Reviewerly/exHarmony huggingface dataset:
Mobile Robot for Decommissioning Work -U-bo Series-

In this research, we aimed to realize a mobile robot that achieves the tasks in a robot contest aiming at realizing a robot towards a decommissioning work. The challenge is to carry 5 [kg] of baggage up to the top of the staircase with a slope of 41 degrees and return to the starting point. In the 1st contest, we realized a robot (U-bo I) that can move automatically with fewer sensors. In the 2nd contest, we realized a remote control robot (U-bo Mark II) that can carry baggage double the weight of regulations (10 [kg]). Due to the trouble, neither robot was able to achieve the task within the time limit, but we were able to realize the basic technology of the robot for the decommissioning work and training to human resources for the decommissioning work.
Datastorr: a workflow and package for delivering successive versions of 'evolving data' directly into R

The sharing and re-use of data has become a cornerstone of modern science. Multiple platforms now allow easy publication of datasets. So far, however, platforms for data sharing offer limited functions for distributing and interacting with evolving datasets— those that continue to grow with time as more records are added, errors fixed, and new data structures are created. In this article, we describe a workflow for maintaining and distributing successive versions of an evolving dataset, allowing users to retrieve and load different versions directly into the R platform. Our workflow utilizes tools and platforms used for development and distribution of successive versions of an open source software program, including version control, GitHub, and semantic versioning, and applies these to the analogous process of developing successive versions of an open source dataset. Moreover, we argue that this model allows for individual research groups to achieve a dynamic and versioned model of data delivery at no cost.
Weaving the Topics of CHI

This paper provides a comprehensive and novel analysis of the annual conference proceedings of CHI to explore the structure and evolution of the community. Self-awareness is healthy for a diverse and dynamic community, allowing it to anticipate and respond to emerging themes. Instead of using a traditional topic modelling approach to analyze the text of the papers, we adopt a social network analysis approach by analyzing the citation network of papers. After constructing such a citation network, community detection is applied in order to cluster papers into different groups. The keywords of these groups are found to represent different research themes in human-computer interaction, while the growth or decline of these groups is visualized by their paper shares over time. Lastly, we contribute a visualization tool for exploring emerging trends within our community, which can be used to predict likely topics at future CHI conferences.
Deep Rule-Based Aerial Scene Classifier using High-Level Ensemble Feature Descriptor

In this paper, a new deep rule-based approach using high-level ensemble feature descriptor is proposed for aerial scene classification. By creating an ensemble of three pre-trained deep convolutional neural networks for feature extraction, the proposed approach is able to extract more discriminative representations from the local regions of aerial images. With a set of massively parallel IF...THEN rules built upon the prototypes identified through a self-organizing, nonparametric, transparent and highly human-interpretable learning process, the proposed approach is able to produce the state-of-the-art classification results on the unlabeled images outperforming the alternatives. Numerical examples on benchmark datasets demonstrate the strong performance of the proposed approach.
Development of a Novel Bio Inspired Framework for Fertilizer Optimization

Fertilizers enhance nutrients in the soil to improve plant growth and soil fertility. It is necessary to optimize the amount of fertilizers applied to a crop to increase profitability and to reduce environmental damage. Though chemical fertilizers play a significant role in soil nutrients enrichment, they also pose severe threat to the environment, which include augmented water pollution, soil pollution, air pollution and acidification and soil mineral depletion. optimizing the quantity of fertilizers applied to the crop avoid harmful effects of chemical fertilizers on environment and ecology. In this paper, the quantity of fertilizers applied to the crops is optimized by using Fruit Fly optimization (FFO) algorithm. FFO algorithm is a popular bio-inspired optimization algorithm, aimed to resolve complex optimization problems and is inspired by the foraging behaviour of fruit flies. The study area of this research work is Coimbatore region, located in Tamilnadu state, India. The results obtained from FFO have proven that excessive application of fertilizers could be systematically minimized through proper optimization planning thereby reducing the negative impacts of chemical fertilizers on the crops, soil and the environment.
Face replacement based on 2D dense mapping

Face replacement, also known as face swapping, is a worth investing problem in privacy protection, intelligent human-computer interaction and video effects. In our paper, a new approach is proposed to transform identities of the input images into a target identity while preserving poses and facial features. It is considered that 2D face replacement can be replaced by the problem of 2D graphics dense mapping using 3D morphable models projection. For mapping the input facial region and the target, our method uses planar parameterization and affine transformation to establish a one-to-one dense mapping between 2D graphics. It can enable to reduce the effect on the rotation of non-frontal faces without image preprocessing. Our method then adjusts positions of eyes to keep gaze direction of the input images, and stitches the transformed image and the target image based on seamless cloning to show the authenticity of the images.
Twenty Years of The Mixed Reality Laboratory

