Reviewerly/exHarmony huggingface dataset:
WordyThief: A Malicious Spammer

We detail the tradecraft used to discover and exploit a prolific Russian-affiliated malicious spam actor. To the best of our knowledge, this paper is the first description of the actor, whom we call WordyThief, and the first publication demonstrating the application of graph techniques to the identification of malicious spam campaigns. This work contributes to the threat intelligence community both as a technique that can be utilized in daily practice, and as a thorough account of WordyThief, who continues to spread malware in October 2020. We initially discovered isolated malware campaigns using large-scale bipartite graphs created from email metadata. These graphs and related campaign specifics revealed the use of domain names within the spammer's infrastructure devised through dictionary domain generation algorithms (DDGAs). Using a second graph-based technique and time series analysis, we recovered the underlying dictionaries and temporal behavior of the actor. A retrospective review of spam collection and correlation with other Domain Name System (DNS) information led us to conclude that the campaigns were all the work of a single actor. We tracked their activity and substantiated our methods retrospectively, through December 2019. We also leveraged open source intelligence (OSINT) to verify our findings. We found that WordyThief operates a large spam infrastructure and distributes malware that steals personal and financial information from victims. This paper includes not only the scientific methods used to detect the actor, but also detailed descriptions and analyses of several elements of their tactics, techniques, and procedures (TTP). We include an analysis of the actor's tendency to use of aged domains, a text analysis of their emails, use of embedded IP tracking in their campaigns, harvesting of open source images, and an exposition of their evolving exploitation techniques.
Intrinsically Motivated Lifelong Exploration in Reinforcement Learning

Long-term horizon exploration remains a challenging problem in deep reinforcement learning, especially when an environment contains sparse or poorly-defined extrinsic rewards. To tackle this challenge, we propose a reinforcement learning agent to solve hard exploration tasks by leveraging a lifelong exploration bonus. Our method decomposes this bonus into a short-term and a long-term intrinsic reward. The former deals with local exploration - exploring the consequences of short-term decisions, while the latter explicitly encourages deep exploration strategies by remaining large throughout the training process. As formulation of intrinsic novelty, we propose to measure the reconstruction error of an observation given its context to capture flexible exploration behaviors characterized by different time horizons. We demonstrate the effectiveness of our approach in visually rich environments in Minigrid, DMLab, and Atari games. Experimental results validate that our method outperforms baselines in most tasks in terms of scores and exploration efficiency.
Image contrast enhancement using histogram equalization: a bacteria colony optimization approach

Histogram equalization (HE) is a popular method for image contrast enhancement that probabilistically maps the existing tonal levels of image to a new set of intensity levels. Despite simplicity, conventional global HE (GHE) has several limitations including visually disturbing false contouring, loss of original image features and wrong color representation in case of color images. Further modifications of GHE method have shown considerable improvement but many such techniques lack retention of original image characteristics. Computational intelligence algorithms can be a potential paradigm to address those limitations. This paper explores the scope of bacteria colony optimization (BCO) to obtain contrast enhancement while maintaining the original image characteristics. The fitness function has been formulated in frequency domain upon rigorous analysis in comparison with the results of existing contrast enhancement techniques. The paper includes implementation results with standard databases and the comparative evaluations. The comparative visual and objective evaluations confirm potential of BCO for improved performance.
Ceramic Anti-Counterfeiting Technology Identification Method Based on Blockchain

This paper proposes a blockchain-based ceramic anti-counterfeiting technology identification method. The method mainly extracts the size and position of the underglaze bubbles formed when the ceramic is fired in the kiln to form the ceramic fingerprint, and constructs the ceramic fingerprint and the ceramic fingerprint. The only correspondence that cannot be forged. In order to eliminate the malicious operation of ceramic fingerprints by a key third party in the storage and sharing process, the non-tamperable modification of the data on the blockchain is used to combine ceramic fingerprints and blockchain technology, so that each ceramic can form its own unique Proof of identity. Experiments have proved that this method can creatively solve the various shortcomings of the current anti-counterfeiting traceability technology applied to ceramics. Through the new technology and new model of anti-counterfeiting traceability of ceramics, ceramic fingerprints and blockchain, it has subverted the current market The anti-counterfeiting traceability system enables everyone to implement ceramic anti-counterfeiting traceability, which is a fundamental technological innovation in the field of anti-counterfeiting traceability.
직업능력개발훈련 참여도 향상을 위한 방안 연구

