Reviewerly/exHarmony huggingface dataset:
Adiabatic speedup in cutting a spin chain via zero-area pulse control

The adiabatic quantum information processing task requires that the evolution of a system must be kept in its instantaneous eigenstate. However, normally the adiabaticity will be ruined due to the interaction between the system and the noisy environment in its long evolution time. Here, in this paper, we show that zero-area pulse control can be used to realize the adiabatic process in a nonadiabatic regime. A concrete example is provided where one spin chain is cut into two chains. The pulse function is applied in the laboratory frame and suitable pulse conditions are obtained numerically. We find that compared with the pulse conditions obtained in the adiabatic frame, the results are similar for low-energy-level systems but tend to deviate when the system's energy level increases. We then obtain the pulse conditions theoretically by writing the control Hamiltonian in the adiabatic frame. It is found that a sequence of pulses with intensities tuned by a time-dependent energy difference is required to guarantee an effective adiabatic speedup.
Study of Depression Detection using Deep Learning

Deep learning is becoming an important method of machine learning and has proven to provide its strength in data sciences and artificial intelligence. It works with unstructured and unlabeled data. Deep learning is more accurate than conventional classifier-based machine learning. The reason for high accuracy is managing by a count of hidden layers, application of activation functions, weighted activation functions, etc. In this work, a new weighted activation function is being proposed to get high accuracy for deep learning. The weighted activation function is going to be applied and tested on a depression dataset to evaluate the intensity of depression.
Analysis Of Genetic Algorithm, Particle Swarm Optimization and Simulated Annealing On Benchmark Functions

Optimization is a crucial part of every field that requires computation and plays an important role in modern optimization. Metaheuristics algorithms are stochastic algorithms designed in such a way that they don't require the calculation of gradient for optimization. Hence they can be used for optimizing non-linear functions too. Since most real-world optimization problems are non-linear in nature, algorithms provide a sufficiently good solution (close to actual solution) for such optimization problems. This paper is aimed to explain the Genetic Algorithm(GA), Simulated Annealing(SA), and Particle Swarm Optimization(PSO) and compare their performance on benchmark functions. Benchmark functions provides a baseline measurement of the performance of algorithms and allows the algorithm to be compared to some other algorithms in the same manner. The algorithm runs on these functions with an aim to locate an optimal position. The performance and the results obtained can be affected by the parameter setting or operations inside each method. Based on the results, PSO performed better compared to GA and SA in obtaining best minimum fitness.
Straightforward Color Image Compression Using True-Mean Multi-Level Block Truncation Coding

A new color image compression based on block truncation coding is proposed. After integer color transformation, the new method for encoding all 4 RVs in the 4-level BTC effectively is applied to Y component. The chromatic components are down-sampled and encoded with 2-level BTC. By simple level adjustment, the encoder and decoder know the number of levels in each block. A variable-length bitmap adjusts to the current level number and raises the compression ratio. With true means as RVs without approximation, the new BTC coder can reach 1~2 dB higher PSNR than some BTC-based coders in the literature.
Modeling learning in knowledge space theory through bivariate Markov processes

Bivariate Markov processes (BMPs) described by Ephraim and Mark (2012) consist of a pair of stochastic processes in the continuous time, one observable and the other latent, that are jointly Markov. In the present article, the navigation behavior and the learning process of a user of a web-based tutoring system are jointly modeled as BMPs constrained by assumptions that are coherent with the concepts of competence-based knowledge space theory. Such constraints are expressed as formal assumptions about the web-based system and about the nature of the learning process. Scenarios are considered where the observed process is the navigation of an individual through the pages of an intelligent tutoring system, whereas the latent learning process consists of transitions among states in a competence structure. The approach seems to be rather general and flexible in modeling learning scenarios with different assumptions. As an example, BMP models are developed for some exemplary scenarios. Maximum likelihood parameter estimation via expectation–maximization algorithm is presented. The results of a simulation study showed that the parameter values are well-recovered by the estimation algorithm. The results of the application of a bivariate Markov model to the real data of students navigating the intelligent tutoring system Stat-Knowlab showed that the proposed approach provides useful insight into students’ learning processes.
A Survey of Continuous Collision Detection

