Reviewerly/exHarmony huggingface dataset:
Layered Ensemble Learning for Effective Binary Classification

Often when a single learner fails to produce accurate results in prediction of class labels in supervised learning tasks, an ensemble of classifiers is applied. Ensemble classifiers have been quite successful in recent years to solve complex machine learning problems in different domains. In this paper, we present a novel multi-layered ensemble classifier for solving binary classification tasks. Our layered ensemble technique performs the classification in multiple stages or layers. In each layer a single classifier is applied that divides the instances into two sets according to the predicted labels. In the later stages or layers these subsets undergo further learning by another set of single classifiers and this process goes on until the classification performance is satisfactory or the subsets produced in these way are too small. The over-fitting issues are dealt by the termination criteria. On a selected benchmark datasets used to test our method, it performs significantly better compared to other state-of-the-art ensemble classifier.
The Challenges Facing the Development of AI in Africa

In this article, we provide a non-exhaustive list of solutions and initiatives developed by using Artificial Intelligence (AI) to solve recurring problems in Africa. Despite the existing initiatives, the continent is not fully benefiting from AI. The challenges facing Africa are numerous and diverse. At the forefront are related challenges such as difficult access to large amounts of data, quality of data, data storage, and data regulatory policy. AI technologies require a good quality connection and considerable energy requirements. Unfortunately, the container suffers from lack of broadband and energy infrastructure. Also, the operation of AI requires a skilled workforce while the education system is not conducive to the development of AI skills and schools and universities do not provide enough young graduates in the labor market with a strong competence in AI. In addition, there is a lack of effective policy and funding for AI research and innovation. Nevertheless, initiatives to promote the development of AI on the continent already exist. In addition, we propose possible solutions, from funding for ecosystem building to infrastructure development, education redesign, and national and regional strategy and policy development so that AI can finally be a key technology for Africa's sustainable development.
An Analysis of Violation and Flouting Maxim on Teacher-Students Interaction in English Teaching and Learning Process

The objectives of this research are to analyze the type of violation and flouting the maxim of teacher-student interaction in the English teaching and learning process in MA Hasyim Asyâ€™ari Bangsri and also find the most frequently produced between violation and flouting of the maxim. This research used the descriptive qualitative method. The data was obtained from the conversation between the teacher and students during the teaching and learning process and analyzed by categorizing utterances based on the violation and flouting maxim theory of the Cooperative Principle. The data were collected through the observation then analyzed by using the technique which is suggested by Miles and Huberman that consists of data reduction, data display, and drawing conclusion or data verification. The result of the research showed that there are four types of maxims that are violated by the teacher and students. These are the maxim of quantity 11 (50%), the maxim of quality 5 (22.73%), the maxim of relation 1 (4.54%), and the maxim of manner 5 (22.73%). The dominant violation was the maxim of quantity with 11 occurrences. Based on the findings, there are three types of flouting maxims in which the most of flouting maxim that is occurred was the maxim of relation. They are divided into 1 (14.29%) maxim of quantity, 2 (28.57%) maxim of quality, and 4 (57.14%) maxims of relation. In conclusion, the most produced between the violation and flouting was the violation of maxim with 22 (75.86%) of total occurrences. Meanwhile, the proportion of flouting maxim was 7 (24.14%).
A Saliency-Based Patch Sampling Approach for Deep Artistic Media Recognition

We present a saliency-based patch sampling strategy for recognizing artistic media from artwork images using a deep media recognition model, which is composed of several deep convolutional neural network-based recognition modules. The decisions from the individual modules are merged into the final decision of the model. To sample a suitable patch for the input of the module, we devise a strategy that samples patches with high probabilities of containing distinctive media stroke patterns for artistic media without distortion, as media stroke patterns are key for media recognition. We design this strategy by collecting human-selected ground truth patches and analyzing the distribution of the saliency values of the patches. From this analysis, we build a strategy that samples patches that have a high probability of containing media stroke patterns. We prove that our strategy shows best performance among the existing patch sampling strategies and that our strategy shows a consistent recognition and confusion pattern with the existing strategies.
VIDEO MARKETING IN EDUCATION: ATTRACTING APPLICANTS USING THE YOUTUBE SERVICE

Internet communications have become an effective and popular tool for integrated marketing communications both to achieve a synergistic effect in large-scale marketing campaigns and to implement short-term tasks, communicate with target audiences, maintain reputation, promote the brand, etc. One of the most effective Internet marketing tools is video marketing on social networks. The article considers the problem of using video marketing as an Internet marketing tool to attract prospective university students (applicants) using the YouTube service, as well as presents a theoretical analysis of the patterns of occurrence and types of video marketing. Based on the results of an expert survey, the advantages and opportunities of using video marketing in the university's marketing campaign are determined, and recommendations on using video marketing to attract university applicants are provided.
Using High-Performance Computers to Enable Collaborative and Interactive Composition with DISSCO

