Reviewerly/exHarmony huggingface dataset:
AAG: A Model for Attack Behavior Judgment in CTF-style Cyber Security Training

Capture the Flag (CTF) competitions become popular to engage people with the world of cybersecurity. In CTF-style cybersecurity training, judging trainees’ attack behavior in terms of attack time, methods, and consequences is the key to know trainee ability and thus to provide more targeted training. Previous work focuses on consequences and lacks judgment on attack methods, which cannot provide enough information for trainers to assess trainee ability. To solve this problem, we propose a model called Attack Action Graph (AAG) to help judge trainees’ behavior. By creating AAG, we characterize predictable and unpredictable attack methods and establish an association between attack methods and consequences. Then we update AAG based on feature detection technology to judge attack behavior. In the experiment based on a typical enterprise network, AAG helps us to achieve real-time judgment of all predicted attack behavior. We find that only 5% of the trainees have unpredicted attack behavior. After extracting the characteristics of unpredicted attack behavior and doing offline judgment, we find new attack methods and cheating behavior.
Fengyun-4A Meteorological Satellite Data Service System

This paper introduces the characteristics and the new technologies applied in Fengyun-4A(FY-4A) Data Service System. The system is an important part of Fengyun-4(FY-4) series satellite ground application system, and also is the new generation platform for meteorological satellite data sharing services. The system integrates several high-performance servers, high availability disk array, large-scale automated tape library as well as operation system, database, storage software and application software, all of which constitutes archive and service application clusters. The system adopts cloud-computing technology, big data technology, WEBGIS technology to establish a stable, reliable and secure operational satellite data archive platform, which not only provides data retrieval and download services, but also provides various auxiliary means and information assistance.
On the Breakdown of the Controlled Environment Paradigm in Norwegian Archival Repositories

The rules and arrangements that govern transfer of digital records to the archive repositories in Norway, rely on a controlled environment paradigm. This paradigm is the basis for assuming the authenticity and evidential values of the archives. The concept of a paradigm is borrowed from the theory of science, but it can also be relevant to fields of practice. In the theory of science, it denotes distinct concepts, methods and thought patterns that guide what contributions are perceived as valid within a field. If or when a paradigm ceases to provide adequate guidance in a field, it may break down, possibly leaving the field in need of a new paradigm. The discussion in this paper apply a theory of different responses to paradigm breakdowns in order to explore stakes and opportunities at a point of crisis for the current paradigm on transfer of born-digital records to archive repositories.
Developing a competency model for double-qualified teachers based on Factor Analysis Method

Objective:By Factor Analysis Method, develop a competency model preliminary.Methods:Based on the early research results of "double-qualified" teachers in vocational colleges, through principal component analysis to extract common factors and was named to develop a competency model preliminary.Results: In this study, initially develop a competency model including 6 dimensions, 40 competencies.Conclusions:Factor Analysis Method in the initial developing a competency model is practical, the conclusion consistent with the actual situation, and can serve as a basis for further research.
Conversation Moderator: A Mobile App for Tracking Individual Speaking in Group Conversations

We can naturally observe in group dynamics when some people speak too much, others too little, or when one person consistently speaks over another, however, we often have little objective data to support such assertions. Even when such data can be collected, it is not often processed quickly enough to provide quality feedback to affect behavior. We developed the conversation moderator to provide feedback on the engagement of people speaking in the meeting through a friendly and intuitive user interface. The application utilizes machine learning-based speaker diarization to parse the audio recording into separate individuals, and the results are presented in a variety of visual representations. This includes total speaking time for each person, a scrollable piano chart indicating the time course of each speaker throughout the conversation, and additional statistics on speaking intervals. Diarization was performed with CMU SPHINX for MFCC conversion to extract audio features relevant for speech recognition and the LIUM toolkit to perform the necessary clustering and sequence estimation to distinguish each speaker. This application provides unbiased information on speakers with the goal of facilitating group discussions and aiding in the study of human social interaction.
Mutual Context-based Word Prediction for Internet Messenger Chat

