bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling Long-Term Dependencies from Videos Using Deep Multiplicative Neural Networks
Authors: Si Wen , Liu Cong , Bi Zhongqin , Shan Meijing
Published: July 2020

Understanding temporal dependencies of videos is fundamental for vision problems, but deep learning–based models are still insufficient in this field. In this article, we propose a novel deep multiplicative neural network (DMNN) for learning hierarchical long-term representations from video. The DMNN is built upon the multiplicative block that remembers the pairwise transformations between consecutive frames using multiplicative interactions rather than the regular weighted-sum ones. The block is slided over the timesteps to update the memory of the networks on the frame pairs. Deep architecture can be implemented by stacking multiple layers of the sliding blocks. The multiplicative interactions lead to exact, rather than approximate, modeling of temporal dependencies. The memory mechanism can remember the temporal dependencies for an arbitrary length of time. The multiple layers output multiple-level representations that reflect the multi-timescale structure of video. Moreover, to address the difficulty of training DMNNs, we derive a theoretically sound convergent method, which leads to a fast and stable convergence. We demonstrate a new state-of-the-art classification performance with proposed networks on the UCF101 dataset and the effectiveness of capturing complicate temporal dependencies on a variety of synthetic datasets.
Deep Learning for Arabic Error Detection and Correction
Authors: Alkhatib Manar , Monem Azza Abdel , Shaalan Khaled
Published: July 2020

Research on tools for automating the proofreading of Arabic text has received much attention in recent years. There is an increasing demand for applications that can detect and correct Arabic spelling and grammatical errors to improve the quality of Arabic text content and application input. Our review of previous studies indicates that few Arabic spell-checking research efforts appropriately address the detection and correction of ill-formed words that do not conform to the Arabic morphology system. Even fewer systems address the detection and correction of erroneous well-formed Arabic words that are either contextually or semantically inconsistent within the text. We introduce an approach that investigates employing deep neural network technology for error detection in Arabic text. We have developed a systematic framework for spelling and grammar error detection, as well as correction at the word level, based on a bidirectional long short-term memory mechanism and word embedding, in which a polynomial network classifier is at the top of the system. To get conclusive results, we have developed the most significant gold standard annotated corpus to date, containing 15 million fully inflected Arabic words. The data were collected from diverse text sources and genres, in which every erroneous and ill-formed word has been annotated, validated, and manually revised by Arabic specialists. This valuable asset is available for the Arabic natural language processing research community. The experimental results confirm that our proposed system significantly outperforms the performance of Microsoft Word 2013 and Open Office Ayaspell 3.4, which have been used in the literature for evaluating similar research.
Use of Intelligent Voice Assistants by Older Adults with Low Technology Use
Authors: Pradhan Alisha , Lazar Amanda , Findlater Leah
Published: July 2020

Voice assistants embodied in smart speakers (e.g., Amazon Echo, Google Home) enable voice-based interaction that does not necessarily rely on expertise with mobile or desktop computing. Hence, these voice assistants offer new opportunities to different populations, including individuals who are not interested or able to use traditional computing devices such as computers and smartphones. To understand how older adults who use technology infrequently perceive and use these voice assistants, we conducted a 3-week field deployment of the Amazon Echo Dot in the homes of seven older adults. While some types of usage dropped over the 3-week period (e.g., playing music), we observed consistent usage for finding online information. Given that much of this information was health-related, this finding emphasizes the need to revisit concerns about credibility of information with this new interaction medium. Although features to support memory (e.g., setting timers, reminders) were initially perceived as useful, the actual usage was unexpectedly low due to reliability concerns. We discuss how these findings apply to other user groups along with design implications and recommendations for future work on voice-user interfaces.
Capturing Subjective First-Person View Shots with Drones for Automated Cinematography
Authors: Ashtari Amirsaman , Stevšić Stefan , Nägeli Tobias , Bazin Jean-Charles , Hilliges Otmar
Published: July 2020

We propose an approach to capture subjective first-person view (FPV) videos by drones for automated cinematography. FPV shots are intentionally not smooth to increase the level of immersion for the audience, and are usually captured by a walking camera operator holding traditional camera equipment. Our goal is to automatically control a drone in such a way that it imitates the motion dynamics of a walking camera operator, and, in turn, capture FPV videos. For this, given a user-defined camera path, orientation, and velocity, we first present a method to automatically generate the operator’s motion pattern and the associated motion of the camera, considering the damping mechanism of the camera equipment. Second, we propose a general computational approach that generates the drone commands to imitate the desired motion pattern. We express this task as a constrained optimization problem, where we aim to fulfill high-level user-defined goals, while imitating the dynamics of the walking camera operator and taking the drone’s physical constraints into account. Our approach is fully automatic, runs in real time, and is interactive, which provides artistic freedom in designing shots. It does not require a motion capture system, and works both indoors and outdoors. The validity of our approach has been confirmed via quantitative and qualitative evaluations.
UAVs vs. Pirates
Authors: Zhang Ruiwen , Holvoet Tom , Song Bifeng , Pei Yang
Published: July 2020

