Reviewerly/exHarmony huggingface dataset:
Design of Timeline-Based Planning Systems for Safe Human-Robot Collaboration

During the last decade, industrial collaborative robots have entered assembly cells supporting human workers in repetitive and physical demanding operations. Such human-robot collaboration (HRC) scenarios entail many open issues. The deployment of highly flexible and adaptive plan-based controllers is capable of preserving productivity while enforcing human safety is then a crucial requirement. The deployment of plan-based solutions entails knowledge engineers and roboticists interactions in order to design well-suited models of robotic cells considering both operational and safety requirements. So, the ability of supporting knowledge engineering for integrating high level and low level control (also from non-specialist users) can facilitate deployment of effective and safe solutions in different industrial settings. In this chapter, we will provide an overview of some recent results concerning the development of a task planning and execution technology and its integration with a state of the art Knowledge Engineering environment to deploy safe and effective solutions in realistic manufacturing HRC scenarios. We will briefly present and discuss a HRC use case to demonstrate the effectiveness of such integration discussing its advantages.
Continuous Testing in the Development of IoT Applications

Internet-of-Things has evolved with the increased volume of data of interconnecting the physical world to the Internet. Cloud provides the underlying power for IoT to become a disrupting technology, revolutionizing the cloud, as we know it. IoT applications need to adapt frequently and rapidly to new requests. Increasing competition and rapidly changing market needs require from nowadays companies' flexibility and fast time to market of their products. To achieve these goals more and more organizations are relying on new IT technologies and software development processes. Though there are many publications around DevOps as a methodology and the use of a process for continuous integration and continuous development, there are fewer solutions provided which incorporate a software delivery pipeline incorporating all processes: continuous business planning, agile development, continuous integration, continuous deployment and continuous testing. The paper proposes a DevOps architecture and agile tools to be used in the implementation of a fully integrated DevOps pipeline based on open-source. Furthermore, continuous testing is covering test best practices and methods, which along with the DevOps pipeline can be widely implemented in any cloud environment, regardless of the cloud provider chosen.
Multi-level Image Thresholding Segmentation Using 2D Histogram Non-local Means and Metaheuristics Algorithms

One of the goals for the multi-level image thresholding segmentation is to divide the image into several homogeneous regions without overlapping. The performance of segmentation approaches when are used 1D histogram-based methods are unsatisfactory as they consider the gray level of an image only and do not deal with spatial correlation among the pixels. The alternative is to use a 2D histogram that permits to handle the situations described above. This chapter explains the use of PSO and SCA metaheuristics algorithms to find the best thresholds for images segmentation, using the two-dimensional (2D) histogram non-local means and Rényi entropy as an objective function. To compare the performance of the results it uses the method 2DNLMeKGSA propose by H. Mittal and M. Saraswat. The methods have tested on five images from the Berkeley Segmentation Dataset and Benchmark (BSDS300) in terms of subjective and objective evaluations.
The Function of the Establishment of Digital Archives in Colleges and Universities to the Optimization of Resources

Abstract Now it has entered the era of big data information, with a large number and various types of information resources. The same situation exists in colleges and universities, and the traditional information resource optimization method is difficult to deal with massive data, and the cost is very high. The optimization of information resources can help schools and other units to develop better. In view of the above problems, this paper proposes to optimize the resources through the establishment of university digital archives. This paper first analyzes the basic connotation of digital archives in the new era, and then puts forward the effective path of the establishment of digital archives in colleges and universities under the big data environment. Finally, the paper analyzes the function of digital archives in resource optimization. The results show that the establishment of digital archives in colleges and universities has been widely praised by teachers and students, and the utilization rate of resources has been significantly improved, with an increase of 38.2%.
An Improved Particle Swarm Optimization Algorithm Based on Entropy and Fitness of Particles

Particle swarm optimization (PSO) is an algorithmic technique for optimization by solving a wide range of optimization problem. This paper presents an improved PSO. The proposed algorithm consists of two parts. Firstly, population initialization method based on entropy is proposed. Secondly, an improved accelerated learning coefficient is proposed. In this algorithm, a modified velocity updating formula of the particle is used, where the randomness in the course of updating particle velocity and the acceleration coefficient is relatively decreased. The entropy of each dimension is calculated to decrease the randomness of swarm and increase population diversity. Each particle has a different learning rate according to its fitness during evolution, which can balance the global and local searching ability of the population and avoid falling into local optimum. Experimental results show that, the proposed algorithm remarkably improves the ability of PSO to jump out of the local optima and significantly enhance the convergence precision.
An Efficient 2-opt Operator for the Robotic Task Sequencing Problem

