Reviewerly/exHarmony huggingface dataset:
Energy-Efficient RAR3 Password Recovery with Dual-Granularity Data Path Strategy

Password recovery tools are used to recover lost passwords and regain access to precious data. Due to the extremely large time and energy consumption of password recovery, efficient hardware accelerators are demanded to accelerate the recovery process. However, simple and regular data interconnect paths between data sources and non-blocking hash pipelines are hard to construct for Roshal ARchive version 3 (RAR3) algorithm based on field programmable gate array (FPGA) devices. The difficulty comes from the fact that the message format of the hash pipeline inputs vary with the password length and the secure hash algorithm 1 (SHA-1) iteration phase. To attack this problem, a dual-granularity data path adjustment strategy is proposed to eliminate the randomness of message block formats caused by the irregularity of password length and to efficiently schedule the data through the regular data interconnect paths. Experimental results show that the proposed hardware accelerator for RAR3 password recovery is 3.3 × more energy-efficient than a state-of-the-art implementation Hashcat on NVIDIA GTX 1060 GPU.
Using Machine Learning to Prioritize Automated Testing in an Agile Environment

Automated software testing is an integral part of most Agile methodologies. In the case of the Scrum Agile methodology, the definition of done includes the completion of tests. As a software project matures, however, the number of tests increases to such a point that the time required to run all the tests often hinders the speed in which artifacts can be deployed. This paper describes a technique of using machine learning to help prioritize automated testing to ensure that tests which have a higher probability of failing are executed early in the test run giving the programmers an early indication of problems. In order to do this, various metrics are collected about the software under test including Cyclomatic values, Halstead-based values, and Chidamber-Kemere values. In addition, the historical commit messages from the source code control system is accessed to see if there had been defects in the various source classes previously. From these two inputs, a data file can be created which contains various metrics and whether or not there had been defects in these source files previously. This data file can then be sent to Weka to create a decision tree indicating which measurements indicate potential defects. The model created by Weka can then then be used in future to attempt to predict where defects might be in the source files and then prioritize testing appropriately.
FingMag

Interacting with a smartwatch is difficult owing to its small touchscreen. A general strategy to overcome the limitations of the small screen is to increase the input vocabulary. A popular approach to do this is to distinguish fingers and assign different functions to them. As a finger identification method for a smartwatch, we propose FingMag, a machine-learning-based method that identifies the finger on the screen with the help of a ring. For this identification, the finger's touch position and the magnetic field from a magnet embedded in the ring are used. In an offline evaluation using data collected from 12 participants, we show that FingMag can identify the finger with an accuracy of 96.21% in stationary geomagnetic conditions.
Family-Based Sleep Technologies

Sleep is a critical component of overall wellness, and pervasive and ubiquitous computing technologies have shown promise for allowing individuals to track and manage their sleep quality. However, sleep quality is also affected by interpersonal factors, especially for families with young children. In this study, we adopted a family informatics approach to understand opportunities and challenges for sleep technologies at the family level. We conducted home-based interviews with 10 families with young children, asking them about their current practices, values, and perceived role for technology. We describe challenges across three phases: bedtime, nighttime, and waking. We show that family-based sleep technologies may have the greatest impact by supporting family activities and rituals, encouraging children's independence, and providing comfort.
Interaction Design &amp; Prototyping for Immersive Analytics

Immersive Analytics is concerned with the design and evaluation of interactive next-generation interfaces that support human understanding, data analysis, and decision making. New immersive technologies present many opportunities for enhancing humans' experiences with data interaction, but also present many challenges, a subset of which are specific to the analytics domain. This workshop is centered around a set of group prototyping sessions, aimed at identifying new approaches to existing design challenges. In addition to giving perspective on opportunities and difficulties faced by future designers, these exercises will also explore new prototyping methods and tools for the design of interactive data-centric interfaces. This part-day workshop aims to build new ties between the existing immersive analytics community with researchers across many disciplines of the CHI community.
Towards Metrics of Meaningfulness for Tech Practitioners

HCI and the tech industry are increasingly interested in designing products that afford meaningful user experiences. Yet while several metrics of meaningfulness have been suggested, their utility and relevance for industry is unclear. We conducted workshops with 9 welfare technology companies and presented them with different metrics from existing literature in HCI, psychology, and industry, to evaluate their product and consider how relevant designing for meaningfulness is for them in their practice. We point to four metrics which companies considered particularly relevant, and suggest that further defining metrics of meaningfulness in HCI would be beneficial to both academia and industry.
Switch Mode Based Deep Fractional Interpolation in Video Coding