The mixed reality lab has now been a staple of the CHI community for twenty years. From its founding in 1999 through to today, we have placed our relationship with art and artists at the forefront of our research methods. In this retrospective exhibition, we present some of our most recent and exciting work, alongside some of our archived works, and ask viewers to consider twenty years of CHI research and innovation - not just from our lab, but from the whole CHI community. Back in 1999 when we started, Virtual Reality was the exciting new technology. A lot has changed since then.
Fast Context-Adaptive Bit-Depth Enhancement via Linear Interpolation

To fill in the huge gap between the rapid development of modern display devices with high dynamic range (HDR) and the mainstream media source with a low bit depth of 8, bit-depth enhancement (BDE) that reconstructs high bit-depth images from low bit-depth ones emerges as an important problem. Early works generally introduce the annoying artifacts and then plenty of context-aware algorithms are proposed recently but at the cost of high computational complexity. In this paper, we propose a fast yet efficient context-adaptive algorithm via linear interpolation. For gradual color transition areas, the interpolation is performed along the direction bisecting the self-defined virtual effective boundaries, while for local peak/bottom areas, the interpolation is carried out based on the minimum distance to intensity steps caused by quantization. By deliberately designing the interpolation direction and endpoints for different types of local contexts, the proposed algorithm can well reconstruct natural images with low computational complexity. The experimental results show that the proposed algorithm outperforms the state-of-the-art algorithms on average in terms of both objective and subjective evaluations.
Exploring Uses of Augmented Reality in Participatory Marketing

This paper is an exploration of Augmented Reality applications to Participatory Marketing and overviews initial findings in this area of research. Participatory Marketing is the concept of marketing with customers rather than at them, and can potentially turn AR users form passive consumers to (pro-)active co-creators of this future media. We conducted a preliminary investigation to focus on possible challenges and opportunities.
Evaluation of ACE properties of traditional SQL and NoSQL big data systems

Traditional SQL and NoSQL big data systems are the backbone for managing data in cloud, fog and edge computing. This paper develops a new system and adopts the TPC-DS industry standard benchmark in order to evaluate three key properties, availability, consistency and efficiency (ACE) of SQL and NoSQL systems. The contributions of this work are manifold. It evaluates and analyses the tradeoff between the ACE properties. It provides insight into the NoSQL systems and how they can be improved to be sustainable for a more wide range of applications. The evaluation shows that SQL provides stronger consistency, but at the expense of low efficiency and availability. NoSQL provides better efficiency and availability but lacks support for stronger consistency. In order for NoSQL systems to be more sustainable they need to implement transactional schemes that enforce stronger consistency as well as better efficiency and availability.
Selection of the number of clusters in functional data analysis

Identifying the number K of clusters in a dataset is one of the most difficult problems in clustering analysis. A choice of K that correctly characterizes the features of the data is essential for building meaningful clusters. In this paper we tackle the problem of estimating the number of clusters in functional data analysis by introducing a new measure that can be used with different procedures in selecting the optimal K. The main idea is to use a combination of two test statistics, which measure the lack of parallelism and the mean distance between curves, to compute criteria such as the within and between cluster sum of squares. Simulations in challenging scenarios suggest that procedures using this measure can detect the correct number of clusters more frequently than existing methods in the literature. The application of the proposed method is illustrated on several real datasets.
A Research-Led Practice-Driven Digital Forensic Curriculum to Train Next Generation of Cyber Firefighters

Lack of skilled digital forensic professionals is seriously affecting the everyday life of everyone as businesses and law enforcement are struggling to fill the bare minimum number of digital investigator positions. This skills shortage can hinder incident response, with organizations failing to put effective measures in place following a cyber-attack or to gather the digital evidence that could lead to the successful prosecution of malicious insiders and cyber-criminals. It therefore makes the connected world less secure and digital economies less reliable, affecting everyone in their ecosystems. The commercial and public sectors are looking to higher education institutions to produce quality graduates equipped to enter the digital forensics profession. This paper presents our proposed research-led, practice-driven digital forensics curriculum. The curriculum is designed to respond to employers’ needs and is built on the experience of running a successful Cyber Security program at Birmingham City University in the industrial heartland of the UK. All students will take a common set of modules in the first semester, but will be given the opportunity to specialize in digital forensics in the second semester and in their summer project, enabling them to graduate with the degree of MSc Digital Forensics.
CNN-Based Bi-Prediction Utilizing Spatial Information for Video Coding

