Reviewerly/exHarmony huggingface dataset:
THE RELATIONSHIP BETWEEN AUDIENCE ENGAGEMENT AND THE ABILITY TO PERCEIVE PITCH, TIMBRE, AZIMUTH AND ENVELOPMENT OF MULTIPLE SOURCES

It is well known in psychology that sounds perceived as close to a listener engage our attention, while more distant sounds can be ignored. But the physical properties of sound that lead to engagement are poorly understood. The phenomenon is well known by directors of cinema and drama, who demand that theatres and sound systems have high presence, and not just adequate intelligibility. This is achieved by bringing the audience close to the performers, attenuating both early and late reflections, and using highly directional loudspeakers for cinema. When sounds are perceived as distant it is not just engagement that suffers. Precise localization of instruments in ensembles becomes impossible, and instrumental voices – distinct in a historic hall – become blended into mere harmonies. Historic concert venues tended to be small, with sound absorbing stages and audience areas. Sonic intimacy and clarity of musical lines was taken for granted. But the physical properties that make a sound seem close to a listener are poorly understood, and there is no current acoustic measure for specifying the type of clarity that provokes audience attention. An overly reverberant, “well blended” sound has become the design goal for modern chamber music and orchestral venues, even though a few great halls demonstrate that it is possible to have both clarity and reverberation at the same time over a wide range of seats. This paper describes the physics of sound that provides engagement, and how reflections interact to reduce it. An impulse response based measure for engagement is described, along with a means of determining the degree of engagement from live recordings of speech. Time and equipment permitting, the author will demonstrate that the right combination of direct sound and reflections can provide engagement, localization, reverberation, and envelopment at the same time. The improvement in sound quality is dramatic.
Proving Type Class Laws for Haskell

Type classes in Haskell are used to implement ad-hoc polymorphism, i.e. a way to ensure both to the programmer and the compiler that a set of functions are defined for a specific data type. All instances of such type classes are expected to behave in a certain way and satisfy laws associated with the respective class. These are however typically just stated in comments and as such, there is no real way to enforce that they hold. In this paper we describe a system which allows the user to write down type class laws which are then automatically instantiated and sent to an inductive theorem prover when declaring a new instance of a type class.
Hybrid Fuzzy Neural Search Retrieval System

Search engines are crucial for information gathering systems (IGS). New challenges face search engines concerning automatic learning from user requests. In this paper, a new hybrid intelligent system is proposed to enhance the search process. Based on a Multilayer Fuzzy Inference System (MFIS), the first step is to implement a scalable system to relay logical rules in order to produce three classifications for search behavior, user profiles, and query characteristics from analysis of navigation log files. These three outputs from the MFIS are used as inputs for the second step, an Adaptive Neuro-Fuzzy Inference System (ANFIS). The training process of the ANFIS replaced the rules by adjusting the weights in order to find the most relevant result for the search query. This proposed system, called MFIS-ANFIS, is implemented as an experimental system. The system performance is evaluated using quantitative and comparative analysis. MFIS-ANFIS aimed to be the core of intelligent and reliable search process.
A randomized least squares solver for terabyte-sized dense overdetermined systems

We present a fast randomized least-squares solver for distributed-memory platforms. Our solver is based on the Blendenpik algorithm, but employs multiple random projection schemes to construct a sketch of the input matrix. These random projection sketching schemes, and in particular the use of the randomized Discrete Cosine Transform, enable our algorithm to scale the distributed memory vanilla implementation of Blendenpik to terabyte-sized matrices and provide up to ×7.5 speedup over a state-of-the-art scalable least-squares solver based on the classic QR algorithm. Experimental evaluations on terabyte scale matrices demonstrate excellent speedups on up to 16,384 cores on a Blue Gene/Q supercomputer.
The Relationship Between Agnostic Selective Classification, Active Learning and the Disagreement Coefficient