For the rising hazard of pirate attacks, unmanned aerial vehicle (UAV) swarm monitoring is a promising countermeasure. Previous monitoring methods have deficiencies in either adaptivity to dynamic events or simple but effective path coordination mechanisms, and they are inapplicable to the large-area, low-target-density, and long-duration persistent counter-piracy monitoring. This article proposes a self-organized UAV swarm counter-piracy monitoring method. Based on the pheromone map, this method is characterized by (1) a reservation mechanism for anticipatory path coordination and (2) a ship-adaptive mechanism for adapting to merchant ship distributions. A heuristic depth-first branch and bound search algorithm is designed for solving individual path planning. Simulation experiments are conducted to study the optimal number of plan steps and adaptivity scaling factor for different numbers of UAVs. Results show that merely decreasing revisit intervals cannot effectively reduce pirate attacks. Without the ship-adaptive mechanism, the proposed method reduces up to 87.2%, 43.2%, and 5.5% of revisit intervals compared to the Lèvy Walk method, the sweep method, and the baseline self-organized method, respectively, but cannot reduce pirate attacks; while with the ship-adaptive mechanism, the proposed method can reduce pirate attacks by up to 6.7% compared to the best of the baseline methods.
An Image Privacy Protection Algorithm Based on Adversarial Perturbation Generative Networks
Authors: Tong Chao , Zhang Mengze , Lang Chao , Zheng Zhigao
Published: July 2020

Today, users of social platforms upload a large number of photos. These photos contain personal private information, including user identity information, which is easily gleaned by intelligent detection algorithms. To thwart this, in this work, we propose an intelligent algorithm to prevent deep neural network (DNN) detectors from detecting private information, especially human faces, while minimizing the impact on the visual quality of the image. More specifically, we design an image privacy protection algorithm by training and generating a corresponding adversarial sample for each image to defend DNN detectors. In addition, we propose an improved model based on the previous model by training an adversarial perturbation generative network to generate perturbation instead of training for each image. We evaluate and compare our proposed algorithm with other methods on wider face dataset and others by three indicators: Mean average precision, Averaged distortion, and Time spent. The results show that our method significantly interferes with DNN detectors while causing weak impact to the visual quality of images, and our improved model does speed up the generation of adversarial perturbations.
Special Issue Proposal: Conversational Search and Recommendation
Authors: Hauff Claudia , Kiseleva Julia , Sanderson Mark , Zamani Hamed , Zhang Yongfeng
Published: July 2020

The rapid growth in speech and small screen interfaces, particularly on mobile devices, has significantly influenced the way users interact with intelligent systems to satisfy their information needs. The growing interest in personal digital assistants, such as Amazon Alexa, Apple Siri, Google Assistant, and Microsoft Cortana, demonstrates the willingness of users to employ conversational interactions. In this special issue, we focus on interactions with information seeking goals. This includes conversational search and recommendation. Given the importance of the topic to both academia and industry and the recent availability of multiple public datasets in this area, we believe that the time is right to propose a special issue on this topic, and ACM Transactions on Information Systems is the perfect venue for it.
ANALYSIS OF OBSTRUCTIVE SLEEP APNEA DISORDER WITH ACCURACY PREDICTION USING SVM FOR SMART ENVIRONMENT
Authors: Ramamurthy Madhumitha , Krishnamurthi Ilango , S Vimal , Annamalai Suresh
Published: July 2020

One of the most crucial sleep disorders that has a direct hit on the quality of life is sleep apnea disorder. Declined memory and disorders related to personality are some of the consequences of Sleep Apnea Disorder. Identifying the difference between normal and abnormal levels of snoring sound is important for the detection of sleep apnea. Diagnosis of the sleep apnea usually takes place in hospitals under the direct supervision of medical professionals. Frequent visits to hospital for diagnosis tend to be an inconvenience to the elderly. Along with it, usage of body monitors also acts as a parameter in disrupting sleep. To overcome them, the analysis equipment is placed in the usual sleeping environment of the patient and LM393 sound sensor is used to detect the snoring levels. The analysis between the normal and the acquired snoring levels using threshold values and SVM helps confirm the presence or absence of the sleep apnea disorder.
Physiotherapy Over a Distance
Authors: Aggarwal Deepti , Ploderer Bernd , Hoang Thuong , Vetere Frank , Bradford Mark
Published: July 2020

