Reviewerly/exHarmony huggingface dataset:
Unsupervised Cross-Modal Retrieval by Coupled Dual Generative Adversarial Networks

Textual-visual cross-modal retrieval has become a hot research topic in both computer vision and natural language processing communities. However, existing deep cross-modal hashing methods either rely on amounts of labeled information or have no ability to learn an accuracy correlation between different modalities. In this paper, we address the unsupervised cross-modal retrieval problem using a novel framework called coupled dual generative adversarial networks (CDGAN). This framework consists of two cycle networks: a text-to-image-to-text(t2t) network and an image-to-text-to-image(i2i) network. The t2t network is used to learn the relation among an original text, the generated image and the generated text using the similarity of original and generated image-text, and the i2i network is used to learn the relation among an original image, the generated text and the generated image. Therefore, two groups of mixed hash codes of image-text are learned in this framework. Furthermore, our proposed CDGAN seamlessly couples these two cycle networks with generative adversarial mechanism so that the hash codes can be optimized simultaneously. Extensive experiments show that our framework can well match images and sentences with complex content, and it can achieve the state-of-the-art cross-modal retrieval results on two popular benchmark datasets.
Block-based compressed sensing algorithm for image compressed and transmission in visible spectral remote sensing imaging system

High-resolution visible spectral remote sensing images enriches human cognition of the world, also brings challenges to the image storage and transmission. Traditional compression method can reduce the transmission bandwidth, also cause defects such as long computing time, high algorithm complexity, and large errors. Compressed sensing can reconstruct high-quality images which is similar with the original image. It provides a new method for remote sensing image compression and reconstruction. This paper analyzes the advantages of blockbased compressed sensing algorithm, and proves that block-based compressed sensing algorithm is superior to nonblock-based compressed sensing algorithm from the perspective of algorithm complexity. This paper modifies the smoothed projected Landweber (SPL) algorithm to make it suitable for the color image. According to the simulation results, it is obvious that the block-shaped compressed sensing algorithm can effectively reduce the transmission cost and ensure the reconstruction quality of the compressed images.
Application of Web Program Development Based on Struts Framework

Abstract As we all know, Struts framework based on the MVC design pattern is one of the most widely used frameworks for Web development nowadays to improving the code reusability and flexibility, which separates the display and logic in Web application and development. This paper first analyzes the Struts framework and its implementation of model, view and controller layers, then summarizes the advantages and disadvantages of Struts framework. Meanwhile, some details should be paid attention to during development.
INFORMATION AND EDUCATIONAL AUTHORIZED ENVIRONMENT FOR THE STUDY OF THE DISCIPLINE

The Relevance of the study is determined by the need to reduce the intensity of the educational process of cadets of Junior courses, due, in particular, the large volume of the discipline "Theoretical mechanics" and the lack of experience of studying in a military University.
Research on technology of panoramic projection

In this paper, we discussed the quality of image and the difficulty of the two models, multi-projector panorama system and fish-eye-projector system, according to principles and experimental operations. The multi-projector stitching model mainly analyzes simplified operations and the cooperation of softwares and hardwares in the course of the projection and optimization. Based on fish-eye-lens, we also designd a fish-eye-projector made of fish-eys-lens and ordinary projector to analyze the quality of lens from the perspective of a normal user.
Intermediation Family: Workspace for Sharing Spatial Design among Multiple Users

This paper presents a novel workspace for sharing a virtual space among multiple users with tangible interfaces and multiple perspectives. The multiple users can share a virtual space such as a spatial design through first-person perspectives (FPPs) and third-person perspectives (TPPs), and can discuss it while switching between the FPPs and TPPs. The workspace, named the Intermediation Family, is composed of box-shaped movable tangible interfaces, a fixed large display, a simple removable HMD and a computer that makes a virtual space. Since the tangible interface plays as both a user's avatar and a camera object for the FPP in the virtual space, the user can observe the virtual space from the FPP on a display mounted on the tangible interface while moving it. When the user wants a more immersive experience, she/he can also use the HMD. Moreover, the users can observe the entire virtual space through the TPP on the fixed large display. The multiple users can share the spatial design by switching their perspectives and exchanging the tangible interface and the HMD one after another. In this paper, two applications were implemented to hear opinions of some users. One is a virtual walk-through application, the other is a hands-on room layout application.
Hardware implementation of the bicubic interpolation for vein enhancement biometric system

