bluuebunny/crossref_metadata_2025 huggingface dataset:
FINANCIAL DIGITALIZATION: BANKS, FINTECH, BIGTECH, AND CONSUMERS
Authors: VALVERDE SANTIAGO CARBÓ , FERNÁNDEZ FRANCISCO RODRÍGUEZ
Published: July 2020

This article explores some recent macroeconomic and microeconomic approaches to financial digitalization and the relationship between banks, FinTech and BigTech. It also deals with new approaches to identify the adoption and implications of financial digitalization by consumers. We show competition between traditional banks and tech companies is mostly driven by their relative ability to manage information sharing. Regulation is still considering ways of providing a level playing field while industry participants are reacting with a mixture of strategies, many of them based on cooperation. The paper also shows there are different ways in which customers access financial digital channels and new approaches from matching learning and brain studies to identify behavioral patterns in financial digitalization decisions.
Learning Context-dependent Personal Preferences for Adaptive Recommendation
Authors: Higuchi Keita , Tsuchida Hiroki , Ohn-Bar Eshed , Sato Yoichi , Kitani Kris
Published: July 2020

We propose two online-learning algorithms for modeling the personal preferences of users of interactive systems. The proposed algorithms leverage user feedback to estimate user behavior and provide personalized adaptive recommendation for supporting context-dependent decision-making. We formulate preference modeling as online prediction algorithms over a set of learned policies, i.e., policies generated via supervised learning with interaction and context data collected from previous users. The algorithms then adapt to a target user by learning the policy that best predicts that user’s behavior and preferences. We also generalize the proposed algorithms for a more challenging learning case in which they are restricted to a limited number of trained policies at each timestep, i.e., for mobile settings with limited resources. While the proposed algorithms are kept general for use in a variety of domains, we developed an image-filter-selection application. We used this application to demonstrate how the proposed algorithms can quickly learn to match the current user’s selections. Based on these evaluations, we show that (1) the proposed algorithms exhibit better prediction accuracy compared to traditional supervised learning and bandit algorithms, (2) our algorithms are robust under challenging limited prediction settings in which a smaller number of expert policies is assumed. Finally, we conducted a user study to demonstrate how presenting users with the prediction results of our algorithms significantly improves the efficiency of the overall interaction experience.
A Method and Analysis to Elicit User-Reported Problems in Intelligent Everyday Applications
Authors: Eiband Malin , Völkel Sarah Theres , Buschek Daniel , Cook Sophia , Hussmann Heinrich
Published: July 2020

The complex nature of intelligent systems motivates work on supporting users during interaction, for example, through explanations. However, as of yet, there is little empirical evidence in regard to specific problems users face when applying such systems in everyday situations. This article contributes a novel method and analysis to investigate such problems
as reported by users:
We analysed 45,448 reviews of four apps on the Google Play Store (Facebook, Netflix, Google Maps, and Google Assistant) with sentiment analysis and topic modelling to reveal problems during interaction that can be attributed to the apps’ algorithmic decision-making. We enriched this data with users’ coping and support strategies through a follow-up online survey (N = 286). In particular, we found problems and strategies related to content, algorithm, user choice, and feedback. We discuss corresponding implications for designing user support, highlighting the importance of user control and explanations of output rather than processes.
How avatar gender may influence users intention to use Second Life environment: An empirical study
Authors: BASSFAR ZAID
Published: July 2020

The use of virtual environments to transform individuals? use of modern technology has gained a special attention lately. The role of gender in the virtual space has also been viewed as a contributor to individuals? use of modern technology. This study investigated the impact of avatar gender on users? intention to use the Second Life (SL) environment in a university context. Two avatars of male and female characteristics were designed and used in the SL environment. A total of 74 SL users were involved in two learning sessions (with male female avatars). A questionnaire was used to capture users? perceptions of ease of use, usefulness, attitude, and behavioral intention to use the SL space. SL users had positive intentions to use the SL environment for various learning purposes when they are provided with the preferred gender appearance. Offering opposite gender characteristics can help stimulate users? interaction with the avatar, thus facilitating the learning process and building the sense of technology effectiveness.
Extending Signal Temporal Logic with Quantitative Semantics by Intervals for Robust Monitoring of Cyber-physical Systems
Authors: Zhong Bingzhuo , Jordan Claudius , Provost Julien
Published: July 2020

