Reviewerly/exHarmony huggingface dataset:
Subsampled turbulence removal network

We present a deep-learning approach to restore a sequence of turbulence-distorted video frames from turbulent deformations and space-time varying blurs. Instead of requiring a massive training sample size in deep networks, we purpose a training strategy that is based on a new data augmentation method to model turbulence from a relatively small dataset. Then we introduce a subsampled method to enhance the restoration performance of the presented GAN model. The contributions of the paper is threefold: first, we introduce a simple but effective data augmentation algorithm to model the turbulence in real life for training in the deep network; Second, we firstly purpose the Wasserstein GAN combined with $\ell_1$ cost for successful restoration of turbulence-corrupted video sequence; Third, we combine the subsampling algorithm to filter out strongly corrupted frames to generate a video sequence with better quality.
Domain Transfer based Data Augmentation for Neural Query Translation

Query translation (QT) serves as a critical factor in successful cross-lingual information retrieval (CLIR).Due to the lack of parallel query samples, neural-based QT models are usually optimized with synthetic data which are derived from large-scale monolingual queries.Nevertheless, such kind of pseudo corpus is mostly produced by a general-domain translation model, making it be insufficient to guide the learning of QT model.In this paper, we extend the data augmentation with a domain transfer procedure, thus to revise synthetic candidates to search-aware examples.Specifically, the domain transfer model is built upon advanced Transformer, in which layer coordination and mixed attention are exploited to speed up the refining process and leverage parameters from a pre-trained cross-lingual language model.In order to examine the effectiveness of the proposed method, we collected French-to-English and Spanish-to-English QT test sets, each of which consists of 10,000 parallel query pairs with careful manual-checking.Qualitative and quantitative analyses reveal that our model significantly outperforms strong baselines and the related domain transfer methods on both translation quality and retrieval accuracy. 1
IMPROVED PREDICTIVE SPARSE DECOMPOSITION METHOD WITH DENSENET FOR PREDICTION OF LUNG CANCER

Lung cancer is the second most common form of cancer in both men and women. It is responsible for at least 25% of all cancer-related deaths in the United States alone. Accurate and early diagnosis of this form of cancer can increase the rate of survival. Computed tomography (CT) imaging is one of the most accurate techniques for diagnosing the disease. In order to improve the classification accuracy of pulmonary lesions indicating lung cancer, this paper presents an improved method for training a densely connected convolutional network (DenseNet). The optimized setting ensures that code prediction error and reconstruction error within hidden layers are simultaneously minimized. To achieve this and improve the classification accuracy of the DenseNet, we propose an improved predictive sparse decomposition (PSD) approach for extracting sparse features from the medical images. The sparse decomposition is achieved by using a linear combination of basis functions over the L2 norm. The effect of dropout and hidden layer expansion on the classification accuracy of the DenseNet is also investigated. CT scans of human lung samples are obtained from The Cancer Imaging Archive (TCIA) hosted by the University of Arkansas for Medical Sciences (UAMS). The proposed method outperforms seven other neural network architectures and machine learning algorithms with a classification accuracy of 95%.
Complaint Identification in Social Media with Transformer Networks

Complaining is a speech act extensively used by humans to communicate a negative inconsistency between reality and expectations.Previous work on automatically identifying complaints in social media has focused on using feature-based and task-specific neural network models.Adapting state-of-the-art pre-trained neural language models and their combinations with other linguistic information from topics or sentiment for complaint prediction has yet to be explored.In this paper, we evaluate a battery of neural models underpinned by transformer networks which we subsequently combine with linguistic information.Experiments on a publicly available data set of complaints demonstrate that our models outperform previous state-of-the-art methods by a large margin achieving a macro F1 up to 87.
Specific Emitter Identification Based on Square Integral Bispectrum Features

Specific Emitter Identification is an important technology employed to identify the unique radio transmitter at the physical level regardless of specific information carried. RF fingerprint contains rich nonlinear characteristics and reflects the differences of internal components in radio transmitters. Radio frequency fingerprint extraction and identification has been extensively adopted to enhance the security of radio frequency communication in both military and civilian use. On the basis of time-frequency analysis of emitter signals, this paper proposes an individual emitter recognition algorithm based on square integral bispectrum features, and uses softmax classifier to classify and recognize five types of emitter individuals, and studies the influence of square integral bispectrum features dimensions and Gaussian noise on recognition results. The algorithm can effectively reduce the feature dimensions and greatly improve the correct recognition rate of emitter. The experiment results demonstrate the practicality of our proposal.
A multi-type and decentralized data transaction scheme based on smart contracts and digital watermarks

