Reviewerly/exHarmony huggingface dataset:
Low-overhead pieceable fault-tolerant construction of logical controlled-phase circuit for degenerate quantum code

We propose a non-transversal but pieceable fault-tolerant conversion circuit that is used to convert encoded information between five-qubit code and seven-qubit CSS code. Since a syndrome extraction circuit requiring fewer ancillary qubit resources would facilitate the realization of large-scale quantum computations, we further adapt a flag-assisted fault-tolerant syndrome measurement scheme to reduce the cost of ancillary preparation. Numerical simulations are also performed to further analyze the performance of our conversion method.
A Knowledge-Enhanced Neural Sequence Labelling Model for Named Entity Recognition on Noisy User-Generated Contents

In recent years, named entity recognition (NER) on user-generated content (UGC) has attracted extensive attention due to its potential widespread application prospects, such as breaking news detection and products recommendation. In this paper, we present a novel knowledge-enhanced neural sequence labelling model (KNSLM), which incorporates external entity knowledge to augment the understanding of UGC with character, word and external entity knowledge. The proposed KNSLM model addresses both text denormalization and data sparsity issues, which cannot be solved directly by current sequence labelling methods. We empirically show that this KNSLM model relieves such issues, which obtaining excellent results in UGC dataset on User-generated Text (WNUT-2017). The experimental results show this KNSLM model achieves entity and surface F1 score 55.09% and 53.12%, respectively.
Research on the Effectiveness Design of Information System Software Test Case Based on Clustering Algorithms

Abstract With the rapid development of software industry, the scale of software becomes larger and larger, and the complexity of software becomes higher and higher. The test coverage criterion traverses to get the corresponding test path and deletes the redundant path according to the constraints, then analyses and eliminates the contradictions in each test scenario to get the consistent requirement scenario, and finally generates the test case set of interlocking software. Test cases are the basis of test execution. Effective writing of test cases plays an important role in the whole testing process of information system. There are constraints (such as dependencies and mutual exclusions) between input items or parameter values, and the constraints certainly exist. From the initial frequent pattern mining to closed pattern mining, maximum pattern mining, incremental mining, topic interest measurement, privacy protection, data flow and other types of data mining association rules.
Class-Specific Interferometric Phase Estimation Using Patch-Based Importance Sampling

Interferometric phase (InPhase) estimation, that is, the denoising of modulo- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\pi $ </tex-math></inline-formula> phase images from sinusoidal <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2\pi $ </tex-math></inline-formula> -periodic and noisy observations, is a challenging inverse problem with wide applications in many coherent imaging techniques. This paper introduces a novel approach to InPhase restoration based on an external data set and importance sampling. In the proposed method, a class-specific data set of clean patches is clustered using a mixture of circular symmetric Gaussian (csMoG) distributions. For each noisy patch, a “home-cluster”, i.e., the closest cluster in the external data set, is identified. An InPhase estimator, termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Shift-invariant Importance Sampling (SIS)</i> estimator, is developed using the principles of importance sampling. The SIS estimator uses samples from the home-cluster to perform the denoising operation. Both the clustering mechanism and the estimation technique are developed for complex-valued signals by taking into account patch shift invariance, which is an important property for an efficient InPhase denoiser. The effectiveness of the proposed algorithm is shown using experiments conducted on a semi-real InPhase data set constructed using human face images and medical imaging applications involving real magnetic resonance imaging (MRI) data. It is observed that, in most of the experiments, the SIS estimator shows better results compared to the state-of-the-art algorithms, yielding a minimum improvement of 1 dB in peak signal-to-noise ratio (PSNR) for low to high noise levels.
New Branch Optimization Design Based on RefineNet

Usually, in semantic segmentation, researchers only use the final output layer for the network training, which means, we ignore information from other layers. To this situation, we design new branches for output, which can output rough small-scale prediction from the middle of the network. With the new branches, the middle of the network will get better control and guidance in the process of training, and also we can make better use of balanced semantic information and spatial information in the middle of the network. With the addition of new branches, more information can be used in the network training. This makes mIoU accuracy 4% increase on CamVid dataset based on RefineNet.
Parallel Differential Evolution with Variable Population Size for Global Optimization

