Reviewerly/exHarmony huggingface dataset:
Anthropomorphizing artificial intelligence: towards a user-centered approach for addressing the challenges of over-automation and design understandability in smart homes

Smart home, as a comfort and leisure providing equipment/device/object instead of its initial role of to serve, support, monitor and deliver therapy, is facing many challenges towards its successful integration with user's lifestyles. Among the current technical and social challenges – interoperability, reliability, privacy, security, consumer's lack of awareness about its benefits, costs and complexity of technology, maintenance costs, lack of incentive for internet providers etc. – take the lead among many others. Limiting the scope, this paper attempts to address challenges of (a) over-automation – a feeling regarding advanced technologies of being out-of-control, and (b) difficulty in understandability of equipment's/device's/object's design – users must be straight-forwardly able to operate them without any particular operational knowledge. Literature from the disciplines of artificial intelligence (hereinafter AI), product design (industrial engineering), human factors (ergonomics), social psychology, communication and sociology (sociological experiments) have been incorporated in this study to understand the psychology of human thought and cognition, everyday things and designer's ability of utilizing these psychologies and of users for addressing, first, the role that a smart home can play as a leisure equipment/device/object, and secondly, granting its user the control and ease of operability they desire.
The Performance of Crop Yield Forecasting Model based on Artificial Intelligence

This paper presents the performance of crop yield forecasting model by comparing the accuracy of the crop yield forecasting models based on artificial intelligence. To compare the error values, researchers use 2 statistical values which are the Coefficient of Multiple Determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) and the Root Mean Square Error (RMSE). The model has the highest R2 value and the lowest RMSE value will be the appropriate model. The models used for comparison in this paper include Linear Regression, Decision Tree Regression, K-Nearest Neighbors Regression, Support Vector Regression, and Multilayer Perceptron Network. The learning dataset comes from the survey of planting area and yield of lychee in Thailand during 2004-2018 and dataset in 2019 to test the performance of the model. The results show that Support Vector Regression has the highest accuracy in forecasting, followed by Multilayer perceptron network, Linear Regression, Decision Tree Regression and k-Nearest Neighbors Regression, respectively.
The Phenomenon of «Pedagogical Reality» in the Content of General Pedagogical Training of Future Teachers

The philosophical and psychological aspects of the study of the concepts ‘reality’ and ‘real world’ have been characterized. The essential features of the phenomenon ‘pedagogical reality’ have been revealed with the account of the peculiarities of the modern stage of the development of pedagogical theory and practice, as well as the common and the different in the concepts of ‘pedagogical reality’ and ‘pedagogical real world’. The peculiarities of the forms of cognition of pedagogical reality (sensory, scientific, artistic and figurative) and the corresponding principles – integrity, unity of analysis and synthesis, contextuality, axiological implication, etc. have been determined. On the basis of the content analysis, the leading forms of representation of the phenomenon ‘pedagogical reality’ in modern textbooks on pedagogy, problems and contradictions in the representation of pedagogical reality in the content of general pedagogical training of future teachers have been identified. It has been emphasised that there is a need to use the phenomenological and&nbsp; hermeneutic methodological approaches in the&nbsp; representation of pedagogical reality in the process of general pedagogical training of future teachers.
Work-in-Progress—Determining the Efficacy of Virtual Reality through Experientiality and Biometric

This paper presents work-in-progress of a research project attempting to determine the efficacy of virtual reality in a science-focused education scenario called SolarVR. The research is informed from both academic literature and the heuristic experiences of researchers involved over the past ten years. An antidisciplinary approach to collaboration aims to support the increasing scale of experientiality of student developers. Physiological measures of participants aim to determine VR effectiveness whilst engaged in an immersive task. The blending of quantitative data, informed with insights from qualitative interpretation, emphasizes user-determined creativity for pedagogically appropriate learning in VR.
Convivial design heuristics for software systems

Illich's notion of conviviality centres on the balance between individual freedom to act and collective freedom from domination. This balance, or tension, is present in the design of most user-facing computer systems, and especially in the design of programming systems. Software lore has arisen with at best a skewed perspective on such issues, having developed from an industrial viewpoint.
An Efficient GPU Framework for Parallelizing Combinatorial Optimization Heuristics

