Reviewerly/exHarmony huggingface dataset:
Pre-existing semantic associations contribute to memorability of visual changes in a scene

Ecologically important attributes in a natural scene arise not only from isolated objects, but also from semantic associations among objects in the scene. How these associations affect scene memory is yet fully understood. Here, we investigate this issue by examining how semantic associations of scene elements affect scene memorability using online crowdsourced methods. Study 1 asked mTurk participants to label objects in 1024 scene images and provide subjective ratings of object relatedness, complexity, valence, and arousal of these scenes (~20 participants per image). We calculated semantic associations among scene elements based on Global Vectors of verbal labels. We found that this measure reliably predicted participants’ subjective ratings of scene object relatedness. In Study 2, participants studied a set of 36 images randomly sampled from the 1024 images (~55 participants per image). During a later recognition test, a test image could repeat a study image (“old”) or have an object added or removed from the image (“changed”). Participants reported whether a test image was “old” or “changed." While certain scenes were consistently recognized across participants in “old” trials, we found that scene memorability in “changed” trials varied by type of change and semantic associations among scene elements. Observers often failed to detect a change when an object related to other elements was added to the scene. In contrast, observers more successfully identified a change when an object related to other elements was removed from the scene. Low-level features (e.g., changed area), scene complexity, valence, or arousal could not explain these observations. These results suggest that pre-existing semantic associations impede retrieval by assimilating additional items into a scene but facilitate associative retrieval of missing scene elements. Collectively, our findings indicate that scene memorability is a property of pre-existing associative long-term memory, separable from contributions of lower-level perceptual features.
A Hybrid 2D-3D Tangible Interface for Virtual Reality

Virtual Reality (VR) controllers are widely used for easy object selection and manipulation as a primary 3D input method in the virtual environment. Mobile devices with touchscreens like smartphones or tablets provide precise 2D tangible inputs. This research combines a VR controller and a touch-based smartphone to create a novel hybrid 2D-3D interface for enhanced VR interaction. We present the interface design and its implementation and also demonstrate four featured scenarios with the hybrid interface.
Supporting Students’ Basic Science Process Skills by Augmented Reality Learning Media

Science process skills (SPS) are one of the skills needed in the chemistry learning process, in order to gain a complete understanding of the concepts. This skills categorized into two types; basic and integrated. This research aimed to develop a product of Augmented Reality (AR) learning media then to know its feasibility, practicality, and effectiveness on students’ basic SPS on acid base learning. The research adopted Research and Development (R&amp;D) with 4-D model consisted of define, design, develop, and disseminate stages. Questionnaire was used to measure the feasibility and practicality. Then, the obtained data were analyzed by qualitative and quantitative descriptive. In addition, SPS test was used to measure the effectiveness of AR learning media and the data were analyzed by the test of One-way ANOVA, Tukey’s Post Hoc test, and General Linear Model (GLM) Univariate. The result showed that the AR learning media is feasible, practical, and effective with big influence to support students’ basic SPS on acid base learning.
Drawings reveal accurate visual information in memory after just 100 ms of exposure

When we view an image for the first time, we rapidly capture its gist, followed by more detailed visual information. Most studies have employed visual recognition or verbal recall tasks to quantify the contents within the gist and details of a visual memory. However, utilizing drawings as a memory measure could reveal more about these representations and their timescales. Here, we conducted three different experiments through Amazon Mechanical Turk to test gist and detail content through drawn memories. The main experiment consisted of a drawing task, where participants (N=120) were exposed to real-world scene images for different lengths of time, varying from 100 ms to 10,000 ms. After a 500 ms delay, they were then asked to draw the image from memory in as much detail as possible. Drawings were monetarily rewarded based on number of objects present in their drawings, to motivate participants. Two separate scoring experiments asked for different participants to determine whether drawings included objects present in the original stimulus (N=410), and whether there were false object insertions not present in the original stimulus (N=91). First, we observed that participants were able to successfully complete memory drawings across all time scales, with no differences in number of successful drawings between the 100 ms and 10,000 ms conditions. Second, false objects were found to be rarely present, with only one false object added to drawings on average in the fastest condition of 100 ms. However, we also observed that more correct objects were drawn from memory in the longest exposure condition, 10 seconds, for all tested images, and retention drastically improved for exposures greater than 500 milliseconds. In sum, while people were generally quite good at conveying the gist of the image at all presentation time scales, the exact accuracy of their stimulus recreation improved with longer exposures.
Hot Spot Mining and Analysis Model of Sports Microblog Culture Public Opinion Based on Big Data Environment

