Reviewerly/exHarmony huggingface dataset:
Understanding the Boundaries between Policymaking and HCI

There is a growing body of literature in HCI examining the intersection between policymaking and technology research. However, what it means to engage in policymaking in our field, or the ways in which evidence from HCI studies is translated into policy, is not well understood. We report on interviews with 11 participants working at the intersection of technology research and policymaking. Analysis of this data highlights how evidence is understood and made sense of in policymaking processes, what forms of evidence are privileged over others, and the work that researchers engage in to meaningfully communicate their work to policymaking audiences. We discuss how our findings pose challenges for certain traditions of research in HCI, yet also open up new policy opportunities for those engaging in more speculative research practices. We conclude by discussing three ways forward that the HCI community can explore to increase engagement with policymaking contexts.
Personas and Identity

Personas are valuable tools to help designers get to know their users and adopt their perspectives. Yet people are complex and multiple identities have to be considered in their interplay to account for a comprehensive representation otherwise, personas might be superficial and prone to activate stereotypes. Therefore, the way users' identities are presented in a limited set of personas is crucial to account for diversity and highlight facets which otherwise would go unnoticed. In this paper, we introduce an approach to the development of personas informed by social identity theory. The effectiveness of this approach is investigated in a qualitative study in the context of the design process for an e-learning platform for women in tech. The results suggest that considering multiple identities in the construction of personas adds value when designing technologies.
Orthogonal polynomial embedded image kernel

Preprocessing operations of images and video frame sequences are beneficial in computer vision algorithms. For example, smoothing frames is used to eliminate noise; while computing frame gradient in x-direction and y-direction is used for frame feature extraction or for finding frame edges. Such operations involve convolving operators (image kernels) with an image precomputing moments will add extra computation cost to computer vision algorithm. In case of video, the computational time accumulatively increased because of the convolution operation for each frame is performed. To overcome this problem, a mathematical model is established for computing preprocessed frame moments via embedding the operator (image kernel) in the orthogonal polynomial (OP) functions. The experimental results show that the computation time for feature extraction using the proposed method is noticeably reduced in the both trends: image size and moment selection order. The average speed up ratio of the proposed method to traditional method is 3x, 5x, 8x, and 40x for moment selection ratio 100%, 25%, 10%, and 5%, respectively. In addition, the percentage reduction in processing time for small image size is ~ 99% and for large image size is ~ 40%.
Influence of image quality on the identification of psyllids using convolutional neural networks

Convolutional Neural Networks (CNNs) usually require large datasets to be properly trained. Although techniques such as transfer learning can relax those requirements, gathering sufficient labelled data to cover all the variability associated to the problem at hand is often costly and time consuming. A way to minimise this challenge would be gathering the training data under laboratory conditions, using high quality sensors capable of generating images with superior resolution, sharpness and contrast. The downside of this approach is that the resulting dataset will most likely lack the variety that can be found under more realistic conditions. This work investigates this trade-off between image quality and dataset representativeness, that is, if a CNN trained with images captured by a scanner in laboratory would be able to reliably recognise psyllids in smartphone images captured under more realistic conditions. A total of 1276 images were used in the experiments, half acquired using a flatbed scanner and half acquired using two different brands of smartphones. Experiments were carried out using Squeezenet CNNs and a 10-fold cross-validation strategy. Accuracies ranged from less than 70% using only scanned images, to around 90% when only smartphone images were employed, indicating that more realistic conditions are essential to guarantee the robustness of the trained network. Scanned images were useful when the training set containing realistic images was not enough to cover all the variability found in the experiments, but were otherwise innocuous.
As Light as You Aspire to Be

Supporting exercise adherence through technology remains an important HCI challenge. Recent works showed that altering walking sounds leads people perceiving themselves as thinner/lighter, happier and walking more dynamically. While this novel approach shows potential for physical activity, it raises critical questions impacting technology design. We ran two studies in the context of exertion (gym-step, stairs-climbing) to investigate how individual factors impact the effect of sound and the duration of the after-effects. The results confirm that the effects of sound in body-perception occur even in physically demanding situations and through ubiquitous wearable devices. We also show that the effect of sound interacted with participants' body weight and masculinity/femininity aspirations, but not with gender. Additionally, changes in body-perceptions did not hold once the feedback stopped; however, body-feelings or behavioural changes appeared to persist for longer. We discuss the results in terms of malleability of body-perception and highlight opportunities for supporting exercise adherence.
Knowledge Base Question Answering With Attentive Pooling for Question Representation

