Reviewerly/exHarmony huggingface dataset:
Dialogue Breakdown and Confusion between Elements and Category

Avoiding dialogue breakdown is important in HRI and HAI. In this paper, we investigated why dialogue breakdown occurs. We hypothesize that confusion between elements and category is one important reason. Elements means individual strange utterances, and category means the entire ability and performance of a robot. We hypothesized that a user who confuses elements and category will tend to lose the motivation to continue interacting with a robot or agent when the robot or agent makes a strange utterance. To verify this hypothesis, we conducted an experiment. We asked participants to perform a memory task cited in a previous work. We separated them into two groups, the no-confusion group and confusion group, and we showed them a movie in which a robot made a mistake on a math problem. After that, we asked them in a questionnaire about their impression of the robot. We conducted a t-test between the two groups for each question. As a result, the participants who confused elements and category tended to brand the robot as having low ability and performance when it made a mistake, and those who were not confused did not have reduced trustworthiness in the robot or reduced motivation for continuing to interact when the robot made a mistake. These results supports our hypothesis.
Identity-based Data Possession Verification Scheme for Multi-Replica Storage in Cloud

Abstract In the cloud storage environment, how to efficiently and dynamically complete the integrity verification of multi-user and multi-replica data is a challenging problem. Based on the bilinear mapping signature mechanism and the multi-branch authentication tree, we propose an identity-based integrity verification scheme of multi-replica data for cloud, which solves the problem of large public key storage and management overhead in signature algorithm based on traditional public key cryptosystems. The proof shows that our scheme satisfies the robustness and has the function of protecting privacy. The results of analysis indicate that, compared with the existing similar schemes, our scheme has higher communication efficiency and computing performance.
State-of-the-Art survey of deep learning based sketch retrieval

Sketch based image retrieval (SBIR) is an important research issue in computer vision, which is a flexible and convenient way to retrieve target images. How to minimize the difference between the sketch domain and the image domain is the key to the SBIR method. Deep learning methods have used in image processing research widely, which break through the limitations of traditional methods, which extract high-dimensional features from a large amount of data and have been proved to effectively solve the cross-domain modeling issues. The research focuses on the deep learning-based sketch retrieval method and reviews the related research work, in the aspects of deep feature extraction model, coarse-grained and fine-grained retrieval based on deep learning, category generalization. Finally, the challenges and future research directions of sketch retrieval are summarized and prospected.
Video Augmentation Method for the Facilitation of Skill Learning in Karate

This paper describes an image processing method that can facilitate skill learning in karate using recorded karate competition videos. The proposed method compensates for a partially filmed karate court in the input video image by generating a complete court image via a network-based generator. To evaluate our method, a player-focused video was augmented with complete competition field information. The augmented video would be useful for observing both players' actions as well as the player positioning within the entire competition court. In particular, a complete court image generator was developed based on a generative adversarial network (GAN) framework to perform this video augmentation.
Tone Recognition Database of Electronic Pipe Organ Based on Artificial Intelligence

In the past few decades, artificial intelligence technology has experienced rapid development, and its application in modern industrial systems has grown rapidly. This research mainly discusses the construction of a database of electronic pipe organ tone recognition based on artificial intelligence. The timbre synthesis module realizes the timbre synthesis of the electronic pipe organ according to the current timbre parameters. The audio time domain information (that is, the audio data obtained by file analysis) is framed and windowed, and fast Fourier transform (FFT) is performed on each frame to obtain the frequency domain information of each frame. The harmonic peak method based on improved confidence is used to identify the pitch, obtain the fundamental tone of the tone, and calculate its multiplier. Based on the timbre parameters obtained in the timbre parameter editing interface, calculate the frequency domain information of the synthesized timbre of each frame, and then perform the inverse Fourier transform to obtain the time domain waveform of each frame; connect the time domain waveforms of different frames by the cross-average method to obtain the time-domain waveform of the synthesized tone (that is, the audio data of the synthesized tone). After collecting the sound of the electronic pipe organ, the audio needs to be denoised, and the imported audio file needs to be parsed to obtain the audio data information. Then, the audio data are frequency-converted and the timbre characteristic information is analyzed; the timbre parameters are obtained through the human-computer interaction interface based on artificial intelligence, and the timbre of the electronic pipe organ is generated. If the timbre effect is not satisfactory, you can re-edit the timbre parameters through the human-computer interaction interface to generate timbre. During the experiment, the overall recognition rate of 3762 notes and 286 beats was 88.6%. The model designed in this study can flexibly generate electronic pipe organ sound libraries of different qualities to meet the requirements of sound authenticity.
Bank-based financial development and foreign direct investment in sub-Saharan African countries: a dynamic causal linkage

