Reviewerly/exHarmony huggingface dataset:
Interval Type-3 Fuzzy Adaptation of the Bee Colony Optimization Algorithm for Optimal Fuzzy Control of an Autonomous Mobile Robot

In this study, the first goal is achieving a hybrid approach composed by an Interval Type-3 Fuzzy Logic System (IT3FLS) for the dynamic adaptation of α and β parameters of Bee Colony Optimization (BCO) algorithm. The second goal is, based on BCO, to find the best partition of the membership functions (MFs) of a Fuzzy Controller (FC) for trajectory tracking in an Autonomous Mobile Robot (AMR). A comparative with different types of Fuzzy Systems, such as Fuzzy BCO with Type-1 Fuzzy Logic System (FBCO-T1FLS), Fuzzy BCO with Interval Type-2 Fuzzy Logic System (FBCO-IT2FLS) and Fuzzy BCO with Generalized Type-2 Fuzzy Logic System (FBCO-GT2FLS) is analyzed. A disturbance is added to verify if the FBCO-IT3FLS performance is better when the uncertainty is present. Several performance indices are used; RMSE, MSE and some metrics of control such as, ITAE, IAE, ISE and ITSE to measure the controller’s performance. The experiments show excellent results using FBCO-IT3FLS and are better than FBCO-GT2FLS, FBCO-IT2FLS and FBCO-T1FLS in the adaptation of α and β parameters.
Cyberbullying Detection using Machine Learning

It is an age of the Internet and electronic media, and social media platforms are one of the most frequently used communication medium nowadays. But some people use these sites for malicious purpose and among those negative aspects "Cyberbullying" is prevalent. Cyberbullying is a form of bullying done through electronic means and is used to insult or harm others. Many researchers have proposed solutions and strategies to overcome this menace, but sarcasm is one aspect of it that still needs to be touched. This study aims to highlight previous researchers and to propose an approach to detect cyberbullying along with the element of sarcasm included in it. The results proved that SVM classifier performed better than other classifiers.
Learning from Different text-image Pairs: A Relation-enhanced Graph Convolutional Network for Multimodal NER

Multimodal Named Entity Recognition (MNER) aims to locate and classify named entities mentioned in a (text, image) pair. However, dominant work independently models the internal matching relations in a pair of image and text, ignoring the external matching relations between different (text, image) pairs inside the dataset, though such relations are crucial for alleviating image noise in MNER task. In this paper, we primarily explore two kinds of external matching relations between different (text, image) pairs, i.e., inter-modal relations and intra-modal relations. On the basis, we propose a Relation-enhanced Graph Convolutional Network (R-GCN) for the MNER task. Specifically, we first construct an inter-modal relation graph and an intra-modal relation graph to gather the image information most relevant to the current text and image from the dataset, respectively. And then, multimodal interaction and fusion are leveraged to predict the NER label sequences. Extensive experimental results show that our model consistently outperforms state-of-the-art works on two public datasets. Our code and datasets are available at https://github.com/1429904852/R-GCN.
Improved Dwarf Mongoose Optimization for Constrained Engineering Design Problems

This paper proposes a modified version of the Dwarf Mongoose Optimization Algorithm (IDMO) for constrained engineering design problems. This optimization technique modifies the base algorithm (DMO) in three simple but effective ways. First, the alpha selection in IDMO differs from the DMO, where evaluating the probability value of each fitness is just a computational overhead and contributes nothing to the quality of the alpha or other group members. The fittest dwarf mongoose is selected as the alpha, and a new operator ω is introduced, which controls the alpha movement, thereby enhancing the exploration ability and exploitability of the IDMO. Second, the scout group movements are modified by randomization to introduce diversity in the search process and explore unvisited areas. Finally, the babysitter's exchange criterium is modified such that once the criterium is met, the babysitters that are exchanged interact with the dwarf mongoose exchanging them to gain information about food sources and sleeping mounds, which could result in better-fitted mongooses instead of initializing them afresh as done in DMO, then the counter is reset to zero. The proposed IDMO was used to solve the classical and CEC 2020 benchmark functions and 12 continuous/discrete engineering optimization problems. The performance of the IDMO, using different performance metrics and statistical analysis, is compared with the DMO and eight other existing algorithms. In most cases, the results show that solutions achieved by the IDMO are better than those obtained by the existing algorithms.
A modified cuckoo search algorithm implemented with SCA and PSO for multi-robot cooperation and path planning