While data sharing cross management is increasingly popular, there are challenges in such a decentralized data sharing mode, especially in terms of transaction fairness and copyright protection. However, few schemes consider both fairness and copyright protection in the current literature. In this paper, we propose a flexible and Ethereum-based scheme to facilitate copyright protection in data sharing blockchain, by using watermark-based policies. This allows us to achieve properties such as preventing the re-selling of data (e.g., partial data, combined data, and leaked data). We also design five algorithms to automatically guarantee data sharing fairness and proactively protect data copyright, by using smart contracts. In other words, several new security features are introduced to existing transaction schemes, while still achieving feasible performance. The response times for each key step of smart contract in the proposed scheme is between 0.41 s and 0.91 s.
SESAM at SemEval-2020 Task 8: Investigating the Relationship between Image and Text in Sentiment Analysis of Memes

This paper presents our submission to task 8 (memotion analysis) of the SemEval 2020 competition.We explain the algorithms that were used to learn our models along with the process of tuning the algorithms and selecting the best model.Since meme analysis is a challenging task with two distinct modalities, we studied the impact of different multimodal representation strategies.The results of several approaches to dealing with multimodal data are therefore discussed in the paper.We found that alignment-based strategies did not perform well on memes.Our quantitative results also showed that images and text were uncorrelated.Fusion-based strategies did not show significant improvements and using one modality only (text or image) tends to lead to better results when applied with the predictive models that we used in our research.
Filtering Back-Translated Data in Unsupervised Neural Machine Translation

Unsupervised neural machine translation (NMT) utilizes only monolingual data for training.The quality of back-translated data plays an important role in the performance of NMT systems.In back-translation, all generated pseudo parallel sentence pairs are not of the same quality.Taking inspiration from domain adaptation where in-domain sentences are given more weight in training, in this paper we propose an approach to filter back-translated data as part of the training process of unsupervised NMT.Our approach gives more weight to good pseudo parallel sentence pairs in the back-translation phase.We calculate the weight of each pseudo parallel sentence pair using sentence-wise round-trip BLEU score which is normalized batch-wise.We compare our approach with the current state of the art approaches for unsupervised NMT.
An Energy-Aware Host Resource Management Framework for Two-Tier Virtualized Cloud Data Centers

The high energy consumption of cloud data centers is one of the key issues restricting the future development of the cloud computing industry.For two-tier virtualized data centers in which containers are deployed on VMs, this paper conducts an in-depth study of this problem from the aspect of resource management.First, the working model of two-tier virtualized data center is defined.By mapping tasks to containers and using VMs to isolate different jobs, this model improves the resource utilization of hosts while ensuring the isolation and security of jobs.Then, an energy-aware host resource management framework is constructed, which includes two algorithms.The initial static placement is a two-tier scheduling algorithm, including the load balancing alternate placement and the two-sided matching methods to complete the placement of containers to VMs and VMs to hosts respectively.The runtime dynamic consolidation algorithm takes the initial placement scheme as input, and utilizes the dynamic consolidation method to use the least active hosts to meet the real-time resource requirements of containers.Finally, simulation experiments compared with related algorithms are conducted using real workload traces.The results show that the proposed two algorithms have better performance in host resource utilization, number of active hosts, number of container migrations and SLA metric.And the entire framework achieves an energy-saving effect of 13.8% at least.
A SMOTe based Oversampling Data-Point Approach to Solving the Credit Card Data Imbalance Problem in Financial Fraud Detection

Credit card fraud has negatively affected the market economic order, broken the confidence and interest of stakeholders, financial institutions, and consumers.Losses from card fraud is increasing every year with billions of dollars being lost.Machine Learning methods use large volumes of data as examples for learning to improve the performance of classification models.Financial institutions use Machine Learning to identify fraudulent patterns from the large amounts of historical financial records.However, the detection of credit card fraud remains as a significant challenge for business intelligence technologies as most datasets containing credit card transactions are highly imbalanced.To overcome this challenge, this paper proposed the use of the data-point approach in machine learning.An experimental study was conducted applying Oversampling with SMOTe, a data-point approach technique, on an imbalanced credit card dataset.State-of-the-art classical machine learning algorithms namely, Support Vector Machines, Logistic Regression, Decision Tree and Random Forest classifiers were used to perform the classifications and the accuracy was evaluated using precision, recall, F1-score, and the average precision metrics.The results show that if the data is highly imbalanced, the model struggles to detect fraudulent transactions.After using the SMOTe based Oversampling technique, there was a significant improvement to the ability to predict positive classes.
A Secret-Sharing Based MPC Protocol for Boolean Circuits with Good Amortized Complexity

