Reviewerly/exHarmony huggingface dataset:
Knowledge Distillation via Route Constrained Optimization

Distillation-based learning boosts the performance of the miniaturized neural network based on the hypothesis that the representation of a teacher model can be used as structured and relatively weak supervision, and thus would be easily learned by a miniaturized model. However, we find that the representation of a converged heavy model is still a strong constraint for training a small student model, which leads to a higher lower bound of congruence loss. In this work, we consider the knowledge distillation from the perspective of curriculum learning by teacher's routing. Instead of supervising the student model with a converged teacher model, we supervised it with some anchor points selected from the route in parameter space that the teacher model passed by, as we called route constrained optimization (RCO). We experimentally demonstrate this simple operation greatly reduces the lower bound of congruence loss for knowledge distillation, hint and mimicking learning. On close-set classification tasks like CIFAR and ImageNet, RCO improves knowledge distillation by 2.14% and 1.5% respectively. For the sake of evaluating the generalization, we also test RCO on the open-set face recognition task MegaFace. RCO achieves 84.3% accuracy on one-to-million task with only 0.8 M parameters, which push the SOTA by a large margin.
Evaluation of Deep Learning Techniques in Sentiment Analysis from Twitter Data

This study presents a comparison of different deep learning methods used for sentiment analysis in Twitter data. In this domain, deep learning (DL) techniques, which contribute at the same time to the solution of a wide range of problems, gained popularity among researchers. Particularly, two categories of neural networks are utilized, convolutional neural networks(CNN), which are especially performant in the area of image processing and recurrent neural networks (RNN) which are applied with success in natural language processing (NLP) tasks. In this work we evaluate and compare ensembles and combinations of CNN and a category of RNN the long short-term memory (LSTM) networks. Additionally, we compare different word embedding systems such as the Word2Vec and the global vectors for word representation (GloVe) models. For the evaluation of those methods we used data provided by the international workshop on semantic evaluation (SemEval), which is one of the most popular international workshops on the area. Various tests and combinations are applied and best scoring values for each model are compared in terms of their performance. This study contributes to the field of sentiment analysis by analyzing the performances, advantages and limitations of the above methods with an evaluation procedure under a single testing framework with the same dataset and computing environment.
Desktop-based safety training using 360-degree panorama and static virtual reality techniques: A comparative experimental study

Virtual reality (VR)-based approaches have been used to facilitate safety knowledge transfer and increase hazard awareness by providing safe and controlled experiences of unsafe scenarios in construction safety training applications. However, the long development times and high computational costs associated with existing VR methods have posed significant challenges to using such VR-based safety training platforms. Unlike VR settings that deliver computer-generated reproductions of the environment, 360-degree panorama can create true-to-reality simulations of construction jobsites. This research developed and compared two hazard-identification training platforms based on VR and 360-degree panorama. Construction students and professionals participated in an experiment to determine their perception of realism and evaluate their hazard-identification skills. It was found that students perceived the 360-degree panorama conditions as more realistic than the VR conditions, but professionals perceived no difference between them. Moreover, differences were found in the average hazard identification index (HII) scores for all participants, with higher scores for the VR conditions than for the 360-degree panorama conditions. Finally, it was found that there was an inverse correlation between the presence scores and the average HII scores for the participants in the study.
DeepXplore

Deep learning (DL) systems are increasingly deployed in safety- and security-critical domains such as self-driving cars and malware detection, where the correctness and predictability of a system's behavior for corner case inputs are of great importance. Existing DL testing depends heavily on manually labeled data and therefore often fails to expose erroneous behaviors for rare inputs. We design, implement, and evaluate DeepXplore, the first white-box framework for systematically testing real-world DL systems. First, we introduce neuron coverage for measuring the parts of a DL system exercised by test inputs. Next, we leverage multiple DL systems with similar functionality as cross-referencing oracles to avoid manual checking. Finally, we demonstrate how finding inputs for DL systems that both trigger many differential behaviors and achieve high neuron coverage can be represented as a joint optimization problem and solved efficiently using gradient-based search techniques. DeepXplore efficiently finds thousands of incorrect corner case behaviors (e.g., self-driving cars crashing into guard rails and malware masquerading as benign software) in state-of-the-art DL models with thousands of neurons trained on five popular datasets such as ImageNet and Udacity self-driving challenge data. For all tested DL models, on average, DeepXplore generated one test input demonstrating incorrect behavior within one second while running only on a commodity laptop. We further show that the test inputs generated by DeepXplore can also be used to retrain the corresponding DL model to improve the model's accuracy by up to 3%.
On the role of knowledge graphs in explainable AI

