Reviewerly/exHarmony huggingface dataset:
Constraint saliency based intelligent camera for enhancing viewers attention towards intended face

Visual Saliency decides the focus of attention towards a region in a scene. When we talk about attending faces in a crowd or set of multiple faces, our focus of attention does not go equal for all the faces. The biasness of human's visual system towards a particular face, occurs due to some dominant features of it, over rest of the faces. So, for the faces which are not intrinsically salient in a scene, there is a requirement of increment of attentiveness. The current study effort to improve the saliency of a face (in a set of multiple faces), which is not significantly salient. It can be achieved by enhancing the contrast of the intended face with its surrounding faces, in terms of visual low-level features like intensity, color, etc. Modification of such feature values will change its potential to attract observer's gaze. But excesses change will destroy the originality of the image. Therefore, the problem of enhancing saliency of a target face is framed as an optimization (maximization) problem under some constraints. This concept can be applied to develop a saliency based intelligent camera having the power of enhancing the attractiveness of a particular face in the crowd and in the taken photograph, the enhanced face may give more attention to the viewers. Experiment has been conducted on grayscale as well as the colour images. Moreover, effect of saliency on faces wearing jewellery, has also been measured.
Employing Topical Relations in Semantic Analysis of Traffic Videos

Motion patterns in traffic video can be directly exploited to generate high-level descriptions of video content, which can be used for rule mining and abnormal event detection. The most recent and successful unsupervised methods for complex traffic scene analysis are based on topic models. In this paper, a topic related sparse topical coding framework is proposed for more effectively discovering motion patterns in traffic videos.
Formalized Conceptual Spaces with a Geometric Representation of Correlations

The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a similarity space and concepts are represented by convex regions in this space. After pointing out a problem with the convexity requirement, we propose a formalization of conceptual spaces based on fuzzy star-shaped sets. Our formalization uses a parametric definition of concepts and extends the original framework by adding means to represent correlations between different domains in a geometric way. Moreover, we define various operations for our formalization, both for creating new concepts from old ones and for measuring relations between concepts. We present an illustrative toy-example and sketch a research project on concept formation that is based on both our formalization and its implementation.
On invariant fields of vectors and covectors

Let Fq be the finite field of order q. Let G be one of the three groups GL(n,Fq), SL(n,Fq) or U(n,Fq) and let W be the standard n-dimensional representation of G. For non-negative integers m and d we let mW⊕dW⁎ denote the representation of G given by the direct sum of m vectors and d covectors. We exhibit a minimal set of homogeneous invariant polynomials {ℓ1,ℓ2,…,ℓ(m+d)n}⊆Fq[mW⊕dW⁎]G such that Fq(mW⊕dW⁎)G=Fq(ℓ1,ℓ2,…,ℓ(m+d)n) for all cases except when md=0 and G=GL(n,Fq) or SL(n,Fq).
Boson–boson pure-dephasing model with non-Markovian properties

Understanding decoherence processes is crucial in the study of open quantum systems. In this paper, we discuss the mechanism of pure-dephasing process with a newly proposed boson-boson model, namely, a bosonic field coupled to another bosonic bath in thermal equilibrium. Our model is fully solvable and can reproduce the pure-dephasing process which is usually described by the well-known spin-boson model, therefore offering a new perspective to understanding decoherence processes in open quantum systems of high dimension. We also show that this model admits a generically non-Markovian dynamics with respect to various different non-Markovian measures.
Begin with the End in Mind: A Unified End-to-End Quality-of-Experience Monitoring, Optimization, and Management Framework

There has been an increasing consensus in the video distribution industry that the design and operation of the full video delivery chain need to be driven by the quality of experience (QoE) appropriate to the end users. Here, we propose a new framework that uses a unified end-to-end solution to produce consistent QoE scores at all points along the delivery chain under the same evaluation criterion. This is a framework that produces a clear picture instantaneously to operation engineers, managing executives, and content creators about how video QoE degrades along the chain; a framework that allows immediate issue identification, localization, and resolution; a framework that enables quality and resource usage optimization; and a framework that provides reliable predictive metrics for long-term strategic resource and infrastructure allocations. The main challenge in the implementation of such a framework is to create a unified QoE metric that not only accurately predicts human QoE but is also lightweight and versatile, readily plugged into multiple points in the video delivery chain. We show that the SSIMPLUS metric offers the best promise, and we demonstrate the benefits of the proposed framework and QoE metric using bandwidth optimization as an example.
Reference terms identification of cited articles as topics from citation contexts

