Reviewerly/exHarmony huggingface dataset:
Under the Background of Internet +, the Practical Research of Mobile Learning Model in Vocational Practice Teaching Guidance

Since the 18th National Congress of the Communist Party of China, the development of vocational education has been placed in a particularly prominent position. The state has successively issued several important issues such as the file “the National Vocational Education Reform Implementation Plan” and “the State Council’s Decision on Accelerating the Development of Modern Vocational Education” teaching practice is the most important practice in higher vocational education. Under the background of the Internet +, the mobile learning mode such as the rain classroom and the micro-curriculum is the core, guiding the teaching practice of higher vocational teaching. It can effectively use information means to achieve the purpose of distance instruction. Effectively solve the theoretical and technical problems students encounter in the Post practice.
국내 중국 관련 학과 교육과정 분석: 전공 교과과정 편성 실태 및 개선방안

This study analyzes the curricula, department names, and established courses of Chineserelated departments in Korean universities, and identifies curricular problems to explore solutions to the identified problems. The object of the analysis includes all departments of Chinese-related studies, including both public and private universities in Korea.
MOBILE APPLICATIONS FOR EDUCATION – AN INTERACTIVE WAY FOR TEACHING AND LEARNING

Learning and teaching nowadays could be done at a different stage of our lives. Mobile technologies and applications support and facilitate the learning process because they provide easy and fast access to learning resources at any time and place. These applications can be successfully combined and used as a tool for formal and non-formal learning, offering interactive features that activate learners and create a new and familiar learning environment. This article aims to provide examples of educational mobile applications, their main features, as well as ways to be effectively used in the educational process.
Is Seeing Believing? Video Forgery through Object-based Editing Techniques

Videos are generally considered to provide stronger forensic evidences than still images. This is why surveillance video is often used in investigations and information-gathering as an important and credible piece of evidence. It is evident that the ever-increasing capabilities of digital video processing have made surveillance video easy to operate and can even change images to one's liking. Although video editing software (such as Adobe Premiere) can be used to easily tamper with digital video without leaving visible clues, it requires proficient editing techniques and processing time. This study focuses on object-based video editing technology. It shows how this object-based video editing technology could adjust the spatial domain properties of objects in the video, such as deletion, displacement, and copying. It is also possible to adjust the object in the time domain so that the temporal relationship between objects changes or even reverses. These features show the potential problems of video as evidence. On the other hand, the same technology can be applied positively to eliminate unwanted objects in the video to make the video smoother, clearer and more accurate.
6 Round Improbable Differential Characteristic on M-PRESENT Using Undisturbed Bits

Undisturbed bit is one of the s-box weaknesses because it can use to construct improbable differential characteristic. That is as the first step of the improbable differential attack. In 2014, Tezcan succeeded in using the undisturbed bit to finding nine rounds of the improbable differential characteristic on the PRESENT with probability p'=2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-9.29</sup> . In 2017, Afifah and Sari Agustini H found another the improbable differential characteristic with higher probability consist of two characteristics with probability p'=2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-8.91</sup> and a characteristic with probability p'=2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-8.68</sup> . In this research, s-box of the PRESENT has replaced with So of the I-PRESENT algorithm. The algorithm called as the M-PRESENT algorithm. Then we construct improbable differential characteristic to know the effect and comparison of the result using a different s-box. Based on the results of the research, we obtained the six rounds of the improbable differential characteristic on M-PRESENT with the highest probability of 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-9.68</sup> as many as eight characteristics. The urgency of this research is for consideration when we design an algorithm not to use s-box that has an undisturbed bit. If the probability of the improbable differential characteristic is high, then an algorithm can be said to be increasingly vulnerable to the improbable differential cryptanalysis.
SCARF

This paper proposes a context data management framework for efficient context data collection and evaluation to support context-aware applications. The proposed framework named SCARF leverages two architectures: a server-based centralized architecture and an edge-based architecture to address the problem of scalability of context collection and evaluation. SCARF collects the context data efficiently from resource constrained mobile/edge and multiple in-situ sensors (not resource constrained) deployed in smart environments. In particular, SCARF takes a set of contextual queries from applications and optimizes the rate at which the context data needs to be collected and/or transmitted from the sensor to the server. Furthermore, SCARF decides whether the code for evaluating context should be executed on the server or on the edge. Execution on the edge will result in reduced communication between the server and the edge but can result in low performance due to resource constraints of the edge. On the other hand, evaluation of context on the server, will require SCARF to transmit sensor data from the sensor to the server but can result in high performance due to high resource availability of the server. SCARF is an adaptive framework which generates an optimal context acquisition plan and a context evaluation plan such that the overall latency is minimized.
VITA Search - An Intelligent Multimodal Search and Archive System for Online Media Resources

