Reviewerly/exHarmony huggingface dataset:
Direct propagation of network QoS distribution to subjective QoE for Video on Demand applications using VP9 codec

The increasing popularity of the Video on Demand (VoD) applications like Netflix and YouTube has caused a large increase in video streaming over the network. To increase subscriptions and avoid customer churn, service providers are keen to evaluate the end-user experience more effectively. In this paper, we present an approach that can be used to evaluate the distribution of the Quality of Experience (QoE) metric evaluated from the measured Quality of Service (QoS) distributions. By using the network QoS measurements of delay and jitter, we find the probability density functions (PDF) for these parameters. Focussing on VoD applications employing improved VP9 codec (as used by Netflix) we use subjective testing and regression to map QoE onto jitter. The resulting QoE model and theory of direct propagation of probability density function was used to evaluate the distribution of QoE. We found that the empirical delay and jitter measurements follow a lognormal distribution whose characteristic parameters, sigma ( σ) and mu ( μ), vary for different network providers in the UK. Our results indicate that, even for slight variations of the underlying jitter distribution, the QoE distribution shows significant variations. These variations were recorded and presented as confidence intervals for four leading UK network providers.
Motion-Planning for Unicycles using the Invariant-Set Motion-Planner

This paper adapts the invariant-set motion-planner to systems with unicycle-like dynamics. The invariant-set motion-planner is a motion-planning algorithm that uses the positive-invariant sets of the closed-loop dynamics to find a collision-free path to a desired target through an obstacle filled environment. The main challenge in applying the invariant-set motion-planner to unicycles is that the positive invariant sets of the unicycle under discontinuous feedback control have complex geometry. Thus, we develop numerically efficient mathematical tools for detecting collisions. We demonstrate the invariant-set motion-planner for unicycles in an automated perpendicular parking case study.
Remittances and corruption in Nigeria

Purpose In this paper, we investigate the impact of remittances on control of corruption in Nigeria for a period of 1986–2016. Design/methodology/approach The study uses ARDL modeling framework, dynamic OLS estimation, variance decomposition and impulse response analysis to examine the relationship between the two variables. Findings The study finds that remittances significantly improve the control of corruption in Nigeria. We further examine the robustness test of the results using dynamic OLS estimation, variance decomposition and impulse response analysis. Our results remain significant and consistent to the earlier one reported in ARDL framework which supports the extant literature. Practical implications Our study suggests that international remittances can be used, through the cross-border transfer of norms and practices, to significantly impact the socioeconomic progresses of a country by reducing corruption. Originality/value The existing studies on the relationship between corruption and remittances document conflicting results. In addition, study on corruption - remittances nexus that specifically focuses on any African country is largely absent despite the fact that most of the countries in the region are recognized as highly corrupt. This paper provides insights on how remittances can be used as part of tool kits to control corruption in African nation.
How can courts take into account the uncertainty in a likelihood ratio?

As legal practitioners and courts become more aware of scientific methods and evidence evaluation, they are demanding measures of the reliability of expert opinion. In particular, there are calls for error rates to accompany opinion evidence in comparative forensic sciences. While error rates or confidence intervals can be useful for those disciplines that claim to identify the source of a trace, the call for these statistical tools has extended to sciences that present opinions in the form of a likelihood ratio. In this article we argue against presenting both a likelihood ratio and numerical measures of its uncertainty. We explain how the LR already encapsulates uncertainty. Instead we consider how sensitivity analyses can be used to guide the presentation of LRs that are informative to the court and not unfair to defendants.
Green Containerized Service Consolidation in Cloud

In the presence of latency sensitive geo-distributed applications, users require fast service for their queries. Cloud computing provides physical servers from its data center in order to process user requests. The cloud data center consumes a huge amount of energy due to lack of management of the data center servers as the container-based service consolidation is a nontrivial task. Since the containers require less resource footprint, consolidating it in servers might make resource availability sparse. In order to reduce the energy consumption of the cloud data center, we have proposed a green container-based consolidation of the services so that the maximum number of servers can be put into idle mode without affecting the application quality of experience. The service consolidation problem has been formulated as an optimization problem considering minimization of total energy consumption of the data center as the objective, and an algorithm named Energy Aware Service consolidation using baYesian optimization (EASY) has been proposed to solve the optimization. We have evaluated the EASY algorithm in simulation using python. The experimental results have shown that EASY improves the total energy consumption of the data centers. This improvement comes at the cost of a small increase of service response time as there exists a trade-off between energy consumption and service response time.
Supervised Speaker Diarization Using Random Forests: A Tool for Psychotherapy Process Research