Fractional interpolation is a significant technology in motion compensation of video coding. It generates sub-pixel level reference samples in inter prediction to facilitate temporal redundancy removal between video frames. Recently, some methods explore to introduce the deep learning technique for fractional interpolation and have obtained better compression results. However, existing deep learning based methods still treat fractional interpolation as a traditional interpolation problem but fail to adjust it to the motion compensation scenario. In this paper, we design a switch mode based deep fractional interpolation method to introduce integer pixels of different positions to the interpolation of sub-pixel position samples. By switching between integer pixels of different positions, our method can infer the sub-pixels with smaller variations and achieve better fractional interpolation results. Consequently the motion compensation performance can be further improved. Experimental results have also verified the efficiency of the switch mode based deep fractional interpolation. Compared with High Efficiency Video Coding, our method achieves 2.8% bit saving on average and up to 6.2% bit saving under low-delay P configuration.
Spatial-Temporal Visual Attention Model for Video Quality Assessment

Objective assessment for videos has developed with mature technique. However, there are still some challenges, such as mimicking the behavior that human-beings do when they watch a video. In this paper, we introduce a model for full-reference (FR) video quality assessment (VQA) which is based on visual attention, optical flow, spatio-temporal slice (STS) images and center bias map. The experimental results show that our proposed model has better performance in wireless transmission distortion than other models in the LIVE video quality database.
Learning and Searching Pseudo-Boolean Surrogate Functions from Small Samples

When searching for input configurations that optimise the output of a system, it can be useful to build a statistical model of the system being optimised. This is done in approaches such as surrogate model-based optimisation, estimation of distribution algorithms, and linkage learning algorithms. This article presents a method for modelling pseudo-Boolean fitness functions using Walsh bases and an algorithm designed to discover the non-zero coefficients while attempting to minimise the number of fitness function evaluations required. The resulting models reveal linkage structure that can be used to guide a search of the model efficiently. It presents experimental results solving benchmark problems in fewer fitness function evaluations than those reported in the literature for other search methods such as EDAs and linkage learners.
Long-term secure management of large scale Internet of Things applications

The Internet of Things (IoT) is an emerging paradigm, where the ubiquitous devices can form the networks and connect to Internet. Security and management of devices remain open challenges for the IoT. We adopt the management framework of industry consortium THREAD, where a group of devices cooperating to accomplish the same task (called policy) are administrated by a designated device called commissioner and together they form a policy group. All these policy groups are further managed by a centralized server. In this hierarchical network structure, the secure distribution of the policy information, access control, and group key from the centralized server to commissioner and its peers become challenging given the pervasive, complex and heterogeneous properties of devices. To solve this, we propose protocols/mechanisms along with a variant of Broadcast Encryption called Secure Identity-Based Broadcast Encryption (SIBBE) and demonstrate the feasibility for secure distribution of information to the IoT devices from centralized server. Most of the related work is based on the Attribute-based Encryption (ABE) for IoT devices, which has scalability issues with the number of attributes. Our experimental and simulation evaluations show that our scheme outperforms the existing schemes in terms of scalability, latency, and communication overhead.
From Motions to Emotions

This work investigates classification of emotions from MoCap full-body data by using Convolutional Neural Networks (CNN). Rather than addressing regular day to day activities, we focus on a more complex type of full-body movement - dance. For this purpose, a new dataset was created which contains short excerpts of the performances of professional dancers who interpreted four emotional states: anger, happiness, sadness, and insecurity. Fourteen minutes of motion capture data are used to explore different CNN architectures and data representations. The results of the four-class classification task are up to 0.79 (F1 score) on test data of other performances by the same dancers. Hence, through deep learning, this paper proposes a novel and effective method of emotion classification, which can be exploited in affective interfaces.
Methods for Investigating Mental Models for Learners of APIs

Despite almost all software development involving application programming interfaces (APIs), there is surprisingly little work on how people use APIs and how to evaluate and improve the usability of an API. One possible way of investigating the usability of APIs is through the user's mental model of the API. Through discussions with the developers and UX practitioners at Google along with our own evaluations, a distributed data processing API called Apache Beam has been identified as difficult to use and learn. In our on-going study, we investigate methods for understanding users' mental models of distributed data processing and how this understanding can lead to design insights for Beam and its documentation. We present our novel approach, which combines a background interview with two natural programming elicitation segments: the first designed for participants to express a high level mental model of a data processing API while the second asks questions contextualized to a data processing task to see how participants apply their conceptual understanding to a more specific situation. Our method shows promise as pilot participants expressed a "dataflow" mental model that matched one way that Beam has been described, resulting in a potential design modification.
Classical random walk with memory versus quantum walk on a one-dimensional infinite chain