Monitoring is the core procedure of runtime verification of cyber-physical systems (CPS) and provides an evaluation of a signal with respect to a given specification. For formally specifying requirements with time constraints for CPS, Signal Temporal Logic (STL) is a well-known specification language with powerful semantics. However, with existing semantics of STL it is not feasible to monitor signals with spatial deviation and time delay. Therefore, we introduce
STL with Quantitative Interval Semantics
to solve this problem. Based on this newly developed semantics, we derived an algorithm called
RoMoTeS
(Robust Monitoring for Temporal Specifications) to monitor a signal with finite length with respect to an STL formula. It provides a real-valued interval for every point in time, which contains all possible signed distances between the deviation polytope determined by the measured data point (exposed to deviation and delay) and the permissive space specified by an STL specification. Furthermore, it is proven that no satisfaction is reported when the signal is potentially falsifying such a specification. Finally, an automatic transmission controller model was used as a case study to show the applicability and usefulness of the proposed algorithm.
Adaptive Weighted Finite Mixture Model
Authors: Xu Xin , Fu Yanjie , Wu Jingyi , Wang Yuqi , Huang Zeyu , Fu Zhiguo , Yin Minghao
Published: July 2020

It is significant for real estate investors to understand how the construction environments and building characteristics impact the housing unit price. However, it is challenging for identifying the complex feature-influence from construction environments and building characteristics. It is also hard to alleviate the heterogeneity of real estate. In this article, we propose a framework named Adaptive Weighted Finite Mixture Model to identify the feature-influence and simultaneously alleviate the ill effect of heterogeneity. Applying this framework, we can predict the housing unit price based on the corresponding features. Besides, we discover that the feature-influence exists in the dissimilarity among similar cities. Specifically, we adaptively learn the weights of features to identify the feature-influence, and we model the estimation of the housing unit price with the feature-influence into a finite mixture model. We utilize the Principle Component Analysis algorithm to obtain a low-dimensional representation of housing features. The low-dimensional representation reduces the computational cost of model learning, and it avoids a potential catastrophe of the singular matrix inversion during the process of learning model parameters, which are estimated by the Expectation Maximization algorithm. To avoid the blind search for the latent group number used in the proposed framework, we employ the pre-clustering result as a guide of the searching range of the latent group numbers. We conduct numerous experiments on three real datasets from Shenyang, Changchun, and Harbin, which are the three provincial capital cities that have similar geography, economics, and cultures. The experimental results demonstrate the effectiveness of the proposed framework.
Secure DNA Motif-Finding Method Based on Sampling Candidate Pruning
Authors: Xia Kaijian , Wu Xiang , Mao Yaqing , Wang Huanhuan
Published: July 2020

With the continuous exploration of genetic research, gradually exposed privacy issues become the bottleneck that limits its development. DNA motif finding is an important study to understand the regulation of gene expression; however, the existing methods generally ignore the potential sensitive information that may be exposed in the process. In this work, we utilize the

-differential privacy model to provide provable privacy guarantees which is independent of attackers’ background knowledge. Our method makes use of sample databases to prune the generated candidate motifs to lower the magnitude of added noise. Furthermore, to improve the utility of mining results, a strategy of threshold modification is designed to reduce the propagation and random sampling errors in the mining process. Extensive experiments on actual DNA databases confirm that our approach can privately find DNA motifs with high utility and efficiency.
Data Hiding
Authors: Singh A. K.
Published: July 2020

With the widespread growth of digital information and improved internet technologies, the demand for improved information security techniques has significantly increased due to privacy leakage, identity theft, illegal copying, and data distribution. Because of this, data hiding approaches have received much attention in several application areas. However, those approaches are unable to solve many issues that are necessary to measure in future investigations. This survey provides a comprehensive survey on data hiding techniques and their new trends for solving new challenges in real-world applications. The notable applications are telemedicine, 3D objects, mobile devices, cloud/distributed computing and data mining environments, chip and hardware protection, cyber physical systems, internet traffic, fusion of watermarking and encryption, joint compression and watermarking, biometric watermarking, watermarking at the physical layer, and many other perspectives. Further, the potential issues that existing approaches of data hiding face are identified. I believe that this survey will provide a valuable source of information for finding research directions for fledgling researchers and developers.
Privacy-preserving Time-series Medical Images Analysis Using a Hybrid Deep Learning Framework
Authors: Yue Zijie , Ding Shuai , Zhao Lei , Zhang Youtao , Cao Zehong , Tanveer M. , Jolfaei Alireza , Zheng Xi
Published: July 2020

Time-series medical images are an important type of medical data that contain rich temporal and spatial information. As a state-of-the-art, computer-aided diagnosis (CAD) algorithms are usually used on these image sequences to improve analysis accuracy. However, such CAD algorithms are often required to upload medical images to honest-but-curious servers, which introduces severe privacy concerns. To preserve privacy, the existing CAD algorithms support analysis on each encrypted image but not on the whole encrypted image sequences, which leads to the loss of important temporal information among frames. To meet this challenge, a convolutional-LSTM network, named HE-CLSTM, is proposed for analyzing time-series medical images encrypted by a fully homomorphic encryption mechanism. Specifically, several convolutional blocks are constructed to extract discriminative spatial features, and LSTM-based sequence analysis layers (HE-LSTM) are leveraged to encode temporal information from the encrypted image sequences. Moreover, a weighted unit and a sequence voting layer are designed to incorporate both spatial and temporal features with different weights to improve performance while reducing the missed diagnosis rate. The experimental results on two challenging benchmarks (a Cervigram dataset and the BreaKHis public dataset) provide strong evidence that our framework can encode visual representations and sequential dynamics from encrypted medical image sequences; our method achieved AUCs above 0.94 both on the Cervigram and BreaKHis datasets, constituting a significant margin of statistical improvement compared with several competing methods.
Cosmos+ OpenSSD
Authors: Kwak Jaewook , Lee Sangjin , Park Kibin , Jeong Jinwoo , Song Yong Ho
Published: July 2020

