Reviewerly/exHarmony huggingface dataset:
High‐Fidelity Universal Quantum Controlled Gates on Electron‐Spin Qubits in Quantum Dots Inside Single‐Sided Optical Microcavities

Abstract Semiconductor quantum‐dot (QD) spins hold great promise in quantum information science and technology. The implementation of high‐fidelity quantum gates on QD‐spin qubits is of great importance. Here, two schemes are presented to implement two universal quantum controlled gates, that is, the two‐qubit controlled‐not gate and the three‐qubit Toffoli gate, on stationary electron‐spin qubits in QDs inside single‐sided optical microcavities, based on the cavity‐assisted photon scattering under the balance condition. Compared with the previous schemes based on the ideal giant circular birefringence, the present schemes use the balance condition of the single‐sided QD‐cavity system, which can be achieved in both the weak‐ and strong‐coupling regimes of cavity quantum electrodynamics. Under the balance condition, the noise caused by the unbalanced reflectance between the coupled and uncoupled QD‐cavity systems can be efficiently depressed, so that the fidelity of each quantum gate operation can be increased to unity in principle. The schemes exhibit the possibility of realizing high‐fidelity and scalable quantum computing with single QD spins and single photons using the feasible and robust light–matter quantum interface. These high‐fidelity quantum controlled gates can lead to more efficient construction of quantum circuits for quantum computing, and provide a convenient avenue for quantum networking.
Detecting Incongruity between News Headline and Body Text via a Deep Hierarchical Encoder

Some news headlines mislead readers with overrated or false information, and identifying them in advance will better assist readers in choosing proper news stories to consume. This research introduces million-scale pairs of news headline and body text dataset with incongruity label, which can uniquely be utilized for detecting news stories with misleading headlines. On this dataset, we develop two neural networks with hierarchical architectures that model a complex textual representation of news articles and measure the incongruity between the headline and the body text. We also present a data augmentation method that dramatically reduces the text input size a model handles by independently investigating each paragraph of news stories, which further boosts the performance. Our experiments and qualitative evaluations demonstrate that the proposed methods outperform existing approaches and efficiently detect news stories with misleading headlines in the real world.
Numerical method for computing equilibria in economic system models with labor force

There are number of researches on mathematic and economic employments as well as on the management methods of economic systems and processes. This article is devoted to the management methods of the economic system known as the labour market, the labour market attributes that should be followed for some labour resource management are defined.
YODA: Enabling computationally intensive contracts on blockchains with Byzantine and Selfish nodes

One major shortcoming of permissionless blockchains such as Bitcoin and Ethereum is that they are unsuitable for running Computationally Intensive smart Contracts (CICs).This prevents such blockchains from running Machine Learning algorithms, Zero-Knowledge proofs, etc. which may need nontrivial computation.In this paper, we present YODA, which is to the best of our knowledge the first solution for efficient computation of CICs in permissionless blockchains with guarantees for a threat model with both Byzantine and selfish nodes.YODA selects one or more execution sets (ES) via Sortition to execute a particular CIC off-chain.One key innovation is the MultI-Round Adaptive Consensus using Likelihood Estimation (MIRACLE) algorithm based on sequential hypothesis testing.MIRACLE allows the execution sets to be small thus making YODA efficient while ensuring correct CIC execution with high probability.It adapts the number of ES sets automatically depending on the concentration of Byzantine nodes in the system and is optimal in terms of the expected number of ES sets used in certain scenarios.Through a suite of economic incentives and technical mechanisms such as the novel Randomness Inserted Contract Execution (RICE) algorithm, we force selfish nodes to behave honestly.We also prove that the honest behavior of selfish nodes is an approximate Nash Equilibrium.We present the system design and details of YODA and prove the security properties of MIRACLE and RICE.Our prototype implementation built on top of Ethereum demonstrates the ability of YODA to run CICs with orders of magnitude higher gas per unit time as well as total gas requirements than Ethereum currently supports.It also demonstrates the low overheads of RICE.
Enhancement Clustering Evaluation Result of Davies-Bouldin Index with Determining Initial Centroid of K-Means Algorithm

Abstract K-Means is one of the most popular clustering algorithms because it is easy and simple when implemented. However, clustering results from K-Means are very sensitive to the selection of initial centroid. Better clustering results are often obtained after several experiments. In this study, Sum of Squared Error (SSE) was used as an approach to determine initial centroid of K-Means algorithm. If the SSE value is smaller then the data in one cluster will be more homogeneous and certainly give a good cluster result. In this study, Sum of Squared Error (SSE) was used as an approach to determine initial centroid of K-Means algorithm. Testing was performed on 3 datasets and the number of clusters 2, 3 and 4. From the test, the average value of Davies-Bouldin Index (DBI) for 3 datasets was 0.2427, while the Simple determine initial centroid of K-Means algorithm obtained an average DBI value of 0.2805. These results prove that clustering with method of determining initial centroid of K-Means algorithm based on Sum of Squared Error minimum able to improve clustering result and enhance DBI value obtained by simple determine initial centroid of K-Means algorithm.
Dynamic Past and Future for Neural Machine Translation