In video coding, slice-level and block-level weighted bi-prediction are used for scenes with temporal brightness variation. However, there are still structured residuals when applying weighted bi-prediction in slice and block level. Recently, CNN-based bi-prediction has achieved remarkable success on reducing significant structured residuals, in which bi-predictor is generated by CNN model using two reference blocks as inputs. Inspired by high spatial correlation of pixels, this paper uses spatial neighboring pixels of both current block and two reference blocks as the additional information of the proposed CNN model to further reduce residual and generate a more accurate bi-predictor. Moreover, by comparing AMVP and merge/skip mode, this paper illustrates that CNN-based bi-prediction is more efficient for merge/skip mode than for AMVP mode. Experimental results show that proposed method reaches 3.46% BD-rate saving for random access configuration on average compared to HM 16.15.
Implementation of Enhanced MD5 Algorithm using SSL to Ensure Data Integrity

Data integrity is the main concern of this paper. An application was developed to implement the Enhanced MD5 Algorithm in Secured Socket Layer (SSL). Furthermore, this application secures the integrity of data sent and received via communication link using SSL. This paper also analyzed the time and space complexity of the Enhanced MD5 Algorithm. The time complexity obtained was 27+7n+2n^2+n log n and space complexity were O (n^2). During uploading and downloading processes, the developed application generated the same hashes for the same files. This means that if an SSL uses a strong standard-based encryption algorithm, data are protected. Using a strong encryption algorithm, data security is assured amidst attacker's interception during transmission.
Disconnect

As the lines between the digital and analog worlds become increasingly blurred, it is nearly impossible to traverse modern life without creating a digital footprint. This integration is so deep-rooted into the fabric of society, that if one attempted to choose to disconnect from today's hyperconnected world, one would have to move away from civilization. Weiser's vision of the omni-present, ubiquitous computer of the 21st century [21] has been realized, but at a cost. With invisible interfaces we forego the ability to recognize when we are being watched, heard or influenced by external actors. This paper takes a bottom-up approach of using design fiction narratives to explore how to design mechanisms of control (MoC) that may help reinstate human control and agency over our data. Preliminary results show emergent themes pertaining to data access, governance and sharing; the forms of MoC; as well as methodological lessons.
Handwritten Cube Recognition and Evaluation Based on Perspectives for Sketch Training System

A sketch is useful as a communication tool. By expressing ideas using sketches, we can explain the information we want to convey to others in an easy-to-understand way. In general, learning sketches is done by experienced teacher feedback. If we can do this approach with technology, we can expect to provide sketch education opportunities to many people. In our system, utilizing the fact that cube sketch can be evaluated by perspective, we recognize and evaluate the sketch of the learner's cube and educate sketch acquisition by feeding back the mistake. The developed system is evaluated by testing whether the handwritten cube can be recognized and whether we can point out the mistakes of the learner's cube correctly.
Towards a requirements language for modeling emotion in videogames

The1 videogame development process must focus on the user satisfaction. Failing to comply with this may jeopardize the success of the videogame, and even put at risk the owner company. Requirements Engineering is essential to elicit the needs and desires of the systems' stakeholders involved in the development, purchase and use of the videogames. The elicited requirements are the basis for the game specification document which is then the major driver for the construction of the videogame. The success of a game involves concerns, such as game strategy, plot and goal of the game, attractive interfaces, efficient algorithms, and, very important, the emotions that the game triggers on its players. This work focuses exactly on the requirements that specify the trigger for different types of emotions. Modeling such emotion requirements has not been much explored, maybe due to the high degree of subjectivity inherent to any emotion. This work proposes a modeling language to represent and model the requirements associated with emotions. These models can be analyzed to identify emotions and conflicting situations in the early stages of the game development.
Technology Meets Fashion

The introduction of technology into the worlds of fashion and haute couture, has made it possible for fashion designers and technologists to create and experiment with garments and wearables in a variety of novel and expressive forms. Several of these haute couture garments infused with technology are shown on international runways and can ultimately influence the design of consumer fashion and wearable products. Within this context, I describe my dissertation which aims to explore and understand the role of technology throughout the process of design and fabrication in the haute tech couture domain and uncover broader implications for the design of wearables.
An Enhanced Privacy-Preserving Recommender System

