Reviewerly/exHarmony huggingface dataset:
Privacy Preservation for Versatile Pay-TV Services

In pay-TV services, content is encrypted and transmitted to subscribers. Each subscriber has a security module that holds a decryption key(s) for the encrypted content. A set-top box or a smart card is often used as the security module. When a subscriber wants to obtain the same services outside the home, the subscriber has to bring the security module. However, even if the security module is a card, it is not easy to take it out because of the structure of TV sets and set-top boxes. As a way of improving current pay-TV services, Ogawa, Tamura, and Hanaoka (OTH17) proposed a system using an attribute-based encryption scheme (ABE). ABE is used to restrict the time and location at which a subscriber can obtain the service. However, OTH17 requires a third trusted party (TTP) for key and ciphertext generation; thus, the TTP knows the time and location of the subscriber. This means that the subscriber's private information is disclosed to the party. Here, we propose a system that avoids disclosure of private data by adding a multi-party computation (MPC). In addition, MPC makes the TTP unnecessary.
A Fast Way to Evaluate Environmental Monitoring Results by Excel

Based on much more data and complicated evaluation in environmental monitoring data processing, his paper uses Excel function to edit self-defined IF function to evaluate environmental monitoring data. Taking pH, dissolved oxygen, permanganate index, chemical oxygen demand, ammonia nitrogen and total phosphorus data processing as examples, the process of establishing IF function is explained.
The Impact of the Observation Period for Detecting P2P Botnets on the Real Traffic Using BotCluster

In recent years, many studies on peer-to-peer (P2P) botnet detection have exhibited the excellent detection precision on synthetic logs collected from the testbed. However, most of them do not evaluate their effectiveness on real traffic. In this paper, we use our BotCluster to analyze real traffic from April 2nd to April 15th, 2017, collected as Netflow format, with three time-scopes for detecting P2P botnet activities in two campuses (National Cheng Kung University (NCKU) and National Chung Cheng University (CCU)). Three time-scopes including single-day, three-day, and weekly observation period applied to the same traffic logs for revealing the influence of the observation period on P2P botnet detection. The experiments show that with the weekly observation period, the precision can increase 10% from 84% to 94% on the combined traffic logs of two campuses.
A Combined Deep Learning and Semi-supervised Classification Algorithm for LS Area

In real world, there are many areas with Large images but only Small labelled (we called LS area), in there supervised and unsupervised algorithm can’t work well, but semi-supervised technology exploiting patterns both in labelled and unlabeled data to get labels can work well. The classification accuracy directly depends on the features extracted from the images. Recently, with the emergence and successful deployment of deep learning techniques for image classification, more research on getting features is directed to deep learning techniques. This paper proposes a combined semi-supervised classifier and pre-trained deep CNN model algorithm—CDLSSC (Combined Deep Learning and Semi-Supervised Classification) for LS area. The transfer learning that has been tested and verified in some areas is used to extract features in this algorithm. The method CDLSSC is evaluated on three image datasets and achieves superior performance. We apply it to the Terra-Cotta Warriors image classification area and get super results, which means that it can be used in cultural relic’s area successfully.
Improved Slope One Algorithm Using Multi-weight and Auxiliary Items

Slope one algorithm is widely used in recommendation systems. Although slope one algorithm is simple and efficient, the correlation between items and data sparsity issue are still the main issues. This paper proposes a multi-weight slope one algorithm, which obtains the correlation between items from multiple aspects, and introduces auxiliary items in relatively sparse scoring data to improve the recommendation effect. Experimental results on the MovieLens show that the recommended effect of multiple weight is better than the single weight. In the case of the sparseness of the data, the MAE value is reducing by 3% when using auxiliary items.
Storytelling Research of Virtual Image Based on User Experience

The VR image not only has the watching characteristic of movie, the immersion of drama and the interaction of game, but also its unique conception. At present, a new type of narrative mode adapted to VR image features has not yet reached a consensus. The purpose of this paper is to analyze the importance of research on the user experience to VR image creation, so as to point out the urgency of using iterative thinking in creating works, at the same time, to explore the possibility of collaborative creation by utilizing the creators and users of intelligent tool. The key to construct a narrative systematic frame which accords with the VR image’s characteristics of visual, audition, touch, taste and touch is put forward. This paper adopts the methods of qualitative research, which includes desktop research and network user research as well as the quantitative researches, so as to conduct the validation studies about the habit, feeling, expectation and participation of users when they experience VR image. Finally, the conception of narrative systematic framework that is suitable for VR image and the suggestions for future research direction are put forward.
Unbundling institutional determinants of innovation