In this paper we present work on intelligent multimodal search and archive system, in which the scientific findings obtained in the work on recognition of Kazakh and Russian speeches, language identification and spoken term detection methods were applied. The paper describes the goals and objectives, the architecture, as well as the subsystem modules of the developed system. The VITA Search system allows for accurately determining the exact time of the required spoken information in the data in Kazakh and Russian languages from various broadcast channels. The speech recognition unit uses the Kaldi toolkit to generate lattices from the raw audio data. An acoustic model trained using deep neural networks shows significant results. The word error rate on the train set for recognition of Kazakh speech was 3.86, and for Russian speech - 9.85. Moreover, we integrated a language identification model trained using Long Short-Term Memory Recurrent Neural Networks in order to select the correct model for the input audio. Regarding spoken term detection, we applied word and proxy-based approaches to search for keyword terms among the lattices.
Damped Sinusoidal Exploration Decay Schedule to Improve Deep Q-Networks-Based Agent Performance

Environments in which the rewards are sparse or occur rarely prove to be some of the most challenging for reinforcement learning (RL) agents and can take a long time to train. The decision between attempting to gain new experience, or using the knowledge already acquired to perform what is thought to be the optimal action in a given state, is computed by the exploration factor. This problem has been debated as the exploration–exploitation trade-off, which simply means choosing between what the agent already knows, and new things the agent can potentially discover. In this paper, an attempt is made to construct a new sinusoidal ε-decay function, which steadily decreases the exploration factor and increases performance. This approach is inspired from the sinusoidal curve which causes an “oscillation” of values. It is expected to stabilize and speed up a DQN-based agent’s learning process, preventing it from diverging even after long periods. The new exploration decay equation can be adopted for other RL algorithms as well.
A Deep Quadruplet Network for Local Descriptor Learning

The generation of a distinguishing feature for local patch matching is a main task in computer vision. Recently, local patch descriptors via deep convolutional neural network (CNN) with a triplet loss have achieved a promising performance. In this paper, we propose a brand new quadruplet loss, which achieves higher accuracy than other pairwise loss and triplet loss methods. Our loss is inspired by a uniform distribution. It separates non-matching examples by means of hard sampled non-matching pairs in a batch, meanwhile uses the random sampled non-matching examples to keep non-matching pairs satisfying a uniform distribution. By applying the proposed quadruplet loss to the L2Net CNN architecture leads to a more compact descriptor named QuadrupletNet. From our experiments, QuadrupletNet shows higher performance than other state-of-the-art loss functions e.g. the Triplet loss, as indicated on both Brown and Hpatches datasets.
Perceptual Image Hashing: Tolerant to Brightness and Contrast Corrections Method Based on Cumulative Histogram Slicing

Perceptual image hashing is used in a wide range of practical applications which include content image authentication, digital watermarking, pattern recognition, computer vision and database fast duplicate image retrieval. Existing techniques are not well suited for the significant brightness and contrast corrections. The main point is that such manipulations can lead to information loss due to the histogram truncation in cases when pixel values are out of the dynamic range. In order to address the issue a novel technique is suggested. Cumulative histogram slices as a pivot for the subsequent image features calculations are used. The points of slicing are calculated in a way they are robust to content preserving manipulations such as brightness and contrast corrections. This approach allows one to handle situations when some of the content slices are lost due to the pixel value overflow. On the other hand, if one tampers image content within any existing slice it will then be detected by comparing the correspondent calculated and provided hash values. Experiment results show that the suggested method has sufficient sensitivity to detect image tampering whereas being tolerant to even significant brightness and contrast corrections. The memory consumption allows one to use the proposed method with the digital watermarking schemes.
Comparative Study Of Classification Techniques On Occurrence Of Breast Cancer Disease