Continuous collision detection (CCD) is a key technology in the field of virtual surgery, cloth simulation and robot motion planning. It can accurately detect the first time of contact between objects and returns collision information such as penetration depth, friction and repulsive force, etc., have a wide range of application and important research value. By analyzing the processing framework of continuous collision detection algorithm in detail, the current research status of continuous collision detection is systematically reviewed from perspectives of two phases respectively. In broad-phase, the recent achievements of space decomposition and sweep and prune are introduced. In narrow-phase, the research status of intelligent optimization based algorithm and image-space based algorithm is illustrated. Besides, the development of bounding volume hierarchy (BVH) is analyzed and discussed. After that, the performance and innovative achievements of self-collision detection in deformable objects are summarized and analyzed. Finally, the challenges and future trends of algorithm research are pointed out.
A Projection-Based Human Motion Recognition Algorithm Based on Depth Sensors

Exercise monitoring systems for rehabilitation are usually not able to pinpoint the exact part for patients' exercise. The research objective is to develop the projection-based motion recognition (PMR) algorithm based on depth data and wide-accepted methods to solve this matter. We regard a motion trajectory as a combination of basic posture units, and then project the basic posture units onto a 2-D space via a projection mapping. Each motion trajectory is transformed to a 2-D motion trajectory map by sequentially connecting the basic posture units involved in the motion trajectory. Finally, we employ a convolutional neural network (CNN)-based classifier to classify the trajectory maps. Accurate classification rate reaches as high as 95.21%. The originality of PMR algorithm lies in (1) it has the generalization capability to some extent since it only adopts popular methods and contains an essential and comprehensive mechanism; (2) the resultant trajectory map may reveal the information about how well a patient execute the rehabilitation assignments.
FPGA Implementation and Acceleration of Convolutional Neural Networks

In many areas like pattern recognition, object detection, and image classification, Convolutional Neural Networks(CNN) are becoming prevalent. FPGA implementation of a trained CNN model and accelerating the CNN model by parallelizing the operations within layers is proposed in this paper. The evaluation of the proposed model is done using the MNIST digit dataset and is later observed for other datasets as well. The evaluation is done based on the parameters such as accuracy, resource utilization, and speed up a achieved upon parallelization. It is observed that the accuracy obtained on FPGA is the same as the accuracy obtained on the computer and the model achieved maximum speed up of 2.586 on FPGA after parallelization.
Adaptive colour restoration and detail retention for image enhancement

Computer vision-based crowd understanding and analysis technology has been widely used in public safety due to the rapid growth of population and the frequent occurrence of various accidents. Improving imaging quality is the key to improve the performance of crowd analysis, density estimation, target recognition, segmentation, and detection in computer vision tasks. Due to the complex imaging environment such as fog and low illumination, some images taken in outdoor environment often have the problems of colour distortion, lack of details, and the poor imaging quality, which affect the subsequent visual tasks. To improve the imaging quality and visual effect, an adaptive colour restoration and detail retention-based method is proposed for image enhancement. First, to overcome the problem of colour distortion caused by low illumination and fog, a multi-channel fusion based adaptive image colour restoration method is proposed. To make the enhancement result more consistent with human observation, the detail retention-based method is applied to enhance the details. Experimental results demonstrate that the authors' results are effective and outperform the compared methods both in visual and objective evaluations.
Improved encryption scheme based on the automorphism group of the Ree function field

The article describes an improved implementation of the encryption scheme based on the automorphism group of Ree function field. Our proposal is to use the Ree function field automorphism group to encrypt the whole group with key bindings. We extended the logarithmic signature to the entire group and got changed the encryption algorithm to protect against a sequential key recovery attack.
Predictive lithological mapping based on geostatistical joint modeling of lithology and geochemical element concentrations

The spatial analysis and interpretation of lithological and geochemical sampling information are central in mineral prospecting and initial geological-mining exploration to delineate exploration targets and locate economic mineralization. This work compares two geostatistical approaches for the spatial prediction of lithological classes through a case study in mineral prospection, considering lithological and geochemical information at a set of surface samples. Both approaches calculate the probabilities of occurrence of the lithological classes at unsampled locations and select the most probable class as the predicted lithology. A split-sample technique is used to assess their performance, with the predictions being made at a testing data subset on the basis of the information of a training subset. The first approach relies on a cokriging of the lithological class indicators and yields an accuracy score (percentage of matches between true and predicted lithological classes) of 90.5%, while the second approach, consisting of a plurigaussian modeling of the classes, increases this score to 92.6%. Unlike the former approach, it also provides consistent outcomes of both the lithological classes and the geochemical covariates, which is valuable for mineral prospectivity mapping.
Fixed simplex coordinates for angular margin loss in CapsNet

