Reviewerly/exHarmony huggingface dataset:
Nasseh method to visualize high-dimensional data

Today's ever-increasing application of high-dimensional data sets makes it necessary to find a way to fully comprehend them. One of these ways is visualizing data sets. However, visualizing more than 3-dimensional data sets in a fathomable way has always been a serious challenge for researchers in this field. There are some visualizing methods already available such as parallel coordinates, scatter plot matrix, RadViz, bubble charts, heatmaps, Sammon mapping and self organizing maps. In this paper, an axis-based method (called Nasseh method) is introduced in which familiar elements of visualization of 1-, 2- and 3-dimensional data sets are used to visualize higher dimensional data sets so that it will be easier to explore the data sets in the corresponding dimensions. Nasseh method can be used in many applications from illustrating points in high-dimensional geometry to visualizing estimated Pareto-fronts for many-objective optimization problems.
Employee Retention in Agile Project Management

In today's contemporary world, one of the most significant challenges facing most organizations is the challenge of employee retention. To remain efficient in an extremely competitive world, it is important for organizations to invest in measures that enhance the productivity and motivation of their workforce. In essence, this is the key to remaining relevant. Agile project management is the newest trend in project management methods that support concepts of flexibility and continuous improvements which help to keep an organization's workforce efficient and motivated and reflect positively on employee retention. In this theoretical paper, we aim to critically analyze how the various Agile methods support employee retention better than traditional approaches by matching employee retention's best practices within its approach. In this work, we found that agile systems implicitly handle several job satisfaction factor and thus help with employee retention without extra effort done by human resource management in traditional approaches.
Digital image correlation with a moving camera using structure from motion calibration

The application of digital image correlation (DIC) in structural monitoring can benefit from the use of remote controlled image acquisition systems such as unmanned aerial vehicles (UAVs) in cases where the structure to be assessed is of difficult accessibility. However, this configuration does not always allow normal camera calibration procedures to be followed. In this work, a camera is manually moved around an object prepared with a speckle pattern, in a reference state and later in a loaded state, in an attempt to emulate the behaviour of a UAV. A structure from motion (SfM) algorithm is applied to the captured images in order to obtain camera calibration parameters for each image, using the features found in the object’s surroundings. An image pair from each state is chosen and 3D DIC is performed on the object using the previously obtained calibration.
BTDP

Bilinear models are very powerful in multimodal fusion tasks like Visual Question Answering. The predominant bilinear methods can all be seen as a kind of tensor-based decomposition operation that contains a key kernel called “core tensor.” Current approaches usually focus on reducing the computation complexity by applying low-rank constraint on the core tensor. In this article, we propose a novel bilinear architecture called Block Term Decomposition Pooling (BTDP), which not only maintains the advantages of previous bilinear methods but also conducts sparse bilinear interactions between modalities. Our method is based on Block Term Decompositions theory of tensor, which will result in a sparse and learnable block-diagonal core tensor for multimodal fusion. We prove that using such a block-diagonal core tensor is equivalent to conducting many “tiny” bilinear operations in different feature spaces. Thus, introducing sparsity into the bilinear operation can significantly increase the performance of feature fusion and improve VQA models. What is more, our BTDP is very flexible in design. We develop several variants of BTDP and discuss the effects of the diagonal blocks of core tensor. Extensive experiments on two challenging VQA-v1 and VQA-v2 datasets show that our BTDP method outperforms current bilinear models, achieving state-of-the-art performance.
Estimation of maintainability parameters for object-oriented software using hybrid neural network and class level metrics