Wearable technologies offer potential in supporting assessment of lower limb movements in video consultations, which otherwise are challenging to assess. Yet there remains a limited understanding of how such technologies can be used to improve video consultations in a hospital setting and how they contribute to the clinician-patient interactions over a distance. In this article, we report on the findings of the field evaluation of a wearable technology—
SoPhy. SoPhy
consists of a pair of sensor embedded socks that capture the lower limb movements of a patient and a web-interface to visualise these movements for the remote physiotherapist. Our study demonstrates that
SoPhy
helped the physiotherapist in identifying the subtle differences in patients’ movements across all six phases of a consultation.
SoPhy
increased the confidence of the physiotherapist and guided more accurate assessment of the patients.
SoPhy
visualisation enhanced the overall clinician-patient communication and offered a better understanding of the therapy goals to the patients. Using the characteristics of the visualisations, patients were able to plan specific goals. We discuss how
SoPhy
helped in addressing challenges in video consultations experienced by a physiotherapist, and beyond that, how it enabled collective reflection between therapist and patient.
SIAT
Authors: Yengui Ameni , Neji Mahmoud
Published: July 2020

The introduction of digital technologies into documentation methods for cultural heritage and archeology made it possible to develop new tools for the acquisition and management of information collected for multidisciplinary studies. These tools are beginning to assert themselves as supports privileged for the description, analysis and comprehension of the objects of study. SIAT is a first step toward the creation of a set of methods, modern tools of analysis, and research in history and archeology. The objective of this approach is to define a continuum of information between all phases of the process from image acquisition, video, and spatial data to the construction of semantically enriched representations by integrating multi-dimensional aspects, multi-scales, multi-layer, and multi-temporal. This research tries to provide a definition and development of a set of data processing modules that can be used by specialists in the conservation of cultural heritage.
SODECL
Authors: Avramidis Eleftherios , Lalik Marta , Akman Ozgur E.
Published: July 2020

Stochastic differential equations (SDEs) are widely used to model systems affected by random processes. In general, the analysis of an SDE model requires numerical solutions to be generated many times over multiple parameter combinations. However, this process often requires considerable computational resources to be practicable. Due to the embarrassingly parallel nature of the task, devices such as multi-core processors and graphics processing units (GPUs) can be employed for acceleration.

Here, we present SODECL (https://github.com/avramidis/sodecl), a software library that utilizes such devices to calculate multiple orbits of an SDE model. To evaluate the acceleration provided by SODECL, we compared the time required to calculate multiple orbits of an exemplar stochastic model when one CPU core is used, to the time required when using all CPU cores or a GPU. In addition, to assess scalability, we investigated how model size affected execution time on different parallel compute devices.

Our results show that when using all 32 CPU cores of a high-end high-performance computing node, the task is accelerated by a factor of up to ≈6.7, compared to when using a single CPU core. Executing the task on a high-end GPU yielded accelerations of up to ≈4.5, compared to a single CPU core.
Trustworthy and Transparent Third-party Authority
Authors: Xu Runhua , Joshi James
Published: July 2020

Recent advances in
cryptographic
approaches, such as Functional Encryption and Attribute-based Encryption and their variants, have shown significant promise for enabling public clouds to provide secure computation and storage services for users’ sensitive data. A crucial component of these approaches is a
third-party authority (TPA)
that must be trusted to set up public parameters, provide private key service, and so on. Components of deployed cryptographic mechanisms such as the
certificate authorities (CAs)
, which are the TPAs of the underlying PKI for the SSL/TLS protocol, have faced several types of attacks (e.g.,
stealthy targeted
and
censorship
attacks), and certificate
mis-issuance
problems. Such practical challenges indicate that the successful deployment of newer emerging cryptographic schemes will also significantly depend on the trustworthiness of the TPAs. Furthermore, recently proposed decentralized TPA approaches that lower the threshold on the conditions required for an entity to become an authority can make the trust issue much worse. To address this issue, we propose an
authority transparency
framework to ensure the trustworthiness of TPAs of recent and emerging advanced cryptographic schemes. The framework includes a formal model and a
secure logging
-based approach to implement the framework. Further, to address the issues related to privacy, we also present a
privacy-preserving authority transparency
approach. We present security analysis and performance evaluation to show that
authority transparency
achieves the security and performance goals.
Optimally Self-Healing IoT Choreographies
Authors: Seeger Jan , Bröring Arne , Carle Georg
Published: July 2020