Previous studies have shown that neural machine translation (NMT) models can benefit from explicitly modeling translated (PAST) and untranslated (FUTURE) source contents as recurrent states (Zheng et al., 2018).However, this less interpretable recurrent process hinders its power to model the dynamic updating of PAST and FUTURE contents during decoding.In this paper, we propose to model the dynamic principles by explicitly separating source words into groups of translated and untranslated contents through parts-to-wholes assignment.The assignment is learned through a novel variant of routing-by-agreement mechanism (Sabour et al., 2017), namely Guided Dynamic Routing, where the translating status at each decoding step guides the routing process to assign each source word to its associated group (i.e., translated or untranslated content) represented by a capsule, enabling translation to be made from holistic context.Experiments show that our approach achieves substantial improvements over both RNMT and Transformer by producing more adequate translations.Extensive analysis demonstrates that our method is highly interpretable, which is able to recognize the translated and untranslated contents as expected. 1
A Hybrid Algorithm for Multi-Objective Scientific Workflow Scheduling in IaaS Cloud

With the increase in deployment of scientific workflow applications on an IaaS cloud computing environment, the distribution of workflow tasks to particular cloud instances to decrease runtime and cost has emerged as an important challenge. The cloud workflow scheduling is a well-known NP-hard problem. In this paper, we propose a new approach for multi-objective workflow scheduling in IaaS clouds offering a limited amount of instances and a flexible combination of instance types, and present a hybrid algorithm combining genetic algorithm, artificial bee colony optimization and decoding heuristic for scheduling workflow tasks over the available cloud resources while trying to optimize the workflow makespan and cost simultaneously. The proposed algorithm is evaluated for real-world scientific applications by a simulation process. The simulation results show that our proposed scheduling algorithm performs better than the current state-of-the-art algorithms. We validate the results by the Wilcoxon signed-rank test.
A user requirement oriented web service discovery approach based on logic and threshold petri net

In recent years, the number of Web services has increased significantly. Web service discovery has drawn much attention with the development of Web service applications and big data analysis. Under this circumstance, traditional Web service discovery strategies cannot adequately meet high user requirements due to the efficiency and precision of service discovery is low. In order to improve the accuracy and efficiency of service discovery, a user requirement oriented Web service discovery approach based on Petri nets is proposed in this study. A data preprocessing strategy of Web service is first designed. Then, a service clustering method is proposed based on Petri nets, which can conduct service cluster head generation, service cluster composition, and service discovery. The proposed method utilizes a superior data preprocessing method. Using simulation experiments, the efficiency and precision of Web service discovery are illustrated. Finally, the application value of the approach on real Web service is discussed.
Multi-Direction Dictionary Learning Based Depth Map Super-Resolution With Autoregressive Modeling

3D depth cameras have become more and more popular in recent years. However, depth maps captured by these cameras can hardly be used in 3D reconstruction directly because they often suffer from low resolution and blurring depth discontinuities. Super resolution of depth maps is necessary. In depth maps, the edge areas play more important role and demonstrate distinct geometry directions compared with natural images. However, most existing super-resolution methods ignore this fact, and they can not handle depth edges properly. Motivated by this, we propose a compound method that combines multi-direction dictionary sparse representation and autoregressive (AR) models, so that the depth edges are presented precisely at different levels. In the patch level, the depth edge patches with geometry directions are well represented by the pre-trained multi-directional dictionaries. Compared with a universal dictionary, multiple dictionaries trained from different directional patches can represent the directional depth patch much better. In the finer pixel level, we utilize an adaptive AR model to represent the local correlation patterns in small areas. Extensive experimental results on both synthetic and real datasets demonstrate that, the proposed model outperforms state-of-the-art depth map super-resolution methods in terms of both quantitative metrics and subjective visual quality.
A context-aware embeddings supported method to extract a fuzzy sentiment polarity dictionary

