Reviewerly/exHarmony huggingface dataset:
Verifiable and Privacy-Preserving Association Rule Mining in Hybrid Cloud Environment

Nowadays it is becoming a trend for data owners to outsource data storage together with their mining tasks to cloud service providers, which however brings about security concerns on the loss of data integrity and confidentiality. Existing solutions seldom protect data privacy whilst ensuring result integrity. To address these issues, this paper proposes a series of privacy-preserving building blocks. Then an efficient verifiable association rule mining protocol is designed under hybrid cloud environment, in which the public unreliable cloud and semi-honest cloud collaborate to mine frequent patterns over the encrypted database. Our scheme not only protects the privacy of datasets from frequency analysis attacks, but also verifies the correctness of mining results. Theoretical analysis demonstrates that the scheme is semantically secure under the threat model. Experimental evaluations show that our approach can effectively detect faulty servers while achieving good privacy protection.
Estimation of Vehicle Pose with Monocular Camera

Object recognition and pose estimation are fundamental computer vision problems. It is important for autonomous driving. In this article, we developed method with pose estimation of rigid objects. We decided this problem on the basis monocular images obtained bya moving vehicle.
2-Convex Polyominoes: Non-Empty Corners

A polyomino P is called 2-convex if for every two cells there exists a monotone path included in P with at most two changes of direction.This paper studies the geometrical properties of a sub-class of 2-convex polyominoes called 1,1 2L α where the upper left corner and the lower right corner of the po- lyomino each contains only one cell.
Optimal allocation strategy for temperature and humidity based on improved glowworm swarm optimization algorithm

Aiming at the problems that the factors such as strong temperature and humidity coupling, nonlinear time delay and other factors of flower vending machine which affects the optimal matching of the power of the machine. This paper proposed a temperature and humidity optimization allocation strategy based on a variable-step adaptive glowworm swarm optimization algorithm. By studying flower storage constraints, temperature and humidity coupling characteristics and analyzing the influence of temperature and humidity coupling coefficient on the operation cost of flower vending machine. This paper established a temperature and humidity power optimization matching model of the machine with the goal of the optimal economy, and used fuzzy control and improved glowworm swarm optimization algorithm to adjust compensation parameters and optimize the objective function respectively. Simulation results showed that the optimized allocation strategy proposed in this paper effectively reduced the energy consumption of the equipment and improved the economic efficiency of the whole machine
On the formalization of asynchronous first passage algorithms

The formalization of First Passage schemes is revisited and the emerging of a conceptual contradiction is underlined. We then show why, despite such a contradiction, the numerical results are not explicitly affected. Through a different formalization of the problem, we recast the current principles of the algorithm in a more solid conceptual framework, and numerical evidence gives further justification to our claims.
Material Matters

Whether you aspire to Weiser's [4] disappearing technology of ubiquitous computing or Negroponte's [3] atoms to bits or something in-between, there is something inescapably material about the digital. How much more so when the goal is hybridity: the bridging of atoms with bits. In this keynote, I examine the intersection of this hybridity with human values-in the present and in the longer term. I situate my remarks in the observation that our scientific and technological capacities have and will continue to far surpass our moral ones - that is our ability to use wisely and humanely the knowledge and tools that we develop. My reflections are grounded in the intellectual traditions of value sensitive design [1] and multi-lifespan design [2]. I take up such questions as: What human values and human impacts are implicated by hybridity and, in particular, hybrid materials? From this perspective of human values, are all hybrid materials the same? Is embedding computation in my glove the same as in my finger? Or are some materials deserving of special status? And, if so, in what ways? What cultural considerations come to the fore? What of non-humans? Turning to metaphor and mental models, what metaphors should be developed for hybrid materials? And in what ways will those highlight and hide embedded computation? What mental models should be associated with these materials? How will these models position people to think about their relationship with hybrid materials, including when to use them, how to adapt them, when to dispose of them, and when to eschew them? More generally, what constitutes responsible innovation around hybrid materials and how should we go about pursuing it? To gain purchase on these questions, I use the frame of value scenarios and anticipatory futures. My comments highlight the importance of taking the material impacts of the digital seriously. At stake is nothing less than what sort of society we want to live in and how we experience our humanity.
Method for Improving the Statistical Properties of Pseudo-random Number Generators

