Reviewerly/exHarmony huggingface dataset:
A Fuzzy Approach for Classification and Novelty Detection in Data Streams Under Intermediate Latency

Novelty detection is an important topic in data stream classification, as it is responsible for identifying the emergence of new concepts, new patterns, and outliers. It becomes necessary when the true label of an instance is not available right after its classification. The time between the classification of an instance and the arrival of its true label is called latency. This is a common scenario in data streams applications. However, most classification algorithms do not consider such a problem and assume that there will be no latency. On the other hand, a few methods in the literature cope with the existence of infinite latency and novelty detection in data streams. In this work, however, we focus on the scenario where the true labels will be available to the system after a certain time, called intermediate latency. Such a scenario is present in the stock market and weather datasets. Moreover, aiming for more flexible learning to deal with the uncertainties inherent in data streams, we consider the use of fuzzy set theory concepts. Therefore, we propose a method for classification and novelty detection in data streams called Fuzzy Classifier with Novelty Detection for data streams (FuzzCND). Our method uses an ensemble of fuzzy decision trees to perform the classification of new instances and applies the concepts of fuzzy set theory to detect possible novelties. The experiments showed that our approach is promising in dealing with the emergence of new concepts in data streams and inaccuracies in the data.
An under-sampling technique for imbalanced data classification based on DBSCAN algorithm

In the classification problem, the classification accuracy will be influenced by the training data significantly. However, data sets distribution in real-world applications, is mostly imbalanced. Imbalanced data sets mean that most of the samples are in one class named the majority class, whereas the other class named the minority class has little samples. In these situations, most of the classifiers confront the problem, because they designed to classify samples that are distributed between classes equally. Therefore, selecting a suitable training set is an essential step in the domain of imbalanced data classification. In this paper, a novel and effective under-sampling technique is presented to select the suitable samples of majority class using the well-known DBSCAN algorithm. According to this algorithm, the most appropriate samples from the majority class are selected, and other majority class samples will be removed to balance the training set. Experimental results over fifteen imbalanced data sets demonstrate the supremacy of the proposed method compared with six other preprocessing methods.
HMDPose: A large-scale trinocular IR Augmented Reality Glasses Pose Dataset

Augmented Reality Glasses usually implement an Inside-Out tracking. In case of a driving scenario or glasses with less computation capabilities, an Outside-In tracking approach is required. However, to the best of our knowledge, no public datasets exist that collects images of users wearing AR glasses. To address this problem, we present HMDPose, an infrared trinocular dataset of four different AR Head-mounted displays captured in a car. It contains sequences of 14 subjects captured by three different cameras running at 60 FPS each, adding up to more than 3,000,000 labeled images in total. We provide a ground truth 6DoF-pose, captured by a submillimeter accurate marker-based tracker. We make HMDPose publicly available for non-profit, academic use and non-commercial benchmarking on ags.cs.uni-kl.de/datasets/hmdpose/.
VR Dodge-ball: Application of Real-time Gesture Detection from Wearables to ExerGaming

Wearable technologies are applied to various areas nowadays, including health care and physical training. In previous work, we developed ExerSense, an algorithm to segment, classify, and count different predefined motions in real-time using correlation of acceleration. Using ExerSense, we developed a Virtual Reality (VR) Dodge-ball game. A watch-like wearable device is mounted to the right wrist and connected wireless to a head-mounted display (HMD). ExerSense enables to recognize real-time ball throwing gesture. Dodging opponent's ball in 3-Dimensional virtual space is detected using the sensors embedded in the HMD. We found that wearable devices have an advantage for virtual exergames against the camera-based approaches: there is no space and place limitation. Additionally, the study shows that exergames could be implemented with only general usage devices.
Cloud computing resource scheduling based on improved ANN model takeaway order volume forecast

In recent years, with the change of people’s living habits, food delivery has become more and more popular. Along with this is the rapid growth of takeaway distribution. It is significant to improve the accurate prediction of the take-out order quantity since it can reduce unnecessary losses for the merchants. The Cloud computing system is the main platform for processing massive data. Applying cloud computing resource scheduling to the forecasting of takeaway orders will greatly help merchants. The research purpose of this paper is to study the cloud computing resource scheduling based on improved ANN model takeaway order volume prediction. This paper improves the ANN model and proposes a cloud computing resource scheduling method based on improved ANN model takeaway order volume prediction. An experimental test was performed to verify the reliability of the proposed method. The results of this study show that the proposed method can accurately predict takeaway orders and contributes to reasonable resource scheduling.
ELMo+Gated Self-attention Network Based on BiDAF for Machine Reading Comprehension