Composers do not usually collaborate with other composers but, for the last half century, open works were created that invite performers to implement details left undetermined or even decide the order in which various sections of the composition are to be played. Chance operations were also used in the writing of musical pieces and, in music generated with the assistance of computers, controlled randomness found its place. This article proposes a platform designed to encourage collaborative and interactive composition on high-performance computers with DISSCO (Digital Instrument for Sound Synthesis and Composition). DISSCO incorporates random procedures as well as deterministic means of defining the components of a piece. It runs efficiently on the Comet supercomputer of the San Diego Supercomputing Center and uses the Jupyter notebook environment to integrate the end-to-end processes with a user. These tools, the implementation platform, and the collaboration management are discussed in detail. Comments regarding aesthetic implications of the partnership between one or more humans and computer—considered a bona fide collaborator—are also provided. Possible future developments are supplied at the end.
Nonlinear intensity measurement for multi-source images based on structural similarity

Feature-based algorithms are widely used in automatic matching of multi-source images (e.g., LiDAR, optical, infrared, map, and SAR images). However, it remains a challenging task to find sufficient correct correspondences for image pairs in the presence of significant noise and nonlinear intensity differences. To solve this problem, this paper proposes a novel feature descriptor named the histogram of maximum phase congruency (HMPC), which is based on the structural properties of images. Then, a novel distance formula is designed by normalizing the phase orientation and histogram value to calculate the similarity. Furthermore, the precise bilateral matching principle and consistency-checking algorithm based on the phase orientation are used to perform matching between the corresponding point sets. Benefiting from combinatorial features, the proposed method can effectively capture the structural information of images and present robust matching performance for complex texture structures and noise images compared to that of the sole feature, and it has been tested with a variety of SAR, LiDAR, optical,and map datas. The results demonstrate that the proposed HMPC achieves a more robust and accurate matching performance than many state-of-the-art methods.
Hands of the World intercultural project: developing student teachers' digital competences through contextualised learning

This paper reports on the impact on student teachers’ professional skills, knowledge and attitudes of engaging in the eTwinning international Hands of the World (HOTW) project which connects over 2000 students and their teachers in 50 schools across the world to undertake a wide range of educational collaborative work, supported by digital and online technologies. The University of Dundee’s HOTW project won the eTwinning prize for the best project two years running and is the only university to have won this annual prize. Student teachers are working in a world where digital technology is firmly embedded and undergoing rapid expansion and change. This study examined the experiences of student teachers as they engaged in a global project to develop their knowledge and understanding of intercultural learning using ICT. An explanatory sequential mixed method design analyzed data publicly available on YouTube™ and Padlet™. Two main data sets were used: responses to professional development webinars and reflections on participating in the project. Data were analyzed thematically focusing on ICT competence, pedagogy and relevance. Participation in the project enhanced the students' ICT competence and confidence to use and explore technology for current and future teaching practice through contextualization and social learning. Our analysis enabled us to identify that the Covid-19 lockdown had a positive impact on the students' learning due to time, space, and relevance. This paper demonstrates that engagement in a contextualized project enabled student teachers to develop their ICT competences and that for many, lockdown provided a conducive learning environment.
Permissioned Blockchain: Securing Industrial IoT Environments

With the significantly increased use of the Industrial Internet of Things (IIoT), it is believed that this technology will revolutionize industrial applications and infrastructures by connecting several industrial assets.But it is getting prone to many cyberattacks and security issues.The emerging security challenges of IIoT can have a devastating effect since it deals with mission and safety-critical systems.Thus, it becomes extremely important to address the security vulnerabilities and susceptibilities of this technology.Blockchain, being one of the most significant solutions to several technologies' security problems, can play a vital role in improving the security of IIoT.Therefore, this paper proposes to use a Hyperledger Fabric Blockchain-enabled IIoT that guarantees the security of the communication medium, data storage, access, and sharing between the IIoT devices and ensures to provide limited access to the authorized identities only.This system also monitors the user access and makes sure that the transactions are performed according to their roles defined by the Certificate Authority (CA) and Membership Service Provider (MSP).Moreover, this paper presents the findings on the implementation of the blockchain network and addresses the key challenges.It evaluates the performance of the proposed network and discusses the key areas to be improved.Finally, the paper describes the benefits of the permissioned blockchain for IIoT and presents a future direction for further research and study.
Equidistant distribution loss for person re-identification

