Reviewerly/exHarmony huggingface dataset:
Research for Designing for Emotions

Designing for emotion is very well established in older design disciplines such as advertising, fashion, graphic, interior and product design, and has made good progress in more recent disciplines such as service design. Design work integrates a range of creative practices with technical and business considerations. There is a continuum of practices from highly demanding technology design to highly creative craft work. Even at the most technical extreme, there are vital creative aspects, especially when faced with challenges that are novel for an area of design, such as designing for emotional aspects of technology usage. The outcomes of novel creative practices cannot be known in advance, making it impossible to fully plan creative work. Researching for design—that is, in support of truly novel creative design work—must carefully consider the work practices of the designers who are expected to benefit from new theories, knowledge, information, processes or procedures. Any research on emotions that assumes good science can be automatically applied to any design context will severely limit its potential impact and reach. It is unlikely to be attractive research that can be realistically applied. This chapter begins with the consideration of how emotions are considered in design, summarises the history of an affective focus in human–computer interaction and reviews the author’s practical support for an affective focus within interaction design. It then relates these three reviews to some key perspectives from creative design research to support an initial agenda for effective impact for research for affective design.
Dual Thresholding based Breast cancer detection in Mammograms

Early stage detection of Breast cancer may reduce the number of cases in which women lost their life. Motivation of proposed work is to automate the process of breast cancer detection which may strengthen the decision of Radiologist. In proposed work, two thresholding techniques are used in parallel to differentiate the pixels of interest from noninterest (less informative like background pixels). One of these techniques is based on Histogram Peak Analysis (HPA), with the objective to choose the peak which maximizes the interclass standard deviation. Second technique is extended version of famous Otsu's thresholding. Morphological operations along with image reconstruction are used for artifact removal and image enhancement. Feature extraction and classification of images in to malignant (cancerous) or non-cancerous is obtained using a convolutional neural network. Mammographic Image Analysis Society (MIAS) database is used for performing experimental results.
Integrating Differential Privacy in the Statistical Disclosure Control Tool-Kit for Synthetic Data Production

A standard approach in the statistical disclosure control tool-kit for producing synthetic data is the procedure based on multivariate sequential chained-equation regression models where each successive regression includes variables from the preceding regressions. The models depend on conditional Bayesian posterior distributions and can handle continuous, binary and categorical variables. Synthetic data are generated by drawing random values from the corresponding predictive distributions. Multiple copies of the synthetic data are generated and inference carried out on each of the data sets with results combined for point and variance estimates under well-established combination rules. In this paper, we investigate whether algorithms and mechanisms found in the differential privacy literature can be added to the synthetic data production process to raise the privacy standards used at National Statistical Institutes. In particular, we focus on a differentially private functional mechanism of adding random noise to the estimating equations of the regression models. We also incorporate regularization in the OLS linear models (ridge regression) to compensate for noisy estimating equations and bound the global sensitivity. We evaluate the standard and modified multivariate sequential chained-equation regression approach for producing synthetic data in a small-scale simulation study.
Adversarial Cross-Lingual Transfer Learning for Slot Tagging of Low-Resource Languages

Slot tagging is a key component in a task-oriented dialogue system. Conversational agents need to understand human input by training on large amounts of annotated data. However, most human languages are low-resource and lack annotated training data for slot tagging task. Therefore, we aim to leverage cross-lingual transfer learning from high-resource languages to low-resource ones. In this paper, we propose an adversarial cross-lingual transfer model with multi-level language shared and specific knowledge to improve the slot tagging task of low-resource languages. Our method explicitly separates the model into the language-shared part and language-specific part to transfer language-independent knowledge. To refine shared knowledge in the latent space, we add a language discriminator and employ adversarial training to reinforce feature separation. Besides, we adopt a novel multi-level feature transfer in an incremental and progressive way to acquire multi-granularity shared knowledge. To mitigate the discrepancies between the feature distributions of language specific and shared knowledge, we propose the neural adapters to fuse features from different sources. Experiments show that our proposed model consistently outperforms monolingual baseline with a statistically significant margin up to 2.09%, even higher improvement of 12.21% in the zero-shot setting. Further analysis demonstrates that our method could effectively alleviate data scarcity of low-resource languages.
Automated Attendance System using Few-Shot Learning Approach

