Reviewerly/exHarmony huggingface dataset:
Developing Amaia: A Conversational Agent for Helping Portuguese Entrepreneurs—An Extensive Exploration of Question-Matching Approaches for Portuguese

This paper describes how we tackled the development of Amaia, a conversational agent for Portuguese entrepreneurs. After introducing the domain corpus used as Amaia’s Knowledge Base (KB), we make an extensive comparison of approaches for automatically matching user requests with Frequently Asked Questions (FAQs) in the KB, covering Information Retrieval (IR), approaches based on static and contextual word embeddings, and a model of Semantic Textual Similarity (STS) trained for Portuguese, which achieved the best performance. We further describe how we decreased the model’s complexity and improved scalability, with minimal impact on performance. In the end, Amaia combines an IR library and an STS model with reduced features. Towards a more human-like behavior, Amaia can also answer out-of-domain questions, based on a second corpus integrated in the KB. Such interactions are identified with a text classifier, also described in the paper.
VR Interface for Designing Multi-view-Camera Layout in a Large-Scale Space

Attention has been focused on sports broadcast, which used a free-viewpoint video that integrates multi-viewpoint images inside a computer and reproduces the appearance observed at arbitrary viewpoint. In a multi-view video shooting, it is necessary to arrange multiple cameras to surround the target space. In a large-scale space such a soccer stadium, it is necessary to determine where the cameras can be installed and to understand what kind of multi-view video can be shot. However, it is difficult to get such information in advance so that “location hunting” is needed usually. This paper presents a VR interface for supporting the preliminary consideration of multi-view camera arrangement in large-scale space. This VR interface outputs the multi-view camera layout on the 3D model from the shooting requirements for multi-view camera shooting and the viewing requirements for observation of the generated video. By using our interface, it is expected that the labor and time required to determine the layout of multi-view cameras can be drastically reduced.
The fundamental and pioneering role of the Geological Survey in China's social development

Abstract The China Geological Survey (CGS) is affiliated to the Ministry of Natural Resources of the People's Republic of China, and is responsible for the unified deployment, organization and implementation of basic, public and strategic geological surveying and mineral exploration, providing basic geological data and information for economic and social development, and responding to public demands. Over the past 20 years, the CGS has been focusing on the national key development strategies and undertaking geological surveys to support the nation's resources and energy security, regional economic development, disaster prevention and mitigation, poverty alleviation, and other related undertakings. Specifically, the CGS has been playing a role in the following areas: assessment of mineral resources, energy resources, urban geology and geo-environment, as well as offering update and application of geodata for public and several other services.
Efficient Target Detection Technique Using Image Matching Via Hybrid Feature Descriptors

Image matching is one of the most famous applications in computer vision and robotics. It is used for real time target detection and recognition systems. An ideal image matching technique should be robust to different image transformations such as scaling, illumination, noise and rotation. Different feature descriptors and detectors such as SIFT, SURF, BRISK, AKAZE and ORB have been introduced previously. However, each one of them has its own weak points in their matching performance. In this paper, we firstly perform a comprehensive comparison between such image matching techniques and their performance on different datasets of images. Then, we introduce a hybrid technique that combine between different feature descriptors. The experimental results showed that the proposed hybrid technique has improved the robustness of the image matching process. The conducted comparative analysis based on the execution time, number of keypoints detected and number of inliers (good matches after outliers’ rejection) has revealed the power of combined ORB and BRISK feature descriptors, outperforming the other feature descriptors combinations in enhancing the accuracy of matching and detection tasks.
Stronger Adversarial Attack: Using Mini-batch Gradient

Recent years, convolutional neural network (CNN) has achieved excellent performance in computer vision, but recent researches show that they're very vulnerable to adversarial examples, which results security risks to practical applications of CNN. Research on the generation algorithm of adversarial examples is conducive to evaluate the robustness of CNNs and promotes the study of defense algorithm. Although existing attack algorithms have achieved good performance in white-box manner, they behave unsatisfactorily in black-box manner. In this paper, we propose the mini-batch gradient based iterative method which could dramatically improve the attack ability in black-box manner. The previous iterative methods such as Iterative Fast Gradient Sign Method (I-FGSM) always make the generated adversarial examples fall into over-fitting, however we use minibatch gradient information to help them jump out of the local minimum. Moreover, the proposed method can also be considered as self-ensemble or implicit ensemble attack thus have a stronger attack power. The parallel randomization layer plays core role for providing the mini-batch gradient and the implement of it is very simple since there are no trainable variables. In order to further improve the attack success rate, we combined the momentum and the proposed method. We conducted several experiments on seven networks and the highest success rate of black-box attack has reached 94.1%, which is close to the whitebox manner. Since the proposed method greatly improve the transferability of adversarial examples, we think it could help evaluate the robustness of various deep networks.
Graph Pattern Matching: Capturing Bisimilar Subgraph

