Reviewerly/exHarmony huggingface dataset:
Challenges in Coding, DSP and Parallel Operation of Quantum Key Distribution and Coherent Data Transmission

We discuss challenges and recent progress on coding, digital signal processing and joint transmission with classical data channels, for quantum key distribution.
Deep Learning as a Service Based on Encrypted Data

With the widespread promotion of 4G/5G wireless network and the rapid development of Internet of Things (IoT), more and more enterprises begin to make use of their powerful computing power and technology to provide pre-trained deep neural networks for ordinary users to help them complete classification, regression, image recognition, NLP and other services. This not only brings convenience to people, but also easily causes the leakage of users' private data. In this paper, we combine deep learning with homomorphic encryption algorithm and design a deep learning network model based on secure Multi-party computing (MPC). In the whole process, we realize that the cloud only owns the encryption samples of users, and users do not own any parameters or structural information related to the model. In the experimental part, we input the encrypted Mnist and Cifar-l0 datasets into the model for testing, and the results show that the classification accuracy rate of the encrypted Mnist can reach 99.21%, which is very close to the result under plaintext. The classification accuracy rate of encrypted cifar-l0 can reach 91.35%, slightly lower than the test result in plaintext, and better than the existing deep learning network model that can realize data privacy protection.
Service Clustering Method Based on Knowledge Graph Representation Learning

With the changing of users’ requirements, the number of Web services is growing rapidly. It has been a popular research field to discover the suitable service accurately and quickly in service computing research. At present, most of the Web services published on the Internet are described in natural language. This trend is becoming more and more obvious. Existing service clustering methods are not only limited to a specifically structured document but also rarely consider the relationship between services into semantic information. In response to the problems mentioned above, this paper suggests a Service Clustering method based on Knowledge Graph Representation Learning (SCKGRL). This method firstly crawled the services data from ProgrammableWeb.com, use natural language tools to process the web services description document, and obtain the service function information set. Secondly, we constructed the service knowledge graph by using the service-related information, the triples are converted into vectors and minimize the dimension of service feature vectors due to the knowledge representation learning method. Finally, the services were clustered by the Louvain algorithm. The experiments show that SCKGRL gives better performance compared with other methods, such as LDA, VSM, WordNet, and Edit Distance, which provides on-demand service more accurately.
Stabilizer of SMPC Throughput using Neural Networks

In view of the successful application of deep learning mainly in the field of image recognition, applications of deep learning in the fields of communication and computer networks are being explored. In these fields, systems that follow proper theoretical calculations and procedures are set up. However, due to the large amount of data to be processed, proper processing takes time, and the aspect that deviates from the theory due to the inclusion of uncertain disturbances sometimes appears. Therefore, deep learning or nonlinear approximation by neural networks may be useful in some cases. The communication method SMPC we have studied measures the throughput by a set of packets called train at the destination node and feeds it back to the source node. By comparing it with the transmission rate, the source node detects congestion on the transmission route and adjusts the packet transmission interval. However, there is a problem that the throughput fluctuates during packets pass through the route, and if it is fed back directly, the transmission rate fluctuates significantly, which causes the fluctuation of the throughput larger and the average throughput even lower. It is not desirable to change the transmission rate unnecessarily. In this study, we tried to stabilize the transmission rate by incorporating prediction and learning by neural network. Optimal transmission rates were predicted by a neural network from the throughput data measured by the destination node, and the results are learned by another neural network to generate a stabilizer. A simple moving average method and a stabilizer using three types of neural networks, MLP, RNN, and LSTM, were built into the transmission controller of the SMPC. It was shown that not only can the fluctuation be reduced, but also the average throughput improves. It is shown that deep learning can be used as a device to predict and output stable values from data with complicated time fluctuations that are difficult to analyze.
CREATION OF FOREIGN LANGUAGE ATMOSPHERE IN TEACHING ENGLISH

During practical study it was offered a new model of the English language education for students, that was tested in current conditions of traditionally common at Azerbaijan national universities. The need to create this model is due to the dissatisfaction of the professional community with the results of such training. This paper consists of two parts. The first part briefly analyzes a number of interacting extralinguistic factors that differently influence the process of learning English by the students in the classrooms, in small training groups, under the guidance of the teacher according to standard methods. The second part of this paper is a description of the foreign language environment model in the single educational atmosphere of a technical university. The task set by the developers of this model is not only to reduce the negative and strengthen the positive influence of the external factors complex, but also to stimulate students for study of English through foreign-language socialization in academic and professionally oriented discourse. The principles of a model organization of the single foreign language atmosphere, a system of uniform control and assessment of English language proficiency are presented in this paper. Preliminary results obtained during the testing of this model were analyzed. The main advantages of the training organization through this model, which allows to increase motivation for students to learn English have been considered during the research.
Collaborative artificial bee colony k-mean clustering algorithm for mixed data set