The various software metrics proposed in the literature can be used to evaluate the quality of software systems written in object-oriented manner. These metrics are broadly categorized into two subcategories i.e., system level software metrics and class level software metrics. In this work, ten different types of class level metrics are considered as an input to develop one model for predicting software maintainability of object-oriented software system. These models are developed using three types of neural networks, i.e., artificial neural network, radial basis function network, and functional link artificial neural network. In this study, a hybrid algorithm based on genetic algorithm (GA) with gradient descent algorithm has been proposed to find optimal weights of these neural networks. Since accuracy of the prediction model is highly dependent on the class level metrics, they are considered as input of the models. So, five different feature selection techniques are used in this study to identify the best set of features with an objective to improve the accuracy of software maintainability prediction model. The effectiveness of these models are evaluated using four evaluation metrics, i.e., MAE, MMRE, RMSE, and SEM. In this work, parallel computing concept has been also considered with an objective to reduce the model training time. The results show that the model developed using the proposed hybrid algorithm based on GA with gradient descent algorithm give better results as compared to the work presented by other authors in literature. The results also show that feature selection techniques obtain better results for predicting maintainability as compared to all metrics. The experimental results show that parallel computing is beneficial in reducing the model training time.
Face recognition system based on continuous one-state model

Face recognition (FR) has received an important concern in our contemporary life, especially in control and security applications. This work proposes a novel method for tackling the problem of FR using the minimum number of states (i.e. one state) of continuous Hidden Markov Model (HMM). The contribution of the proposed method can be viewed in the use of continuous one-state HMM, which has not yet been used in other approaches. Furthermore, the concept that early adopted, regarding the relationship between facial regions and number of states, is invalidated, accordingly. The implementation of this method can be summarized as follows: i) filtering the face image using a median filter, ii) reduction of additional noise and image size using multiple level of discrete wavelet transform (DWT), iii) training the outcomes of the DWT using one-state HMM of continuous output density with one Gaussian mixture coefficient, and iv) recognizing images for validation. The advantage of using continuous output densities appears in conducting noisy images, such that the proposed method highly reduces the effect of noise. The simulation results present that the recognition rate is about 100% despite the presence of 25% and 50% of impulsive noise densities on the images of the ORL and Yale databases, respectively.
A Novel Collision-Free Path Planning Modeling and Simulation Methodology for Robotical Arms Using Resistive Grids

SUMMARY Path planning represents planning collision-free strategies to move from starting point to ending point. These strategies can be carried out for known and unknown environments. Recently, a novel and reduced CPU-time modeling and simulation methodology for path planning in known environment based on resistive grids (RGs) has been introduced. In this work, a novel modified version of Resistive Grid Path Planning Methodology (RGPPM) methodology is presented with the purpose of exploring collision-free path planning for robotic arms. This extension of the methodology allows to numerically relate positions in the RG with angular values of the robotic systems. In addition, it is possible to include obstacles in the configuration space, and therefore collision detection can be established for RGs. Finally, the variation of links for robotic arms and obstacles for configuration space is explored by simulating different scenarios.
Teaching graduate students how to review research articles and respond to reviewer comments

This article deals with teaching students how to review research articles of other authors and how to respond to reviewer comments related to their own articles. By teaching these skills, one educates students how to do something which is undoubtedly much more important for their professional development: how to properly do their own research and how to properly write their own research articles. The guidelines given are primarily intended for students of computer science. Nevertheless, the notions presented here are applicable to areas other than computing. Since there are so many branches within computing, each one characterized by its own particular features, this article also includes a section on variations of presented methodology, which are of interest for each particular branch of computer science covered by the authors of this article. In general, the review process consists of two main parts: formal review and quality review. Led by the lack of results in the available literature, an analysis based on readability index evaluates average article acceptance rates, review times, and delay period for publishing accepted articles depending on the following: article types, willingness of reviewers to do reviews, journal impact factor, and the scientific field. Quality review must be taken into account for all types of articles. Appendix A describes specific experiences from various scientific fields. Appendix B gives specific guidelines for a number of fields covered by the authors of this article.
MEvo: a framework for effective macro sets evolution