We are living in a world where artificial intelligence is slowly reshaping the world. Artificial Intelligence systems reduce the error rate and can work very efficiently and with incredible speed and precision. Nowadays, Artificial Intelligence is being used to make the technology smarter so it can take decisions and minimize human efforts.An “Automatic attendance system using facial recognition” is one of the smart implementations of Artificial Intelligence. When we stand in front of the camera, it will take our photo and match it with the photos it has in its database. Once the photo is matched with a photo from the database, the system marks that person's attendance as present in the table and stores it in the database so that we can view the attendance sheet as needed.In this paper we are mainly focusing on one of the major problems in making these systems i.e., requirement of large training data. As the machine learning and deep learning model require a large training data for its model to work with high accuracy, it become a tedious task to collect large no of photos per person. So, in the proposed model we are trying to minimize the training data up to 5 photos per person in a way that it doesn't affect the accuracy of the whole system. We are using Convolutional neural network and pretrained FaceNet model and we are getting an accuracy of approx. 98% with 100 different faces.
Research on English Vocabulary Translation Technology Based on Dynamic Bilingual Corpus

The goal of this study is to use modern Internet technology to collect dynamic English-language contrastive corpora in a large-scale information database, build a bilingual dynamic corpus in Chinese and English, and use bilingual translation memory to complete bilingual assistance based on computer-assisted translation Construction of translation search engine system. The main content of this article mainly includes the design of constructing the corpus and the implementation of the translation search engine system. In terms of building a dynamic corpus, according to the general process of Web content mining, the paper first uses web spiders to search and collect raw data in the Internet's massive information database, and then preprocesses and rearranges them. After purification and identification, the Two-page bilingual texts and two-page bilingual texts are used for word segmentation matching and web page structure matching, respectively. Chinese-English bilingual translation corpora are extracted and stored in the database. The deployment of the auxiliary translation search engine system is based on the construction of a dynamic corpus. The indexing tool is used to index the corpus, and manual review and user feedback modules are added to provide users with Chinese-English bilingual auxiliary translation services.
Seg-Hashnet: Semantic Segmentation Based Unsupervised Hashing

Hashing based multimedia retrieval methods have been widely studied because of the advantages of computation efficiency and low storage cost. Generally, there are two ways of doing hashing: Supervised and unsupervised hashing. Supervised hashing was known to be more efficient and popular. However, in many real applications, only unsupervised method work because there are no labels. But unsupervised hashing method still face some challenges such as how to get semantic information from unlabeled data. To address this problem, in this paper, we propose a deep unsupervised hashing method based on semantic segmentation (Seg-HashNet). Specifically, a pre-trained semantic segmentation model is used to mine informative semantic information to guide the learning process and generate highquality hash codes. Extensive experiments on two public datasets have been performed, which showed that Unsupervised Semantic Segmentation hashing is superior to the latest unsupervised hashing method in image retrieval tasks.
Same but Different: A Comparison of Players’ Perceptions and Motivational Factors in Two Commercially Available AR Games

Abstract In 2019, the Harry Potter-themed augmented reality (AR) game Wizards Unite was released, one which employed the same game mechanics as Pokémon Go . In order to explore the similarities and differences in players’ perceptions, motivations to play, and missing functions in Wizards Unite and Pokémon Go , we conducted a web-based survey which targeted active players of Wizards Unite 14 weeks after its release. The results are compared to a survey applying the same methodology and questions directed at active players of Pokémon Go . An important reason why many players started playing Wizards Unite was that they were already Harry Potter fans. For Wizards Unite players the motivation stemming from public attention was lower compared to that for Pokémon Go players. The main motivating factors drawing players to these games include fun, curiosity, and being physically active. The AR function was not mentioned as a motivating factor for either game. The aspect of players being able to collect and complete the game was more of a motivation for playing Pokémon Go . Although the game mechanisms are similar, we identified specific differences in the motivational factors of the two games. The main difference is the misfit between the general game story with hunting and collecting within the Harry Potter universe. Nevertheless, the theme of the game and its background story can be important long-term motivating factors of Wizards Unite . The findings in this paper provide insights into how one can create AR games that meet players’ needs.
Deep Adversarial Active Learning With Model Uncertainty For Image Classification

Active learning aims at selecting and labeling as few samples as possible to train a good task model. Most existing methods rely on various heuristics to iteratively select a single sample in each active learning loop, thus cannot tackle large datasets efficiently. In this paper, we propose a new batch-mode active learning method, which can plug model prediction uncertainty into adversarial batch selection to ensure the selected samples are representative in unlabeled data, complementary to labeled data, and beneficial for model training. Experiments on four benchmark image datasets validate the effectiveness and efficiency of the proposed method for active image classification in comparison with the state-of-the-art methods.
Performance Evaluation of Various ANN Architectures Using Proposed Cost Function