Quantum walk is a very useful tool for building quantum algorithms due to the faster spreading of probability distributions as compared to a classical random walk. Comparing the spreading of the probability distributions of a quantum walk with that of a mnemonic classical random walk on a one-dimensional infinite chain, we find that the classical random walk could have a faster spreading than that of the quantum walk conditioned on a finite number of walking steps. Quantum walk surpasses classical random walk with memory in spreading speed when the number of steps is large enough. However, in such a situation, quantum walk would seriously suffer from decoherence. Therefore, classical walk with memory may have some advantages in practical applications.
Innovative Education and Engagement Tools for Rheumatology and Immunology Public Engagement with Augmented Reality

Rheumatoid arthritis (RA) affects around 1% of the population, which places a heavy burden on society and has severe consequences for the individuals affected. The early diagnosis and implementation of disease-modifying anti-rheumatic drugs significantly increase the chance of achieving long-term sustained remission. Therefore, raising awareness of RA amongst the general public is important in order to decrease the time of diagnosis of the disease. Augmented reality (AR) can be tremendously valuable in a teaching and learning context, as the coexistence of real and virtual objects aids learners in understanding abstract ideas and complicated spatial relationships. It has also been suggested that it raises motivation in users through interactivity and novelty. In this chapter we explore the use AR in public engagement, and detail the design, development and evaluation of a blended learning experience utilising AR. A set of informative printed posters was produced, enhanced by an accompanying interactive AR application. The main user testing was conducted with 27 participants at a science outreach event at the Glasgow Science Centre. Findings report mean positive attitudes regarding all aspects of the study, highlighting the potential of AR for public engagement with topics such as RA.
Oversubscribing micro-clouds with energy-aware containers scheduling

Cloud computation is being pushed to the edge of the network, towards Micro-clouds, to promote more energy efficiency and less latency when compared to heavy resourced centralized datacenters. This trend will enable new markets and providers to fill the current gap. There are however challenges in this design: (i) devices have less resources, leading to a frequent use of oversubscription (ii) lack of economic incentives to both provider and application owner to cope with less than full requests fulfilled. To support this trend, the virtualization layer of Micro-clouds is currently dominated by containers, which have a small memory footprint and strong isolation properties. We propose an extension to Docker Swarm, a widely used containers orchestrator, with an oversubscribing scheduling algorithm, based on improving resources utilization to levels where the energy efficiency is maximized. This solution improves CPU and memory utilization over Spread and Binpack (Docker Swarm strategies). Although we introduce a small overhead in scheduling times, our solution manages to allocate more requests, with a successful allocation rate of 83% against 57% of current solutions, measured on the scheduling of real CPU- and memory-intensive workloads (e.g. Video encoding, Key-value storages and a Deep-learning algorithm).
Age invariant face recognition using Frangi2D binary pattern

The field of computer vision is devoted to discovering algorithms, data representations and computer architectures that embody the principles underlying visual capabilities. Computer vision is an interdisciplinary field that deals with how computers can be made for gaining high level understanding from digital images or videos. While very promising result has been shown on face recognition related problems, age invariant face recognition still relics a challenge. Facial appearance of a human varies over time, which results in substantial intra-class variations. In order to address this problem, we propose Frangi2D method for normalization, Linear Binary pattern (LBP) for feature extraction and Sparse Representation Classifier (SRC). Extensive results on a well-known public domain face aging dataset: MORPH. The experimental results show the superiority of our proposed method in age invariant face recognition.
MUSCLE

Smart contracts are applications that are deployed and executed on a blockchain's decentralised infrastructure. Many smart contract applications rely on data that resides outside the blockchain. However, while traditional web applications can communicate with trustworthy data sources directly through the Internet, this is not possible for smart contracts because their execution must be deterministic. Bringing external data into the blockchain has been a topic of research since the first introduction of Ethereum. A system that can provide this data to smart contracts is called an oracle. The primary requirement in designing oracles is that the authenticity of the data must be publicly verifiable, which can be achieved through signatures. However, transmitting data to the blockchain and performing the verification is costly, especially if applications require data from multiple sources. In that case, current approaches would need to retrieve the data from each source separately. In this paper, we present the concept of MUlti-Source oraCLE (MUSCLE) for retrieving data from multiple sources, which we believe to be the first to focus on the multi-source scenario. We implement five variants of MUSCLE, each using a different signature or aggregate signature scheme and compare their performance with two oracles that are based on TLS-N, which represents the current state of the art. Our results show that the ECDSA-based MUSCLE features the lowest total gas expenditure, while the BGLS-based oracle provides lower transaction and storage costs.
Centralized State Sensing using Sensor Array on Wearable Device