Combinatorial optimization problems are often NP-hard and too complex to be solved within a reasonable time frame by exact methods. Heuristic methods, which do not offer a convergence guarantee could obtain some satisfactory resolution for combinatorial optimization problems. However, it is not only very time-consuming for Central Processing Units (CPU) but also very difficult to obtain an optimized solution when solving large problem instances. So, parallelism can be a good technique for reducing the time complexity, as well as improving the solution quality. Nowadays Graphics Processing Units (GPUs) have evolved supporting general purpose computing. GPUs have become many core processors, multithreaded, highly parallel with high bandwidth memory and tremendous computational power due to the market demand for high definition and real time 3D graphics. Compared to CPU threads, GPU threads are very lightweight which means that changing the context between two threads is not a costly operation. So, GPU cores provide a low-cost opportunity to parallelizing metaheuristics for combinatorial optimization problems. The major issues for GPU parallelization are: 1) efficient distribution of data processing between GPU and CPU, 2) efficient parallelism control for the thread synchronization, 3) efficient memory management for the optimization of data transfer between the different memories, the capacity constraints of these memories, etc. Our proposed work aims to designing an efficient GPU framework which can efficiently deal with these important parallelization issues with GPU while parallelizing heuristics methods like hill climbing, simulated annealing algorithm etc. for solving large optimization problems.
BlueCompass: A Case Study of an In-Building Hospital Information and Navigation System

An in-building hospital information and navigation system called BlueCompass is designed and developed as a mobile application in this work. BlueCompass aims to provide a path finding and information of each service unit of Songklanagarind Hospital which is a case study hospital in this work. QR code and Bluetooth beacon technologies are utilized for pinpointing the user and the service unit locations. The path finding feature provide four results including the overall path as a text, the overall path as a map similar to the Google Map Direction option, the voice and the augmented reality option. To achieve the sustainability of the system, BlueCompass also allows its information to be maintained and updated via a web interface. Even though BlueCompass is designed specifically for Songklanagarind Hospital, the idea can be used for other area.
Hardware/Software Co-design for XML-Document Processing

Algorithms and tools for XML-documents processing are reviewed. The document analyzer algorithms, which are implemented in the stack state machine, are considered. A need for high-speed reconfigurable hardwired XML-request analyzers is determined. The SM16 processor architecture core is developed which effectively evaluates the stack-based parsing algorithms and is implemented in the field-programmable gate array (FPGA). The processor has the stack architecture with three additional stack blocks, hash-table, and instructions that accelerate the execution of parsing operations. The hardware/software FPGA-based system, which has the main processor and tens to hundreds of SM16 executive processor elements, is proposed. This system is scalable for different amounts of packets streams. It efficiently processes XML-documents and can be easily reconfigured to the given document grammars, allowing to adapt to fast-changing modern-world communications.
Non-malleable Encryption: Simpler, Shorter, Stronger

One approach toward basing public-key encryption (PKE) schemes on weak and credible assumptions is to build “stronger” or more general schemes generically from “weaker” or more restricted ones. One particular line of work in this context was initiated by Myers and Shelat (FOCS ’09) and continued by Hohenberger, Lewko, and Waters (Eurocrypt ’12), who provide constructions of multi-bit CCA-secure PKE from single-bit CCA-secure PKE. It is well known that encrypting each bit of a plaintext string independently is not CCA-secure—the resulting scheme is malleable. We therefore investigate whether this malleability can be dealt with using the conceptually simple approach of applying a suitable non-malleable code (Dziembowski et al., ICS ’10) to the plaintext and subsequently encrypting the resulting codeword bit by bit. We find that an attacker’s ability to ask multiple decryption queries requires that the underlying code be continuously non-malleable (Faust et al., TCC ’14). Since, as we show, this flavor of non-malleability can only be achieved if the code is allowed to “self-destruct,” the resulting scheme inherits this property and therefore only achieves a weaker variant of CCA security. We formalize this new notion of so-called indistinguishability under self-destruct attacks (IND-SDA) as CCA security with the restriction that the decryption oracle stops working once the attacker submits an invalid ciphertext. We first show that the above approach based on non-malleable codes yields a solution to the problem of domain extension for IND-SDA-secure PKE, provided that the underlying code is continuously non-malleable against (a reduced form of) bit-wise tampering. Then, we prove that the code of Dziembowski et al. is actually already continuously non-malleable against bit-wise tampering. We further investigate the notion of security under self-destruct attacks and combine IND-SDA security with non-malleability under chosen-ciphertext attacks (NM-CPA) to obtain the strictly stronger notion of non-malleability under self-destruct attacks (NM-SDA). We show that NM-SDA security can be obtained from basic IND-CPA security by means of a black-box construction based on the seminal work by Choi et al. (TCC ’08). Finally, we provide a domain extension technique for building a multi-bit NM-SDA scheme from a single-bit NM-SDA scheme. To achieve this goal, we define and construct a novel type of continuous non-malleable code, called secret-state NMC, since, as we show, standard continuous NMCs are insufficient for the natural “encode-then-encrypt-bit-by-bit” approach to work.
Washroom Sign Detection Using Convolutional Neural Network in Natural Scene Images