The results of evolutionary algorithms depends on population diversity that normally decreases by increasing the selection pressure from generation to generation. Usually, this can lead evolution process to get stuck in local optima. The study is focused on mechanisms to avoid this undesired phenomenon by introducing parallel differential evolution that decompose a monolithic population into more variable-sized sub-populations, which evolve independently of each other. The proposed parallel algorithm operates with individuals having some characteristics of agents, e.g., they act autonomously by selecting actions, with which they affect the state of environment. This incorporates two additional mechanisms: aging, and adaptive population growth, which direct the decision-making by individuals. The proposed parallel differential evolution was applied to the CEC’18 benchmark function suite, while the produced results were compared with some traditional stochastic nature-inspired population-based and state-of-the-art algorithms.
Active Learning for Road Lane Landmark Inventory with Random Forest in Highly Uncontrolled LiDAR Intensity Based Image

Road landmark inventory is becoming an important industry for the maintenance of transport infrastructures among others. Several commercial sensors are available witch include LiDAR sensors allowing to capture up to 1.5 million data point per second. We obtain an intensity based image from the LiDAR point cloud intensity. The landmark detection is posed as a two class classification problem that may be solved by some standard approaches, for example, Random Forest (RF). Besides model parameter selection, a central problem is the construction of the labeled dataset due to human labor cost and the highly uncontrolled conditions of the data capture. We propose an open ended Active Learning approach with a human operator in the loop who can start the Active Learning process when detection quality is degraded by the change in data condition in order to achieve adaptation to them. As an additional contribution, we have assessed the ability of Active Learning to overcome the issues raised by highly class imbalanced dataset, reaching a True Pixel Ratio value of 0.98.
Peng-Type ZNN Model Attempted for Online Diagonalization of Time-Varying Symmetric Matrix

Matrix diagonalization (or termed, matrix eigen-decomposition) is a vital part of matrix theory. Different from static matrix diagonalization problem, in this paper, the more challenging problem, i.e., time-varying symmetric matrix diagonalization problem, is mainly researched. For solving this problem, the ZNN (i.e., Zhang neural network) design method is employed, which has been formally proposed for finding online solutions of various time-varying problems. Besides, the try-and-error method is adopted to simplify an intermediate model. Then, an effective model termed Peng-type ZNN model is proposed and discussed. In order to verify the validity of the proposed model, three examples are considered. Simulation results illustrate that the steady-state errors are limited to a relatively small level, and the steady-state errors can be controlled by changing the diagonal matrix in the model. Therefore, the effectiveness of the proposed Peng-type ZNN model is testified.
Solving Combinatorial Problems through Off-Policy Reinforcement Learning Methods

In recent years, reinforcement learning (RL) has emerged as a strong candidate to solve many decision-making problems. It fundamentally combines the benefits of both deep neural networks and control engineering practices to accomplish human-level or even superior competency in decision-concise systems. This work extends this very idea and proves the applicability of RL algorithms to combinatorial puzzles such as river-crossing puzzle and seating arrangement problems. In that regard, this work applied quality-networks (QN), deep-quality-networks (DQN), and double-deep-quality-networks (DDQN) to environments mentioned above, and afterward, evaluated their performance through analyzing reward and performance plots. The study concludes that all these methods effectively completed the puzzle tasks with unnoticeable performance differences for the given environments.
The Analysis of Product Evaluation and Sales Characteristic Model Based on Data Mining

Since a lot of online evaluation information is accumulated with the application and development of e-commerce industry, sentiment analysis technology can be used to mine and utilize it effectively, so as to provide reference for e-commerce industry management. First, a sentiment semantic classification dictionary was constructed based on the frame semantics theory. Then, based on the dictionary and some rules, the sentiment of online reviews was analyzed. Finally, through the test of online review data, a higher accuracy rate and recall rate were obtained, which proved the validity and effectiveness of the research method.
Competency Frameworks as Activators for curriculum development in ICT courses: A comparative study

An ICT skills framework maps all the related skills and the expectations of the industry. Creating improved curricula to address the needs of an ever-changing industry should be informed by the requirements of regularly revised ICT competency frameworks. This study compares multiple international ICT competency frameworks for use as a guide in the development of relevant ICT curricula. The investigation of ICT frameworks should, therefore, be considered for the possibility of helping educational systems to prepare a new and relevant labour pool. The research presents significant differences and similarities of ICT competency frameworks that must be considered during the design of ICT curricula, as these should influence the development of ICT curricula.
A discourse theoretical model for determining the limits of free speech on campus

