Reviewerly/exHarmony huggingface dataset:
SAVUNMA SİSTEM ÇİZİMLERİNİN ESER OLARAK KORUNMASI

Defense systems include machine and electronics-based systems with different functions, various building elements and works such as maps and software. The drawing can be related to an object, its production or explanation, as well as a concept or an event. FSEK 2 is a general regulation regarding the protection of drawings according to Turkish Copyright Law. This regulation, like other drawings, applies to the protection of copyrights on drawings related to defense systems. Drawings for defense systems must meet the protection requirements in order to be protected as works. Accordingly, the drawings must have the originality of the author, have a technical and scientific nature, and have no aesthetic qualities. Drawings containing aesthetic qualities as well as technical and scientific nature are preserved as scientific work, not fine art.
Hubble: An End to End Approach for Anomaly Detection in Network Traffic

Fast and accurate detection of abnormal network traffic is of great significance to improve the stability and security of the network. In recent years, the research on anomaly detection of network traffic based on deep neural network has made substantial progress. However, existing network traffic anomaly detection methods mainly focus on learning new feature representations to anomaly detection methods, lead to data-inefficient learning and suboptimal anomaly scoring. Furthermore, they are typically designed as unsupervised learning methods due to the lack of large-scale labeled anomaly data. As a result, they are difficult to leverage the prior knowledge when such information is available as in many real-world anomaly detection applications. To tackle the aforementioned fundamental challenges, in this paper we introduce Hubble, a end to end anomaly detection framework for efficiently, accurately, and quickly detecting the abnormal information embedded in network traffic data. There are three key components combined to successfully achieve the above objectives, the feature extractor for fast traffic encoding learning, the anomaly scorer for accurate detection anomaly data, and the multi- classifier for diverse abnormal types. The extensive results show that Hubble can be trained substantially improving abnormal detection accuracy by 25.1% and reducing detection time up to 44.6% on average compared to state-of-the-art methods in network traffic.
3D Super-resolution Optical Imaging Using Deep Image Prior

Deep learning based super-resolution methods have received much attention, especially unsupervised super-resolution due to the difficulty of collecting images pairs (low-resolution and high-resolution images from the same scenario) in many fields, such as optics. Optical imaging is typical technique in advance optical measurement equipment and optical super-resolution imaging has received much attention. In this paper, a novel model, deep image prior with design surface model (DIP-DSM), based on deep image prior to improve the resolution of optical imaging is presented. It makes use of single image instead of using random input in which the design surface model is regarded as prior information. To validate the model, a series of experiments are conducted, and the results show the superiority of the proposed model as compared with deep image prior. Furthermore, the performance of different neural networks are explored and it is find that the U-Net achieve best reconstruction quality and reach to PSNR, 32.937.
A Method for Automatic Counting and Labeling of Cells Stained with Microporous Membrane

Cells in the blood can be screened through a membrane. But the method of manually identifying the number of cells is time-consuming and laborious, and there may even be some errors. Therefore, an effective image recognition method was adopted in this paper to automatic count and label the stained cells on the membrane after filtration. We tested the images of cells without the influence of the membrane, and compared them with the actual number of cells with 90% accuracy. Under the influence of the filter membrane, the accuracy is about 80%. Therefore, this method can be used to calculate the number of cells in the images relatively accurately and efficiently.
Research on mission planning of multiple AUVs based on hybrid strategy

In order to solve the problem of local optimal solution and slow convergence speed of standard genetic algorithm, this paper combines simulated annealing method and improved genetic algorithm to propose a task planning model based on hybrid algorithm. Through the analysis of the AUV task planning problem, this paper simulates and compares the task planning results of the standard genetic algorithm, the improved genetic algorithm and the hybrid algorithm. The simulation results show that the task planning model based on the hybrid algorithm can effectively improve the algorithm convergence speed, avoid local minima, and balance the tasks between AUVs.
Calibration method for measuring angle device with CCD

Angle measuring accuracy is the important technique index of the measuring angle device with CCD. The calibration methods for CCD camera have some disadvantages, such as the high requirements to the used devices and the complicated operation processes. This paper proposed a new calibration method based on the triangle principle using cross target, the corresponding mathematics model is established. The results of the academic analysis and the contrast experiment show that the new method is correct, and its accuracy can be controlled. It is easy to be popularized and applied because of the strong adaptability and the simple operation.
Beyond the Scene: A Comparative Analysis of Two Storytelling-based Conversational Agents

