Reviewerly/exHarmony huggingface dataset:
Mitigating Privacy Leak by Injecting Unique Noise into the Traffic of Smart Speakers

In recent years, in the Internet, it is common to encrypt communication lines for the assumption that the contents of communication are eavesdropped, but even if the communication lines are secure, there are many cases in which the possibility of the contents of communication being leaked to a third party by a side-channel attack is not taken into account. Although it is important that the contents of all communication are not known by the third party, the information related to privacy may be leaked unintentionally by only encrypting traffics. In this study, we made smart speakers, an IoT device that has started to penetrate into our daily lives, to perform eight kinds of activities, and used their traffic data to estimate their activities with CNN, and we were able to estimate the activities with 98% accuracy. As a counter measure, we propose a method to reduce the accuracy of estimation by adding dummy packets to their communication traffic as noise. While adding random noise only reduced the accuracy of our machine learning model to 0.5 with 800 [packets/100msec] of noise, by adding well-designed noise, we were able to reduce the accuracy to 0.28 with 200 [packets/100msec] of noise of the same model. In this study, we made smart speakers, an IoT device that has started to penetrate into our daily lives, to perform eight kinds of activities, and used their traffic data to estimate their activities with CNN, and we were able to estimate the activities with 98% accuracy. As a counter measure, we propose a method to reduce the accuracy of estimation by adding dummy packets to their communication traffic as noise. While adding random noise only reduced the accuracy of our machine learning model to 0.5 with 800 [packets/100msec] of noise, by adding well-designed noise, we were able to reduce the accuracy to 0.28 with 200 [packets/100msec] of noise of the same model. While adding random noise only reduced the accuracy of our machine learning model to 0.5 with 800 [packets/100msec] of noise, by adding well-designed noise, we were able to reduce the accuracy to 0.28 with 200 [packets/100msec] of noise of the same model.
Statistical Methods for Analyzing and Processing Data Components When Recognizing Visual Objects in the Space of Key Point Descriptors

In this paper, we propose the improvement of structural pattern recognition techniques in computer vision systems. We performed the transformation of the key point descriptors space into the space of the data statistical distributions to increase the speed of data processing. These distributions are based on the set of component values obtained by partitioning descriptors into non-intersecting fragments. The compression of data into the fixed set of bits allows to simplify processing and to reduce the quantity of computation operations. BRISK or ORB are suggested to be used as key point detectors, because they form binary descriptors which greatly simplifies processing and analysis. A number of traditional and up-to-date statistical approaches have been proposed and analyzed to determine the relevance of object descriptions by their distribution values and according to the significance level: the chi-square criterion, Renyi divergence, the non-parametric criterion and z-criterion. Additionally, the usage of Manhattan distance between the values of the distribution matrices was tested. The experimental part of the investigations presents the results of calculations and software modeling of the proposed methods for the icons dataset. It is shown, that the statistical distribution models are able to separate set of features effectively even in the case of a small number of bits in the data fragment. An improvement of distinguishing level is confirmed when increasing the size of the fragment in the description structure. The implementation of statistical distributions reduced processing time by hundreds of times preserving sufficient recognition quality.
A Study of Computer Aided Translation Based on Artificial Intelligence Technology

Abstract Artificial intelligence technology has been widely used in various fields, and has become the inevitable trend of social development. Based on artificial intelligence technology, this paper studies the computer-aided translation. Firstly, the concept and development of artificial intelligence technology are briefly introduced. Secondly, the function and application of computer-aided translation under the background of artificial intelligence are analysed. Thirdly, the application of artificial intelligence in computer translation is expounded. Finally, the development of computer-aided translation is prospected.
Garbage Sorting System Based on Composite Layer CNN and Multi-Robots

Abstract Object detection technology is the key problem in the garbage sorting system. There are many difficulties in identifying the target in the garbage, such as poor visibility and targets being coverage. The existing image processing methods are difficult to detect the target objects in the garbage sorting system. To solve this problem, this paper proposes a garbage sorting system based on convolution neural network and robots. The CNN network combines the features of three different network layers and predicts the location and angle information of the target objects at the same time, which we called composite layer. Coupled with the training solution of classification regression and angle prediction, the performance of object detection is further improved. The entire sorting system is composed of three subsystems: vision system, electrical system and mechanical system. The vision system is responsible for the image processing and send the position and attitude information to the electrical system. The electrical system controls the different robots to do sorting motion. We construct a TestSet which has 23983 objects to be detected. The experiment results show that our system’s Drop rate is 1.7% and False rate is 4.8%, which is good enough to meet the needs.
Quantified Temporal Alethic Boulesic Doxastic Logic