In the industrial Internet of Things domain, applications are moving from the Cloud into the Edge, closer to the devices producing and consuming data. This means that applications move from the scalable and homogeneous Cloud environment into a potentially constrained heterogeneous Edge network. Making Edge applications reliable enough to fulfill Industry 4.0 use cases remains an open research challenge. Maintaining operation of an Edge system requires advanced management techniques to mitigate the failure of devices. This article tackles this challenge with a twofold approach: (1) a policy-enabled failure detector that enables adaptable failure detection and (2) an allocation component for the efficient selection of failure mitigation actions. The parameters and performance of the failure detection approach are evaluated, and the performance of an energy-efficient allocation technique is measured. Finally, a vision for a complete system and an example use case are presented.
An Efficient Service Function Chaining Placement Algorithm in Mobile Edge Computing
Authors: Wang Meng , Cheng Bo , Chen Junliang
Published: July 2020

Mobile Edge Computing (MEC) is a promising network architecture that pushes network control and mobile computing to the network edge. Recent studies propose to deploy MEC applications in the Network Function Virtualization (NFV) environment. The mobile network service in NFV is deployed as a Service Function Chaining (SFC). In the dynamic and resource-limited mobile network, SFC placement aiming at optimizing resource utilization is a challenging problem. In this article, we solve the SFC placement problem in the MEC-NFV environment. We formulate the SFC placement problem as a weighted graph matching problem, including two sub-problems: a graph matching problem and an SFC mapping problem. To efficiently solve the graph matching problem, we propose a Linear Programming–(LP) based approach to calculate the similarity between VNFs and physical nodes. Based on the similarity, we design a Hungarian-based algorithm to solve the SFC mapping problem. Evaluation results show that our proposed LP-based solutions outperform the heuristic algorithms in terms of execution time and resource utilization.
Toward a Sustainable Handling of Interlinear-Glossed Text in Language Documentation
Authors: List Johann-Mattis , Sims Nathaniel A. , Forkel Robert
Published: July 2020

While the amount of digitally available data on the worlds’ languages is steadily increasing, with more and more languages being documented, only a small proportion of the language resources produced are sustainable. Data reuse is often difficult due to idiosyncratic formats and a negligence of standards that could help to increase the comparability of linguistic data. The sustainability problem is nicely reflected in the current practice of handling interlinear-glossed text, one of the crucial resources produced in language documentation. Although large collections of glossed texts have been produced so far, the current practice of data handling makes data reuse difficult. In order to address this problem, we propose a first framework for the computer-assisted, sustainable handling of interlinear-glossed text resources. Building on recent standardization proposals for word lists and structural datasets, combined with state-of-the-art methods for automated sequence comparison in historical linguistics, we show how our workflow can be used to lift a collection of interlinear-glossed Qiang texts (an endangered language spoken in Sichuan, China), and how the lifted data can assist linguists in their research.
Exploring How Persons with Dementia and Care Partners Collaboratively Appropriate Information and Communication Technologies
Authors: Hwang Amy S. , Jackson Piper , Sixsmith Andrew , Nygård Louise , Astell Arlene , Truong Khai N. , Mihailidis Alex
Published: July 2020

Persons with dementia and their care partners have been found to adapt their own technological arrangements using commercially available information and communication technologies (ICTs). Yet, little is known about these processes of technology appropriation and how care practices are impacted. Adopting a relational perspective of care, we longitudinally examined how four family care networks appropriated a new commercial ICT service into their existing technological arrangements and care practices. Cross-case analysis interpreted collaborative appropriation to encompass two interrelated processes of
creating and adapting technological practices
and
negotiating and augmenting care relationships
. Four driving forces were also proposed:
motivating meanings
that actors ascribe to the technology and its use; the
learnability
of the technology and actors’
resourcefulness
; the establishment of
responsive and cooperative care practices
; and the qualities of
empathy and shared power in care relationships
. The importance of technological literacy, learning, meaning-making, and the nature and quality of care relationships are discussed. Future work is urged to employ longitudinal and naturalistic approaches, and focus design efforts on promoting synergistic care relationships and care practices.
C
ANOPY
Authors: Panwar Nisha , Sharma Shantanu , Wang Guoxi , Mehrotra Sharad , Venkatasubramanian Nalini
Published: July 2020

This article focuses on the new privacy challenges that arise in smart homes. Specifically, the article focuses on inferring the user’s activities—which may, in turn, lead to the user’s privacy—via inferences through device activities and network traffic analysis. We develop techniques that are based on a cryptographically secure token circulation in a ring network consisting of smart home devices to prevent inferences from device activities, via
device workflow
, i.e., inferences from a coordinated sequence of devices’ actuation. The solution hides the device activity and corresponding channel activities, thus preserving the individual’s activities. We also extend our solution to deal with a large number of devices and devices that produce large-sized data by implementing parallel rings. Our experiments also evaluate the performance in terms of communication overheads of the proposed approach and the obtained privacy.
RVSDG
Authors: Reissmann Nico , Meyer Jan Christian , Bahmann Helge , Själander Magnus
Published: July 2020

Intermediate Representations (IRs) are central to optimizing compilers as the way the program is represented may enhance or limit analyses and transformations. Suitable IRs focus on exposing the most relevant information and establish invariants that different compiler passes can rely on. While control-flow centric IRs appear to be a natural fit for imperative programming languages, analyses required by compilers have increasingly shifted to understand data dependencies and work at multiple abstraction layers at the same time. This is partially evidenced in recent developments such as the Multi-Level Intermediate Representation (MLIR) proposed by Google. However, rigorous use of data flow centric IRs in general purpose compilers has not been evaluated for feasibility and usability as previous works provide no practical implementations.