As semiconductor technology has advanced, many storage systems have begun to use non-volatile memories as storage media. The organization and architecture of storage controllers have become more complex to meet various design requirements in terms of performance, response time, quality of service (QoS), and so on. In addition, due to the evolution of memory technology and the emergence of new applications, storage controllers employ new firmware algorithms and hardware modules. When designing storage controllers, engineers often evaluate the performance impact of using new software and hardware components using software simulators. However, this technique often yields limited evaluation accuracy because of the difficulty of modeling complex operations of components and the interactions among them. In this article, we present a reconfigurable flash storage controller design that serves as a rapid prototype. This design can be synthesized into a field-programmable gate array device and used in a realistic performance evaluation environment. We show the usefulness of our design by demonstrating the performance impact of design parameters.
Toward Translating Raw Indoor Positioning Data into Mobility Semantics
Authors: Li Huan , Lu Hua , Chen Gang , Chen Ke , Chen Qinkuang , Shou Lidan
Published: July 2020

Indoor mobility analyses are increasingly interesting due to the rapid growth of raw indoor positioning data obtained from IoT infrastructure. However, high-level analyses are still in urgent need of a concise but semantics-oriented representation of the mobility implied by the raw data. This work studies the problem of translating raw indoor positioning data into
mobility semantics
that describe a moving object’s mobility event (What) someplace (Where) at some time (When). The problem is non-trivial mainly because of the inherent errors in the uncertain, discrete raw data. We propose a three-layer framework to tackle the problem. In the
cleaning
layer, we design a cleaning method that eliminates positioning data errors by considering indoor mobility constraints. In the
annotation
layer, we propose a split-and-match approach to annotate mobility semantics on the cleaned data. The approach first employs a density-based splitting method to divide positioning sequences into split snippets according to underlying mobility events, followed by a semantic matching method that makes proper annotations for split snippets. In the
complementing
layer, we devise an inference method that makes use of the indoor topology and the mobility semantics already obtained to recover the missing mobility semantics. The extensive experiments demonstrate that our solution is efficient and effective on both real and synthetic data. For typical queries, our solution’s resultant mobility semantics lead to more precise answers but incur less execution time than alternatives.
Planar Graphs Have Bounded Queue-Number
Authors: Dujmović Vida , Joret Gwenaël , Micek Piotr , Morin Pat , Ueckerdt Torsten , Wood David R.
Published: July 2020

We show that planar graphs have bounded queue-number, thus proving a conjecture of Heath et al. [66] from 1992. The key to the proof is a new structural tool called
layered partitions
, and the result that every planar graph has a vertex-partition and a layering, such that each part has a bounded number of vertices in each layer, and the quotient graph has bounded treewidth. This result generalises for graphs of bounded Euler genus. Moreover, we prove that every graph in a minor-closed class has such a layered partition if and only if the class excludes some apex graph. Building on this work and using the graph minor structure theorem, we prove that every proper minor-closed class of graphs has bounded queue-number.

Layered partitions have strong connections to other topics, including the following two examples. First, they can be interpreted in terms of strong products. We show that every planar graph is a subgraph of the strong product of a path with some graph of bounded treewidth. Similar statements hold for all proper minor-closed classes. Second, we give a simple proof of the result by DeVos et al. [31] that graphs in a proper minor-closed class have low treewidth colourings.
The Information Resilience Framework
Authors: Blay Karen Banahene , Yeomans Steven , Demian Peter , Murguia Danny
Published: July 2020

The quality of information is crucial to the success of asset delivery, management, and performance in the Digitised Architecture, Engineering, Construction, and Operations (DAECO) sector. The exposure and sensitivity of information to threats during its lifecycle leaves it vulnerable, affecting the intrinsic, relational, and security dimensions of information quality. A resilient information lifecycle perspective that identifies capabilities and requirements is therefore needed to assure information quality amid threats. This research develops and presents an information resilience (IR) framework by drawing on the theories of resilience, information quality, and vulnerability. In developing the framework, the critical incident technique was employed in interviewing 30 professionals (average of 40 minutes) in addition to reviewing seven project-documents across three digitally-driven infrastructure projects (making up 324 pages of data). The validated capabilities and requirements identified from this study have been collated into the framework and this highlights the need for cognitive-driven capabilities and process-driven requirements in DAECO.
Evaluation of Shared Resource Allocation Using SAND for ABR Streaming
Authors: Pham Stefan , Heeren Patrick , Schmidt Calvin , Silhavy Daniel , Arbanowski Stefan
Published: July 2020

