Reviewerly/exHarmony huggingface dataset:
On Scaling Contrastive Representations for Low-Resource Speech Recognition

Recent advances in self-supervised learning through contrastive training have shown that it is possible to learn a competitive speech recognition system with as little as 10 minutes of labeled data. However, these systems are computationally expensive since they require pre-training followed by fine-tuning in a large parameter space. We explore the performance of such systems without fine-tuning by training a state-of-the-art speech recognizer on the fixed representations from the computationally demanding wav2vec 2.0 framework. We find performance to decrease without fine-tuning and, in the extreme low-resource setting, wav2vec 2.0 is inferior to its predecessor. In addition, we find that wav2vec 2.0 representations live in a low dimensional subspace and that decorrelating the features of the representations can stabilize training of the automatic speech recognizer. Finally, we propose a bidirectional extension to the original wav2vec framework that consistently improves performance.
dK-Projection: Publishing Graph Joint Degree Distribution with Node Differential Privacy

Network data has great significance for commercial and research purposes. However, most networks contain sensitive information about individuals, thereby requiring privacy-preserving mechanisms to publish network data while preserving data utility. In this paper, we study the problem of publishing higher-order network statistics, i.e., joint degree distribution, under strong mathematical guarantees of node differential privacy. This problem is known to be challenging, since even simple network statistics (e.g., edge count) can be highly sensitive to adding or removing a single node in a network. To address this challenge, we propose a general framework of publishing dK-distributions under node differential privacy, and develop a novel graph projection algorithm to transform graphs to \(\theta \)-bounded graphs for controlled sensitivity. We have conducted experiments to verify the utility enhancement and privacy guarantee of our proposed framework on four real-world networks. To the best of our knowledge, this is the first study to publish higher-order network statistics under node differential privacy, while enhancing network data utility.
The Impact on Digitalization on Financial Sector Performance

The aim of this study is to analyze the effects of digitalization and artificial intelligence applications on financial performance of banks which emerged as a result of developments in information and communication technologies (ICT). In this paper, the role of financial system in the modern economy, as well as the impact of technological change and financial innovation on the structure of the financial sector are tried to be explained. In this context, the literature and its findings are evaluated to explain the impact of financial innovations classified as new production processes and new products or services on the performance of financial sector. Finally, important examples of how fintech transform the structure of the banking sector are provided. In this analysis, technical efficiency scores of banks representing the performance of the financial sector were used as the main determinant variable. The data set of 26 commercial banks active in the Turkish banking sector between the years of 2010 and 2016 constitute the sample size of the study. While technical efficiency scores of each bank are obtained by Data Envelopment Analysis (DEA), the effect of digitalization on financial performance is estimated by using “truncated regression model combined with bootstrap confidence intervals.” According to the results of the truncated regression model, digitalization has a positive effect on financial performance.
Multi-Client Functional Encryption for Separable Functions

In this work, we provide a compiler that transforms a single-input functional encryption scheme for the class of polynomially bounded circuits into a multi-client functional encryption (MCFE) scheme for the class of separable functions. An n-input function f is called separable if it can be described as a list of polynomially bounded circuits \(f^1, \dots , f^n\) s.t. \(f(x_1, \dots , x_n)= f^1(x_1)+ \dots + f^n(x_n)\) for all \(x_1,\dots , x_n\). Our compiler extends the works of Brakerski et al. [Eurocrypt 2016] and of Komargodski et al. [Eurocrypt 2017] in which a generic compiler is proposed to obtain multi-input functional encryption (MIFE) from single-input functional encryption. Our construction achieves the stronger notion of MCFE but for the less generic class of separable functions. Prior to our work, a long line of results has been proposed in the setting of MCFE for the inner-product functionality, which is a special case of a separable function. We also propose a modified version of the notion of decentralized MCFE introduced by Chotard et al. [Asiacrypt 2018] that we call outsourceable mulit-client functional encryption (OMCFE). Intuitively, the notion of OMCFE makes it possible to distribute the load of the decryption procedure among at most n different entities, which will return decryption shares that can be combined (e.g., additively) thus obtaining the output of the computation. This notion is especially useful in the case of a very resource consuming decryption procedure, while the combine algorithm is non-time consuming. We also show how to extend the presented MCFE protocol to obtain an OMCFE scheme for the same functionality class.
Research on Lane Line Segmentation Algorithm Based on Deeplabv3