A more stationary and discriminative embedding is necessary for robust classification of images. We focus our attention on the newel CapsNet model and we propose the angular margin loss function in composition with margin loss. We define a fixed classifier implemented with fixed weights vectors obtained by the vertex coordinates of a simplex polytope. The advantage of using simplex polytope is that we obtain the maximal symmetry for stationary features angularly centred. Each weight vector is to be considered as the centroid of a class in the dataset. The embedding of an image is obtained through the capsule network encoding phase, that is identified as digitcaps matrix. Based on the centroids from the simplex coordinates and the embedding from the model, we compute the angular distance between the image embedding and the centroid of the correspondent class of the image. We take this angular distance as angular margin loss. We keep the computation proposed for margin loss in the original architecture of CapsNet. We train the model to minimise the angular between the embedding and the centroid of the class and maximise the magnitude of the embedding for the predicted class. The experiments on different datasets demonstrate that the angular margin loss improves the capability of capsule networks with complex datasets.
Enhancing Model Robustness by Incorporating Adversarial Knowledge into Semantic Representation

Despite that deep neural networks (DNNs) have achieved enormous success in many domains like natural language processing (NLP), they have also been proven to be vulnerable to maliciously generated adversarial examples. Such inherent vulnerability has threatened various real-world deployed DNNs-based applications. To strength the model robustness, several countermeasures have been proposed in the English NLP domain and obtained satisfactory performance. However, due to the unique language properties of Chinese, it is not trivial to extend existing defenses to the Chinese domain. Therefore, we propose AdvGraph, a novel defense which enhances the robustness of Chinese-based NLP models by incorporating adversarial knowledge into the semantic representation of the input. Extensive experiments on two real-world tasks show that AdvGraph exhibits better performance compared with previous work: (i) effective – it significantly strengthens the model robustness even under the adaptive attacks setting without negative impact on model performance over legitimate input; (ii) generic – its key component, i.e., the representation of connotative adversarial knowledge is task-agnostic, which can be reused in any Chinese-based NLP models without retraining; and (iii) efficient – it is a light-weight defense with sub-linear computational complexity, which can guarantee the efficiency required in practical scenarios.
Improving Dialogue Response Generation Via Knowledge Graph Filter

Current generative dialogue systems tend to produce generic dialog responses, which lack useful information and semantic coherence. An promising method to alleviate this problem is to integrate knowledge triples from knowledge base. However, current approaches mainly augment Seq2Seq framework with knowledge-aware mechanism to retrieve a large number of knowledge triples without considering specific dialogue context, which probably results in knowledge redundancy and incomplete knowledge comprehension. In this paper, we propose to leverage the contextual word representation of dialog post to filter out irrelevant knowledge with an attention-based triple filter network. We introduce a novel knowledge-enriched framework to integrate the filtered knowledge into the dialogue representation. Entity copy is further proposed to facilitate the integration of the knowledge during generation. Experiments on dialogue generation tasks have shown the proposed framework’s promising potential.
What's my App?

With the spread of broadband Internet, Real-Time Communication (RTC) platforms have become increasingly popular and have transformed the way people communicate. Thus, it is fundamental that the network adopts traffic management policies that ensure appropriate Quality of Experience to users of RTC applications. A key step for this is the identification of the applications behind RTC traffic, which in turn allows to allocate adequate resources and make decisions based on the specific application's requirements. In this paper, we introduce a machine learning-based system for identifying the traffic of RTC applications. It builds on the domains contacted before starting a call and leverages techniques from Natural Language Processing (NLP) to build meaningful features. Our system works in real-time and is robust to the peculiarities of the RTP implementations of different applications, since it uses only control traffic. Experimental results show that our approach classifies 5 well-known meeting applications with an F1 score of 0.89.
The Semantics of Comparatives: A Difference-Based Approach

Abstract Degree semantics has been developed to study how the meanings of measurement and comparison are encoded in natural language. Within degree semantics, this paper proposes a difference-based (or subtraction-based) approach to analyze the semantics of comparatives. The motivation is the measurability and comparability of differences involved in comparatives. The main claim is that comparatives encode a subtraction equation among three scalar values: two measurements along an interval scale and the difference between them. We contribute two innovations: (i) using interval arithmetic to implement subtraction, and (ii) analyzing comparative morpheme -er/more as an additive particle, denoting the default, most general, positive difference. Our analysis inherits existing insights in the literature. Moreover, the innovations bring new conceptual and empirical advantages. In particular, we address the interpretation of comparatives containing than-clause-internal quantifiers and various kinds of numerical differentials. We also account for three puzzles with regard to the scope island issue, the monotonicity of than-clauses, and the discourse status of the standard in comparison.
Technology of Image Steganography and Steganalysis Based on Adversarial Training

