Reviewerly/exHarmony huggingface dataset:
Fast Single-shot Ship Instance Segmentation Based on Polar Template Mask in Remote Sensing Images

Object detection and instance segmentation in remote sensing images is a fundamental and challenging task, due to the complexity of scenes and targets. The latest methods tried to take into account both the efficiency and the accuracy of instance segmentation. In order to improve both of them, in this paper, we propose a single-shot convolutional neural network structure, which is conceptually simple and straightforward, and meanwhile makes up for the problem of low accuracy of single-shot networks. Our method, termed with SSS-Net, detects targets based on the location of the object's center and the distances between the center and the points on the silhouette sampling with non-uniform angle intervals, thereby achieving abalanced sampling of lines in mask generation. In addition, we propose a non-uniform polar template IoU based on the contour template in polar coordinates. Experiments on both the Airbus Ship Detection Challenge dataset and the ISAIDships dataset show that SSS-Net has strong competitiveness in precision and speed for ship instance segmentation.
Online Spatiotemporal Action Detection and Prediction via Causal Representations

In this thesis, we focus on video action understanding problems from an online and real-time processing point of view. We start with the conversion of the traditional offline spatiotemporal action detection pipeline into an online spatiotemporal action tube detection system. An action tube is a set of bounding connected over time, which bounds an action instance in space and time. Next, we explore the future prediction capabilities of such detection methods by extending an existing action tube into the future by regression. Later, we seek to establish that online/causal representations can achieve similar performance to that of offline three dimensional (3D) convolutional neural networks (CNNs) on various tasks, including action recognition, temporal action segmentation and early prediction.
THE EFFECT OF SPECIAL EXERCISES ON DEVELOPING EXPLOSIVE AND DISTINCTIVE FORCE AT SPEED AND ACHIEVING DISCUS THROW FOR PEOPLE WITH CEREBRAL PALSY F36 - F37

Training the effectiveness of discus throwing for the handicapped depends on the athlete's fast strength in the muscles of the aiming arm, chest, trunk and shoulders, and through the researcher's experience in the field of handicapped sport and for the researcher to be a trainer in the Paralympic Athletics Federation, and the importance of the research lies in how to prepare a set of applied exercises Special training according to the use of one of the training methods, which is the fixed muscle strength training for players with disabilities for the effectiveness of discus throwing with disabilities for cerebral palsy (F-36 -F-37) to develop special fitness capabilities for this category according to the mechanism of muscular work for this method and to raise the level of We achieved this event, the research problem crystallized in the weakness of these players in some special fitness capabilities that are reflected negatively on the level of achievement for this event, and the research aimed to prepare special exercises for players throwing a disk with disabilities for cerebral palsy and to identify the impact of special exercises in developing the explosive and distinctive strength With the special speed of discus players with disabilities for cerebral palsy F36 -F-37 for individuals in the sample.The researcher used the one-group experimental approach, and determined the research sample by the intentional method, which included players with disabilities, cerebral palsy category F-36, as the training method that lasted for a period of (8) weeks with three units was applied using the auxiliary tools, and after obtaining the raw results It was statistically treated, and the researchers concluded that exercises have a positive effect in developing physical abilities and consequently the development of achievement for disabled players with disabilities is cerebral palsy among the individuals in the sample.
Share Withholding Attack in Blockchain Mining: Technical Report

Cryptocurrency achieves distributed consensus using proof of work (PoW). Prior research in blockchain security identified financially incentivized attacks based on withholding blocks which have the attacker compromise a victim pool and pose as a PoW contributor by submitting the shares (earning credit for mining) but withholding the blocks (no actual contributions to the pool). We advance such threats to generate greater reward advantage to the attackers while undermining the other miners and introduce the share withholding attack (SWH). SWH withholds shares to increase the attacker's reward payout within the pool, in contrast to the prior threats withholding blocks, and rather builds on the block-withholding threats in order to exploit the information about the impending block submission timing, challenging the popularly established assumption that the block submission time is completely random and unknown to miners. We analyze SWH's incentive compatibility and the vulnerability scope by identifying the critical systems and environmental parameters which determine the attack's impact. Our results show that SWH in conjunction with block withholding yield unfair reward advantage at the expense of the protocol-complying victim miners and that a rational miner will selfishly launch SWH to maximize its reward profit. We inform the blockchain and cryptocurrency research of the novel SWH threat and include the potential countermeasure directions to facilitate such research and development.
APVAS: Reducing Memory Size of AS\_PATH Validation by Using Aggregate Signatures