As interest in vocational competency development projects increases, support is increasing. In this study, in order to study ways to improve participation in vocational competency development training, related factors are selected and the main factors influencing them are derived. The main factors were influencing factors were identified and ways to improve participation rate of vocational competency development were studied. Variables were selected through prior research, and data were collected and analyzed through in-depth group interviews. Based on factor analysis and reliability analysis, as a result of confirming the factors that influence the plan for improving participation in vocational competency development training by applying the backward stage selection (condition) method of logistic regression analysis, the case of respect for minorities in the 7-point Likert scale The closer to 7 was, the higher the probability of being classified as having never participated, and the other factors except minority respect, Likert scale closer to 7 indicates higher probability of bening classified as having never participated, except for the minority respect factor. Therefore, it was confirmed that lowering the scale of the factor of respect for minority, which is job satisfaction, and increasing the factors of individual characteristics such as annual salary and organizational commitment, training system for job satisfaction, and quality assurance have effects on improving participation rates in vocational competency development training.
A Pseudo Random Binary Sequence Generator Based on Chaotic Logistic Maps

One of the major applications of Pseudo-Random Numbers is cryptography. Cryptography is a technique of securing information and communications through the use of codes so that only those people for whom the information is intended can understand it and process it thus preventing unauthorized access to information. Attackers can bypass cryptography, hack into computers that are responsible for data encryption and decryption, and exploit weak implementations, such as the use of default keys. To tackle this problem, logistic chaotic maps are used to generate a Pseudo-Random Binary Sequence (PRBS). The output of the designed PRBS Generator has successfully passed all the performance assessments of the NIST test suite. Hence a highly efficient Cryptographic Pseudo Random Bit Sequence Key is developed which not only has good performance in terms of statistical properties but can also achieve high product throughput if realized by FPGA hardware.
DNS-Morph: UDP-Based Bootstrapping Protocol for Tor

Tor is a popular system for anonymous communication and censorship circumvention on the web, this puts Tor as a target for attacks by organizations and governmental bodies whose goal is to hinder users’ ability to connect to it. These attacks include deep packet inspection (DPI) to classify Tor traffic as well as legitimate Tor client impersonation (active probing) to expose Tor bridges. As a response to Tor-blocking attempts, the Tor community has developed Pluggable Transports (PTs), tools that transform the appearance of Tor’s traffic flow. In this paper we introduce a new approach aiming to enhance the PT’s resistance against active probing attacks, as well as white-listing censorship by partitioning the handshake of the PT from its encrypted communication. Thus, allowing mixing different PTs, e.g., ScrambleSuit for the handshake and FTE for the traffic itself. We claim that this separation reduces the possibility of marking Tor related communications. To illustrate our claim, we introduce DNS-Morph: a new method of transforming the handshake data of a PT by imitating a sequence of DNS queries and responses. Using DNS-Morph, the Tor client acts as a DNS client which sends DNS queries to the Tor bridge, and receives DNS responses from it. We implemented and successfully tested DNS-Morph using one of the PTs (ScrambleSuit), and verified its capabilities.
Formally Verified Switching Logic for Recoverability of Aircraft Controller

Abstract In this paper, we investigate the design of a safe hybrid controller for an aircraft that switches between a classical linear quadratic regulator (LQR) controller and a more intelligent artificial neural network (ANN) controller. Our objective is to switch safely between the controllers, such that the aircraft is always recoverable within a fixed amount of time while allowing the maximum time of operation for the ANN controller. There is a priori known safety zone for the LQR controller operation in which the aircraft never stalls, over accelerates, or exceeds maximum structural loading, and hence, by switching to the LQR controller just before exiting this zone, one can guarantee safety. However, this priori known safety zone is conservative, and therefore, limits the time of operation for the ANN controller. We apply reachability analysis to expand the known safety zone, such that the LQR controller will always be able to drive the aircraft back to the safe zone from the expanded zone (“recoverable zone") within a fixed duration. The “recoverable zone" extends the time of operation of the ANN controller. We perform simulations using the hybrid controller corresponding to the recoverable zone and observe that the design is indeed safe.
Cryptochain approach to Cloud Computing Security: Case study, Challenges, and Solutions

Abstract Cryptochain has attracted the new generation of FinTech technologies because of its disruptive security that adapts to teaching time. In particular, it provides security with peer-to-peer authentication, encryption, and hash generation. According to the FinTech sector, the security market’s technology sector is predicted to grow to $ 20 billion at the end of 2021. With this, the cryptochain can operate across the whole Internet of Things (Cloud) ecosystem; its programs are predicted to expand. Cloud technology has been widely welcomed in all tech fields for its efficiency and reliability. This paper will discuss the theory of cryptochain technology and elated thermal research methods. We will learn how to sync cryptochain security on a cloud with its secure data solutions.
Mathematical modeling of learning processes based on the theory of control