Machine reading comprehension (MRC) has always been a significant part of artificial intelligence and the focus in the field of natural language processing (NLP). Given context paragraph, to answer its query, we need to encode complex interaction between the question and the context. In the late years, with the rapid progress of neural network model and attention theory, MRC has made great advances. Especially, attention theory has been widely used in MRC. However, the accuracy of the previous classic baseline model has some upside potential and some of them did not take into account the long context dependence and polysemy. In this paper, for resolving the above problems and further improve the model, we introduce ELMo representations and add a gated self-attention layer to the Bi-Directional Attention Flow network (BIDAF). In addition, we employ the feature reuse method and modify the linear function of answer layer to further improve the performance. In the experiment of SQuAD, we prove this model greatly exceeds the baseline BIDAF model and its performance is close to the average level of human test, which proves the validity of this model.
Sequential Logic and Pipelining in Chip-Based Electronic-Photonic Digital Computing

The recent rapid progress in integrated photonics has catalyzed the development of integrated optical computing in this post-Moore's law era. Electronic-photonic digital computing, as a new paradigm to achieve high-speed and power-efficient computation, has begun to attract attention. In this paper, we systematically investigate the optical sequential logic and pipelining in electronic-photonic computing, which together offer a solution to potential problems in latency and power budget as the size of electronic-photonic computing circuits scales up considerably to achieve much more complex functions. Pipelining and sequential logic open up the possibility of high-speed very-large-scale electronic-photonic digital computing.
ADKT: Adaptive Deep Knowledge Tracing

Deep Learning based Knowledge Tracing (DLKT) has been shown to outperform other methods due to its strong representational ability. However, DLKT models usually exist a common flaw that all learners share the same model with identical network parameters and hyper-parameters. The drawback of doing so is that the learned knowledge state for each learner is only affected by the specific learning sequence, but less reflect the personalized learning style for each learner. To tackle this problem, we proposes a novel framework, called Adaptive Deep Knowledge Tracing (ADKT), to directly introduce personalization into DLKT. The ADKT framework tries to retrain an adaptive model for each learner based on a pre-trained DLKT model and trace the knowledge states individually for each learner. To verify the effectiveness of ADKT, we further develop the ADKVMN (Adaptive Dynamic Key-Value Memory Network) model by combining the ADKT and the classic DKVMN, which has been widely referred as a state-of-the-art DLKT model. With extensive experiments on two popular benchmark datasets, including the ASSISTments2009 and ASSISTments2015 datasets, we empirically show that ADKVMN has superior predictive performance than DKVMN.
Logics of (In)sane and (Un)reliable Beliefs

Abstract Inspired by an interesting quotation from the literature, we propose four modalities, called ‘sane belief’, ‘insane belief’, ‘reliable belief’ and ‘unreliable belief’, and introduce logics with each operator as the modal primitive. We show that the four modalities constitute a square of opposition, which indicates some interesting relationships among them. We compare the relative expressivity of these logics and other related logics, including a logic of false beliefs from the literature. The four main logics are all less expressive than the standard modal logic over various model classes, and the logics of sane and insane beliefs are, respectively, equally expressive as the logics of unreliable and reliable beliefs on any class of models. The logics of reliable and unreliable beliefs are then combined into a bimodal logic, which turns out to be equally expressive as the standard modal logic. Despite this, we cannot obtain a complete axiomatization of the minimal bimodal logic, by simply translating the axioms and rules of the minimal modal logic $\textbf{K}$ into the bimodal language. We then introduce a schematic modality which unifies reliable and unreliable beliefs and axiomatize it over the class of all frames and also the class of serial frames. This line of research is finally extended to unify sane and insane beliefs and some axiomatizations are given.
Read, write, develop: the socio-economic impact of literacy in South Africa