Abstract The paper develops a set of quantified temporal alethic boulesic doxastic systems. Every system in this set consists of five parts: a ‘quantified’ part, a temporal part, a modal (alethic) part, a boulesic part and a doxastic part. There are no systems in the literature that combine all of these branches of logic. Hence, all systems in this paper are new. Every system is defined both semantically and proof-theoretically. The semantic apparatus consists of a kind of $$T \times W$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>T</mml:mi><mml:mo>×</mml:mo><mml:mi>W</mml:mi></mml:mrow></mml:math> models, and the proof-theoretical apparatus of semantic tableaux. The ‘quantified part’ of the systems includes relational predicates and the identity symbol. The quantifiers are, in effect, a kind of possibilist quantifiers that vary over every object in the domain. The tableaux rules are classical. The alethic part contains two types of modal operators for absolute and historical necessity and possibility. According to ‘boulesic logic’ (the logic of the will), ‘willing’ (‘consenting’, ‘rejecting’, ‘indifference’ and ‘non-indifference’) is a kind of modal operator. Doxastic logic is the logic of beliefs; it treats ‘believing’ (and ‘conceiving’) as a kind of modal operator. I will explore some possible relationships between these different parts, and investigate some principles that include more than one type of logical expression. I will show that every tableau system in the paper is sound and complete with respect to its semantics. Finally, I consider an example of a valid argument and an example of an invalid sentence. I show how one can use semantic tableaux to establish validity and invalidity and read off countermodels. These examples illustrate the philosophical usefulness of the systems that are introduced in this paper.
A label-oriented loss function for learning sentence representations

Neural network methods which leverage word-embedding obtained from unsupervised learning models have been widely adopted in many natural language processing (NLP) tasks, including sentiment analysis and sentence classification. Existing sentence representation generation approaches which serve for classification tasks generally rely on complex deep neural networks but relatively simple loss functions, such as cross entropy loss function. These approaches cannot produce satisfactory separable sentence representations because the usage of cross entropy may ignore the sentiment and semantic information of the labels. To extract useful information from labels for improving the distinguishability of the obtained sentence representations, this paper proposes a label-oriented loss function. The proposed loss function takes advantage of the word-embeddings of labels to guide the production of meaningful sentence representations which serve for downstream classification tasks. Compared with existing end-to-end approaches, the evaluation experiments on several datasets illustrate that using the proposed loss function can achieve competitive and even better classification results.
Path Planning by Search Algorithms in Graph-Represented Workspaces

Path planning is an essential task in autonomous mobile robotics that demands to navigate following a minimum-cost path, which involves partitioning the landscape in nodes and the use of combinatorial optimization methods to find the optimal sequence of nodes to follow. Traditional algorithms such as the A* and Dijkstra are computationally efficient in landscapes with a reduced number of nodes. Most of the practical applications require to use a significantly large number of nodes up to the point that the problem might be computationally explosive. This work contributes to state-of-the-art with two heuristics for the A* algorithm that allows finding the optimal path in landscapes with a large number of nodes. The heuristics used the Euclidean and Manhattan distance in the estimation function. We present a comparative analysis of our proposal against the Dijkstra’s and A* algorithms. All the experiments were achieved using a simulation-platform specially designed for testing important algorithm features, such as the grid size, benchmark problems, the design of custom-made test sceneries, and others. Relevant results are drawn to continue working in this line.
Applicant Mapping of Predicting Seriousness in Choosing Private University using Self Organizing Map (SOM) Algorithm

The admission of new students is an initial effort made by PTS, but many applicants have less serious interest, so it has a negative impact on universities. Universities have to work very hard, to find prospective students, when the competition is getting tougher from time to time. Therefore we need a system that can predict, between truly serious and less serious applicants. Self Organizing Map (SOM) application, is a method of artificial neural network (ANN) that can be used to make predictions. Prediction of applicants is done by mapping the characteristics of the applicants, based on nine indicators obtained from the applicant's input data. The nine indicators consist of pathways of entry, distance from home to university, applicant's job, father's job, mother's occupation, parent's income, year of graduation, school of origin, and official services. The system with SOM prediction can do two mappings, namely serious registrants and less serious registrants. Comparison between predicted data and reality in the field, on the MAPE value or the average percentage error value each year. In 2017 it was 0.0065%, 2018 was 0.00067%, and in 2019 it was 0.0047%. From the results of predictive research using SOM, it can be seen that the trend pattern of registrant interest is the same as the data in the field which shows a decreasing graph from year to year.
Querying APIs with SPARQL

