of the demand environment on competitive dynamics. Two new constructs—preference overlap and preference symmetry—are introduced to characterize the relationships among the preferences of different market segments. The article presents a formal model that examines how these relationships lead to the emergence of different competitive regimes. The model is analyzed using computer simulation. The theory and model results hold implications for understanding the dynamics of disruptive technologies and suggest new indicators for assessing disruptive threats. Copyright © 2002 John Wiley & Sons, Ltd.
Will the internet promote democracy? search engines, concentration of online news readership, and e-democracy

ABSTRACT Challenging the assumption that the Internet is far more diverse than traditional forms of media, this study provides evidence that newspaper readership is more concentrated online than offline. Further, results suggest that the observed high concentration of readership on the Internet is associated with the presence of search engines that benefit a small number of top news outlets. Search engines allow people to access to new information at a lower cost, but this benefit may come at a price; the traffic referred by search engines goes mostly to a small number of top national news providers.
Decentralized strategy for car-like robot formations

This work proposes a control architecture for the consistent motions of a group of nonholonomic robotic vehicles. We present a decentralized control strategy that permits each vehicle to autonomously compute an optimal trajectory by using only locally generated information. In addition, to reach the desired positions in the robot formation, each robot follows a trajectory that is adapted thanks to reactive terms incorporated in the path planning process. This allows the robot to avoid obstacles and to maintain communication with the rest of the formation. We provide the proof of the convergence of this algorithm. Simulation results validate and highlight the efficiency and relevance of this method.
Charting Physical Literacy Journeys Within Physical Education Settings

Physical literacy is creating significant interest worldwide due to its holistic nature and the potential it has to impact on people’s lives. It is underpinning many physical education programs, coaching strategies, health initiatives, and policymakers’ decisions. However, the complex philosophical and holistic nature of the concept has meant that methods used to chart/assess/measure progress have been very much dependent on the pedagogues interpretation of the concept. This paper will provide a review of current practices and issues related to charting/assessing/measuring progress of an individual’s journey. It will go on to highlight considerations that, we suggest, should be made by any organization developing methods to chart/assess/measure progress.
Does Guilt Affect Performance? Evidence from Penalty Kicks in Soccer

Does guilt affect performance? Exploiting a novel measure of the justification of penalty calls, we find that unjustified penalty calls negatively affect penalty conversion rates, and that this effect increases with social norms of trust. Exploiting the variance arising from players who do not play in their countries of origin by including the norms of both the league and the kickers’ countries of origin, we separate the constraints on egoism into two categories: internal sanctions, such as guilt, and external sanctions, such as shame. We find that both guilt and shame affect the performance of penalty kickers.
Optimal resource allocation In base stations for mobile wireless communications

Telecommunications provides a rich source of interesting and often challenging optimisation problems. This thesis is concerned with a series of mixed-integer non-linear optimisation problems that arise in mobile wireless communications systems. The problems under consideration arise when mobile base stations have an Orthogonal Frequency-Division Multiple Access (OFDMA) architecture, where there are subcarriers for data transmission and users with various transmission demands. In such systems, we simultaneously allocate subcarriers to users and power to subcarriers, subject to various constraints including certain quality of service (QoS) constraints called rate constraints. These problems can be modelled as Mixed Integer Non-linear Programmes (MINLP). When we began the dissertation, we had the following main aims: To design an exact algorithm for the subcarrier and power allocation problem with rate constraints (SPARC), the objective of which is to maximise total data transmission rate of the entire system. To design an exact algorithm for the fractional subcarrier and power allocation problem with rate constraints (F-SPARC) problem in order to maximise system efficiency, i.e.: total data transmission rate divided by total power supplied to the system. To design a heuristic algorithm for the F-SPARC problem. To design a heuristic algorithm for the SPARC problem in dynamic settings, where user demand changes very frequently. Along the way, however, we discovered a new approach to a broad family of problems, which includes the F-SPARC as a special case. These problems are called mixed-integer fractional programs with indicator variables, and they are dealt with in Chapter 3.
University choice: which attributes matter when you are paying the full price?