In this paper, the causal relationship between financial development and foreign direct investment in sub-Saharan African (SSA) countries is examined. Three proxies of financial development, namely bank deposits, deposit money bank assets, and liquid liabilities have been used to examine this linkage. Using a multivariate panel Granger-causality model, the study found that the causal relationship between financial development and foreign direct investment is dependent on the variable used to measure the level of financial development. The relationship also varies over time. Overall, the study found a causal flow from FDI to financial development to predominate, at least in the short run. The study, therefore, recommends that policies aimed at attracting foreign direct investment inflows should be prioritised in SSA countries in the short run, in order to foster the development of the financial sector in the region.
Inaugural Speech Classification with Named Entities and Key Phrases

The amount of online textual data is massive and is beyond homo sapiens' manual understanding. The techniques to analyze the massive textual data have been continuously developed. Machine learning techniques are providing excellent tools to help our understanding the huge amount of textual data by providing a succinct summary of documents, recommendation of articles, etc. Using named entities and key phrases can help better understanding the textual data and provides a smaller feature set than using unigram features. Our work shows that terms of named entities and key phrases actually provide truly meaningful results in classifying documents into target classes and hence provides the positive direction toward massive data classifications by using a smaller feature set. In this work, we classified inaugural speeches of the US presents by using named entities and key phrases into two parties: Republican and Democratic parties. We utilized a supervised learning approach (Support Vector Machines) and achieved 100% accuracy of performance in classifying inaugural speeches by using named entities and key phrases related to patriotic terms.
Fast scene labeling via structural inference

Scene labeling or parsing aims to assign pixelwise semantic labels for an input image. Existing CNN-based models cannot leverage the label dependencies, while RNN-based models predict labels within the local context. In this paper, we propose a fast LSTM scene labeling network via structural inference. A minimum spanning tree is used to build the image structure for constructing semantic relationships. This structure allows efficient generation of direct parent–child dependencies for arbitrary levels of superpixels, and thus structural relationships can be learned with LSTM. In particular, we propose a bi-directional recurrent network to model the information flow along the parent–child path. In this way, the recurrent units in both coarse and fine levels can mutually transfer the global and local context information in the entire image structure. The proposed network is extremely fast, and it is 2.5× faster than the state-of-the-art RNN-based models. Extensive expseriments demonstrate that the proposed method provides a significant improvement in learning the label dependencies, and it outperforms state-of-the-art methods on different benchmarks.
First Leap Towards Development of Dialogue System for Autonomous Bus

This paper describes the dialogue system for the autonomous bus. Without driver onboard in an autonomous bus, a passenger needs someone to talk to when in need. In that scenario, the dialogue system in this work helps a passenger to manage travel plan. The system is designed to work in both chat-oriented and goal-oriented conversations. The internal design is rule-based utterance matching. We also describe the database, which can be easily expandable by the administrator for future development. The dialogue system deployment on android smartphone interface is demonstrated in this paper.
Analysis of Influencing Factors of Teaching Effect Based on Structural Equation Model

Structural equation model is a multivariate statistical analysis method. It can not only test some unpredictable abstract ideas, but also design parameters for the causal connection model between independent variables and dependent variables. Among them, the analysis of various latent variables is based on the verification factor analysis technology. The research first collects various relevant data, derives the latent variables and measurement variables, then composes the measurement model, and then verifies the adaptability of the measurement model structure mode through actual data collection. When such suitable factors are determined, a causal model based on path analysis technology for latent variables, that is, a structural model, can be used for parameter design. This article uses the structural equation model as an analysis tool, starting from the three aspects of teachers, cases, and students to evaluate the application effect of the case teaching method in the classroom teaching of intellectual property law course and construct a structural equation model of the influencing factors of the case teaching effect. To understand students’ satisfaction with case teaching effects and related influencing factors, the research results show that the effect of case teaching is jointly affected by the level of teachers, case selection, and student response. Among them, teacher factors have the greatest impact on case teaching effects, followed by case factors, and students have the least.
Toward community answer selection by jointly static and dynamic user expertise modeling