Abstract Although the amount of RDF data has been steadily increasing over the years, the majority of information on the Web is still residing in other formats, and is often not accessible to Semantic Web services. A lot of this data is available through APIs serving JSON documents. In this work we propose a way of extending SPARQL with the option to consume JSON APIs and integrate this information into SPARQL query answers, obtaining a language that combines data from the “traditional” Web to the Semantic Web. Our proposal is based on an extension of the SERVICE operator with the ability to connect to JSON APIs. With the aim of evaluating these queries as efficiently as possible, we show that the main bottleneck is the amount of API requests, and present an algorithm that produces “worst-case optimal” query plans that reduce the number of requests as much as possible. We note that the analysis of this algorithm is studied in terms of an algorithm for evaluating relational queries with access methods with the minimal number of access queries, which is of independent interest. We show the superiority of the worst-case optimal approach in a series of experiments that take existing SPARQL benchmarks, and augment them with the ability to connect to JSON APIs in order to obtain additional information.
Question Answering System based on Diease Knowledge Base

In this article, our main concern is the question and answer based on Chinese disease knowledge base, which is a limited domain question. For this type of question and answer, there are mainly three steps: (1) First, identify the entity and attribute of the question raised by the user; (2) Then, turn the entity and attribute into a structured query; (3) Finally use the structure Inquiries can be found in the disease knowledge base. According to the above three steps, the problems we need to solve are: (1) Missing Chinese question database about diseases asked by users; (2) Chinese disease knowledge base is missing; (3) There is a cascading error in the current mainstream pipeline method. For questions 1 and 2, we used crawler technology to crawl relative disease questions and structured disease knowledge bases on disease question and answer websites and disease websites, respectively. We named the disease question database and knowledge database as DieaseQuestion and DieaseBase respectively. For problem 3, we propose a joint model to identify the entities and attributes in the question. In this article, we prove the effectiveness of our model on the DieaseQuestion dataset.
A systematic review of unsupervised approaches to grammar induction

Abstract This study systematically reviews existing approaches to unsupervised grammar induction in terms of their theoretical underpinnings, practical implementations and evaluation. Our motivation is to identify the influence of functional-cognitive schools of grammar on language processing models in computational linguistics. This is an effort to fill any gap between the theoretical school and the computational processing models of grammar induction. Specifically, the review aims to answer the following research questions: Which types of grammar theories have been the subjects of grammar induction? Which methods have been employed to support grammar induction? Which features have been used by these methods for learning? How were these methods evaluated? Finally, in terms of performance, how do these methods compare to one another? Forty-three studies were identified for systematic review out of which 33 described original implementations of grammar induction; three provided surveys and seven focused on theories and experiments related to acquisition and processing of grammar in humans. The data extracted from the 33 implementations were stratified into 7 different aspects of analysis: theory of grammar; output representation; how grammatical productivity is processed; how grammatical productivity is represented; features used for learning; evaluation strategy and implementation methodology. In most of the implementations considered, grammar was treated as a generative-formal system, autonomous and independent of meaning. The parser decoding was done in a non-incremental, head-driven fashion by assuming that all words are available for the parsing model and the output representation of the grammar learnt was hierarchical, typically a dependency or a constituency tree. However, the theoretical and experimental studies considered suggest that a usage-based, incremental, sequential system of grammar is more appropriate than the formal, non-incremental, hierarchical view of grammar. This gap between the theoretical as well as experimental studies on one hand and the computational implementations on the other hand should be addressed to enable further progress in computational grammar induction research.
Use of Open Training Portals to Host Developed STEM Courses