The current hype of Artificial Intelligence (AI) mostly refers to the success of machine learning and its sub-domain of deep learning. However, AI is also about other areas, such as Knowledge Representation and Reasoning, or Distributed AI, i.e., areas that need to be combined to reach the level of intelligence initially envisioned in the 1950s. Explainable AI (XAI) now refers to the core backup for industry to apply AI in products at scale, particularly for industries operating with critical systems. This paper reviews XAI not only from a Machine Learning perspective, but also from the other AI research areas, such as AI Planning or Constraint Satisfaction and Search. We expose the XAI challenges of AI fields, their existing approaches, limitations and opportunities for Knowledge Graphs and their underlying technologies.
PARN: Position-Aware Relation Networks for Few-Shot Learning

Few-shot learning presents a challenge that a classifier must quickly adapt to new classes that do not appear in the training set, given only a few labeled examples of each new class. This paper proposes a position-aware relation network (PARN) to learn a more flexible and robust metric ability for few-shot learning. Relation networks (RNs), a kind of architectures for relational reasoning, can acquire a deep metric ability for images by just being designed as a simple convolutional neural network (CNN)[23]. However, due to the inherent local connectivity of CNN, the CNN-based relation network (RN) can be sensitive to the spatial position relationship of semantic objects in two compared images. To address this problem, we introduce a deformable feature extractor (DFE) to extract more efficient features, and design a dual correlation attention mechanism (DCA) to deal with its inherent local connectivity. Successfully, our proposed approach extents the potential of RN to be position-aware of semantic objects by introducing only a small number of parameters. We evaluate our approach on two major benchmark datasets, i.e., Omniglot and Mini-Imagenet, and on both of the datasets our approach achieves state-of-the-art performance. It's worth noting that our 5-way 1-shot result on Omniglot even outperforms the previous 5-way 5-shot results.
Jointly Learning Kernel Representation Tensor and Affinity Matrix for Multi-View Clustering

Multi-view clustering refers to the task of partitioning numerous unlabeled multimedia data into several distinct clusters using multiple features. In this paper, we propose a novel nonlinear method called joint learning multi-view clustering (JLMVC) to jointly learn kernel representation tensor and affinity matrix. The proposed JLMVC has three advantages: (1) unlike existing low-rank representation-based multi-view clustering methods that learn the representation tensor and affinity matrix in two separate steps, JLMVC jointly learns them both; (2) using the “kernel trick,” JLMVC can handle nonlinear data structures for various real applications; and (3) different from most existing methods that treat representations of all views equally, JLMVC automatically learns a reasonable weight for each view. Based on the alternating direction method of multipliers, an effective algorithm is designed to solve the proposed model. Extensive experiments on eight multimedia datasets demonstrate the superiority of the proposed JLMVC over state-of-the-art methods.
GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition

Recent adversarial learning research has achieved very impressive progress for modelling cross-domain data shifts in appearance space but its counterpart in modelling cross-domain shifts in geometry space lags far behind. This paper presents an innovative Geometry-Aware Domain Adaptation Network (GA-DAN) that is capable of modelling cross-domain shifts concurrently in both geometry space and appearance space and realistically converting images across domains with very different characteristics. In the proposed GA-DAN, a novel multi-modal spatial learning structure is designed which can convert a source-domain image into multiple images of different spatial views as in the target domain. A new disentangled cycle-consistency loss is introduced which balances the cycle consistency and greatly improves the concurrent learning in both appearance and geometry spaces. The proposed GA-DAN has been evaluated for the classic scene text detection and recognition tasks, and experiments show that the domain-adapted images achieve superior scene text detection and recognition performance while applied to network training.
Digital competence of early childhood education teachers: attitude, knowledge and use of ICT

The main objective of the research described here was to learn how young learners self-evaluate their digital competence. A non-experimental and descriptive quantitative methodology was employed, an electronic survey being used to collect the data. Among the main results, we can highlight that these learners self-evaluate their attitude towards Information and Communication Technologies (ICT) as favourable, their handling of them as moderate and their knowledge of them as scarce. It became clear that they do not have a level of digital competence suitable for being called ‘digital natives’, nor sufficient ability to use ICT in their academic life or in their professional future.
Spectral Feature Transformation for Person Re-Identification