We present a new secure multiparty computation protocol in the preprocessing model that allows for the evaluation of a number of instances of a boolean circuit in parallel, with a small online communication complexity per instance of 10 bits per party and multiplication gate. Our protocol is secure against an active dishonest majority, and can also be transformed, via existing techniques, into a protocol for the evaluation of a single "well-formed" boolean circuit with the same complexity per multiplication gate at the cost of some overhead that depends on the topology of the circuit. Our protocol uses an approach introduced recently in the setting of honest majority and information-theoretical security which, using an algebraic notion called reverse multiplication friendly embeddings, essentially transforms a batch of evaluations of an arithmetic circuit over a small field into one evaluation of another arithmetic circuit over a larger field. To obtain security against a dishonest majority we combine this approach with the well-known SPDZ protocol that operates over a large field. Structurally our protocol is most similar to MiniMAC, a protocol which bases its security on the use of error-correcting codes, but our protocol has a communication complexity which is half of that of MiniMAC when the best available binary codes are used. With respect to certain variant of MiniMAC that utilizes codes over larger fields, our communication complexity is slightly worse; however, that variant of MiniMAC needs a much larger preprocessing than ours. We also show that our protocol also has smaller amortized communication complexity than Committed MPC, a protocol for general fields based on homomorphic commitments, if we use the best available constructions for those commitments. Finally, we construct a preprocessing phase from oblivious transfer based on ideas from MASCOT and Committed MPC.
A “Learned” Approach to Quicken and Compress Rank/Select Dictionaries

We address the well-known problem of designing, implementing and experimenting compressed data structures for supporting rank and select queries over a dictionary of integers. This problem has been studied far and wide since the end of the '80s with tons of important theoretical and practical results.Following a recent line of research on the so-called learned data structures, we first show that this problem has a surprising connection with the geometry of a set of points in the Cartesian plane suitably derived from the input integers. We then build upon some classical results in computational geometry to introduce the first "learned" scheme for implementing a compressed rank/select dictionary. We prove theoretical bounds on its time and space performance both in the worst case and in the case of input distributions with finite mean and variance.We corroborate these theoretical results with a large set of experiments over datasets originating from a variety of sources and applications (Web, DNA sequencing, information retrieval and natural language processing), and we show that a carefully engineered version of our approach provides new interesting space-time trade-offs with respect to several well-established implementations of Elias-Fano, RRR-vector, and random-access vectors of Elias γ/δ-coded gaps.
SynPo-Net—Accurate and Fast CNN-Based 6DoF Object Pose Estimation Using Synthetic Training

Estimation and tracking of 6DoF poses of objects in images is a challenging problem of great importance for robotic interaction and augmented reality. Recent approaches applying deep neural networks for pose estimation have shown encouraging results. However, most of them rely on training with real images of objects with severe limitations concerning ground truth pose acquisition, full coverage of possible poses, and training dataset scaling and generalization capability. This paper presents a novel approach using a Convolutional Neural Network (CNN) trained exclusively on single-channel Synthetic images of objects to regress 6DoF object Poses directly (SynPo-Net). The proposed SynPo-Net is a network architecture specifically designed for pose regression and a proposed domain adaptation scheme transforming real and synthetic images into an intermediate domain that is better fit for establishing correspondences. The extensive evaluation shows that our approach significantly outperforms the state-of-the-art using synthetic training in terms of both accuracy and speed. Our system can be used to estimate the 6DoF pose from a single frame, or be integrated into a tracking system to provide the initial pose.
Improving eye–computer interaction interface design: Ergonomic investigations of the optimum target size and gaze-triggering dwell time

The Midas touch is reflected by the interactive feedback of interface functional elements, and a low level of spatial accuracy is related to the interaction area. This study tried to solve these two problems from the perspective of human-computer interactions and ergonomics. Two experiments were conducted to explore the optimum target size and gaze-triggering dwell time of the eye–computer interaction (ECI) system. Experimental Series 1 was used as the pre-experiment to identify the size that has a greater task completion rate. Experimental Series 2 was used as the main experiment to investigate the optimum gaze-triggering dwell time by using a comprehensive evaluation of the task completion rate, reaction time, and NASA-TLX (Task Load Index). In Experimental Series 1, the optimal element size was determined to be 256 × 256p x2. The conclusion of Experimental Series 2 was that when the dwell time is set to 600 ms, the efficiency of the interface is the highest, and the task load of subjects is minimal as well. Finally, the results of Experiment Series 1 and 2 have positive effects on improving the usability of the interface. The optimal control size and the optimal dwell time obtained from the experiments have certain reference and application value for interface design and software development of the ECI system.
A Lattice-Based Homomorphic Proxy Re-Encryption Scheme with Strong Anti-Collusion for Cloud Computing