The latest development in cognitive technologies are helping us understand emotions and sentiments with unprecedented precision. Polarity detection is the key enabler to sentiment analysis and typically relies on experimental dictionaries, where terms are assigned polarity scores, yet lacking contextual information and based on human inputs and conventions. In this article, we present a novel approach to automatically extract a polarity dictionary from a particular domain, the stock market, without human intervention and addressing the scaling and thresholding problem. Our approach tracks the price changes of particular stocks over time, using it as a guiding polarity value. The magnitude of the price variation for a particular stock is then attributed to the financial news about this stock in corresponding period of time and that is what we use as our working corpus. On top of that, we derive the so-called binned corpus and apply the well-known TF–IDF information retrieval techniques to compute the TF–IDF value for each term. These values are then disseminated within the neighbourhood of each term based on the embeddings-enabled cosine distance. After introducing the problem and providing the background information, we thoroughly describe our method and all the components required to implement the system. Last but not least, we assign the terms to fuzzy linguistic labels and provide a volatility metric indicating how reliable our scores are depending on their distribution of occurrences in the corpus. To show how our approach works, we implement it for the Euro Stoxx 50 from January 2018 to March 2019 and discuss the results compared with typical approaches, pointing out potential improvements for further research work.
An Efficient Cryptosystem for Video Surveillance in the Internet of Things Environment

Surveillance systems paradigm envisions the pervasive interconnection and cooperation of interactive devices over the Internet infrastructure. Nevertheless, dissemination and processing of surveillance video amid the Internet of Things (IoT) applications become a susceptible issue due to the large volume and the significant information of these data. Moreover, surveillance devices on IoT have very limited resources such as memory and storage. The actual security methods are not quite appropriate for surveillance IoT systems. Thus, a particular cryptosystem technique is required for surveillance data security. In this paper, we propose an efficient cryptosystem to secure IoT-based surveillance systems. The proposed cryptosystem framework contains three parts. First, a lightweight automatic summarization technique based on a fast histogram-clustering approach is used to extract the keyframes from the surveillance video. Then, we employ a discrete cosine transform (DCT) technique to compress the extracted data size. Finally, the proposed framework performs an efficient image encryption algorithm by employing a discrete fractional random transform (DFRT). The testing results and analysis confirm the features of the proposed cryptosystem on surveillance systems. The proposed framework is fast and ensures secure and efficient real-time processing by minimizing the transmission cost and storage.
Current research opportunities of image processing and computer vision

Image processing and computer vision is an important and essential area in today’s scenario. Several problems can be solved through computer vision techniques. There are a large number of challenges and opportunities which require skills in the field of computer vision to address them. Computer vision applications cover each band of the electromagnetic spectrum and there are numerous applications in every band. This article is targeted to the research students, scholars and researchers who are interested to solve the problems in the field of image processing and computer vision. It addresses the opportunities and current trends of computer vision applications in all emerging domains. The research needs are identified through available literature survey and classified in the corresponding domains. The possible exemplary images are collected from the different repositories available for research and shown in this paper. The opportunities mentioned in this paper are explained through the images so that a naive researcher can understand it well before proceeding to solve the corresponding problems. The databases mentioned in this article could be useful for researchers who are interested in further solving the problem. The motivation of the article is to expose the current opportunities in the field of image processing and computer vision along with corresponding repositories. Interested researchers who are working in the field can choose a problem through this article and can get the experimental images through the cited references for working further.
A Tweakable Image Encryption Algorithm Using an Improved Logistic Chaotic Map

This paper aims to improves the chaotic behavior of classical logistic chaotic map for image encryption.First, this classical technique was enhanced, and the effectiveness of the improved technique was verified by the bifurcation diagram and Lyapunov exponent, in comparison to the classical technique.On this basis, an efficient tweakable image encryption algorithm was proposed to protect the security of digital image transmission.The proposed algorithm adopts a confusion-diffusion architecture.With the aid of the tweak, each original image is encrypted as multiple different images using the same secret key-stream.The experimental results prove that the proposed algorithm, despite its simplicity, can withstand several types of attacks through image encryption.The research results shed new light on the data security in the transmission of digital images.
Gender Classification in Human Face Images for Smart Phone Applications Based on Local Texture Information and Evaluated Kullback-Leibler Divergence