Signal acquisition from the wrist using wearable device is usually corrupted by noise, sometimes up to a level where the noise completely dominates the signal. In this paper, we use the acquisition of photoplethysmography (PPG) signal as an example to demonstrate our proposed algorithm implemented on a sensor array. To increase the chances of acquiring clean PPG signal, we develop a uniform linear sensor array located above the radial artery. We also develop a wearable device to integrate our sensor array and our proposed algorithm. We propose centralized state sensing (CSS), a centralized algorithm suited to our sensor array, to increase the efficiency in multi-sensor estimation. To support our proposed algorithm, we use heart rate estimation as an example and compare it with the estimation of a single sensor and the statistical mean of the estimation of multiple sensors. Experimental results demonstrate that our proposal achieve a reduction in mean absolute error of 26.8%, compared to the estimation result obtained using the average of the estimation of all sensors. Operating on a 80 MHz processor, our algorithm introduces a 8 ms overhead, making it highly suitable for wearable devices.
Robust maneuver strategy of observer for bearings‐only tracking

Abstract For the bearings‐only tracking problem, an observer's maneuver can enhance the accuracy of estimation. However, it may be idealistic to expect the observer to move in the free space during reconnaissance missions, because threats in the motion space can restrict the motion space of the observer. In this study, considering the constraint of threats avoidance on the motion space of the observer, an optimal maneuver strategy was proposed. Firstly, the adaptive cubature Kalman Filter method was established to improve the robustness and accuracy of state estimation. Then the finite horizon Markov Decision VProcess approach was improved to generate the optimal maneuver policy, in which the quantization method was responsible for discretizing the process and providing the transition matrix for Markov Decision Process, and the novel reward function was proposed as the criterion for optimization. Finally, the framework of the method was established. The numerical results verified the feasibility and advantage of the proposed method by comparing them with state of the art research in the field.
Total Variability Layer in Deep Neural Network Embeddings for Speaker Verification

The total variability model (TVM) has been extensively used as a tool to obtain a vector representation of the sources of variability present in a signal. However, recent studies have shown that embeddings derived from a deep neural network (DNN) architecture can provide significant performance improvement over TVM for the speaker verification task. In this letter, we show that TVM can also be reformulated in a manner that enables the integration of a DNN within the model. In addition, we show that this TVM architecture can also be incorporated as one of the layers within a DNN embedding system. Through experiments on speakers in the wild (SITW) corpus, we show that the inclusion of total variability layer in a DNN embedding system provides around 20% relative improvement in equal error rate performance.
A Visual Analytics Approach to Exploration of Hotels in Overlaid Drive-Time Polygons of Attractions

Given multiple attractions to go to, choosing hotels is a Bichromatic Reverser k Nearest Neighbor searching problem. Many efforts have been made to address the problem in the domain of spatial databases. However, most of them suffer from the difficulty of visual selection and comparison. To integrate human intuition into the analysis process and facilitate information communication with users, geospatial visualization techniques have been developed. Unfortunately, too much information on the map, such as hotels and attractions, and drive-time polygons, could overwhelm users. Following the focus+context principle of information visualization, we propose a visual analytics approach to provide users with the flexibility of making their hotel choice. It consists of three main components: an overlap-free and space-efficient clustering algorithm to reduce the searching space for attractions in the map view (MapView), visual comparison of attraction-hotel drive-time in brushable Small Multiples View (SMView), and the coordination between the two views. We demonstrate the utility of this approach and explain how MapView and SMView can help address the problem.
Nova: Diffused Database Processing Using Clouds of Components [Vision Paper]

Nova proposes a departure from today's complex monolithic database management systems (DBMSs) as a service using the cloud. It advocates a server-less alternative consisting of a cloud of simple components that communicate using high speed networks. Nova will monitor the workload of an application continuously, configuring the DBMS to use the appropriate implementation of a component most suitable for processing the workload. In response to load fluctuations, it will adjust the knobs of a component to scale it to meet the performance requirements of the application. The vision of Nova is compelling because it adjusts resource usage, preventing either over-provisioning of resources that sit idle or over-utilized resources that yield a low performance, optimizing total cost of ownership. In addition to introducing Nova, this vision paper presents key research challenges that must be addressed to realize Nova. We explore two challenges in detail.