Reviewerly/exHarmony huggingface dataset:
On efficiency of artifact lookup strategies in digital forensics

In recent years different strategies have been proposed to handle the problem of ever-growing digital forensic databases. One concept to deal with this data overload is data reduction, which essentially means to separate the wheat from the chaff, e.g., to filter in forensically relevant data. A prominent technique in the context of data reduction are hash-based solutions. Data reduction is achieved because hash values (of possibly large data input) are much smaller than the original input. Today's approaches of storing hash-based data fragments reach from large scale multithreaded databases to simple Bloom filter representations. One main focus was put on the field of approximate matching, where sorting is a problem due to the fuzzy nature of the approximate hashes. A crucial step during digital forensic analysis is to achieve fast query times during lookup (e.g., against a blacklist), especially in the scope of small or ordinary resource availability. However, a comparison of different database and lookup approaches is considerably hard, as most techniques partially differ in considered use-case and integrated features, respectively. In this work we discuss, reassess and extend three widespread lookup strategies suitable for storing hash-based fragments: (1) Hashdatabase for hash-based carving (hashdb), (2) hierarchical Bloom filter trees (hbft) and (3) flat hash maps (fhmap). We outline the capabilities of the different approaches, integrate new extensions, discuss possible features and perform a detailed evaluation with a special focus on runtime efficiency. Our results reveal major advantages for fhmap in case of runtime performance and applicability. hbft showed a comparable runtime efficiency in case of lookups, but hbft suffers from pitfalls with respect to extensibility and maintenance. Finally, hashdb performs worst in case of a single core environment in all evaluation scenarios. However, hashdb is the only candidate which offers full parallelization capabilities, transactional features, and a Single-level storage.
Typing on Split Keyboards with Peripheral Vision

Split keyboards are widely used on hand-held touchscreen devices (e.g., tablets). However, typing on a split keyboard often requires eye movement and attention switching between two halves of the keyboard, which slows users down and increases fatigue. We explore peripheral typing, a superior typing mode in which a user focuses her visual attention on the output text and keeps the split keyboard in peripheral vision. Our investigation showed that peripheral typing reduced attention switching, enhanced user experience and increased overall performance (27 WPM, 28% faster) over the typical eyes-on typing mode. This typing mode can be well supported by accounting the typing behavior in statistical decoding. Based on our study results, we have designed GlanceType, a text entry system that supported both peripheral and eyes-on typing modes for real typing scenario. Our evaluation showed that peripheral typing not only well co-existed with the existing eyes-on typing, but also substantially improved the text entry performance. Overall, peripheral typing is a promising typing mode and supporting it would significantly improve the text entry performance on a split keyboard.
Visualizing Multidimensional Health Status of Data Centers

Monitoring data centers is challenging due to their size, complexity, and dynamic nature. This project proposes a visual approach for situational awareness and health monitoring of high-performance computing systems. The visualization requirements are expanded on the following dimensions: (1) High performance computing spatial layout, (2) Temporal domain (historical vs. real-time tracking), and (3) System health services such as temperature, CPU load, memory usage, fan speed, and power consumption. To show the effectiveness of our design, we demonstrate the developed prototype on a medium-scale data center of 10 racks and 467 hosts. The work was developed using feedback from both industrial and acadamic domain experts.
Quantum interference of multi-photon at beam splitter with application in measurement-device-independent quantum key distribution

Abstract Despite the relative simplicity of the traditional Hong–Ou–Mandel (HOM) interferometer setup, it is of fundamental interest in various quantum optics applications and quantum information technologies. In this article, multi-photon interference using the original HOM interferometer setup is analyzed. More specifically, for any photon number state with Gaussian spectral distribution entering the beam splitter, the general analytical solution is calculated. The result is then used to study the coincident probability for coherent sources (laser) and squeezed coherent state. We also look into the potential benefits of implementing the squeezed coherent state in discrete-variable Measurement-Device-Independent Quantum Key Distribution and find that by optimizing the squeezing parameter, the error rate can be significantly reduced. This in turn also enhances the secret key rate performance over the coherent state.
ERP: An elastic resource provisioning approach for cloud applications