Traditional learning methods focus on the training of neural networks. However robustness or fault tolerance or sensitivity of its input to output which plays an important role while designing a neural network are not considered appropriately. Purpose of this paper is the optimization of various feed forward artificial neural network (FFANN) architectures with hyper-parmeter selection and using proposed cost function AvgNew.This paper, has proposed a cost function which when applied to TDFFANN(tailored deep feed forward artificial neural network) and other FFANN architectures makes it more fault-tolerant, sensitive or robust. Result obtained using variants of MLP(Multi Layer Perceptron) and FFANN(feed forward artificial neural network) proves AvgNew to be efficient.
A Supervised Anonymous Issuance Scheme of Central Bank Digital Currency Based on Blockchain

The emergence of digital currency is seen as a new major revolution in the form of currency. In this revolution, central banks around the world have been focusing on exploring the development and implementation of central bank digital currency (CBDC). Up to now, CBDC is still in the stage of research, and there is no successful experience or precedent worldwide. Existing schemes based on the “indirect CBDC” model, which are deployed on a transparent blockchain, have the problem of financial data leakage of commercial banks. In this paper, we propose a supervised anonymous issuance (SAI) scheme of CBDC based on blockchain using a “special” anonymous multi-receiver certificateless signcryption scheme proposed and zero-knowledge proofs, which guarantees the anonymity of commercial banks and the confidentiality of the amount under the supervision of the central bank. Our scheme is secure under some cryptographic assumptions. In the practical instantiation of the SAI scheme, transactions are about 2 KB and take under 6 ms to verify.
Website Design For Non-Cash Customer Transaction Data Visualization In XYZ Bank

The development of information technology, which is followed by an increasingly high level of bank competition, encourages the banking or non-banking sector to be more innovative in providing various alternative non-cash payment services that are safe, fast and efficient, and are global in nature. With the increasing number of non-cash transactions from bank customers, a website design is needed to visualize customer non-cash transaction data. This study aims to design customer websites. Website design method used is the Waterfall method with several steps, like Communication (Project Initiation & Requirement Gathering), Planning (Estimating, Scheduling, Tracking), Modeling (Analysis & Design) and Construction (Code & Test). The design produces a website for users. The website for visualizing customer noncash transaction data generated, it will make it easier for customers to see, analyze and control expenses made through visualizing customer non-cash transaction data.
Metaheuristics Approaches for the Travelling Salesman Problem on a Spherical Surface

Traveling salesman problem in which all the vertices are assumed to be on a spherical surface is a special case of the conventional travelling salesman problem. There are exact and approximate algorithms for the travelling salesman problem. As the solution time is a performance parameter in most real-time applications, approximate algorithms always have an important area of research for both researchers and engineers. In this chapter, approximate algorithms based on heuristic methods are considered for the travelling salesman problem on the sphere. Firstly, 28 test instances were newly generated on the unit sphere. Then, using various heuristic methods such as genetic algorithms, ant colony optimization, and fluid genetic algorithms, the initial solutions for solving test instances of the traveling salesman problem are obtained in Matlab®. Then, the initial heuristic solutions are used as input for the 2-opt algorithm. The performances and time complexities of the applied methods are analyzed as a conclusion.
Tensor-Based Light Field Denoising By Exploiting Non-Local Similarities Across Multiple Resolutions

Light field is a kind of 4D signal that contains rich information about position and angle of light, which can express the scene more accurately. Light field is easily affected by noise for the hardware sensitivity. This paper utilizes the intrinsic tensor sparsity model and integrates super-resolution(SR) into a unified light field denoising method based on tensor operation. Avoiding vectorization, we make full use of correlation of light field. By exploiting SR method, we avoid sub-pixel mis-alignment in the searching process of similar patch. Experimental results validate that our proposed method outperforms the state-of-art methods in terms of both objective and subjective quality on the HCI light field old dataset.
A high performance machine learning algorithm TspINA; scheduling multifariousness destined tasks by better efficiency

The ability to successfully execute task scheduling for any computing system is a performance measurement in any multi-processing environment, such as working of cloud or continuous scheduling and much more. Over the years, for many cloud deployments, cost-effective resource scheduling (CERS) algorithms and dynamic and integrated resource scheduling (DAIRS) algorithms were commonly used. But these algorithms have certain drawbacks in terms of retention and efficient task response time and setting threshold values along with causing lot of overhead. Through this paper we try to put forward an algorithm based on machine learning (ML) paradigm, Task scheduling process Improved Novel Algorithm (TspINA). Our proposed algorithm explores the methods used by CERS and DAIRS algorithms and provides a reasonable enhancement by integrating machine learning features techniques for prior-learning and persistent adaptation to minimize the completion time for a defined series of objectives and tasks. Our suggested methodology would be further compared to the existing CERS and DAIRS algorithms by using generic datasets and the outcomes will be correlated with the amount of machine effort, delay and responsiveness.
Multi-Modal Fusion With Observation Points For Skeleton Action Recognition