Steganography has made great progress over the past few years due to the advancement of deep convolutional neural networks (DCNN), which has caused severe problems in the network security field. Ensuring the accuracy of steganalysis is becoming increasingly difficult. In this paper, we designed a two-channel generative adversarial network (TGAN), inspired by the idea of adversarial training that is based on our previous work. The TGAN consisted of three parts: The first hiding network had two input channels and one output channel. For the second extraction network, the input was a hidden image embedded with the secret image. The third detecting network had two input channels and one output channel. Experimental results on two independent image data sets showed that the proposed TGAN performed well and had better detecting capability compared to other algorithms, thus having important theoretical significance and engineering value.
Preventing Failures by Dataset Shift Detection in Safety-Critical Graph Applications

Dataset shift refers to the problem where the input data distribution may change over time (e.g., between training and test stages). Since this can be a critical bottleneck in several safety-critical applications such as healthcare, drug-discovery, etc., dataset shift detection has become an important research issue in machine learning. Though several existing efforts have focused on image/video data, applications with graph-structured data have not received sufficient attention. Therefore, in this paper, we investigate the problem of detecting shifts in graph structured data through the lens of statistical hypothesis testing. Specifically, we propose a practical two-sample test based approach for shift detection in large-scale graph structured data. Our approach is very flexible in that it is suitable for both undirected and directed graphs, and eliminates the need for equal sample sizes. Using empirical studies, we demonstrate the effectiveness of the proposed test in detecting dataset shifts. We also corroborate these findings using real-world datasets, characterized by directed graphs and a large number of nodes.
Review of Text analysis Based on Deep Learning

Text analysis using data mining technology can acquire and discover implicit knowledge, which is a process from text information description and feature extraction to knowledge formation. The spatial representation of text data and semantic information can be simplified and identified using end-to-end deep learning algorithms. This paper reviews the text analysis based on deep learning. Firstly, this article analyzes the process of the text learning, then text analysis learning models are summarized, including convolutional neural networks, recurrent neural networks, and deep learning algorithm fusion and so on. Furthermore, the applications of text analysis based on deep learning are introduced.
Enabling Simultaneous Content Regulation and Privacy Protection for Cloud Storage Image

The population of cloud computing greatly facilitates the sharing of explosively generated image today. While benefiting from the convenient of cloud, the privacy protection mechanism that commonly applied in cloud service makes the spreading of illegal and harmful data very hard to be detected or controlled. Such a realistic threat should be seriously treated, yet is largely overlooked in the literature. To address this issue, we propose the first cloud service framework that can simultaneously provide privacy protection and content regulation for the cloud storage image. In specific, we design a secure multi-party computation (MPC) protocol to protect the data privacy via random projection. By leveraging the distance preserving properties residing in random projection, we propose a privacy-preserving principal component analysis (PCA)-based recognition approach over the random projection domain to achieve content matching while respecting the data privacy. To facilitate the efficiency, we implement our system under the compressive sensing (CS) framework. Due to the compression effect of CS, the proposed cloud service can achieve remarkable reduction on the computation and communication complexity of the content matching process. Theoretical analysis and experimental results both show that our system can achieve privacy assurance and acceptable recognition performance, while with high efficiency.
Classifying Breast Histopathology Images with a Ductal Instance-Oriented Pipeline

In this study, we propose the Ductal Instance-Oriented Pipeline (DIOP) that contains a duct-level instance segmentation model, a tissue-level semantic segmentation model, and three-levels of features for diagnostic classification. Based on recent advancements in instance segmentation and the Mask RCNN model, our duct-level segmenter tries to identify each ductal individual inside a microscopic image; then, it extracts tissue-level information from the identified ductal instances. Leveraging three levels of information obtained from these ductal instances and also the histopathology image, the proposed DIOP outperforms previous approaches (both feature-based and CNN-based) in all diagnostic tasks; for the four-way classification task, the DIOP achieves comparable performance to general pathologists in this unique dataset. The proposed DIOP only takes a few seconds to run in the inference time, which could be used interactively on most modern computers. More clinical explorations are needed to study the robustness and generalizability of this system in the future.