Elasticity is the key technique to provisioning resources dynamically in order to flexibly meet the users' demand. Namely, the elasticity is aimed at meeting the demand at any time. However, the aforementioned approaches usually provision virtual machines (VMs) in a coarse-grained manner just by the CPU utilization. Actually, two or more elements are needed for the performance metric, including the CPU and the memory. It is challenging to determine a suitable threshold to efficiently scale the resources up or down. In this paper we present an elastic scaling framework that is implemented by the cloud layer model. First we propose the elastic resource provisioning (ERP) approach on the performance threshold. The proposed threshold is based on the Grey relational analysis (GRA) policy, including the CPU and the memory. Secondly, according to the fixed threshold, we scale up the resources from different granularities, such as in the physical machine level (PM-level) or virtual machine level (VM-level). In contrast, we scale down the resources and shut down the spare machines. Finally, we evaluate the effectiveness of the proposed approach in real workloads. The extensive experiments show that the ERP algorithm performs the elastic strategy efficiently by reducing the overhead and response time.
Graph-based word sense disambiguation in Telugu language

In Natural Language Processing, word sense disambiguation (WSD) is an open challenge which improves the performance of the applications such as machine translation and information retrieval system. Many verbal languages will have many ambiguous words. The meaning of these ambiguous words differ per context. To choose the correct meaning of the word in the given context is known as WSD. In this article, the proposed work is to develop a WSD system using machine learning technique and knowledge-based approach for Telugu language. The knowledge resource used to develop the WSD system is Lexical Knowledge Base (LKB). The efficiency of WSD system is good when compared with other unsupervised approaches.
High Speed and Low Area Complexity Extended Euclidean Inversion Over Binary Fields

Finite field arithmetic is the building block of public-key cryptographic schemes, which are used widely for security and privacy of consumer electronics hardware and software systems. Since inversion is the most expensive finite field arithmetic operation, this paper proposes a novel, fast, and compact inverter over binary fields based on the traditional extended Euclidean algorithm (EEA). The proposed design outperforms the reported inverters in terms of area and speed. A modified EEA algorithm is presented and the design space of inversion over GF(2m) is explored in order to allow for parallelism and concurrency, resolving the disturbing issues of variables alignment, before and during each inversion iteration. Polynomial division and multiplication are revisited, in order to derive their iterative equations, which are suitable for systolic array implementation. Then, a concurrent divider/multiplier is developed using a systematic methodology, while the resulting systolic architecture is utilized to build the EEA-based inverter. Finally, the complexity of the proposed design is analyzed and compared with efficient inverters in the literature, showing the lowest area-time complexity.
Failing with Style

Failure is a common artefact of challenging experiences, a fact of life for interactive systems but also a resource for aesthetic and improvisational performance. We present a study of how three professional pianists performed an interactive piano composition that included playing hidden codes within the music so as to control their path through the piece and trigger system actions. We reveal how apparent failures to play the codes occurred for diverse reasons including mistakes in their playing, limitations of the system, but also deliberate failures as a way of controlling the system, and how these failures provoked aesthetic and improvised responses from the performers. We propose that creative and performative interfaces should be designed to enable aesthetic failures and introduce a taxonomy that compares human approaches to failure with approaches to capable systems, revealing new creative design strategies of gaming, taming, riding and serving the system.
Developing E-Module for fluids based on problem-based learning (PBL) for senior high school students

Technology in Information and Communication is growing rapidly. It also brings a tremendous impact on education. The aim of this research was developing e-modul based on Problem Based Learning. This research develops android-assisted learning media (e-module) which focuses on fluids materials for grade XI senior high school students. The research method used is Research and Development (R & D) with the ADDIE models (Analysis, Design, Development, Implement, and Evaluation). The validation results from the physics material experts was 86,3% with excellent interpretation, validation by the expert of instructional media was 83,3% with very good interpretation, validation result from the learning expert was 84,8% with very good interpretation and result of legibility test was 83% with very good interpretation. This indicate that e-module for fluids materials can be used by students as a material which fun and adds the knowledge for students.
Analysing compliance and application of usability guidelines on efficient and understandable controls

Usability is the ease with which a user can navigate through a website. Your goal is to improve the user experience during your interaction with the website. Currently, there are many guidelines and recommendations whose objective is to enhance the user experience. But no standard or list includes these recommendations in a single guide. Usability, unlike accessibility, can be more intuitive. Because the developers themselves are in turn users and can use intuition during the development of websites. One can think that there is an innate behavior during the use of the usability guidelines and that therefore a user guide is not necessary. This research aims: 1) to collect all the usability guidelines and define their objectives. 2) seeks to find out if there is indeed an innate behavior of developers when they apply these guidelines. This research presents usability guidelines compiled and classified into five groups: Recommendations to reduce "noise" Follow conventions Give information quickly and comprehensible Efficient and understandable controls for users to enter information Give answers descriptive and understandable to the actions of the users The purpose of the work is to analyze the recommendations (4) Data entry, efficient and understandable controls for users to enter information. To analyze this objective, the behavior of 20 web developers without training or experience in usability is studied. The experiment aims to reach conclusions about whether there is innate behavior on the part of IT engineers. Or if they need specific training. The objectives are: 1) to know which recommendations are the most forgotten by web developers. 2) to see the importance of the recommendations, in web developers view.
Nostalgin