The \textit{BGPsec} protocol, which is an extension of the border gateway protocol (BGP), uses digital signatures to guarantee the validity of routing information. However, BGPsec's use of digital signatures in routing information causes a lack of memory in BGP routers and therefore creates a gaping security hole in today's Internet. This problem hinders the practical realization and implementation of BGPsec. In this paper, we present APVAS (AS path validation based on aggregate signatures), a new validation method that reduces memory consumption of BGPsec when validating paths in routing information. To do this, APVAS relies on a novel aggregate signature scheme that compresses individually generated signatures into a single signature in two ways, i.e., in sequential and interactive fashions. Furthermore, we implement a prototype of APVAS on \textit{BIRD Internet Routing Daemon} and demonstrate its efficiency on actual BGP connections. Our results show that APVAS can reduce memory consumption by 80\% in comparison with the conventional BGPsec.
Analysis on Linguistics Research Directions in the Age of Big Data

Abstract With the development of science and technology in recent years, concepts such as big data and databases frequently appear in people’s fields of vision. Among them, big data and the construction of foreign language corpora have become common practice in the field of academic research. It has a certain understanding by consulting related journals, papers and books. This article starts with what big data is, what it means to build a corpus for linguistic research, and the situation of linguistic research in the context of big data, and briefly explains the current status of data and linguistic research in the context of big data discussion and at the same time hopes to have a more comprehensive understanding and understanding of the database.
POSEIDON: Privacy-Preserving Federated Neural Network Learning

In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an $N$-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural network training. It employs multiparty lattice-based cryptography to preserve the confidentiality of the training data, the model, and the evaluation data, under a passive-adversary model and collusions between up to $N-1$ parties. To efficiently execute the secure backpropagation algorithm for training neural networks, we provide a generic packing approach that enables Single Instruction, Multiple Data (SIMD) operations on encrypted data. We also introduce arbitrary linear transformations within the cryptographic bootstrapping operation, optimizing the costly cryptographic computations over the parties, and we define a constrained optimization problem for choosing the cryptographic parameters. Our experimental results show that POSEIDON achieves accuracy similar to centralized or decentralized non-private approaches and that its computation and communication overhead scales linearly with the number of parties. POSEIDON trains a 3-layer neural network on the MNIST dataset with 784 features and 60K samples distributed among 10 parties in less than 2 hours.
Analysis of the 2015 Revised Korean Languages Curriculum in terms of the OECD Education 2030 competencies

The purpose of this study is to analyze the characteristics of the Korean language curriculum through curriculum content mapping (CCM) in the OECD Education 2030 project, which is later compared with the international curricular of the national languages. The CCM framework consists of a “competency code” that presents OECD competencies and a “content code” that contains the content of the curriculum. In this study, the degree of the competencies in the Korean language curriculum is analyzed and compared with the international curricular in terms of the OECD perspective. The result is analyzed as follows. First, foundational literacies of Korean were similar to the international national language curricular. Second, in the competencies of skills, attitudes and values, “problem-solving” and “learning about learning” were higher in the Korean language curriculum than the OECD average, “critic thinking” was similar to, and “persistence” was lower than the OECD average. Third, in terms of key concepts, both “student agency” and “co-agency” were higher in the Korean language curriculum. Fourth, “creating values” and “solving dilemmas” in terms of the degree of reflection of CCM framework competency related to transformational competencies, the Korean Language was higher than the average of OECD member countries in terms of “creating new values” and “resolving tensions and dilemmas” were higher but “taking responsibilities” were lower than the OECD countries. Fifth, the competencies in the compound literacies were also compared. These comparison results provide as reference materials that describes the suitability of the Korean language curriculum and contributes to future revisions of the Korean language curriculum for the students in 2030s.
THE MOTIVATIONAL COMPONENT OF POSTGRADUATE EDUCATION LECTURERS’ PERSONAL READINESS TO WORK AT CHANGES