Nowadays, automated guided vehicles (AGVs) are widely used in many real life applications, such as in modern factories and logistics systems. Generally, an AGV is assigned a number of random tasks, and different execution orders of the tasks correspond to different moving distances. Therefore, it is important to determine the task execution order. This problem is known as the robotic task sequencing problem, which could be transformed to an equivalent asymmetric travelling salesman problem (ATSP). In the field of TSP, the 2-opt movements is a basic operator which has been widely used by local-search based heuristics. However, for the ATSP, given an incumbent solution with N tasks, the current best method requires a complexity of O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) to evaluate all the O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) possible 2-opt based movements, and thus, the computational cost is too expensive, especially for large-scale instances. This paper develops a series of data structures, to reduce the above time complexity from O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) to O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). Experimental results based on 60 task sets indicate that, the proposed method can significantly improve the efficiency of 2-opt based local search.
The Quantum Shor Algorithm Simulated on FPGA

Shor algorithm is a key milestone in the quantum computing history. It has greatly promoted the development of quantum computing since it is supposed to crack the RSA cryptosystems. Simulation technology is an important strategy in quantum computing research, but the software simulation can only simulate the final result rather than the behavior and hardware structure. This paper proposes a hardware simulation strategy for quantum Shor algorithm. The hardware simulation is closer to the execution process of quantum computation than software simulation. It helps to understand how quantum computer works more concrete, and guide the design of quantum computer more specifically. According to the workflow of quantum computer, this paper divides the Shor algorithm into quantum part and classical part. In our experiment, the quantum part is simulated on FPGA device, and the classical part is executed on CPU host. FPGA device communicates with CPU through PCIe channel, and realizes the modules of quantum register, Hadamard gate, quantum Fourier transform (QFT), quantum measure and so on. Our experiment support 17 qubit simulation in Intel A10 FPGA device, and the execution performance is consistent with our theoretical analysis.
Predicting Drug Sale Quantity Using Machine Learning

Medication is one of the essential parts of a patient's treatment. Therefore, it is important to have good medication storage administration in order to have effective medication storage. This study aimed to find a proper model used for the prediction of medication purchase amount by using machine learning to analyze medication purchasing amounts in the form of time series. In this research, the first 10 medicines in AV group were chosen. Then, Multilayer Perceptron (MLP), Long Shot-Term Memory (LSTM), and 1D Convolutional neural network with LSTM models were used together with Rolling Windows which were used to predict the purchase amount of each model. The periods of prediction were at 1 month, 3 months, and 6 months. The efficacy of each model was compared using their errors. CNN-LSTM model produces the better forecasting results. The result also shows that 1-month forecasting period is suitable for medicines that specific to disease. The 3-month forecasting period is suitable for commonly used medicines. The 6-month forecasting period is suitable for the medicines for chronic diseases.
CPPCD: A Token-Based Approach to Detecting Potential Clones

Most state-of-the-art clone detection approaches are aimed at finding clones accurately and/or efficiently. Yet, whether a code fragment is a clone often varies according to different people's perspectives and different clone detection tools. In this paper, we present CPPCD (CP-based Potential Clone Detection), a novel token-based approach to detecting potential clones. It generates CP (clone probability) values and CP distribution graphs for developers to decide if a method is a clone. We have evaluated our approach on large-scale software projects written in Java. Our experiments suggest that the majority of clones have CP values greater than or equal to 0.75 and that CPPCD is an accurate (with respect to Type-1, Type-2, and Type-3 clones), efficient, and scalable approach to detecting potential clones.
Error correction schemes for fully correlated quantum channels protecting both quantum and classical information

