Reviewerly/exHarmony huggingface dataset:
Examining Primary Education Teachers’ Perceptions of TPACK and the Related Educational Context Factors

The study presented in this paper was designed to examine Greek primary education teachers’ perceptions of their knowledge and skills with regards to the integration of ICT in their instructional practices. By adopting the Technological Pedagogical Content Knowledge framework, we developed an extended TPACK scale, including two sub-constructs, i.e., a) the Educational Context and b) teachers’ self-assessment of TPACK abilities. The analysis of the results showed that the participants appear to believe that they have a good level of knowledge with regards to the primary TPACK domains, i.e., content, pedagogy and technology. However, most teachers perceived them separately and were not able to integrate their TPACK knowledge in order to design and implement meaningful ICT-based interventions in their classroom practice. Statistically significant differences were recorded in relation to teachers’ factors, i.e., gender, teaching experience and training about ICT in education. Finally, the findings showed that teachers’ efforts to implement ICT-based interventions in their classrooms could be affected by factors related to the educational context in the Greek primary schools. (Keywords: TPACK, ICT in primary education; ICT integration skills; educational context)
Cost-sensitive dual-bidirectional linear discriminant analysis

In most previous cost-sensitive feature extraction methods, the image matrix needs to be converted into vectors. The conversion always leads to a high computation complexity and small sample size problem. To address these issues, we propose a matrix-feature extraction method for face recognition, Cost-sensitive Dual-Bidirectional Linear Discriminant Analysis (CB2LDA). It is based on 2D image matrices, which greatly reduces the computation complexity and the probability of falling into small sample size problems. The proposed methods extract 2D matrix features from a diagonal block matrix containing both image matrix A and its transposition AT. With the block matrix, the scatter information in both directions is simultaneously considered in the projections, which helps to preserve the underlying data structure in images. Moreover, it aims to preserve the best cost-weighted discriminative information in the facial images, such that the misclassification costs reach a lower level. The experimental results validate the effectiveness and efficiency of the proposed method.
Bit-Level Optimized Neural Network for Multi-Antenna Channel Quantization

Quantized channel state information (CSI) plays a critical role in precoding design which helps reap the merits of multiple-input multiple-output (MIMO) technology. In order to reduce the overhead of CSI feedback, we propose a deep learning based CSI quantization method by developing a joint convolutional residual network (JC-ResNet) which benefits MIMO channel feature extraction and recovery from the perspective of bit-level quantization performance. Experiments show that our proposed method substantially improves the performance.
Off-line handwritten signature verification using compositional synthetic generation of signatures and Siamese Neural Networks

In this work, we propose the use of Siamese Neural Networks to help solve the off-line handwritten signature verification problem with random forgeries in a writer-independent context. Our proposed solution can be used on new signers without the need for any additional training. Also, we have analyzed three types of synthetic data to increase the amount of samples and the variability needed for training deep neural networks: augmented data samples from GAVAB dataset, a proposal of compositional synthetic signature generation from shape primitives and the GPDSSynthetic dataset. The first two approaches are “on-demand” generators and they can be used during the training stage to produce a potentially infinite number of synthetic signatures. In our approach, we initially trained Siamese Neural Networks using signatures from GAVAB dataset and different combinations of synthetic data. The best verification results were obtained when combining original and synthetic signatures for training. Additionally, we tested our approach on the GPSSynthetic, MCYT, SigComp11 and CEDAR datasets demonstrating the generalization capabilities of our proposal.
Binary search tree image encryption with DNA

Symmetric image encryption methods allow users to encrypt the image and hide it from others. Only those that possess the private key can decrypt the data in order to view the content. This paper proposed a new symmetric image encryption method using the concepts of Deoxyribonucleic Acid (DNA) sequence and Binary Search Tree (BST). The method initiates by generating the secret key. Next, the number of nodes in candidate BST is determined deterministically prior to create the candidate BST. Then, the plain image and the relevant candidate BST are converted to the relevant DNA sequences. Afterwards, the proposed method proceeds by superimposing DNA-BST over the DNA image in order to apply XOR function. Finally, the DNA image is converted to the cipher image. The experimental results approve the robustness of the proposed method against well-known attacks.
Automatic Source Code Summarization with Extended Tree-LSTM

