Reviewerly/exHarmony huggingface dataset:
Harnessing the linguistic signal to predict scalar inferences

Pragmatic inferences often subtly depend on the presence or absence of linguistic features.For example, the presence of a partitive construction (of the) increases the strength of a so-called scalar inference: listeners perceive the inference that Chris did not eat all of the cookies to be stronger after hearing "Chris ate some of the cookies" than after hearing the same utterance without a partitive, "Chris ate some cookies".In this work, we explore to what extent neural network sentence encoders can learn to predict the strength of scalar inferences.We first show that an LSTM-based sentence encoder trained on an English dataset of human inference strength ratings is able to predict ratings with high accuracy (r = 0.78).We then probe the model's behavior using manually constructed minimal sentence pairs and corpus data.We find that the model inferred previously established associations between linguistic features and inference strength, suggesting that the model learns to use linguistic features to predict pragmatic inferences.
A video coverless information hiding algorithm based on semantic segmentation

Abstract Due to the fact that coverless information hiding can effectively resist the detection of steganalysis tools, it has attracted more attention in the field of information hiding. At present, most coverless information hiding schemes select text and image as transmission carriers, while there are few studies on emerging popular media such as video, which has more abundant contents. Taking the natural video as the carrier is more secure and can avoid the attention of attackers. In this paper, we propose a coverless video steganography algorithm based on semantic segmentation. Specifically, to establish the mapping relationship between secret information and video files effectively, this paper introduces the deep learning based on semantic segmentation network to calculate the statistical histogram of semantic information. To quickly index the sender’s secret message to the corresponding video frame, we build a three-digit index structure. The receiver can extract the valid video frame from the three-digit index information and restore the secret information. On the one hand, the neural network is trained through the original image and the noisy image in this scheme; therefore, it can not only effectively resist the interference of noises, but also accurately extract the robust deep features of the image. The frames of video generate the robust mapping to the secret information after the semantic information statistics. On the other hand, semantic segmentation belongs to pixel-level segmentation, which has high requirements for network parameters, so it is difficult for attackers to decrypt and recover secret information. Since this scheme does not modify the primitiveness of video data, it can effectively resist steganalysis tools. The experimental results and analysis show that the video coverless information hiding scheme has a large capacity and a certain resistance to noise attack.
Bi-level Probabilistic Feature Learning for Deformable Image Registration

We address the challenging issue of deformable registration that robustly and efficiently builds dense correspondences between images. Traditional approaches upon iterative energy optimization typically invoke expensive computational load. Recent learning-based methods are able to efficiently predict deformation maps by incorporating learnable deep networks. Unfortunately, these deep networks are designated to learn deterministic features for classification tasks, which are not necessarily optimal for registration. In this paper, we propose a novel bi-level optimization model that enables jointly learning deformation maps and features for image registration. The bi-level model takes the energy for deformation computation as the upper-level optimization while formulates the maximum \emph{a posterior} (MAP) for features as the lower-level optimization. Further, we design learnable deep networks to simultaneously optimize the cooperative bi-level model, yielding robust and efficient registration. These deep networks derived from our bi-level optimization constitute an unsupervised end-to-end framework for learning both features and deformations. Extensive experiments of image-to-atlas and image-to-image deformable registration on 3D brain MR datasets demonstrate that we achieve state-of-the-art performance in terms of accuracy, efficiency, and robustness.
Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation

Open Domain dialog system evaluation is one of the most important challenges in dialog research.Existing automatic evaluation metrics, such as BLEU are mostly referencebased.They calculate the difference between the generated response and a limited number of available references.Likert-score based self-reported user rating is widely adopted by social conversational systems, such as Amazon Alexa Prize chatbots.However, selfreported user rating suffers from bias and variance among different users.To alleviate this problem, we formulate dialog evaluation as a comparison task.We also propose an automatic evaluation model CMADE (Comparison Model for Automatic Dialog Evaluation) that automatically cleans self-reported user ratings as it trains on them.Specifically, we first use a self-supervised method to learn better dialog feature representation, and then use KNN and Shapley to remove confusing samples.Our experiments show that CMADE achieves 89.2% accuracy in the dialog comparison task.Our implementation is available at
A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model