Digital communication, being a part of everyday life, keeps everyone connected on the fly mostly via chat and text messaging. While chatting and texting the users are assisted in typing by many predicted and suggested words provided by the keyboard. These keyboards' systems do not use the context of users and do not consider the mutual condition of both the users in a conversation as well. Context is defined as the physical and psychological condition of a user and mutual context is defined as a mutualization of contexts of two users engaged in a conversation. If mutual contexts of the users are considered for predicting words, higher accuracy of correct prediction of the next word in a conversation can be achieved. We implemented the mutual context of users in a typical conversation through a simulation tool and achieved satisfactory results.
A Hypothesis Testing Approach to Sharing Logs with Confidence

Logs generated by systems and applications contain a wide variety of heterogeneous information that is important for performance profiling, failure detection, and security analysis. There is a strong need for sharing the logs among different parties to outsource the analysis or to improve system and security research. However, sharing logs may inadvertently leak confidential or proprietary information. Besides sensitive information that is directly saved in logs, such as user-identifiers and software versions, indirect evidence like performance metrics can also lead to the leakage of sensitive information about the physical machines and the system. In this work, we introduce a game-based definition of the risk of exposing sensitive information through released logs. We propose log indistinguishability, a property that is met only when the logs leak little information about the protected sensitive attributes. We design an end-to-end framework that allows a user to identify risk of information leakage in logs, to protect the exposure with log redaction and obfuscation, and to release the logs with a much lower risk of exposing the sensitive attribute. Our framework contains a set of statistical tests to identify violations of the log indistinguishability property and a variety of obfuscation methods to prevent the leakage of sensitive information. The framework views the log-generating process as a black-box and can therefore be applied to different systems and processes. We perform case studies on two different types of log datasets: Spark event log and hardware counters. We show that our framework is effective in preventing the leakage of the sensitive attribute with a reasonable testing time and an acceptable utility loss in logs.
A Framework for Analyzing Book Covers and Co-purchases using Object Detection and Data Mining Methods

Co-purchase analysis is becoming an essential task for e-commerce businesses as it enables to predict new purchases and understand customer behavior. In this paper, we propose a novel framework to analyze books sales from co-purchase and visual data. An existent dataset created from Amazon co-purchase data was extended to include visual information from book covers. Deep Learning was used to perform object detection and Data mining techniques were used to analyze relationships concerning book co-purchases and the influence of visual information on books category. Our results revealed several interesting relationships between co-purchases and predominant colors in the cover, as well as between objects and categories.
Morse Telegraph Detection and Demodulation Algorithm Research Based on Wavelet Transform and Clustering

Morse telegraph is an important communication mode for emergency communication, which is ancient and vital, and it is of great significance to realize its demodulation. In this paper, Morse signal is detected by the short-time Fourier transform on the main frequency points. After taking the wavelet transform of the signal, the beginning and the end of the Morse code points and dashes are accurately estimated by the local extreme detection method, and the length distribution of the pulse and interval are calculated. The method of clustering is used to classify the points, dashes and different intervals, and then the telegram is decoded according to Morse code coding rules. Experiments proves that this method has certain anti-noise performance, and it can detect and decode Morse telegraph under the condition of high signal noise.
Research on the Model Construction and Development of Computer Information Acquisition System

Abstract Nowadays, the number of crimes committed by computers is increasing, so computer forensics technology has become the focus of people’s research and attention. This paper designs the module structure of the computer information acquisition system, and focuses on the realization of the key technologies of the computer information acquisition system. It discusses the application of data mining on the web, and focuses on the analysis of web text mining technology. This paper proposes a method to acquire UNIX kernel information at the user level, and introduces a utility program to acquire index node table in UNIX system. At the same time, a prototype of a professional news information acquisition system is proposed. Taking the prototype of the system as the background, the application of information acquisition on the web is realized by using relevant technologies of web information search and web text mining.
A Horizontal Attack on SM9 Signature Generation

In this paper, we will present a horizontal attack on SM9 signature generation. The horizontal attack can make use of the information from multiple locations on one curve of SM9 signature generation to exploit the secret key. Since only one curve is needed in this attack, the conventional countermeasures like message blinding and key randomization would not work. We accomplished the security evaluation of SM9 signature generation on Sasebo-GII. The experiment results show that a vulnerable realization could leak the information of all key bits.
An Improvement on the Authentication and Encryption of Ceph in Unreliable Environments