We present the Regionalized Value State Dependence Graph (RVSDG) IR for optimizing compilers. The RVSDG is a data flow centric IR where nodes represent computations, edges represent computational dependencies, and regions capture the hierarchical structure of programs. It represents programs in demand-dependence form, implicitly supports structured control flow, and models entire programs within a single IR. We provide a complete specification of the RVSDG, construction and destruction methods, as well as exemplify its utility by presenting Dead Node and Common Node Elimination optimizations. We implemented a prototype compiler and evaluate it in terms of performance, code size, compilation time, and representational overhead. Our results indicate that the RVSDG can serve as a competitive IR in optimizing compilers while reducing complexity.
Creating Hardware Component Knowledge Bases with Training Data Generation and Multi-task Learning
Authors: Hsiao Luke , Wu Sen , Chiang Nicholas , Ré Christopher , Levis Philip
Published: July 2020

Hardware component databases are vital resources in designing embedded systems. Since creating these databases requires hundreds of thousands of hours of manual data entry, they are proprietary, limited in the data they provide, and have random data entry errors.

We present a machine learning based approach for creating hardware component databases directly from datasheets. Extracting data directly from datasheets is challenging because: (1) the data is relational in nature and relies on non-local context, (2) the documents are filled with technical jargon, and (3) the datasheets are PDFs, a format that decouples visual locality from locality in the document. Addressing this complexity has traditionally relied on human input, making it costly to scale. Our approach uses a rich data model, weak supervision, data augmentation, and multi-task learning to create these knowledge bases in a matter of days.

We evaluate the approach on datasheets of three types of components and achieve an average quality of 77 F1 points—quality comparable to existing human-curated knowledge bases. We perform application studies that demonstrate the extraction of multiple data modalities including numerical properties and images. We show how different sources of supervision such as heuristics and human labels have distinct advantages that can be utilized together to improve knowledge base quality. Finally, we present a case study to show how this approach changes the way practitioners create hardware component knowledge bases.
Neural Co-training for Sentiment Classification with Product Attributes
Authors: Bai Ruirui , Wang Zhongqing , Kong Fang , Li Shoushan , Zhou Guodong
Published: July 2020

Sentiment classification aims to detect polarity from a piece of text. The polarity is usually positive or negative, and the text genre is usually product review. The challenges of sentiment classification are that it is hard to capture semantic of reviews, and the labeled data is hard to annotate. Therefore, we propose
neural co-training
to learn the semantic representation of each review using the neural network model, and learn the information from unlabeled data using a co-training framework. In particular, we use the attention-based bi-directional Gated Recurrent Unit (Att-BiGRU) to model the semantic content of each review and regard different categories of the target product as different views. We then use a co-training framework to learn and predict the unlabeled reviews with different views. Experiment results with the Yelp dataset demonstrate the effectiveness of our approach.
Improved Heuristic Data Management and Protection Algorithm for Digital China Cultural Datasets
Authors: Shang Rui , Li Xia
Published: July 2020

In the present scenario sustainable management and protection of digital cultural datasets are considered as a significant area of research. In the recent past, the protection and management of cultural data are facing several new challenges and opportunities. Though several researchers explored their work on managing and protecting cultural data, efficiently and reliability of the present data management algorithm seems to be more complicated due to its incompetence in managing data in an optimized manner. This work presents an improved heuristic big data management algorithm for cultural datasets which is considered as a new discipline of digital cultural heritage specially established for strengthening strategic and interdisciplinary research. The scientific operation and management mechanism of digital protection of cultural heritage is experimentally validated and results show promising outcomes.
GPU Fast Convolution via the Overlap-and-Save Method in Shared Memory
Authors: Adámek Karel , Dimoudi Sofia , Giles Mike , Armour Wesley
Published: July 2020

We present an implementation of the overlap-and-save method, a method for the convolution of very long signals with short response functions, which is tailored to GPUs. We have implemented several FFT algorithms (using the CUDA programming language), which exploit GPU shared memory, allowing for GPU accelerated convolution. We compare our implementation with an implementation of the overlap-and-save algorithm utilizing the NVIDIA FFT library (cuFFT). We demonstrate that by using a shared-memory-based FFT, we can achieved significant speed-ups for certain problem sizes and lower the memory requirements of the overlap-and-save method on GPUs.
Zeroploit
Authors: Rangan Ram , Stephenson Mark W. , Ukarande Aditya , Murthy Shyam , Agarwal Virat , Blackstein Marc
Published: July 2020

