Reviewerly/exHarmony huggingface dataset:
Federated Acoustic Model Optimization for Automatic Speech Recognition

Traditional Automatic Speech Recognition (ASR) systems are usually trained with speech records centralized on the ASR vendor's machines. However, with data regulations such as General Data Protection Regulation (GDPR) coming into force, sensitive data such as speech records are not allowed to be utilized in such a centralized approach anymore. In this demonstration, we propose and show the method of federated acoustic model optimization in order to solve this problem. This demonstration does not only vividly show the underlying working mechanisms of the proposed method but also provides an interface for the user to customize its hyperparameters. With this demonstration, the audience can experience the effect of federated learning in an interactive fashion and we wish this demonstration would inspire more research on GDPR-compliant ASR technologies.
Toward Kinecting cognition by behaviour recognition-based deep learning and big data

Abstract The majority of older people wish to live independently at home as long as possible despite having a range of age-related conditions including cognitive impairment. To facilitate this, there has been an extensive focus on exploring the capability of new technologies with limited success. This paper investigates whether MS Kinect (a motion-based sensing 3-D scanner device) within the MiiHome (My Intelligent Home) project in conjunction with other sensory data, machine learning and big data techniques can assist in the diagnosis and prognosis of cognitive impairment and hence prolong independent living. A pool of Kinect devices and various sensors powered by minicomputers providing internet connectivity are being installed in up to 200 homes. This enables continuous remote monitoring of elderly residents living alone. Passive and off-the-shelf sensor technologies were chosen to implement data acquisition specifically from sources that are part of the fabric of the homes, so that no extra effort is required from the participants. Various constraints including environmental, geometrical and big data were identified and appropriately dealt with. A visualization tool (MAGID) was developed for validation and verification of numerous behavioural activities. Then, a subset of data, from twelve pensioners aged over 65 with age-related cognitive decline and frailty, were collected over a period of 6 months. These data were subjected to several machine learning algorithms (multilayer perceptron neural network, neuro-fuzzy and deep learning) for classification and to extract routine behavioural patterns. These patterns were then analysed further to ascertain any health-related information and their attributes. For the first time, important routine behaviour related to Activities of Daily Living (ADL) of elderly people with cognitive and physical decline has been learnt by machine learning techniques from selected sample data obtained by MS Kinect. Medically important behaviour, e.g. eating, walking, sitting, was best learnt by deep learning with accuracy of 99.30% during training stage and average error rate of 1.83% with maximum of 12.98% during the implementation phase. Observations obtained from the application of the above learnt behaviours are presented as trends over a period of time. These trends, supplemented by other sensory signals, have provided a clearer picture of physical (in)activities (including falls) of the pensioners. The calculated behavioural attributes related to key indicators of health events can be used to model the trajectory of health status related to cognitive decline in a home setting. These results, based on a small number of elderly residents over a short period of time, imply that within the results obtained from the MiiHome project, it is possible to find indicators of cognitive decline. However, further studies are needed for full clinical validation of these indications in conjunction with assessment of cognitive decline of the participants.
Bidding System using Blockchain

Block chain is the latest technology in the IT industry for securing blocks of data. Secure online bidding system is designed with blockchain technology using hyper ledger saw tooth framework. Currently, there is a lot of data integrity problems with respect financial services in the market. Block chain overcomes this flaw by decentralizing the information. In the proposed work, the client can connect to the validator network to put an item or commodity of their choice for auction. Other users can view the items and connect to the validator network to bid their items using user interface. All the information regarding the items are stored on the Blockchain. Information is safe and integrity is achieved. Detailed records cannot be altered and the highest bidder has to make the purchase. Once the purchase is done, nobody can alter the data. Information is safe and integrity is achieved as both seller and bidder are enabled with security.
Expanding Wave Function Collapse with Growing Grids for Procedural Map Generation

This paper proposes a method to augment the basic Wave Function Collapse algorithm with the Growing Grid neural network for procedural map generation. The system accept a higher level description of the map, in the form of an image, and the system returns a list of solutions based on learning parameters and module constraints. First, a description of the technical implementation of the system is given. Second, the augmented WFC variant is evaluated from a user experience perspective, in the context of a first person cognitive map formation.
Distributed mining of time-faded heavy hitters