The paper puts forward an intelligent approach that deals with the computation of an optimal path with collision avoidance for the stick-carrying twin moving from a pre-assumed start position to a predefined goal position. It has been solved through the efficient implementation of modified cuckoo search, sine cosine algorithm, and particle swarm optimization to design a hybrid algorithm aimed at using the communal advantages of the search and position update ability of these algorithms. The benefits are realized by integrating the egg-laying behavior of the cuckoo species to achieve an efficient global search strategy with modified parameters, local search strategy of particle swarm optimization, and greedy approach of sine cosine algorithm. The proposed algorithm is validated using 10 standard benchmark functions, computer simulation using C language, and real robot platform using Epuck robot to illustrate minimal time, shortest distance, collision avoidance, path smoothness, synchronized action, and reduced energy usage in terms of the path traveled, execution time, the number of steps, and the number of turns in the static as well as the dynamic environment.
Privacy-Enhancing Technology and Everyday Augmented Reality

Fundamental to Augmented Reality (AR) headsets is their capacity to visually and aurally sense the world around them, necessary to drive the positional tracking that makes rendering 3D spatial content possible. This requisite sensing also opens the door for more advanced AR-driven activities, such as augmented perception, volumetric capture and biometric identification - activities with the potential to expose bystanders to significant privacy risks. Existing Privacy-Enhancing Technologies (PETs) often safeguard against these risks at a low level e.g., instituting camera access controls. However, we argue that such PETs are incompatible with the need for always-on sensing given AR headsets' intended everyday use. Through an online survey (N=102), we examine bystanders' awareness of, and concerns regarding, potentially privacy infringing AR activities; the extent to which bystanders' consent should be sought; and the level of granularity of information necessary to provide awareness of AR activities to bystanders. Our findings suggest that PETs should take into account the AR activity type, and relationship to bystanders, selectively facilitating awareness and consent. In this way, we can ensure bystanders feel their privacy is respected by everyday AR headsets, and avoid unnecessary rejection of these powerful devices by society.
Device-independent quantum key distribution using random quantum states

We Haar uniformly generate random states of various ranks and study their performance in an entanglement-based quantum key distribution (QKD) task. In particular, we analyze the efficacy of random two-qubit states in realizing device-independent (DI) QKD. We first find the normalized distribution of entanglement and Bell-nonlocality which are the key resource for DI-QKD for random states ranging from rank-1 to rank-4. The number of entangled as well as Bell-nonlocal states decreases as rank increases. We observe that the decrease in the secure key rate is more pronounced in comparison with that of the quantum resource with the increase in rank. We find that the pure state and Werner state provide the upper and lower bound, respectively, on the minimum secure key rate of all mixed two-qubit states possessing the same magnitude of entanglement under general as well as optimal collective attack strategies.
Using Infographics to Teach the Evidence Analysis Process to Senior Undergraduate Students