By analyzing the development characteristics of sports microblog cultural public opinion under the big data environment and the specific needs of automatic public opinion monitoring, the paper designs a sports microblog cultural public opinion hotspot mining system structure model, and describes the main functions and implementation methods of each layer. The paper first uses tools such as ICTCLAS and AntConc to extract hot words, then describes the standardized data representation, and finally uses the Chameleon clustering algorithm to achieve clustering and topic extraction of hot blog posts. This method will provide information support for timely discovering sensitive information and grasping the hot spots of sports microblog culture and public opinion.
A Comparative Analysis of Cohesion in Narrative Text between Korean L1 and L2 Speakers

This paper aims to analyze the differences between Korean L1 group and L2 group in cohesive expression in narrative text writing. The previous studies have been pointed out that the Korean L2 texts lack cohesive expression, but not enough contrast studies have been done comparing cohesive expressions between L1 and L2 texts. Thus, it was difficult for L2 speakers to recognize how their textual composition should be modified in comparison to L1 speakers. This paper analyzes the characteristics of L1 text that L2 speakers can refer to for text writing. It examined the method of lexical cohesion, the use of tense forms, the use of sentential conjunctions and conjunctive endings for semantic cohesion, and the use of determinative phrases as representative language devices in cohesive expression. There was no significant difference between the two groups in the expression of lexical cohesion, which was considered to be related to the lack of substitutive pronouns in Korean. In use of the tense form, the two groups showed a distinct contrast, with L2 speakers using the tense form as a deitic function, while L1 speakers used it as a modal function. In addition, although both groups used sentential conjunctions and conjunctive endings as cohesive devices, the L1 group used sentential conjunctions much more diversely and more often. In contrast, the L2 group showed a relatively high dependence on conjunctive endings than sentential conjunctions. Finally, semantic cohesion by determinative phrase differed significantly in the two groups. In L1 text, unlike in L2, noun phrases in each sentential component have been modified by a determinative phrase. In conclusion, L2 speakers need to be trained with the use of sentential conjunctions, along with the modal function of tense forms. In addition, they need to use determinative phrases for semantic cohesion more often. (Jeonju University)
Abnormal Event Detection in a Surveillance Scene Using Convolutional Neural Network

Automatic abnormal event detection in a surveillance scene is very significant because of more consciousness about public safety. Because of usefulness and complexity, currently, it is an open research area. In this manuscript, the authors have proposed a novel convolutional neural network (CNN) model to detect an abnormal event in a surveillance scene. In this work, CNN is used in two ways. Firstly, it is used for both feature extraction and classification. In a second way, CNN is used for feature extraction, and support vector machine (SVM) is used for classification. Without any pre-processing, the proposed model gives better results compared to state-of-the-art methods. Experiments are carried out on four different publicly available benchmark datasets and one combined dataset, which contains all images of four datasets. The performance is measured by accuracy and area under the ROC (receiver operating characteristic) curve (AUC). The experimental results determine the efficacy of the proposed model.
Artificial Intelligence Based on Modular Reinforcement Learning and Unsupervised Learning

The progress observed on simple multilayer perceptrons regarding convolutional neural networks and recurrent neural networks can be an accurate example in this manner. The deeper the networks become, the more difficult it is to optimize the millions of parameters by just sparse rewards. Reducing the parameters that are optimized via RL can make the problem much easier. Machine Learning is a sub-area of artificial intelligence. It is a data-driven method and aims to recognize patterns in data. Data is transferred to the model, then a prediction is made by it. The obtained output of the model is compared with the expected output, known as the ground truth. The difference between the prediction of the model and the ground truth is a cost with which the model is afterwards trained. In the course of the development in the last decade, it was impressively shown that all ML algorithms mentioned above gained performance when used in combination with deep neural networks.
Management and Operations

In our penultimate chapter, we are going to look at monitoring through the use of email alerts, logging, SNMP, and setting up maintenance windows, and we will finish by exploring the REST API. We will start with the basic requirements of any good network: alerting and logging.
A new representation of S-boxes for algebraic diﬀerential cryptanalysis