Due to disabilities, visually impaired or blind people face difficulties to recognize washroom sign in public places by themselves. In natural scene images, so many objects are present that are similar to human shaped male or female washroom sign making it more difficult to detect and classify between male and female washroom sign. Moreover, at a certain distance, a human body is also look like washroom sign, where system might get confused to classify between a real human figure and a human shaped washroom sign. Focusing on this issue, deep learning- based methods are proposed to detect common patterns of washroom signs in natural images. In this proposed method, MSER algorithm is used for object detection, Geometrical properties algorithm is used for text part and unwanted part removal and then region of interest has been detected by bounding box algorithm, at last CNN is used to classify washroom sign images into three different classes, i.e. ‘washroom sign’, ‘female washroom sign’ and ‘men washroom sign’. Our CNN classifier gives an accuracy with 96%- 99%. The experimental results were compared with other methods such as SVM, HOG, AdaBoost, MCT to compare the accuracy results with our proposed method which is described in Proposed Method.
Work-in-Progress—Virtual Agents in Teaching: A Study of Human Aspects

Students require human intelligence and social interaction in the form of academic assistance at different times of their study period. Their desire to get and find academic assistance varies and is dependent on many factors such as attendance mode, personal situation, semester timetables, and assessment due dates. Providing students with access to this expertise when it is needed and to large numbers of students is problematic. Virtual Agents (VAs) seek to provide a technology-enabled social element to encourage and provide timely support to aid students' learning. We have implemented 4 unit-specific VIRtual Teaching Assistants (VIRTAs) across 2 universities to provide support to answer student's questions about various aspects of the unit. In this work-in-progress paper, we present the usage patterns of students to show how many questions were asked by students and at what point of time in the semester the questions were asked addressing the desire to find assistance when required from VIRTA.
Strong security of linear ramp secret sharing schemes with general access structures

A secret sharing scheme is a cryptographic technique to protect a secret from loss and leakage by dividing it into shares. A ramp secret sharing scheme can improve efficiency in terms of the information ratio by allowing partial information about the secret which is composed of several sub-secrets to leak out. The notion of strong security has been introduced to control the amount of information on every subset of the sub-secrets that unauthorized sets can obtain. However, there have been proposed few methods to construct strongly secure schemes for general access structures. Furthermore, all existing methods need strong assumptions, which lead to a loss of efficiency and a limitation of access structures. In this paper, we show that any ramp secret sharing scheme which is linear over a sufficiently large field can be transformed into a strongly secure scheme with the same access structure preserving the information ratio. Since our method only requires linearity, our strongly secure schemes can realize arbitrary access structures and achieve smaller information ratio than the previous schemes.
Gesture Controlled Wheel Chair and Convertible Wheel Chair into Stretcher

Wheelchair and stretcher are commonly used as medical equipment for the transportation. People who cannot walk due to physiological or physical illness, injured or any disability use the wheelchair, but they need the help of someone to sleep on a bed (in case of patient stretcher). New feature is introduced like convertible wheelchair which can be converted into stretcher which can be controlled depending on need. The project work is divided in two parts as follows: 1) Gesture controlled wheelchair 2) Convertible wheelchair into stretcher.
Machine Learning based Language Modelling of Code Switched Data

With the rapid increase of internet users all over the world, social media platforms have risen at a tremendous pace. Code-switched languages (When the speaker alternates between two or more languages eg. Hinglish, Hindi words written in English) are a popular medium of communication on social media. They are characterized by the lack of grammatical structure and variation in spellings. These linguistic constraints combined with lack of data cause ambiguity making the task of text classification on code-switched data difficult. In this paper, we have proposed a Language Modelling (LM) based approach to text classification of Hinglish text. We approach this problem by building a Universal Language Model Fine-tuning using AWD-LSTM architecture on a Hindi-English code-switched (Hinglish) corpus collected from various blogging sites. The language model is able to encode important information about the code-switched data and can be quickly fine-tuned on a given Hinglish dataset and achieve good results. We evaluated the performance of our model on the code-switched aggression detection TRAC-1 dataset, Hinglish Offensive Tweet (HOT) dataset and humour-classification dataset. Experiments on these datasets using our proposed method were able to surpass the previously reported results.
Spiral of Silence and Its Application in Recommender Systems