Infographics have been described as compelling visual formats that can be quickly comprehended and used for presenting complex information. 1 Otten J.J. Cheng K. Drewnowski A. Infographics and public policy: Using data visualization to convey complex information. Health Aff. 2015; 34: 1901-1907 Crossref PubMed Scopus (74) Google Scholar Infographic design summarizing results from the review of research articles involves interpreting data and presenting it in a graphic format; however, infographics are more than static graphs. They are a complex compilation of connected images that convey an idea. 2 Davis M. Quinn D. Visualizing text: The new literacy of infographics. Reading Today. 2013/2014; 31: 16-18 Google Scholar Engaging visual processing has the potential to enhance learning, 3 Heath S.B. Seeing our way into learning. Camb J Educ. 2000; 30: 121-132 Crossref Scopus (54) Google Scholar because visual memory has been found to be superior to auditory memory. 4 Cohen M.A. Horowitz T.S. Wolfe J.M. Auditory recognition memory is inferior to visual recognition memory. Proc Natl Acad Sci U S A. 2009; 106: 6008-6010 Crossref PubMed Scopus (127) Google Scholar Also, visual information is preferred by approximately 65% of the population. 5 McManus K. Help me see. IE. 2012; 44: 18 Google Scholar This may explain the popularity of infographics and their widespread use by advertisers, news organizations, nonprofits, government agencies, and professional organizations to communicate their messages. 6 Skau D. Harrison L. Kosara R. An evaluation of the impact of visual embellishments in bar charts. Comput Graph Forum. 2015; 34: 221-230 Crossref Scopus (37) Google Scholar
A high magnification UV lens for high temperature optical strain measurements

Digital Image Correlation (DIC) measures full-field strains by tracking displacements of a specimen using images taken before and after deformation. At high temperatures, materials emit light in the form of blackbody radiation, which can interfere with DIC images. To screen out that light, DIC has been recently adapted by using ultraviolet (UV) range cameras, lenses, and filters. Before now, UV-DIC had been demonstrated at the centimeter scale using commercially available UV lenses and filters. Commercial high-magnification lenses using visible light have also been used for DIC. However, there is currently no commercially available high-magnification lens that will allow images to be taken (a) in the UV range, (b) at a submillimeter scale, and (c) from a relatively long working distance separating a specimen inside a test chamber and a camera outside the chamber. In this work, a custom UV high-magnification lens is demonstrated to perform high-magnification, high-temperature DIC measurements. To demonstrate the capabilities of this lens, a series of thermo-mechanical tests was run on a stainless-steel ring specimen. Two UV cameras performed simultaneous measurements: one at lower magnification using a commercial UV lens, and one with the custom high-magnification UV lens. At room temperature, the custom lens produces sufficiently bright images to perform DIC, while at high temperature (demonstrated to 900 °C) the images retained sufficient contrast while avoiding oversaturation. The lens can detect submillimeter rigid motion and tensile strains from long working distances and high magnification. These tests show that the custom lens is suitable for use in high-magnification UV-DIC measurements.
Machine learning for medical diagnosis: A neural network classifier optimized via the directed bee colony optimization algorithm

A fast and accurate method for diagnosis is required in the medical domain. The existing techniques use classification methodology but are not accurate and fast enough or viable for medical diagnosis. In this chapter, we present a high performance artificial neural network (ANN) optimized via the directed bee colony (DBC) algorithm. The methodological analysis is utilized to diagnose cancer, diabetes, and heart disease. The performance analysis is done on three principal variants for diagnosis: classification accuracy, uniqueness of solution, and running time. The relatively low performance of earlier techniques remains a critical barrier for the successful transaction of these techniques for medical diagnosis. The ANN is trained with a newly developed DBC algorithm, which mimics the group food search and decision-making strategy of bees to avoid the premature convergence to local minima. The coalition of bees incorporates the parallelism in an optimal solution search, which reduces the running time. The algorithm results in a unique solution after every run, thereby increasing the medical viability of the proposed method. Extensive comparison with other evolutionary algorithms needs to be done and is tabulated, which highlights its upshots in terms of uniqueness, classification accuracy, and running time. A comprehensive comparison from different meta-analytic studies is done on 16 other algorithms. In two different criterion methods, DBC has been ranked second and first. In terms of uniqueness of the answer, DBC has been ranked first both times. Also, the running time is approximately 101% and 21% more than DBC by GA and PSO.
Encouraging Password Manager Adoption by Meeting Adopter Self-Determination Needs

