Reviewerly/exHarmony huggingface dataset:
GRNN Based an Intelligent Technique for Image Inpainting

Image inpainting is usually framed as a constrained image generation problem. It is a method that helps to reconstruct the lost or deteriorated parts of images as well as video. The main focus in image inpainting techniques is how precisely to can generate the corrupted pixels in an image. In this paper, we tried using a single pass learning algorithm which greatly reduce time to train the model. The objective of the proposed model is to reconstruct large continuous regions of missing or deteriorated parts of an image. In this paper, GRNN based model along with some image inpainting techniques is being used. Each image is divided into two sections: the missing part that is to be reconstructed, and the context. The network would work identically for arbitrary removals not just for regions having particular shapes such as square or rectangles. Final evaluation is based on the Mean Squared Error (MSE) and Peak Signal to Noise Ratio (PSNR) between the corrupted image and the original image for the regions which is to be regenerate.
The bicentennial history of the electromagnetic telegraph (from Ørsted’s experiments to social networks)

In 1820, Hans Christian Ørsted noticed the influence of electric current on the deflection of a magnetic needle. This discovery became the theoretical basis for the creation of electromagnetic telegraph. This review describes the previous methods of transmitting information. The contribution of Russian and foreign inventors to the development of telegraph communication, as well as the current state of this technology, is considered.
Correlation Power Analysis for SM4 Based on ISOMAP

In Correlation Power Analysis (CPA), the problem is that the attacking rate becomes slow when there are many power sampling points. We introduced a method of preprocessing power data based on ISOMAP. Before performing CPA, this method uses the ISOMAP algorithm to reduce the dimension of power matrix. Under the guarantee of the intrinsic dimension of the power consumption matrix, the computational complexity of the CPA is reduced. At the same time, after the dimension is reduced, the data correlation is enhanced, which improves the attack efficiency of CPA.
Competitive Analysis with Graph Embedding on Patent Networks

Advanced competitive analysis is increasingly becoming important in business analytics. A key component of strategic research is to collect and review information from multiple unstructured sources to identify major competitors and their technology development trends. Topic modeling techniques such as Latent Dirichlet Allocation (LDA) have been applied to competitive analysis, which mainly use the semantic similarities between documents to infer competitive relationships. In this study, we propose using graph embedding methods to learn the implicit competitive relationships between firms. Using patent networks with patents and organizations as nodes, we learn the embeddings of nodes which are then used to cluster organizations into groups. Organizations within the same groups are considered competitors. We applied three graph embedding methods: node2vec, metapath2vec, and GraphSAGE to learn node embeddings. Two of these methods use the structural information in patent networks: node2vec for homogeneous networks and metapath2vec for heterogeneous networks. While, GraphSAGE uses both the structure and content information in the patent network. The results are compared with a baseline author-topic modeling method. The graph embedding methods outperform the author-topic modeling approach in learning the competitive relationships. A case study, examining the evolution of competitors over multiple years, shows the graph embedding method learns meaningful node embeddings.
Study on Chinese Open Domain Question Answering based on Support Vector Machine

For reason of providing precise answer to users which is better than traditional search engine, question answering (QA) has become a hot research spot. Currently, most researches focus on Knowledge base and community based question answering systems, studies on extracting answer from plain text are rarely mentioned. This paper studied open domain question answering based on support vector machine. A large scale synonym set was borrowed to improve system performance at every stage. Two kinds of new semantic features were introduced to extract answers from text. In order to verify effectiveness our methods, we implemented our experiment in the domain of Chinese and designed a prototype system named XiaoQ. Experimental data shows that our system works well at a promising level which proved that this paper has some reference value.
Use Computer Audio Technology to Compare Piano Singing and Legato Based on “Particle” Playing Method

Abstract This paper first introduces the basic concepts of playing and legato, and discusses the importance and difference of playing and legato. Then computer audio technology is used for audio contrast analysis. This paper expounds the connotation of piano singing in order to provide reference for piano learners and piano teachers.
Network Application Classification with DNN Model

Abstract Due to the development of information technology, there are many kinds of Internet applications. Many network applications not only bring great challenges to the quality of network services but also have an impact on Internet security. In order to efficiently use network resources and provide effective management and control measures for network managers, network traffic needs to be classified. At present, many traffic classification algorithms are not accurate enough, which affect the evaluation and further study of the network. Therefore, the innovation of this paper is to propose a network application classification model. This model uses Deep Neural Network (DNN). Different pretreatment methods are used for data sets to increase the accuracy. This model is compared with other models to prove the effectiveness of the work.
Eye Tracking and Measurement of Eye Rotation Using a Small Camera Installed roughly next to the Eye