The learning process is considering as object of optimal control, for process controlling offered sequence of innovative approaches. Given a functional structure didactic system "teacher-reader" and on this bases created cyclic structure of calculation experience. The obtained results are graphically displayed.
Implementation of Metaheuristics with Extreme Learning Machines

This chapter approaches the topic of Extreme Learning Machines (ELM); this technique is implemented in a single layer feedforward neural network and provides to the neural network with a fast performance since it is not necessary to adjust weights and hidden layer biases. ELM is implemented and combined with three metaheuristic algorithms. The selected algorithms belong to the family of Swarm intelligence such as Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC) and Grey Wolf Optimization (GWO); these algorithms are applied to optimize the hidden biases and input weights and are implemented for improving the performance along with techniques of Extreme learning machines in data classification. Also, the performance and behavior of each algorithm can be observed. The data employed in this work consist of information about Australian credit, diabetes detection and hearth disease. The investigation result show that GWO obtained better results in the first dataset and PSO are better in the second and third datasets.
Towards Incorporating AI into the Mission Planning Process

While there are numerous powerful tools to support Navy mission planning, the mission planning process still remains a hybrid planning activity across human operators and advanced tools. Advances in artificial intelligence (AI) have seen an increase in interest and use in the mission planning environment. Yet traditional approaches typically focus on optimizing the performance of the individual operator or the mission planning tool, not the joint problem solving that is needed for the human and AI team necessary in these envisioned mission planning environments. For example, while AI-based approaches can offload extensive processing by the human operator, outcomes of AI-based tools are seldom presented in ways that make it readily understand by a human or fit in with the overall process. This results in new and additional work for the operator as they must manually translate these outcomes into actionable information. There are also specific characteristics of AI-based approaches to consider, such as accounting for data limitations; many mission planning environments have data availability constraints or do not capture the right data. New methods are needed to specifically consider the benefits of AI-based approaches, ensure that AI derived insights are communicated effectively to operators, and optimally support operators in their own mission planning workflow. In this paper, we describe an overall approach for incorporating AI into the mission planning process. First, to consider how best to leverage the unique capabilities and constraints of a human operator and artificial intelligence technologies as a cooperative team throughout the mission planning process, we will frame the human operator and AI system as a single unit and take a joint cognitive system (JCS) analysis approach. This approach will identify the key information to jointly create the most effective mission plan most efficiently. Next, we describe a novel approach, neural policy programs (NPPs) to address some of the critical challenges of incorporating AI into the mission planning process. Finally, we describe our approach for extending to the StarCraft II game and creating a pre-game planning stage to prototype our approaches.
Variable mesh optimization applied to fringe pattern demodulation using a Bézier surface

We present a parametric method to carry out a demodulation process in complex fringe pattern images with either open or closed fringes; this method is based on the parallel demodulation algorithm and introduces a novel way, to the best of our knowledge, to approximate the phase map using the Bezier surface control points. For this study, a recently developed population meta-heuristic called Variable Mesh Optimization is introduced to implement the optimization process. The results of the proposed method improve the phase error and the run time scores in comparison with other global optimization algorithms, which address this type of problem.
The Woods: A Mixed-Reality Multiplayer Cooperative Game

While loneliness in our real lives is increasingly recognized as having dire physical, mental and emotional consequences, cooperative games have also been shown to build empathy and provide positive social impact. In this paper, we present ‘The Woods’, a local cooperative mixed reality game that provides players with face-to-face interactions in pursuit of a shared goal using augmented reality and 4-channel audio spatialization panning. This paper discusses the technical aspects of the game, the design rationale and development process, and the resulting player experience. The goal of this research is to develop a narrative driven AR game that provides social benefits by prompting players to problem-solve collaboratively, and to leverage the physical and digital experience as fully as possible.
The equivalence of linear codes implies semi-linear equivalence

We prove that if two linear codes are equivalent then they are semi-linearly equivalent. We also prove that if two additive MDS codes over a field are equivalent then they are additively equivalent.
Cybersecurity Awareness on Video Conferencing Service During Coronavirus Pandemic