Abstract Why are some nations wealthy, while others remain mired in poverty? Prior literature suggests technological innovation and strong institutions are uniquely conducive to long‐term economic growth. Property rights institutions (PRIs) afford protection from expropriation by the government and political elites, while contracting institutions (CIs) allow private parties to conduct commercial transactions. In this study, we systematically examine the relative effects of PRIs and CIs on innovation. Using an instrumental variable (IV) approach and two‐stage least squares regression framework, our results suggest that PRIs have a strong positive effect on innovation, while CIs do not. Constraint on executive, protection from expropriation of the government, and protection of private property are proxies for PRIs, while legal formalism, procedural complexity, and number of procedures are proxies for CIs. We find strong empirical evidence that PRIs have statistically significant effects both on innovation intensity and time lag in innovation adoption, while the impacts of CIs on these measures of innovation are generally insignificant. Our findings are relevant to policymakers today as developing countries look to innovation to reignite their economies and avoid the middle‐income trap.
D2M: Mobility-Aware Dynamic Data Multicasting in Software-Defined Data Center Networks

In this paper, we study the problem of mobility-aware dynamic data multicasting in software-defined Data Center Networks (DCNs) in the presence of Internet of Things (IoT) devices. In the existing literature, researchers studied that due to unbalanced traffic distribution in DCN, the network throughput and delay degrade significantly. Moreover, with the integration of heterogeneous IoT devices, the difficulty in achieving balanced traffic distribution increases. Hence, there is a need to design an efficient data multicasting scheme in DCN, while integrating the software-defined network architecture. In this work, we propose a Dynamic Data Multicasting scheme, named D2M, using single leader multiple follower Stackelberg game for ensuring high utilization of network capacity and efficient load balancing. In D2M, the controller acts as the leader, and the switches act as the followers. The leader decides the distribution of flow-rules among the switches for ensuring efficient load-balancing. We represent bandwidth distribution as a pseudo-Cournot competition, where each follower decides the optimal bandwidth to be allocated for each flow. The existence of at least one Nash equilibrium using D2M is ensured. Through simulation, we observed that using D2M, the network throughput increased by 6.13&#x2013;95.32% than using existing schemes, while ensuring 21.32&#x2013;99.29% reduction in delay.
Skip-Pose Vectors: Pose-based motion embedding using Encoder-Decoder models

This paper proposes a pose-based unsupervised embedding learning method for action recognition. To classify human action based on the similarity of motions, it is important to establish a good feature space such that similar motions are mapped to similar vector representations. On the other hand, learning a feature space with this property with a supervised approach requires huge training samples, tailored supervised key-points, and action categories. Although the labeling cost of keypoints is decreasing day by day with improvement of 2D pose estimation methods, labeling video category is still problematic work due to the variety of categories, ambiguity and variations of videos. To avoid the need for such expensive category labeling, following the success of “Skip-Thought Vectors”, an unsupervised approach to model the similarity of sentences, we apply its idea to contiguous pose sequences to learn feature representations for measuring motion similarities. Thanks to handling human action as 2D poses instead of images, the model size can be small and easy to handle, and we can augment the training data by projecting 3D motion capture data to 2D. Through evaluation on the JHMDB dataset, we explore various design choices, such as whether to handle the actions as a sequence of poses or as a sequence of images. Our approach leverages pose sequences from 3D motion capture and improves its performance as much as 61.6% on JHMDB.
Algorithmic Stability Theory

The notion of Stability [, , ] allows to answer a fundamental question in learning theory: which are the properties that a learning algorithm \(\mathscr {A}\) should fulfill in order to achieve good generalization performance? Stability answers this question in a very intuitive way: if \(\mathscr {A}\) selects similar models, even if the training data are (slightly) modified, then we can be confident that the learning algorithm is stable.
User Interface Prototyping Toolkit (UIPT)

A design philosophy implementation in the form of a User Interface Prototyping Toolkit (UIPT) constitutes a software application for use by Fleet users, researchers, designers, and developers to aid in rapid engagement with end users in an agile design process. The toolkit allows rapid development of user interfaces (UIs) for collaborative exploration and validation of new concepts of operations (CONOPS) and new technology for decision-making and situational awareness. This improvement in exploration and analysis of software systems applies no matter the production stage. This product allows researchers, designers and developers to quickly develop new or modify existing UIs as designers work alongside Fleet users in a collaborative design effort. The design effort applies to existing or novel missions utilizing tasks and CONOPs as drivers for meaningful requirements and design input. The UIPT toolkit effort is targeted to Command, Control, Communications, Computers, Intelligence, Surveillance and Reconnaissance (C4ISR) and Cyber missions for the U.S. Navy. UIPT enables the Navy to significantly reduce the risks associated with pursuing revolutionary technology such as autonomous vehicles, artificial intelligence and distributed sensing in terms of the design of the user interface. The UIPT toolkit provides the ability to explore, analyze, develop and test systems that maximize human interaction and efficiency via the interface for these new and evolving CONOPs and systems.
Internet Users Authentication via Artificial Intelligence