Purpose Taking motivation from South Africa's ranking of 50 out of 50 countries who participated in the Progress in International Reading Literacy Study of Grade four learners in 2016, this paper examines the social and economic outcomes of literacy in South Africa. Design/methodology/approach Using unemployment, income and crime rates as proxies for social and economic outcomes, the effect of literacy is examined by employing the fixed- and random-effects techniques to estimate a panel data covering nine provinces in South Africa from 2008 to 2017. Findings The results show that literacy rate worsens unemployment but improves crime rate and income per capita across South African provinces. Practical implications Policymakers need to consider an expanded view of literacy by extending investments to cover financial and technology literacy in addition to functional literacy to fully maximise the benefits of education. Originality/value To the authors’ best knowledge, this is the first empirical assessment of literacy outcomes in South Africa.
Maze Generation Based on Difficulty using Genetic Algorithm with Gene Pool

Game level design is one of the most important element of developing an enjoyable video game. Besides, game with difficult and dynamic level can make players more exciting. This paper presents a new method of generating a video game level using a genetic algorithm. The proposed method is called gene pool integrates learning. This method implemented in feature selection so that this method is general enough to be used for multiple different types of games. This paper uses some training data to scan good patterns and store all of them in a gene pool. Furthermore, the genetic algorithm is used to find the combination of patterns that can produce the best result. The gene pool also records the quality of each gene so it can learn the pattern which most commonly found in multiple levels. For testing, this research develops a custom game with complicated rules that are hard to represent by a simple 2D array compared to the previously attempted work. The result of this research shows that the method can generate many complicated levels at once. Overall, levels generated using this method on average requires almost 3 times more steps to solve than the dataset.
Point-Based Deep Neural Network for 3D Facial Expression Recognition

3D data are an important resource for many computer-based applications, as they provide valuable depth cues about the full geometry of 3D associated objects. They become even more valuable as regards 3D face/ facial expression recognition using deep learning. Indeed, two main challenges remain under study. The first is how to resume 3D faces with a discriminative representation from a 3D point cloud while exploiting an adequate Deep Neural Network (DNN). The second is the lack of large 3D facial datasets. To address the first issue, we propose to exploit solely geometric information while applying DNN. Hence, in order to deal with high resolution face scans with a rich point cloud representation, we extract point-based representations using various sampling strategies. Different keypoint sets are used, ranging from a small set of points of interest (i.e. landmarks) to point sets sampled from a curve-based representation, as well as scale-invariant feature transform keypoints. As for the second issue and in order to overcome overfitting caused mainly by the lack of large labelled datasets while applying DNN, we propose to generate new realistic-like facial expressions using non-rigid registration techniques. The effectiveness of the suggested approach is demonstrated through conducting experiments on the BU-3DFE database. The quantitative evaluation and comparison with the recently developed state of the art show the competitiveness of the proposed 3D facial expression recognition approach.
Edge‐based effective active appearance model for real‐time wrinkle detection

Abstract Background Recently, the field of face and facial features has been progressively studied. The features of facial expression have gained increasing attention for related applications. The wrinkle is the most representative feature, and its research and applications have been topics of high interest. Wrinkles play an important role in face feature analysis. They have been widely used in applications, such as age estimation, skin texture classification, expression recognition, and simulation. Purpose Existing approaches to the image‐based analysis of wrinkles as texture not as curvilinear discontinuity and wrinkle detection mainly have focused on detecting wrinkles on forehead position, which is usually horizontal linear shapes, while the detection of the nasolabial wrinkle is not well understood due to their variety of shapes and complexity. Method In this paper, we present a nasolabial wrinkle line detecting effective algorithm based on the Active appearance model and Hessian filter to improve localization results by creating unique initial shapes of the wrinkle lines for each input face image. Results Experimental results show that the proposed method is capable of tracking curve wrinkle lines, thus allowing to detect complexly structured wrinkle lines. This work demonstrates results illustrated the competitiveness of the proposed method in detecting nasolabial wrinkle lines. Conclusion In our study, this was introduced the effectiveness of changing the structure of AAM and successfully applied in wrinkle line localizing, although competitive results are achieved by the proposed wrinkle detection method.
Standardization of digital technologies of simulator systems as a method of ensuring reliability of conditions of service of power engineering facilities (part 3)