In order to solve the problems of low accuracy of lane line detection caused by variation of light intensity, the blocking of pedestrians, vehicles and green plants, a lane line segmentation algorithm combining the enhanced network Retinexnet and the instance segmentation network Deeplabv3 was proposed. Firstly, Retinexnet network was used to enhance the original images to improve the image contrast and clarity. Then, lane line detection and segmentation were carried out based on the segmentation network Deeplabv3, at the same time, the shape analysis algorithm was introduced. Experimental results showed that the proposed method could segment the lane lines well in the changeable road environment and at night, and the forward detection rate of the proposed method could reach up to 94.6%, which improved the detection accuracy by about 2 percentage points compared with direct Deeplabv3 network.
Fast and Robust Stratified Self-Calibration Using Time-Difference-Of-Arrival Measurements

In this paper we study the problem of estimating receiver and sender positions using time-difference-of-arrival measurements. For this, we use a stratified, two-tiered approach. In the first step the problem is converted to a low-rank matrix estimation problem. We present new, efficient solvers for the minimal problems of this low-rank problem. These solvers are used in a hypothesis and test manner to efficiently remove outliers and find an initial estimate which is used for the subsequent step. Once a promising solution is obtained for a sufficiently large subset of the receivers and senders, the solution can be extended to the remaining receivers and senders. These steps are then combined with robust local optimization using the initial inlier set and the initial estimate as a starting point. The proposed system is verified on both real and synthetic data.
Face recognition from image patches using an ensemble of CNN-local mesh pattern networks

Face recognition is one of the superior methods among the biometric identification system. Demand for face recognition is increased recently because of the availability of data from security and surveillance cameras. This work proposes a face recognition method using an ensemble of convolutional neural network trained on original and the textural feature maps from image patches. The original face image divided into smaller patches and a textural descriptor is applied on them. The textural feature map image of all patches and the original patches are input to two pre-trained ResNet50 networks. Input images are used to fine-tune the networks. The predictions of the two networks are combined by majority voting ensemble technique to get the final labels. The proposed system achieves an accuracy of 98.05%. The system performance is also evaluated using different measures like precision, recall, and F-measure.
Learning Software Quality Assurance with Bricks

Software Quality Assurance (SQA) and Software Process Improvement (SPI) are topics of crucial importance for software engineers; however, teaching them in a lecture room comes with several limitations due to lack of practical experience. With that in mind, we created KUALI-Brick, a LEGO-based activity that brings SQA and SPI concepts together applying them in order to successfully build a LEGO city. This hands-on activity has been carried out in a fourth-year Software Engineering course at the University of Canterbury, with current results showing high levels of fun, increased engagement and an improved learning experience. We present a step-by-step guide to replicate the activity as well as lessons learned after conducting the activity for three consecutive years.
The Professionalization of Compliance

: In the last three decades, compliance has grown from an ad hoc job function into a sophisticated profession. In this chapter, I describe the development of the field and current challenges in the design of effective compliance programs. I discuss several opportunities, particularly around the use of data and analytics, that have the potential to enhance compliance efficiency and efficacy.
Coping with Messiness in Ethnography: Authority, Bias and Immersion in ethnographic Fieldwork in the non-Western World

Ethnography has firmly established its position in the Human-Computer Interaction (HCI) community. Many studies have benefited from following ethnographic approaches to arrive at a grounded and comprehensive understanding of the respective research context. Applying that to the non-Western world, however, comes with challenges for researchers. Aside from ethical concerns which have been addressed in the past, we want to use this workshop to foster conversations and discussions on authority, bias and immersion when conducting ethnographic field work in the non-Western world – especially as a Western researcher. The main objective of this workshop is to exchange experiences and to identify common aspects and ways of overcoming, coping with or even embracing the messiness in ethnographic work and derive guidelines based on these discussions.
Let’s Sketch! A Hands-on Introductory Course on Sketching in HCI

Sketching is a universal activity but an often overlooked skill – yet it can benefit researchers and practitioners in HCI – sketching has proven to be a valuable addition to skill-sets in academic and industrial contexts. Many individuals lack the confidence to take up sketching after years of non-practice, but it is possible to re-learn, improve, and apply this skill in practical ways. This course is a sketching journey, from scribbles and playful interpretations, to helpful theory, storytelling and practical applications. Attending individuals will learn techniques and applied methods for utilizing sketching within the context of HCI, guided by experienced instructors.
LFIR2Pose: Pose Estimation from an Extremely Low-resolution FIR image Sequence