One of the topical areas of innovative development of education is STEM education. It involves the formation of a future specialist spatial understanding of the logical connections between events or processes with approaches to their solution. Such formation can be realized through interdisciplinary communication, considering how some fundamental knowledge is put into practice. As a rule, basic and professional courses are taught separately, so most students do not form a holistic picture of knowledge. In this way, students slowly learn their professional skills. Therefore, a training course was created that combines two disciplines Probability Theory and Information Theory between which there is a strong link. Open training portals allow you to use similar courses. The course is being tested in groups of students of the specialty Secondary Education Informatics.
An approach to support information security risk assessment

In today's world, the importance of information security is growing. That is why many organizations are building information security management systems. The basis of these systems is risk management. This article presents some issues related to risk assessment in building information security management systems. Based on the structuring of the problems and the difficulties encountered in risk assessment, means have been proposed to solve some of these problems. An attempt has been made to summarize the proposed measures and to build an approach for organizing the functionalities of a software product to support information security risk assessment, as an element of the information security risk management process.
Direction-Sensitivity Features Ensemble Network for Rotation-Invariant Face Detection

Recent deep learning based Rotation-Invariant Face Detection (RIFD) algorithms make efforts to explore a mapping function from face appearance to the rotation-in-plane (RIP) orientation. Most methods propose to predict RIP angles in a coarse-to-fine cascade regression style and improve the overall RIFD performance. However, the problem of suboptimal between the models of training phase and testing phase cannot be solved because of its cascaded nature. The weakness of ambiguous mapping between face appearance and its real orientation would also degrade the performance considerably. In this paper, we propose a novel Direction-Sensitivity Features Ensemble Network for rotation-invariant face detection (DFE-Net) which learns an end-to-end convolutional model for RIFD from coarse to fine. Specifically, the incline bounding box regression is implemented by introducing angle prediction based on improved SSD. A Direction-Sensitivity Features Ensemble Module (DFEM) is adopted in the network to progressively focus on the awareness of face angle information, which can learn and accurately extract features of rotated regions and locate rotated faces precisely. Finally, we add multi-task loss to guide the learning process to captures consistent face appearance-orientation relationships. Extensive experiments on two challenging benchmarks demonstrate that the proposed framework achieves favorable performance and consistently outperforms the state-of-the-art algorithms.
Visualization of Numerical Association Rules by Hill Slopes

Association Rule Mining belongs to one of the more prominent methods in Data Mining, where relations are looked for among features in a transaction database. Normally, algorithms for Association Rule Mining mine a lot of association rules, from which it is hard to extract knowledge. This paper proposes a new visualization method capable of extracting information hidden in a collection of association rules using numerical attributes, and presenting them in the form inspired by prominent cycling races (i.e., the Tour de France). Similar as in the Tour de France cycling race, where the hill climbers have more chances to win the race when the race contains more hills to overcome, the virtual hill slopes, reflecting a probability of one attribute to be more interesting than the other, help a user to understand the relationships among attributes in a selected association rule. The visualization method was tested on data obtained during the sports training sessions of a professional athlete that were processed by the algorithms for Association Rule Mining using numerical attributes.
VSEW: an early warning system for volcanic and seismic events

Forecasting of imminent seismic and/or volcanic events can drastically reduce the loss of human life by evacuating the inhabitants residing in the neighboring areas. An integrated system, called Volcano-Seismic Early Warning (VSEW), is proposed, capable of classifying the alarm scenario in three levels (soft, medium and hard) for the forecast of seismic-volcanic phenomena of medium-high intensity. The system is based on threshold levels, calculated statistically, of some seismic and volcanic precursors (historical and recent earthquakes, volcanic tremor, concentration of magmatic gases, soil temperature). In active volcanic areas, the analysis of satellite images can highlight the soil temperature gradients between adjacent crustal areas, which is a critical criterion for identifying deep thermal anomalies linked to the rise of magmatic bodies. After being processed by a central server, the selected geological data are made available to mobile devices (smartphones, tablets, notebooks) to which they are presented in graphs and tables. An acoustic-luminous alarm will warn the user when the threshold values of one or more geophysical parameters have been reached or exceeded: the intensity of the signal emitted will be proportional to the risk of a potential eruption or earthquake. The implementation of this system aims at providing a valid alert instrument in areas characterized by high volcanic and seismic risk.
Learning based and Context Aware Non-Informative Comment Detection

This report introduces the approach that we have designed and implemented for the DeClutter challenge of Doc-Gen2, which detects non-informative code comments. The approach combines both comment based text classification and code context based prediction. Based on the approach, our "fduse" team achieved the best F1 score (0.847) in the competition.