The homomorphic proxy re-encryption scheme combines the characteristics of a homomorphic encryption scheme and proxy re-encryption scheme. The proxy can not only convert a ciphertext of the delegator into a ciphertext of the delegatee, but also can homomorphically calculate the original ciphertext and re-encryption ciphertext belonging to the same user, so it is especially suitable for cloud computing. Yin et al. put forward the concept of a strong collusion attack on a proxy re-encryption scheme, and carried out a strong collusion attack on the scheme through an example. The existing homomorphic proxy re-encryption schemes use key switching algorithms to generate re-encryption keys, so it can not resist strong collusion attack. In this paper, we construct the first lattice-based homomorphic proxy re-encryption scheme with strong anti-collusion (HPRE-SAC). Firstly, algorithm TrapGen is used to generate an encryption key and trapdoor, then trapdoor sampling is used to generate a decryption key and re-encryption key, respectively. Finally, in order to ensure the homomorphism of ciphertext, a key switching algorithm is only used to generate the evaluation key. Compared with the existing homomorphic proxy re-encryption schemes, our HPRE-SAC scheme not only can resist strong collusion attacks, but also has smaller parameters.
Efficient Hybrid Supervision for Instance Segmentation in Aerial Images

Instance segmentation in aerial images is of great significance for remote sensing applications, and it is inherently more challenging because of cluttered background, extremely dense and small objects, and objects with arbitrary orientations. Besides, current mainstream CNN-based methods often suffer from the trade-off between labeling cost and performance. To address these problems, we present a pipeline of hybrid supervision. In the pipeline, we design an ancillary segmentation model with the bounding box attention module and bounding box filter module. It is able to generate accurate pseudo pixel-wise labels from real-world aerial images for training any instance segmentation models. Specifically, bounding box attention module can effectively suppress the noise in cluttered background and improve the capability of segmenting small objects. Bounding box filter module works as a filter which removes the false positives caused by cluttered background and densely distributed objects. Our ancillary segmentation model can locate object pixel-wisely instead of relying on horizontal bounding box prediction, which has better adaptability to arbitrary oriented objects. Furthermore, oriented bounding box labels are utilized for handling arbitrary oriented objects. Experiments on iSAID dataset show that the proposed method can achieve comparable performance (32.1 AP) to fully supervised methods (33.9 AP), which is obviously higher than weakly supervised setting (26.5 AP), when using only 10% pixel-wise labels.
Exploring Digital Literacy Levels and Technology Integration Competence of Turkish Academics

&lt;p style="text-align:justify"&gt;Today’s individuals are expected to have skills in many areas as a natural consequence of the advances that have been taking place in society and technology. Particularly in developed countries, these skills are also called 21&lt;sup&gt;st&lt;/sup&gt; century skills. Critical, creative and reflective thinking, problem solving and keeping up with the digital age (digital literacy) are some of these skills. Universities play a significant role in raising qualified individuals in our country. Updating the training programs, keeping up with the era and having a say in the digital world makes it a necessity for people who give education in these areas to be competent. From this point of view, this study aimed to explore the digital literacy levels of Turkish academics working in faculties of education and the perceptions of students towards technology integration competence of the academics. In this study, quantitative cross-sectional design was preferred. While selecting the participants, purposeful sampling method was used, and two different participant groups (academics and prospective teachers) were included in the process. Two scales with validity and reliability in the literature were used as data collection tools in the research. In the data collection process, firstly, a survey was administered to academics working in faculties of education, and then another one was conducted with prospective teachers. The results obtained were subjected to quantitative data analysis via SPSS 24 and AMOS 24 software, and descriptive and inferential statistics were generated. The results revealed that the variables of department, age and grade level created a significant difference in the digitalization of academics, whereas the gender variable significantly contributed only to the perceptions of prospective teachers.&lt;/p&gt;
A Novel Adaptive Gradient Compression Scheme: Reducing the Communication Overhead for Distributed Deep Learning in the Internet of Things

Distributed deep learning deployed in an edge computing environment is a promising approach for extracting accurate information from raw sensor data from Internet of Things (IoT). But the distributed training suffers from heavy communication overheads between a master node and multiple compute nodes due to frequent transmission of gradients, which limits the training efficiency of the distributed deep learning. In this article, we propose a novel algorithm named ProbComp-LPAC (ProbComp: probability compression and LPAC: layer parameters adaptive compression), which can reduce the communication overhead and improve the training efficiency of the distributed deep learning. ProbComp-LPAC adopts a probability equation to select the gradients and uses different compression rates in different layers of deep neural networks. Comparing with other methods, such as adaptive compression (AdaComp) and lazily aggregated quantized compression (LAQ), the performance of ProbComp-LPAC is not only faster in the training speed but also higher in the accuracy of the test.