Password managers are a potential solution to the password conundrum, but adoption is paltry.We investigated the impact of a recommender application that harnessed the tenets of self-determination theory to encourage adoption of password managers.This theory argues that meeting a person's autonomy, relatedness and competence needs will make them more likely to act.To test the power of meeting these needs, we conducted a factorial experiment, in the wild.We satisfied each of the three self-determination factors, and all individual combinations thereof, and observed short-term adoption of password managers.When all self-determination factors were satisfied, adoption was highest, while meeting only the autonomy or relatedness needs individually significantly improved the likelihood of adoption.
PCA of Fe-oxides MLA data as an advanced tool in provenance discrimination and indicator mineral exploration: Case study from bedrock and till from the Kiggavik U deposits area (Nunavut, Canada)

Magnetite and hematite grains from the 0.25–0.5 mm and 0.5–2.0 mm ferromagnetic fractions of ten till samples collected up-ice, overlying and down-ice of the Kiggavik U deposits (Nunavut, Canada), as well as eight bedrock samples from Kiggavik igneous and metasedimentary basement and overlying sedimentary rocks were characterized for their grain size and mineral association using optical microscopy, scanning electron microscopy (SEM) and mineral liberation analysis (MLA). Principal component analysis (PCA) was used to evaluate the MLA data for Fe-oxide mineral association and grain size distribution. PCA shows that mineralogical and granulometric differences in Fe-oxides from Kiggavik igneous rocks distinguish them from that of Kiggavik metasedimentary and sedimentary rocks. In addition, The PCA results indicate that the composition and abundance of minerals associated/intergrown with Fe-oxides are not only different in various till samples, but also in different size fractions of the same sample. Higher proportions of hornblende, quartz, gahnite, grunerite, apatite, chromite and sulfides are intergrown with Fe-oxides in the 0.5–2.0 mm till fraction, as compared to the 0.25–0.5 mm fraction in which Fe-oxides are mostly associated with pyroxene, titanite, rutile, feldspars, calcite and zircon. The mineral associations and grain sizes of proximal bedrocks are reflected in smaller size fractions of Kiggavik till, whereas detrital grains in the 0.5–2.0 mm fraction of Kiggavik till may have originated from distal sources. PCA also shows that Fe-oxides from the Kiggavik bedrock and till can be discriminated from those of volcanogenic massive sulfide (VMS) deposits because of smaller grain sizes and higher abundances of sulfides, gahnite, axinite, corundum, hypersthene and pyroxene intergrown with VMS Fe-oxides. This study emphasizes the importance of selecting suitable representative grain size fractions of till, or other sediments, when using indicator minerals for exploration. The results of PCA of Fe-oxides MLA data are consistent with the results of using Fe-oxides geochemical data in provenance discrimination of Kiggavik till.
Exploiting syntactic and neighbourhood attributes to address cold start in tag recommendation

Many state-of-the-art tag recommendation methods were designed considering that an initial set of tags is available in the target object. However, the effectiveness of these methods greatly suffer in a cold start scenario in which those initial tags are absent (although other features of the target object, such as title and description, may be present). To tackle this problem, previous work extracts candidate terms directly from the text associated with the target object or from similar/related objects, and use statistical properties of the occurrence of words, such as term frequency (TF) and inverse document frequency (IDF), to rank the candidate tags for recommendation. Yet, these properties, in isolation, may not be enough to effectively rank candidate tags, specially when they are extracted from the typically small and possibly low quality texts associated with Web 2.0 objects. In this work, we analyze various syntactic patterns (e.g., syntactic dependencies between words in a sentence) of the text associated with Web 2.0 objects that can be exploited to identify and recommend tags. We also propose new tag quality attributes based on these patterns, including them as new evidence to be exploited by state-of-the-art Learning-to-Rank (L2R) based tag recommenders. We evaluate our tag recommendation methods using real data from four Web 2.0 applications, finding that, for three out of our four datasets, the inclusion of our new proposed syntactic tag quality attributes brings improvements to two L2R-based tag recommenders with gains of up to 17% in precision. Furthermore, we find that recommendations provided by these methods can be further expanded exploiting the target object’s neighbourhood (i.e., similar objects). Our characterization and feature importance analysis results show that our syntactic attributes can indeed help discriminate relevant from non-relevant tags, being complementary to other, more traditional, tag quality attributes, particularly for datasets in which the textual features are short and / or present low quality.
Distance Learners’ Use of Handheld Technologies