This paper presents an integral comparison related to epidemiological data for the most commonly occurring disease among women, the breast cancer. It compares different techniques of linear Classification by using the collected dataset and give the finding regarding the efficiency of one technique over the other. It will help the doctors and experts to take accurate decisions and predict the right stage of the disease. The examination made in the proposal between various procedures to discover which methods will perform all the more precisely then the other to distinguish and give more data with respect to the malignant growth. It can also help to find about the reoccurrence of breast cancer and what treatment must be suggested to the potential patient.
A Data Analysis Pipeline for Identifying Periodic Processes during Drosophila Development

In this paper, we propose a computational pipeline that can be used to unearth periodic processes and their regulators in any organism along with the networks governing such periodicity. Our approach is based on mining periodic subgraphs from temporal gene networks. Specifically, we collected 30 time varying gene networks inferred from temporal expression profiles of 588 Drosophila genes, computed periodic subgraphs in those networks, and analyzed them in a number of ways in order to discover their biological significance. Our results show that the largest connected component in the periodic subgraphs as well as hub genes and dense subgraphs in that component are highly enriched in periodically active gene-functions. We also devised a way to find the regulators of these periodic functions. We show the superiority of our approach as compared to a baseline method that computes aperiodic subgraphs by showing that similar analysis on aperiodic subgraphs fails to find any periodic or specific gene function. To the best of our knowledge, this work is the first of its kind in the field of network biology.
A Real-Time Feedback Approach Based on Semantic Clustering for Poverty Alleviation Problem

Poverty has always been one of the most acute social problems in the world. In order to eradicate poverty, the Chinese government has invested a lot of manpower and material resources, and promised to lead all poor areas and poor people into a well-off society by 2020. In this process, the county government as the main battlefield of poverty alleviation, the complex work of poverty alleviation has brought considerable pressure to grass-roots cadres and helping cadres. For the sake of improving the efficiency of support work, we propose a real-time feedback approach based on semantic clustering for poverty alleviation problem, through which we can build a bridge between grass-roots cadres and decision makers. In this method, we first use a fast label extraction method to quickly extract important feature words from the complicated help information. Secondly, we use unsupervised text clustering method to identify important poverty alleviation problems from these feature words, so as to provide a reference for the poverty alleviation workers to carry out their work in an orderly and targeted manner. The experimental results for different regions show that the poverty alleviation problem identified by our proposed method can reflect regional characteristics.
Software Elderly-Oriented Interaction and UI Design from the Perspective of Service Design

This paper analyzes the pain points, problems and needs existing in the use of software by the elderly, and studies the cognitive and perceptual characteristics and problems of the elderly. The method and principle of systematically carrying out integrated innovation based on user’s whole process experience from the perspective of service design is put forward.
An Authentic and Secure Printed Document from Forgery Attack by Combining Perceptual Hash and Optical Character Recognition

The implementation of physical or printed documents is still commonly used. Among them are certificates, forms, diplomas and important letters. This has the potential to be exploited by irresponsible parties by falsifying documents or forgery attacks. In 2014, Husain et al conduct research on the scheme of creating physical documents along with verification of physical documents using the hash of the OCR value and embedded in the QR Code. The scheme is stated to detect forgery attacks. In 2015, Wang et al conduct research on perceptual hash schemes applied to image-based soft file documents. The hash is considered robust or resistant to noise and filtering and sensitive to document forgery. In this final project, the implementation of OCR proposed by Husain et al in applying the QR Code will be modified. The modification by inserting Perceptual hash value in the QR Code. The application is built using the Python programming language and is desktop-based. Based on the test results, it was concluded that the application had fulfilled all the requirements and designs and the application could produce authentic physical documents that secure from forgery attacks.
Research on the Construction Technology of Knowledge Graph in Aviation

Abstract In order to alleviate the overload of airspace information under the network environment and integrate the scattered and useful airspace information, the technical architecture of knowledge graph in the aviation field is proposed. This paper describes the technical architecture of aviation knowledge graph construction, including knowledge extraction, knowledge fusion, knowledge processing, knowledge update and other key technologies and applications. Finally, an instance of knowledge graph for air combat is constructed. The knowledge graph discovers the hidden relationship between task and maneuver through resource sharing and information mining, which can be used for mission optimization and maneuver prediction to assist air combat decision-making.
Conflict and Balance Between Big Data Investigation and Personal Information Protection