Deep Learning is a popular Machine Learning algorithm that is widely used in many areas in current daily life. Its robust performance and ready-to-use frameworks and architectures enables many people to develop various Deep Learning-based software or systems to support human tasks and activities. Traffic monitoring is one area that utilizes Deep Learning for several purposes. By using cameras installed in some spots on the roads, many tasks such as vehicle counting, vehicle identification, traffic violation monitoring, vehicle speed monitoring, etc. can be realized. In this paper, we discuss a Deep Learning implementation to create a vehicle counting system without having to track the vehicles movements. To enhance the system performance and to reduce time in deploying Deep Learning architecture, hence pretrained model of YOLOv3 is used in this research due to its good performance and moderate computational time in object detection. This research aims to create a simple vehicle counting system to help human in classify and counting the vehicles that cross the street. The counting is based on four types of vehicle, i.e. car, motorcycle, bus, and truck, while previous research counts the car only. As the result, our proposed system capable to count the vehicles crossing the road based on video captured by camera with the highest accuracy of 97.72%.
Feature-Based Diversity Optimization for Problem Instance Classification

Understanding the behaviour of heuristic search methods is a challenge. This even holds for simple local search methods such as 2-OPT for the Travelling Salesperson Problem (TSP). In this article, we present a general framework that is able to construct a diverse set of instances which are hard or easy for a given search heuristic. Such a diverse set is obtained by using an evolutionary algorithm for constructing hard or easy instances which are diverse with respect to different features of the underlying problem. Examining the constructed instance sets, we show that many combinations of two or three features give a good classification of the TSP instances in terms of whether they are hard to be solved by 2-OPT.
EFANet: Exchangeable Feature Alignment Network for Arbitrary Style Transfer

Style transfer has been an important topic both in computer vision and graphics. Since the seminal work of Gatys et al. first demonstrates the power of stylization through optimization in the deep feature space, quite a few approaches have achieved real-time arbitrary style transfer with straightforward statistic matching techniques. In this work, our key observation is that only considering features in the input style image for the global deep feature statistic matching or local patch swap may not always ensure a satisfactory style transfer; see e.g., Figure 1. Instead, we propose a novel transfer framework, EFANet, that aims to jointly analyze and better align exchangeable features extracted from the content and style image pair. In this way, the style feature from the style image seeks for the best compatibility with the content information in the content image, leading to more structured stylization results. In addition, a new whitening loss is developed for purifying the computed content features and better fusion with styles in feature space. Qualitative and quantitative experiments demonstrate the advantages of our approach.
Autonomous and Cooperative Design of the Monitor Positions for a Team of UAVs to Maximize the Quantity and Quality of Detected Objects

This letter tackles the problem of positioning a swarm of UAVs inside a completely unknown terrain, having as objective to maximize the overall situational awareness. The situational awareness is expressed by the number and quality of unique objects of interest, inside the UAVs' fields of view. YOLOv3 and a system to identify duplicate objects of interest were employed to assign a single score to each UAVs' configuration. Then, a novel navigation algorithm, capable of optimizing the previously defined score, without taking into consideration the dynamics of either UAVs or environment, is proposed. A cornerstone of the proposed approach is that it shares the same convergence characteristics as the block coordinate descent (BCD) family of approaches. The effectiveness and performance of the proposed navigation scheme were evaluated utilizing a series of experiments inside the AirSim simulator. The experimental evaluation indicates that the proposed navigation algorithm was able to consistently navigate the swarm of UAVs to “strategic” monitoring positions and also adapt to the different number of swarm sizes. Source code is available at https://github.com/dimikout3/ConvCAOAirSim.
Blockchain technology in maritime industry

Maritime industry is one of the most globally connected industries that include transportation of numerous types of goods and documents across the world. With that said, it is safe to say that abundance of financial and paper-trail transactions are made every day in order for goods to be transported from one place to another. The scope of this paper is to show that by implementing blockchain technology savings in time and money could be generated. This paper presents costs of container freights and rates in the last few years and assumes possible future costs of container freights and rates if blockchain based technology is implemented. Additionally, by using comparative method economical and time value of “traditional” bill of lading is compared with a blockchain bill of lading solution. It is also important to mention the potential impact of the blockchain technology on the world environment and ecology by reducing global paper consumption and emissions from vehicles that are used in the transportation process. This paper also gives a descriptive and comprehensive overview of current and future applications of blockchain technology in maritime industry.
THE RESPONSE OF UKRAINIAN TEACHERS TO СOVID-19: CHALLENGES AND NEEDS IN THE USE OF DIGITAL TOOLS FOR DISTANCE LEARNING