The article describes peculiarities of the motivational component in the structure of a postgraduate education lecturer’s readiness to work at changes. The problem of the development of postgraduate education lecturers’ motivation to professional work in the conditions of social changes is discussed. The set of factors that influence the motivational behaviour are identified and described, namely, they are: lecturers’ needs, motives, interests, goals and incentives. The experimental research has identified motives that are important for postgraduate education lecturers: social, pragmatic, prestigious, professional, personal or professional self-improvement motives. Different indicators of postgraduate education lecturers’ personal readiness should be developed depending on their organizational, professional, age and gender characteristics, which was determined with the analysis of variance. Insufficient work motivation of postgraduate education lecturers within their personal readiness was determined in general and depending on their gender-age and organizational and professional characteristics, in particular. The obtained experimental data suggest that postgraduate educational institutions must prepare their employees to be ready to work in changing working conditions. In the addition to meeting the lecturers’ pragmatic needs, it is necessary to give them opportunities and to reveal hidden resources of their Self and personal self-improvement. Thus, a corrective program should be developed for postgraduate education lecturers as an auxiliary method increasing the motivational component of their personal readiness. &nbsp
MetaSimulator: Simulating Unknown Target Models for Query-Efficient Black-box Attacks.

Many adversarial attacks have been proposed to investigate the security issues of deep neural networks. For the black-box setting, current model stealing attacks train a substitute model to counterfeit the functionality of the target model. However, the training requires querying the target model. Consequently, the query complexity remains high and such attacks can be defended easily by deploying the defense mechanism. In this study, we aim to learn a generalized substitute model called MetaSimulator that can mimic the functionality of the unknown target models. To this end, we build the training data with the form of multi-tasks by collecting query sequences generated in the attack of various existing networks. The learning consists of a double-network framework, including the task-specific network and MetaSimulator network, to learn the general simulation capability. Specifically, the task-specific network computes each task's meta-gradient, which is further accumulated from multiple tasks to update MetaSimulator to improve generalization. When attacking a target model that is unseen in training, the trained MetaSimulator can simulate its functionality accurately using its limited feedback. As a result, a large fraction of queries can be transferred to MetaSimulator in the attack, thereby reducing the high query complexity. Comprehensive experiments conducted on CIFAR-10, CIFAR-100, and TinyImageNet datasets demonstrate the proposed approach saves twice the number of queries on average compared with the baseline method. The source code is released on this https URL .
Joint Adversarial Training of Speech Recognition and Synthesis Models for Many-to-One Voice Conversion Using Phonetic Posteriorgrams

This paper presents a method for many-to-one voice conversion using phonetic posteriorgrams (PPGs) based on an adversarial training of deep neural networks (DNNs).A conventional method for many-to-one VC can learn a mapping function from input acoustic features to target acoustic features through separately trained DNN-based speech recognition and synthesis models.However, 1) the differences among speakers observed in PPGs and 2) an over-smoothing effect of generated acoustic features degrade the converted speech quality.Our method performs a domain-adversarial training of the recognition model for reducing the PPG differences.In addition, it incorporates a generative adversarial network into the training of the synthesis model for alleviating the oversmoothing effect.Unlike the conventional method, ours jointly trains the recognition and synthesis models so that they are optimized for many-toone VC.Experimental evaluation demonstrates that the proposed method significantly improves the converted speech quality compared with conventional VC methods.
The ability to speak Indonesian through the seminar method

In Indonesian lessons, 4 areas must be studied including listening, speaking, writing, and reading. In social terms, people need speaking skills, especially speaking skills in Indonesian well. Speaking Indonesian correctly is not only needed in social society but also needed in the world of work after graduating in the world of education. The objectives of this study are (1) to find out how the Indonesian language skills of grade VIIIA students of SMP Dharma Praja Denpasar in applying the seminar method (2) to find out what mistakes occur when speaking Indonesian at Dharma Praja Middle School for the implementation of the seminar. Based on the results of this study it can be concluded that the results of speaking of eighth-grade students of SMP Dharma Praja through the seminar method on the assessment that 95% of students can achieve grades even exceeding the KKM value of 76. Especially in the section of diction and grammar, the value of students is 100, 72 students who got a value of 100, namely 27 people who are included in the predicate very good, students who get a value of 72, namely 3 people who are included in the predicate enough.
User Intention Recognition and Requirement Elicitation Method for Conversational AI Services