Advances in conversational interfaces have facilitated the widespread use of chatbots in assisting humans in their daily lives such as in retail services, healthcare, and education. With the use of Natural Language Processing techniques (NLP), chatbots can leverage on storytelling strategies to engage children in meaningful conversations to narrate and share their stories. In this paper, we compared two existing conversational storytelling agents, Orsen and EREN, by analyzing the dialog logs to examine children's engagement when sharing their stories with these agents. Taking three dimensions - language production, flow maintenance, and affect; into consideration, results show that the ability of EREN to recognize emotions expressed in the child's input text can encourage the children to be more open to sharing their stories. Even though the agent cannot generate affect-rich responses, its ability to recognize the emotions made children feel thankful and appreciative of the agent for listening.
GASKT: A Graph-Based Attentive Knowledge-Search Model for Knowledge Tracing

Knowledge tracking (KT) is a fundamental tool to customize personalized learning paths for students so that they can take charge of their own learning pace. The main task of KT is to model the learning state of the students, however the process is quite involved. First, due to the sparsity of real-world educational data, the previous KT models ignore the high-order information in question-skill; second, the long sequence of student interactions poses a demanding challenge for KT models when dealing with long-term dependencies, and the last, due to the complexity of the forgetting mechanism. To address these issues, in this paper, we propose a Graph-based Attentive Knowledge-Search Model for Knowledge Tracing (GASKT). The model divides problems and skills into two types of nodes, utilizing R-GCN to thoroughly incorporate the relevance of problem-skill through embedding propagation, which reduces the impact of sparse data. Besides, it employs the modified attention mechanism to address the long-term dependencies issue. For the attention weight score between questions, on the basis of using the scaled dot-product, the forgetting mechanism is fully considered. We conduct extensive experiments on several real-world benchmark datasets, and our GASKT outperforms the state-of-the-art KT models, with at least 1% AUC improvement.
Extended Cyclic Codes Sandwiched Between Reed-Muller Codes

The famous Barnes-Wall lattices can be obtained by applying Construction D to a chain of Reed-Muller codes. By applying Construction <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{D}^{(\text{cyc})}$</tex> to a chain of extended cyclic codes sandwiched between Reed-Muller codes, Hu and Nebe (J. London Math. Soc. (2) 101 (2020) 1068–1089) constructed new series of universally strongly perfect lattices sandwiched between Barnes- Wall lattices. In this paper, we explicitly determine the minimum weight codewords of those codes for some special cases.
AudiTEE: Efficient, General-Purpose and Privacy-Preserving Audit for Distributed Ledgers

Privacy-preservation and effective auditing are two desirable but challenging requirements on distributed ledgers.To meet the requirements, this paper presents an auditing scheme, called as AudiTEE, which can audit a distributed ledger in a generic, efficient, and privacy-preserving manner.AudiTEE leverages Trusted Execution Environment (TEE) to generate confidential but auditable transactions and realize arbitrary, efficient and confidential audit on them.Unfortunately, TEE suffers from some inherent barriers and is itself not a complete solution for fast audit.To tackle these challenges, AudiTEE takes advantage of KAMT (K-anonymity Authentication Based on Merkle Tree) protocol for efficient management on account and user-defined anonymous transactions.Further, to achieve a complete and fast audit with unlinkability, TEE doesn't process through all but only a comparatively small part of transactions according to a special ktag attached on each transaction to ensure that a user cannot hide transactions from auditor even when auditor is blind with who is involved in each transaction on the ledger.Apart from the above, AudiTEE allows flexible control on user behaviors.We implement a concrete instance of AudiTEE under a bank setting and demonstrate the scalability with all its core functionalities.
Notating Deconstruction: What Can Ethnomusicological Transcription Learn from the Notational Practices of Contemporary Composers?

Abstract Drawing on Jacques Derrida’s (1981) approach to deconstructing Platonic dichotomies, this article argues that any notational system is inherently structuring and should be subjected to deconstructive efforts. Further, my contention is that this deconstruction can be realized in how notation itself is used, in what I refer to as ‘deconstructive notation’. This article looks at how notation has been used by composers to deconstruct the categories of Western staff notation, opening up the ways in which music is studied as an invitation to all those engaged in notating music to make room in their work for deconstructive notational play.
The readiness of the future teacher to form students' functional literacy