Adaptive bitrate media streaming clients adjust the quality of media content depending on the current network conditions. The shared resource allocation (SRA) feature defined in MPEG-SAND (server and network assisted DASH) allows servers to allocate bandwidth to streaming clients. This enables coordination and prioritization of clients that are connected to the same network bottleneck (e.g., to maximize the number of clients that can play back a stream fluently). In this article, we evaluate different bandwidth limitation strategies and analyze the effects on the clients. For this purpose, a testbed using multiple Raspberry Pis was created. The results show that in various scenarios, SRA improves the fairness and the QoE of streaming sessions. Solely allocating a maximum quality level to the client is not sufficient in some cases. Therefore, additional means, such as limiting bandwidth on the client or traffic shaping with software-defined networking for SRA, are evaluated.
Deep Neural Network--based Machine Translation System Combination
Authors: Zhou Long , Zhang Jiajun , Kang Xiaomian , Zong Chengqing
Published: July 2020

Deep neural networks (DNNs) have provably enhanced the state-of-the-art natural language process (NLP) with their capability of feature learning and representation. As one of the more challenging NLP tasks, neural machine translation (NMT) becomes a new approach to machine translation and generates much more fluent results compared to statistical machine translation (SMT). However, SMT is usually better than NMT in translation adequacy and word coverage. It is therefore a promising direction to combine the advantages of both NMT and SMT. In this article, we propose a deep neural network--based system combination framework leveraging both minimum Bayes-risk decoding and multi-source NMT, which take as input the N-best outputs of NMT and SMT systems and produce the final translation. In particular, we apply the proposed model to both RNN and self-attention networks with different segmentation granularity. We verify our approach empirically through a series of experiments on resource-rich Chinese⇒English and low-resource English⇒Vietnamese translation tasks. Experimental results demonstrate the effectiveness and universality of our proposed approach, which significantly outperforms the conventional system combination methods and the best individual system output.
Few-shot Food Recognition via Multi-view Representation Learning
Authors: Jiang Shuqiang , Min Weiqing , Lyu Yongqiang , Liu Linhu
Published: July 2020

This article considers the problem of few-shot learning for food recognition. Automatic food recognition can support various applications, e.g., dietary assessment and food journaling. Most existing works focus on food recognition with large numbers of labelled samples, and fail to recognize food categories with few samples. To address this problem, we propose a Multi-View Few-Shot Learning (MVFSL) framework to explore additional ingredient information for few-shot food recognition. Besides category-oriented deep visual features, we introduce ingredient-supervised deep network to extract ingredient-oriented features. As general and intermediate attributes of food, ingredient-oriented features are informative and complementary to category-oriented features, and thus they play an important role in improving food recognition. Particularly in few-shot food recognition, ingredient information can bridge the gap between disjoint training categories and test categories. To take advantage of ingredient information, we fuse these two kinds of features by first combining their feature maps from their respective deep networks and then convolving combined feature maps. Such convolution is further incorporated into a multi-view relation network, which is capable of comparing pairwise images to enable fine-grained feature learning. MVFSL is trained in an end-to-end fashion for joint optimization on two types of feature learning subnetworks and relation subnetworks. Extensive experiments on different food datasets have consistently demonstrated the advantage of MVFSL in multi-view feature fusion. Furthermore, we extend another two types of networks, namely, Siamese Network and Matching Network, by introducing ingredient information for few-shot food recognition. Experimental results have also shown that introducing ingredient information into these two networks can improve the performance of few-shot food recognition.
Exploring Impact of Profile Data on Code Quality in the HotSpot JVM
Authors: Wade April W. , Kulkarni Prasad A. , Jantz Michael R.
Published: July 2020

Managed language virtual machines (VM) rely on dynamic or just-in-time (JIT) compilation to generate optimized native code at run-time to deliver high execution performance. Many VMs and JIT compilers collect
profile
data at run-time to enable profile-guided optimizations (PGO) that customize the generated native code to different program inputs. PGOs are generally considered integral for VMs to produce high-quality and performant native code.