The number of Internet users increases and the Internet is part of people’s daily lives, as a result, the behavior of the user becomes free and informal. This is the basis of the assumption that the manner of user actions on the Internet has become a factor that can be used by authentication using artificial intelligence. In turn, existing works related to users’ web browsing behavior-based authentication with using machine learning do not analyze some important behavioral user’s characteristics, such as patterns of behavior or user behavior on a frequently visited resource. It causes to suggest own features and check their contribution to the accuracy of the system. The aim of this work is to study the possibility of introducing a map of clicks, bigrams, trigrams of frequent web pages and their domains, evaluation of the contribution of added features. In this work, we replace the web pages’ genre classification by domain classification and don’t take into account the spikes in views. We have created a system based on artificial intelligence. As a work result, we have shown a significant improvement in the accuracy of the system using the click map and a slight improvement in the use of bigrams and trigrams.
Semantic BI-Embedded GRU for Fill-in-the-Blank Image Question Answering

Image question answering is becoming a very attractive topic in the field of computer vision and natural language processing in recent years. In this work, we propose a novel Semantic Bi-Embedded Gated Recurrent Unit (SBE-GRU) method to answer fill-in-the-blank style multiple choice questions for images. Different from the single network, we use the SBE-GRU model to learn the high-level semantic information existing in images. To learn the semantic mapping of an image from the visual level to the language level, we feed the visual-language mappings into a stacked GRU. Moreover, to choose the right answer in the candidate options more simply and effectively, we regard the answer sentence as an answer list while training. In the extensive experiments, the proposed method can get better results compared with the state-of-the-art and CCA methods.
Simulation Analysis Based on Behavioral Experiment of Cooperative Pattern Task

We have a behavior experiment using pattern task abstracting cooperative behaviors that require intention estimation and action switching to specific goals. And we have analyzed strategies to adjust cooperative intention estimations. In this research, we constructed an agent model that have three strategies of “random selection”, “self-priority selection”, and “other agent’s target pattern estimation”. Moreover the decision making process was verified by simulation.
Stochastic program synthesis via recursion schemes

Stochastic synthesis of recursive functions has historically proved difficult, not least due to issues of non-termination and the often ad hoc methods for addressing this. We propose a general method of implicit recursion which operates via an automatically-derivable decomposition of datatype structure by cases, thereby ensuring well-foundedness. The method is applied to recursive functions of long-standing interest and the results outperform recent work which combines two leading approaches and employs 'human in the loop' to define the recursion structure. We show that stochastic synthesis with the proposed method on benchmark functions is effective even with random search, motivating a need for more difficult recursive benchmarks in future. This paper summarizes work that appeared in [1].
Spectral Invariant Operations in the p-valued Spectral Domain

The paper studies the generation of ternary bent functions by permuting the circular Vilenkin-Chrestenson spectrum of a known bent function. We call this spectral invariant operations in the spectral domain, in analogy to the spectral invariant operations in the domain of the functions. Furthermore, related generalized permutations are derived to obtain new bent functions in the original domain.
Efficient PatchMatch-Based Image Registration and Retargeting for Cartoon Animation

Image registration and retargeting has been playing a significant role in creating sorts of visual effects in animation production pipeline, attracting plenty of researchers working on. However, previous methods are slow due to physically or geometrically based complicated computations and often rely on user assistants or external devices to capture the motion data, reducing their practicability. In this paper, we propose a novel approach to capture a path motion contained in a source animation in a fully automatic manner and reuse it to new characters with different appearances. The key idea is to employ visual difference based random search procedure to automatically track the motion and combine the moving least square based deformation technique to endue the new object with the captured motion. Our experiments demonstrate that our method achieves good results with high quality and performance.
Research on Telemedical Monitoring System Based on Homomorphic Encryption

Social trends such as the accelerating aging of the population and the prevalence of chronic non-communicable diseases have made high-quality and long-term medical care gradually become the focus of attention of the whole society. Telemedical monitoring technology can greatly reduce the demand of hospital-centered medical resources, but increase the risk of data disclosure of personal privacy at the same time. How to ensure the privacy data security of patient in the course of telemedicine is an urgent problem to be solved in the development of new medical system. Based on the technology of homomorphic encryption, this paper studies the feasibility and specific encryption implementation of the telemedical surveillance system, after obtaining the patient indicators, and creates a prototype of a real-time early warning system for hospital, family and individual patients. At last a simulation test is designed on the Windows platform. The results and conclusion provide some reference for the future research of “cloud medicine” technology and give some advise on the application of homomorphic encryption technology.
Unambiguous discrimination of nonorthogonal entangled quantum states in cavity QED