In this paper, hardware implementation of bicubic interpolation for vein enhancement biometric system is proposed. This paper present hardware architecture that enable pipelining computation with less hardware resource that elude redundant computation, reduce computation complexity and improve the throughput. The architecture utilized parallel pipeline hardware to achieve fast processing time and high throughput. Using temporary interpolation points (TIPs), the offchip memory is accessed only once and redundant memory access is avoided. The TIPs are shifted using shift registers between blocks so its do not need to be calculated again. For the interpolation process, instead of using multipliers, shifters are used to reduce hardware cost and size. Shift registers are used as intermediate buffer between horizontal and vertical interpolation so that memory buffer or FIFO is not required. Experimental results and analysis show that the proposed hardware implementation architecture is efficient, use less hardware resources and speeds up the processing time.
Multidimensional QoE Prediction of WebRTC Video Communication with Machine Learning

Web Real-Time Communication (WebRTC) technology has quickly become popular as a video conferencing platform since it enables cost-effective peer-to-peer communication via browsers. In order to test cost-effectiveness of using WebRTC technology, it is necessary to dispose of user feedback through Quality of Experience (QoE) scores, considering various Influence Factors (IFs). Several studies have analyzed the impact of Context IFs (CIFs) on perceived QoE so far. This paper considers the impact of two CIFs, i.e., conversational task and different types of WebRTC-based applications on the QoE of video communication. In this sense, it focuses on several QoE dimensions, i.e., the overall satisfaction, efficiency, ease of use, and acceptance of WebRTC. QoE scores were collected by survey, and then statistically analyzed using Analysis of Variance (ANOVA) and Pearson correlation. Multi-Layer Perceptron (MLP) Artificial Neural Network (ANN) model has been developed in order to predict QoE based on its dimensions (i.e., overall satisfaction, efficiency, ease of use, and acceptance). Results indicate that the developed MLP model has a medium strong ability to make accurate QoE predictions.
Accurate and Efficient Traffic Sign Detection with a Guided Region Enlarging Algorithm

Traffic signs detection is a hot and important topic in the research field of computer vision and lots of applications, e.g., driver assistance system and autonomous vehicle, etc. Thanks to the development of the convolutional neural network, object detection has achieved promising results in recent years. Nevertheless, traffic sign detection still faces two difficulties due to the characteristics of traffic signs. The first difficulty is the detection of small and blurred traffic signs. The second difficulty is that training samples are sparse and imbalanced. For tackling these two difficulties, Guided Region Enlarging Algorithm (GREA) is proposed in the paper. The GREA consists of two sub-modules, Potential region estimation network (PREN) and NMS-Cropping. The PREN obtains a potential object region of each input image first. Then, the potential object region is enlarged by the NMS-Cropping algorithm for subsequent augmenting training. The GREA makes use of features of a small and blurred traffic sign to enhance the performance of a traffic sign detector. To comprehensively evaluate the proposed approach, two traffic sign datasets include the Swedish traffic sign data set and the Tsinghua-Tencent 100k data set are utilized in the experiment. Experimental results demonstrate the efficiency of the proposed GREA, and our approach is comparable to the state-of-the-art approaches.
Short Paper

Memory-trace Obliviousness (MTO) is a noninterference property: programs that enjoy it have neither explicit nor implicit information leaks, even when the adversary can observe the program counter and the address trace of memory accesses. Probabilistic MTO relaxes MTO to accept probabilistic programs. In prior work, we developed λobliv, whose type system aims to enforce PMTO [2]. We showed that λobliv could typecheck (recursive) Tree ORAM [6], a sophisticated algorithm that implements a probabilistically oblivious key-value store. We conjectured that λobliv ought to be able to typecheck more optimized oblivious data structures (ODSs)[8], but that its type system was as yet too weak.
Cognitive Code Quality Improvement with Pattern Recognition and Recommendation by Examples

Several groups work on automated design pattern recognition or automated refactoring. The goal of our paper is to present examples for ugly program code and its transformation into a pretty version. These examples are useful to test and teach their systems. What does ugly code and pretty code mean? In an ugly code there is redundancy, which means that we have anomalies. Pretty code means that it has the same functionality as the ugly one, it follows a design pattern, and there is no redundancy. Our work is motivated by the fact, that we could not find a similar work, although, we work on an automated design pattern tool, so we think that there is a need for ugly and pretty code examples. This work contains examples for the design patterns: Null Object, Template Method, State, and Strategy. We do not discuss how to refactor the ugly code into a pretty one. The program codes are presented in Java.
Backhand Display: A Wearable Device for the Back of the Hand