In this work, we study and quantify the performance benefits of PGOs, understand the importance of profiling data quantity and quality/accuracy to effectively guide PGOs, and assess the impact of individual PGOs on VM performance. The insights obtained from this work can be used to understand the current state of PGOs, develop strategies to more efficiently balance the cost and exploit the potential of PGOs, and explore the implications of and challenges for the alternative ahead-of-time (AOT) compilation model used by VMs.
DIAC
Authors: Li Xinyi , Zhang Lei , Shen Xipeng
Published: July 2020

This article tackles the problem of detecting and solving potential conflicts among independently developed apps that are to be installed into an open Internet-of-Things (IoT) environment. It provides a new set of definitions and categorizations of the conflicts to more precisely characterize the nature of the problem, and it proposes a representation named “IA Graphs” for formally representing IoT controls and inter-app interplays. Based on the definitions and representation, it then describes an efficient conflict detection algorithm. Combining conflict categories, seriousness indicator, and conflict frequency, an innovative solution policy for solving various detected conflicts is developed, which also takes into account user preference and interest by providing interactive process. It implements a compiler and runtime software system that integrates all the proposed techniques together into a comprehensive solution. Experiments on SmartThings apps validate its significantly better detection efficacy over prior methods and effectiveness of conflict solution with user preference.
GMAI
Authors: Calderón Alejandro J. , Kosmidis Leonidas , Nicolás Carlos F. , Cazorla Francisco J. , Onaindia Peio
Published: July 2020

Critical real-time systems require strict resource provisioning in terms of memory and timing. The constant need for higher performance in these systems has led industry to recently include GPUs. However, GPU software ecosystems are by their nature closed source, forcing system engineers to consider them as black boxes, complicating resource provisioning. In this work, we reverse engineer the internal operations of the GPU system software to increase the understanding of their observed behaviour and how resources are internally managed. We present our methodology that is incorporated in GMAI (GPU Memory Allocation Inspector), a tool that allows system engineers to accurately determine the exact amount of resources required by their critical systems, avoiding underprovisioning. We first apply our methodology on a wide range of GPU hardware from different vendors showing its generality in obtaining the properties of the GPU memory allocators. Next, we demonstrate the benefits of such knowledge in resource provisioning of two case studies from the automotive domain, where the actual memory consumption is up to 5.6× more than the memory requested by the application.
Formal Verification of Spacecraft Control Programs
Authors: Lukyanov Georgy , Mokhov Andrey , Lechner Jakob
Published: July 2020

Verification of correctness of control programs is an essential task in the development of space electronics; it is difficult and typically outweighs design and programming tasks in terms of development hours. This article presents a verification approach designed to help spacecraft engineers reduce the effort required for formal verification of low-level control programs executed on custom hardware.

The verification approach is demonstrated on an industrial case study. We present a REDuced instruction set for Fixed-point and INteger arithmetic (REDFIN), a processing core used in space missions, and its formal semantics expressed using the proposed metalanguage for state transformers, followed by examples of verification of simple control programs.
EncoDeep
Authors: Samragh Mohammad , Javaheripi Mojan , Koushanfar Farinaz
Published: July 2020

This article proposes EncoDeep, an end-to-end framework that facilitates encoding, bitwidth customization, fine-tuning, and implementation of neural networks on FPGA platforms. EncoDeep incorporates nonlinear encoding to the computation flow of neural networks to save memory. The encoded features demand significantly lower storage compared to the raw full-precision activation values; therefore, the execution flow of EncoDeep hardware engine is completely performed within the FPGA using on-chip streaming buffers with no access to the off-chip DRAM. We further propose a fully automated optimization algorithm that determines the flexible encoding bitwidths across network layers. EncoDeep full-stack framework comprises a compiler that takes a high-level Python description of an arbitrary neural network. The compiler then instantiates the corresponding elements from EncoDeep Hardware library for FPGA implementation. Our evaluations on MNIST, SVHN, and CIFAR-10 datasets demonstrate an average of 4.65× throughput improvement compared to stand-alone weight encoding. We further compare EncoDeep with six FPGA accelerators on ImageNet, showing an average of 3.6× and 2.54× improvement in throughput and performance-per-watt, respectively.
Fast and Energy-Efficient State Checkpointing for Intermittent Computing
Authors: Ahmed Saad , Bhatti Naveed Anwar , Alizai Muhammad Hamad , Siddiqui Junaid Haroon , Mottola Luca
Published: July 2020

Intermittently powered embedded devices ensure forward progress of programs through state checkpointing in non-volatile memory. Checkpointing is, however, expensive in energy and adds to the execution times. To minimize this overhead, we present DICE, a system that renders differential checkpointing profitable on these devices. DICE is unique because it is a software-only technique and efficient because it only operates in volatile main memory to evaluate the differential. DICE may be integrated with reactive (Hibernus) or proactive (MementOS, HarvOS) checkpointing systems, and arbitrary code can be enabled with DICE using automatic code-instrumentation requiring no additional programmer effort. By reducing the cost of checkpoints, DICE cuts the peak energy demand of these devices, allowing operation with energy buffers that are one-eighth of the size originally required, thus leading to benefits such as smaller device footprints and faster recharging to operational voltage level. The impact on final performance is striking: with DICE, Hibernus requires one order of magnitude fewer checkpoints and one order of magnitude shorter time to complete a workload in real-world settings.
Oblivious Resampling Oracles and Parallel Algorithms for the Lopsided Lovász Local Lemma
Authors: Harris David G.
Published: July 2020

The Lovász Local Lemma (LLL) shows that, for a collection of “bad” events B in a probability space that are not too likely and not too interdependent, there is a positive probability that no events in B occur. Moser and Tardos (2010) gave sequential and parallel algorithms that transformed most applications of the variable-assignment LLL into efficient algorithms. A framework of Harvey and Vondrák (2015) based on “resampling oracles” extended this to sequential algorithms for other probability spaces satisfying a generalization of the LLL known as the Lopsided Lovász Local Lemma (LLLL).