The article is devoted to the issues of increasing the statistical safety of pseudo-random number generators by equalizing the properties of the generated sequences. The traditional generator structure assumes two stages: the first provides good statistical properties and a long period of the formed sequence, the second is responsible for unpredictability and cryptographic resistance. In this case, the second stage can lead to deterioration in the statistical properties of the sequence formed by the generator. An approach based on adding to the traditional two-stage generators architecture of third-stage is considered. A variant of the third stage construction is proposed. The possibilities of applying the proposed approach to improve the statistical properties of modern ciphers are described. The results of statistical properties researches of the generators with implemented third stage are given.
E-Commerce Consumer Privacy Protection Based on Differential Privacy

With the advent of the era of big data, human society is facing unprecedented opportunities and challenges. In many fields where big data is widely used, e-commerce is undoubtedly a crucial one. While the advanced data analysis technology penetrates into every link of e-commerce transaction, it not also brings about convenience, but also causes many adverse effects. On the one hand, this paper analyzed both the current situation of consumer privacy security on e-commerce platform and the increasingly mature application of differential privacy in various IT fields. It is found to be promising that with its good feature of privacy ambiguity, consumer privacy protection in differential privacy mechanism can meet the requirements of a more robust development of e-commerce in big data era. On the other hand, this paper briefly discussed about consumer protection in legal level, inspired by relevant researches done on GDPR.
Research on Key Feature Extraction and Position Accurate Tracking Based on Computer Vision Image

At present, the difficulty in video image recognition mainly focuses on video image segmentation and video image recognition. In video image segmentation, the effect of segmentation has affected by changes in lighting. The active vision technology of coded structured light illumination is used, and only one image is used. Under the condition that the camera system parameters are unknown, it is not necessary to calibrate the three-dimensional scene in the real three-dimensional Euclidean space for position. The human-computer interaction based on video image recognition in this paper is an important research topic of current intelligent human-computer interaction. Because vision-based video image recognition used a camera to capture video image information without the need to purchase expensive equipment, it is more natural and convenient to operate and meets the requirements of human-centeredness and freedom. Computer vision-based video image research has important theoretical value and application prospects, so we believe that the research on video image recognition will receive more attention and development.
Learning trajectory of three dimensions’ topic through analytical geometry approach

The purpose of this research is to create a learning trajectory of the distance, angle, and angular measure concept in three dimensions. The underlying problem of this research was the discovery of learning obstacles in using the learning trajectory which was built in the Euclidean geometry approach that already existed, such as the lack of students' spatial sense, teachers' capability, and learning activities' habit. The method used in this research is a qualitative research, particularly prospective analysis part of the didactical design research through observational learning, literary studies of topics' related material, and interview. Based on the results of research and discussion, the vector topic learning activity was too emphasized on the algebraic form, so the learning trajectory that was recently compiled through analytic geometry approach, required an emphasis on exploration of the vector geometric representation. The new learning trajectory, i.e. through analytical geometry approach, that has been formed can be useful to address the problem of learning trajectory before. For future research, we consider to develop a didactical design with this learning trajectory as the main learning framework.
Compact in‐memory representation of large graph databases for efficient mining of maximal frequent sub graphs

Summary Complex networks have been used in many scientific disciplines like sociology, microbiology, and telecommunication to represent the interactions among them. Graphs are generally used for representing such complex networks. Mining significant frequent patterns from graph databases has been a challenging area of research. A number of sub graph mining algorithms have been proposed for finding frequent fragments in molecular databases. A very few algorithms have been proposed for mining frequent patterns from large communication networks. All these algorithms perform well on medium size networks and fail on very large graphs. The scalability of these algorithms has been an issue because of the enormous memory requirements and also due to the exponential number of frequent sub graphs possible. In this paper, we propose a compact way of representing graph databases and also use it in a maximal frequent sub graph mining algorithm. The algorithm is found to be efficient and scalable to very large graph databases.
Properties of the physical space, the information space and out-structure space of information

Human beings live in the physical space, think and judge in the information space, and act back in the physical space. Their thinking and judgment greatly depend on information accumulation and the environmental stimulation in their physical spaces. Therefore, their cognition and actions are formed under the guidance of both the physical space and information space. Based on the three-element engineering model, this paper takes the present characteristics of time to reinterpret the properties and relationships among the physical space, information space and out-structure space of information. The findings of this analysis are intended to provide a precise guide for people on their individual or collective life in society, especially as a result of the increasing nature of information processing ability in the present era. As well, the findings of this study are anticipated to provide some guidance for people's thoughts concerning the developing society, which they can use to understand the anticipated nature of society in the future.
Automatic Failure Detection and Correction for Real-Time Object Tracking with Kernelized Correlation Filter