Algebraic cryptanalysis can be used to break (small versions of) block ciphers with small data complexity.If we have access to a large number of P-C pairs, algebraic cryptanalysis can be combined with differential techniques.Differential characteristic produces extra linear equations, which can be used to augment the original algebraic system.In our experiments with algebraic differential cryptanalysis, we have developed a different technique to represent the system.In our new method, we model a single P-C pair based encryption, but we use the differential to restrict the equations that model active S-boxes.An algebraic system created with our new model is smaller, and can theoretically be solved faster.Our experiments show that the advantage depends on the overall number of P-C pairs available and whether the chosen differential characteristic is correctly estimated.One of the advantages of the new method is that it can use partial information from the differential and still determine a correct solution faster than both the standard algebraic attack and the standard algebraic-differential attack.
Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset

Visually-grounded spoken language datasets can enable models to learn cross-modal correspondences with very weak supervision. However, modern audio-visual datasets contain biases that undermine the real-world performance of models trained on that data. We introduce Spoken ObjectNet, which is designed to remove some of these biases and provide a way to better evaluate how effectively models will perform in real-world scenarios. This dataset expands upon ObjectNet, which is a bias-controlled image dataset that features similar image classes to those present in ImageNet. We detail our data collection pipeline, which features several methods to improve caption quality, including automated language model checks. Lastly, we show baseline results on image retrieval and audio retrieval tasks. These results show that models trained on other datasets and then evaluated on Spoken ObjectNet tend to perform poorly due to biases in other datasets that the models have learned. We also show evidence that the performance decrease is due to the dataset controls, and not the transfer setting.
A Prototypical Network Approach for Evaluating Generated Emotional Speech

The collection of emotional speech data is a time-consuming and costly endeavour.Generative networks can be applied to augment the limited audio data artificially.However, it is challenging to evaluate generated audio for its similarity to source data, as current quantitative metrics are not necessarily suited to the audio domain.We explore the use of a prototypical network to evaluate four classes of generated emotional audio with this in mind.We first extract spectrogram images from WAVEGAN generated audio and other audio augmentation approaches, comparing similarity to the class prototype and diversity within the embedding space.Furthermore, we augment the source training set with each augmentation type and perform a classification to explore the generated audio plausibility.Results suggest that quality and diversity can be quantitatively observed with this approach.In the chosen context, we see that WAVEGAN generated data is recognisable as a source data class (F1-score 43.6 %), and the samples add similar diversity as unseen source data.This result leads to more plausible data for augmentation of the source training set -achieving up to 63.9 % F1 which is a 3.5 % improvement over the source data baseline.
Solving QAP with Auto-parameterization in Parallel Hybrid Metaheuristics

The Quadratic Assignment Problem (QAP) is one of the most challenging combinatorial optimization problems with many real-life applications. Currently, the best solvers are based on hybrid and parallel metaheuristics, which are actually highly complex and parametric methods. Finding the best set of tuning parameters for such methods is a tedious and error-prone task. In this paper, we propose a strategy for auto-parameterization of QAP solvers. We show evidence that auto-parameterization can further improve the quality of computed solutions. Our auto- parameterization scheme relieves the user from having to find the right parameters while providing a high quality solution.
Commentary: Worshipping the internet – the perils of serving an unpredictable master – a commentary on Aboujaoude and Gega (2021)

Numerous links have been established between cybertechnology and mental health problems. Although the direction of causality in the relationship between internet misuse and mental disturbance remains unclear, the cyberworld may contribute to the shaping of psychopathology in various ways. These include internet-enhanced impulsivity, disinhibition and aggression, attentional problems, difficulties in coping with online information and selective and constraining online interactions with likeminded individuals. One of the imperatives of public health is to help internet users, especially those who are vulnerable such as children and adolescents, to be aware of the risks involved in interactions with the digital technologies and educate them about circumventing the dangers and seeking help.
Product click-through rate prediction model integrating self-attention mechanism

In the commodity click-through rate prediction task, existing deep learning models implicitly construct combinatorial features and cannot know the optimal order of the combinatorial features that can be learned; at the same time, the intrinsic correlation between features is ignored, and invalid feature combinations will bring unnecessary noise to the model. To address these problems, a product click-through prediction model (ACDeepFM) incorporating a self-attentive mechanism is proposed, which first uses the self-attentive mechanism to mine the intrinsic connections among input features and adaptively models the weights of input features. Then a compressed interaction network is added to precisely mine the effect of different order combinations of features on the model prediction results. Then deep neural networks are added to fit complex interaction scenarios between users and items. Finally, the information extracted from the self-attentive mechanism module, the deep neural network module and the compressed interaction network module are fed into the subsequent multilayer perceptron layer to further learn meaningful combinatorial features. Experimental results on two publicly available datasets show that the proposed model achieves higher AUC values and lower Logloss values relative to FM, DNN, DeepFM and xDeepFM models, validating the effectiveness of the ACDeepFM model.
CACS—Compliant Access Control Scheme for Populated Cloud User Services

