Reviewerly/exHarmony huggingface dataset:
Implementing A Wireless Network Infrastructure To Enhance Et Curriculum

During the summer of 2001, a team of students was brought together to design, install, and test an 802.11b wireless local area network (WLAN).The team was tasked with installing a WLAN in and between two buildings on the Texas A&M main campus that house the Engineering Technology and Industrial Distribution Department (ETID).The primary goal of the WLAN installation was to provide an infrastructure that would be used by Engineering Technology students and faculty for educational and research purposes.The Wireless Infrastructure Project (WIP) became a real world classroom/laboratory environment for the team members to learn about wireless communications, networking, and authorization and authentication security and to gain practical experience in project management, technical communications and presentation skills.Additionally, the Electronics and Telecommunications Engineering Technology Program is developing innovative ways to leverage the new infrastructure that depart from traditional administrative uses.Professors and students are developing coursework that utilize the infrastructure for the monitoring and control of mobile platforms while WIP students are adding new labs that expand the scope of teaching wireless network design and security.Through the WIP initiative, an innovative resource has been created that will add significantly to engineering technology education both in the classroom and the laboratory.
PROBLEMS OF QUALIMETRY OF GIFTEDNESS

The article deals with the problems of the qualimetry of giftedness caused by the lack of a conventional definition of the term giftedness and its quantitative criteria.The method of calculation of child giftedness' level and the quantitative measure of its expression is offered.On the basis of the proposed technique, the difference between comprehensive and special giftedness is demonstrated.Proposals for realization of some trends of research in giftedness are provided with the aim of their comparison with the studies of various authors.
Quantum trajectories for environment in superposition of coherent states

We derive stochastic master equations for a quantum system interacting with a Bose field prepared in a superposition of continuous-mode coherent states. To determine a conditional evolution of the quantum system we use a collision model with an environment given as an infinite chain of not interacting between themselves qubits prepared initially in a entangled state being a discrete analogue of a superposition of coherent states of the Bose field. The elements of the environment chain interact with the quantum system in turn one by one and they are subsequently measured. We determine a conditional evolution of the quantum system for continuous in time observations of the output field as a limit of discrete recurrence equations. We consider the stochastic master equations for a counting as well as for a diffusive stochastic process.
Visual Classification With Multikernel Shared Gaussian Process Latent Variable Model

Multiview learning methods often achieve improvement compared with single-view-based approaches in many applications. Due to the powerful nonlinear ability and probabilistic perspective of Gaussian process (GP), some GP-based multiview efforts were presented. However, most of these methods make a strong assumption on the kernel function (e.g., radial basis function), which limits the capacity of the real data modeling. In order to address this issue, in this paper, we propose a novel multiview approach by combining a multikernel and GP latent variable model. Instead of designing a deterministic kernel function, multiple kernel functions are established to automatically adapt various types of data. Considering a simple way of obtaining latent variables at the testing stage, a projection from the observed space to the latent space as a back constraint has also been simultaneously introduced into the proposed method. Additionally, different from some existing methods which apply the classifiers off-line, a hinge loss is embedded into the model to jointly learn the classification hyperplane, encouraging the latent variables belonging to the different classes to be separated. An efficient algorithm based on the gradient decent technique is constructed to optimize our method. Finally, we apply the proposed approach to three real-world datasets and the associated results demonstrate the effectiveness and superiority of our model compared with other state-of-the-art methods.
Improving term candidates selection using terminological tokens

Abstract The identification of reliable terms from domain-specific corpora using computational methods is a task that has to be validated manually by specialists, which is a highly time-consuming activity. To reduce this effort and improve term candidate selection, we implemented the Token Slot Recognition method, a filtering method based on terminological tokens which is used to rank extracted term candidates from domain-specific corpora. This paper presents the implementation of the term candidates filtering method we developed in linguistic and statistical approaches applied for automatic term extraction using several domain-specific corpora in different languages. We observed that the filtering method outperforms term candidate selection by ranking a higher number of terms at the top of the term candidate list than raw frequency, and for statistical term extraction the improvement is between 15% and 25% both in precision and recall. Our analyses further revealed a reduction in the number of term candidates to be validated manually by specialists. In conclusion, the number of term candidates extracted automatically from domain-specific corpora has been reduced significantly using the Token Slot Recognition filtering method, so term candidates can be easily and quickly validated by specialists.
Temporal Self-Organization: A Reaction–Diffusion Framework for Spatiotemporal Memories