Speaker diarization is the practice of determining who speaks when in audio recordings. Psychotherapy research often relies on labor intensive manual diarization. Unsupervised methods are available but yield higher error rates. We present a method for supervised speaker diarization based on random forests. It can be considered a compromise between commonly used labor-intensive manual coding and fully automated procedures. The method is validated using the EMRAI synthetic speech corpus and is made publicly available. It yields low diarization error rates (M: 5.61%, STD: 2.19). Supervised speaker diarization is a promising method for psychotherapy research and similar fields.
Algorithm for Multi Keyword Search Over Encrypted Data in Cloud Environment

Cloud Computing offers storage resources as well as network and computing resources to the organizations. This eliminates the high infrastructure cost for the organizations that are using these services as they can now dynamically pay for these services, i.e., pay per use model, which is followed by most of the cloud providers. As the organization does not locally host these resources, these are comparatively far easier to manage and use than the traditional infrastructural resources. As a result of these factors, the popularity of cloud computing is increasing continuously. But this transfer of data and applications to the cloud server also creates some challenges. It poses problems that must be dealt with properly to ensure a secure cloud computing environment. As more and more sensitive data is being uploaded on the cloud in the present scenario, the privacy and security concerns associated with the data is continuously increasing. To address this, issue the data is stored on the cloud in the encrypted form. Also, as the amount of data stored is usually tremendous, so an efficient search scheme is also necessary. So here, we deal with two significant aspects of cloud computing: Encryption and Searching. We are proposing a secure and efficient encryption scheme to encrypt the data stored in the cloud as well as the queries along with a multi-keyword search scheme to search over the encrypted cloud data.
Reliability and Availability analysis of a cloud computing transition system under multiple failures

Purpose To analyze the performance of multistate cloud computing transition system through the various reliability measures is the purpose of this paper. Design/methodology/approach In this article, a mathematical model for a multistate cloud computing transition system with various types of failures has been analyzed by using the Markov process, supplementary variable technique and Laplace transformation. Findings Various reliability measures such that reliability, availability, mean time to failure (MTTF), mean time to repair and cost analysis have also been analyzed. This article presents some geographic illustrations for the practical utility of the model. Originality/value The authors developed a mathematical model to analyze the reliability of the cloud computing transition system by considering the possible failures.
Off-Shell Quantum Fields to Connect Dressed Photons with Cosmology

The anomalous nanoscale electromagnetic field arising from light–matter interactions in a nanometric space is called a dressed photon. While the generic technology realized by utilizing dressed photons has demolished the conventional wisdom of optics, for example, the unexpectedly high-power light emission from indirect-transition type semiconductors, dressed photons are still considered to be too elusive to justify because conventional optical theory has never explained the mechanism causing them. The situation seems to be quite similar to that of the dark energy/matter issue in cosmology. Regarding these riddles in different disciplines, we find a common important clue for their resolution in the form of the relevance of space-like momentum support, without which quantum fields cannot interact with each other according to a mathematical result of axiomatic quantum field theory. Here, we show that a dressed photon, as well as dark energy, can be explained in terms of newly identified space-like momenta of the electromagnetic field and dark matter can be explained as the off-shell energy of the Weyl tensor field.
Prism Removes Consensus Bottleneck for Smart Contracts

The performance of existing permissionless smart contract platforms such as Ethereum is limited by the consensus layer. Prism is a new proof-of-work consensus protocol that provably achieves throughput and latency up to physical limits while retaining the strong guarantees of the longest chain protocol. This paper reports experimental results from implementations of two smart contract virtual machines, EVM and MoveVM, on top of Prism and demonstrates that the consensus bottleneck has been removed. Code can be found at https://github.com/wgr523/prism-smart-contracts.
A systematic approach to multi-task learning from time-series data