The article features the results of a survey on the challenges and needs of the Ukrainian teachers in the implementation of distance education in the conditions of quarantine caused by the COVID-19 pandemic. The survey was carried out by the Comparative Studies Department for Information and Education Innovations of the Institute of Information Technologies and Learning Tools of the NAES of Ukraine in 2020. The study is based on the 607 responses of educators from all-over Ukraine to the online questionnaire conducted between March 27 and April 4, 2020. The content of the questionnaire covered the following topics: educational qualification of teachers, number of screen hours, problems with the access to necessary information, access to the Internet, digital tools that teachers use for distance learning, teachers’ needs in the professional development regarding the use of ICT. The obtained answers allowed providing recommendations for public education authorities, decision makers, institutions of secondary, higher and postgraduate education on the improvement of teachers’ digital competence, organization of online communication and Internet access on the workplace to digital tools for distance education. The recommendations are based on the responses of the international educational community and organizations (OECD, PISA, UNESCO, and Council of Europe) to COVID-19, in comparison with the Ukrainian realities reflected in the online survey. The novelty of the study presented in the article lies in the fact that it gives a response to the needs that Ukrainian teachers identified in the first weeks of the quarantine. It can be used to achieve long-term practical results in providing support for schools, teachers, school administrators and students in the organization of distance learning as well as in overcoming barriers to teachers' access to information on the use of digital distance learning tools.
HDR Reconstruction Based on the Polarization Camera

The recent development of the on-chip micro-polarizer technology has made it possible to acquire - with the same ease of operation as a conventional camera - spatially aligned and temporally synchronized polarization images simultaneously in four orientations. This development has created new opportunities for interesting applications including those in robotics. In this letter, we investigate the use of this sensor technology in high-dynamic-range (HDR) imaging. Specifically, observing that natural light can be attenuated differently by varying the direction of the polarization filter, we treat the multiple images captured by the polarization camera as a set captured at different exposure times, useful to the reconstruction of an HDR image. In our approach, we first study the radiometric model of the polarization camera, and relate the polarizer direction, degree and angle of polarization of light to the exposure time of a pixel in the polarization images. Subsequently, by applying the standard radiometric calibration procedure of a camera, we recover the camera response function. With multiple polarization images at known pixel-specific exposure times, we can then proceed to estimate the irradiance maps from the images and generate an HDR image. Two datasets are created to evaluate our approach, and experimental results show the dynamic range by our approach can be increased by an amount dependent on light polarization. We also use two robotics experiments on feature matching and visual odometry to demonstrate the potential benefit of this increased dynamic range.
The Knowledge Acquisition Bottleneck Problem in Multilingual Word Sense Disambiguation

Word Sense Disambiguation (WSD) is the task of identifying the meaning of a word in a given context. It lies at the base of Natural Language Processing as it provides semantic information for words. In the last decade, great strides have been made in this field and much effort has been devoted to mitigate the knowledge acquisition bottleneck problem, i.e., the problem of semantically annotating texts at a large scale and in different languages. This issue is ubiquitous in WSD as it hinders the creation of both multilingual knowledge bases and manually-curated training sets. In this work, we first introduce the reader to the task of WSD through a short historical digression and then take the stock of the advancements to alleviate the knowledge acquisition bottleneck problem. In that, we survey the literature on manual, semi-automatic and automatic approaches to create English and multilingual corpora tagged with sense annotations and present a clear overview over supervised models for WSD. Finally, we provide our view over the future directions that we foresee for the field.
Explaining and Exploiting the Advantages of Down-sampled Lexicase Selection

In genetic programming, parent selection is ordinarily based on aggregate measures of performance across an entire training set. Lexicase selection, by contrast, selects on the basis of performance...
An Efficient Searchable Public-Key Authenticated Encryption for Cloud-Assisted Medical Internet of Things