Abstract Data mining techniques are used to extract useful patterns from a large data set. k-mean algorithm is one of the most famous partitioning clustering algorithm. But, Euclidean distance is sensitive to outliers and is suitable to only numeric values. Real time datasets have mixed attribute values, missing values and measurements are not in the standard format.The proposed algorithm extends the ability of the kmean algorithm to use a mixed simil arity measure to find the similarity between data objects for clustering mixed datasets.For imp uting missing values, correlation based data imputation is used.In addition, k-mean output depends on the initial cluster centre and local optima suffers from the number of clusters(k). In order to improve the efficiency of the k-mean algorithm, Artificial Bee Colony Optimization (ABC) based clustering algorithm is suggested. ABC is successful at exploring the search space, but endures in leveraging the search space. Collaborative search is used to amplify the search quality of bees to amplify the search quality of bees employees. To determine the number of clusters for the given data set, the Elbow method is used. In order to evaluate the outcome of the proposed algorithm, real time datasets are used. The results showed that the proposed method performs well compared to comparative algorithms.
Creating Interdisciplinary Courses in the Process of Studying a Foreign Language at Nonlinguistic University

The purpose of the study is to identify educational and methodological foundations for creating interdisciplinary integrative courses. The authors consider the content and language integrated approach to studying foreign languages at nonlinguistic universities. Scientific novelty of the research lies in the fact that possibility of logical relationship between different disciplines at the university in the process of learning a foreign language is determined. As a result of the experimental study, a developed and tested author’s curriculum of an interdisciplinary course proved its effectiveness in accordance with the developed success indicators for organisation of interdisciplinary courses.
WITHDRAWN: Trusted Data Collection Gateway for BlockChain Traceability Applications and Edge Computing

Blockchain technology has Traceability as the realization solution of decentralized trust systems. In this system, the model business is still guaranteed by the underlying blockchain need to understand the best blockchain possible. Design science methods have practical prototypes developed that can change opportunities and challenges from a perspective. This is exemplified by adopting innovative project technologies that help blockchain deliver effective project calculations and resource allocation. Blockchain technology reduces regulatory approval delays, and the management of advanced clinical trial data plays an important role in the medical field. The frequency of spreading false reports is increasing. The trendy supply chain is diverse and can streamline investor contact and not confirm public panic and irrational behavior in existing platforms. Therefore, the development of blockchain-based tracking systems is essential to gaining credibility and credibility from public and government agencies. This is a proposed method on data collected from various external sources and improving various blockchain applications and opportunities and tracking systems.
Constructing an Integrated Classifiers for Identifying Authenticity and Sentiment Analysis- A Case of Hotel Reviews

Most of the consumers will search for the reviews of products or service to help them make a decision to shopping or not, so they consider the authenticity of comments is very important. Moreover, company will collect positive and negative comments to realize their position within the market, and sentiment analysis is regarded as a beneficial tool. Most of the researches only focused on Identifying authenticity or sentiment analysis, our study attempted to solve both of two issues. We constructed an integrated classifier based on text mining approaches for Identifying authenticity and sentiment analysis, the data which this study used was collected by Amazon Mass Intelligence Platform and Trip Advisor hotel review website. There are two experiments tested in this study which are mixed judgment and judge by step. The mixed judgment was analyzing real vs. fake and positive vs. negative reviews into a matrix. Additionally, judge by step was analyzing the reality of reviews then conducting sentiment analysis. With the experimental results, the system will figure out the keywords through two theories--sentiment analysis and rumor/lie theory, based on sentiment analysis the comments could divide into positive and negative. Furthermore, if the user would like to know the comments are fake or not, then based on the rumor/lie theory. During the process, the experiments were not only use a single classifier in order to pick the best model but use the method of integrating to enhance the accuracy of classifier. The results of this study illustrated the comparison between judge by step and mixed judgment. The accuracy which in step method (77.29%) is better than mixed judgment (68.13%). This study found out the step methods is more complicate than mixed judgement, but the accuracy and F- measure are the better.
A Novel Detection Method of Power System Subsynchronous Oscillation Based on Sample Entropy