This paper proposes an algorithm that estimates tracking failure and track the target object again. The proposed algorithm consists of three steps: 1) Tracking target object with KCF tracker. 2) Estimating tracking failure by analysing correlation values. 3) Re-capturing the target object using multiple search windows and re-tracking. Experimental results shows that the proposed algorithm effectively corrects tracking failure situations.
Fast local outlier detection algorithm using K kernel space

Outlier detection can detect a small amount of data which containing valuable information from a large number of data, and it has become a hot topic in data mining. In this article, a new algorithm is proposed, which is fast local outlier detection algorithm using K kernel space. It is proposed to solve problem of the detection efficiency is not high because of the unevenness of density distribution in the outlier detection algorithm based on density, and the running time of the algorithm is obviously increased after introducing the reverse K nearest neighbors algorithm. By introducing K kernel space this algorithm divides the objects in data set into near K neighborhood points and far K neighborhood points, and reduces the number of data points which computation of the reverse K neighborhood, so as to reduce the running time of the algorithm. The accuracy of outlier detection is improved by introducing reachable distance and reachable density to reduce the statistical fluctuation of distance. Finally, the effectiveness of the proposed algorithm is shown by simulation data set and real data set.
Fuzzy Co-clustering for Categorization of Subjects in Questionnaire Considering Responsibility of Each Question

Categorization of subjects is a basic approach for summarizing the results of various questionnaires. Co-clustering is a technique for simultaneous co-clustering of mutually familiar objects and items such that each co-cluster is formed by the subject group with their typical questions. This research considers such a situation that a questionnaire is designed for finding some pre-defined categories, which are characterized by some typical questions, but some questions may not be necessarily fit to the target categories. Then, fuzzy co-clustering is performed in conjunction with evaluation of the responsibility of each question for the target categorization. The proposed fuzzy memberships are constructed under a hierarchical structure of category characterization and question evaluation. The characteristic feature of the proposed method is demonstrated through numerical experiments with an artificial data set.
Engagement in Earnings Conference Calls: A Multi-Method Examination

Research on conference calls documents that the question and answer (Q&A) portion is informative to markets. However, prior studies focus on the attributes of the participating individuals, primarily managers and analysts. We instead use the conversation as our unit of analysis, and examine whether conversational engagement between managers and analysts in earnings calls is informative to market participants. Using an experiment, we first demonstrate that linguistic style matching (LSM), a form of verbal coordination, is a reasonable empirical proxy for conversational engagement. We next use a quasi-experiment to confirm that investors can detect differences in engagement. Finally, using a hand-collected archival dataset comprised of audio recordings and textual transcripts from over 2,400 earnings calls, we show that LSM in manager-analyst conversations during the Q&A is positively associated with absolute stock returns during the conversation, suggesting that conversations with greater engagement are more informative to capital markets and facilitate price formation.
Analysis Report on Attacks and Defence Modeling Approach to Cyber Security

The researcher stated that critical analysis on attacks and defense modeling approach to cyber security which is one of the significant research issues in the computing environment. The researcher focused on some of the factors such as control the side of damage, perform forensic analysis, executive standard counter measures, perform threat detection and hunting, and gather threat intelligence. These all are the defense parameters which are stated to modeling approach in cyber security. During the research the researcher evaluated the security parameters including password strength, fraud detection system, firewall and others security parameters which are significant with respect to cyber attach and environment. The researcher shown statistical report on industries impacted by cyber-attacks worldwide as of September 2017. The researcher stated that passwords are basic cyber-security tool that people encounter nearly every day to prevent valuable data and highly sensitive information from unauthorized persons. The researcher represented an attack and defense modeling approach with the help of cyber security attributes such as information security, network security, operational security, end-user protection and application security. The researcher also stated that a case study of the 2016 Korean cyber command compromise: the victim of a prospering cyber-attack that allowed access to internal networks. Per usual with massive scale attacks against South Korean entities, the hack was straight off attributed to DPRK. Case study-2: cyber warfare conflict analysis and case studies, to analysis historical cyber warfare incidents from the past to the current and capture relevant information in a very information acquisition section.
The Role of the Academic Librarian in the Sciences of Food and Health