A selective classifier (f,g) comprises a classification function f and a binary selection function g, which determines if the classifier abstains from prediction, or uses f to predict. The classifier is called pointwise-competitive if it classifies each point identically to the best classifier in hindsight (from the same class), whenever it does not abstain. The quality of such a classifier is quantified by its rejection mass, defined to be the probability mass of the points it rejects. A rejection rate is achieved if the rejection mass is bounded from above by O(1/m) where m is the number of labeled examples used to train the classifier (and O hides logarithmic factors). Pointwise-competitive selective (PCS) classifiers are intimately related to disagreement-based active learning and it is known that in the realizable case, a fast rejection rate of a known PCS algorithm (called Consistent Selective Strategy) is equivalent to an exponential speedup of the well-known CAL active algorithm. We focus on the agnostic setting, for which there is a known algorithm called LESS that learns a PCS classifier and achieves a fast rejection rate (depending on Hanneke's disagreement coefficient) under strong assumptions. We present an improved PCS learning algorithm called ILESS for which we show a fast rate (depending on Hanneke's disagreement coefficient) without any assumptions. Our rejection bound smoothly interpolates the realizable and agnostic settings. The main result of this paper is an equivalence between the following three entities: (i) the existence of a fast rejection rate for any PCS learning algorithm (such as ILESS); (ii) a poly-logarithmic bound for Hanneke's disagreement coefficient; and (iii) an exponential speedup for a new disagreement-based active learner called ActiveiLESS.
Entanglement manipulation beyond local operations and classical communication

When a quantum system is distributed to spatially separated parties, it is natural to consider how the system evolves when the parties perform local quantum operations with classical communication (LOCC). However, the structure of LOCC channels is exceedingly complex, leaving many important physical problems unsolved. In this paper, we consider generalized resource theories of entanglement based on different relaxations to the class of LOCC. The behavior of various entanglement measures is studied under non-entangling channels, as well as the newly introduced classes of dually non-entangling and positive partial transpose (PPT)-preserving channels. In an effort to better understand the nature of LOCC bound entanglement, we study the problem of entanglement distillation in these generalized resource theories. We first show that unlike LOCC, general non-entangling maps can be superactivated, in the sense that two copies of the same non-entangling map can, nevertheless, be entangling. On the single-copy level, we demonstrate that every NPT entangled state can be converted into an LOCC-distillable state using channels that are both dually non-entangling and having a PPT Choi representation and that every state can be converted into an LOCC-distillable state using operations belonging to any family of polytopes that approximate LOCC. We then turn to the stochastic convertibility of multipartite pure states and show that any two states can be interconverted by any polytope approximation to the set of separable channels. Finally, as an analog to k-positive maps, we introduce and analyze the set of k-non-entangling channels.
Corrigendum to “Inductive-data-type systems” [Theoret. Comput. Sci. 272 (1–2) (2002) 41–68]

In a previous work (Abstract data type systems, Theoret. Comput. Sci. 173 (2) (1997)), the last two authors presented a combined language made of a (strongly normalizing) algebraic rewrite system and a typed lambda-calculus enriched by pattern-matching definitions following a certain format, called the “General Schema”, which generalizes the usual recursor definitions for natural numbers and similar “basic inductive types”. This combined language was shown to be strongly normalizing. The purpose of this paper is to reformulate and extend the General Schema in order to make it easily extensible, to capture a more general class of inductive types, called “strictly positive”, and to ease the strong normalization proof of the resulting system. This result provides a computation model for the combination of an algebraic specification language based on abstract data types and of a strongly typed functional language with strictly positive inductive types.
Real-Time Collision Detection for Deformable Characters with Radial Fields

Many techniques facilitate real-time collision detection against complex models.These typically work by pre-computing information about the spatial distribution of geometry into a form that can be quickly queried.When models deform though, expensive pre-computations are impractical.We present radial fields: a variant of distance fields parameterised in cylindrical space, rather than Cartesian space.This 2D parameterisation significantly reduces the memory and computation requirements of the field, while introducing minimal overhead in collision detection tests.The interior of the mesh is defined implicitly for the entire domain.Importantly, it maps well to the hardware rasteriser of the GPU.Radial fields are much more application-specific than traditional distance fields.For these applications -such as collision detection with articulated characters-however, the benefits are substantial.
Structure-Based Intensity Propagation for 3-D Brain Reconstruction With Multilayer Section Microscopy

Microscopy is widely used for brain research because of its high resolution and ability to stain for many different biomarkers. Since whole brains are usually sectioned for tissue staining and imaging, reconstruction of 3D brain volumes from these sections is important for visualization and analysis. Recently developed tissue clearing techniques and advanced confocal microscopy enable multilayer sections to be imaged without compromising the resolution. However, noticeable structure inconsistence occurs if surface layers are used to align these sections. In this paper, a structure-based intensity propagation method is designed for the robust representation of multilayer sections. The 3D structures in reconstructed brains are more consistent using the proposed methods. Experiments are conducted on 367 multilayer sections from 20 mouse brains. The average reconstruction quality measured by the structure consistence index increases by 45% with the tissue flattening method and 29% further with the structure-based intensity propagation.
Fast Adaptive Bases Algorithm for Non-rigid Image Registration