In order to detect the sub-synchronous oscillation(SSO) in power systems rapidly and accurately, a novel test method based on sample entropy(SEn) is presented in this paper. By analyzing the variation trend of SEn value of the rotor shafting torque in continuous time period, the complexity of the original signal during different time window could be acquired to judge whether SSO occurs and what kind of SSO has happened in the system. This method has better anti-noise ability and real time property while it does not need large sets of data. Simulation results of the IEEE first standard model test system verified the effectiveness and practicability of the proposed method.
Packrat Parsers Can Support Multiple Left-recursive Calls at the Same Position

One of the common problems with the recursive descent parsing method is that when parsing with a left-recursive grammar, the parsing does not terminate because the same parsing function is recursively invoked indefinitely without consuming the input string. Packrat parsing, which is a variant of recursive descent parsing method that handles grammars described in parsing expression grammars (PEGs) by backtracking, is also affected by the above problem. Although naive backtracking parsers may exhibit an exponential execution time, packrat parsers achieve a linear time complexity (for grammars that are not left-recursive) by memoizing the result of each call to the parsing functions. Some methods have been proposed to solve the problem of left recursion in packrat parsers. In these methods, memoization tables in packrat parsers are modified to limit the depth of the recursive calls. By calling the same parsing function repeatedly while increasing the limit, the parsed range in the input string is expanded gradually. These methods have problems in that multiple occurences of left-recursive calls at the same input position cannot be handled correctly, and some of the grammars that does not include left recursion cannot be handled. In this research, we propose and implement a new packrat parser to address these problems. This packrat parser can handle multiple occurences of left-recursive calls at the same position in the input by giving priority to the most recently used rule when gradually increasing the parsed range of the recursion. In the evaluation of the proposed method, in addition to the grammars including left recursion manageable by the methods proposed in existing studies, we confirmed that our approach supports the grammars that cannot be handled by those existing methods.
Density-Based Mining Algorithms for Dynamic Data: An Incremental Approach

Traditionally, data mining algorithms deal with a static set of input. The major bottlenecks with this class of algorithms may include redundant computation, higher latency along with increased consumption of available resources. Given the essence of dealing with dynamic data, this work focused on designing incremental mining algorithms specifically in the field of density-based clustering and outlier detection. The primary reason being density-based algorithms shows robustness in finding clusters of varying granularity or extracting outliers from variable density regions. Through these works, we proposed incremental extensions to two density-based clustering algorithms: MBSCAN, SNN-DBSCAN and an outlier detection algorithm KNNOD. The incremental extensions to MBSCAN (iMass) and KNNOD (KAGO) are approximate in nature supporting single point insertions. While for SNN-DBSCAN, we proposed exact incremental algorithms ( $$\text {BISDB}_\mathrm{add}$$ and $$\text {BISDB}_\mathrm{del}$$ ) supporting both insertion and removal of data in batch mode. iMass obtained a maximum efficiency upto an order of 2.28 ( $$\approx $$ 191 times) maintaining a mean clustering accuracy of around 60.375%. $$\text {BISDB}_\mathrm{add}$$ and $$\text {BISDB}_\mathrm{del}$$ achieved high efficiency upto an order of 3, 4, respectively. The clusters obtained through both these algorithms were identical to SNN-DBSCAN. KAGO outperformed KNNOD by achieving a maximum efficiency upto an order of 3.9 ( $$\approx $$ 8304 times) across two intrusion detection datasets and a bidding data pertaining to a search engine. On evaluating outliers using these datasets, the Rand-Index and F1-score pertaining to KAGO showed an average improved accuracy of around 3.3%.
Analysis and Optimization of Computer Trajectory Based on Compound Eye Vision System

Abstract With the rapid development of science and technology, people have higher and higher requirements for target detection technology, which requires us to solve the disadvantages of traditional cameras, such as the difficulty of panoramic photography and continuous tracking motion. However, compound eye insects have a visual system composed of fewer neurons, which can achieve rapid detection and capture of objects of interest. Therefore, based on the principle of compound eye vision, a bionic compound eye panoramic detection system is designed in this paper, which can realize panoramic real-time detection through multiple sub eye cameras. Through the compound eye vision system, we can accurately locate the pan tilt controller, which can provide a comprehensive panoramic motion image. Through the principle of automatic windowing, we can acquire the image of moving area by multi-layer window. Then, through the relevant image processing methods, such as binarization, median filtering, spatial filtering, moving object detection, edge detection and other methods, we can get a relatively clear moving object contour and trajectory analysis, which can achieve full field of view, high sensitivity detection and tracking.
Diction on the Tantan Application: A Semantic Study