Person re-identification (re-id) is a technology that matches images of a certain pedestrian among non-overlapping cameras. Recently, many approaches based on deep learning have been studied comprehensively. Most of these methods try to learn discriminative pedestrian features via elaborated network architectures and one or multi loss functions to maximize inter-class separability and intra-class compactness. However, the class imbalance characteristics from the datasets are ignored, and the discriminative information cannot be completely exploited. In this paper, we propose a novel loss function termed as Equidistant Distribution Loss (EDL) to improve the imbalance problem that exists in re-id tasks. Specifically, we first normalize the learned features in the embedding layer and the weights in the last fully connected layer, so that both the features and the weights can be projected on a hypersphere space. After that, we directly impose an equidistance constraint among the weights to guide the learned features spread uniformly in the sphere space. The proposed EDL can effectively address the local squeeze or imbalance problem caused by imbalanced samples, making full use of feature space and profiting the performance. Extensive experiments on re-id datasets, including Market-1501, DukeMTMC-reID are conducted to demonstrate the preferable performance of the proposed method.
A Cryptographic based Approach for Data Hiding in Advanced Video Sequences

Data hiding in video streams became more popular in the present world, since there is a high frequency of data communication over the internet. Hiding the data in video streams provides more security as well as increases embedding capacity than hiding inside the images. The quantity of information to be embedded into the video increases, it can badly influence the video excellence make it inappropriate for certain appliances. The main concerns in data hiding in videos are its high visual excellence, increased hiding capacity, video stream size etc. In this paper, a new data hiding technique is proposed in compressed H.264 Video Streams. At first, the information to be embedded is encrypted using Cryptography approach. The Cryptographic approach helps to encrypt the plain information based on the elliptic points produced by choosing the large prime number. The encrypted data is embedded into the transformed DCT coefficients of I, B and P video frames. The experiment is conducted for different set of video sequences. The results shows that the proposed method yields better performance in terms of Peak signal to noise ratio (PSNR), Structural similarity index (SSIM) and Video quality measure (VQM) when compare to existing methods.
An Empirical Comparison of Resampling Ensemble Methods of Deep Learning Neural Networks for Cross-Project Software Defect Prediction

Software defect prediction is one of the most important quality assurance activities during software development.This paper contributes empirical insights into the effectiveness of three resampling ensemble methods (bagging, boosting, and dagging) of Deep Learning Neural Networks (DLNN) for cross-project software defect prediction, compared to individual DLNN.An empirical study was conducted using five datasets.The results indicate that the bagging ensembles of DLNN offer only 0.24% increase in accuracy, on average, compared to the individual DLNN models, whereas the boosting and dagging ensembles degrade the accuracy.Furthermore, the results show that the three resampling ensembles of DLNN outperform the individual DLNN models in precision; with a maximum improved precision by 25.15% on average using the boosting ensembles.The results however indicate that none of the resampling ensembles improve the recall.Lastly, for a balanced performance in terms of both precision and recall, the results indicate improvements ranging from 0.98% on average by applying the bagging ensembles to 11.67% on average by applying the boosting ensembles.
Comparison of Reinforcement Learning Activation Functions to Improve the Performance of the Racing Game Learning Agent

Recently, research has been actively conducted to create artificial intelligence agents that learn games through reinforcement learning. There are several factors that determine performance when the agent learns a game, but using any of the activation functions is also an important factor. This paper compares and evaluates which activation function gets the best results if the agent learns the game through reinforcement learning in the 2D racing game environment. We built the agent using a reinforcement learning algorithm and a neural network. We evaluated the activation functions in the network by switching them together. We measured the reward, the output of the advantage function, and the output of the loss function while training and testing. As a result of performance evaluation, we found out the best activation function for the agent to learn the game. The difference between the best and the worst was 35.4%.
Development and realization of home online teaching system based on video data analysis

With the outbreak of the COVID-19, most students can only take classes online at home. However, many students cannot consciously control their learning behaviors when they are in class at home, and some negative learning behaviors such as sleeping on the table or wandering have appeared. These negative learning behaviors greatly affect the effectiveness of learning. In order to deal with these problems, a online teaching system based on human action recognition is developed to assist teachers in real-time capturing the class status of classmates and improve the efficiency of home-based online teaching. Here, the system uses the OpenPose human body gesture recognition algorithm to obtain the key points of the student's body in class in front of the camera, and recognizes the student's class behavior through the analysis of the coordinates. The RTMP protocol is used to solve the problem of audio and video transmission during the live broadcast. We have conducted experiments to show the effectiveness of our system for analyzing the status of students and evaluating the online class.
Fast DCTTS: Efficient Deep Convolutional Text-to-Speech