Recent controversies concerning freedom of expression on university campuses have raised the question of how the limits of free speech can be determined in a justified way in a pluralistic public space such as the campus. The article addresses this question from the viewpoint of two complementary theoretical perspectives: Rainer Forst’s respect conception of toleration, and the discourse theory of democracy developed by Jürgen Habermas and Seyla Benhabib. These theories are argued to provide a non-arbitrary, impartial and procedural model for determining the limits of free speech on campus. Deriving primarily from the discourse model, the article suggests that the limits of freedom of expression on campus should be determined by collective deliberative processes involving the affected students. Moreover, it is argued that, instead of prohibiting controversial topics or views, the university administration and teachers should focus on establishing procedural rules of rational deliberation. This is argued to increase students’ understanding of the nature of legitimate democratic discussion and thus accomplish the university’s educational task of fostering students’ ability to use their freedom of speech in a responsible way.
Research on Integrated Energy Technology of Green Campus Combined with Wind, Solar, Storage, Charging, Industry, Academia and Research

Abstract The research on the integration of wind, solar, storage, charging, industry, academia and research is an important embodiment of promoting the traditional energy system to step forward into the energy Internet system. The green smart campus is also a typical demonstration of the energy Internet. In this paper, an integrated construction scheme of wind, solar, storage, charging, industry, academia and research is put forward in combination with the actual situation. In order to realize multi-energy complementarity in a more scientific way, the operation mode of the micro-grid system is studied and analyzed in depth under grid-connected and off-grid modes, so that the emergency power for critical loads can be guaranteed when power is lost. The scheme provides a set of new and concrete solutions for colleges and universities to realize green and smart campus.
Recognition of circular workpiece in complex environment

With the development of science and technology, Using computer vision technology to detect and identify products has become a common application. As a common geometric figure in industrial production, circle often needs to be detected and recognized. In this paper, in order to solve the problem of the identification of the round workpiece in the complex environment, it is studied. Traditional Hough algorithm transforms image from original image space to parameter space, In the parameter space, we use some parameter form which most boundary points satisfy as the description of the curve in the image. industrial production environment is complex, Even if we try to eliminate noise or improve Hough algorithm, Still can't improve its recognition rate. For this reason, this paper proposes a method combining YOLOv3 deep learning system with Hough algorithm, which greatly improves the recognition rate of circular workpiece in complex industrial environment.
Tumor Growth Modeling: State Estimation with Maximum Likelihood and Particle Filtering

In this study, we combined the Maximum Likelihood Estimator from our previous works with a Sequential Importance Resampling (SIR) particle filter to estimate the states of the stochastic Gompertz tumor growth model. We also implemented a parallel version in CUDA for the SIR filter in order to reduce its execution time. Extensive simulations with synthetic data were run to examine whether the SIR filter can provide more accurate state estimates in respect to the Normalized Mean Squared Deviation criterion compared to those provided by the deterministic Gompertz model. Moreover, we monitored and compared the execution time of the SIR's parallel and sequential implementations for different numbers of particles. The results showed that the SIR filter can estimate the system's states very accurately, even at the early tumor growth stages. Additionally, the parallel implementation that ran on the GPU was way more efficient than the implementation that ran on the CPU. By combining the Maximum Likelihood Estimator (MLE) with an SIR filter, we were able to obtain very accurate estimates of the tumors' volume. Furthermore, the execution time for the SIR filter was significantly decreased by taking advantage of the GPUs ability to perform a very large number of computations in parallel.
Distant Supervision for Keyphrase Extraction using Search Queries

Keyphrase extraction aims at automatically selecting small set of phrases in a document, that best describe its main ideas. There is great need for better methods of keyphrase extraction in the absence of labeled data, as currently unsupervised algorithms fail to achieve adequate performance, compared to their supervised counterparts. In this paper we suggest a widely applicable distant supervision framework based on auxiliary data from query logs. By propagating information from queries and subsequent consumption of content, weak labels are produced, transforming the problem into the easier supervised task. Evaluation on a large dataset shows the superiority of this approach over unsupervised alternatives.
A Covert Information Transmission Scheme Based on High Frequency Acoustic Wave Channel

With the development of Internet and communication technology, the security threats of mobile communication network emerge one after another. Using sound wave and covert channel, it’s possible to find a covert security communication method based on high frequency sound waves at short distance. Initially, we use technologies such as channel coding and decoding, message authentication, signal synchronization, signal modulation and demodulation. Then we designed a multi-thread based sound wave acquisition and playback scheme. We also constructed a one-way P2P high frequency acoustic wave covert channel with good basic performance. On this basis, we introduced the error control mechanism and other related technologies and designed a two-way alternating information sharing scheme. Finally, the information security application of high frequency acoustic wave covert channel is realized on mobile devices.
Fast Video Classification with CNNs in Compressed Domain