There have been significant advances in machine learning due to the profusion in data collection and computing resources. However, the need for large annotated datasets to train machine learning models remains a problematic constraint. To address the limitation of annotated data for personalized prediction, we propose a framework to enrich annotated time-series (TS) sensing data by way of transfer learning with new multi-task learning (MTL) models. Compared to previous MTL approaches for TS, this work introduces three contributions. First, we propose a systematic method to examine the efficiency of MTL approaches by exploring options for three key characteristics of MTL with TS: the choice of features that efficiently capture temporally dynamic information, the similarity measure that effectively models the commonality and uniqueness across tasks being learned, and the choice of regularization for achieving the best tradeoff between a model's generalizability and accuracy. Second, we present an MTL deep learning model that is shown to achieve state-of-the-art performance for personalized human activity recognition from time-series. Experimental results on three benchmark activity recognition datasets and one activity recognition in-the-wild dataset show that the proposed framework provides performance gains over prior work while presenting a unified approach for designing MTL solutions for personalized time-series classification problems.
Memory corruption attacks within Android TEEs

Many security-critical services on mobile devices rely on Trusted Execution Environments (TEEs). However, due to the proprietary and locked-down nature of TEEs, the available information about these systems is scarce. In recent years, we have witnessed several exploits targeting all major commercially used TEEs, which raises questions about the capabilities of TEEs to provide the expected integrity and confidentiality guarantees. In this paper, we evaluate the exploitability of TEEs by analyzing common flaws from the perspective of an adversary. We provide multiple vulnerable TEE applications for OP-TEE, a reference implementation for TEEs, and elaborate on the steps necessary for their exploitation on an Android system. Our vulnerable examples are inspired by real-world exploits seen in-the-wild on commercially used TEEs. With this work, we provide developers and researchers with introductory knowledge to realistically assess the capabilities of TEEs. For these purposes, we also make our examples publicly available.
Extraction and Evaluation of Knowledge Entities from Scientific Documents

The goal of this workshop is to engage the related communities in open problems in the extraction and evaluation of knowledge entities from scientific documents. This workshop entitles this cutting-edge and cross-disciplinary direction Extraction and Evaluation of Knowledge Entity (EEKE), highlighting the development of intelligent methods for identifying knowledge claims in scientific documents, and promoting the application of knowledge entities. The website of this workshop is at: https://eeke2020.github.io/.
A Hybrid Approach to Procedural Generation of Roguelike Video Game Levels

Algorithmic generation of data, known as procedural content generation, is an attractive prospect within the game development industry as a means of creating infinitely fresh and varied content. In this paper, we present an approach to level generation for roguelike dungeon style levels, based on our examination of the suite of existing approaches used in formal research. Our generator aims to create simple dungeon style level layouts that are always playable. We utilise a hybrid technique combining context free grammars to generate a description of levels and a cellular automata inspired process to generate the physical space. The generator proves successful at consistently generating dungeon layouts that maintain completability at all times with sufficient variation, when accounting for the occasional need for corrective actions. We conclude that there is substantial value in hybrid approaches to automated level design and propose a new heuristic by which to assess dungeon style level content.
Automated Generation of FMEAs using SysML for Reliability, Safety, and Cybersecurity

In this paper, an automated FMEA generation capability using the SysML modeling language and described its application to a simple SCADA computer network was described. The outputs produced by the tool (implemented as a SysML plug-in) from this analysis were presented, and the resultant insights into the design were described. The fundamental innovation in our approach is the identification and enumeration of all failure propagation paths and the detailed documentation of the failure transformations, detection measures, mitigation measures and protective measures that can be applied to these devices to prevent or mitigate the impact of the anomaly. By doing so, we can expand the traditional FMEA approach to analysis of cyberattack vectors. Because our approach is automated and can be readily integrated into a system development effort using Model Based Systems Engineering (MBSE), the analysis can be readily repeated throughout the design and can be used frequently to assess a system design, identify weaknesses, and take corrective actions to create a more resilient and robust system.
AI Safety Landscape From short-term specific system engineering to long-term artificial general intelligence

AI Safety is an emerging area that integrates very different perspectives from mainstream AI, critical system engineering, dependable autonomous systems, artificial general intelligence, and many other areas concerned and occupied with building AI systems that are safe. Because of this diversity, there is an important level of disagreement in the terminology, the ontologies and the priorities of the field. The Consortium on the Landscape of AI Safety (CLAIS) is an international initiative to create a worldwide, consensus-based and generally-accepted knowledge base (online, interactive and constantly evolving) of structured subareas in AI Safety, including terminology, technologies, research gaps and opportunities, resources, people and groups working in the area, and connection with other subareas and disciplines. In this note we summarise early discussions around the initiative, the associated workshops, its current state and activities, including the body of knowledge, and how to contribute. On a more technical side, I will cover a few spots in the landscape, from very specific and short-term safety engineering issues appearing in specialised systems, to more long-term hazards emerging from more general and powerful intelligent systems.
Artificial Intelligence for Video-based Learning at Scale