We describe a new structural property that holds for most known resampling oracles, which we call “obliviousness.” Essentially, it means that the interaction between two bad-events
B
,
B
′
depends only on the randomness used to resample
B
and not the precise state within
B
itself.

This property has two major consequences. First, combined with a framework of Kolmogorov (2016), it leads to a unified parallel LLLL algorithm, which is faster than previous, problem-specific algorithms of Harris (2016) for the variable-assignment LLLL and of Harris and Srinivasan (2014) for permutations. This gives the first RNC algorithms for rainbow perfect matchings and rainbow Hamiltonian cycles of
K

n

.

Second, this property allows us to build LLLL probability spaces from simpler “atomic” events. This gives the first resampling oracle for rainbow perfect matchings on the complete
s
-uniform hypergraph
K

n

(
s
)

and the first commutative resampling oracle for Hamiltonian cycles of
K

n

.
B
3

-Tree
Authors: Cha Hokeun , Nam Moohyeon , Jin Kibeom , Seo Jiwon , Nam Beomseok
Published: July 2020

In this work, we propose

B
3
-tree

, a hybrid index for persistent memory that leverages the byte-addressability of the in-memory index and the page locality of B-trees. As in the byte-addressable in-memory index,

B
3
-tree

is updated by 8-byte store instructions. Also, as in disk-based index,

B
3
-tree

is failure-atomic since it makes every 8-byte store instruction transform a consistent index into another consistent index without the help of expensive logging. Since expensive logging becomes unnecessary, the number of cacheline flush instructions required for

B
3
-tree

is significantly reduced. Our performance study shows that

B
3
-tree

outperforms other state-of-the-art persistent indexes in terms of insert and delete performance. While

B
3
-tree

shows slightly worse performance for point query performance, the range query performance of

B
3
-tree

is 2x faster than FAST and FAIR B-tree because the leaf page size of

B
3
-tree

can be set to 8x larger than that of FAST and FAIR B-tree without degrading insertion performance. We also show that read transactions can access

B
3
-tree

without acquiring a shared lock because

B
3
-tree

remains always consistent while a sequence of 8-byte write operations are making changes to it. As a result,

B
3
-tree

provides high concurrency level comparable to FAST and FAIR B-tree.
Practical Accuracy Estimation for Efficient Deep Neural Network Testing
Authors: Chen Junjie , Wu Zhuo , Wang Zan , You Hanmo , Zhang Lingming , Yan Ming
Published: July 2020

Deep neural network (DNN) has become increasingly popular and DNN testing is very critical to guarantee the correctness of DNN, i.e., the accuracy of DNN in this work. However, DNN testing suffers from a serious efficiency problem, i.e., it is costly to label each test input to know the DNN accuracy for the testing set, since labeling each test input involves multiple persons (even with domain-specific knowledge) in a manual way and the testing set is large-scale. To relieve this problem, we propose a novel and practical approach, called PACE (which is short for
P
ractical
AC
curacy
E
stimation), which selects a small set of test inputs that can precisely estimate the accuracy of the whole testing set. In this way, the labeling costs can be largely reduced by just labeling this small set of selected test inputs. Besides achieving a precise accuracy estimation, to make PACE more practical it is also required that it is interpretable, deterministic, and as efficient as possible. Therefore, PACE first incorporates clustering to interpretably divide test inputs with different testing capabilities (i.e., testing different functionalities of a DNN model) into different groups. Then, PACE utilizes the MMD-critic algorithm, a state-of-the-art example-based explanation algorithm, to select prototypes (i.e., the most representative test inputs) from each group, according to the group sizes, which can reduce the impact of noise due to clustering. Meanwhile, PACE also borrows the idea of adaptive random testing to select test inputs from the minority space (i.e., the test inputs that are not clustered into any group) to achieve great diversity under the required number of test inputs. The two parallel selection processes (i.e., selection from both groups and the minority space) compose the final small set of selected test inputs. We conducted an extensive study to evaluate the performance of PACE based on a comprehensive benchmark (i.e., 24 pairs of DNN models and testing sets) by considering different types of models (i.e., classification and regression models, high-accuracy and low-accuracy models, and CNN and RNN models) and different types of test inputs (i.e., original, mutated, and automatically generated test inputs). The results demonstrate that PACE is able to precisely estimate the accuracy of the whole testing set with only 1.181%∼2.302% deviations, on average, significantly outperforming the state-of-the-art approaches.
The Influence of Robot Number on Robot Group Perception—A Call for Action
Authors: Wullenkord Ricarda , Eyssel Friederike
Published: July 2020