In recent years, it has become popular to upload patients’ medical data to a third-party cloud server (TCS) for storage through medical Internet of things. It can reduce the local maintenance burden of the medical data and importantly improve accuracy in the medical treatment. As remote TCS cannot be fully trusted, medical data should be encrypted before uploading, to protect patients’ privacy. However, encryption makes search capabilities difficult for patients and doctors. To address this issue, Huang et al. recently put forward the notion of Public-key Authenticated Encryption with Keyword Search (PAEKS) against inside keyword guessing attacks. However, the existing PAEKS schemes rely on time-consuming computation of parings. Moreover, some PAEKS schemes still have security issues in a multiuser setting. In this paper, we propose a new and efficient PAEKS scheme, which uses the idea of Diffie-Hellman key agreement to generate a shared secret key between each sender and receiver. The shared key will be used to encrypt keywords by the sender and to generate search trapdoors by the receiver. We prove that our scheme is semantically secure against inside keyword guessing attacks in a multiuser setting, under the oracle Diffie-Hellman assumption. Experimental results demonstrate that our PAEKS scheme is more efficient than that of previous ones, especially in terms of keyword searching time.
News Text Summarization Based on Multi-Feature and Fuzzy Logic

In the last 70 years, the automatic text summarization work has become more and more important because the amount of data on the Internet is increasing so fast, and automatic text summarization work can extract useful information and knowledge what user's need that could be easily handled by humans and used for many purposes. Especially in people's daily life, news text is the type of text most people are exposed to. In this study, a new automatic summarzation model for news text which based on fuzzy logic rules, multi-feature and Genetic algorithm (GA) is introduced. Firstly, the most important feature is word features, we score each word and extracted words that exceeded the preset score as keywords and because news text is a special kind of text, it contains many specific elements, such as time, place and characters, so sometimes these special news elements can be extracted directly as keywords. Second is sentence features, a linear combination of these features shows the importance of each sentence and each feature is weighted by Genetic algorithm. At last, we use fuzzy logic system to calculate the final score in order to get automatic summarization. The results of the proposed method was compared with other methods including Msword, System19, System21, System 31, SDS-NNGA, GCD, SOM and Ranking SVM by using ROUGE assessment method on DUC2002 dataset show that proposed method outperforms the aforementioned methods.
Analytical Upper Bound for the Error on the Discretization of Uncertain Linear Systems by using the Tensor Product Model Transformation

This work provides analytical upper bounds on the discretization error of uncertain linear systems.The Tensor Product Model Transformation is used to approximate the derived discretized system, with a reduced number of vertices.Digital state feedback controllers are then designed for the discretized system, for comparison to other available work in the current literature.
HAM: Hidden Anchor Mechanism for Scene Text Detection

Direct regression and anchor are the two mainly effective and prevailing mechanisms in the paradigm of scene text detection. However, the use of direct regression-based methods may be challenging during optimization without the help of anchors as references. Unfortunately, the anchor-based methods always suffer from the careful design of the anchors, degrading the robustness to complex scenes. To address the above-mentioned problems, we propose a novel hidden anchor mechanism (HAM) especially for scene text detection. The predictions of anchors are innovatively regarded as hidden layers, and the weighted sum of the predictions is integrated into a direct regression-based network. Hence, the architecture of our HAM still has the characteristic of simplicity as with direct regression-based methods. Moreover, it is easier to optimize anchors as references with this type of method than with direct regression-based methods. In this way, our network can take advantage of both direct regression and anchor mechanisms. In addition, we decouple three kinds of one-dimensional anchors from three-dimensional anchors, greatly reducing the number of anchors in text bounding box matching without performance degradation. We also propose a post-processing technique for long text detection, named iterative regression box (IRB), which takes a few additional computational costs and can be easily generalized to other methods. Experiments on several public datasets demonstrate that the proposed method achieves state-of-the-art performance. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hjbplayer/HAM</uri> .
Big Data-Driven Abnormal Behavior Detection in Healthcare Based on Association Rules

Healthcare insurance frauds are causing millions of dollars of public healthcare fund losses around the world in various ways, which makes it very important to strengthen the management of medical insurance in order to guarantee the steady operation of medical insurance funds. Healthcare fraud detection methods can reduce the losses of healthcare insurance funds and improve medical quality. Existing fraud detection studies mostly focus on finding normal behavior patterns and treat those violating normal behavior patterns as fraudsters. However, fraudsters can often disguise themselves with some normal behaviors, such as some consistent behaviors when they seek medical treatments. To address these issues, we combined a MapReduce distributed computing model and association rule mining to propose a medical cluster behavior detection algorithm based on frequent pattern mining. It can detect certain consistent behaviors of patients in medical treatment activities. By analyzing 1.5 million medical claim records, we have verified the effectiveness of the method. Experiments show that this method has better performance than several benchmark methods.