Answer selection, ranking high-quality answers first, is a significant problem for the community question answering sites. Existing approaches usually consider it as a text matching task, and then calculate the quality of answers via their semantic relevance to the given question. However, they thoroughly ignore the influence of other multiple factors in the community, such as the user expertise. In this paper, we propose an answer selection model based on the user expertise modeling, which simultaneously considers the social influence and the personal interest that affect the user expertise from different views. Specifically, we propose an inductive strategy to aggregate the social influence of neighbors. Besides, we introduce the explicit topic interest of users and capture the context-based personal interest by weighing the activation of each topic. Moreover, we construct two real-world datasets containing rich user information. Extensive experiments on two datasets demonstrate that our model outperforms several state-of-the-art models.
Encryption Image using HiSea Algorithm Based on Android Operating System

The mobile phone environment represents one of the important environments in encryption various multimedia (audio, image, and video) , and this depends on the type of algorithm used in the encryption process, as phones have limited memory and computational resources, Therefore the selection of the algorithm must be compatible with the mobile environment in terms of speed, safety and flexibility in addition to choosing an algorithm that The simplicity and safety of the image encryption process was investigated with lightweight and efficient computing. In this paper, Hybrid Cube Encryption (HiSec) was used. When implementing this algorithm in a smart phone environment, the results showed the ease of encrypting images and retrieving the original image, in addition to that it only requires small computational resources, and the algorithm was very effective in encrypting images on mobile phones. This suggested method has been implemented in the mobile environment with android OS. The proposed method has been programmed in JAVA, and the method has been tried on different types of mobile phones (such as Huawei Nova 2, Huawei Nova 7, HTC, NOT 8, Galaxy S 20, and HONOR).
Checking for model failure and for prior-data conflict with the constrained multinomial model

Multinomial models can be difficult to use when constraints are placed on the probabilities. An exact model checking procedure for such models is developed based on a uniform prior on the full multinomial model. For inference, a nonuniform prior can be used and a consistency theorem is proved concerning a check for prior-data conflict with the chosen prior. Applications are presented and a new elicitation methodology is developed for multinomial models with ordered probabilities.
Marginal Utility Approach for Quality of Service Evaluation in IT infrastructure

IT-services are corner stones of any computing IT-infrastructures. In order to make them working properly their quality should be evaluated, measured and controlled. A lot of researches were conducted on quality of service evaluation, and many approaches were discovered. However, there is still need for connecting user satisfaction as subjective parameter with objective parameters or key performance indicators. Possible way for connecting those characteristics is to use marginal utility function in order to define direct dependency between quality of experience and quality of service. This kind of solution was proposed in this research.
Missed diagnoses detection by adversarial learning

Missed diagnosis has been a serious public health issue in clinical diagnosis and treatment, it may cause disease deterioration and reduce cure rate. In recent years, many deep learning approaches have been proposed for automated medical image classification. In this study, we propose a new methodology to detect the images missed diagnosed by deep learning classifiers. Based on the intermediate feature maps of deep learning classifiers, the proposed model can detect missed diagnosed sample and reduce the missed diagnoses rate, with no obvious decrease in the accuracy. The proposed model is constructed using generative adversarial networks and autoencoders to learn consistent mapping from data space to latent space, and is trained with adversarial examples. After training, the output of the discriminator is used to recognize missed diagnosed samples. The method is evaluated on different network architectures and various types of medical image datasets and achieves promising results. Compared with other state-of-the-art approaches, the proposed method shows superior performance on most datasets.
Simulation and Classification of Mobile Communication Base Station Downlink Signal Based on CNN

In recent years, with the rapid deployment of fifth-generation base stations, mobile communication signals are becoming more and more complex. How to identify and classify those signals is a current research hotspot. In this paper, we evaluated several CNN-based automatic modulation classification models on eight types of cellular signals generated with MatlabR2020a and expressed in time-domain or frequency-domain. The experimental results showed that all the tested models with different forms of inputs got a classification accuracy of about 80% when the signal-to-noise ratio was higher than 5 dB. Also, using the time-domain signals as inputs got the highest classification accuracy when the signal-to-noise ratio was low, whereas using frequency-domain signals as inputs greatly reduced the computational time relative to using time-domain signals.
Identification of Analogue Modulated Signal via Artificial Intelligence