In this work we propose a scheme in the context of superconducting cavities to unambiguously discriminate non-orthogonal quantum field states when one of them is one of the four Bell states. The present work, which generalizes an earlier proposal [1] dealing with non-entangled states, also makes use of a single high Q cavity, a sample of three-level atoms, Ramsey zones and selective detectors, thus consisting of an oversimplification from the experimental view.
Increasing genetic programming robustness using simulated dunning-kruger effect

Robustness is a key characteristic of genetic programming candidate solutions, providing the ability to maintain a satisfactory level of performance under dynamic and uncertain environments. In this paper we perform experiments on Tartarus problem instances[2] exploring the hypothesis that the introduction of a fitness distribution bias, inspired by the Dunning-Kruger effect [5], will lead to an increase in the diversity and robustness of candidate solutions.
Recent Advances in Attribute-Based Signatures

Attribute-based signatures are an advanced signature primitive that allows a signer to prove the possession of attributes with minimum level of disclosure of his privacy. In particular, by issuing a signature, the singer can prove the possession of a set of attributes that satisfies some public policy, while hiding any further information on the attributes beyond the fact that the attributes satisfy the public policy. This primitive has many interesting applications, for example, anonymous credentials, attribute-based messaging, and secret leaking. There are two main performance measures of attribute-based signatures. The first is expressiveness of the policy, namely, the computational class with which the policy is described, while the other is the computational overhead, for example, the signature size. While initial schemes mainly tired to investigate a trade-off between expressiveness and computational overhead, recently the research moves to aiming at achieving very expressive policy. Such constructions includes the construction for non-monotone span programs as the policy, those for unbounded circuits from bilinear maps from lattices in the random oracle model, and from lattices in the standard model, those for Turing machines and nondeterministic finite automata, and that for unbounded arithmetic branching programs. Another direction in this research area is to extend the functionality of the attribute-based signature primitive. This direction includes constructing traceable attribute-based signatures, decentralized attribute-based signatures, decentralized traceable attribute-based signatures, and hierarchical attribute-based signatures. In this talk, we give a brief overview of the recent advances in the area of attribute-based signatures, pick up some of the recent schemes, and introduce the key techniques for these constructions.
An Idea Support Method and a Tool for New Product Development

In order to develop a new product, we have to create more valuable user experiences in the product. The value of the product is able to satisfy the goals of stake holders of the product. Creating the value depends greatly on developers’ inspirations and ideas. However, it is hard for humans to have good inspirations and ideas constantly. Therefore, in order to produce such ideas constantly, we need an engineering method. When we support developers to create new ideas systematically and continuously, we have to solve some problems. In order to solve these problems, we developed a method that was integrated into our empirical knowledge with KAOS, persona analysis, scenario analysis, etc. As a result, it becomes possible to analyze the user experiences based on their roles and, to add functions that improve the user experiences. In order to validate the effectiveness of the method, we applied the method to an actual product development. We also developed a tool for the method. We close this paper with the results of its validation and our future work.
Facial Expression Recognition Based on Complete Local Binary Pattern and Convolutional Neural Network

Facial expression recognition is a challenging problem in computer vision as changes in the background and location of the same person may also lead to difficulties in the recognition. In order to improve the poor effect of facial expression recognition algorithms due to it’s easy to be affected by illumination and posture in real life environment, a facial expression recognition algorithm based on CLBP and convolutional neural network is proposed. The algorithm uses HOG feature and Adaboost algorithm to cut out images with face and eyes, extracts CLBP fusion feature images, constructs a seven-layer convolutional neural network model with two convolutional and pooling layers, two full connection layers and one Softmax classification layer to recognize six expressions of anger, aversion, fear, happiness, sadness and surprise. Four sets of comparative experiments are designed on the public data set CK+, a recognition rate of 98.60% is achieved, better than the existing mainstream methods.
The Research Process

Research is a process that requires not only time but considerable effort. Research is intended to answer a specific question that is pertinent to a field of study. The research question or study purpose determines the type of research approach taken. Prior to conducting research, it is important to determine if the research must be approved by an institutional review board to ensure that it is being conducted in an ethically sound manner. After the study implementation, the researcher has the obligation to write about the research process. This assists other researchers by providing additional knowledge to the literature surrounding the research topic.