Current methods for skeleton-based action recognition compute features based on the given skeleton joint information. We show that introducing new observation points in skeleton motion sequences and using them to create fused representations from multiple modalities such as joints and bones, can enhance the discriminative power of the original modalities. Moreover, such representations can be used to create new streams in multi-stream networks that fuse constructively with other streams trained on the original modalities, effectively exhibiting a dual behaviour and collectively boosting the performance of the network even further. We present one possible multi-modal fusion system with a single observation point that can easily be incorporated in existing networks and improves state-of-the-art results on the two popular J-HMDB and Kinetics-Skeleton action recognition datasets.
Assessing the Human Factor of Cybersecurity: Can Surveys Tell the Truth?

Survey-based measuring plays an important role in exploring human behavior. In organizational context, self-reporting of behaviors, attitudes, norms etc. can often lead to people responding in line with expectations rather than reality. Particularly when answering sensitive questions, respondents can disguise the truth for various reasons. This is called a social desirability effect (SDE) and poses a key problem in the field of behavioral studies because it can significantly bias the findings of research. A number of methods to prevent or detect SDE exist. The aim of the paper is to test selected techniques for decreasing SDE in survey-based measuring of information security behavior and to propose an improved scale, minimally susceptible to SDE. We used a cross-sectional survey design with a split-ballot experiment across three companies of critical infrastructure in Slovenia (n = 414). Four groups of employees received versions of information security behavior scale with different combinations of negative, positive and forgiving item wording. No universal group and item type effect of forgiving and alternating item wording was found with testing of the Balanced Inventory of Desirable Responding (BIDR) scale. However, it turns out that the content of items matters because one of methods perform differently for different types of behavioral items. Moreover, the part of analysis showed that combination of forgiving and alternating item wording might be effective in minimizing SDE. Items with best properties were chosen to establish new information security behavior scale. The majority of items were chosen from groups with alternating item wording, especially the one combining positive and forgiving items.
Transformer Decoder Based Reinforcement Learning Approach for Conversational Response Generation

Developing a machine that can hold an engaging conversation with a human is one of the main challenges in designing a dialogue system in the field of natural language processing. Responses generated by neural conversational models with log-likelihood training methods tend to lack informativeness and diversity. We address the limitation of log-likelihood training in dialogue generation models, and we present the Reinforce Transformer decoder model, our new approach for training the Transformer decoder based conversational model, which incorporates proximal policy optimization techniques from re-inforcement learning with the Transformer decoder architecture. We specifically examine the use of our proposed model for multi-turn dialogue response generation in a real word human to a human dataset. To verify the effectiveness of our proposed framework, we evaluate our model on the Reddit dialogues data, which is a real word human to a human dataset. Experiments show that our proposed response generating model in a dialogue achieves significant improvement over recurrent sequence-to-sequence models and also the state of the art Transformer based dialogue generation models based on diversity and relevance evaluation metrics.
Consideration of How Different Rearview Presentations Used for Electronic Mirrors on Automobiles Affect Human Spatial Cognition

Recent automobile development has led to the installation of rearview cameras or electronic mirrors that could display a rearview image to the driver through a monitor in the driver’s seat and it expected to provide drivers with higher spatial cognition. In this research, we clarify the magnification function that allows optimal cognition of the rear approaching vehicles for drivers. We conduct the experiment introducing Camera monitor system under the same size and location constraints that traditional rearview mirrors face. We use a simple driving simulator and provide participants with a wide rearview by changing the magnification according to the viewing angle. Participants estimate the distance from approaching vehicles that change lanes and press the button. We recorded the distance the button was pressed and a subjective evaluation questionnaire. We analyzed from the point of view that it is safer to perceive the vehicle to vehicle distance to be closer than its actual distance. It was revealed that a pattern that follows the downward convex with no magnification region and a linear function without no magnification region led to the safest presentation of the vehicle to vehicle distance. It was also confirmed that some subjective evaluations of these patterns were higher. In addition, the presentation difference of the vehicle to vehicle distance between one function and the function that is no magnification could be estimated by a model using the least-squares method.