In recent years, chat-bot has become a new type of intelligent terminal to guide users to consume services. However, it is criticized most that the services it provides are not what users expect or most expect. This defect mostly dues to two problems, one is that the incompleteness and uncertainty of user's requirement expression caused by the information asymmetry, the other is that the diversity of service resources leads to the difficulty of service selection. Conversational bot is a typical mesh device, so the guided multi-rounds Q$\&$A is the most effective way to elicit user requirements. Obviously, complex Q$\&$A with too many rounds is boring and always leads to bad user experience. Therefore, we aim to obtain user requirements as accurately as possible in as few rounds as possible. To achieve this, a user intention recognition method based on Knowledge Graph (KG) was developed for fuzzy requirement inference, and a requirement elicitation method based on Granular Computing was proposed for dialog policy generation. Experimental results show that these two methods can effectively reduce the number of conversation rounds, and can quickly and accurately identify the user intention.
Crime Management and Reporting System

To deliver next generation police and law enforcement reporting tools, and setting up intelligence platforms that agencies use to take incoming incident reports, lessen live employee resources and allow these enforcement agencies to reallocate resources to much needed community areas.
Effect of Morality for Automated Negotiating Agents: A Preliminary Result

In this paper, we analyze the effect of morality in agent-based automated negotiations. Much attention has been given to the axiomatic decision-making model based on self-control under the presence of temptation. Here, self-control and shame are modeled as the mental costs of morality, and they are explicitly incorporated in the utility function. This is because the recent advances in behavioral economics have experimentally proved that real decision making is not simply based on a single value criterion but on multiple value criteria. Gul and Pesendorfer [] first proposed a decision-making model based on self-control and temptation (thus called the GP model), and since then it has been extended. Dillenberger and Sadowski [] proposed a shame (morality)-based utility model based on the GP model, in which an agent faces the trade-off between its selfishness and morality. In this paper, we apply a morality-based utility model to automated negotiation agents and analyze the effect of morality on automated negotiation and the related consensus-making.
JQF: Optimal JPEG Quantization Table Fusion by Simulated Annealing on Texture Images and Predicting Textures

JPEG has been a widely used lossy image compression codec for nearly three decades. The JPEG standard allows to use customized quantization table; however, it's still a challenging problem to find an optimal quantization table within acceptable computational cost. This work tries to solve the dilemma of balancing between computational cost and image specific optimality by introducing a new concept of texture mosaic images. Instead of optimizing a single image or a collection of representative images, the simulated annealing technique is applied to texture mosaic images to search for an optimal quantization table for each texture category. We use pre-trained VGG-16 CNN model to learn those texture features and predict the new image's texture distribution, then fuse optimal texture tables to come out with an image specific optimal quantization table. On the Kodak dataset with the quality setting $Q=95$, our experiment shows a size reduction of 23.5% over the JPEG standard table with a slightly 0.35% FSIM decrease, which is visually unperceivable. The proposed JQF method achieves per image optimality for JPEG encoding with less than one second additional timing cost. The online demo is available at https://matthorn.s3.amazonaws.com/JQF/qtbl_vis.html
Variations in Child-Child and Child-Adult Interactions

The aim of the present study is to investigate communication strategies used by twenty upper-primary school students in two types of interactions in Spanish. In the first phase of the study, students were paired with level and aged matched peers. Their task was to describe how to get to the particular place of the city on the basis of the map. During the second part of the study, they conducted short interviews with Spanish native speakers. Those two types of interactions were recorded and then transcribed in order to find out what communication strategies were the most commonly used by participants. The results clearly show that, even though participants were beginner learners of Spanish, they managed to successfully convey the message with the help of a wide array of communication strategies. Depending on the dyad students worked in, they used a variety of methods that enabled them to interact in those pairs. The most conspicuous differences were observed in terms of appeals of help and switches to English (L2) and Polish (L1).
The Key Techniques of Constructing the Database of Treatment Measures for Hidden Troubles in Electric Power System

This article adopts a research method that combined with theoretical analysis and system design, analyzes multiple dimensions of common safety hazard events, uses support vector machine classification algorithms to filter and mine valuable information in massive data, and establishes a common safety hazard event feature set. This kind of technology can realize the automatic classification of safety hazards and extract their characteristic information, introduce the semantic analysis, word segmentation clustering method to determine the typical processing measures of events, and generate knowledge items to add methods and models to the processing measures database, which can solve the grid safety hazard data entry specifications, In-depth analysis and other issues.