With the surge of deep learning techniques, the field of person re-identification has witnessed rapid progress in recent years. Deep learning based methods focus on learning a discriminative feature space where data points are clustered compactly according to their corresponding identities. Most existing methods process data points individually or only involves a fraction of samples while building a similarity structure. They ignore dense informative connections among samples more or less. The lack of holistic observation eventually leads to inferior performance. To relieve the issue, we propose to formulate the whole data batch as a similarity graph. Inspired by spectral clustering, a novel module termed Spectral Feature Transformation is developed to facilitate the optimization of group-wise similarities. It adds no burden to the inference and can be applied to various scenarios. As a natural extension, we further derive a lightweight re-ranking method named Local Blurring Re-ranking which makes the underlying clustering structure around the probe set more compact. Empirical studies on four public benchmarks show the superiority of the proposed method. Code is available at https://github.com/LuckyDC/SFT_REID.
Coverless steganography based on image retrieval of DenseNet features and DWT sequence mapping

Recently, researches have shown that coverless image steganography can resist the existing steganalysis tools effectively. On this basis, a novel coverless image steganography algorithm based on image retrieval of DenseNet features and DWT sequence mapping is proposed in this paper. Firstly, DenseNet convolutional neural network model in deep learning is used to extract the features of image datasets. Supervised learning is adopted to retrieve the image, and the retrieval results can be used as the information carrier. Secondly, the selected images are divided into 4 × 4 sub-blocks for block discrete wavelet transform. Then the DWT coefficient are calculated based on the low-frequency components after block transformation, and the coefficients between blocks are scanned according to the Zigzag scan, such that the robust feature sequences are generated. Finally, the secret information is divided into segments with the same length as the feature sequence, and an inverted index is established with feature sequence, the position of blocks, DWT coefficient and image path. The image with the same feature sequence as the secret information segment is selected through index as the carriers. The experimental results and analysis show that this method has better robust and security performance resisting most image attacks compared with the state-of-the-art methods.
Coupled Adversarial Training for Remote Sensing Image Super-Resolution

Generative adversarial network (GAN) has made great progress in recent natural image super-resolution tasks. The key to its success is the integration of a discriminator which is trained to classify whether the input is a real high-resolution (HR) image or a generated one. Arguably, learning a strong discriminative prior is essential for generating high-quality images. However, in remote sensing images, we discover, through extensive statistical analysis, that there are more low-frequency components than natural images, which may lead to a “discrimination-ambiguity” problem, i.e., the discriminator will become “confused” to tell whether its input is real or not when dealing with those low-frequency regions, and therefore, the quality of generated HR images may be deeply affected. To address this problem, we propose a novel GAN-based super-resolution algorithm named coupled-discriminated GANs (CDGANs) for remote sensing images. Different from the previous GAN-based super-resolution models in which their discriminator takes in a single image at one time, in our model, the discriminator is specifically designed to take in a pair of images: a generated image and its HR ground truth, to make better discrimination of the inputs. We further introduce a dual pathway network architecture, a random gate, and a coupled adversarial loss to learn better correspondence between the discriminative results and the paired inputs. Experimental results on two public data sets demonstrate that our model can obtain more accurate super-resolution results in terms of both visual appearance and local details compared with other state of the arts. Our code will be made publicly available.
Blockchain and Internet of Things: A bibliometric study

Blockchain is a distributed, decentralized and immutable digital ledger which records transactions across a global network of computers where the information is highly secure. Since its merger with other domains has solved numerous relevant problems, it has potential to resolve the issues of privacy and security in the domain of Internet of Things (IoT). IoT is reshaping the world into smart cities, smart farming, smart grids, smart transport, smart home, and smart healthcare systems. Thus, application of Blockchain in the domain of IoT gives birth to a new domain of Blockchain in IoT (BIoT). This research presents a bibliometric analysis of articles in BIoT domain, covering papers published in top journals and conferences, and finds research trends. It also explores diverse research areas, the most influential publications, top publication venues, top funding agencies and future research direction. This research can be a good learning point for young researchers to find attractive relevant research insights within BIoT.
STNReID: Deep Convolutional Networks With Pairwise Spatial Transformer Networks for Partial Person Re-Identification