Self-organizing maps (SOMs) find numerous applications in learning, clustering, and recalling spatial input patterns. The traditional approach in learning spatiotemporal patterns is to incorporate time on the output space of a SOM along with heuristic update rules that work well in practice. Inspired by the pioneering work of Alan Turing, who used reaction–diffusion equations to explain spatial pattern formation, we develop an analogous theoretical model for a spatiotemporal memory to learn and recall temporal patterns. The contribution of the paper is threefold: 1) using coupled reaction–diffusion equations, we develop a theory from first principles for constructing a spatiotemporal SOM and derive an update rule for learning based on the gradient of a potential function; 2) we analyze the dynamics of our algorithm and derive conditions for optimally setting the model parameters; and 3) we mathematically quantify the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">temporal plasticity</i> effect observed during recall in response to the input dynamics. The simulation results show that the proposed algorithm outperforms the SOM with temporal activity diffusion, neural gas with temporal activity diffusion and spatiotemporal map formation based on a potential function in the presence of correlated noise for the same data set and similar training conditions.
Towards understandable personalized recommendations: Hybrid explanations

Nowadays, personalized recommendations are widely used and popular. There are a lot of systems in various fields, which use recommendations for different purposes. One of the basic problems is the distrust of users of recommended systems. Users often consider the recommendations as an intrusion of their privacy. Therefore, it is important to make recommendations transparent and understandable to users. To address these problems, we propose a novel hybrid method of personalized explanation of recommendations. Our method is independent of recommendation technique and combines basic explanation styles to provide the appropriate type of personalized explanation to each user. We conducted several online experiments in the news domain. Obtained results clearly show that the proposed personalized hybrid explanation approach improves the users? attitude towards the recommender, moreover, we have observed the increase of recommendation precision.
Interactive Camera Network Design Using a Virtual Reality Interface

The traditional literature on camera network design focuses on constructing automated algorithms. These require problem-specific input from experts in order to produce their output. The nature of the required input is highly unintuitive, leading to an impractical workflow for human operators. In this work we focus on developing a virtual reality user interface allowing human operators to manually design camera networks in an intuitive manner. From real world practical examples we conclude that the camera networks designed using this interface are highly competitive with, or sometimes even superior to, those generated by automated algorithms, but the associated workflow is more intuitive and simple. The competitiveness of the human-generated camera networks is remarkable because the structure of the optimization problem is a well known combinatorial NP-hard problem. These results indicate that human operators can be used in challenging geometrical combinatorial optimization problems, given an intuitive visualization of the problem.
Alternative evaluation functions for the cyclic bandwidth sum problem

One essential element for the successful application of metaheuristics is the evaluation function. It should be able to make fine distinctions among the potential solutions in order to avoid producing wide plateaus (valleys) in the fitness landscape, on which detecting a promising search direction could be hard for certain local search strategies. In the specific case of the cyclic bandwidth sum (CBS) problem, the heuristics reported have used directly the objective function of the optimization problem to assess the quality of potential solutions. Nevertheless, such a conventional function does not allow to efficiently establish preferences among distinct potential solutions. In order to cope with this important issue, three new more refined evaluation functions for the CBS problem are introduced in this paper. An in-depth comparative analysis considering the conventional and the three proposed evaluation functions is carried out and presented. It includes an assessment of their: (a) discrimination potential, (b) consistency with regard to the primary objective of the CBS problem, and (c) practical usefulness within two different algorithms, best improvement local search and iterated local search. A validation of the experimental results by means of a meticulous statistical significance analysis revealed that proposing more informative evaluation schemes for the CBS problem could be a useful means of improving the performance of metaheuristics. Indeed, our iterated local search implementation, using an alternative evaluation function, surpassed the best solutions yielded by the state-of-the-art algorithms and allow us to attain new better upper bounds for 14 out of 20 well-known benchmark instances.
Video Highlight Detection via Region-Based Deep Ranking Model