Textual content of citations has been widely applied to access the impact of research outputs, observe knowledge transfer across domains and generate summaries of research articles. These applications, however, experience the problem of correct identification of citational context windows, and alternately use text in a fixed-size window around the citation instance. Citation contexts might contain terms not referenced by the target citation. When application requirement is only the reference terms, this observation ought to be taken into account. This paper tries to find out whether the reference terms of a target citation can be represented as topics derived from its citation contexts or not. We, specifically, consider cases where complete statements are referred to by a citation and cases where there are no explicit reference terms. Evaluation results show that in a majority of cases, reference terms of a citation can be replaced with topics generated from its contexts.
Kernel Machines: Introduction

Kernels are a type of similarity measures between observed patterns. By exploiting an important mathematical property, they provide new pattern representation and, at the same time, new perspectives to solve many machine learning problems. In this article, we will describe and motivate the main idea of the kernel approach. Notice that, while the usage of kernels is based on well founded theoretical arguments, we will confine our discussion to the main intuitive functional concepts and definitions.
Prediction with a flexible finite mixture-of-regressions

Finite mixture regression (FMR) is widely used for modeling data that originate from heterogeneous populations. In these settings, FMR can offer increased predictive power compared to more traditional one-class models. However, existing FMR methods rely heavily on mixtures of linear models, where the linear predictor must be given as an input. A flexible FMR model is presented using a combination of the random forest learner and a penalized linear FMR. The performance of the new method is assessed by predictive log-likelihood in extensive simulation studies. The method is shown to achieve equal performance with the existing FMR methods when the true regression functions are in fact linear and superior performance in cases where at least one of the regression functions is nonlinear. The method can handle a large number of covariates, and its predictive ability is not greatly affected by surplus variables.
Retrieval performance analysis of multibiometric database using optimized multidimensional spectral hashing based indexing

Demand for efficient retrieval of data from biometric databases is increasing due to its widespread authentication applications that range from e-passport to attendance system. Major research contribution in this area is by using tree based indexing methods and data independent random hashing methods. In this work, the data dependent hashing technique using optimized multidimensional spectral hashing that uses hash table lookup is employed. Palmprint, Iris and Face biometric features are generated using GIST, optimized features are fused based on bio-inspired cuckoo search algorithm and then converted into binary hash code. The compact binary codes representing the fused features form the multibiometric database on which the retrieval performance is analyzed. Simulation results obtained indicate that the hit rate and penetration rate have improved considerably for the desired recognition accuracy.
Statistical Inference Techniques

This article presents the most common hypothesis testing procedures useful for bioinformatics applications. The hypothesis tests can be divided into two categories: parametric and nonparametric. The first category includes those tests based on the assumption of knowing the distribution of the sampling population(s) and inference is drawn on one or more unknown parameter(s); the second category contains those tests that are “distribution-free”, hence generally requiring much less assumptions. For each test, we will present the mathematical assumptions under which it is applicable and the test statistics used for making inference.
A GIS-based tool for the China National Mineral Resource Assessment Initiative: A case study of gold deposits in the Bayan Obo Group, Inner Mongolia, China

This paper describes a software tool and its application to the China National Mineral Resource Assessment Initiative (CNMRAI). The initiative was carried out throughout China from 2007 to 2013, creating collaborations among geological societies from institutions such as academies, universities, companies, and local geological surveys. A geographic information system (GIS)-based tool was designed, providing functions for database management of the multiple original spatial datasets, modelling, targeting, estimation, and visualization to assist participants in assessing mineral resource potentials. During this initiative, geologists used the tool to generate mineral potential maps and assess undiscovered resources for the 25 major types of mineral resources in China. First, the requirements of the tool were introduced; second, the framework and major functions of the tool were delineated and developed; and third, a case study of gold resource assessment from a project of the CNMRAI in the Bayan Obo region, Inner Mongolia, China, was discussed. In addition, the mineral potential map was displayed by terrain rendering. This paper also provides interpretations of the results of a prospectivity analysis based on conceptual geological models. Discussions of the new requirements and techniques for modelling and assessing will be used to further develop the Mineral Resources Assessment Software (MRAS).
Refinement and resolution of just-in-time requirements in open source software and a closer look into non-functional requirements

