Reviewerly/exHarmony huggingface dataset:
Rotational Objects Recognition and Angle Estimation via Kernel-Mapping CNN

Convolutional neural network (CNN) has become the mainstream method in the field of image recognition for its excellent ability to feature extraction. Most of the CNNs increase the classification accuracy for the rotational objects by imposing the network with rotation invariance or equivariance property, which causes the loss of the targets orientation information. This paper attempts to achieve objects recognition and angle or orientation estimation simultaneously without additional network training. To this end, we propose the matching criterion and the kernel-mapping convolutional neural network (KM-CNN). It has been shown that when the kernel satisfies the matching criterion, the output remains the same. Based on this study, we apply rotation transformation to the KM-CNN. Besides, the KM-CNN with the rotation by shifting pixel method and octagonal convolutional kernels can solve the mismatching problem caused by the rotations. The KM-CNN with the kernel sharing central weights gives the near state-of-art results in target recognition and angle estimation on benchmark datasets MNIST, GTSRB and Caltech-256.
Code Division Multiplexing and Machine Learning Based Reversible Data Hiding Scheme for Medical Image

In this paper, a new reversible data hiding (RDH) scheme based on Code Division Multiplexing (CDM) and machine learning algorithms for medical image is proposed. The original medical image is firstly converted into frequency domain with integer-to-integer wavelet transform (IWT) algorithm, and then the secret data are embedded into the medium frequency subbands of medical image robustly with CDM and machine learning algorithms. According to the orthogonality of different spreading sequences employed in CDM algorithm, the secret data are embedded repeatedly, most of the elements of spreading sequences are mutually canceled, and the proposed method obtained high data embedding capacity at low image distortion. Simultaneously, the to-be-embedded secret data are represented by different spreading sequences, and only the receiver who has the spreading sequences the same as the sender can extract the secret data and original image completely, by which the security of the RDH is improved effectively. Experimental results show the feasibility of the proposed scheme for data embedding in medical image comparing with other state-of-the-art methods.
Enhancing Machine Reading Comprehension With Position Information

When people do the reading comprehension, they often try to find the words from the passages which are similar to the question words first. Then people deduce the answer based on the context around these similar words. Therefore, the position information may be helpful in finding the answer rapidly and is useful for reading comprehension. However, previous attention-based machine reading comprehension models typically focus on the interaction between the question and the context representation without considering the position information. In this paper, we introduce the position information to machine reading comprehension and investigate the performance of the position information. The position information is experimented in three different ways: 1) position encoder; 2) attention mechanism; and 3) position mapping embedding. By experimenting on TriviaQA dataset, we have demonstrated the effectiveness of position information.
Comparing coherence measures for X states: Can quantum states be ordered based on quantum coherence?

Quantum coherence is an essential resource for quantum information processing and various quantitative measures of it have been introduced. However, the interconnections between these measures are not yet understood properly. Here, using a large set of randomly prepared $X$ states and analytically obtained expressions of various measures of coherence (e.g., relative entropy of coherence, $l1$ norm of coherence, coherence via skew information, and first-order coherence), it is established that these measures of quantum coherence cannot be used to perform ordering of a set of quantum states based on the amount of coherence present in a state. Further, it is shown that for a given value of quantum coherence measured by the relative entropy of coherence, maximally nonlocal mixed states of $X$ type (which are characterized by maximal violation of the CHSH inequality) have maximum quantum coherence as measured by $l1$ norm of coherence. In addition, the amount of coherence measured by $l1$ norm of coherence for a Werner state is found to be always less than that for a maximally nonlocal mixed state even when they possess an equal amount of coherence measured by the relative entropy of coherence. These resource theory based measures of coherence are not observed to show any relation with the first-order coherence, while its maximum (hidden coherence) is found to be more connected to concurrence both being basis independent quantities. These observations could be of use in obtaining a deeper understanding of the interconnections between various measures of quantum coherence.
Modal Decomposition of Feedback Delay Networks

Feedback delay networks (FDNs) belong to a general class of recursive filters which are widely used in sound synthesis and physical modeling applications. We present a numerical technique to compute the modal decomposition of the FDN transfer function. The proposed pole finding algorithm is based on the Ehrlich-Aberth iteration for matrix polynomials and has improved computational performance of up to three orders of magnitude compared to a scalar polynomial root finder. The computational performance is further improved by bounds on the pole location and an approximate iteration step. We demonstrate how explicit knowledge of the FDN's modal behavior facilitates analysis and improvements for artificial reverberation. The statistical distribution of mode frequency and residue magnitudes demonstrate that relatively few modes contribute a large portion of impulse response energy.
Mapping fuzzy RDF(S) into fuzzy object‐oriented databases