In this article, we first characterize register operand value locality in shader programs of modern gaming applications and observe that there is a high likelihood of one of the register operands of several multiply, logical-and, and similar operations being zero, dynamically. We provide intuition, examples, and a quantitative characterization for how zeros originate dynamically in these programs. Next, we show that this dynamic behavior can be gainfully exploited with a profile-guided code optimization called
Zeroploit
that transforms targeted code regions into a zero-(value-)specialized fast path and a default slow path. The fast path benefits from zero-specialization in two ways, namely: (a) the backward slice of the
other
operand of a given multiply or logical-and can be skipped dynamically, provided the only use of that other operand is in the given instruction, and (b) the forward slice of instructions originating at the given instruction can be zero-specialized, potentially triggering further backward slice specializations from operations of that forward slice as well. Such specialization helps the fast path avoid redundant dynamic computations as well as memory fetches, while the fast-slow versioning transform helps preserve functional correctness. With an offline value profiler and manually optimized shader programs, we demonstrate that
Zeroploit
is able to achieve an average speedup of 35.8% for targeted shader programs, amounting to an average frame-rate speedup of 2.8% across a collection of modern gaming applications on an NVIDIA® GeForce RTX™ 2080 GPU.
Batch-file Operations to Optimize Massive Files Accessing
Authors: Yang Yang , Cao Qiang , Yao Jie , Jiang Hong , Yang Li
Published: July 2020

Existing local file systems, designed to support a typical
single-file access mode
only, can lead to poor performance when accessing a batch of files, especially small files. This single-file mode essentially serializes accesses to batched files one by one, resulting in a large number of non-sequential, random, and often dependent I/Os between file data and metadata at the storage ends. Such access mode can further worsen the efficiency and performance of applications accessing massive files, such as data migration. We first experimentally analyze the root cause of such inefficiency in batch-file accesses. Then, we propose a novel batch-file access approach, referred to as BFO for its set of optimized
Batch-File Operations
, by developing novel
BFOr
and
BFOw
operations for fundamental read and write processes, respectively, using a
two-phase access
for metadata and data jointly. The BFO offers dedicated interfaces for batch-file accesses and additional processes integrated into existing file systems without modifying their structures and procedures. In addition, based on BFOr and BFOw, we also propose the novel batch-file migration
BFOm
to accelerate the data migration for massive small files. We implement a BFO prototype on ext4, one of the most popular file systems. Our evaluation results show that the batch-file read and write performances of BFO are consistently higher than those of the traditional approaches regardless of access patterns, data layouts, and storage media, under synthetic and real-world file sets. BFO improves the read performance by up to 22.4× and 1.8× with HDD and SSD, respectively, and it boosts the write performance by up to 111.4× and 2.9× with HDD and SSD, respectively. BFO also demonstrates consistent performance advantages for data migration in both local and remote situations.
Machine Learning Methods for Data Association in Multi-Object Tracking
Authors: Emami Patrick , Pardalos Panos M. , Elefteriadou Lily , Ranka Sanjay
Published: July 2020

Data association is a key step within the multi-object tracking pipeline that is notoriously challenging due to its combinatorial nature. A popular and general way to formulate data association is as the NP-hard multi-dimensional assignment problem. Over the past few years, data-driven approaches to assignment have become increasingly prevalent as these techniques have started to mature. We focus this survey solely on learning algorithms for the assignment step of multi-object tracking, and we attempt to unify various methods by highlighting their connections to linear assignment and to the multi-dimensional assignment problem. First, we review probabilistic and end-to-end optimization approaches to data association, followed by methods that learn association affinities from data. We then compare the performance of the methods presented in this survey and conclude by discussing future research directions.
Study on Automated Approach to Recognize Characters for Handwritten and Historical Document
Authors: S Dhivya , G Usha Devi
Published: July 2020

Script recognition is the mechanism of automatic script analysis and recognition whereby intensive study has been carried out and a significant amount of papers on this problem have been released over the past. But there are still a few issues to be solved, particularly in Indian historical manuscripts. This literature examines the Script recognition with reference to multi-script document and different historical scripts such as Kurdish-Latin, Devanagari, Grantha, Arabic handwritten characters, Bangladesh, Devanagari and Gurumukhi, ancient Chinese, Arabic, Nam Character, Greek, Nastalique Urdu, Georgian handwritten, Nandinagari, and Hebrew, which provide the course of study that focuses on the framework for script recognition. This review concentrates on scope of prediction, dataset type, the methods used for data preprocessing, and measures of performance used for analysis. On the basis of this survey, Current research constraints have been recognized and future study specifications are emphasized in the area of modeling historical manuscripts.