Because the eyeball is a sphere, the pupil center coordinate is distorted and this caused the issue of low eye tracking accuracy with the dark pupil eye tracking. The authors introduce a new eye tracking method that does not reduce the accuracy of estimates even when the pupil camera is installed roughly next to the human eye. In this method, the user is asked to look at a minimum of six points of light that include the corners of a pentagon that is slanted towards the inside corner of the eye and the center of gravity of that pentagon. The authors also introduce a method to measure eye rotation by using a camera fixed in the same manner as the aforementioned eye tracking. This method uses the characteristic images of the blood vessels in the whites of the eyeball. A template image is selected based on three criteria: First, the blood vessel must be thick and have good dark/light contrast; second, there must not be any external light sources reflected; and third, there must not be blood vessels with a similar shape nearby.
Cryptanalysis and improvement of “Game theoretic security of quantum bit commitment”

Recently, Zhou et al. proposed a quantum bit commitment (QBC) protocol by using Bell states. In this paper, we propose a cryptanalysis strategy for the protocol and demonstrate that the sender can successfully change her commitment without being detected by the recipient with a probability 1-(1/2)n, where n is the number of rounds in executing the protocol. Hence, the protocol does not meet the binding property which shows its insecurity. In order to overcome the shortcomings, we propose an improved version of the protocol. The analysis illustrates that the improvement meets the security requirements.
Performance Analysis on Enhanced Round Function of SIMECK Block Cipher

There are various Lightweight Block Ciphers (LBC) nowadays that exist to meet the demand on security requirements of the current trend in computing world, the application in the resource-constrained devices, and the Internet of Things (IoT) technologies. One way to evaluate these LBCs is to conduct a performance analysis. Performance evaluation parameters seek appropriate value such as encryption time, security level, scalability, and flexibility. Like SIMECK block cipher whose algorithm design was anchored with the SIMON and SPECK block ciphers were efficient in security and performance, there is a need to revisit its design. This paper aims to present a comparative study on the performance analysis of the enhanced round function of the SIMECK Family block cipher. The enhanced ARX structure of the round function on the three variants shows an efficient performance over the original algorithm in different simulations using the following methods of measurement; avalanche effect, runtime performance, and brute-force attack. Its recommended that the enhanced round function of the SIMECK family be evaluated by different security measurements and attacks.
Enemy inside: salesperson fraud detection in the insurance industry

In the insurance industry, fraud can be performed because of salesperson's bonuses and commissions. This problem reduces the reputation of the insurance industry. With the decrease of reputation, the trust of the customer to the industry decreases and thus, the acquisition of new customers becomes difficult. This study aims to estimate the probability of salesperson to fraud with historical data by using machine learning methods. The industry's reputation is maintained if fraud can be detected in advance.
The Matter of Shape: A computational Approach to Making in Architectural Heritage (Survey)

Shapes can express not only visual but also functional and material aspects of designs. Recent studies in computational making, as well as earlier research on weights and colors in shape grammars, have done much to explore how shapes in computations may inscribe material attributes. This is both relevant and essential in deciphering past design knowledge from architectural heritage. With examples from traditional stone relief carving and tile mosaics of thirteenth century Anatolia, our research, reviewed here, emphasizes a neglected design knowledge: how the tools and techniques applied on the material surface factor into and determine the final shape, and what constitutes it. The discussion extends to ways of capturing and modeling the physical information from built heritage, and how consequent representations of shapes in these models may incorporate material attributes, in order to increase our understanding of past architectural and building traditions.
IoTRemedy: Non-Intrusive Rule Decomposition for User Privacy in Modern IoT Platforms

With the pervasiveness and popularity of Internet of Thing (IoT) devices in smart homes, end users gain a lot of valuable services at the expense of data leakage risks, which eventually leads to disclosure of user privacy. Specifically, device data are usually reported to an IoT platform's backend cloud for it to run services such as home automation. Various vulnerabilities of the cloud and insider attacks can put the data collected by clouds in jeopardy. To mitigate data leakage in home automation, we propose a new data flow control framework IoTRemedy, which reduces data leakage by transparently performing most automation rule operations within the home network and only reports necessary data to the cloud for executing operations which cannot be localized. To this end, we design a rule decomposition algorithm for splitting each real rule that was executed by clouds into a localized rule (LRule) and a cloud rule (CRule), based on eight specific situations; thus, the resultant rules accomplish the same functions while retaining at local as much data as possible. We evaluate the performance of IoTRemedy by interfacing with two popular platforms: SmartThings and IFTTT. The result validates the effectiveness of IoTRemedy.
No Free Lunch: Free at Last!