Just-in-time (JIT) requirements are characterized as not following the traditional requirement engineering approach, instead focusing on elaboration when the implementation begins. In this experience report, which is an invited extension of a recently published work [1], we first analyze both functional and non-functional JIT requirements from three successful open source software (OSS) projects, including Firefox, Lucene, and Mylyn, to explore the common activities that shaped those requirements. We identify a novel refinement and resolution process that all studied requirements followed from their inception to complete realization and subsequent release. Noticing some interesting phenomena more specific to non-functional requirements (NFRs), we conduct a second study examining 50 NFRs from the aforementioned three subject systems. This study reveals a slightly different process followed in the refinement and resolution of NFRs. It also provides further evidence that NFR refinement and resolution activities focus more on implementation refinement rather than requirement elaboration in the studied systems. Our research provides new insights into how OSS project teams create quality features from simple initial descriptions of JIT requirements, uncovers activities more particular to JIT requirements engineering (RE) for NFRs, and opens new avenues for further research in the emerging JIT RE field.
Convolutional Networks for Object Category and 3D Pose Estimation from 2D Images

Current CNN-based algorithms for recovering the 3D pose of an object in an image assume knowledge about both the object category and its 2D localization in the image. In this paper, we relax one of these constraints and propose to solve the task of joint object category and 3D pose estimation from an image assuming known 2D localization. We design a new architecture for this task composed of a feature network that is shared between subtasks, an object categorization network built on top of the feature network, and a collection of category dependent pose regression networks. We also introduce suitable loss functions and a training method for the new architecture. Experiments on the challenging PASCAL3D+ dataset show state-of-the-art performance in the joint categorization and pose estimation task. Moreover, our performance on the joint task is comparable to the performance of state-of-the-art methods on the simpler 3D pose estimation with known object category task.
Low-memory and high-performance architectures for the CCSDS 122.0-B-1 compression standard

Two low-memory and high-performance architectures for the CCSDS 122.0-B-1 standard are proposed. They use novel memory organizations to reduce the total memory requirements in order to be implemented in a single FPGA device. The architectures were implemented in radiation-hardened and commercial FPGA devices. Based on the experimental results for the case of Virtex5QV radiation-hardened device, the throughput is 135 MSamples/sec for image with 12 bits/pixel and horizontal resolution up 8192 pixels. Also, the proposed architectures outperform the existing one in terms of the memory requirements and area.
A Third Strike Against Perfect Phylogeny

Perfect phylogenies are fundamental in the study of evolutionary trees because they capture the situation when each evolutionary trait emerges only once in history; if such events are believed to be rare, then by Occam's Razor such parsimonious trees are preferable as a hypothesis of evolution. A classical result states that 2-state characters permit a perfect phylogeny precisely if each subset of 2 characters permits one. More recently, it was shown that for 3-state characters the same property holds but for size-3 subsets. A long-standing open problem asked whether such a constant exists for each number of states. More precisely, it has been conjectured that for any fixed number of states $r$ there exists a constant $f(r)$ such that a set of $r$-state characters $C$ has a perfect phylogeny if and only if every subset of at most $f(r)$ characters has a perfect phylogeny. Informally, the conjecture states that checking fixed-size subsets of characters is enough to correctly determine whether input data permits a perfect phylogeny, irrespective of the number of characters in the input. In this article, we show that this conjecture is false. In particular, we show that for any constant $t$, there exists a set $C$ of $8$-state characters such that $C$ has no perfect phylogeny, but there exists a perfect phylogeny for every subset of at most $t$ characters. Moreover, there already exists a perfect phylogeny when ignoring just one of the characters, independent of which character you ignore. This negative result complements the two negative results ("strikes") of Bodlaender et al. (1992,2000). We reflect on the consequences of this third strike, pointing out that while it does close off some routes for efficient algorithm development, many others remain open.
On the k-Medoids Model for Semi-supervised Clustering

Clustering is an automated and powerful technique for data analysis. It aims to divide a given set of data points into clusters which are homogeneous and/or well separated. A major challenge with clustering is to define an appropriate clustering criterion that can express a good separation of data into homogeneous groups such that the obtained clustering solution is meaningful and useful to the user. To circumvent this issue, it is suggested that the domain expert could provide background information about the dataset, which can be incorporated by a clustering algorithm in order to improve the solution. Performing clustering under this assumption is known as semi-supervised clustering. This work explores semi-supervised clustering through the k-medoids model. Results obtained by a Variable Neighborhood Search (VNS) heuristic show that the k-medoids model presents classification accuracy compared to the traditional k-means approach. Furthermore, the model demonstrates high flexibility and performance by combining kernel projections with pairwise constraints.
Can media and text analytics provide insights into labour market conditions in China?