We study efficient quantum error correction schemes for the fully correlated channel on an n-qubit system with error operators that assume the form $$\sigma _x^{\otimes n}$$, $$\sigma _y^{\otimes n}$$, $$\sigma _z^{\otimes n}$$. Previous schemes are improved to facilitate implementation. In particular, when n is odd and equals $$2k+1$$, we describe a quantum error correction scheme using one arbitrary qubit $$\sigma $$ to protect the data state $$\rho $$ in a 2k-qubit system. The encoding operation $$\sigma \otimes \rho \mapsto \Phi (\sigma \otimes \rho )$$ only requires 3k CNOT gates (each with one control bit and one target bit). After the encoded state $$\Phi (\sigma \otimes \rho )$$ goes through the channel, we can apply the inverse operation $$\Phi ^{-1}$$ to produce $${\tilde{\sigma }} \otimes \rho $$ so that a partial trace operation can recover $$\rho $$. When n is even and equals $$2k+2$$, we describe a hybrid quantum error correction scheme using any one of the two classical bits $$\sigma \in \{|ij{\rangle }{\langle }ij|: i, j \in \{0,1\}\}$$ to protect a 2k-qubit state $$\rho $$ and two classical bits. The encoding operation $$\sigma \otimes \rho \mapsto \Phi (\sigma \otimes \rho )$$ can be done by $$3k+2$$ CNOT gates and a single-qubit Hadamard gate. After the encoded state $$\Phi (\sigma \otimes \rho )$$ goes through the channel, we can apply the inverse operation $$\Phi ^{-1}$$ to produce $$\sigma \otimes \rho $$ so that a perfect protection of the two classical bits $$\sigma $$ and the 2k-qubit state is achieved. If one uses an arbitrary two-qubit state $$\sigma $$, the same scheme will protect 2k-qubit states. The scheme was implemented using MATLAB, Mathematica, Python and the IBM’s quantum computing framework qiskit.
A Distributed Log Management Method using a Blockchain Scheme

A cyber attack necessitates preventing the spread of damage by quickly responding to the incident. To investigate the situation accurately, we must collect, manage, and maintain log data on devices related to the system. We herein propose a distributed management method for logs using a blockchain scheme. The blockchain scheme has the following two features. One makes managed data tamper resistant. The other makes access to the same data possible with multiple terminals, thereby increasing access availability. A log management method that adapts these features can provide log data that are guaranteed to be complete when needed. This paper presents our evaluation of the feasibility of our method in a prototype.
Maximum Entropy based Urdu Named Entity Recognition

Urdu is widely spoken and a national language of Pakistan. The language covers huge variety from others languages as well therefore known as “Lashkari Zuban” (a mixture of different languages. We have performed experiments on Urdu Named Entity Recognition (NER) using model-based approach. NER is the task for the identification and classification of named entities from the given text therefore; name, place, organization, time/date, etc. The task has an important role for automated systems, information extraction, machine learning and artificial intelligence. A lot of work has been done for European languages but the task for South Asian languages is its development stage. We chose Urdu language as it is our national language but still there are a lot of challenges in Urdu language as the language as very limited resources and it is also free structured language. Our research has been conducted on IJCNLP-08 dataset which is IOB (inside Outside Beginning) tagged using maximum entropy model. Precision, Recall and F-measure are used to evaluate the accuracy of the model.
Orbital Instability of Chaotic Laser Diode with Optical Injection and Electronically Applied Chaotic Signal

We numerically studied the chaotic dynamics of a laser diode (LD) system with optical injection, where a chaotic signal, which is generated by an LD with optical feedback, is applied to the drive current of the master LD. To quantify the orbital instability of the slave LD, the Lyapunov exponent was calculated as a function of the optical injection ratio between the master and slave LDs and the optical feedback ratio of the applied signal. We found that the Lyapunov exponent was increased and the orbital instability was enhanced by applying a chaotic signal when the inherent system without the applied signal was in a “window”. Next, we investigated the orbital instability of the slave LD in terms of statistical and dynamical quantities of the applied chaotic signal. The maximal value of the Lyapunov exponent for a certain range of the injection ratio was calculated and we showed that a chaotic pulsation is suitable for enhancing the orbital instability of the LD system. We then investigated chaos synchronization between the LDs. It is concluded that the orbital instability of an LD with optical injection can be enhanced by applying chaotic pulsation without chaos synchronization.
Optical Flow Based Solar Irradiance Forecasting in Satellite Images

Prediction of cloud movement is essential for solar irradiance forecasting. However, most of the cloud tracking and predicting methods at present refer to ground-based images. This paper provides a new viewpoint from satellite images to analyze cloud movement. We choose Shi-Tomasi's method to pick out feature points, filter the image with Gaussian kernel and track the cloud by Lucas-Kanade optical flow methods. We also evaluate detailed implementations of different filters and sampling methods with our own metrics. The result indicates that the satellite image is high-resolution enough for the task of cloud tracking and irradiance forecasting and optical flow is a suitable method for this task.
A Summarization Generation Method for E-Courseware on the Internet Platform