What did it feel like to walk through a city from the past? In this work, we describe Nostalgin (Nostalgia Engine), a method that can faithfully reconstruct cities from historical images. Unlike existing work in city reconstruction, we focus on the task of reconstructing 3D cities from historical images. Working with historical image data is substantially more difficult, as there are significantly fewer buildings available and the details of the camera parameters which captured the images are unknown. Nostalgin can generate a city model even if there is only a single image per facade, regardless of viewpoint or occlusions. To achieve this, our novel architecture combines image segmentation, rectification, and inpainting. We motivate our design decisions with experimental analysis of individual components of our pipeline, and show that we can improve on baselines in both speed and visual realism. We demonstrate the efficacy of our pipeline by recreating two 1940s Manhattan city blocks. We aim to deploy Nostalgin as an open source platform where users can generate immersive historical experiences from their own photos.
Lake

Mobile app designers aim to develop the best mobile software interfaces in the least amount of time, and rely on testing ideas with prototypes in lieu of building costly, fully functioning applications. Yet, designers cannot effectively prototype some complex app experiences, including augmented reality applications like Pokémon GO, because existing tools lack the needed features, or because prototyping in them is too time intensive to be feasible. To solve this problem, we introduce Lake, a mobile application prototyping tool that enables the creation of complex mobile applications with the same ease as paper prototyping. By leveraging the Wizard of Oz technique used in paper prototyping in our digital medium, we enable designers to prototype at the same low cost as paper, but at a much higher fidelity. Through a pilot study (N=6), we find that designers are able to gather organic in-context feedback from complex prototypes made with Lake.
Efficient Region of Interest Detection for Liver Segmentation using 3D CT Scans

Deep learning has become a methodology of choice in medical imaging; one of the applications being classification tasks. The research presented in this paper aims to obtain a region of interest for liver segmentation with the aid of a convolutional neural network to classify 2D slices of a 3D CT volume. This is done by classification of slices to detect slices containing the pelvis and chest so that they can be removed while maintaining the abdomen within which the liver occurs. The presented approach is evaluated on the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2007 grand challenge datasets and the evaluation metrics used are accuracy, recall and precision. The presented approach proved to perform well and the classification models achieved an accuracy rate of 0.99 for pelvis slice classification and 0.97 for chest slice classification.
Multisensory Virtual Environment for Fire Evacuation Training

Growing organizational safety awareness is propelling the interest in development of novel solutions for fire training. Significant effort in this area has previously gone into designing various immersive virtual reality (VR) systems in hopes of enabling safe exploration and experiencing the consequences of ones actions in scenarios that would be too hazardous in the real life. Yet, the fact that VR generally lacks the sensory feedback of a real life fire situation has been found to impede a sense of realism and validity of such experiences. As part of our efforts to mitigate this issue, we now present a new prototype fire evacuation simulator designed to deliver not just audiovisual feedback, but likewise real-time heat and scent stimulation.
Computational Modeling in Human-Computer Interaction

We propose a workshop on rapidly emerging topic of Computational Modeling in HCI to address the challenges of increasing complexity of human behaviors we are able to track and collect today. The goal of this workshop is to reconcile two seemingly competing approaches to computational modeling: theoretical modeling, which seeks to explain behaviors vs. algorithmic modeling, which seeks to predict behaviors. The workshop will address: 1) convergence of the two approaches at the intersection of HCI, 2) updates to theoretical and methodological foundations, 3) bringing disparate modeling communities to CHI, and 4) sharing datasets, code, and best practices. This workshop seeks to establish Computational Modeling as a theoretical foundation for work in Human-Computer Interaction (HCI) to model the human accurately across domains and support design, optimization, and evaluation of user interfaces to solve a variety of human-centered problems.
MulMapper: Towards an Automated FPGA-Based CNN Processor Generator Based on a Dynamic Design Space Exploration