Neural machine translation models are used to automatically generate a document from given source code since this can be regarded as a machine translation task. Source code summarization is one of the components for automatic document generation, which generates a summary in natural language from given source code. This suggests that techniques used in neural machine translation, such as Long Short-Term Memory (LSTM), can be used for source code summarization. However, there is a considerable difference between source code and natural language: Source code is essentially structured, having loops and conditional branching, etc. Therefore, there is some obstacle to apply known machine translation models to source code.Abstract syntax trees (ASTs) capture these structural properties and play an important role in recent machine learning studies on source code. Tree-LSTM is proposed as a generalization of LSTMs for tree-structured data. However, there is a critical issue when applying it to ASTs: It cannot handle a tree that contains nodes having an arbitrary number of children and their order simultaneously, which ASTs generally have such nodes. To address this issue, we propose an extension of Tree-LSTM, which we call Multi-way Tree-LSTM and apply it for source code summarization. As a result of computational experiments, our proposal achieved better results when compared with several state-of-the-art techniques.
Observability Analysis and Adaptive Information Fusion for Integrated Navigation of Unmanned Ground Vehicles

Integrated navigation of unmanned ground vehicles (UGV) is significant for many advanced intelligent transportation system applications. Adaptive information fusion technique based on observability analysis has a great potential to enhance UGV integrated navigation systems for the capability of high-precision positioning and navigation. In an integrated navigation system, the tolerance against unknown and time-varying observation conditions is a key factor to satisfy the specific requirements of high-precision, self-adaption, and high reliability. Thus, a novel adaptive federated Kalman filter (FKF) is proposed with time-varying information sharing factors based on the criteria for the degree of observability. In addition, an error-state cascaded integration architecture is designed for UGV integrated navigation. Simulation with real datasets gathered from road tests in urban areas showed that the new adaptive integrated navigation system can autonomously update FKF information sharing factors according to the measurement quality and the observability of each navigation error-state. Therefore, the accuracy, robustness, and fault-tolerance ability of the whole system can be effectively improved in a high dynamic environment.
Data-driven decision support under concept drift in streamed big data

Abstract Data-driven decision-making ( $$\mathrm {D^3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>D</mml:mi><mml:mn>3</mml:mn></mml:msup></mml:math> M) is often confronted by the problem of uncertainty or unknown dynamics in streaming data. To provide real-time accurate decision solutions, the systems have to promptly address changes in data distribution in streaming data—a phenomenon known as concept drift. Past data patterns may not be relevant to new data when a data stream experiences significant drift, thus to continue using models based on past data will lead to poor prediction and poor decision outcomes. This position paper discusses the basic framework and prevailing techniques in streaming type big data and concept drift for $$\mathrm {D^3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>D</mml:mi><mml:mn>3</mml:mn></mml:msup></mml:math> M. The study first establishes a technical framework for real-time $$\mathrm {D^3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>D</mml:mi><mml:mn>3</mml:mn></mml:msup></mml:math> M under concept drift and details the characteristics of high-volume streaming data. The main methodologies and approaches for detecting concept drift and supporting $$\mathrm {D^3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>D</mml:mi><mml:mn>3</mml:mn></mml:msup></mml:math> M are highlighted and presented. Lastly, further research directions, related methods and procedures for using streaming data to support decision-making in concept drift environments are identified. We hope the observations in this paper could support researchers and professionals to better understand the fundamentals and research directions of $$\mathrm {D^3}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mi>D</mml:mi><mml:mn>3</mml:mn></mml:msup></mml:math> M in streamed big data environments.
Exploiting Temporal Relationships in Video Moment Localization with Natural Language

We address the problem of video moment localization with natural language, i.e. localizing a video segment described by a natural language sentence. While most prior work focuses on grounding the query as a whole, temporal dependencies and reasoning between events within the text are not fully considered. In this paper, we propose a novel Temporal Compositional Modular Network (TCMN) where a tree attention network first automatically decomposes a sentence into three descriptions with respect to the main event, context event and temporal signal. Two modules are then utilized to measure the visual similarity and location similarity between each segment and the decomposed descriptions. Moreover, since the main event and context event may rely on different modalities (RGB or optical flow), we use late fusion to form an ensemble of four models, where each model is independently trained by one combination of the visual input. Experiments show that our model outperforms the state-of-the-art methods on the TEMPO dataset.
One Voice Fits All?