Research on robot groups has often applied psychological principles underlying group processes between humans to interactions with and between robots. However, such research has failed to test empirically whether these principles indeed apply to the robot context. For instance, the notion of a social group may be interpreted differently when facing human versus robot groups. Basic research on this issue is missing. Therefore, the present experiment aimed at integrating social psychological theorizing and research on robot groups by utilizing the principles of group entitativity. We examined the effect of robot number and similarity on the perception of these robots as a (social) group. To do so, participants saw pictures of one to ten robots, appearing low or high in similarity. Results showed that the aspects eliciting the perception of a social “group” in humans seem to differ from the factors evoking robot group perception. According to our findings, at least three robots seem necessary for the perception of a robot “group” to emerge. Social psychological research, however, has proposed that two persons suffice to elicit the notion of a human social group. Basic research is needed to substantiate assumptions drawn from social psychological theorizing before translating it into human-robot context.
TimeToFocus
Authors: Borghouts Judith , Brumby Duncan P. , Cox Anna L.
Published: July 2020

Many computer tasks involve looking up information from different sources, and these self-interruptions can be disruptive. In this article, we investigate whether giving people feedback on how long they are away from their task influences their self-interruption behaviour. We conducted a contextual inquiry on self-interruption behaviour in an office workplace. Participants were observed to postpone physical interruptions until a convenient moment in the task if they were expected to take time. In contrast, observations revealed that digital interruptions were addressed immediately; participants reported these were presumed to be quick to deal with. To increase awareness of time spent on digital interruptions, we developed TimeToFocus, a notification tool showing people the duration of their interruptions while working on a task. A field study deployment of TimeToFocus in an office workplace found that feedback on the duration of interruptions made participants reflect on what they were doing during interruptions. They reported that they used this insight to avoid task-irrelevant activities. To confirm whether participants’ perceptions of the benefit of the tool could be measured, we conducted an online experiment, where participants had to retrieve information from an email sent to their personal email addresses and enter it into a spreadsheet. Participants who used our tool made shorter interruptions, completed the spreadsheet task faster and made fewer data entry errors. We conclude that feedback on the length of interruptions can assist users in focusing on their primary task and thus improve productivity.
Socio-Technical Aspirations for Children with Special Needs
Authors: Sharma Sumita , Avellan Tero , Linna Juhani , Achary Krishnaveni , Turunen Markku , Hakulinen Jaakko , Varkey Blessin
Published: July 2020

Society’s expectations and assistance for children with special needs is rooted in its cultural, societal, and political backdrop. Previous work on the role of culture on assistive or adaptive technology design for children with special needs identified a three-part framework: lifestyle, socio-technical infrastructure, and monetary and informational resources. Through our work in India, we proposed a fourth dimension to this framework: socio-technical aspirations. We defined socio-technical aspirations as the individual- or community-driven ambition and desire to own or use a specific technology for personal benefit or societal acceptance or both. In Finland, we interviewed four parents of children enrolled in a rehabilitation program, with the aim to understand their expectations from and current usage of technology. Findings from Finland reveal a desire for technology for children with special needs to be more engaging than what is currently available. We also identified several attributes that can contribute to socio-technical aspirations in a given context, including but not limited to: the level of inclusiveness supported in the school, which directly affects how technology is viewed with respect to the social acceptance it provides; the socio-technical aspirations of the child and how they are perceived and met by the parents and teachers; and previous technology experience of the various stakeholders involved in raising a child with special needs, which determines their attitude toward technology for not only for themselves but also for the child. In this article, we validate the dimension of socio-technical aspirations to strengthen our case for incorporating stakeholder’s socio-technical aspirations for technology designed or adapted for children with special needs.
A Survey on Device-free Indoor Localization and Tracking in the Multi-resident Environment
Authors: Ngamakeur Kan , Yongchareon Sira , Yu Jian , Rehman Saeed Ur
Published: July 2020

Indoor device-free localization and tracking can bring both convenience and privacy to users compared with traditional solutions such as camera-based surveillance and RFID tag-based tracking. Technologies such as Wi-Fi, wireless sensor, and infrared have been used to localize and track people living in care homes and office buildings. However, the presence of multiple residents introduces further challenges, such as the ambiguity in sensor measurements and target identity, to localization and tracking. In this article, we survey the latest development of device-free indoor localization and tracking in the multi-resident environment. We first present the fundamentals of device-free localization and tracking. Then, we discuss and compare thetechnologiesused in device-free indoor localization and tracking. After discussing the steps involved in multi-resident localization and tracking including target detection, target counting, target identification, localization, and tracking, thetechniquesrelated to each step are classified and discussed in detail along with the performance metrics. Finally, we identify the research gap and point out future research directions. To the best of our knowledge, this survey is the most comprehensive work that covers a wide spectrum of the research area of device-free indoor localization and tracking.
The Fundamental Uncertainties of Mothering
Authors: Devendorf Laura , Andersen Kristina , Kelliher Aisling
Published: July 2020