The big data investigation mode has many unparalleled advantages in traditional investigation mode. It can actively carry out investigation activities, effectively improve the detection rate, and protect the legal rights of criminal suspects to avoid extorting confessions by torture. However, the big data investigation mode is also easy to conflict with the protection of citizens’ personal information rights, which is likely to cause violations of citizens’ personal information rights. Therefore, it is necessary to guide the implementation of the relevant laws, regulate the relevant information acquisition procedures, increase the intensity of infringements, and improve the ideological awareness of investigators, so as to guide the development of big data detection models.
Improvisation of Minimax Algorithm with Multi Criteria Decision Maker (MCDM) in the Intelligent Agent of Card Battle Game

Choosing the right algorithm for Intelligent Agent is very important in determining the next step. Actions taken by an Intelligent Agent with less optimum algorithm can be figured out easily by the player competing with it. This certainly makes it easier for the player to win the game. Card Battle is a game that compares the strength of both card decks. They competed to determine who has the best cards on deck to reduce the enemy health point. Intelligent Agent need to act as the competing enemy called Non-Player Character (NPC). Presently, Intelligent Agent on Card Battle Games still use the Minimax Algorithm. This algorithm thinks only to counter enemy summoned card without considering other things such as enemy health point left, the cost for summoning a card, etc. Improvisation of Minimax Algorithm with Multi Criteria Decision Maker (MCDM) provides a new way of thinking for the decision making during the game. The results of this study increase the difficulty of the NPC by making it possible for the Intelligent Agent to consider more game properties and counter the player cards.
Verification of C Programs using Annotations

In the rapidly-changing modern world, computer programming and software engineering hold tremendous significance. Technology is neither good nor evil but in the right hands, they can provide powerful benefits to the society. Therefore, the need to write correct programs has become very crucial in the current world. The absolute correctness of a program can be proven by testing dynamically using well-defined test cases, but testing can never show the absence of errors and is largely impractical for programs with no bounded input range. Hence, proving a program's correctness at compile time still remains a challenge. A more theoretical and feasible approach of checking the correctness of a program is by using logical reasoning and mathematical proofs. This paper delves into checking the correctness of a C program by converting the given C code to an equivalent code in Dafny, a static-program verifier created by Microsoft Research. Dafny was designed to provide a simple introduction to formal verification and it has been extensively used to verify some challenging algorithms.
Comparison of the Energy Efficiency in Fat-Tree and B-Cube Based Data Center Network

Nowadays, in order to accommodate the growth in demand for computing resources, adding more power and cooling capacity to existing data centers or build new data centers is required. However, both of these options are extremely expensive. Therefore, improving energy efficiency has become a paramount necessity for data centers. The right architecture and topology design will be able to produce effective use of energy and increase performance. Taking into account the servers and network without the cooling aspect in data center energy efficiency, and the increasing trend of network simulator in data center network research, this paper aims to answer the emerging question by comparing two most common network topologies (Fat-Tree and B-Cube) in data center using Network Simulator 3 (NS3) tool. Despite having difference in nature, those two topologies have an equalized form to be compared. In this research, the focus of energy efficiency study is to determine whether the difference of nature in Fat-Tree and B-Cube topology will affect the energy consumption in the data center using an assumption of energy consumption for a certain network device. The result, despite the small amount, B-Cube is proven to be 0.003141% better than Fat-Tree in term of energy use.
Structurally evolving component-port-connector architectures of centrally controlled systems

The increasing complexity of software variants demands for variation management techniques suitable for industrial practice. As "clone-and-own" with subsequent manual evolution still is a popular method to create software variants, this leads to product lines that are difficult to maintain and evolve and can produce conflicts when changes occur in both, product line and variant. Where general approaches to differencing and merging handcrafted changes to products perform suboptimally, respecting assumptions on the structure of the architecture can reduce the differencing search space and yield better results. We present a novel method for differencing and merging hierarchical component-port-connector architectures based on the Focus calculus. It leverages assumptions on the distribution of components to facilitate calculating differences between architecture versions and deriving delta bundles to update software products to changes in the underlying product line. Through a (preliminary) survey with 27 participants, we compared the results of our method with the results of manually differencing. The survey showed that this method yields deltas and merge results that are considered correct by the participants. Overall, we found that including assumptions about the architectural style and that grouping related deltas into compact bundles can greatly facilitate merging manually created and evolved products back into their underlying product lines.