When a smart device talks, what should its voice sound like? Voice-enabled devices are becoming a ubiquitous presence in our everyday lives. Simultaneously, speech synthesis technology is rapidly improving, making it possible to generate increasingly varied and realistic computerized voices. Despite the flexibility and richness of expression that technology now affords, today's most common voice assistants often have female-sounding, polite, and playful voices by default. In this paper, we examine the social consequences of voice design, and introduce a simple research framework for understanding how voice affects how we perceive and interact with smart devices. Based on the foundational paradigm of computers as social actors, and informed by research in human-robot interaction, this framework demonstrates how voice design depends on a complex interplay between characteristics of the user, device, and context. Through this framework, we propose a set of guiding questions to inform future research in the space of voice design for smart devices.
Fair Loss: Margin-Aware Reinforcement Learning for Deep Face Recognition

Recently, large-margin softmax loss methods, such as angular softmax loss (SphereFace), large margin cosine loss (CosFace), and additive angular margin loss (ArcFace), have demonstrated impressive performance on deep face recognition. These methods incorporate a fixed additive margin to all the classes, ignoring the class imbalance problem. However, imbalanced problem widely exists in various real-world face datasets, in which samples from some classes are in a higher number than others. We argue that the number of a class would influence its demand for the additive margin. In this paper, we introduce a new margin-aware reinforcement learning based loss function, namely fair loss, in which each class will learn an appropriate adaptive margin by Deep Q-learning. Specifically, we train an agent to learn a margin adaptive strategy for each class, and make the additive margins for different classes more reasonable. Our method has better performance than present large-margin loss functions on three benchmarks, Labeled Face in the Wild (LFW), Youtube Faces (YTF) and MegaFace, which demonstrates that our method could learn better face representation on imbalanced face datasets.
Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification

Most existing person re-identification(Re-ID) approaches achieve superior results based on the assumption that a large amount of pre-labelled data is usually available and can be put into training phrase all at once. However, this assumption is not applicable to most real-world deployment of the Re-ID task. In this work, we propose an alternative reinforcement learning based human-in-the-loop model which releases the restriction of pre-labelling and keeps model upgrading with progressively collected data. The goal is to minimize human annotation efforts while maximizing Re-ID performance. It works in an iteratively updating framework by refining the RL policy and CNN parameters alternately. In particular, we formulate a Deep Reinforcement Active Learning (DRAL) method to guide an agent (a model in a reinforcement learning process) in selecting training samples on-the-fly by a human user/annotator. The reinforcement learning reward is the uncertainty value of each human selected sample. A binary feedback (positive or negative) labelled by the human annotator is used to select the samples of which are used to fine-tune a pre-trained CNN Re-ID model. Extensive experiments demonstrate the superiority of our DRAL method for deep reinforcement learning based human-in-the-loop person Re-ID when compared to existing unsupervised and transfer learning models as well as active learning models.
Creating sense of presence in a virtual reality experience: Impact on neurophysiological arousal and attitude towards a winter sport

Highlights• Cognitive involvement increased sense of presence in a virtual reality experience.• Increase in sense of presence positively affected neurophysiological arousal.• Increase in sense of presence positively affected attitude towards sport.
DualFog-IoT: Additional Fog Layer for Solving Blockchain Integration Problem in Internet of Things

Integration of blockchain and Internet of Things (IoT) to build a secure, trusted and robust communication technology is currently of great interest for research communities and industries. But challenge is to identify the appropriate position of blockchain in current settings of IoT with minimal consequences. In this article we propose a blockchain-based DualFog-IoT architecture with three configuration filter of incoming requests at access level, namely: Real Time, Non-Real Time, and Delay Tolerant Blockchain applications. The DualFog-IoT segregate the Fog layer into two: Fog Cloud Cluster and Fog Mining Cluster. Fog Cloud Cluster and the main cloud datacenter work in a tandem similar to existing IoT architecture for real-time and non-real-time application requests, while the additional Fog Mining Cluster is dedicated to deal with only Delay Tolerant Blockchain application requests. The proposed DualFog-IoT is compared with existing centralized datacenter based IoT architecture. Along with the inherited features of blockchain, the proposed model decreases system drop rate, and further offload the cloud datacenter with minimal upgradation in existing IoT ecosystem. The reduced computing load from cloud datacenter doesn't only help in saving the capital and operational expenses, but it is also a huge contribution for saving energy resources and minimizing carbon emission in environment. Furthermore, the proposed DualFog-IoT is also being analyzed for optimization of computing resources at cloud level, the results presented shows the feasibility of proposed architecture under various ratios of incoming RT and NRT requests. However, the integration of blockchain has its footprints in terms of latent response for delay tolerant blockchain applications, but real-time and non-real-time requests are gracefully satisfying the service level agreement.
Learning Object-Specific Distance From a Monocular Image