No Free Lunch (NFL) Theorem in M/L is rigid and inflexible, which states that “No particular classifier can outperform all the other classifiers for every dataset”. In this paper we present a MISD machine that runs multiple classifiers in parallel against a given dataset, implementing a “Swiss Army Knife” to combine many different classifiers and review their performance, effortlessly. The service will be hosted as a public service over the internet for any Machine Learning practitioner to experiment with datasets.
Heritage Education Experience: Creating and Sharing Collective Memories

This article presents a study of a ubiquitous learning application named YouuHE. It was developed to support Heritage Education experiences, enabling local citizens to create and share collective memories about historical places. Users can receive personalized content according to location and personal interest. This study lasted 20 weeks and the participants were all seniors who participated in a pre-existing coexistence group. The preliminary results identified 3 motivations aspects related to the learning process. In addition, the support offered by the application shows itself effective as a potential resource to create and share memories in this kind of ubiquitous heritage education experiences.
Mineral identification of rocks from Pohon Batu hot springs in West Seram using FTIR spectroscopy

Abstract This study demonstrates the Fourier Transform Infrared (FTIR) spectroscopy which has been applied to identification of mineral rocks from Pohon Batu hot springs, West Seram regency, Province of Maluku. FTIR spectroscopic interpretation are absorbance or transmittance spectra as a function of wavelength that shows the content of organic compounds found in rocks. From the conditions of mineral formation and presence of various impurity ions and elements also influence the intensity and position of the IR absorption peaks. This study identified the strong peaks of minerals like kaolinite at 3618 cm −1 , montmorillonite at 1612 cm −1 , organic carbon at 2924 cm −1 , quartz at 1874 cm −1 , calcite at 1797 cm −1 , albite at 1026 cm −1 , illite at 756 cm −1 and hematite at 532 cm −1 . From the result of FTIR analysis indicates that this area has undergone hydrotermal alteration thus becoming a secondary minerals. Rock-forming minerals in the hydrothermal alteration process are influenced by the presence of temperature, pressure in geothermal fluids and the decomposition of plants and animals.
A Multi-Weight Strategy for Container Consolidation

Finding energy efficient management strategies while satisfying the Quality of Service (QoS) requirements is one of the critical challenges in Clouds. Virtual Machine consolidation has proved its efficiency in dealing with the energy issue in Cloud environments. Recently, containers are increasingly gaining popularity with regards to virtual machines (VMs) and going to be major deployment model especially at the edge of the network. Hence, it becomes critical to address energy efficiency from container's point of view. Over the past few years, there have been several attempts for container consolidation heuristics. Unfortunately, most of the proposed strategies took container consolidation from VM's point of view. In fact, they considered only one single deployment model in which containers are deployed on virtual machines. On the basis of such assumption, container consolidation is achieved through VMs consolidation. The fact stating that containers can be deployed as well in bare metal requires to take container consolidation from a different point of view. This paper focuses on container consolidation issue with regards to the bare metal deployment model and suggests a new consolidation strategy. The performance of our strategy is evaluated using the Docker Platform and is compared against two other consolidation strategies.
The use of hyperlinking as evidential practice in Danish online hate speech

Abstract Using data from readers’ comments to news articles from a national Danish newspaper, the article addresses the nature and function of hyperlinks as evidential practice in relation to xenophobic hate speech. Hyperlinks refer to the use of URL addresses to link to websites; hate speech is understood broadly as stigmatising discourse. Adopting a discursive approach to evidentiality that accounts for a range of phenomena including source of knowledge, participant roles, epistemic stance and interactional force, hate speech related hyperlinks and their evidential functions were identified. While not prevalent in number, hyperlinks serve to legitimise negative stances towards minority groups but also support counter speech targeting prejudicial views. Links can be used as part of processes of metaphorical shift and sarcasm as well as to provoke hate speech in comment threads. As URL addresses are frequently textual, they can have evidential functions independent of the material that they link to.
Visual Perception Adaptive Image Processing for HD Microdisplay