Non-rigid image registration is an important preprocessing step in many medical image applications, but it is also a very computation intensive task. In this paper, a fast adaptive bases (FAB) non-rigid image registration with local optimization method is proposed to speed up the free-form deformation with radial basis functions. Our proposed algorithm applies matched area identification in the initialization process and adaptive support size for basis function in the misregistration region identification process. Moreover, higher grid point density for local optimization is used in the identified misregistration regions. With our proposed algorithm, the registration speed is found significantly increased while maintaining the registration accuracy. The overall running time is reduced by preserving the advantage of computing local deformation on disjoint regions instead of trying to solve the problem globally. Performance evaluation of our proposed method was conducted using simulated and clinical brain dataset with standard measures based on root mean square error (RMSE) and normalized mutual information (NMI) similarity measure. Experimental results showed that notable improvement on speed was achieved as compared to the conventional regular and adaptive grid approaches.
A new face feature point matrix based on geometric features and illumination models for facial attraction analysis

In this paper, we propose a 81-point face feature points template that used for face attraction analysis. This template is proposed that based on the AAM model, according to the geometric characteristics and the illumination model. The experimental results demonstrate that, the attraction of human face can be analyzed by the feature vector analysis of human face image quantification and the influence of light intensity on the attraction of human face. By taking the appropriate algorithm, the concept of facial beauty attractiveness can be learned by machine with numeric expressions.
SOCA-DSEM: A well-structured SOCA development systems engineering methodology

Service-oriented Software Engineering (SOSE) is a software engineering paradigm focused on Service-oriented Computing Applications (SOCAs), for what SOCA development methodologies are required. Recent studies on SOCA development methodologies revealed theoretical and practical deficiencies. Thus, academicians and practitioners must adapt development methodologies from other paradigms or use the available partial SOCA development methodologies. Also, since the high acceptance of agile approaches, we claim new well-structured and balanced agility-rigor methodologies are required. Then, this paper proposes a new SOCA Development Systems Engineering Methodology, including its description, the explanation of its theoretical foundations and the illustration of its use with a prototype of a running example. Two pilot empirical evaluations on usability metrics are also reported. Findings support both theoretical adequacy and positive perceptions from the evaluators. While further empirical tests are required for gaining more conclusive evidences our preliminary results are encouraging.
Analyzing the impacts of socio-economic factors on French departmental elections with CoDa methods

The vote shares by party on a given subdivision of a territory form a vector called composition (mathematically, a vector belonging to a simplex). It is interesting to model these shares and study the impact of the characteristics of the territorial units on the outcome of the elections. In the political economy literature, few regression models are adapted to the case of more than two political parties. In the statistical literature, there are regression models adapted to share vectors including Compositional Data (CoDa) models, but also Dirichlet models, and others. Our goal is to discuss and illustrate the use CoDa regression models for political economy models for more than two parties. The models are fitted on French electoral data of the 2015 departmental elections.
Which Factorization Machine Modeling Is Better: A Theoretical Answer with Optimal Guarantee

Factorization machine (FM) is a popular machine learning model to capture the second order feature interactions. The optimal learning guarantee of FM and its generalized version is not yet developed. For a rank k generalized FM of d dimensional input, the previous best known sampling complexity is O[k3d · polylog(kd)] under Gaussian distribution. This bound is sub-optimal comparing to the information theoretical lower bound O(kd). In this work, we aim to tighten this bound towards optimal and generalize the analysis to sub-gaussian distribution. We prove that when the input data satisfies the so-called τ-Moment Invertible Property, the sampling complexity of generalized FM can be improved to O[k2d · polylog(kd)/τ2]. When the second order self-interaction terms are excluded in the generalized FM, the bound can be improved to the optimal O[kd · polylog(kd)] up to the logarithmic factors. Our analysis also suggests that the positive semi-definite constraint in the conventional FM is redundant as it does not improve the sampling complexity while making the model difficult to optimize. We evaluate our improved FM model in real-time high precision GPS signal calibration task to validate its superiority.
A Scalable Approach for 360° Feedback in Cooperative Learning