Many enterprises are adopting deep learning algorithms in their everyday tasks faster than ever. Convolutional Neural Networks (CNNs) in particular are being used widely due to the impressive performance in various application areas. FPGAs, on the other hand, are becoming a promising hardware platform for various deep learning algorithms including CNN. However, optimized and efficient FPGA design requires an expert with hardware design skills. This is particularly a challenge for deep learning practitioners who would like to accelerate their algorithm without worrying about the underlying hardware knowledge required to accomplish that in FPGAs. In this work we are proposing an automated framework, MulMapper, that can generate a functional and synthesized CNN processor hardware IP (using Vivado HLS) for Zynq-based FPGAs, given Caffe-based CNN definition file. We created a dynamic and novel design space utilizing Target Device Resource, Target Core Mode and Target Data Width as design space dimensions. MulMapper explores the design space in these three dimensions and proposes the optimum design points. We tested MulMapper framework on common CNN architectures, LeNet, CNP and CIFAR-10. It has been verified that early-stage MulMapper can lead to synthesis of resource-optimized CNN processor hardware IP that can be used for many regular CNN variants. Comparison with the state-of-the-art shows that architectures generated using MulMapper obtained up to 25-29× DSP48 and 13-20× on-chip memory reduction, with up to 0.35 GOP/sec performance.
A Dynamic Part-Attention Model for Person Re-Identification

Person re-identification (ReID) is gaining more attention due to its important applications in pedestrian tracking and security prevention. Recently developed part-based methods have proven beneficial for stronger and explicit feature descriptions, but how to find real significant parts and reduce miscorrelation between images to improve accuracy of ReID still leaves much room to improve. In this paper, we propose a dynamic part-attention (DPA) method based on masks, which aims to improve the use of variable attention parts. Particularly, a two-branch network with a dynamic loss function is designed to extract features of the global image and the parts of the body separately. With the comprehensive but targeting learning strategy, the proposed method can capture discriminative features based, but not depending on, masks, which guides the whole network to focus on body features more consciously and achieves more robust performance. Our method achieves rank-1 accuracy of 91.68% on public dataset Market1501, and experimental results on three public datasets indicate that the proposed method is effective and achieves favorable accuracy when compared with the state-of-the-art methods.
Designing Computational Materiality

This paper articulates the design, manufacturing, and deployment of transTexture, a digital lamp features dynamic kinetic textures and changeable lighting effects. We deployed transTexture in the homes of two domain expert participants in a preliminary study to explore how computational materiality of interaction can fit a changing everyday environment. We conducted two semi-structured interviews with domain expert participants at the beginning and end of the field study. Based on the lived experiences uncovered in our initial findings, we elaborate on two notions that describe types of user engagements with computational materiality: transformation and Integration. We conclude with lessons learned from our study that will guide our future research on computational materiality.
HCI Across Borders and Intersections

The HCI Across Borders (HCIxB) community has been growing in recent years, thanks in particular to the Development Consortium at CHI 2016 and the HCIxB Symposia at CHI 2017 and 2018. This year, we propose an HCIxB symposium that continues to build scholarship potential of early career HCIxB researchers, strengthening ties between more and less experienced members of the community. We especially invite scholarship with a focus on intersections, examining and/or addressing multiple forms of marginalization (e.g. race, gender, class, among others).
Learning From Observation of Tabletop Knotting Using a Simple Task Model

This paper describes a method for teaching robots tabletop knotting tasks via a human demonstration. Simple teaching-and-playback approaches cannot be applied to rope manipulation tasks because the repeatability of a rope's shape is not high due to its deformation. In this study, we introduce the concept of unit manipulations that are basic operations in knotting procedures, to design a description method for tabletop knotting. The proposed method consists of two phases: a teaching phase and a reproduction phase. In the teaching phase, a human demonstrates a knotting procedure in front of a robot, and the robot interprets it as a sequence of unit manipulations. In the reproduction phase, the robot automatically reproduces the same knot from the sequence. The intermediate unit manipulation description enables this despite the rope's deformation and the kinematic differences between humans and robots. We evaluated the proposed method to confirm its effectiveness using a system with a dual-arm robot.
Evaluation of approaches proposed to avoid overlap of markers in visualizations based on multidimensional projection techniques

Multidimensional projection techniques provide graphical representations computed based on instance similarities to enable the analysis of abstract and possibly large data sets. However, when the data set size grows these graphical representations can hardly avoid overlap among markers. To overcome this issue, while some techniques attempt to remove overlap after multidimensional projection, some projection techniques were developed considering non-overlapping constraints. In this work, we present an analysis of four overlap removal techniques and two projection techniques considering non-overlapping. The evaluation was performed according to five metrics that consider structural and similarity relations, and based on the results we provide a guide to use a technique according to the data set and analysis goals.