Abstract Referring to a huge usage of the analogue modulated signal in both civilian and military applications, and impotency of identification it, the importance is came from many reasons also to how to get an algorithm to recognize an analogue modulated signal via AI for high performance of identification and simple to use. The identification is a middle-step between recognizing and signal demodulation. Selecting the most suitable variables are for input modulated signal and with (AI) assistant to identify some of analogue modulated signals such as (AM, DSB, SSB, and FM). Using simulation with Gaussian noise (0 dB to 10 dB) and AI. Classification system consisting from 3 parts: 1-Pre-processing stage which is talking about Feature keys extraction did. The element keys extraction did so as to get input include keys for the AAMR classifier, 2-Training stage which talked about the preparation, and Analog programmed adjustment acknowledgment (AAMR) classifier advancement., and 3-Testing stage which talked on the classifier performance evaluated using 25% of all generated data as a test data. Using two algorithms (SCG and CONJGRAD), In the SCG, the possibility of each single key feature for each single modulation scheme with variance SNR results got. A couple of methods compared and the results show that the performance of the AI based recognition system is superior to the decision classifiers based on way with less SNR values. Both of the suggested identification systems are capable to discriminate (AM, DSB, SSB, and FM) at SNR&gt;5 dB with a success rate 100%.
The content of the concept “quality of education” and the definition of methods to measure it

The concept “quality of education” is widely used in modern educational practice; however, in teaching, there is no common understanding of the pedagogical phenomenon that underlies this concept. In this regard, it is necessary to present reflections on the content of such terms as “education” and “quality” in relation to the cultural and historical prerequisites for the formation of the category “quality of education” and various approaches to its content. Besides, the article touches upon different approaches to the procedures for measuring the quality of education and distinguishes their strengths and weaknesses. The authors encourage the reader to think about the problems of defining the quality of education as a pedagogical phenomenon in modern conditions through cultural and historical traditions. The study results in the consideration of various aspects of the evolution of such terms as “quality” and “education” in Russian pedagogy, which have influenced the modern interpretations of the concept “quality of education” and the approaches to its measurements. The article reveals the advantages of a practical understanding of the quality of education, as well as the risks associated with the transformation of education into a service and the absolutization of its quantitative parameters in measuring the quality of education. The authors raise the questions that remain open and require their solution despite the continued interest in the problems of education quality in pedagogical theory and practice. The novelty of the research is provided by taking into consideration Russian cultural and historical traditions of the formation of such terms as “education” and “quality” and the justification of the possible ways of their use in modern approaches to the measurements of quality of education, as well as the justification of the importance of using the applied aspect of the quality of education in Russian educational practice.
Application of AI Technology in Defense of Big Data Network Security

Abstract Artificial intelligence technology is a new technology based on computer science and technology, combined with Internet technology. It has been widely used in all walks of life. With the development of information technology, the coverage of the Internet is wider, more and more users are involved, and the transmission and sharing of information is more and more convenient. This makes people’s request for information security rise to a higher level. The arrival of the big data era has put forward higher requirements for the security protection of the information technology security personnel for the complex data. Artificial intelligence technology can solve this problem well in the application of big data security defense. Artificial intelligence technology can not only enhance the security of network information, but also will change the way people live in the future with the wider application in the network, which will profoundly affect people’s life in the information age.
New Countermeasures Against Differential Fault Attacks

As a kind of classical side channel attacks, differential fault attacks (DFA) has some favorable properties, such as the flexible fault injections, lower attack complexity and so on, which usually induces a serious threat to the engineering application of encryption algorithms. At present, how to resist DFA has became an important research topic. In this paper, a new general protection scheme against DFA is proposed by combining the basic idea of fault detection and infective countermeasure. Specifically, in this scheme, some fresh random values are used to scramble the difference of ciphertexts and the infective function so that the secret key information in the encryption process cannot be derived. In particular, the new scheme can not only resist to double fault attacks, but also can quickly determine the exact fault injected location of the target encryption cipher. It has the advantages of higher accuracy, easier implementation and less time complexity. As a application, the protection scheme for the Pyjamask authentication encryption algorithm is also presented, where Pyjamask cipher is current one of the second round candidate algorithms of lightweight ciphers competition by the National Institute of Standards and Technology (NIST). Moreover, the Skinny encryption algorithm is used as the encryption function for infective calculation to scramble the secret key information of cipher. It is illustrated this scheme can effectively protect the Pyjamask cipher against DFA in practical simulations.