In Automated Planning, generating macro-operators (macros) is a well-known reformulation approach that is used to speed-up the planning process. Nowadays, given the number of existing techniques, a large number of macros is already available or can be easily extracted. Most of the macro generation techniques aim for using the same set of generated macros for each planner and every problem instance in a given domain. Although they provide ‘general improvement’, the effect of macros might vary a lot for different planners. Moreover, the impact of macros on structurally different problem instances than the training ones can be potentially very detrimental. Evidently, this limits the exploitation of macros in real-world planning applications, where the structure of problem instances can often change as well as the exploited planning engine can change from time to time. In this paper, we propose the Macro sets Evolution (MEvo) approach. MEvo has been designed for overcoming the aforementioned issues in order to improve the performance of domain-independent planners by dynamically selecting promising macros – taken from a given pool – while solving continuous streams of problem instances. Our extensive empirical study, involving more than 1,000 planning problem instances and 8 state-of-the-art planning engines, demonstrates effectiveness and efficiency of MEvo.
Blur Invariant Template Matching Using Projection onto Convex Sets

Blur is a common phenomenon in image acquisition that negatively influences recognition rate if blurred images are used as a query in template matching. Various blur-invariant features and measures were proposed in the literature, yet they are often derived under conditions that are difficult to satisfy in practise, for example, images with zero background or periodically repeating images and classes of blur that are closed under convolution. We propose a novel blur-invariant distance that puts no limitation on images and is invariant to any kind of blur as long as the blur has limited support, non-zero values and sums up to one. A template matching algorithm is then derived based on the blur-invariant distance, which projects query images on convex sets constructed around template images. The proposed method is easy to implement, it is robust to noise and blur size, and outperforms other competitors in this area.
Policy-Driven Middleware for Multi-Tenant SaaS Services Configuration

The multi-tenancy architecture allows software-as-a-service applications to serve multiple tenants with a single instance. This is beneficial as it leverages economies of scale. However, it does not cope with the specificities of each tenant and their variability; notably, the variability induced in the required quality levels that differ from a tenant to another. Hence, sharing one single instance hampers the fulfillment of these quality levels for all the tenants and leads to service level agreement violations. In this context, this article proposes a policy-driven middleware that configures the service according to the non-functional requirements of the tenants. The adopted approach combines software product lines engineering and model driven engineering principles. It spans the quality attributes lifecycle, from documenting them to annotating the service components with them as policies, and it enables dynamic configuration according to service level agreements terms of the tenants.
Parallel Streaming Random Sampling

This paper investigates parallel random sampling from a potentially-unending data stream whose elements are revealed in a series of element sequences (minibatches). While sampling from a stream was extensively studied sequentially, not much has been explored in the parallel context, with prior parallel random-sampling algorithms focusing on the static batch model. We present parallel algorithms for minibatch-stream sampling in two settings: (1) sliding window, which draws samples from a prespecified number of most-recently observed elements, and (2) infinite window, which draws samples from all the elements received. Our algorithms are computationally and memory efficient: their work matches the fastest sequential counterpart, their parallel depth is small (polylogarithmic), and their memory usage matches the best known.
Constructing and predicting school advice for academic achievement

Educational tests can be used to estimate pupils' abilities and thereby give an indication of whether their school type is suitable for them. However, tests in education are usually conducted for each content area separately which makes it difficult to combine these results into one single school advice. To help with school advice, we provide a comparison between both domain-specific and domain-agnostic methods for predicting school types. Both use data from a pupil monitoring system in the Netherlands, a system that keeps track of pupils' educational progress over several years by a series of tests measuring multiple skills.
A Pulse-gated, Neural Implementation of the Backpropagation Algorithm

For some time, it has been thought that backpropagation of errors could not be implemented in biophysiologically realistic neural circuits. This belief was largely due to either 1) the need for symmetric replication of feedback and feedforward weights, 2) the need for differing forms of activation between forward and backward propagating sweeps, and 3) the need for a separate network for error gradient computation and storage, on the one hand, or 4) nonphysiological backpropagation through the forward propagating neurons themselves, on the other. In this paper, we present spiking neuron mechanisms for gating pulses to maintain short-term memories, controlling forward inference and backward error propagation, and coordinating learning of feedback and feedforward weights. These neural mechanisms are synthesized into a new backpropagation algorithm for neuromorphic circuits.
Detecting Misalignments between System Security and User Perceptions: A Preliminary Socio-technical Analysis of an E2E email Encryption System