In recent years, Convolutional Neural Network (CNN) has proven to be one of the most successful tools for video-based action recognition. As the most popular method for video action recognition, the two-stream CNNs method, utilizing the optical flows, is not available for real-time applications because of the high computation requirement. In this paper, we present that accelerating the CNN architecture by replacing optical flows with the Motion Vector (MVs) can achieve a faster process speed that can be used in real-time applications. The MVs is designed to extracted information directly from the compressed video bitstreams. We explored how the proposed video classification method gives the very impressive result. First, using motion vector via taking raw video bitstream as input to directly predict action classes without explicitly computing optical flow. Secondly, we demonstrate a strong base-line two-stream ConvNet using pre-train models and transfer learning for our both spatial stream and temporal stream. The finding of our approach proves to be significantly faster than the original two-stream approaches, and achieves high accuracy and satisfies real-time requirement.
Integrative activity of a point of sale and the product dissemination with augmented reality

An Integrative Activity uses the skills and knowledge provided in various subjects to solve practical problems, with an individual and/ or group approach. Specifically in this article we work on a Point of Sale system and the Diffusion of Products with Augmented Reality, developed over three semesters and involving courses of Software Engineering, Computer Human Interaction, Design of Virtual Environments and Computing for Mobile Devices. The activity applies knowledge of software development models, usability, 3D modeling, augmented reality and development of web applications for mobile devices. At the end of this activity, the student has managed to develop complete software, from planning until testing phases.
Dynamic Segment Array and Search

Array and data searching technique is very important for data structure or any database system for storing data into memory and retrieval of data from memory. There is a concept, known as an Array which has a power of storing bulk or group of homogeneous data. There should be appropriate algorithm to store bulk of data into Array and to retrieve any data at the time of utilization. A best searching technique can save the time of user and don’t take more extra memory to search a particular data. There are three types of array [1] (a) One dimensional (b) Two dimensional (c) Multi dimensional. In which data is stored sequentially commencing from index = 0 to index = Max. in this Array we can apply two searching technique. We can apply binary search if array is sorted else we utilize linear search to retrieve a particular data from Array. The Time complexity of linear search in unsorted array [2, 3] is O(1) at best cases O(n) at average cases and O(n) at worst cases. The goal of this master thesis is to minimize the time complexity in average case and worst cases. This paper introducing a new concept of array, we can say this “DYNAMIC SEGMENT ARRAY” in this type of array we can divide the array into 11 segments. We can store data in particular segment of Array which do not interfere the other segment of Array. A special searching technique known as “DYNAMIC SEGMENT SEARCH” is applied to retrieve a required data item from a particular segment. The Time complexity of “DYNAMIC SEGMENT SEARCH” in unsorted integer array is T(1) + c at best cases T(n/11) + C at average cases and T(2n/11) + c at worst cases. We achieved the goal by saving the time up to 80% in data comparison.
Migrating legacy Web applications

Since the adoption of portable devices like smartphones or smartwatches, the usage of applications has rapidly increased for supporting diverse daily activities from connecting with friends to measuring running distance. Since the Apps are an ubiquitous tool in everyday life, it is also an opportunity for a new digital business model. However, there are legacy applications implementing the core business process and rules that are not designed to run properly on new mobile devices. The migration of legacy applications by developing a new version from scratch introduces risk and uncertainty. This study proposes an approach to assist the migration of legacy Apps so that available functionality is prioritized, the experience of User Interface is re-thought, and the new design is evaluated with a preliminary running version of the legacy app. The proposed method is based on a Markov chain model. An experiment on ten participants shows that a dynamic user interface that implements the Markov model provides the users with a better usability. This work suggests that the heuristics make possible a smart and proactive interface design.
A Non-intrusive Correction Algorithm for Classification Problems with Corrupted Data

A novel correction algorithm is proposed for multi-class classification problems with corrupted training data. The algorithm is non-intrusive, in the sense that it post-processes a trained classification model by adding a correction procedure to the model prediction. The correction procedure can be coupled with any approximators, such as logistic regression, neural networks of various architectures, etc. When the training dataset is sufficiently large, we theoretically prove (in the limiting case) and numerically show that the corrected models deliver correct classification results as if there is no corruption in the training data. For datasets of finite size, the corrected models produce significantly better recovery results, compared to the models without the correction algorithm. All of the theoretical findings in the paper are verified by our numerical examples.