In this paper, we propose a method for human pose estimation from a Low-resolution Far-InfraRed (LFIR) image sequence captured by a 16 × 16 FIR sensor array. Human body estimation from such a single LFIR image is a hard task. For training the estimation model, annotation of the human pose to the images is also a difficult task for human. Thus, we propose the LFIR2Pose model which accepts a sequence of LFIR images and outputs the human pose of the last frame, and also propose an automatic annotation system for the model training. Additionally, considering that the scale of human body motion is largely different among body parts, we also propose a loss function focusing on the difference. Through an experiment, we evaluated the human pose estimation accuracy with an original data set, and confirmed that human pose can be estimated accurately from an LFIR image sequence.
SCA Net: Sparse Channel Attention Module for Action Recognition

Channel attention has shown its great performance recently when it was incorporated into deep convolutional neural networks. However, existing methods usually require extensive computing resources due to their involuted structure, which further increase the computational burden of 3D CNNs. In this paper, a lightweight sparse channel attention (SCA) module implemented by efficient group convolution is proposed, which adopts the idea of sparse channel connection and involves much fewer parameters but brings clear performance gain. Meanwhile, to solve the lack of local channel interaction brought by group convolution, a dominant function called Aggregate-Shuffle-Diverge (ASD) is leveraged to enhance information flow over each group with no additional parameters. We also adjust the existing mainstream 3D CNNs by employing 3D convolution factorization, so as to further reduce the parameters. Our SCA module can be flexibly incorporated into most existing 3D CNNs, all of which can achieve a perfect trade-off between performance and complexity on action recognition task with factorized I3D or 3D ResNext backbone networks. The experimental results also indicate that the resulting network, namely, SCA Net can achieve outstanding performance on UCF-101 and HMDB-51 datasets.
Velocity Guided Amplification of View Rotation for Seated VR Scene Exploration

This paper presents a velocity guided amplification approach for head rotation in VR headsets, enabling the amplification factor to be dynamically changed according to head rotation velocity, and keep it within the comfort range. We first conducted experiments to investigate the effects of head rotation velocity on human sense towards the virtual view rotation and propose the velocity guided amplification approach. We then performed extensive evaluation by comparing our amplification method with existing linear mapping methods that use constant amplification factors. Results demonstrate that users achieve the best performance on given tasks with less discomfort when using our technique.
Designing SciberPunks as Future Personas for More than Human Design

In this case study we describe the evolution of a new method for creating future personas, called SciberPunks, for use in sustainable city design scenarios. SciberPunks channel the voice of the environment and have special abilities for feeling and expressing data, such as the ability to taste it, or communicate it through living tattoos on the skin. The aim was to examine how environmental data could act as a bridge between people and nature, to encourage empathy towards 'more-than-human' perspectives. We engaged 5 participants in activities designed to lead them through a process of engaging with information and data in the process of building their personas. The activities utilised arts-based methods as we were interested in the experiential aspects of engaging with data and how we might foster creative and sensory experiences with it. Activities included drawing, writing and performing and were framed by a single story that took participants on a journey through time: past, present and future. Activities took place online, due to COVID-19. Overall, participants produced 5 characters, including a shaman, a shape-shifter and a fairy, all with special skills for connecting to nature and/or to each other.
Network and Content-Dependent Bitrate Ladder Estimation for Adaptive Bitrate Video Streaming

In this paper, a method is presented to estimate bitrate ladders on the basis of both content complexity and network traces. Unlike previous methods, the proposed algorithm is independent of the adaptive bitrate (ABR) control scheme used by the client. The proposed approach was evaluated in a simulation on the basis of real-life throughput measurements over a large period of time and across multiple ABR control schemes. Unlike in previous studies, perceptual quality metrics that account for the effect of both coding and stalling on video quality are used in the evaluations. Results show that the proposed method performs consistently over time and across ABR control schemes. Thus, this method will enable video streaming service providers to encode videos by using their knowledge of the network performance. This will ultimately enable network usage to be optimized and the quality of experience to be improved.
Mitigating Remote Code Execution Vulnerabilities: A Study on Tomcat and Android Security Updates

The security of web-applications has become increasingly important in recent years as their popularity has grown exponentially. More and more web-based enterprise applications deal with sensitive personal and private information, which, if compromised, can not only lead to system downtime, but can also cause mean millions of dollars in damages to the organization. It is critical to protect web-applications from the constant onslaught of hacker attacks. Remote Code Execution (RCE) attacks are one of the most prominent security threats for software systems, especially Java-based systems. In the current study, we have studied the security update reports for RCE vulnerabilities published by two Java-based projects: Apache Tomcat and Android. We analyzed and categorized the code-fixes (i.e., patches/updates) that were applied to mitigate/fix fifty-one (51) RCE vulnerabilities in the two above-mentioned Java projects. Our analysis showed that a significant majority of the RCE vulnerabilities found in Java projects can be mitigated with just five (5) types/categories of code-fixes. Overall, our goal was to study RCE vulnerabilities in an effort to provide programmers with a handy list of code-fixes, thus making it easier for them to effectively mitigate known RCE vulnerabilities in their own Java-based applications.
A Fingerprint Image Enhancement Method Based on Anisotropic Diffusion and Shock Filtering