The Resource Description Framework (RDF) and RDF Schema (RDFS), being a recommendation of World Wide Web Consortium (W3C), have been widely used to exchange and reason information about resources on the web. It is a common case that information about resources may be uncertain under an environment of open web. To deal with uncertainties in resource information, we propose, in this paper, a general fuzzy RDF(S) model, which contains a fuzzy RDFS layer and a fuzzy RDF layer. In particular, we investigate how to formally map the fuzzy RDF(S) model to the fuzzy object-oriented database model in the paper. We develop mapping rules and implement a prototype system to demonstrate the feasibility of our approach.
Effective Data-Driven Technology for Efficient Vision-Based Outdoor Industrial Systems

Vision systems are the core information collection module in outdoor industrial systems such as factory inspection robots. However, haze greatly reduces working efficiency. Existing dehazing methods have two problems-first, they are not specifically designed for the industrial systems; second, these methods include several assumptions in their design processes and imaging models, leading to unsatisfactory results. In this article, an approach for single image dehazing is proposed to improve the efficiency of outdoor vision-based systems. First, a novel haze imaging model is proposed based on the dichromatic atmospheric scattering model. It considers the effects of multiple scattering and involves fewer assumptions. Then a data-driven technique called sparse representation is used to solve this model. Considering a haze image, a distorted and blurred version of a fine image, every patch is presented using dedicatedly prepared over-complete dictionaries and is traced back to a haze-free image. Quantitative and qualitative comparisons on a number of real-world haze images demonstrate that the proposed approach not only is more stable but also leads to better dehazing results.
On Reform of College English Teaching Based on the FiF Smart Learning Platform

The world’s educational reforms evolve dramatically along with the rapid development of science and technology, and promoting education informatization has become the national strategy of many countries. To implement the the Action Plan for Education Informatization 2.0 issued by the Ministry of Education of the People’s Republic of China and further integrate the information technology into classroom teaching to achieve innovations in teaching , Leshan Normal University has introduced the FiF (For Ideal Future) Smart Learning Platform , developed by iFLYTEK Co.,Ltd.. Giving a full play to the platform’s functions, teachers are actively practicing the graded and classified College English teaching reform, as well as flipped classroom model and blending teaching model, thereby having improved the efficiency in teaching. The teaching empirical research has suggested that the FiF Smart Teaching Platform plays a positive role in improving students’ oral English and comprehensive English ability.
Comments on “Publicly Verifiable Computation of Polynomials Over Outsourced Data With Multiple Sources”

With more organizational and individual data owners storing their data in the cloud, there is a corresponding need to ensure that such outsourced data can be computed by the data owners or some authorized third parties. In other words, the cloud server performs the requested computation, and returns the computation result to the requesting party. Such a paradigm is referred to as outsourced computation in the literature. A challenge is how one can ensure the correctness of the returned result, and this has been extensively studied in the literature. For example, in 2017, Song <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> . proposed a protocol for publicly verifiable computation of polynomials over outsourced data with multiple sources. However, we reveal that a core building block of their protocol allows an adversary to forge the signatures on the outsourced data. Hence, this invalidates the security of the protocol. We also point out their proposal is yet very interesting and useful, only a little improvement can strengthen their proposal to be secure.
Improvement of Short-Term BIPV Power Predictions Using Feature Engineering and a Recurrent Neural Network

The time resolution and prediction accuracy of the power generated by building-integrated photovoltaics are important for managing electricity demand and formulating a strategy to trade power with the grid. This study presents a novel approach to improve short-term hourly photovoltaic power output predictions using feature engineering and machine learning. Feature selection measured the importance score of input features by using a model-based variable importance. It verified that the normative sky index in the weather forecasted data had the least importance as a predictor for hourly prediction of photovoltaic power output. Six different machine-learning algorithms were assessed to select an appropriate model for the hourly power output prediction with onsite weather forecast data. The recurrent neural network outperformed five other models, including artificial neural networks, support vector machines, classification and regression trees, chi-square automatic interaction detection, and random forests, in terms of its ability to predict photovoltaic power output at an hourly and daily resolution for 64 tested days. Feature engineering was then used to apply dropout observation to the normative sky index from the training and prediction process, which improved the hourly prediction performance. In particular, the prediction accuracy for overcast days improved by 20% compared to the original weather dataset used without dropout observation. The results show that feature engineering effectively improves the short-term predictions of photovoltaic power output in buildings with a simple weather forecasting service.
Effective Optical Braille Recognition Based on Two-Stage Learning for Double-Sided Braille Image