Cloud resources provide ubiquitous service access for the connected users and applications through heterogeneous infrastructures. Being an open platform and permitting anywhere any time access, the resource security requirements are high. This article introduces a Compliant Access Control Scheme (CACS) for retaining the robustness of resource security amid condensed user requests. The proposed scheme ensures flexible access control using proportionate authentication. In this proportionate authentication, the access interval and the users' request density are accounted for granting resource permissions. This authentication requires identity verified shared keys for providing access to the resources. The scheme's performance is verified using access grant time, false rate, and service ratio.
Methods and tools of formation of general indexes for automation of devices in rehabilitative medicine for post-stroke patients

The current processes of recovery of post-infarction and post-stroke patients in the context of the establishment of the institution of family doctors and insurance medicine are considered. It was proposed to introduce modules for automation of recovery devices (MARD) to ensure procedures, quality of life and reduce labor costs during the period of long-term recovery. The forms of presentation of the model of the integral indicator are substantiated, which, in accordance with the requirements of the Ministry of Health, assesses the generalized indicator of the patient's statement (GIPS), the quality of medical services and increases the efficiency of data compression. A consistent application of two Euclidean norms is proposed, which leads indicators of dissimilar physical nature to a limited metric space. The relationship between the lower and upper bounds of the GIPS, the error, the width of the sliding window, and the values of the derivatives was established on the basis of the Taylor series expansion, geometric inequality and limited space. The model for evaluating the GIPS as a lower bound and the method for generating information about its properties are substantiated. A three-level comparator is applied and an vector- indicator (VI) is introduced as an informational addition to the time series. Additional capabilities for intelligent analysis are demonstrated. The model of GIPS through VI is presented. The examples of VI values are used to demonstrate its applicability to the intelligent analysis of the recovery process. Openness, accessibility, transparency of GIPS and VI as tools of KIT is implemented by the princes of public administration (PA) by reducing it to quantitative control and comparison if there are quantitative and qualitative indicators in the list. VI, sliding windows, as PA and KIT tools in software (SW) for a diagnostic conclusion and correction of the course of procedures, are numerically investigated. It is demonstrated on examples of a numerical experiment with software how the combined application of the method for calculating the GIPS and VI effectively affects the compression ratio, increasing it to 60–75 %
Active Learning from an Imbalanced Dataset

The proposed chapter deals with psychological data related to depression, anxiety, and stress to study how the classification and analysis is carried out on imbalanced data. The proposed study not only contributes on providing practical information about the balancing techniques such as synthetic minority oversampling technique but also reveals the strategy for dealing with the working of many existing classification algorithms such as the support vector machine, random forest, XGBoost, etc. on the imbalanced dataset. The present use of evaluation metrics that are solely implied for the imbalanced data classification is also illustrated. It was observed that the ordinary model assessment techniques do not precisely quantify model execution when gone up against imbalanced datasets and that the common techniques such as the logistic regression and decision tree have a predisposition toward classes that have many observations. The attributes of the minority class are treated low and are routinely overlooked. Henceforth, there is a high likelihood of misclassification of the minority class when compared to the majority class. A confusion matrix which contains data about the real and predicted class is used as an assessment standard to check the exhibition of grouping calculation. Rather than going for accuracy, F-score and the area under the curve are considered as the measures to evaluate the classification model.
Ways and Challenges of Product Labelling for the Visually Impaired

The conceptualizing process plays an important role in assisting designers’ creativity in form and styling development. It contributes to representing the cultural elements before product transformation, which has a limited investigation. This research aims to identify the metaphorical form element that conveys the brain impaired as factors of selection and defining form development of the humanoid robot embodiment. Design Protocol Analysis obtains to into design linguistic interpretations and synthesizing design based on perceptual product experience. Findings have outlined the theory of metaphorical form element selection and identification that could represent brain impaired product in assisting humanoid robotic acceptance among autism. Keywords: braille; visually impaired; tactile; labelling tool. eISSN: 2398-4287© 2021. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning &amp; Surveying, Universiti Teknologi MARA, Malaysia. DOI: https://doi.org/10.21834/ebpj.v6iSI5.2933