This study investigates the information seeking behavior of faculty and graduate students in the sciences of food and health. We compare three food science research sources through quantitative and qualitative research methods of interviews, observational studies, and surveys. We aimed to discover what motivates scientists’ information seeking, how they search, what barriers they experience, and how they critically evaluate information sources. We explored faculty perceptions of literature review practice in the field and by their students, their preferences for training and best practice in order to improve research quality. Recommendations for librarians supporting food science and related departments are provided.
Occluded Face Recognition by Identity-Preserving Inpainting

Occluded face recognition, which has an attractive application in the visual analysis field, is challenged by the missing cues due to heavy occlusions. Recently, several face inpainting methods based on generative adversarial networks (GANs) fill in the occluded parts by generating images fitting the real image distributions. They can lead to a visually natural result and satisfy human perception. However, these methods fail to capture the identity attributes, thus the inpainted faces may be recognized at a low accuracy by machine. To enable the convergence of human perception and machine perception, this paper proposes an Identity Preserving Generative Adversary Networks (IP-GANs) to jointly inpaint and recognize occluded faces. The IP-GANs consists of an inpainting network for regressing missing facial parts, a global-local discriminative network for guiding the inpainted face to the real conditional distribution, a parsing network for enhancing structure consistence and an identity network for recovering missing identity cues. Especially, the novel identity network suppresses the identity diffusion by constraining the feature consistence from the early subnetwork of a well-trained face recognition network between the inpainted face and its corresponding ground-true. In this way, it regularizes the inpaintor, enforcing the generated faces to preserve identity attributes. Experimental results prove the proposed IP-GANs capable of dealing with varieties of occlusions and producing photorealistic and identity-preserving results, promoting occluded face recognition performance.
Public Sphere, Virtualization, Rhizome: Communicative Practices of Runet Social Media

In the present work, the interrelation of social media with the philosophical thought of poststructuralism is investigated. The nature of the manifestation of this connection, the reasons for its actualization, the main one being the crisis of confidence in traditional media, is largely related to poststructuralist world outlook and attitude. Particular attention is paid to the structure of social media, the emphasis is on their nonlinearity. The image of the rhizome, described by J. Deleuze and F. Guattari, helps to reveal the peculiarities of the text functioning within the social media space and the formation of an interpretative-commentary complex around it.The latter is capable of changing the initial intentions in many ways, and here correlation with the poststructuralist idea of the "death of the author" is traced. Semantic dominants of this non-linear system are associated both with public expectations and with the activities of certain political actors and business structures. As a result, the interpretative-commentary complex acquires the features of a poly-subject, affecting the subject-object dichotomy, which underlies the classical models of communication. The relationship of social media with the philosophical thought of poststructuralism is also studied in the context of the public sphereideas. Modern interactive media are distinguished by the interpenetration of collective social and individual personal beginnings: the topics that the public and certain social groups consider most relevant are revealed here. In addition, a large number of interpretations are allowed. At the same time, the discussion is mainly limited to the presentation of certain views without establishing common ground, so we cannot yet talk about the space of the classical public sphere. Separately, the issue of increasing image symbols within the communicative space of social media is raised, it is considered in the context of the theory of simulacra by J. Baudrillard. Social media simultaneously acts as a product of the virtual environment, and as a reaction to it, where network activity correlates with other types of activity. In the present work, a conclusion about the complex nature of relationship between social media and modern philosophical thought is made. A systematic study of constructs that have an impact on the communicative practices of social media provides the most objective ideas about this phenomenon.
This Is How I Feel About Complex Systems

It has been around for nearly 40 years since I joined with the academic world in computer science related areas. Because I prefer to concrete real world topics, I, as a system scientist, usually research and develop challenging cutting-edge applications on complex systems. By complex systems, I do not only mean such areas as complex adaptive systems, chaotic, nor fuzzy systems, but, seemingly complicated systems required to be socially implemented. In this short paper, based on my final lecture at Tokyo Institute of Technology, I would like to present how I feel about complex systems and discuss principles of agent-based modeling, interdisciplinary research, and system creation. The paper concludes some comments on what we must do in the future.