This paper proposes a novel two-stage learning framework TS-OBR for double-sided Braille images recognition. In the first stage, a Haar cascaded classifier with the sliding window strategy is adopted to quickly detect Braille recto dots with high confidence. Then a coarse-to-fine de-skewing method is proposed to correct original skewed Braille images, which maximizes the variance of horizontal and vertical projection at different angles. And an adaptive Braille cells grid construction method based on statistical analysis is proposed, which can dynamically generate the Braille cells grid for each Braille image. In the second stage, a decision-level SVM classifier with four classifiers recognition results is used to get recto dots detection results only on intersections of the Braille cells grid. Experimental results on the public double-sided Braille dataset and our Braille exam answer paper dataset show the proposed framework TS-OBR is effective, robust and fast for Braille dots detection and Braille characters recognition.
Multi-Objective Service Placement Scheme Based on Fuzzy-AHP System for Distributed Cloud Computing

With the rapid increase in the development of the cloud data centers, it is expected that massive data will be generated, which will decrease service response time for the cloud data centers. To improve the service response time, distributed cloud computing has been designed and researched for placement and migration from mobile devices close to edge servers that have secure resource computing. However, most of the related studies did not provide sufficient service efficiency for multi-objective factors such as energy efficiency, resource efficiency, and performance improvement. In addition, most of the existing approaches did not consider various metrics. Thus, to maximize energy efficiency, maximize performance, and reduce costs, we consider multi-metric factors by combining decision methods, according to user requirements. In order to satisfy the user’s requirements based on service, we propose an efficient service placement system named fuzzy- analytical hierarchical process and then analyze the metric that enables the decision and selection of a machine in the distributed cloud environment. Lastly, using different placement schemes, we demonstrate the performance of the proposed scheme.
Towards Assessing Argumentation Annotation - A First Step

This paper presents a first attempt at using Walton’s argumentation schemes for annotating arguments in Swedish political text and assessing the feasibility of using this particular set of schemes with two linguistically trained annotators. The texts are not pre-annotated with argumentation structure beforehand. The results show that the annotators differ both in number of annotated arguments and selection of the conclusion and premises which make up the arguments. They also differ in their labeling of the schemes, but grouping the schemes increases their agreement. The outcome from this will be used to develop guidelines for future annotations.
Dependency Parsing for Spoken Dialog Systems

Dependency parsing of conversational input can play an important role in language understanding for dialog systems by identifying the relationships between entities extracted from user utterances.Additionally, effective dependency parsing can elucidate differences in language structure and usage for discourse analysis of human-human versus human-machine dialogs.However, models trained on datasets based on news articles and web data do not perform well on spoken human-machine dialog, and currently available annotation schemes do not adapt well to dialog data.Therefore, we propose the Spoken Conversation Universal Dependencies (SCUD) annotation scheme that extends the Universal Dependencies (UD) (Nivre et al., 2016) guidelines to spoken human-machine dialogs.We also provide ConvBank, a conversation dataset between humans and an opendomain conversational dialog system with SCUD annotation.Finally, to demonstrate the utility of the dataset, we train a dependency parser on the ConvBank dataset.We demonstrate that by pre-training a dependency parser on a set of larger public datasets and finetuning on ConvBank data, we achieved the best result, 85.05% unlabeled and 77.82% labeled attachment accuracy.
Teachers’ Attitudes to Incorporation Digital Means in Teaching Process in Relation to the Subjects they Teach

The paper presents some of the empirical results of the research focused on iden-tification of the ways the primary and secondary school teachers use interactive educational activities and digital means in their teaching practice in dependence on the subjects they teach. The main research question was whether the ways of the use of interactive educational activities and digital means are the same for all teachers, independently on the character of the subject they teach, or whether it depends on the taught subject. As the results have showed, the purposes signifi-cantly differ in dependence on the character of the taught subjects. In the paper, in more detail results for teaching natural science subjects and foreign languages are discussed.
Visual Tracking by Structurally Optimizing Pre-Trained CNN