We propose an end-to-end speech synthesizer, Fast DCTTS, that synthesizes speech in real time on a single CPU thread. The proposed model is composed of a carefully-tuned lightweight network designed by applying multiple network reduction and fidelity improvement techniques. In addition, we propose a novel group highway activation that can compromise between computational efficiency and the regularization effect of the gating mechanism. As well, we introduce a new metric called elastic mel-cepstral distortion (EMCD) to measure the fidelity of the output mel-spectrogram. In experiments, we analyze the effect of the acceleration techniques on speed and speech quality. Compared with the baseline model, the proposed model exhibits improved MOS from 2.62 to 2.74 with only 1.76% computation and 2.75% parameters. The speed on a single CPU thread was improved by 7.45 times, which is fast enough to produce mel-spectrogram in real time without GPU.
ARThings – enhancing the visitors’ experience in museums through collaborative AR

Museum exhibitions are adopting Culturtainment, a new range of emergent media, as means of engaging their visitors through a high level of creative output and consumption, where Augmented Reality becomes a technological challenge for augmenting the visitors' experience in a meaningful way. ARThings defines its scope to extend the concept of touring in an art exhibition of paintings, towards collecting itinerary and interaction analytics, through collaborative AR. A demonstrative prototype has been implemented for the Art Museum of Brasov, Transylvania, Romania, where a User-Acceptance Test has been conducted on 21 participants, together with on-site assessment, positively validating a future adoption.
Multi-Person Pose Estimation on Embedded Device

Tasks based on deep learning have make great progress in accuracy with the development of deep learning in recent years. However, algorithms base on deep learning are both computationally intensive and memory intensive, makes it difficult to deploy on resource-constrained devices. Therefore, it is worth studying how to compress the architecture of existing network while achieving a comparable performance, so that the model can be deployed on devices with limited resources. This paper performs model compression and acceleration in multi-person pose estimation by replacing the feature extraction network, parameter pruning and knowledge distillation, achieve a 2.6× MACs reduction and 2× acceleration but only 25% drop in accuracy compared with a lightweight model. The compressed model can be deployed on the embedded device Jetson Nano with a 12 FPS inference speed.
Radical Counter Network for Robust Chinese Character Recognition

Chinese character recognition has attracted much interest due to its high challenge and various applications. The whole-character modeling method can recognize common characters well but unable to handle unseen situation. Some radical-based modeling methods have successfully achieved great performance in unseen condition but need RNN-based decoder for sequence decoding. Therefore, a compact model which can recognize unseen characters needs to be proposed. First, this paper introduces a novel radical counter network (RCN) to recognize Chinese characters by identifying radicals and spatial structures. The proposed RCN first extracts visual features from input by employing DenseNet as encoder. Then a decoder based on fully connected layer is employed, aiming at synchronously estimating the number of each caption in character. Additionally, we design a multi-task learning to combine global feature extraction capability of whole-character modeling and local feature extraction capability of radical-based modeling, which further improves the model generalization. Experiments on natural scene character dataset demonstrate that the proposed model significantly outperforms WCN by 5.48% and achieve comparable performance with RAN in lower model complexity. That shows great robustness and simplicity of our model.
Swivel: Hardening WebAssembly against Spectre.

We describe Swivel, a new compiler framework for hardening WebAssembly (Wasm) against Spectre attacks. Outside the browser, Wasm has become a popular lightweight, in-process sandbox and is, for example, used in production to isolate different clients on edge clouds and function-as-a-service platforms. Unfortunately, Spectre attacks can bypass Wasm's isolation guarantees. Swivel hardens Wasm against this class of attacks by ensuring that potentially malicious code can neither use Spectre attacks to break out of the Wasm sandbox nor coerce victim code-another Wasm client or the embedding process-to leak secret data. We describe two Swivel designs, a software-only approach that can be used on existing CPUs, and a hardware-assisted approach that uses extension available in Intel 11th generation CPUs. For both, we evaluate a randomized approach that mitigates Spectre and a deterministic approach that eliminates Spectre altogether. Our randomized implementations impose under 10.3% overhead on the Wasm-compatible subset of SPEC 2006, while our deterministic implementations impose overheads between 3.3% and 240.2%. Though high on some benchmarks, Swivel's overhead is still between 9x and 36.3x smaller than existing defenses that rely on pipeline fences.