Graph pattern matching that aims to seek out answer graphs in a data graph matching a provided graph, plays a fundamental role as a part of graph search for graph databases. “Matching” indicates that the two graphs are correlated, such as bisimulation, isomorphism, simulation, etc. The strictness of bisimulation is between simulation and isomorphism. Seldom work has been done to search for bisimulation subgraphs. This research focuses on the problem. The symbol [Formula: see text] is introduced to fundamental modal logic language, thereby yielding [Formula: see text] language; the symbols [Formula: see text] is added for forming [Formula: see text] formulas. Then conclusions about graph bisimulations are shown. Subsequently, a theorem with detailed proof is presented, stating that [Formula: see text] formulas characterize finite directed graphs modulo bisimulation. According to the conclusions and theorem, algorithms for finding subgraphs are proposed. After dividing the query graph, the match graphs undergo the characterization using [Formula: see text] formulas. In the data graphs, by model checking the formulas, the answer graphs exhibiting bisimilarity to the match graphs are able to be captured.
Re-Training StyleGAN - A First Step Towards Building Large, Scalable Synthetic Facial Datasets

StyleGAN is a state-of-art generative adversarial network architecture that generates random 2D high-quality synthetic facial data samples. In this paper we recap the StyleGAN architecture and training methodology and present our experiences of retraining it on a number of alternative public datasets. Practical issues and challenges arising from the retraining process are discussed. Tests and validation results are presented and a comparative analysis of several different re-trained StyleGAN weightings is provided. The role of this tool in building large, scalable datasets of synthetic facial data is also discussed.
Quantum Annealing via Path-Integral Monte Carlo With Data Augmentation

This article considers quantum annealing in the Ising framework for solving combinatorial optimization problems. The path-integral Monte Carlo simulation approach is often used to approximate quantum annealing and implement the approximation by classical computers, which refers to simulated quantum annealing (SQA). In this article, we introduce a data augmentation scheme into SQA and develop a new algorithm for its implementation. The proposed algorithm reveals new insights on the sampling behaviors in SQA. Theoretical analyses are established to justify the algorithm, and numerical studies are conducted to check its performance and to confirm the theoretical findings. Supplementary materials for this article are available online.
Convergence and Parameters Setting of Continuous Hopfield Neural Networks Applied to Image Restoration Problem

Nour-eddine, Joudar Hassan, Ramchoun En-Naimani, Zakariae Mohamed, EttaouilIn image processing, Hopfield neural networks (HNNs) have been successively invested to solve the optimization model of image restoration. However, in the discrete case, HNNs lead to the fluctuation behaviors due to the limitation of the hard limit activators. The continuous model of HNNs generates non feasible solutions owing to the trial-and-error setting values process of the model parameters. To deal with these issues effectively, we suggest a new energy function with appropriate parameters settled by the stability analysis of the network. Performance of our method is demonstrated numerically and visually by several computational tests.
An Efficient Method Based on Region-adjacent Embedding for Text Classification of Chinese Electronic Medical Records

The following topics are dealt with: learning (artificial intelligence); feature extraction; convolutional neural nets; image classification; neural nets; natural language processing; text analysis; pattern clustering; cloud computing; and diseases.
Marketing and Artificial Intelligence

Technologies are changing marketing due to the amount of information available to consumers, along with information being generated by consumers. Marketers face a challenge with greater volume and variety of data generated at a faster rate than ever before along with fragmentation of channels. This data when combined with artificial intelligence presents an opportunity to marketers to provide value add at a granular level and a personalized customer experience round the clock 24/7/365. Treating customers as individuals by offering an optimized personalized offering, sending the right personalized message at the right time through their preferred channel is the promise of data fed into AI algorithms. Artificial intelligence has the potential to transform companies by making sense out of an insanely voluminous variety of data being generated with its ability to serve customers more effectively and efficiently, personalizing at scale.
Fuzzy Recognition by Logic-Predicate Network

predicate networkThe paper presents a description and justification of the correctness of fuzzy recognition by a logic-predicate network.Such a network is designed to recognize complex structured objects that can be described by predicate formulas.The NP-hardness of such an object recognition requires to separate the learning process, leaving it exponentially hard, and the recognition process itself.The learning process consists in extraction of groups of features (properties of elements of an object and the relations between these elements) that are common for objects of the same class.The main result of a paper is a reconstruction of a logic-predicate recognition cell.Such a reconstruction allows to recognize objects with descriptions not isomorphic to that from a training set and to calculate a degree of coincidence between the recognized object features and the features inherent to objects from the extracted group.
Properness and Consistency of Syntactico-Semantic Reasoning using PCFG and MEBN