The set of impressions that a user has about distinct aspects of a system depends on the experience perceived while interacting with the system. Considering the effects of these interactions in a security analysis allows for a new class of security properties in terms of misalignments between the system's technical guarantees and the user's impressions of them. For instance, a property that we call "false sense of insecurity" identifies a situation in which a secure system injects uncertainty in users, thus improperly transmitting the degree of protection that it actually provides; another, which we call "false sense of security", captures situations in which a system instills a false sense of security beyond what a technical analysis would justify. Both situations leave room for attacks. In this paper we propose a model to define and reason about such socio-technical misalignments. The model refers to and builds on the concept of security ceremonies, but relies on user experience notions and on security analysis techniques to put together the information needed to verify misalignment properties about user's impressions and system's security guarantees. We discuss the innovative insight of this pilot model for a holistic understanding of a system's security. We also propose a formal model that can be used with existing model checkers for an automatic analysis of misalignments. We exemplify the approach by modelling one specific application for end-to-end email encryption within which we analyze a few instances of misalignment properties.
A Deep Reinforcement Learning Based Mapless Navigation Algorithm Using Continuous Actions

In this paper, we propose a maples navigation for robot with deep reinforcement learning and continuous actions, in order to investigate the effect of continuous actions for robots mapless navigation. Assuming that the positions of robots can be easily obtained by indoor localization system, the robot agent are trained in a simulation environment to learn mapless navigation policy by taking only obstacle distances and relative positions to the target. Considering the state of robot motion in real world, properly limited range of continuous actions are given to the agent to choose. So the agent output steering angle and moving distance in the range of limitation. We valid that continuous actions allow the agent to have richer explorations, flexible movements and thus higher possibility to reach the navigation target, by experiments of comparing with traditional discrete actions.
A Comparative Study of Using Bag-of-Words and Word-Embedding Attributes in the Spoiler Classification of English and Thai Text

This research compares the effectiveness of using traditional bag-of-words and word-embedding attributes to classify movie comments into spoiler or non-spoiler. Both approaches were applied to comments in English, an inflectional language; and in Thai, a non-inflectional language. Experimental results suggested that in terms of classification performance, word embedding was not clearly better than bag of words. Yet, a decision to choose it over bag of words could be due to its scalability. Between Word2Vec and FastText embeddings, the former was favorable when few out-of-vocabulary (OOV) words were present. Finally, although FastText was expected to be helpful with a large number of OOV words, its benefit was hardly seen for Thai language.
Looking Good Sister! The Use of a Virtual World to Develop Nursing Skills

When developing contemporary nursing curricula the challenge is how to offer students a variety of authentic learning opportunities. Indeed, there is a generally held concern that the tertiary sector finds difficulty in achieving consistent, high quality opportunities for experiential learning. Additionally, these opportunities are often expensive and logistically problematic. A number of disciplines in higher education are opting for the use of contemporary software approaches that serve as valuable adjuncts for the delivery of learning content. This satisfies the students of today who demand experiences that are instantly gratifying, engaging and flexible. Nevertheless and contrastingly, the profession of nursing remains at its core a hands-on profession. Second Life is a three dimensional virtual world, that, due to its ability to promote collaborative, synchronous and immersive learning has been described as a rich social environment. Making decisions about how much technology should be included in the curriculum and at what point in the learner’s journey it should be introduced presents a challenge to nurse educators. This research utilised a created nursing environment in Second Life where activities were embedded within three undergraduate nursing courses. All participating academics, and students were required to create their own avatar. Following the task, semi structured interviews were conducted, transcribed, coded and thematically analysed. The perspectives of practicing Registered Nurses and nursing academics and their students are presented. Observations, field notes and examination of relevant artefacts were also cyclically reviewed and incorporated. Findings from this research relate to the action of avatar creation within a pedagogical experience. It was established that offline characteristics influence a willingness to invest time and creativity when developing an online persona. Evidence of impression management was clear during customisation of the appearance and capabilities of the avatar. The findings suggest that there is relevance and transferability following participation in virtual world nursing experiences. Future investigation could focus on which students are best suited to learning this way and also what level of learning is actually achieved when in the role of a personalised ‘ideal self avatar’.