Video-based learning (VBL) is widespread; however, there are numerous challenges when teaching and learning with video. For instructors, creating effective instructional videos takes considerable time and effort. For students, watching videos can be a passive learning activity. Artificial intelligence (AI) has the potential to improve the VBL experience for students and teachers. This half-day workshop will bring together multi-disciplinary researchers and practitioners to collaboratively envision the future of VBL enhanced by AI. This workshop will be comprised of a group discussion followed by a presentation session. The goal of the workshop is to facilitate the cross-pollination of design ideas and critical assessments of AI approaches to VBL.
BlurNet: Defense by Filtering the Feature Maps

Recently, the field of adversarial machine learning has been garnering attention by showing that state-of-the-art deep neural networks are vulnerable to adversarial examples, stemming from small perturbations being added to the input image. Adversarial examples are generated by a malicious adversary by obtaining access to the model parameters, such as gradient information, to alter the input or by attacking a substitute model and transferring those malicious examples over to attack the victim model. Specifically, one of these attack algorithms, Robust Physical Perturbations (RP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> ), generates adversarial images of stop signs with black and white stickers to achieve high targeted misclassification rates against standard-architecture traffic sign classifiers. In this paper, we propose BlurNet, a defense against the RP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> attack. First, we motivate the defense with a frequency analysis of the first layer feature maps of the network on the LISA dataset, which shows that high frequency noise is introduced into the input image by the RP <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> algorithm. To remove the high frequency noise, we introduce a depthwise convolution layer of standard blur kernels after the first layer. We perform a blackbox transfer attack to show that low-pass filtering the feature maps is more beneficial than filtering the input. We then present various regularization schemes to incorporate this lowpass filtering behavior into the training regime of the network and perform white-box attacks. We conclude with an adaptive attack evaluation to show that the success rate of the attack drops from 90% to 20% with total variation regularization, one of the proposed defenses.
Natural logic knowledge bases and their graph form

This paper describes how knowledge bases can be represented in and reasoned with in natural logic. Natural logic is a regimented fragment of natural language possessing a well-defined logical semantics. As such, natural logic may be considered an attractive alternative among the various knowledge representation logics such as description logics. Our version of natural logic expands formal ontologies with affirmative propositions expressing a variety of relationships between concepts. It comprises (nested) restrictive relative clauses and prepositional phrases and, as a new construct, adverbial prepositional phrases. The natural logic knowledge base is to be used for deductive query answering applying inference rules. This is facilitated by introduction of Datalog as an embedding meta-logic. The inference rules are stated in Datalog and act directly on the natural logic formulations. The knowledge base propositions are decomposed into a graph form enabling path finding between concepts. The examples in the paper are derived from text source life-science descriptions.
Systems Thinking Exercises in Computing Education

Integrating sustainability in computing education entails broadening the scope of the education, but how can that be done while maintaining student engagement? Climate change and species extinction can appear far removed from data structures and algorithms to say the least. In our ongoing work of integrating sustainability in our Media Technology programme, we have addressed this gap by introducing systems thinking games and activities to broaden the scope, as well as by situating the issues addressed in the course in relation to their future profession. In this paper, we present our experiences of introducing and playing systems thinking games, how the systems thinking exercise sessions were conducted, outcomes of the sessions and finally some lessons learnt. Furthermore, we present and analyse changes we did to the exercises and that led to a richer material for discussions in the classroom.
Differential Evolution with Reversible Linear Transformations

Differential evolution (DE) is a well-known type of evolutionary algorithms (EA). Similarly to other EA variants it can suffer from small populations and loose diversity too quickly. This paper presents a new approach to mitigate this issue: We propose to generate new candidate solutions by utilizing reversible linear transformations applied to a triplet of solutions from the population. In other words, the population is enlarged by using newly generated individuals without evaluating their fitness. We assess our methods on three problems: (i) benchmark function optimization, (ii) discovering parameter values of the gene repressilator system, (iii) learning neural networks. The empirical results indicate that the proposed approach outperforms vanilla DE and a version of DE with applying differential mutation three times on all testbeds.