Introduction. Society and the state form an external request for the results of education, proceeding from their priority tasks and interests. Currently, the actual educational result is the functional literacy of students. The achievement of the demanded results of the educational process largely depends on the professionalism of the teacher who implements it. In this connection, the issue of training pedagogical personnel ready and able to form relevant educational results, in particular, the functional literacy of students, by means of individual subject areas, is of particular relevance. The first step to solving this issue is to study the pedagogical phenomenon, which is new for domestic science, "the readiness of future teachers to form students' functional literacy" and the development of its scientifically based model, which is the purpose of this article. Materials and Methods. In the process of work, theoretical (analysis, generalization, systematization, pedagogical modeling) and empirical (open observation, interviewing) research methods were used. Results. The main directions of pedagogical activity focused on the formation of functional literacy of students by means of individual subject areas are highlighted. This made it possible to clarify the content of the concept of "the readiness of future teachers to form the functional literacy of students." The structural-content model of the studied type of readiness is theoretically substantiated, in which motivational, emotional-volitional, cognitive, activity and reflexive-evaluative components are highlighted. The criteria (personal, cognitive, activity and regulatory) and the corresponding indicators for assessing the readiness of students of a pedagogical higher educational institution for the formation of students' functional literacy are formulated. The levels of formation of this readiness are described. Discussion and Conclusions. The implementation of modern education strategies is impossible without training the relevant teaching staff. The development of a scientifically grounded model of the readiness of future teachers to form students' functional literacy is just the beginning of the path to modernizing teacher education. Allocation of the readiness structure and the criterion apparatus for its assessment allows us to determine further guidelines for the design and implementation of the conditions for its formation.
Proof-of-Points for Electronic Health Records in Blockchain

As healthcare specialization grows ever more widespread, it becomes increasingly common for patients to seek healthcare from several different providers. In cases of chronic diseases, like cancer or diabtetes, it becomes necessary to seek different providers for different procedures. Practitioners need access to complete medical history of a patient. Despite the advantages of Electronic Health Records(EHR), sharing is normally done through mail or fax, as feasible, suitable applications for EHR storage that operate with high levels of security are only on the edge of the market.Ecosystems for Health Information Exchange(HIE) have the usual goal of ensuring safe, secure access, as well as diagnostic aids, and a range of usabilty across the practitioners of an entire nation. Centralised EHR systems do not have protection from failure events, as well as the expected security and privacy concerns that severely restrict their scalability.Blockchain makes use of a distributed ledger that provides a complete history of every transaction that has occured in the network. This generation of transactional applications have generated immense hype because of rapid improvements to transparency, while not sacrificing high levels of scalability. Blockchains have provided a high level of success in distributing a shared history to massive numbers of users, while maintaining the data’s integrity. This scalable, decentralised approach has immense benefits for EHR, allowing interaction of medical specialists on a level not seen before. In this paper, we will propose a blockchain network that uses a Proof-Of-Points consensus algorithm which will allow users to produce blocks efficiently and transparently. The proposed application will ensure security through a multi-level authentication method, as well as a fast querying and fast storage guarantees, that will make it more attractive for medical practitioners.
Improved Privacy of Data Transaction in IoT Enabled Privacy Based Algorithm

The conventional methods restricts the number of transactions which enters the global SVM-GMM by implementing a scalable local ledger, but compromising on the peer validation of transactions at local and global level.In this paper, we analyse the security of IoT using privacy based algorithm.It aims at studying the block chain as a potential solution to secure IoT data management within supply chains in Telehealth.It integrate privacy based algorithm for data management that includes: collection, validation, storage and analysis.The study integrates security algorithm in IoT devices using a closed-loop model, which is developed to address the security related concerns in IoT devices in supply chain.The validation shows that the proposed security protocol offers improved data privacy and integrity than the other security mechanisms.
Development and Expansion of Management Software for New Automatic Hematology Analyzer Based on PC/Windows

With the improvement of automation and intelligence, the status of medical examination equipment has greatly improved. The five-differential hematology analyzer adopts the master-slave architecture which is between the management machine and the control machine, the master and slaves conduct command transmission and data interaction through the communication interface. The control system has developed by DSP controller on the basis of the five-differential blood analyzer model, and the system structure and functions of the operation management software have been further developed based on PC/Windows. The communication module, data management module and inspection accuracy have been improved. The system architecture design of the operation management software to expand and manage multiple control machines has been further proposed, systematic maintenance plan has been proposed for the modification and unreasonable maintenance of the existing software during the development and maintenance process, and software related functions have been expanded by the system.
Design and Expansion Development of Embedded System Software for a New Intelligent Hematology Analyzer