The video highlight detection task is to localize key elements (moments of user’s major or special interest) in a video. Most of the existing highlight detection approaches extract features from the video segment as a whole without considering the difference of local features spatially. In spatial extent, not all regions are worth watching because some of them only contain the background of the environment without human or other moving objects, especially when there is lots of clutter in the background. To deal with this issue, we propose a novel region-based model which can automatically localize the key elements in a video without any extra supervised annotations. Specifically, the proposed model produces position-sensitive score maps for local regions in the spatial dimension of the video segment, and then aggregates all position-wise scores with position-pooling operation. The regions with higher response values will be extracted as key elements. Thus more effective features of the video segment are obtained to predict the highlight score. The proposed position-sensitive scheme can be easily integrated into an end-to-end fully convolutional network which aims to update parameters via stochastic gradient descent method in the backward propagation to improve the robustness of the model. Extensive experimental results on the YouTube and SumMe datasets demonstrate that the proposed approach achieves significant improvement over state-of-the-art methods.
Major project risk assessment method based on BP neural network

In order to prevent risks in major projects, it is of great importance to accurately assess risks in advance. Therefore, in this paper, we propose a novel major project risk assessment method with the BP neural network model. Firstly, we propose an index system for major project risk assessment, which is made up of four parts: 1) Schedule risk, 2) Cost risk, 3) Quality risk, and 4) Resource risk. Secondly, we propose a hybrid BP neural network and particle swarm optimization (PSO) model to evaluate risks in major projects. Especially, major project risk assessment results are achieved from the output layers of the BP neural network which is optimized by the PSO algorithm. In our proposed hybrid model, the fitness for each particle is computed through an optimal function, and then the particle can improve its velocity for the next cycle by searching the optimal value. Furthermore, this process should be repeated when the end condition is satisfied. Finally, experimental results demonstrate that the proposed method is able to evaluate risk level of major projects with high accuracy.
Employing Domain Indexes to Efficiently Query Medical Data From Multiple Repositories

Content-based retrieval still remains one of the main problems with respect to controversies and challenges in digital healthcare over big data. To properly address this problem, there is a need for efficient computational techniques, especially in scenarios involving queries across multiple data repositories. In such scenarios, the common computational approach searches the repositories separately and combines the results into one final response, which slows down the process altogether. In order to improve the performance of queries in that kind of scenario, we present the Domain Index, a new category of index structures intended to efficiently query a data domain across multiple repositories, regardless of the repository to which the data belong. To evaluate our method, we carried out experiments involving content-based queries, namely range and k nearest neighbor (kNN) queries, 1) over real-world data from a public data set of mammograms, as well as 2) over synthetic data to perform scalability evaluations. The results show that images from any repository are seamlessly retrieved, sustaining performance gains of up to 53% in range queries and up to 81% in kNN queries. Regarding scalability, our proposal scaled well as we increased 1) the cardinality of data (up to 59% of gain) and 2) the number of queried repositories (up to 71% of gain). Hence, our method enables significant performance improvements, and should be of most importance for medical data repository maintainers and for physicians' IT support.
An Approach for Integrating Mixed Reality into BIM for Early Stage Design Coordination

Building Information Modelling (BIM) and its associated technologies have proved to be one of the most promising developments in the Architectural, Engineering and Construction (AEC) industry. Over the past few decades, the AEC sector has been restricted in its communication of design as a result of single interface methods based on 2D and 3D visualization of information. Thus, most issues with respect to construction are identified fairly late, resulting in costly changes. With the introduction of BIM, many other approaches to data visualization can be leveraged including Mixed Reality (MR) applications for the virtual representation of spaces and objects beyond 3D. MR offers a revolution in the virtual representation of objects and space through context awareness as well as the incorporation of information beyond 3D offering countless opportunities for more effective design visualization and coordination. Despite the capability of MR, however, few examples exist of its application to design coordination in the AEC. In addressing this gap this study proposes a novel methodology for the application of MR in design coordination as well as investigates the impact of introducing MR into BIM workflow with a focus on the identification and avoidance of clashes. A prototypical model of the MR design coordination is presented and discussed. Findings indicate that MR improves design productivity and quality but also highlights potential infrastructure issues inhibiting the mainstreaming of MR for design practice.
A family of heuristic search algorithms for feature model optimization