Although distributed systems are mostly used in internal environments, they are also inevitably exposed to some untrusted environments where some potential security problems exist. The TLS 1.3 [1], a new network security standard, was officially released in August 2018. It is of great significance for the authentication and secure transmission of public keys. At the same time, it can be used as a reference for the implementation of the bi-directional authentication between the subsystems in distributed systems. Based on the principle and thought of TLS1.3, an improvement on the authentication subsystem is proposed, taking Ceph, a distributed storage system, as an example. This paper designed an improved solution for Ceph's communication in an insecure environment.
Features of Mechanical Product Manual and Its Translation Strategies

Abstract Mechanical product manual is a kind of application writing used to introduce the performance, specifications, use and way of maintenance about specific mechanical products. Based on the linguistic adaptation theory, the present paper probes into the research of mechanical product manual translation. The translation of mechanical product manual can offer the target language customers the equivalent product information, publicize the mechanical products, and even attract the potential customers. On this premise, a qualified translator ought to comply with the features of mechanical product manual and adopt the appropriate translation strategies so as to render a high quality translation.
A Comprehensive Study of Attacks on the IoT and its Counter Measures Using Blockchain

The Internet of things (IoT) covers the whole world, and our myriad daily devices are connected with their surrounding environment in order to collect information to perform specific tasks. These IoT devices need to interact and synchronize with each other, and every place is sensing enabled by a wireless sensor network. The server–client model makes managing the communication and synchronization of a few devices easy. However, if there are thousands of devices, during synchronization the IoT suffers from several limitations and issues, which are most challenging due to its complex environment. To overcome these limitations and issues, blockchain can be used to control and configure IoT devices. Blockchain’s original use was for bitcoin (cryptocurrency), but now its use is expanding to many other fields, including the IoT, healthcare, finances, security, and academic and industrial fields. Now that blockchain technology has been realized as the next revolutionary technology, the primary aim of using blockchain in the IoT is to connect things to the Internet in a peer-to-peer manner, and blockchain is the only distributed ledger maintained by a peer-to-peer network where non trusted participations in the network can communicate with each other without a trusted third party. Blockchain capabilities, such as immutability, auditability, transparency, open source, persistency, and anonymity, can solve most architectural shortcoming of the IoT. Among other things, IoT devices require privacy, security, authentication, protection from attacks, affordability, easy deployment, and self-maintenance. The aim of the IoT is to merge both digital and physical worlds into a single ecosystem that makes up a new intelligent era of the Internet.
The Morphology of Life Events

Everything around us including human life is nothing but the collection of diverse events in the flow of time. Being more specific, events are happenings qualified by spatio-temporal attributes, and are worth remembering. For life events, such happenings may include a delineated change in situation like marital status, scholastic, occupation or any other environmental amendment in time. We can think of this chain of events as one's lifeline. In other words, individual's lifeline is a collection of temporally ordered set of events, which interlinked between themselves resulting an intricate system of events. The system becomes even complex when multiple lifelines interact and influence with each other. In this paper, we have analyzed each type of events from the perspective of temporal relational algebra, extended their notions to redefine, and formalized the different temporal connectivity between set of events with the help of mathematical notion. This paper is the extension version of the River metaphor model for personal event management system in which we have removed the ambiguity of time. This extended model helps us to measure multiple event relationship more accurately and investigate the cause and effects of different events that seems to happen arbitrarily.
A Refined Neural Network Recognition Architecture for Blurred Image Semantic Generalization

The technique of generating a caption for a blurred image described by AI still exists the hurdle of recognition. In a blurred image, figuring out a semantic subject is a severe challenge. In this paper, we implement a semantic classifier as an auxiliary oriented filter that combines with a standard dense based caption architecture. This refined architecture is used to categorize a main subject from the media and transform it into a specific predicting range of field. The proposed framework can describe the outcomes and semantical relations that are hidden in an image; they are supposed to deliver a refined natural language sentence through the hierarchical context. Besides, the sample data is evaluated on the proposed approach at the authority benchmark, such as dataset MSCOCO.
A lgorithm Used in a Hybrid Age to bring paradigm shift: across all segments and verticals to create an edge