We present a standard-based methodology to data-mine and eventually compute the results from 360° feedback surveys. We pay special attention to its suitability in the educational and pedagogical sphere, where very little research efforts have been accomplished. A 360° feedback study (also known as multi-rater feedback) provides a holistic view of an appraisee's performance based on the information and opinions gathered from raters about the person being evaluated. An evaluator or appraiser can be any peer, coworker, subordinate, supervisor, customer, classmate, and so on and even the appraisee himself/herself. This type of surveys is usually carried out in big companies/institutions and by specialized human resources departments. The computer tools normally used for the processing of the data generated by these studies are based on simple spreadsheets and ad hoc scripts backed by untapped databases. In this context, we advocate the use of a refined, standardized, and straightforward computing environment based on stored procedures. The Persistent Stored Module and the stored procedures there defined are part of the Structured Query Language ISO norm, and they can be leveraged to perform complex calculations without the need of developing external software or requiring extra network transactions. This approach can also be transferred to general 360° appraising studies and, with special emphasis, to those performed in academic levels. As a testbed, the presented techniques have been put into practice in a multi-rater evaluation experience executed with over 240 high-school students. The technical and pedagogical lessons learned in this initial test can be eventually exported to the three main academic levels in a network of educational centers with more than 44k scholars enrolled. The inclusion of such assessment actions in educational environments enables the fostering of the novel pattern of cooperative learning, as it is also highlighted.
Blockchain-Based Internet Voting: Systems’ Compliance with International Standards

Blockchain has emerged as the technology claiming to change the way services are delivered nowadays, from banking to public administrations. The field of elections, and specifically internet voting, is not an exception. As a distributed audit layer, it is expected that blockchain technology will provide more transparency to such services, while preventing that a simple agent can tamper with electoral electronic data. Elections require a challenging combination of privacy and integrity. However, a system that would store ballots in clear on a public blockchain, while transparent, would not comply with secret suffrage, one key principle for democratic elections. For this reason, the motivation of this paper is to assess the potential of blockchain technology in internet voting. To this end we analyse several blockchain-based internet voting systems and their degree of compliance against a set of commonly accepted properties of internet voting, namely those of the Council of Europe. This has allowed us to identify a set of common features and challenges on how blockchain can contribute to the conduct of e-enabled elections.
Considerations for Cost Estimation of Software Testing Outsourcing Projects

<p class='IJASEITAbtract'>Software testing outsourcing appears to be the best alternative to acquire better software quality with competent ratification by extrinsic parties who have the capability to do it. Through the effort, organizations are peeking to promising benefits constitute in it such as current testing technology, experts, an abridgment of the project’s duration and more concentration on the main organisation’s activity. Along with these benefits, one important reason that encourages the decision is optimization of cost expenditure, which the strategy is perceived as a good move for a competitive organization. However, implementing such preference eventually results in a different outcome. Organizations have to bear the higher cost and incur losses of cost deviation from the expected estimation. The conflicting between cost and benefits raises an important concern of striving better cost estimation for such projects. This paper aims to address this interest by analyzing the existing literature in order to identify the contributing factors towards better cost estimation for software testing outsourcing project-context. The analysis is done using the content analysis method. The results could be divided into two categories; which are the cost items and contributing factors. Cost items consist of direct cost and indirect cost, which refers to the expenses for the project. While the contributing factors consist of people and environment, which are needed to produce accurate cost estimation. The findings provide an insight to excogitate attentively the essentials in the endeavor of improving the exactitude of cost estimation for software testing outsourcing project.
An approach for guiding developers in the choice of security solutions and in the generation of concrete test cases

This paper tackles the problems of choosing security solutions and writing concrete security test cases for software, which are two tasks of the software life cycle requiring time, expertise and experience. We propose in this paper a method, based upon the notion of knowledge base, for helping developers devise more secure applications from the threat modelling step up to the testing one. The first stage of the approach consists of the acquisition and integration of publicly available security data into a data store. This one is used to assist developers in the design of attack-defense trees expressing the attacker possibilities to compromise an application and the defenses that may be implemented. These defenses are given under the form of security pattern combinations, a security pattern being a generic and reusable solution to design more secure applications. In the second stage, these trees are used to guide developers in the test case generation. Test verdicts show whether an application is vulnerable to the threats modelled by an ADTree and whether the consequences of the chosen security patterns are observed from the application (a consequence leading to some observable events partly showing that a pattern is correctly implemented). We applied this approach to web applications and evaluated it on 24 participants. The results are very encouraging in terms of the two criteria: comprehensibility and effectiveness.