Hematology analyzer is widely used in clinical tests in the hospital. In order to meet the needs of clinical diagnosis, various new detection function has been added to hematology analyzer to achieve joint detection of multiple parameters. Based on rapid software design and extended development methods, the design and implementation of a new intelligent hematology analyzer software are explored in this paper, and the overall design of the control node and management node are introduced, also, the software implementation of the CAN communication module and fault detection mechanism are given. The new intelligent hematology analyzer is based on the functional module of existing three-diff standard hematology analyzer, using a distributed system architecture which adopt CAN bus for communication. The CRP detection is attached to the original routine blood test, extending a new detection module only needs to increase the control node and modify the upper level. The test result shows that the new intelligent hematology analyzer based on the distributed architecture has good reliability and scalability, and can realize the rapid expansion of external modules.
Optimized Underwater Manipulator Path Planning to Minimize The Disturbance on Robot

The movement of the underwater manipulator during floating tasks introduces large perturbations to the robot body state. We presents an online path finding and trajectory generation method for an underwater manipulator in such a scenario. The goal of the path planning in this paper is to cause the smallest possible variation in disturbance moments to the robot body during the movement of the robotic arm. Initially, disturbance graphs are obtained for different combinations of robot arm joint angles. A graph search algorithm is then used to find the initial trajectory. In order to make the trajectory smoother and to match the dynamics of the robot, polynomials are used to optimise the trajectory and information on the derivatives of the polynomials is further utilised to constrain the trajectory. To illustrate the effectiveness of this approach, the algorithm proposed in this paper is compared with conventional motion planning methods performing the same task in a simulated environment, and the results show that our approach brings the least variation in disturbance moments.
Recommender System for Online Teaching

This paper focuses on the support process within the online teaching environment, which is currently unsatisfactory because of the very limited size of the course trainers or teachers compared to the massive number of the enrolled learners who need support. Indeed, many of the learners can not appropriate the information they receive and must therefore be guided. Thus, in order to help these learners take advantage of the course they follow, we propose a tool to recommend to each of them an ordered list of “Leader learners” who are able to support him throughout his navigation in the online environment. The recommendation phase is based on a multicriteria decision making approach to periodically predict the set of “Leader learners”. Moreover, since the learners’ profiles are very heterogeneous, we recommend to each learner the leaders who are most appropriate to his profile in order to ensure a good understanding between them. The recommendation we propose is based on the demographic filtering and the Euclidean distance to identify the neighbourhood of the target learner. This method concerns only the higher-education teaching.
Dialogue Act Recognition Using Visual Information

Automatic dialogue management including dialogue act (DA) recognition is usually focused on dialogues in the audio signal. However, some dialogues are also available in a written form and their automatic analysis is also very important.
MSCNN: Steganographer Detection Based on Multi-Scale Convolutional Neural Networks

Steganographer detection aims to find guilty user who spread images containing secret information among many innocent users. Feature extraction is an important step in steganographer detection. The challenge is that the existing steganalytic feature extraction of steganographer detection is mostly based on manually designed features. A steganographer detection algorithm based on multi-scale convolutional networks (MSCNN_SD) is proposed to automatically extract steganalytic features in the paper. MSCNN_SD introduces the residual maps and quantization truncation idea of classical SRM into deep convolution network. MSCNN_SD uses a set of filters to extract rich residual information and uses a number of quantized truncation combinations are introduced to discretize the residual maps. Finally, two parallel deep learning subnets are used to learn the features of different scale residuals. The simulation results illustrate that the proposed MSCNN_SD method is superior to the state-of-the-art method. MSCNN_SD uses a well-trained model of one steganography payload, which can detect different steganography and payloads used by steganographer. At the same time, MSCNN_SD has a good detection effect when the steganographer uses the hybrid steganography strategy .
Text to Code Conversion Using Deep Learning for NLP

Semantic parsing is the problem of mapping natural language sentences to a formal representation like lambda calculus expressions. We work on a specific sub-problem in semantic parsing: producing python code snippets from natural language descriptions. We address this sub-problem using the CoNaLa Dataset in order to improve precision and to help developers in their development phases. For this task, we use Deep Learning (DL) for Natural Language Processing (NLP), based mainly on an Artificial Neuron Networks (ANN). Unlike the simple ANN, which learns in one direction, we use a Recurrent Neural Networks with LSTM cells since it performs better in Machine Translation (MT) task. We get a BLEU score of 15.2 beating the previously reported baseline of the CoNaLa challenge.
Designing for Mindfulness : An Exploration of Interaction Design with Biodata Inspired by Chinese Aesthetics

The Quantified Self (QS) movement and self-tracking technologies in personal informatics are opening up opportunities for users to manage their health and wellbeing. Until now, researchers have exp ...