It is crucial to model missing ratings in recommender systems since user preferences learnt from only observed ratings are biased. One possible explanation for missing ratings is motivated by the spiral of silence theory. When the majority opinion is formed, a spiral process is triggered where users are more and more likely to show their ratings if they perceive that they are supported by the opinion climate. In this paper we first verify the existence of the spiral process in recommender systems by using a variety of different real-life datasets. We then study the characteristics of two key factors in the spiral process: opinion climate and the hardcore users who will give ratings even when they are minority opinion holders. Based on our empirical findings, we develop four variants to model missing ratings. They mimic different components of the spiral of silence based on the spiral process with global opinion climate, local opinion climate, hardcore users, relationships between hardcore users and items, respectively. We experimentally show that, the presented variants all outperform state-of-the-art recommendation models with missing rating components.
Comparing Authoring Workflows and Learning Paths in Web &amp; Mixed Reality Environments

With recent developments in information technology, we can realize learning experiences on the Web and in mixed reality. Many disciplines have profited from this, e.g. anatomy with the possibility to learn with 3D models in both environments. However, designing authoring workflows and learning paths can be different in these environments as the transfer of design ideas can affect the quality of the learning experiences. In this paper, we juxtapose a Web application and a mixed reality application of the same anatomy course. We highlight where differences need to be considered and which modules can be used in both environments, e.g. authentication and gamification. With the practical knowledge presented here, the design of learning experiences on the Web and in mixed reality can be enhanced. To support this, all code is available as open-source software on GitHub.
N-gram models for code completion in Pharo

In this paper, I present applying statistical language models to improve code completion in Pharo. In particular, the goal is to use n-gram models for sorting the completion candidates and, in such a way, increase the relevancy of the suggested completions.
On the Analysis of Space-Amplitude Diagram in Chaotic based Image Encryption

The existing literature has numerous compression and encryption schemes, but the schemes consume more time, storage space and bandwidth, and use less key space, which results in insecure communication. The Arnold Coupled Generalized Logistic Map Lattices (ACGLML) image encryption method introduces the number of permutations as a key space, as the technique is founded on the non-linear coupling of lattices using numerous iterated 2D Arnold cat maps. This method reduces the mutual information among the lattice values from the current 0.1 % to 0%by n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> order permutation. Besides, the diffusion key space is expanded in the range 0.5 to 4 against 3.57 to 4 in current literature using the generalized logistic map. The Space-Amplitude diagram is used to overcome the dynamic variation of lattice values through multiple iterations. A measurable metric has been introduced in this method which would help the system designers to verify the chaotic performance in a new dimension. This paper has compared the ACGLML method with the ACLML on Space-Amplitude diagram, and ACGLML is proved to perform better. The major task of the ACGLML method is encryption and decryption and despite that; it uses conventional wavelet based decompositions for image compression and decompression to ensure the applications in common communication channel.
A Scenario Experience Method with Virtual Reality Technologies for Designing Mixed Reality Services

With the rapid progress of Mixed Reality (MR) technology development, a range of services will available for use in the near future. Given that MR technologies are still evolving, innovating new services that use them can be a difficult process. When designing a new service, scenarios play a vital role in helping to visualize the end product. By using a scenario, designers can explore the opportunities and pitfalls of the service. Prototypes, therefore, are highly desirable for enabling users to experience scenarios immersively. However, current MR devices have serious drawbacks such as a narrow field of view. In this paper, we explore the feasibility of a scenario experience method with virtual reality (VR) technologies for MR service design. We compared a VR based scenario with an MR based scenario by evaluating Ambient Bot, which is an MR service with a 3D character floating around the user that provides information when looked at. By comparing the scenario experience when using the VR device with that of the MR device, we were able to clarify the advantages and disadvantages of the scenario experience in VR.
Work-in-Progress—Legal and Ethical Issues in Immersive Education

An examination of the legal and ethical issues surrounding the expanding use of immersive technology in educational settings. Augmented and virtual reality technologies are becoming widespread, raising new questions and posing new risks that both educators and policymakers must consider. This work-in-progress paper is part of a more extensive investigation into those issues with an analysis of current and developing research, policies, and best practices, particularly in Asia.
Pattern-match-oriented proof writing language

This paper demonstrates how pattern matching for non-free data types can make proofs intuitive.
Contextual machine teaching

Machine learning research today is dominated by a technocentric perspective and in many cases disconnected from the users of the technology. The machine teaching paradigm instead shifts the focus from machine learning experts towards the domain experts and users of machine learning technology. This shift opens up for new perspectives on the current use of machine learning as well as new usage areas to explore. In this study, we apply and map existing machine teaching principles onto a contextual machine teaching implementation in a commuting setting. The aim is to highlight areas in machine teaching theory that requires more attention. The main contribution of this work is an increased focus on available features, the features space and the potential to transfer some of the domain expert's explanatory powers to the machine learning system.