This article presents the results from a longitudinal study of students' choice of university in England. Students were surveyed initially when applying for university (Wave One) and then again when they were about to embark on their chosen course (Wave Two). The results from Wave Two demonstrated a high degree of consistency with the Wave One findings: course and university reputation are far more important and fees are relatively unimportant. However, a key result across both waves was that patterns of utility for students with no parental experience of university were significantly different from students whose parents had attended university. The utility associated with different levels of entry qualifications, of fees and of university and course reputation, differed between social groups. The study suggests that the benefits of going to a highly rated university may be undervalued in families that have no direct experience of higher education. In addition, whilst females are more significantly put off by universities with low entry requirements, the qualitative attitudinal statements included in the follow-up study seem to indicate that so-called ‘softer’ factors may also influence their choice.
Vision based obstacle detection and map generation for reconnaissance

Obstacle detection and map generation is an essential tool for site reconnaissance applications. Further it enables optimal and efficient path planning for mobile agents to navigate in unknown environments. This paper proposes a solution to this problem through a stereo vision-based obstacle detection and depth measurement method for reconnaissance. The proposed approach employs a boundary tracing algorithm to identify the objects in the close vicinity to the vision system. Next, it generates a depth map that contains the obstacles and the distance to each obstacle with respect to the position of the mobile agent. An error model was developed to further improve the accuracy of the depth estimation. Depth maps from multiple angles for a given set of identified obstacles are superimposed in the same coordinate system to generate the aerial map. For objects with sharp edges, the aerial map was generated based on corners identified using a corner detection algorithm. A prototype of the system was implemented and tested in a controlled environment. The results show that percentage estimation error was significantly reduced after the depth calculation was refined using the proposed error model.
Protocol CINEMO: The use of fiction for collecting emotional data in naturalistic controlled oriented context

In this study we have collected a corpus for training model for emotion detection in the context of monitoring artificial agent's by voice. In order to control occurrences of various range of affective state in a modeling purpose, we used acted emotional expression through dubbing play exercises. We observed that some natural affective states occurred during those exercises. We ran a perceptive test in order to validate our annotations: anger and sadness were the more consensual annotations.
THE SCHWARZSCHILD STATIC COSMOLOGICAL MODEL

The present work describes an immersion in 5D of the interior Schwarzschild solution of the general relativity equations. The model-theory is defined in the context of a flat 5D space–time-matter Minkowski model, using a Tolman-like technique, which shows via Lorentz transformations that the solution is compatible with homogeneity and isotropy, thus obeying the cosmological principle. These properties permit one to consider the solution in terms of a cosmological model. In this model, the Universe may be treated as an idealized star with constant density and variable pressure, where each observer can be the "center" of the same. The observed redshift appears as a static gravitational effect which obeys the sufficiently verified and generally accepted square distance law. The Buchdahl stability theorem establishes a limit of distance observation with density dependence.
Twistors in conformally flat Einstein four manifolds

This paper studies the two-component spinor form of massive spin- potentials in conformally flat Einstein four-manifolds. Following earlier work in the literature, a nonvanishing cosmological constant makes it necessary to introduce a supercovariant derivative operator. The analysis of supergauge transformations of primary and secondary potentials for spin- shows that the gauge freedom for massive spin- potentials is generated by solutions of the supertwistor equations. The supercovariant form of a partial connection on a nonlinear bundle is then obtained, and the basic equation of massive secondary potentials is shown to be the integrability condition on super β-surfaces of a differential operator on a vector bundle of rank 3. Moreover, in the presence of boundaries, a simple algebraic relation among some spinor fields is found to ensure the gauge invariance of locally supersymmetric boundary conditions relevant for quantum cosmology and supergravity.
The recognition of emotions from speech using GentleBoost classifier: A comparison approach

The recognition of the internal emotional state of one person plays an important role in several human-related fields. Among them, human-computer interaction has recently received special attention. The current research is aimed at the analysis of segmentation methods and of the performance of the GentleBoost classifier on emotion recognition from speech. The data set used for emotion analysis is Berlin - a database of German emotional speech. A second data set is DES – Danish Emotional Speech data set is used for comparison purposes. Our contribution for the research community consists in a novel extensive study on the efficiency of using distinct numbers of frames per speech utterance for emotion recognition. Eventually, a set of GentleBoost 'committees' with optimal classification rates is determined based on an exhaustive study on the generated classifiers and on different types of segmentation.