Environment perception, including object detection and distance estimation, is one of the most crucial tasks for autonomous driving. Many attentions have been paid on the object detection task, but distance estimation only arouse few interests in the computer vision community. Observing that the traditional inverse perspective mapping algorithm performs poorly for objects far away from the camera or on the curved road, in this paper, we address the challenging distance estimation problem by developing the first end-to-end learning-based model to directly predict distances for given objects in the images. Besides the introduction of a learning-based base model, we further design an enhanced model with a keypoint regressor, where a projection loss is defined to enforce a better distance estimation, especially for objects close to the camera. To facilitate the research on this task, we construct the extented KITTI and nuScenes (mini) object detection datasets with a distance for each object. Our experiments demonstrate that our proposed methods outperform alternative approaches (e.g., the traditional IPM, SVR) on object-specific distance estimation, particularly for the challenging cases that objects are on a curved road. Moreover, the performance margin implies the effectiveness of our enhanced method.
Explainable Machine Learning in Industry 4.0: Evaluating Feature Importance in Anomaly Detection to Enable Root Cause Analysis

In the past recent years, Machine Learning methodologies have been applied in countless application areas. In particular, they play a key role in enabling Industry 4.0. However, one of the main obstacles to the diffusion of Machine Learning-based applications is related to the lack of interpretability of most of these methods. In this work, we propose an approach for defining a `feature importance' in Anomaly Detection problems. Anomaly Detection is an important Machine Learning task that has an enormous applicability in industrial scenarios. Indeed, it is extremely relevant for the purpose of quality monitoring. Moreover, it is often the first step towards the design of a Machine Learning-based smart monitoring solution because Anomaly Detection can be implemented without the need of labelled data. The proposed feature importance evaluation approach is designed for Isolation Forest, one of the most commonly used algorithm for Anomaly Detection. The efficacy of the proposed method is tested on synthetic and real industrial datasets.
Grow and Prune Compact, Fast, and Accurate LSTMs

Long short-term memory (LSTM) has been widely used for sequential data modeling. Researchers have increased LSTM depth by stacking LSTM cells to improve performance. This incurs model redundancy, increases run-time delay, and makes the LSTMs more prone to overfitting. To address these problems, we propose a hidden-layer LSTM (H-LSTM) that adds hidden layers to LSTM's original one-level nonlinear control gates. H-LSTM increases accuracy while employing fewer external stacked layers, thus reducing the number of parameters and run-time latency significantly. We employ grow-and-prune (GP) training to iteratively adjust the hidden layers through gradient-based growth and magnitude-based pruning of connections. This learns both the weights and the compact architecture of H-LSTM control gates. We have GP-trained H-LSTMs for image captioning, speech recognition, and neural machine translation applications. For the NeuralTalk architecture on the MSCOCO dataset, our three models reduce the number of parameters by 38.7× [floating-point operations (FLOPs) by 45.5×], run-time latency by 4.5×, and improve the CIDEr-D score by 2.8 percent, respectively. For the DeepSpeech2 architecture on the AN4 dataset, the first model we generated reduces the number of parameters by 19.4× and run-time latency by 37.4 percent. The second model reduces the word error rate (WER) from 12.9 to 8.7 percent. For the encoder-decoder sequence-to-sequence network on the IWSLT 2014 German-English dataset, the first model we generated reduces the number of parameters by 10.8× and run-time latency by 14.2 percent. The second model increases the BLEU score from 30.02 to 30.98. Thus, GP-trained H-LSTMs can be seen to be compact, fast, and accurate.
MLTL: A multi-label approach for the Tomek Link undersampling algorithm

A large variety of problems are multi-labeled, which made the Multi-Label Classification field become an active topic in the machine learning community. However, real world problems tend to be imbalanced, meaning that some classes may have more samples than others. Learning from imbalanced datasets is a challenging task and for that has attracted the attention of researchers that have proposed some resampling algorithms to address this problem. This work presents two main contributions: A new resampling algorithm for multi-label classification problems named MLTL - Multi-Label Tomek Link, which is based on the standard Tomek Link resampling algorithm; A multi-label imbalanceness API for the Mulan framework. Results in seven well-known datasets showed that MLTL is a competitive technique when compared to other multi-label resampling methods from the literature.
A new permutation-diffusion-based image encryption technique using cellular automata and DNA sequence

In recent years, the security of digital images has constantly been a major topic discussed by researchers. In this paper, we proposed a new structure for image encryption using Deoxyribonucleic Acid (DNA) and Recursive Cellular Automata (RCA). Image encryption takes place in two separate phases. At the permutation phase, a logistic map is employed for cellular shift in the image rows and columns. Then, the DNA and RCA at the diffusion phase are used to change the gray level of pixels to new values. The optimum entropy value obtained in this method was 7.9994 while the correlation coefficient was 0.0001, which indicated good results. Moreover, the newly proposed technique was demonstrated to enhance resistance against a variety of attacks through desirable image encryption.