The article deals with the issues of creating standardized digital simulator systems, in order to ensure reliable service conditions of electric power facilities. The article presents the reasons which prevented all simulator designers in power engineering from ensuring efficient functional properties of simulation systems for operator training. The primary problem was the lack of methods and procedures for developing the simulator’s core subsystem – a mathematical model of a power facility, because the existing standards, norms and technical requirements were only concerned with the structure and functional properties of simulator subsystems. These reasons and circumstances determine the current state of the Russian simulator production; its negative trends are covered in depth in this article. There is also a critical assessment of simulators designed by some of the Russian engineers: these machines are not conducive to improving the quality of training and retraining of operating personnel of power companies. The article identifies the concept of a physical-statistical approach to developing a power facility simulation model: it includes preparing an analytical description of the physical processes at the simulation object, adapted to real processes, taking into account real-time sample observation, collecting and analyzing statistical procedure data, adjusting the model structure and assessing the parameters and states of the facility model. This approach concept defines the main aspect of modern methodology for setting and achieving all goals related to the energy facility modeling for simulator construction. Western sanctions and import substitution requirements dictate the necessity of developing home-grown technology, manufacturing and introducing new equipment in power engineering, including the sphere of simulator production. The designers of Russian simulators should understand that the principles and technical rules of power equipment imported from Western countries, including automatic control systems for such equipment, are different from the ones which the Unified Power System of Russia is based on. It is emphasized that, in real operating conditions of Russian power facilities, reliability of operation, maintenance and repair personnel is essential. It is noted that, in order to improve this reliability, it is necessary to develop a new regulatory document which would fully explore its topic: ensuring consistent and efficient methods for developing the structure and functions of digital technological systems for training power facility personnel.
Investigating Non-Usually Employed Features in the Identification of Architectural Smells

Architectural smells (ASs) negatively affect the maintenance and evolution of software at the architectural level. Most of the current approaches for ASs identification rely on the same small and well-known set of usually employed metrics (UE-Ms) with fixed thresholds. Machine learning (ML) is a promising technique for smell identification as algorithms can learn from a rich set of metrics/features, covering several characteristics of the software and incorporating a certain degree of subjectivity. This has been explored by building datasets with a robust and rich set of features, including not only the UE-Ms but also other non-usually employed metrics (NUE-Ms). However, usually the UE-Ms determine the output of the algorithms, obfuscating other metrics that have the potential to improve the classification. This also leads to inflated and difficult to maintain datasets. In this paper, we investigate the accuracy of some ML algorithms employing only NUE-Ms. We scoped our study in the classification of two smells: God Component and Unstable Dependency. This investigation revealed a set of NUE-Ms that can be also used to identify these smells and the contribution of each one for the classification. Based on this information, software engineers can then build a final dataset just with the potential features. We also briefly present our tool, called InSet, that was used by academics and practitioners to identify smells in their systems. The feedback of them was used as the oracle to compare our tool to other approaches and good results were reached.
Designing for Virtual Touch

"[i miss your touch]" is a web platform that allows people who are in separate locations to co-create a real-time artwork within a shared virtual environment. This platform enables a live collaboration to occur between two participants and PluginHUMAN (the artists). [i miss your touch] responds to participants? movements. PluginHUMAN affect, in real-time, live video streams from participants? webcams. Their affected movements are combined and displayed together, allowing participants to play, dance and make art in a shared virtual environment. This project launched as a rapid response to COVID-19 lockdown and physical distancing rules. Our approach to designing a novel, real-time interactive virtual art experience may benefit game designers and researchers who seek to: provide players with the experience of virtual touch; those exploring embodied play; designers who are providing co-creation opportunities for players; and those interested in the intersection of technology, art and play.
Automatic Text Summarization for Odia Language: A Novel Approach

In the era of Artificial Intelligence, Natural Language Processing (NLP) has become an integral part in the process of development. The methodology aims at making the machine or the computer more interactive with humans saving time. Auto text summarization is one such subfield of NLP that has gained importance in the present scenario as it helps to achieve the task faster. This paper focuses on the morphological aspect of Odia language in the computational scenario. An overview of text summarization process with its progress in Indian language has been discussed. It also suggests a simple statistical method for auto text summarization. The result obtained can be considered significant.
Asymmetric effect of text-chart proximity on reading behavior

Statistical charts complement textual reports by visualizing overall patterns or relations in the data. However, layout algorithms may place charts far from their associated text. Such distant placement can cause reading difficulties, or worse, a misinterpretation. We conducted an eye-tracking experiment comparing reading behaviors in two proximity levels: Placing text and chart on the same page, versus placing them on two different pages. The results indicate that the proximity influences text-reading stronger than chart-reading behavior. We discuss design implications for text-chart layout algorithms and practices. This paper and all data and materials are freely available at https://osf.io/xunt9.