The paper proposes a formal approach for parsing grammatical derivations in the context of the principle of semantic compositionality by defining a mapping between Probabilistic Context Free Grammar (PCFG) and Multi Entity Bayesian Network (MEBN) theory, which is a first-order logic for modelling probabilistic knowledge bases. The principle of semantic compositionality states that meaning of compound expressions is dependent on meanings of constituent expressions forming the compound expression. Typical pattern analysis applications focus on syntactic patterns ignoring semantic patterns governing the domain in which pattern analysis is attempted. The paper introduces the concepts and terminologies of the mapping between PCFG and MEBN theory. Further the paper outlines a modified version of CYK parser algorithm for parsing PCFG derivations driven by MEBN. Using Kullback- Leibler divergence an outline for proving properness and consistency of the PCFG mapped with MEBN is discussed.
READING ANXIETY IN ENGLISH AS A FOREIGN LANGUAGE FOR UNDERGRADUATE STUDENTS IN INDONESIA

Exploring students’ anxiety and its sources in a foreign language have highly paid the attention of many researchers in the field of foreign language teaching and learning, especially in English Language Teaching (ELT). On the other hand, the anxiety and its sources of foreign language learning itself affect the achievement of specific language skills such as reading which has infrequently been investigated. Some studies focused only on Foreign Language Classroom Anxiety and/or also Foreign Language Speaking Anxiety in EFL undergraduate students.  Thus, this research was intended to investigate students’ reading anxiety in English as a Foreign Language (EFL) of the undergraduate student class. There were 37 non-English program undergraduate students participated in this research. The research method was a case study. The data were collected using questionnaires that measured students’ anxiety and the sources of students’ anxiety in reading context, Foreign Language Reading Anxiety Scale designed by Saito et al. (1999) and the questionnaire developed by Ahmad et al. (2013). The research found out that most students identified anxiety in medium level. The sources of students' reading anxiety were based on the material features including unknown vocabulary and unfamiliar culture as the most sources. The personal factors found were afraid of making errors which is less than worry about reading effect. The implication is the lecturers can assist foreign language students to reduce foreign language reading anxiety by improving the students’ self-confidence to deal with the personal factor.
Information Security Awareness (ISA) towards the Intention to Comply and Demographic Factors

Evidence from the literature and observation conducted from various practice in managing project or developing product within the organization suggested that human factors in term of control and direction can be the greatest threats to the organization to ensure the high quality of information security. Therefore, fewer research was conducted towards assessing the level of end-user awareness in a practical way to extract the relationship of demographic factor with user compliance in order to understand the working mechanism. Thus, this study prepares a literature review with the PRISMA model to implement a systematic process in an integrated manner in order to reveal representative information on the subject. Subsequently, a correspondence analysis (CA) was conducted to discover the hidden meaning of the relevant demographic factors that might affect the intention to comply with the organization's policy to protect confidential, sensitive and confidential information. One interesting result stated that ethnicity provides a stronger association by providing a total variation of 0.121, singular value of 0.248, chi-square of 26.208 and standard deviation of 0.063.
k-Anonymity on Graphs Using the Szemerédi Regularity Lemma

Graph anonymization aims at reducing the ability of an attacker to identify the nodes of a graph by obfuscating its structural information. In k-anonymity, this means making each node indistinguishable from at least other k-1 nodes. Simply stripping the nodes of a graph of their identifying label is insufficient, as with enough structural knowledge an attacker can still recover the nodes identities. We propose an algorithm to enforce k-anonymity based on the Szemerédi regularity lemma. Given a graph, we start by computing a regular partition of its nodes. The Szemerédi regularity lemma ensures that such a partition exists and that the edges between the sets of nodes behave quasi-randomly. With this partition to hand, we anonymize the graph by randomizing the edges within each set, obtaining a graph that is structurally similar to the original one yet the nodes within each set are structurally indistinguishable. Unlike other k-anonymization methods, our approach does not consider a single type of attack, but instead it aims to prevent any structure-based de-anonymization attempt. We test our framework on a wide range of real-world networks and we compare it against another simple yet widely used k-anonymization technique demonstrating the effectiveness of our approach.
Research on the Multimedia Corpus Database Construction of Yongjiang River Area Dialect