In this paper, we propose a novel channel pruning method for convolutional neural network (CNN)-based trackers. Pre-trained CNNs are widely used in visual tracking to obtain high-level representations of targets. However, most pre-trained CNNs are trained for other tasks (e.g., VGGNet is trained for image classification), and they require a considerable amount of time to generate features. First, we introduce a dimensionality reduction method considering the information amount and tracking errors to obtain good low-dimensionality features from the last convolutional layer for tracking. Then, a backward channel selection method is proposed to select representative channels layer by layer. In this process, we aim to minimize the target changes and maximize the loss of the background or other objects. Finally, we reconstruct the neural network weights to reduce the information loss of the target with one-shot learning. Experimental results on challenging benchmarks show that the proposed channel pruning method can enhance the tracking performance and reduce the computational requirements.
A Blind Medical Image Watermarking for Secure E-Healthcare Application Using Crypto-Watermarking System

Abstract A reliable medical image management must provide proper security for patient information. Protecting the medical information of the patients is a major concern in all hospitals. Digital watermarking is a procedure prevalently used to secure the confidentiality of medical information and maintain them, which upgrades patient health awareness. To protect the medical information, the robust and lossless patient medical information sharing system using crypto-watermarking method is proposed. The proposed system consists of two phases: (i) embedding and (ii) extraction. In this paper, we securely share three types of patient information, medical image, electronic health record (EHR), and face image from one hospital to another hospital. Initially, all the three inputs are encrypted and the information is concordant. In order to enhance the robustness of the crypto-watermarking system, the obtained bit stream is compressed, and the compressed bit streams are embedded into the cover image. The same process is repeated for the extraction process. The experimentation result is carried out using different medical images with EHR, and the effectiveness of the proposed algorithm is analyzed with the help of peak signal to noise ratio.
Subword Language Model for Query Auto-Completion

Current neural query auto-completion (QAC) systems rely on character-level language models, but they slow down when queries are long.We present how to utilize subword language models for the fast and accurate generation of query completion candidates.Representing queries with subwords shorten a decoding length significantly.To deal with issues coming from introducing subword language model, we develop a retrace algorithm and a reranking method by approximate marginalization.As a result, our model achieves up to 2.5 times faster while maintaining a similar quality of generated results compared to the character-level baseline.Also, we propose a new evaluation metric, mean recoverable length (MRL), measuring how many upcoming characters the model could complete correctly.It provides more explicit meaning and eliminates the need for prefix length sampling for existing rank-based metrics.Moreover, we performed a comprehensive analysis with ablation study to figure out the importance of each component 1 .
Widening the Representation Bottleneck in Neural Machine Translation with Lexical Shortcuts

The transformer is a state-of-the-art neural translation model that uses attention to iteratively refine lexical representations with information drawn from the surrounding context.Lexical features are fed into the first layer and propagated through a deep network of hidden layers.We argue that the need to represent and propagate lexical features in each layer limits the model's capacity for learning and representing other information relevant to the task.To alleviate this bottleneck, we introduce gated shortcut connections between the embedding layer and each subsequent layer within the encoder and decoder.This enables the model to access relevant lexical content dynamically, without expending limited resources on storing it within intermediate states.We show that the proposed modification yields consistent improvements over a baseline transformer on standard WMT translation tasks in 5 translation directions (0.9 BLEU on average) and reduces the amount of lexical information passed along the hidden layers.We furthermore evaluate different ways to integrate lexical connections into the transformer architecture and present ablation experiments exploring the effect of proposed shortcuts on model behavior. 1
Revisiting the Evaluation of Theory of Mind through Question Answering

Theory of mind, i.e., the ability to reason about intents and beliefs of agents is an important task in artificial intelligence and central to resolving ambiguous references in natural language dialogue.In this work, we revisit the evaluation of theory of mind through question answering.We show that current evaluation methods are flawed and that existing benchmark tasks can be solved without theory of mind due to dataset biases.Based on prior work, we propose an improved evaluation protocol and dataset in which we explicitly control for data regularities via a careful examination of the answer space.We show that stateof-the-art methods which are successful on existing benchmarks fail to solve theory-of-mind tasks in our proposed approach.