This paper presents a neural network model for a knowledge base (KB)-based single-relation question answering (SR-QA). This model is composed of two main modules, i.e., entity linking and relation detection. In each module, an embedding vector is computed from the input question sentence to calculate its similarity scores with entity candidates or relation candidates. This paper focuses on attention-based question representation in SR-QA. In the entity linking module, two attentive pooling methods, inner-sentence attention and structure attention, are employed to derive question embeddings, and their performances are compared in experiments. In the relation detection module, a new attentive pooling structure, named multilevel target attention (MLTA), is proposed to utilize the multilevel descriptions of relations. In this structure, the attention weights for aggregating the hidden states of question sentences are calculated using relation candidates as queries at the relation level, word level, and character level. Then, the similarity scores for relation detection are computed by matching questions to relation candidates at all three levels. The experimental results show that our proposed model achieves a state-of-the-art accuracy of 82.29% on the simple questions dataset. Furthermore, the results of ablation tests demonstrate the effectiveness of our proposed MLTA method for question representation.
Finding significant keywords for document databases by two-phase Maximum Entropy Partitioning

This paper investigates the selection of class-specific significant keywords for document databases. We define two types of significant keywords with respect to a document class: Elite and Unique Elite, derived in two phases. Elite Keywords are defined as those that have high term frequencies within the class. To obtain the top partition of distinctively high occurring terms in each class, we employ Maximum Entropy Partitioning (MEP) in the first phase. Our presumption is that the term probabilities within the subset of significant (and non-significant) keywords at the point of maximum entropy are relatively more uniform with respect to each other. Unique Elite keywords are those that are Elite for a particular class, and at the same time have a higher frequency of occurrence only in that class as compared to the other classes. To measure this aspect, in the second phase, we compute the entropy of each Elite keyword across all classes, sort the entropies in the ascending order and again employ MEP to shortlist those Elite keywords that occur uniquely in this class, characterized by distinctively low entropy. Experimental comparisons with the state-of-the-art on benchmark datasets using an ensemble of bagged tree classifiers, establishes the discriminatory powers of the derived keywords.
Visual Decoding of Hidden Watermark in Trained Deep Neural Network

Deep neural network (DNN) is a kind of intellectual property considering its usefulness and cost to develop. This paper proposes watermarking to a trained DNN models to protect its copyright. The proposed method has a remarkable feature for watermark detection process, which can decode the embedded pattern cumulatively and visually. In the experiment, we can embed a specific visual pattern using 5,000 or 60,000 images on the pretrained image classification DNN model. Then the embedded pattern is decoded using 20 images out of the 5,000 or 60,000 images, while a performance degradation to the original image classification task is small. At the conference site, real time and animated visual decoding demonstration is performed.
Double-Bit Quantization and Index Hashing for Nearest Neighbor Search

As binary code is storage efficient and fast to compute, it has become a trend to compact real-valued data to binary codes for the nearest neighbors (NN) search in a large-scale database. However, the use of binary code for the NN search leads to low retrieval accuracy. To increase the discriminability of the binary codes of existing hash functions, in this paper, we propose a framework of double-bit quantization and index hashing for an effective NN search. The main contributions of our framework are: first, a novel double-bit quantization (DBQ) is designed to assign more bits to each dimension for higher retrieval accuracy; second, a double-bit index hashing (DBIH) is presented to efficiently index binary codes generated by DBQ; and third, a weighted distance measurement for DBQ binary codes is put forward to re-rank the search results from DBIH. The empirical results on three benchmark databases demonstrate the superiority of our framework over existing approaches in terms of both retrieval accuracy and query efficiency. Specifically, we observe an absolute improvement on precision of 10%-25% in most cases and the query speed increases over 30 times compared to traditional binary embedding methods and linear scan, respectively.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>k</mml:mi></mml:math> -uniform quantum states arising from orthogonal arrays

A pure quantum state of $N$ subsystems with local dimension $d$ is called a $k$-uniform state if every reduction to $k$ qudits is maximally mixed. Based on a special class of combinatorial design, namely irredundant orthogonal arrays, we show the existence of 2-uniform $N$-qudit states when $N\ensuremath{\ge}4$ and $d$ is a prime power other than 2. In addition, given any Hadamard matrix of order greater than 4, we demonstrate how it can be used to construct 3-uniform multiqubit states. In fact, we find that there exists some 3-uniform $N$-qubit when $N\ensuremath{\ge}8$ except case $N=9$. These give an answer to a question posed by Goyeneche et al. [Phys. Rev. A 90, 022316 (2014)]. Furthermore, starting from a minimal support $k$-uniform state, we show how to generate an orthogonal basis consisting of $k$-uniform states. At last, we derive a series of $(k\ensuremath{-}1)$-uniform $(N\ensuremath{-}1)$-qudit states from a single $k$-uniform $N$-qudit state. The $k$-uniform states are good candidates among the multipartite entangled states.
Creating Elasticity with Enhanced Weighted Optimization Load Balancing Algorithm in Cloud Computing