Present day ideals of good parenting are socio-technical constructs formed at the intersection of medical best practices, cultural norms, and technical innovation. These ideals take shape in relation to the fundamental uncertainty that parents/mothers face, an uncertainty that comes from not knowing how to do what is best for one's children, families, and selves. The growing body of parent-focused smart devices and data-tracking platforms emerging from this intersection frame the responsible parent as one who evaluates, analyzes, and mitigates data-defined risks for their children and family. As these devices and platforms proliferate, whether from respected medical institutions or commercial interests, they place new demands on families and add an implicit emphasis on how humans (often mothers) can be augmented and improved by data-rich technology. This is expressed both in the actions they support (e.g., breastfeeding, monitoring food intake), as well as in the emotions they render marginal (e.g., rage, struggle, loss, and regret). In this article, we turn away from optimization and self-improvement narratives to attend to our own felt experiences as mothers and designers. Through an embodied practice of creating Design Memoirs, we speak directly to the HCI community from our position as both users and subjects of optimized parenting tools. Our goal in this work is to bring nuance to a domain that is often rendered in simplistic terms or frames mothers as figures who could endlessly do more for the sake of their families. Our Design Memoirs emphasize the conflicting and often negative emotions we experienced while navigating these tools and medical systems. They depict our feelings of being at once powerful and powerless, expressing rage and love simultaneously, and struggling between expressing pride and humility. The Design Memoirs serve us in advocating that designers should use caution when considering a problem/solution focus to the experiences of parents. We conclude by reflecting on how our shared practice of making memoirs, as well as other approaches within feminist and queer theory, suggest strategies that trouble these optimization and improvement narratives. Overall, we present a case for designing for mothers who feel like they are just making do or falling short, in order to provide relief from the anxiety of constantly seeking improvement.
Discovering Graph Functional Dependencies
Authors: Fan Wenfei , Hu Chunming , Liu Xueli , Lu Ping
Published: July 2020

This article studies discovery of Graph Functional Dependencies (GFDs), a class of functional dependencies defined on graphs. We investigate the fixed-parameter tractability of three fundamental problems related to GFD discovery. We show that the implication and satisfiability problems are fixed-parameter tractable, but the validation problem is co-W[1]-hard in general. We introduce notions of reduced GFDs and their topological support, and formalize the discovery problem for GFDs. We develop algorithms for discovering GFDs and computing their covers. Moreover, we show that GFD discovery is feasible over large-scale graphs, by providing parallel scalable algorithms that guarantee to reduce running time when more processors are used. Using real-life and synthetic data, we experimentally verify the effectiveness and scalability of the algorithms.
A Survey of Learning Causality with Data
Authors: Guo Ruocheng , Cheng Lu , Li Jundong , Hahn P. Richard , Liu Huan
Published: July 2020

This work considers the question of how convenient access to copious data impacts our ability to learn causal effects and relations. In what ways is learning causality in the era of big data different from—or the same as—the traditional one? To answer this question, this survey provides a comprehensive and structured review of both traditional and frontier methods in learning causality and relations along with the connections between causality and machine learning. This work points out on a case-by-case basis how big data facilitates, complicates, or motivates each approach.
Incremental Discovery of Imprecise Functional Dependencies
Authors: Caruccio Loredana , Cirillo Stefano
Published: July 2020

Functional dependencies (
fd
s) are one of the metadata used to assess data quality and to perform data cleaning operations. However, to pursue robustness with respect to data errors, it has been necessary to devise imprecise versions of functional dependencies, yielding relaxed functional dependencies (
rfd
s). Among them, there exists the class of
rfd
s relaxing on the extent, i.e., those admitting the possibility that an
fd
holds on a subset of data. In the literature, several algorithms to automatically discover
rfd
s from big data collections have been defined. They achieve good performances with respect to the inherent problem complexity. However, most of them are capable of discovering
rfd
s only by batch processing the entire dataset. This is not suitable in the era of big data, where the size of a database instance can grow with high-velocity, and the insertion of new data can invalidate previously holding
rfd
s. Thus, it is necessary to devise incremental discovery algorithms capable of updating the set of holding
rfd
s upon data insertions, without processing the entire dataset. To this end, in this article we propose an incremental discovery algorithm for
rfd
s relaxing on the extent. It manages the validation of candidate
rfd
s and the generation of possibly new
rfd
candidates upon the insertion of the new tuples, while limiting the size of the overall search space. Experimental results show that the proposed algorithm achieves extremely good performances on real-world datasets.