Fingerprint image enhancement is an important part of the pre-processing step of the fingerprint recognition process. The purpose is to repair the degraded fingerprint image, improve the contrast of the fingerprint image, and connect the broken lines. After careful study of the anisotropic diffusion (AD) algorithm, combined with the shock filtering (SF) algorithm, a two-stage fingerprint image enhancement algorithm based on anisotropic diffusion and shock filtering (ADSF) is proposed. The experimental results show that the method can not only preserve the advantages of repairing the ridge gap of coherent enhanced diffusion, but also sharpen the edge of the fingerprint ridge and enhance the contrast of the ridge line.
Feasibility Analysis of Tactical Radio Station Communication Behaviors Cognition

In the field of electronic countermeasures, it is extremely difficult to effectively identify communication behaviors of tactical radio station by analyzing communication protocols of the non-cooperative organization. Therefore, this work mainly explores the possibility of directly figuring out radio station communication behaviors from the perspective of the physical layer, conducts a theoretical analysis with the US military protocol standard as an example, and simulates the communication behaviors signals. Finally, combined with the autoencoder, experiment results show the feasibility of grasping communication behaviors from the perspective of the physical layer.
A Report on Automatic Face Recognition: Traditional to Modern Deep Learning Techniques

In the era of smart digital transformation, automatic face recognition is the way of identification and verification of a person in many applications of security and authentication. If the person's 2D still image or 3D video frame is taken under controlled lighting and frontal face poses, then today automatic face recognition is a solved problem with more than 98 % accuracy. Under ideal condition of images, automatic face recognition outperforms manual face recognition rate. But the problem of uncontrolled illumination, occlusion, tilting of faces, different expressions of faces, use of accessories and hair color, growth of facial hair, aging effect of a person and low-resolution images makes automatic face recognition underperform; it gets defeated by human. Unless this problem is solved on real time, automatic face recognition system cannot be trusted for crucial security applications like e-passport, fraud detection, counter terrorism and mug-shot verification etc. Extensive research is underway to improve this technology. Traditionally Harr cascade classifier methods, histogram of oriented gradients (HoG), principal component analysis (PCA), Eigen-faces, and support vector machine (SVM) classifier are used in face recognition. Now modern deep learning innovation has superseded those traditional machine learning techniques with more computing power in GPUs and TPUs to tackle all variations in input image quality and ever-growing face database. The goal of this paper is to report the journey of face recognition system from traditional to modern techniques to increase precision and recall of the system.
Importance Evaluation of Movie Aspects: Aspect-Based Sentiment Analysis

Sentiment analysis (SA) is useful to analyze movie reviews since sentiment within audience comments is the feedback of movies. The most important aspect of the movie is explored by investigating the sentiment of different aspects of movie reviews of IMDb. Four main movie aspects were evaluated, which include actors, directors, plot, and music. Both sentiment orientation and intensity will be determined with the fine-grained analysis. A filter is created to extract aspect related words within a lexicon modified by us and compute sentiment ratings with VADER. This investigation showed that reviews on the plot have the most significant impact, slightly higher than those of actors. Thus, the experiment gives the result that a good plot is a proxy for a movie to succeed.
Rankings of Countries Based on Rankings of Universities

Although many methods have been designed for ranking universities, there is no suitable system that focuses on the ranking of countries based on the performance of their universities. The overall ranking of the universities in a region can indicate the growth of interests in science among the people of that land. This paper introduces a novel ranking mechanism based on the rankings of universities. Firstly, we introduce and discuss two new rankings of countries, based on the rank of their universities. Secondly, we create rankings of countries according to the selected method, based on the top 12000 universities in webometrics.info (January 2012) and compare rankings of countries in 4 editions (January 2012 to July 2013). Firstly, we introduce two new methods of ranking countries based on their university rankings, Weighted Ranking (WR) and Average Ranking (AR). Secondly, we discuss how the introduced ranking systems, perform in ranking countries based on the two years of data. Thirdly, we choose QS (http://www.topuniversities.com) and webometrics.info as two different classification systems for comparing rankings of countries, based on the top 500 universities in these rankings. Results indicate that the methodology can be used to show the quality of the whole universities of each country used to compare rankings of countries in practice compare to other countries in the world.