This study investigates how and where distance learners use handheld devices and the impact this has on learning habits, access to learning content and quality of work. It analyses the spatial dimension of anytime-anywhere learning and, with a focus on anywhere learning, it explores students’ ongoing negotiation of the flow between and across study locations. The study concludes by proposing two new concepts: the flow of places and place of space. These should help direct the framing of future studies into the places, spaces, and mobility of formal and informal seamless learning. A dataset comprising 446 responses from undergraduate students enrolled at the UK’s largest distance learning university was analysed in respect to three research questions. All age groups, study levels, and disciplines were represented. Five key findings are: most students now use handheld devices for study-related learning; the distribution of study-related learning tasks was similar in all seven study places; there is a strong, statistically-significant correlation between the number of study places in which handheld devices are used and the number of study task types performed; two fifths of students using a handheld device for learning have noticed a change in study habit and benefit to learning; and multiple regression analysis shows three variables (number of study places, number of study tasks, and change in study habits) are predictors of finding it easier to access learning materials and improved quality of learners’ work.
The Design of Hand Gestures for Selecting Virtual Objects

Providing input for the selection of objects in virtual, augmented, and mixed-mode reality can be done with hand-held controllers or hand gestures depending on the complexity and precision required. Free hand gestures have the advantages of eliminating the need for a controller, not needing to see the controls on the controller, and potentially being less fatiguing. However, the designs of hand gestures for object selection have not been thoroughly evaluated for usability and performance. Eighteen participants evaluated four different ray-casting hand gestures (index thrust, index click, palm thrust, and palm click) and three snapback thresholds while selecting 2D targets of different sizes. Dependent variables were mean time to select targets, number of selections not completed, number of incorrect targets selected, and subjective preference. The index thrust and index click gestures were preferred by subjects and had faster mean selection times and lower number of incorrect target selections. There were no significant differences between snapback thresholds on productivity, error, or preference. Overall, the index thrust and index click gestures were associated with greater user confidence, particularly with smaller targets.
Takagi-Sugeno fuzzy regulator design for nonlinear and unstable systems using negative absolute eigenvalue approach

This paper introduces a Takagi-Sugeno (T-S) fuzzy regulator design using the negative absolute eigenvalue (NAE) approach for a class of nonlinear and unstable systems. The open-loop system is initially embodied by the traditional T-S fuzzy model and then, all closed-loop subsystems are combined using the proposed Max-Min operator in place of traditional weighted average operator from the controller side to lessen the coupling virtually and simplify the proposed regulator design. For each virtually decoupled closed-loop subsystem, the composite regulators (i.e., primary and secondary regulators) are designed by the NAE approach based on the enhanced eigenvalue analysis. The Lyapunov function is utilized to guarantee the asymptotic stability of the overall T-S fuzzy control system. The most popular and widely used nonlinear and unstable systems like the electromagnetic levitation system (EMLS) and the inverted cart pendulum (ICP) are simulated for the wide range of the initial conditions and the enormous variation in the disturbance. The transient and steady-state performance of the considered systems using the proposed design are analyzed in terms of the decay rate, settling time and integral errors as IAE, ISE, ITAE, and ITSE to validate the effectiveness of the proposed approach compared to the most popular and traditional parallel distributed compensation (PDC) approach.