CCS Concepts:
Crab-tree
Authors: Wang Chundong , Chattopadhyay Sudipta , Brihadiswarn Gunavaran
Published: July 2020

In recent years, the next-generation non-volatile memory (NVM) technologies have emerged with DRAM-like byte addressability and disk-like durability. Computer architects have proposed to use them to build
persistent memory
that blurs the conventional boundary between volatile memory and non-volatile storage. However, ARM processors, ones that are widely used in embedded computing systems, start providing architectural supports to utilize NVM since ARMv8. In this article, we consider tailoring B+-tree for NVM operated by a 64-bit ARMv8 processor. We first conduct an empirical study of performance overhead in writing and reading data for a B+-tree with an ARMv8 processor, including the time cost of cache line flushes and memory fences for crash consistency as well as the execution time of binary search compared to that of linear search. We hence identify the key weaknesses in the design of B+-tree with ARMv8 architecture. Accordingly, we develop a new B+-tree variant, namely,
c
rash
r
ecoverable
A
RMv8-oriented
B
+-tree (Crab-tree). To insert and delete data at runtime, Crab-tree selectively chooses one of two strategies, i.e., copy on write and shifting in place, depending on which one causes less consistency cost. Crab-tree regulates a strict execution order in both strategies and recovers the tree structure in case of crashes. To further improve the performance of Crab-tree, we employ three methods to reduce software overhead, cache misses, and consistency cost, respectively. We have implemented and evaluated Crab-tree in Raspberry Pi 3 Model B+ with emulated NVM. Experiments show that Crab-tree significantly outperforms state-of-the-art B+-trees designed for persistent memory by up to 2.2× and 3.7× in write and read performances, respectively, with both consistency and scalability achieved.
Foundations, Properties, and Security Applications of Puzzles
Authors: Ali Isra Mohamed , Caprolu Maurantonio , Pietro Roberto Di
Published: July 2020

Cryptographic algorithms have been used not only to create robust ciphertexts but also to generate cryptograms that, contrary to the classic goal of cryptography, are meant to be broken. These cryptograms, generally called puzzles, require the use of a certain amount of resources to be solved, hence introducing a cost that is often regarded as a time delay—though it could involve other metrics as well, such as bandwidth. These powerful features have made puzzles the core of many security protocols, acquiring increasing importance in the IT security landscape. The concept of a puzzle has subsequently been extended to other types of schemes that do not use cryptographic functions, such as CAPTCHAs, which are used to discriminate humans from machines. Overall, puzzles have experienced a renewed interest with the advent of Bitcoin, which uses a CPU-intensive puzzle as proof of work. In this article, we provide a comprehensive study of the most important puzzle construction schemes available in the literature, categorizing them according to several attributes, such as resource type, verification type, and applications. We have redefined the term puzzle by collecting and integrating the scattered notions used in different works, to cover all the existing applications. Moreover, we provide an overview of the possible applications, identifying key requirements and different design approaches. Finally, we highlight the features and limitations of each approach, providing a useful guide for the future development of new puzzle schemes.
Multi-Task Learning for Entity Recommendation and Document Ranking in Web Search
Authors: Huang Jizhou , Wang Haifeng , Zhang Wei , Liu Ting
Published: July 2020

Entity recommendation, providing users with an improved search experience by proactively recommending related entities to a given query, has become an indispensable feature of today’s Web search engine. Existing studies typically only consider the query issued at the current timestep while ignoring the in-session user search behavior (short-term search history) or historical user search behavior across all sessions (long-term search history) when generating entity recommendations. As a consequence, they may fail to recommend entities of interest relevant to a user’s actual information need. In this work, we believe that both short-term and long-term search history convey valuable evidence that could help understand the user’s search intent behind a query, and take both of them into consideration for entity recommendation. Furthermore, there has been little work on exploring whether the use of other companion tasks in Web search such as document ranking as auxiliary tasks could improve the performance of entity recommendation. To this end, we propose a multi-task learning framework with deep neural networks (DNNs) to jointly learn and optimize two companion tasks in Web search engines: entity recommendation and document ranking, which can be easily trained in an end-to-end manner. Specifically, we regard document ranking as an auxiliary task to improve the main task of entity recommendation, where the representations of queries, sessions, and users are shared across all tasks and optimized by the multi-task objective during training. We evaluate our approach using large-scale, real-world search logs of a widely-used commercial Web search engine. We also performed extensive ablation experiments over a number of facets of the proposed multi-task DNN model to figure out their relative importance. The experimental results show that both short-term and long-term search history can bring significant improvements in recommendation effectiveness, and the combination of both outperforms using either of them individually. In addition, the experiments show that the performance of both entity recommendation and document ranking can be significantly improved, which demonstrates the effectiveness of using multi-task learning to jointly optimize the two companion tasks in Web search.
Multiples Over Models
Authors: Fox Sarah E. , Menking Amanda , Eschler Jordan , Backonja Uba
Published: July 2020