Smartwatches and smartphones have their own unique advantages and their combination allows the user to quickly access information. Our proposal, Backhand Display, is a wearable device for the back of the hand. It is a single device that can replace both the smartwatch and the smartphone. Its fundamental performances are evaluated, including text input, vibration notification, and response time between notification and reply. It is confirmed that Backhand Display offers faster text input than the smartwatch, greater notification capability than the smartphone, and quicker response to notifications than either of them. These benefits are supported by the results of a user questionnaire. Though there are several limitations and issues to be resolved, these results show the potential of Backhand Display, a wearable device on the back of the hand.
To Make Sense of Procedurally Generated Dungeons

With the growth of procedural content generation in game development, there is a need for a viable generative method to give context and make sense of the content within game space. We propose procedural narrative as context through objectives, as a useful means to structure content in games. In this paper, we present and describe an artifact developed as a sub-system to the Evolutionary Dungeon Designer (EDD) that procedurally generates objectives for the dungeons created with the tool. The quality of the content within rooms is used to generate objectives, and together with the distributions and design of the dungeon, main and side objectives are formed to maximize the usage of game space and create a proper context.
Performance Improvement in Web Mining Using Classification to Investigate Web Log Access Patterns

Data mining having one of the applications termed as Web mining which helps to eradicate particular information from Web data like Web content, Web structure, and Web usage data. Web service is the technique of detecting suitable service as per requirements and it is applicable in enterprises, industry, and government. Web service is a software arrangement considered to maintain interoperable machine wise interface over a grid. The purpose of this research study is to investigate an efficient methodology of the Web services which plays crucial role for acquiring different services. From this research study, the classification method in Web usage mining can be utilized to find visitor activity information form Web server log which helps the company for getting supporting information and used to specify the evaluation of Web application. The accuracy of our work is acquired better outcome than the existing classification, so it can be used in Web usage mining.
Development of Speaker Recognizer Using I-vectors in Two Programming Environments

The i-vectors with Probabilistic Linear Discriminative Analysis (PLDA) are known to be one of the latest and most advanced techniques in the field of Automatic Speaker Recognition (ASR). The paper focuses on the development of i-vector/PLDA based the ASR systems in two programming environments, namely Python and MATLAB, which are popular among machine-learning community. Comparative evaluation of system performance, in terms of accuracy and computational requirements, for both platforms is presented.
High Order Semantic Relations-Based Temporal Recommendation Model by Collaborative Knowledge Graph Learning

Knowledge graph (KG) as the source of side information has been proven to be useful to alleviate the data sparsity and cold start. Existing methods usually exploit the semantic relations between entities by learning structural or semantic paths information. However, they ignore the difficulty of information fusion and network alignment when constructing knowledge graph from different domains, and do not take temporal context into account. To address the limitations of existing methods, we propose a novel High-order semantic Relations-based Temporal Recommendation (HRTR), which captures the joint effects of high-order semantic relations in Collaborative Knowledge Graph (CKG) and temporal context. Firstly, it automatically extracts different order connectivities to represent semantic relations between entities from CKG. Then, we define a joint learning model to capture high-quality representations of users, items, and their attributes by employing TransE and recurrent neural network, which captures not only structural information, but also sequence information by encoding semantic paths, and to take their representations as the users’/items’ long-term static features. Next, we respectively employ LSTM and attention machine to capture the users’ and items’ short-term dynamic preferences. At last, the long-short term features are seamlessly fused into recommender system. We conduct extensive experiments on real-world datasets and the evaluation results show that HRTR achieves significant superiority over several state-of-the-art baselines.
Measuring visibility of disciplines on Chinese academic web

This study proposes a hierarchy affiliation model (department–school–university) to build network between web entities taking into account the domain names, the topological structure of academic network and the disciplinary characteristics of schools and universities synthetically. The study of the Chinese academic web based on the model shows that at the school level, 68 of 95 disciplines (71.6%) are identified from the directed school network and 71 from the undirected school network, respectively; at the university level, four out of seven broad disciplines are found. Furthermore, according to the comparative result based on three types of relations (hyperlinks, citations and collaborations) among universities, we would like to argue with cautions that the structure on academic web would potentially be more suitable to trace the interests in common between institutions.
Сategories of analysis within activity-oriented counseling