The entire world is looking for a solution that helps us to come out of the current pandemic situation caused by the novel coronavirus. It has not only caused lakhs of death but also the biggest economic shutdown. In this scenario, with the need for social distancing the world needs to have a certain strategy to sustain. Even though online e-commerce has hit new highs, now it is growing even faster with video conferencing service; here the need for awareness of cybersecurity comes into action. Since the entire population is depended on the information technology, all age people without awareness of securing themselves from cyberattack use the technology which creates lots of problem for the end user and the provider of that service. This paper considers the statistics on the cybersecurity awareness on video conferencing service from the different level of users of different age group.
Towards Commonsense Reasoning in AAL Environments

Ambient Assisted Living (AAL) is an application of Smart Environments, dealing with elderlies and their caregivers’ assistance in their daily life within their enhanced apartments. An AAL environment needs constant observation of the inhabitant’s activities to inform caregivers of critical situations respectively to react to them, such as the patient leaving the flat with the stove still on. Setting up an AAL environment is costly and complicated, as all sensors are tailored to the specific situation. Various industrial systems or research activities exist to monitor the environment and apply a rule-based inference to detect the multiple conditions as far as possible. There are, however, a range of standard day-to-day sensors, such as light switches, window sensors etc., which do not directly monitor patient conditions but allow for inference about a situation, e.g. whether a person has left the flat. We call this “lifted” contextual information. Also, there is much uncertainty in such environments, such as sensor malfunctions, power loss, or connectivity issues. Hence, a situation awareness system should freely combine and switch between combinations of sensors for identifying and verifying the current situation, respectively, inferences drawn from it. For example, confirm that the person has left the flat by checking for a webcam movement. This resembles our ability to use commonsense when we look at possible sensor readings on a dashboard. We make plain inferences based on a hypothesis on the given evidence. Such a system needs to make logical connections between different data and contribute to a derived information. We propose developing a logic-based system using the sensor events as evidence for a commonsense reasoning task.
A Nested Unsupervised Learning Model for Classification of SKU’s in a Transnational Company: A Big Data Model

This work seeks to develop a nested non-supervised model that allows a transnational soft drink company to improve its decision-making for the discontinuation of products from its portfolio with the use of unsupervised models from a database with commercial and financial information for all your product line in your most important operation. The integration of different cluster methodologies through a nested non-supervised model allowed to generate a correct identification of the products that should be refined from the catalog due to financial and operational factors. Given the magnitude of the information, a cluster was integrated into a platform for data processing as well as the generation of automatic reports that could be consulted automatically through the cloud. The products identified through the nested unsupervised model made it possible to identify products that had low demand and a low contribution to the utility of the company. Removing said products from the catalog will allow maximizing the profit of the business in addition to not incurring sunk costs related to the production and distribution of low-demand products. The platform developed will allow continuous monitoring of business performance in order to automatically identify the products likely to leave the catalog.
Optimal Community-Generation Methods for Acquiring Extensive Knowledge on Twitter

This paper proposes a method to generate an optimal community on Twitter by grouping users using a solution to the knapsack problem. Most past studies have proposed methods to recommend one user or some candidates, rather than recommending multiple users as candidates. It is quite difficult to recommend candidates as a single group that considers the balance of user characteristics, because grouping in terms of relationships among users is a combinatorial problem, especially on Twitter, as a huge number of users must be handled, so combinatorial explosion occurs easily. Although the combination optimization problem is difficult, with the knapsack problem, it is possible to obtain a solution of good quality within a practical calculation time. In this paper, we calculate the combination of users whose total amount of knowledge is maximized by using “amount of knowledge acquired” as a community evaluation item. In addition, we conduct a subject experiment to evaluate the information obtained from the generated community and the performance of the proposed method.
Cultural Discourse in User Behavior: Transfer of Thought in Keyboard Key Practice

After examining users' keyboard key practice of 95 students in China, we argue that there is a transfer of thought in keyboard key practice of Chinese students. We conceptualize the keys as discourse with cultural factors from the perspective of Cultural Discourse Studies and explore how the users' behaviors deviate the designers' intentions in the key practice. We design a ten-question survey testing the keyboard practice of Chinese students based on the radical behaviorism by Skinner [32], which combines the thoughts and feelings as well as observable behaviors. The survey results indicate that participants generally prefer certain keys and believe each key should not have only one basic function and the keys should be merged according to their operation directions. We then develop a key behavior model by viewing keys as words, and the process of keyboard practice as language comprehension and employment. By dividing the process into designer, discourse, and user; the functions of keys are mapped as designed function and performed function. Results indicate that participants performed poly-functional thought, a universal and powerful thinking method in Chinese language and culture, which may lead to the conflicts with the mono-functional design. We believe the transfer of thought enriches software didactics and inspires a different view of application design.