The human brain consists of billions of neurons which are unified by a mixture of synapses. If “enough” synaptic inputs to the neuron fires, then the neuron will also shoot. This progression is known as “accepted wisdom”. To replicate this procedure of philosophy on computers, we need to have a combination of machine learning and neural networks. This wide-ranging know-how of putting artificial brain into machine is in veracity fascinating, we humans for the reason that of our imaginative brain power has this natural instincts to go ahead of what seems not viable to craft tools and proficiency which becomes an additional room to our everyday verve, which can make decisions on our behalf and make our living tremendous well-organized. It is with this persevere to make humans super productive that the new paradigm shift has started putting artificial intelligence to the computer machines and now time has come to go long mode to build machines which can practically believe like human brains.
Entropy squeezing and coherence for a non-Markovian dissipative qubit system

We examine the impact of the non-Markovian environment on the dynamical behavior of the quantum coherence and entropy squeezing considering a two-level atomic system (qubit) immersed in a reservoir with zero-temperature for two types of non-Markovian environments. We consider a cavity little off-resonance with the transition frequency of the qubit and the case of a non-perfect photonic band gap (PBG). We show that the amount of coherence is dependent on the structure of the environment and influenced through the memory effects. We obtain that the delay and revival of the coherence loss might take place by controlling the detuning of the cavity-qubit system. Whereas, a partial coherence trapping occurs in non-ideal PBG and the decrease of the gap width will destroy the coherence. On the other hand, we show the situation for which the squeezing is occurring and enhanced with respect to the main parameters for the system. Finally, we display the monotonic dependence of the quantum coherence and squeezing on the main model parameters.
Robust Temporal Super-Resolution for Dynamic Motion Videos

It is difficult to apply most video temporal super-resolution studies for real-world scenes because they are optimized for a specific range of characteristics. In this paper, we propose a video temporal super-resolution method that is tolerant to motion diversity and noise. Our proposed method improves its robustness by fine-tuning the pre-trained SPyNet that is trained for videos with simple motions and moderate conditions. Moreover, our proposed network learns to accurately synthesize two frames generated by a backward warping function without requiring any additional information using the architecture of a modified DHDN. This enables our proposed method to efficiently synthesize two warped frames by saving the computational complexity for pre-training and extracting the additional information. Finally, we apply the self-ensemble method, which is commonly used in studies on image processing but not on video processing. The application of the self-ensemble method enables our network to generate stable output frames with improved quality without any additional training. Our proposed network proved its performance by ranking 5th in the AIM 2019 video temporal super-resolution challenge; the performance gap between our proposed network and the 3rd-and 4th-ranked solutions was very small. The source code and pre-trained models are available at https://github.com/BumjunPark/DVTSR.
Expert system on diagnosing children’s illness using Bayesian Method

Abstract Expert system is a computer program that replicates the process of thinking and expert knowledge in solving a particular problem. The expert system can be implemented in wide range of areas, including in the medical field. Children are very susceptible to germs and parents often fear of not being able to detect symptoms of illness usually suffered by children. This expert application system for paediatric diagnosis is an expert system application designed to assist parents to sign in as users and enable them to diagnose the type of illness suffered by children according to the perceived symptoms based on a dynamic knowledge. This knowledge is obtained from various sources including scientific journals, the results of interviews and intensive discussions with medical experts. The knowledge base is structured systematically into a database with several tables including disease tables, symptom tables, and tables on basic rules to support system performance in drawing conclusions. The conclusion in this expert system is drawn using Bayesian Method. The assessment on 30 cases shows that the accuracy of system output is 76.3 percent and the system evaluation on users is 85 percent.
Aprovechamiento de frutas y hortalizas de temporada de la Región de Tabasco, mediante la deshidratación del producto, utilizando una estufa solar

Drying has been the most widely used fruit preservation process for many years. Food dehydration is a process in which heat transfer and mass transfer take place. The main objective of this research is to share the results obtained in the process of dehydration of seasonal fruits and vegetables of the Tabasco region through a dynamic model using a solar stove, through a controllable indirect heating method, which stabilizes the temperature of dehydration, so that there are no alterations in color and flavor of the fruit. The results in the dehydration of seasonal fruits (mango and cambola) are presented in adequate and homogeneous proportions, distributing each piece in the solar stove, exposed to solar energy for three days. A description of the solar drying used, the instrumentation used and the experimental process that was carried out in each of the tests is briefly shown. Solar energy is used as a good source of heat supply for the dehydration of agricultural products and the use of the solar stove gives another alternative to the use of this renewable and sustainable energy, which is available in Tabasco for drying food products, with the purpose of preserving and giving added value to the fruits of this region