We present P2PTFHH (Peer-to-Peer Time-Faded Heavy Hitters) which, to the best of our knowledge, is the first distributed algorithm for mining time-faded heavy hitters on unstructured P2P networks. P2PTFHH is based on the FDCMSS (Forward Decay Count-Min Space-Saving) sequential algorithm, and efficiently exploits an averaging gossip protocol by merging in each interaction the involved peers’ underlying data structures. We formally prove the convergence and correctness properties of our distributed algorithm and show that it is fast and simple to implement. Extensive experimental results, executed on both synthetic and real datasets, confirm that P2PTFHH retains the extreme accuracy and error bound provided by FDCMSS whilst showing excellent scalability. Our contributions are threefold: (i) we prove that the averaging gossip protocol can be used jointly with our augmented sketch data structure for mining time-faded heavy hitters; (ii) we prove the error bounds on frequency estimation; (iii) we experimentally prove that P2PTFHH is extremely accurate and fast, allowing near real time processing of large datasets.
Parallel multi-head dot product attention for video summarization

The dominant video summarization deep learning models are based on recurrent or convolutional neural network with a complex architectures. The best performing models also use attention mechanism. We propose a novel method for supervised, keyframes based video summarization by applying a well known Transformer architecture. Current state of the art method based on self-attention network. These network is simple and robust but still lower than human level. We propose model for video summarization based on a powerful language architecture Transformer which performs a single feed forward and backward pass during training for the entire video sequence to frame score transformation. This approach was adapted for the sequential regression followed by binarized evaluation which allowed to compare predicted summary with human level. Experiments show that model is competitive in performance and have a good parallel training ability also. Our model achieves F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> 48.1 and 50.19 for SumMe in cannonical and augmented setting and 60.12 and 61.5 for TVSum.
DaRLing: A Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries

Abstract The W3C Web Ontology Language (OWL) is a powerful knowledge representation formalism at the basis of many semantic-centric applications. Since its unrestricted usage makes reasoning undecidable already in case of very simple tasks, expressive yet decidable fragments have been identified. Among them, we focus on OWL 2 RL, which offers a rich variety of semantic constructors, apart from supporting all RDFS datatypes. Although popular Web resources - such as DBpedia - fall in OWL 2 RL, only a few systems have been designed and implemented for this fragment. None of them, however, fully satisfy all the following desiderata: (i) being freely available and regularly maintained; (ii) supporting query answering and SPARQL queries; (iii) properly applying the sameAs property without adopting the unique name assumption; (iv) dealing with concrete datatypes. To fill the gap, we present DaRLing , a freely available Datalog rewriter for OWL 2 RL ontological reasoning under SPARQL queries. In particular, we describe its architecture, the rewriting strategies it implements, and the result of an experimental evaluation that demonstrates its practical applicability.
Analysis of Image Processing Techniques to Segment the Target Animal in Non-uniformly Illuminated and Occluded Images

Non-uniformly illuminated images are a class of images that, from a subjective perspective, are difficult to analyze. The excess noise and the lack of properly defined boundaries all contribute to making these images a difficult dataset for any form of analysis or segmentation. This calls for proper feature extraction and specific enhancement to make these images ready for efficient information gathering. This paper aims to visualize the features that can be enhanced using image enhancement techniques to identify the target animal in a non-uniformly illuminated and occluded image, thereby enhancing the recognition power of the proposed system. This paper uses a method to approximately detect and locate the position of the animal in an image. Segmentation Using Region Adjacency Graphs, Interactive Foreground Extraction using GrabCut Algorithm and DeepLab model for semantic image segmentation have also been analyzed.
Comparing the Effects of Paper and Mobile Augmented Reality Instructions to Guide Assembly Tasks

To guide an assembly sequence, instructions are often presented on paper or digital manuals containing photos, diagrams, and texts. Recently, augmented reality (AR) has gained increasing attention as a new way to provide user support and assistance for guiding assembly. To date, although comprehensive studies have compared the effectiveness of augmented reality instructions on Head-Mounted Displays and projectors, only a few investigate the AR instructions on mobile devices such as smartphones and tablets. In this paper, we aim to close this gap by comparing mobile AR instructions to paper-based instructions using a Lego assembly task. Two groups of participants were asked to either use AR instructions or paper-based instructions. Their task effectiveness, efficiency, cognitive load, and motivation were measured. Results indicate that participants who used mobile AR instructions made fewer errors compared to participants who used paper instructions. Furthermore, mobile AR instructions do not hinder task efficiency, cognitive load, and motivation. This study adds empirical evidence to the usability of mobile AR instructions and may help instructional designers gain insights about differences in mobile AR instructions and paper instructions.
Efficient Global MOT Under Minimum-Cost Circulation Framework