A recommender system stores historical data collected over a long period from various users, these are used to predict how new and existing users would rate an item. As user data is stored by the system, this poses threat to user’s privacy. The goal of a privacy preserving recommender system is to hide user ratings from system and yet allow to make recommendations. A recent example is the privacy-preserving recommender scheme proposed by Badsha, Yi and Khalil. Their scheme assumes that the server is semi-honest. However, when the server is malicious an attack is possible, as shown by Mu, Shao and Miglani. In this paper, we propose a simple modification to their scheme, which preserves the privacy of ratings against a malicious server. We demonstrate that the computation and communication costs of modified protocol are reasonable in comparison to original protocol.
Techniques for Measuring the Probability of Adjacency between Carved Video Fragments: The VidCarve Approach

File carving is a powerful technique for both digital forensics investigations and data recovery. It offers the flexibility to recover data stored on digital media independent of the underlying file system. Thus, it can be used in the cases where we need to recover deleted files or when we have a corrupted, overwritten, or unknown file system. In this paper, we present a novel video file carving (VidCarve) framework to recover and reassemble fragmented video files into playable video files. VidCarve consists of four main components: identification and recovery, weight assignment, reassembly, and file construction. This paper focuses on the weight assignment and reassembly processes where the codec specification parameters of carved fragments were overwritten. We propose several weight assignment techniques to estimate the probability of adjacency between video fragments. Based on these weights, the reassembly algorithm recovers the video files by constructing their correct sequences. We provide experimental results for the proposed techniques of weight assignment and reassembly. We claim that the overall accuracy rate can produce forensically sound evidence and play a critical role in the process of digital evidence recovery in many criminal cases.
Double-Volante: Double Cursor Interactions to Move Obstacles for Browsing and Navigation

Based on recently developed 3D computer graphics technology (e.g., hardware and software), there are many Virtual Reality systems and video games with realistic 3D CG. However, navigation methods for browsing 3D scenes have not advanced. Conventional techniques focus on smoothly moving a user’s viewpoint around a pre-defined 3D object in a 3D scene. In contrast, our method allows users to move or translate pre-defined objects interactively to clear their fields of vision. In the real world, it is impossible to move big or heavy objects easily, so we think that moving or translating objects is one of the significant advantages of a virtual 3D world. Moreover, by moving pre-defined objects, a user can see objects behind them. By doing this, the user can decide whether to move or not. These simple manipulations are useful not only with 3D navigation but also with information retrieval and web browsing. In this paper, we describe our Double-Volante interaction and its applications.
On scaling the enumeration of the preferred extensions of abstract argumentation frameworks

Argumentation has emerged as one of the major fields in Artificial Intelligence. In particular, Dung's abstract argumentation framework (AF) has received much attention in the last two decades, and many computational issues have been investigated for different argumentation semantics. Specifically, enumerating the sets of arguments (i.e., extensions) prescribed by an argumentation semantics is arguably one of the most challenging problems for AFs, and this is particularly the case for the well-known preferred semantics.
Modeling Error Rates in Spatiotemporal Moving Target Selection

When we try to acquire a moving target such as hitting a virtual tennis in a computer game, we must hit the target instantly when it flies over our hitting range. In other words, we have to acquire the target in spatial and temporal domains simultaneously. We call this type of task spatiotemporal moving target selection, which we find is common yet less studied in HCI. This paper presents a tentative model for predicting the error rates in spatiotemporal moving target selection. Our model integrates two latest models, the Ternary-Gaussian model and the Temporal Pointing model, to explain the influence of spatial and temporal constraints on pointing errors. In a 12-subject pointing experiment with a computer mouse, our model shows high fitting results with 0.904 R2. We discuss future research directions on this topic and how it could potentially help the design in dynamical user interfaces.
The Gaussian Source-and-Data-Streams Problem

A Gaussian source and two data streams are to be transmitted over a Gaussian broadcast channel: the first stream, the “common stream,” is to be decoded by both receivers, and the second, the “private stream,” only by the strong receiver. Both receivers wish to estimate the source sequence, though with possibly different mean squared-errors. The quadruples of achievable rates and estimation errors are characterized, and it is shown that-once the data rates have been fixed-there is no tension between the estimation errors. Only the “equal bandwidth” case is treated, where the rate at which the source emits symbols is also the rate at which the channel is used.