This paper embraces an arrangement of rules for load balancing to improvise the general execution along with viability in assorted environment of cloud computing. The paper advocates a combination set of rules in light of priority and batch rules, taking points of interest of weighted optimization algorithm and similarly spread current execution algorithms.These algorithm reflects the records of current resource and the capability issue of Central Processing Unit to acquire the targets. Cloud Analyst simulator has been used to evaluate and compare the fused algorithm with different algorithms. The results demonstrated progress in migration time, reaction time and scalability, availability & reliability of resources with energy-consumption and throughput via considering the modern-day aid records and the Central Processing Unit potential factor in comparison with different algorithms.
FoBo

Despite the known benefits of commensal eating, eating alone is becoming increasingly common as people struggle to find time and manage geographical boundaries to enjoy a meal together. Eating alone however can be boring, less motivating and shown to have negative impact on health and wellbeing of a person. To remedy such situations, we undertake a celebratory view on robotic technology to offer unique opportunities for solo-diners to feel engaged and indulged in dining. We present, Fobo, a speculative design prototype for a mischievous robotic dining companion that acts and behaves like a human co-diner. Besides tackling solo-dining, this work also aims to reorient the perception that robots are not always meant to be infallible. They could be erroneous and clumsy, like we humans are.
Accurate traffic light detection using deep neural network with focal regression loss

This paper proposes a method that uses a deep neural network (DNN) to detect small traffic lights (TLs) in images captured by cameras mounted in vehicles. The proposed TL detector has a DNN architecture of encoder-decoder with focal regression loss; this loss function reduces loss of well-regressed easy examples. The proposed TL detector has freestyle anchor boxes that are placed at arbitrary locations in a grid cell of an input image, so it can detect small objects located at borders of the grid cell. We evaluate the proposed TL detector with a focal regression loss on two public TL datasets: Bosch small traffic light dataset, and LISA traffic lights dataset. Compared to state-of-the-art TL detectors, the proposed TL detector achieves 7.19%–42.03% higher mAP on the Bosch-TL dataset and 19.86%–49.16% higher AUC on the LISA-TL dataset.
A Data Sharing Protocol to Minimize Security and Privacy Risks of Cloud Storage in Big Data Era

A cloud-based big data sharing system utilizes a storage facility from a cloud service provider to share data with legitimate users.In contrast to traditional solutions, cloud provider stores the shared data in the large data centers outside the trust domain of the data owner, which may trigger the problem of data confidentiality.This paper proposes a secret sharing group key management protocol (SSGK) to protect the communication process and shared data from unauthorized access.Different from the prior works, a group key is used to encrypt the shared data and a secret sharing scheme is used to distribute the group key in SSGK.The extensive security and performance analyses indicate that our protocol highly minimizes the security and privacy risks of sharing data in cloud storage and saves about 12% of storage space.
Shortest Dubins paths through three points

The 3-Point Dubins Problem (3PDP) consists of steering a Dubins vehicle through three consecutive points with prescribed heading orientations at initial and final points so that the resulting path is the shortest. Characterizing the path of the 3PDP is important because (1) it provides insightful views on the solution paths of the Dubins Traveling Salesman Problem (DTSP) and the Curvature-Constrained Shortest-Path Problem (CCSPP) as they are natural extensions of the 3PDP and (2) some algorithms in the literature for solving the DTSP and the CCSPP require efficient methods for solving the 3PDP. In this paper, Pontryagin's maximum principle is used to show that the path of 3PDP must lie in a sufficient family of 18 types. Moreover, a formula in terms of the parameters of the 3PDP for all the 18 types is established, and this formula reveals the relationship between the unknown orientation angle at mid point and known parameters. By observing that the formula can be converted into some polynomials, the 3PDP can therefore be efficiently solved by finding zeros of those polynomials. Finally, numerical simulations illustrate the developments by comparing with the straightforward discretization-based method.
Utilizing Information Bottleneck to Evaluate the Capability of Deep Neural Networks for Image Classification

Inspired by the pioneering work of the information bottleneck (IB) principle for Deep Neural Networks' (DNNs) analysis, we thoroughly study the relationship among the model accuracy, I ( X ; T ) and I ( T ; Y ) , where I ( X ; T ) and I ( T ; Y ) are the mutual information of DNN's output T with input X and label Y. Then, we design an information plane-based framework to evaluate the capability of DNNs (including CNNs) for image classification. Instead of each hidden layer's output, our framework focuses on the model output T. We successfully apply our framework to many application scenarios arising in deep learning and image classification problems, such as image classification with unbalanced data distribution, model selection, and transfer learning. The experimental results verify the effectiveness of the information plane-based framework: Our framework may facilitate a quick model selection and determine the number of samples needed for each class in the unbalanced classification problem. Furthermore, the framework explains the efficiency of transfer learning in the deep learning area.
The image-based analysis and classification of urine sediments using a LeNet-5 neural network