One of the main steps in human identification is gender classification which can increase the identification accuracy.In many smart phone applications, human identification plays an important role in different reasons such as login permission, sign up certificates, etc.So, accurate gender classification algorithms may increase the accuracy of smart phone applications and reduce its complexity.Also, one of the benefits of gender classification algorithms is for parents to monitor the social network contacts of their child in terms of gender.Different methods have been proposed to do it accurately so far.In all methods, classification accuracy is the main challenge for researchers.But, in smart phone applications, some challenges such as rotation, gray scale variations may reduce the accuracy.In this respect, a rotation invariant approach is proposed in this paper to classify genders in human face images based on improved version of local binary patters (ILBP).Local binary pattern (LBP) is a texture descriptor, which extract local contrast and spatial structure information.Some issues such as noise sensitivity, rotation sensitivity and low discriminative features can be considered as disadvantages of the basic LBP.ILBP solves the above disadvantages using a new theory for binary patterns categorization.The proposed approach includes two stages.First of all, a feature vector is extracted for human face images based on ILBP.Next, Kullback-Leibler divergence classifier is used to classify gender.In this paper Kullback Leibler classifier is evaluated based on log likelihood ratio as distance measure.In the result part, two databases, self-collected and ICPR are used as human face database.Results are compared by different well known methods in this literature that shows the high quality of the proposed approach in terms of accuracy rate.Other main advantages of our approach are rotation invariant, low noise sensitivity, size invariant and low computational complexity.The proposed approach decreases the computational complexity of smartphone applications because of reducing the number of database comparisons.It can also improve performance of the synchronous applications in the smartphones because of memory and CPU usage reduction.
Class-Specific Deep Feature Weighting for Naïve Bayes Text Classifiers

Feature weighting is used to alleviate the conditional independence assumption of Naïive Bayes text classifiers and consequently improve their generalization performance. Most traditional feature weighting algorithms use general feature weighting, which assigns the same weight to each feature for all classes. We focus on class-specific feature weighting approaches, which discriminatively assign each feature a specific weight for each class. This paper uses a statistical feature weighting technique and proposes a new class-specific deep feature weighting method for Multinomial Naïve Bayes text classifiers. In this deep feature weighting method, feature weights are not only incorporated into the classification formulas but they are also incorporated into the conditional probability estimates of Multinomial Naïve Bayes text classifiers. Experimental results for a large number of text classification datasets validate the effectiveness and efficiency of our method.
Color Space Transformation and Multi-Class Weighted Loss for Adhesive White Blood Cell Segmentation

White blood cells (WBCs) are the cells of immune system, protecting against infective diseases and invasion of viruses and bacteria. Their aberrant number, both abnormal increase and decrease, is a sign of an ongoing pathology, a precise evaluation of their number is of the utmost importance as the first step of assessing a potential disease. In blood cell microscopic images, since red blood cells and platelets are similar in color with WBCs, and WBCs are partially adhesive, WBC segmentation for counting is often not resulting in a good performance. Therefore, in this work, a color space transformation is proposed to filter out red blood cells and platelets, which is transforming the blood cell microscopic images of patients with acute lymphoblastic leukemia from RGB color space to HSV to detect and extract WBCs. For precisely segmenting adhesive WBCs in extraction results, we set cell border to the third class, in addition to foreground and background. A weighted cross-entropy loss function based on class weight and distance transformation weight enhanced U-Net to learn cell border features. Our results showed that the method proposed in this paper for WBC segmentation using the data set ALL_IDB1 could achieve an accuracy of 97.92%.
Deep detector classifier (DeepDC) for moving objects segmentation and classification in video surveillance

In this study, the authors present a new approach to segment and classify moving objects in video sequences by combining an unsupervised anomaly discovery framework called DeepSphere and generative adversarial networks. The proposed deep detector classifier employs and validates DeepSphere, which aims mainly to identify the anomalous cases in the spatial and temporal context in order to perform foreground objects segmentation. For post-processing, some morphological operations are considered to better segment and extract the desired objects. Finally, they take advantage of the power of generative models, which recognise the problem of semi-supervised learning as a specific missing data imputation task in order to classify the segmented objects. They evaluate the method with multiple datasets and the results confirm the effectiveness of the proposed approach, which achieves superior performance over the state-of-the-art methods having the capabilities of segmenting and classifying moving objects from videos surveillance.
Editorial – Towards FAIR and SQUARE hydrological data

Data issues were, are, and will remain a core component of the hydrological sciences. Their character and influence on the way the discipline is practiced may vary through time, but their intrinsic...
Nucleus segmentation of cervical cytology images based on multi-scale fuzzy clustering algorithm

In the screening of cervical cancer cells, accurate identification and segmentation of nucleus in cell images is a key part in the early diagnosis of cervical cancer. Overlapping, uneven staining, poor contrast, and other reasons present challenges to cervical nucleus segmentation. We propose a segmentation method for cervical nuclei based on a multi-scale fuzzy clustering algorithm, which segments cervical cell clump images at different scales. We adopt a novel interesting degree based on area prior to measure the interesting degree of the node. The application of these two methods not only solves the problem of selecting the categories number of the clustering algorithm but also greatly improves the nucleus recognition performance. The method is evaluated by the IBSI2014 and IBSI2015 public datasets. Experiments show that the proposed algorithm has greater advantages than the state-of-the-art cervical nucleus segmentation algorithms and accomplishes high accuracy nucleus segmentation results.