With the increase of various online learning platforms, lots of electronic coursewares (e-courseware) are uploaded to the Internet platform for users' learning and sharing. Constructing the summarization for e-courseware through extracting the content of e-courseware is of great importance for users to select and utilize e-courseware for the self-learning process. But the information extraction algorithm for e-courseware has not been fully studied on the Internet Platform. This paper proposes a new method for extracting courseware information based on automatic summarization, and then applies it to generate automatically the summarization covering the knowledge content of e-courseware on the Internet platform. Based on the analysis of e-courseware structure, the method uses the extractive and abstractive automatic digest algorithm to deal with the text of a single slide, and then calculates the similarity of two sentences outputted by the two algorithms to determine the final key information, finally organizes the summarization of e-courseware based on the extracted key information. The experimental results show that the algorithm has a good performance in the summarization and generalization of text information for e-coursewares, which is conducive to the generation of learning notes for users so as to quickly understand the content of e-courseware.
Dynamic Cost Ant Colony Algorithm for Optimize Distributed Database Query

Optimizing query in distributed database is considered as the most important part of a database system. The optimizer tries to find an optimal join order which reduces the query execution cost. Many factors may affect the execution cost of a query, including communication costs, resources, and access to large distributed data sets. When the number of relations and number of joins in a query increases, the complexity of the optimizer also increases. The success of query execution heavily influenced by the search method which is performed using the query optimizer. Processing of queries is considered as NP-hard problem and many researchers are focused on this problem in recent years. Researches are trying to build an appropriate algorithm to seek an optimal solution especially when the size of the database increases. In this paper, an ant colony algorithm as one of the hybrid strategy of evolutionary algorithms is utilized to find a solution for join query optimization problem in the distributed database systems. Unlike traditional ant colony-based query optimization techniques that based on static cost, the suggested model relies on dynamic cost which calculates the cost while the execution plan is built. Using this strategy, the algorithm aims to find an optimal join order which minimizes the total execution time. Experimental results show that the proposed model can handle different number of join entities. Also, the algorithm is affected by the number of ants used. Better results are obtained in case of large joined if the number of used ants increased.
Underwater Image Enhancement with a Total Generalized Variation Illumination Prior

In order to promote the visual quality of degraded underwater images, we develop an innovative variational model based on Retinex with a total generalized variation (TGV) prior on the illumination. The TGV prior is adopted to approximate piecewise smoothness and piecewise linear smoothness of the illumination, which combines the first-order and second-order total variation (TV) to model the variation of illumination. When adopting this illumination prior in the Retinex-based algorithm, the illumination and reflection are well separated, and underwater enhanced results appear more natural and their details and edges are better preserved. Then an efficient iterative optimization method is derived to settle the proposed model via alternately calculating the illumination and the reflection simultaneously. Numerous experiments on both visual results and objective metrics demonstrate the superiority of our method compared with several underwater enhancement methods. In addition, the proposed method can be extended for dehazing, sandstorm removal and low illumination image enhancement, which can illustrate better capacity of our model.
A Real-time Auto-recognition Method for Pointer-meter under Uneven Illumination

Traditional pointer-type meters are widely applied in different environments for its excellent stability. The results of indication recognition limit the progress of the whole industrial process directly. However, gross and random errors often occur in manual reading of precision pointer-type meters. In this paper, we proposed a real time auto-recognition method for pointer-meter which can cope with uneven light and adapt to different environments by using computer vision and image processing technology. Experimental results show that the proposed method is able to recognize pointer meter readings under uneven illumination with high recognition accuracy in a short time.
Learning Rotation Sensitive Neural Network for Deformed Objects' Detection in Fisheye Images

Object detection plays a significant role in an intelligent system equipped with a fisheye camera. The fisheye image captures a wide field-of-view but deforms in the radial direction. The deformation changes the relative angle between the edge of objects and the image. Therefore, a horizontal bounding box cannot perform an accurate description of an object's location and dimension in advanced neural network training. In this paper, we build a rotation sensitive neural network targeting to realize one-stage regression on the fisheye image detection. The oriented bounding box is applied in the object's description and detection. To evaluate our proposed method, we develop a new labelled fisheye image dataset that contains two categories. The network model training takes around 3 hours and achieves 100% precious by the test set.
Towards Semantic Clone Detection via Probabilistic Software Modeling

Semantic clones are program components with similar behavior, but different textual representation. Semantic similarity is hard to detect, and semantic clone detection is still an open issue. We present semantic clone detection via Probabilistic Software Modeling (PSM) as a robust method for detecting semantically equivalent methods. PSM inspects the structure and runtime behavior of a program and synthesizes a network of Probabilistic Models (PMs). Each PM in the network represents a method in the program and is capable of generating and evaluating runtime events. We leverage these capabilities to accurately find semantic clones. Results show that the approach can detect semantic clones in the complete absence of syntactic similarity with high precision and low error rates.