We developed a minimum-cost circulation framework for solving the global data association problem, which plays a key role in the tracking-by-detection paradigm of multi-object tracking (MOT). The global data association problem was extensively studied under the minimum-cost flow framework, which is theoretically attractive as being flexible and globally solvable. However, the high computational burden has been a long-standing obstacle to its wide adoption in practice. While enjoying the same theoretical advantages and maintaining the same optimal solution as the minimum-cost flow framework, our new framework has a better theoretical complexity bound and leads to orders of practical efficiency improvement. This new framework is motivated by the observation that minimum-cost flow only partially models the data association problem and it must be accompanied by an additional and time-consuming searching scheme to determine the optimal object number. By employing a minimum-cost circulation framework, we eliminate the searching step and naturally integrate the number of objects into the optimization problem. By exploring the special property of the associated graph, that is, an overwhelming majority of the vertices are with unit capacity, we designed an implementation of the framework and proved it has the best theoretical computational complexity so far for the global data association problem. We evaluated our method with 40 experiments on five MOT benchmark datasets. Our method was always the most efficient in every single experiment and averagely 53 to 1,192 times faster than the three state-of-the-art methods. When our method served as a sub-module for global data association methods utilizing higher-order constraints, similar running time improvement was attained. We further illustrated through several case studies how the improved computational efficiency enables more sophisticated tracking models and yields better tracking accuracy. We made the source code publicly available on GitHub with both Python and MATLAB interfaces.
Optimal Entanglement-Assisted Quantum Codes With Larger Minimum Distance

Entanglement-assisted quantum error-correcting code (EAQECC) is a new-type quantum code and can be constructed from arbitrary linear codes by sharing entangled states c in advance. In this letter, we explore a family of entanglement-assisted quantum error-correcting codes (EAQECCs) with length (q <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> - 1)/12 from negacyclic codes. Compared with known results in the literatures, our EAQECCs are new and have larger minimum distance.
Efficient sampling from shallow Gaussian quantum-optical circuits with local interactions

Gaussian boson sampling is a candidate model for demonstrating quantum computational advantage in photonic systems. Here, the authors narrow down the range of possible architectures for such a demonstration, by introducing a classical algorithm that can simulate optical systems with shallow-depth circuits and local gates in polynomial time.
Invertible secret sharing: Using meaningful shadows based on Sorted Indexed Code

The need for secret sharing using meaningful shadow images is an important method to protect the transmitted data that does not attract the attention of adversary, which has become obvious in the past few years. The recovery of the cover image without any distortion and security of the embedded secret string efficiently needs to be addressed. This paper proposes an invertible (k,n) secret sharing scheme using meaningful shadow image based on Sorted Indexed Code (SIC) with high embedding capacity. The SIC helps to achieve better randomness between shadow images in terms of NPCR, the closeness of each objective assessment (i.e., PSNR, SSIM, NPCR, and UACI) between the shadows. Hence, by utilizing the SIC and polynomial based threshold secret sharing method the proposed scheme achieves an adequate visual quality of shadow images, complete recovery of the cover image, and increases the embedding capacity. The experimental results show the visual quality of image shares for camouflage purpose of steganography. The visual qualities of shadow images for the proposed scheme are validated through Peak-Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Method (SSIM) analysis. Further, the security of the proposed scheme is analyzed using dissimilarity measures; Number of Pixel Change Rate (NPCR) and Unified Average Changing Intensity (UACI).
On the Use of Web Assembly in a Serverless Context

Abstract This paper considers how WASM can be run in different serverless contexts. A comparison of different serverside WASM runtime options is considered, specifically focused on , and . Next, different options for running WASM within two serverless platforms – Openwhisk and AWS Lambdai – are compared. Initial results show that a solution which uses the built-in WASM supports is found to work better than using the dedicated WASM runtimes but this has limitations and providing more direct integration with WASM runtimes should be explored further.
The Use of the Posterior Probability in Score Differencing