Partial person re-identification (ReID) is a challenging task because only partial information of person images is available for matching target persons. Few studies, especially on deep learning, have focused on matching partial person images with holistic person images. This study presents a novel deep partial ReID framework based on pairwise spatial transformer networks (STNReID), which can be trained on existing holistic person datasets. STNReID includes a spatial transformer network (STN) module and a ReID module. The STN module samples an affined image (a semantically corresponding patch) from the holistic image to match the partial image. The ReID module extracts the features of the holistic, partial, and affined images. Competition (or confrontation) is observed between the STN module and the ReID module, and two-stage training is applied to acquire a strong STNReID for partial ReID. Experimental results show that our STNReID obtains 66.7% and 54.6% rank-1 accuracies on Partial-ReID and Partial-iLIDS datasets, respectively. These values are at par with those obtained with state-of-the-art methods.
Utilization-prediction-aware virtual machine consolidation approach for energy-efficient cloud data centers

In the age of the information explosion, the energy demand for cloud data centers has increased markedly; hence, reducing the energy consumption of cloud data centers is essential. Dynamic virtual machine VM consolidation, as one of the effective methods for reducing energy energy consumption is extensively employed in large cloud data centers. It achieves the energy reductions by concentrating the workload of active hosts and switching idle hosts into low-power state; moreover, it improves the resource utilization of cloud data centers. However, the quality of service (QoS) guarantee is fundamental for maintaining dependable services between cloud providers and their customers in the cloud environment. Therefore, reducing the power costs while preserving the QoS guarantee are considered as the two main goals of this study. To efficiently address this problem, the proposed VM consolidation approach considers the current and future utilization of resources through the host overload detection (UP-POD) and host underload detection (UP-PUD). The future utilization of resources is accurately predicted using a Gray-Markov-based model. In the experiment, the proposed approach is applied for real-world workload traces in CloudSim and were compared with the existing benchmark algorithms. Simulation results show that the proposed approaches significantly reduce the number of VM migrations and energy consumption while maintaining the QoS guarantee.
Efficient Certificateless Aggregate Signature With Conditional Privacy Preservation in IoV

As an extension of traditional vehicular ad hoc networks, the Internet of Vehicles (IoV) enables information collection and dissemination, which brings a lot of convenience and benefits to the intelligent transportation systems. However, the booming IoV confronts a few challenges in the aspects of vehicle location privacy preservation and the authenticity of the transmitted information. In order to meet these challenges, we propose an efficient certificateless aggregate signature scheme with conditional privacy preservation in this article. Our scheme utilizes the technique of full aggregation to reduce the bandwidth resources and computing overhead. Besides, the conditional privacy preservation in IoV system is realized by using pseudonym mechanism. We demonstrate that the proposed scheme is secure against the Type-I and Type-II adversaries in the random oracle under the computational Diffie-Hellman assumption. In addition, the proposed scheme is compared with related works from aspects of computation cost, communication efficiency, and security requirements. The comparison results show that the proposed scheme is efficient, and it is suitable for resource-constrained environments.
FlowNet3D++: Geometric Losses For Deep Scene Flow Estimation

We present FlowNet3D++, a deep scene flow estimation network. Inspired by classical methods, FlowNet3D++ incorporates geometric constraints in the form of point-toplane distance and angular alignment between individual vectors in the flow field, into FlowNet3D [21]. We demonstrate that the addition of these geometric loss terms improves the previous state-of-art FlowNet3D accuracy from 57.85% to 63.43%. To further demonstrate the effectiveness of our geometric constraints, we propose a benchmark for flow estimation on the task of dynamic 3D reconstruction, thus providing a more holistic and practical measure of performance than the breakdown of individual metrics previously used to evaluate scene flow. This is made possible through the contribution of a novel pipeline to integrate point-based scene flow predictions into a global dense volume. FlowNet3D++ achieves up to a 15.0% reduction in reconstruction error over FlowNet3D, and up to a 35.2% improvement over KillingFusion [32] alone. We will release our scene flow estimation code later.
Spam filtering using a logistic regression model trained by an artificial bee colony algorithm

Email spam is a serious problem that annoys recipients and wastes their time. Machine-learning methods have been prevalent in spam detection systems owing to their efficiency in classifying mail as solicited or unsolicited. However, existing spam detection techniques usually suffer from low detection rates and cannot efficiently handle high-dimensional data. Therefore, we propose a novel spam detection method that combines the artificial bee colony algorithm with a logistic regression classification model. The empirical results on three publicly available datasets (Enron, CSDMC2010, and TurkishEmail) show that the proposed model can handle high-dimensional data thanks to its highly effective local and global search abilities. We compare the proposed model’s spam detection performance to those of support vector machine, logistic regression, and naive Bayes classifiers, in addition to the performance of the state-of-the-art methods reported by previous studies. We observe that the proposed method outperforms other spam detection techniques considered in this study in terms of classification accuracy.