This The situation of Yongjiang River area dialect decay is getting worse because of the unique geographical location and economic development. In this paper, firstly, divides the dialect area of the Yongjiang River and defines the dialect regions, the speakers, the survey method and the survey time. Secondly, after the determination of investigation area and relevant candidates, the recording and photography of audio and video are entered. Thirdly, the corpus transliteration is performed, including transcribing the recording or video into text, the corresponding international phonetic transcription and the Mandarin translation. Finally, the database design of ELAN is completed. This study makes up for the narrow situation in which the dialects storage and protection stay traditionally in writing and oral path.
Effect of Data Parameters and Seeding on k-Means and k-Medoids

k-means and k-medoids are arguably the two most popular clustering methods. This paper reports an empirical study of the relative (de)merits of these two methods. We compare their performances under varying data conditions. We also assess the impact of replacing random selection of initial cluster centers, with a systematic approach. The technique employed in our study, for initial centroid selection, is the state-of-art D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -weighing technique used in k-means ++, a variant of k-means. We use an extensive set of well-known and recommended benchmark datasets. These datasets allow for the study of performance under varying data size; varying cluster count, imbalance and overlap; and varying dimensionality. We establish that although k-means is much less accurate than k-medoids (which is grossly inefficient and less scalable), the accuracy gap is reduced significantly by seeding k-means. On the other hand, seeding does not improve k-medoids' accuracy but significantly worsens its efficiency (typically doubles its running time). Increased dimensionality tends to improve the accuracy of both algorithms, while increased overlap tends to worsen them.
Extraction Algorithm of English Text Information From Color Images Based on Radial Wavelet Transform

The Chinese and English text information of color image reflects some important contents of color image to a certain extent, and these texts are extracted automatically, which has important application value in the fields of information retrieval, digital library, web page retrieval and intelligent transportation.In this paper, various signal extraction techniques based on radial wavelet transform modulus Maxima are analyzed, and it is found that these techniques have poor ability to extract weak signals, and the higher requirements for directionality lead to pseudo-boundary phenomena in two-dimensional image extraction results.Based on the correlation denoising method of radial wavelet coefficients, the radial wavelet entropy is introduced into the field of signal extraction, and the complex Morlet radial wavelet is selected as the basis function.A complex Morlet radial wavelet entropy extraction algorithm suitable for extracting the English text characteristics of weak signal color images is proposed.In addition, a method of scene text recognition is proposed.Based on D-SIFT local features and spatio-temporal histogram, this method vectorizes text samples in the framework of Bo Ws model.Because the spatio-temporal histogram can flexibly model the structural information of the text, this method can effectively describe the scene text.On this basis, the selective ensemble learning method is used to improve the performance of the text extractor.In order to expand the application range of the algorithm and improve the running efficiency of the integrated extractor, a model compression method based on color image English text samples and integrated extractor is proposed.The integrated extractor which takes up more space and runs slowly is compressed into an equivalent and more efficient one.In order to reduce the number of pseudo-samples needed in the process of model compression, a method of training local extractor based on color image English text samples is proposed, which greatly reduces the number of pseudo-samples needed and improves the efficiency of the model compression method.
Performance Analysis of Cryptographic Algorithm in FPGA

Elliptic Curve Digital Signature Algorithm (ECDSA) is an asymmetric algorithm used for expanded security with data integrity and authentication in the On-Board Units (OBU's) of Intelligent Transport Systems (ITS). Field programmable gate array is an attractive platform for hardware implementation of cryptographic algorithms, providing better security and performance in response to need. Hence, the various performance parameters of ECDSA such as key size, Look up Table (LUTs) slices and key pair points on the elliptic curve are analyzed in FPGA. In this work, we have designed a 16-bit Elliptic curve cryptography digital signature algorithm. The Elliptic curve cryptography covers all relevant asymmetric cryptographic natives like digital signature and key exchange. ECC key generation, signature generation, and verification generation algorithms were executed for the 16-bit ECDSA in Verilog HDL and then simulated and synthesized using Xilinx-virtex7 and Vivado 2018.3-zed board. It was seen that the number of slices involved by the ECDSA 16-bit algorithm in Xilinx (virtex7) is lesser than the available number of slices.
Research on Investment Forecast of Distribution Based on BP Neural Network

In order to further improve the construction of distribution network, to solve the problem of insufficient investment and excessive surplus, to improve the efficiency of capital utilization, we should to forecast and analyze the investment of distribution network project urgently. Webuild the investment prediction model of distribution network engineering, determine the key elements that affect the project investment cost. We obtain the predicted value of the project after model training, thus provide data reference for the project cost control.