Abstract The development of technology is increasing rapidly. In this digital era, humans are demanded to be more creative in all fields. Otherwise, human will be left behind by the progress of the times. The internet as one of the references of cyberspace surfers also experienced significant developments. One application that is much preferred is the Tantan application. Tantan application is a matchmaking application. The communication process in the tantan application uses language as its bridge. This application applies some requirements the profile photo should be original, no vulgar photos and no violent elements posted. The interesting thing here is the choice of words which should be appropriate to be used by users to avoid words that can offend other speaker. These dictionaries semantically contain connotative and denotative meanings. This research is a qualitative descriptive study. The data in this study were taken from the tantan application user conversation. The data collection is done by taking a screenshot of a conversation that will be used as data. The data obtained will be reduced and the data analysis is done in a descriptive way that is describing the results of observations. System filter languange on application tantan have two theories in classifying words. First, euphemisms is words that have implicit meaning to conceal the intention of and to refine said. example: teteh (sister), anak (children), bikin (make), rambut (hair), hotel (hotel), di atas (above), masuk (entrance) . Second, desfemisme is words that have value not polite or taboo, example: bego (stupid), hina (despicable) .
Design of topic Web crawler based on improved PageRank algorithm

Abstract With the continuous development of network information technology, the network is filled with a large number of all kinds of unstructured data called big data. However, this data is not easily stored in a local database. People realize that it is essential to get useful information from the Internet efficiently. The effort to gather information by human hands has led to the emergence of web crawler technology. However, the existing search engines still have shortcomings in topic similarity judgment and web page sorting algorithm. Therefore, this paper applies PageRank algorithm to topic crawler, constructs a vertical search engine, and introduces topic relevance factor to suppress "topic drift" according to the shortcomings of PageRank algorithm.
SCPORAM: A Hardware Support Scheme for Protecting Memory Access Patterns

Oblivious RAM is a cryptographic protocol for hiding memory access patterns in the un-trusted external storage environment. Recently, Path ORAM has received wide attentions due to its simplicity and efficiency in secure processor design. Unfortunately, the memory bandwidth demand for Path ORAM is still too high, which restricts Path ORAM from further development. But overlapping paths can be cached to reduce memory bandwidth without the loss of security. In this paper, we present SCPORAM (Self Clustering Path ORAM), a clustering method for path merging, which transform memory requests into different categories. For each set, the intersection can be cached for every member sharing. Therefore, SCPORAM has the ability to effectively reduce memory access bandwidth. Based on this algorithm, a new ORAM controller is proposed. Compared to baseline Path ORAM, SCPORAM can reduce system overhead by 53%.
A Shoulder-Surfing Resistant Colour Image-based Authentication Method Using Human Vision Perception with Spatial Frequency

Superimposing the high and low spatial frequencies of two different images allows different image interpretations, depending on the viewing distance between the viewer and the image. Previous works used this technique to produce greyscale keypads for PIN entry to prevent shoulder-surfing. Nevertheless, implementation in colour keypads would preserve the underlying look and feel of most applications which are in colour. Thus, the proposed method uses superimposition to produce hybrid image keypads in colour. From user testing and simulated shoulder surfing attacks, it was shown that the proposed method had a low error rate of 3.57% for user logins while none of the shoulder surfing attacks were successful.
Extending the FOLKI-PIV Algorithm for the Coherent Coregistration of SAR Images

The FOLKI-PIV algorithm, originally developed as a particle image velocimetry technique, is extended to handle complex-valued images and applied to the coherent coregistration of SAR images.
Weak Target Detection in High-Resolution Remote Sensing Images by Combining Super-Resolution and Deformable FPN

Weak target detection plays an important role in military and civilian fields. However, due to the limitation of the target size and the influence of complex background, the detection of weak target is a huge challenge. Therefore, based on high-resolution remote sensing image, this paper proposes a weak target detection network which combines super-resolution and deformable convolution. Firstly, the high-resolution remote sensing image is expanded and enhanced to eliminate the influence of complex background. Secondly, a detection network based on the deformable convolution and feature pyramid network (FPN) is used to solve the problem of less information caused by the fewer target pixels. In addition, this paper establishes a detection dataset only containing weak vehicles. The experimental results show that the proposed method achieves better detection results in the weak target detection problem.