This paper aims to provide an analysis of the theoretical and methodological grounds of Activity-oriented models of psychological counseling practice, along with an author's model of counseling interaction between a psychologist and a client within the framework of Activity theory.&#x0D; In contrast to majority of psychological theories, Activity theory has not yet been fully reflected in psychotherapeutic and counseling practice. As a consequence, we can see the gap in the relevant methodology of psychological care, despite of multiple theoretical and empirical research, conducted within Activity framework.&#x0D; The structure and procedure of Activity-oriented counselling is based on major categories of Activity theory, developed by S. Rubinstein, A. Leontiev, F. Vasilyuk, G. Schedrovitsky, namely: i) unity of consciousness and activity; ii) experiencing as a form of personal activity; iii) categories of activity analysis. Since the subject of counseling is the problem situation experienced by a client, the main purpose of psychological counseling is to convert the problem into achievable tasks, which requires setting a clear logic and sequencing stages of consultative process.&#x0D; It is shown that the consultant guides the client through a series of stages, which involve consistent changes.&#x0D; Those stages include following: situation scanning; positional analysis; problematizing and capturing of contradictions, repositioning and designing of new activity schemes.&#x0D; Activity-oriented counseling offers a comprehensive, context-sensitive method of client`s life story exploration and highly structured procedure, which allows psychologist and the client to retain its overall meaning and logic.&#x0D; Certainly, an active approach in the psychological practice of counselling requires further methodological developments: in particular, the development of protocols of counseling sessions and non-parametric methods of diagnosis of client advancement.
Performance of Recycled Fire-Extinguishing Agents in Terms of Fire-Fighting Performance Management

Since January 28, 2017, domestic fire extinguishers have been manufactured nationwide, and a domestic fire extinguisher can be used for 10 years. Hence, the treatment of the extinguishing agent existing in waste extinguishers is a critical issue economically and environmentally. Accordingly, the manufacturer reuses and recycles the fire extinguishing agent, and this recycling method has been approved for fire extinguishers. In some cases, the quality of reused extinguishing agents and fire extinguishers are uncertain. Therefore, in this study, the physical and chemical properties of recycled extinguishing agents were evaluated through accelerated aging tests to verify the quality of the recycled extinguishing agents. The accelerated aging test results were obtained according to the approval criteria type for the physical and chemical properties of the recycled extinguishing agent. As a substitute effect, it was found that the import substitution effect and resource effect could be regarded as a circulating resource for fire-extinguishing agents that are imported in full quantity, and the price competitiveness of fire extinguishers can be achieved.
Training-the-trainer in Tele-instruction on Collaborative Physical Tasks

Functionality in remote instructional technologies, such as telepointing and remote annotation tools, were designed to support distributed collaboration on physical tasks. Unfortunately, these functionalities have introduced new challenges in effectively conveying instructions and knowledge. Challenges in the use of remote instructional technologies are important as they directly relate to the learning outcomes of training a worker for future real-world practice. My research emphasizes the systematic investigation of the socio-technical challenges in distributed collaboration and the benefits of training-the-trainer in how to effectively use functionality to convey knowledge to a local worker.
Towards the Emergent Theatre: A Novel Approach for Creating Live Emergent Narratives Using Finite State Machines

In this project, we introduce finite state machines as a way to simultaneously connect an audience’s input with performer output during a live performance. Our approach is novel in that we can redefine the traditional notion of an author, by dividing and balancing the responsibilities for creating and developing emergent narrative between three elements: the audience, finite state machines, and the performers. This also allows audience members a large degree of freedom to input into the system, as we can consider audience inaction as a form of productive action. We have developed the Data Generation Engine (DGE), software that generates data to be used by performers for creating and developing narrative and to provide audiences with opportunities to manipulate the DGE’s data generation and distribution mechanisms. We argue that using this approach in a live theatre context allows a consistent narrative to emerge while giving the audience the freedom to engage in the narrative without disrupting the performance.
An Adaptive Utility Quantification Strategy for Penetration Semantic Knowledge Mining

Penetration semantic knowledge mining plays an important role in automated penetration testing. The loss of external utility makes it hard to employ high utility itemset mining algorithms to retrieve the knowledge. To overcome this problem, this paper proposes an adaptive utility quantification strategy which could differentiate and quantify external utility of item effectively. Further, incremental high utility pattern tree structure is adopted to maintain utility information for incremental database so as to facilitate the calculation of external utility. The experimental result turns out that high utility itemsets mining algorithms with proposed adaptive utility quantification strategy could mine penetration testing semantic knowledge from raw penetration testing data effectively and efficiently.