Score differencing is one of the six categories of statistical methods used to detect test fraud (Wollack &amp; Schoenig, 2018) and involves the testing of the null hypothesis that the performance of an examinee is similar over two item sets versus the alternative hypothesis that the performance is better on one of the item sets. We suggest, to perform score differencing, the use of the posterior probability of better performance on one item set compared to another. In a simulation study, the suggested approach performs satisfactory compared to several existing approaches for score differencing. A real data example demonstrates how the suggested approach may be effective in detecting fraudulent examinees. The results in this article call for more attention to the use of posterior probabilities, and Bayesian approaches in general, in investigations of test fraud.
Low-altitude UAV Detection Method Based on One-staged Detection Framework

Illegal flight of unmanned aerial vehicles (UAVs) poses serious threats to the public and national security. With the characteristics of small size and low flight height, UAVs are difficult for the traditional air-defense system to detect. Therefore, to deal with the illegal UAV flight, this paper proposed a state-of-the art low-altitude UAV detection method. Firstly, a large-scale UAV data set including multiple kinds of UAVs is collected and constructed. Then, based on one stage detection framework, the UAV detection (UAVDet) network is presented with the improvement of more detection scales, utilization of focal loss and specific data augmentation. Experiment results show that the proposed UAV detection method has significant improvement on UAV detection performance, and it is competent to achieve real-time and effective UAV detection.
From Code to Natural Language: Type-Aware Sketch-Based Seq2Seq Learning

Code comment generation aims to translate existing source code into natural language explanations. It provides an easy-to-understand description for developers who are unfamiliar with the functionality of source code. Existing approaches to code comment generation focus on summarizing multiple lines of code with a short text, but often cannot effectively explain a single line of code. In this paper, we propose an asynchronous learning model, which learns the code semantics and generates a fine-grained natural language explanation for each line of code. Different from a coarse-grained code comment generation, this fine-grained explanation can help developers better understand the functionality line-by-line. The proposed model adopts a type-aware sketch-based sequence-to-sequence learning method to generate natural language explanations for source code. This method incorporates the type of source code and the mask mechanism with the Long Short Term Memory (LSTM) network via encoding and decoding phases. We empirically compare the proposed model with state-of-the-art approaches on real data sets of source code and description in Python. Experimental results demonstrate that our model can outperform existing approaches on commonly used metrics for neural machine translation.
Comparing Different Developer Behavior Recommendation Styles

Research shows that one of the most effective ways software engineers discover useful developer behaviors, or tools and practices designed to help developers complete programming tasks, is through human-to-human recommendations from coworkers during work activities. However, due to the increasingly distributed nature of the software industry and development teams, opportunities for these peer interactions are in decline. To overcome the deprecation of peer interactions in software engineering, we explore the impact of several system-to-human recommendation systems, including the recently introduced suggested changes feature on GitHub which allows users to propose code changes to developers on contributions to repositories, to discover their impact on developer recommendations. In this work, we aim to study the effectiveness of suggested changes for recommending developer behaviors by performing a user study with professional software developers to compare static analysis tool recommendations from emails, pull requests, issues, and suggested changes. Our results provide insight into creating systems for recommendations between developers and design implications for improving automated recommendations to software engineers.
A Virtual Reality OER Platform to Deliver Phobia-Motivated Experiences

This paper describes an on-going project to develop a Virtual Reality platform to deliver phobia-inspired experiences. These experiences could induce a reaction in the user that may help the user overcome, or alleviate, the phobia. The platform includes monitoring sensors that could be used to measure how much impact the experience is having. The project development has been taking place at a Sino-foreign Higher Education Institution in Mainland China, University of Nottingham Ningbo China (UNNC). UNNC has already been host to a number of OER (Open Educational Resource) development projects, and the current project is also anticipated to eventually be released to the OER community. This paper presents the background, development, and current state of the project. Challenges to project completion, and future work are also outlined.
Precise Noise Calibration for CV-QKD

Continuous-variable quantum-key-distribution (CV-QKD) devices, conveniently based on standard components from the domain of coherent optical communication, can guarantee an information-theoretic secure-key expansion. Their security analysis assumes that all power lost in the channel, as well as channel noise in excess with respect to the shot noise, is due to an eavesdropper limited only by quantum mechanics. Quantifying the excess noise, which typically has significantly less power than the shot noise, is essential for secure-key generation. In this article a heterodyne-receiver setup capable of measuring optical power with an extremely high sensitivity (approximately -130 dBm within a 12.5 MHz bandwidth at 1550 nm, corresponding to a power 40 dB below the shot noise) is analysed and demonstrated in a CV -QKD prototype device. Such accuracy is achieved by means of continuous calibration and long-time averages.