The official Chinese labour market indicators have been seen as problematic given their small cyclical movement and their only partial capture of the labour force. In our paper, we build a monthly Chinese labour market conditions index (LMCI) using text analytics applied to Mainland Chinese-language newspapers over the period from 2003 to 2017. We use a supervised machine learning approach by training a support vector machine classification model. The information content and the forecast ability of our LMCI are tested against official labour market activity measures in wage and credit growth estimations. Surprisingly, one of our findings is that the much-maligned official labour market indicators do contain information. However, their information content is not robust and, in many cases, our LMCI can provide forecasts that are significantly superior. Moreover, regional disaggregation of the LMCI illustrates that labour conditions in the export-oriented coastal region are sensitive to export growth, while those in inland regions are not. This suggests that text analytics can, indeed, be used to extract useful labour market information from Chinese newspaper articles.
Herbrand's theorem as higher order recursion

This article examines the computational content of the classical Gentzen sequent calculus. There are a number of well-known methods that extract computational content from first-order logic but applying these to the sequent calculus involves first translating proofs into other formalisms, Hilbert calculi or Natural Deduction for example. A direct approach which mirrors the symmetry inherent in sequent calculus has potential merits in relation to proof-theoretic considerations such as the (non-)confluence of cut elimination, the problem of cut introduction, proof compression and proof equivalence. Motivated by such applications, we provide a representation of sequent calculus proofs as higher order recursion schemes. Our approach associates to an LK proof π of ⇒∃vF, where F is quantifier free, an acyclic higher order recursion scheme H with a finite language yielding a Herbrand disjunction for ∃vF. More generally, we show that the language of H contains all Herbrand disjunctions computable from π via a broad range of cut elimination strategies.
STAGER: Semantic-Based Framework for Generating Adapters of Service-Based Generic-API for Portable Cloud Applications

In PaaS model, providers have different proprietary APIs, which make developers locked inside a specific platform and not able to easily port their applications among different platforms. So, vendor lock-in problem appeared. One solution to this problem is to use generic APIs with specific adapters. However, any update in a PaaS specific-API makes its corresponding adapter is unusable which causes, what we call, API synchronization problem. Therefore, STAGER (SemanTic-based GenERation of Generic-API Adapters) framework is proposed. STAGER framework provides a semi-automatic adapter generation process, which generates specific adapters of generic APIs for PaaS services (e.g., blob storage and datastore services) for target PaaS platforms. The adapter generation process is based on semantic annotations of the generic APIs and their corresponding PaaS specific-APIs. In order to evaluate STAGER framework, two generic APIs for blob storage and NoSQL datastore services have been proposed. STAGER framework is used to generate the adapters of these generic APIs for two PaaS platforms: Google App Engine (GAE) and Windows Azure. Although there is some overhead for semantically annotating the PaaS APIs, the evaluation results prove the feasibility of STAGER framework and promote the usage of the generated adapters for implementing portable cloud applications.
Type I and Type II codes over the ring 𝔽2 + u𝔽2 + v𝔽2 + uv𝔽2

We discuss self-dual codes over the ring [Formula: see text]. We characterize the structure of self-dual, Type I codes and Type II codes over [Formula: see text] with given generator matrix in terms of the structure of their Torsion and Residue codes. We construct self-dual, Type I and Type II codes over [Formula: see text] for different lengths.
Considerations for parameter configuration on Vortex Particle Swarm Optimization

This paper presents the Vortex Particle Swarm Optimization (VPSO) algorithm and some considerations for the appropriate selection of parameters. The optimization algorithm is composed of two modes: translational and circular movements. Convergence of the swarm to a given optimal point is performed by linear movements, while the exploration stage is characterized by circular movements (i.e. a vortex-like behavior). These emerging behaviors are observed in different living organisms in nature and are a product of swarm (social) interactions. Particularly in the proposed algorithm, the vortex-like behavior allows escaping from local minima. Parameter selection is proposed based on an approximate analysis of the swarm behavior, and the algorithm performance is studied via simulation results of well-known test functions.