Feature models are a well-known formalism for capturing variability, commonalities and configuration rules of software systems. These models are a compact representation of the set of products in a software product line or configurations of a system at runtime, in terms of features and logical relationships among them. The feature model optimization problem consists of selecting a valid product from the model that satisfies a set of resource or business restrictions and optimizes an objective function commonly related to user preferences. This problem, although computationally intractable, has been addressed in several works with different algorithms. However, these approaches appeal to simplifications of the problem or present drawbacks that limit their application. For example, several approaches do not contemplate feature interactions, and some of them do not guarantee exact solutions or even valid solutions satisfying complex constraints. In this article, we propose a novel algorithm called CSA that overcomes the performance and common weaknesses of existing approaches. CSA can be parameterized with a set of classic search strategies (Backtracking, Branch & Bound, and Best-First Search) and heuristics that allow us to leverage solution optimality and search efficiency. This makes CSA appropriate for automating decisions both at design-time, where exact solutions are generally required, and at run-time, where selection must be done efficiently but sub-optimal solutions are acceptable. The algorithm supports different formats of objective functions, including multi-linear polynomial functions that are capable of representing feature interactions. We present an analysis to validate algorithm properties, and then a series of experiments with synthetic and real models to empirically compare CSA with existing alternatives to show the benefits of our approach. In our analysis, CSA showed to be complete, exact, and scalable for searching approximate solutions. The empirical results showed that the approximate variant of CSA can reach an optimality degree of 99%, against a 84% and 93% reached by other approximate alternatives based on genetic and greedy algorithms respectively. In terms of response time, CSA performed a 72% better than other approximate algorithms. Compared to other exact approaches, CSA improves response time on specific problem types. Furthermore, CSA was evaluated with problem instances involving feature interactions, showing that performance properties scale properly when the number of feature interactions increases.
Statistical enhancement of airborne gamma-ray uranium anomalies: Minimizing the lithological background contribution in mineral exploration

A methodological proposal is presented for uranium prospection using multiple regression of aerogeophysical data (gamma-spectrometry and magnetometry) via a case study in a large and geologically complex area in central Brazil, where known uranium deposits occur. The model seeks to identify and minimize the background variability in uranium concentration for the different lithotypes present in the study area. The multiple regression model is obtained based on the thorium channel data, the eTh/K, eU/K ratios and the magnetic field analytical signal amplitude. Areas with high uranium concentration, in various lithotypes, are highlighted by the subtraction of the regression model obtained from the uranium values measured during the aerial survey. The study area covers part of the southeastern region of the Goiás state, including the Catalão I Complex, which hosts a phosphate mine and includes well-known uranium anomalies as well as rare earth elements associated with the phosphate deposits. The proposed method reveals the presence of anomalous areas as a function of the independent variables, represented by the geophysical properties and the different lithotypes that constitute the geological environment. Besides areas with high uranium contents associated with the Catalão I Complex, new anomalous areas associated with other types of geological environments could be identified. The targets found by the application of the proposed method, were checked in the field via geochemical analysis of rock samples in addition to in situ measurements with a portable gamma-spectrometer. Field measurements showed that, in all cases analyzed, uranium contents presented values above the average concentration of this radioelement in the earth's crust. Geochemical analyzes results from the collected samples confirmed our results by revealing uranium levels ranging from 3.5 to 15.9 ppm. The presented method was successful in identifying specific sites of high uranium concentration via aerogeophysical data, previously concealed by the geological background.
Digital legacy: Designing with things

This paper explores how theories of things can create new forms of agency for the dead. It considers how meaning is constructed through the use or translation of our diverse collections and environments online. These memorials and rituals offer a plurality of narratives, experiences and esthetics, which have the potential to give a wider scope for constructing a durable biography after death. The paper draws conceptual links between digital and physical materials and aims to expand interdisciplinary discourse around the way design can create new forms of legacy through rethinking the role of digital things in our lives.
Bridging the double-gap in circularity. Addressing the intention-behaviour disparity in fashion

(2019). Bridging the double-gap in circularity. Addressing the intention-behaviour disparity in fashion. The Design Journal: Vol. 22, Running with Scissors: 13th International Conference of the European Academy of Design, Dundee, 10-12th April, 2019, edited by Louise Valentine, Joanna Bletcher and Leon Cruickshank, pp. 901-914.
Making the child-computer interaction field grow up

The Interactions website (interactions.acm.org) hosts a stable of bloggers who share insights and observations on HCI, often challenging current practices. Each issue we'll publish selected posts from some of the leading and emerging voices in the field.