With the development of 5G networks, a new wave of development is ushered in the field of virtual reality (VR). The small physical size, lightweight, low power consumption, and high brightness make microdisplays the core components of VR. In order to improve the user's immersive experience and reduce the screen window effect, microdisplays are required to have higher pixel density and faster refresh speeds. However, this demand results in a large amount of data transmission and poor real‐time rendering of image information. When the human eye observes an image, the gaze point changes due to the interest of the viewer. To solve this problem, we propose an image compression algorithm based on visual perception adaptation to reduce data transmission bandwidth. The experimental results show that when sight distance is 2, the maximum comprehensive compression ratio is 42.834, the minimum comprehensive compression ratio is 11.415, the minimum clock frequency is 17.033MHz, and the maximum clock frequency is 63.916MHz. The algorithm effectively reduces the data transmission bandwidth and meets the real‐time rendering of VR application, and the rendering effect is good.
Linear Nearest Neighbor Realization of Quantum Circuits Using Clustering and Look-ahead Policy

In the last couple of years, quantum computing has come out as emerging trends of computation not only due to its immense popularity but also for its commitment towards physical realization of quantum circuit in on-chip units. At the same time, the process of physical realization has faced several design constraints and one such problem is nearest neighbor (NN) enforcement which demands all the operating qubits to be placed adjacent in the implementable circuit. Though SWAP gate embedment can transform a design into NN architecture, it still creates overhead in the design. So, designing algorithms to restrict the use of SWAPs bears high importance. Considering this fact, in this work, we are proposing a heuristic-based improved qubit placement strategy for efficient implementation of NN circuit. Two different design policies are being developed here. In the first scheme, a global reordering technique based on clustering approach is shown. In the second scheme, a local reordering technique based on look-ahead policy is developed. This look-ahead strategy considers the impact over the gates in the circuit and thereby estimates the effect using a cost metric to decide the suitable option for SWAP implementation. Furthermore, the joint use of both the ordering schemes also has been explored here. To ascertain the correctness of our design algorithms, we have tested them over a wide range of benchmarks and the obtained results are compared with some state-of-the-art design approaches. From this comparison, we have witnessed a considerable reduction on SWAP cost in our design scheme against the reported works’ results.
The practical dilemma and reform path of university-enterprise collaborative education for Auditing major in the Big Data Era

Abstract Based on the demand of applied audit talents in the Big Data Era, the paper puts forward the reform path of university-enterprise collaborative education mode for Auditing major through the analysis of the practical dilemma of the mode. By continuously deepening the connotation and extension of university-enterprise cooperation, the government, enterprises and universities can achieve all-round and high-quality cooperation in professional construction, personnel training and other aspects.
Tracking customers in crowded retail scenes with Siamese Tracker

Object tracking is a fundamental domain in computer vision, especially multiple human tracking, which is an important task to solve many key problems, including traffic counting, behavior analysis of in-store customers. It is very difficult to handle these problems in heavily crowded scenes because of pedestrian occlusions. Recent research works focus on tracking-by-detection, which breaks tracking problem down into two steps: detect objects then associate them with groups by motion or features. Recent object detectors still do not have the accuracy-speed balance and do not work perfectly in a crowded area. In this paper, we propose a new method for human tracking, which is less depended on the instability of the object detector and inherits the success of Siamese Visual Tracking in recent years. While Siamese-family trackers work on a single object, we extend the problem by proposing a strategy that manages across multiple tracker and combines a human detector as a semi-supervisor for tracker location correction. Additionally, the detector-tracker associating strategy adapts the transformation of human poses and the acquisition of new pedestrians during the tracking process. Our method outperforms the state-of-the-art algorithms on our crowded scenes dataset.
ECHO: A Tool for Empirical Evaluation Cloud Chatbots

A chatbot is a software that interacts with humans by conducting conversations via textual or auditory methods. Chatbots have recently been used for plethora of applications including travel, medical, education, retail etc. Several cloud-based platforms (e.g. IBM, Amazon, Google, Microsoft) are available for developing and deploying chatbots. However, there is a lack of an evaluation methodology and a tool for evaluating chatbots comprehensively. Current approaches for comparing cloud-based chatbots are manual and rely on expert's judgement. In this short paper, we propose, devise, implement and demonstrate a tool namely ECHO for empirical evaluation of cloud-based chatbots. ECHO is capable of automatically evaluating multiple cloud-based chatbots and report the outcomes of the comparative evaluation. We validate the efficacy of ECHO by conducting comparative evaluation of 3 popular cloud-based chatbots in 2 different question-answering application scenarios with 3 levels of complexities.