In this work, we presented a deep learning approach based on the LeNet-5 network for analysing and classifying recognisable shapes in urine sample images. The approach is based on shape analysis to recognise and classify red blood cells, white blood cells, epithelial cells and crystals observed under microscopes in urine samples. We modified the LeNet-5 neural network by changing the numbers of output nodes and convolutional layers. We compared the results of our method with those obtained by traditional feature extraction followed by back-propagation neural networks. Our testing showed that our method achieved a higher accuracy, sensitivity and specificity. The performance of our method demonstrated its broad applicability in urine sample analysis.
Estimating solar irradiance using sky imagers

Abstract. Ground-based whole-sky cameras are now extensively used for the localized monitoring of clouds. They capture hemispherical images of the sky at regular intervals using a fish-eye lens. In this paper, we propose a framework for estimating solar irradiance from pictures taken by those imagers. Unlike pyranometers, such sky images contain information about cloud coverage and can be used to derive cloud movement. An accurate estimation of solar irradiance using solely those images is thus a first step towards the short-term forecasting of solar energy generation based on cloud movement. We derive and validate our model using pyranometers colocated with our whole-sky imagers. We achieve a better performance in estimating solar irradiance and in particular its short-term variations compared to other related methods using ground-based observations.
Exploring Word-gesture Text Entry Techniques in Virtual Reality

Efficient text entry is essential to any computing system. However, text entry methods in virtual reality (VR) currently lack the predictive aid and physical feedback that allows users to type efficiently. The state of the art methods such as using physical keyboards with tracked hand avatars require a complex setup which might not be accessible to the majority of VR users. In this paper, we propose two novel ways to enter text in VR: 1) Word-gesture typing using six degrees of freedom (6DOF) VR controllers; and 2) word-gesture typing using pressure-sensitive touchscreen devices. Our early stage pilot experiment shows that users were able to type at 16.4 WPM and 9.6 WPM on the two techniques respectively without any training, while an expert's typing speeds reached up to 34.2 WPM and 22.4 WPM. Users subjectively preferred the VR controller method over the touchscreen one in terms of usability and task load. We conclude that both techniques are practical and deserve further study.
Blockchain-Based Anonymous Reporting Scheme With Anonymous Rewarding

Since the ancient times, there exist all kinds of criminals in the world. In order to punish them, most authorities usually encourage the people to provide the evidence of a crime. To avoid the revenges from the criminals, it is necessary to study the anonymous reporting scheme with anonymous rewarding. In this paper, for the first time, we propose the novel concept of blockchain-based anonymous reporting scheme with anonymous rewarding (BB2AR). Our BB2AR scheme solves the open problem: how to realize the anonymous reporting and the anonymous rewarding simultaneously? First, we formalize the system model, definition, and security model of BB2AR. Second, we propose a concrete BB2AR scheme based on the elliptic curve public key cryptography. Our formal proof shows that our BB2AR scheme satisfies the anonymous reporting and the anonymous rewarding simultaneously. Finally, we analyze its efficiency and provide its prototype implementation. From the comparison, our BB2AR scheme is more efficient and practical.
BlockSLaaS: Blockchain Assisted Secure Logging-as-a-Service for Cloud Forensics

Cloud computing has become a prominent and widespread technology nowadays. However, it agonized due to incremental serious security issues. To solve these issues forensic techniques needs to be applied in cloud. Log is a paramount element in forensic investigations to reveal 3W i.e. who, what, when of happened suspicious activity. That’s the reason, secure preservation and investigation of different logs is an essential job for cloud forensics. Due to very little control over the clouds, it’s very difficult to collect authentic logs from cloud environment while preserving integrity and confidentiality. Till today, forensic investigator has to trust Cloud Service Provider (CSP), who collect the logs from individual sources of cloud environment. However, untrusted stakeholders of cloud and malicious entities from outside the cloud can collude with each other to alter the logs after the fact and remain untraceable. Thus, validity of the provided logs for forensics can be questionable. In this paper, we proposed forensic aware blockchain assisted secure logging-as-a-service for cloud environment to securely store and process logs by tackling multi-stakeholder collusion problem and ensuring integrity & confidentiality. The integrity of logs is ensured using immutable property of blockchain technology. Cloud Forensic Investigator (CFI) can only be able to access the logs for forensic investigation by BlockSLaaS, which preserves confidentiality of logs.