In this article, we describe our efforts to retrace and reimagine period tracking technology—or, mobile applications designed to support the documentation and quantification of menstrual cycle data. In their current form, these systems often encourage those who menstruate to extract intimate information about the body (e.g., consistency or color of menstrual flow, physical and emotional symptoms), while promising to predict fertility and offer insight into managing one's period. In doing so, these technologies subtly dictate the forms of knowledge and types of relationships menstruators are expected to establish with their bodies (i.e., transactional or instrumentalized). Through historical analysis and a series of participatory experiments, we offer a vision for menstrual sensemaking that expands on these forms of interaction and ways of knowing to emphasize
multiplicity
and
dimensionality
rather than models, predictability, or a user's relation to averages or norms.
SCANet
Authors: Li Yantao , Hu Hailong , Zhu Zhangqian , Zhou Gang
Published: July 2020

Continuous authentication monitors the security of a system throughout the login session on mobile devices. In this article, we present SCANet, a two-stream convolutional neural network--based continuous authentication system that leverages the accelerometer and gyroscope on smartphones to monitor users’ behavioral patterns. We are among the first to use two streams of data—frequency domain data and temporal difference domain data—from the two sensors as the inputs of the convolutional neural network (CNN). SCANet utilizes the two-stream CNN to learn and extract representative features and then performs the principal component analysis to select the top 25 features with high discriminability. With the CNN-extracted features, SCANet exploits the one-class support vector machine to train the classifier in the enrollment phase. Based on the trained CNN and classifier, SCANet identifies the current user as a legitimate user or an impostor in the continuous authentication phase. We evaluate the effectiveness of the two-stream CNN and the performance of SCANet on our dataset and BrainRun dataset, and the experimental results demonstrate that CNN achieves 90.04% accuracy, and SCANet reaches an average of 5.14% equal error rate on two datasets and takes approximately 3 s for user authentication.
Troubling Design
Authors: Søndergaard Marie Louise Juul
Published: July 2020

The euphemism “female trouble” (discreetly referring to women's health experiences) suggests that trouble is linked to women and bodily transitions women can go through. However, trouble is not only a noun; it is also a verb with a strong feminist tradition. In this article, I present
troubling design
; a theoretically grounded and practice-oriented design program for designing with women's health.
Troubling design
brings to matter how trouble is an implicit condition of designing ethical and responsible technologies for women's health. By designing with trouble,
troubling design
encourages designers and researchers to develop knowledge embedded in criticality and questioning status quo, in order to expand their perspectives on designing with intimate and tabooed bodily experiences. Grounded in feminist theory and research-through-design,
troubling design
contributes with analytical and generative design knowledge articulated through design examples and three practices: staying with the wrong, curious visiting, and collective imagining.
Contrast Pattern Mining in Paired Multivariate Time Series of a Controlled Driving Behavior Experiment
Authors: Li Qingzhe , Zhao Liang , Lee Yi-Ching , Lin Jessica
Published: July 2020

The controlled experiment is an important scientific method for researchers seeking to determine the influence of the intervention, by interpreting the contrast patterns between the temporal observations from control and experimental groups (i.e., paired multivariate time series (PMTS)). Due to recent technological advances and the growing popularity of sensing technology such as in-vehicle sensors and activity trackers, time series data is experiencing explosive growth in both size and complexity. This is threatening to overwhelm the interpretation of control experiments, which conventionally rely on human analysts. Thus, it is imperative to develop automated methods that are expected to simultaneously characterize and detect the interpretable contrast patterns in PMTS generated by controlled experiments. However, a few challenges prohibit existing methods from directly addressing this problem: (1) handling the coupling of contrast identification and pattern characterization, (2) dynamically characterizing the patterns in PMTS, and (3) mining the contrast patterns in multiple PMTS with ubiquitous individual differences. Therefore, we propose a novel framework to mine interpretable contrast patterns based on the dynamic feature dependencies for PMTS through optimization. The proposed framework simultaneously characterizes the dynamic feature dependency networks for PMTS and detects the contrast patterns. Specifically, we characterize the generative process of PMTS as a probabilistic model defined by pairwise Markov random fields whose likelihoods are maximized using our group graphical lasso. The model is then generalized to handle multiple PMTS and solved by proposing a customized algorithm based on the expectation-maximization framework. Extensive experiments demonstrate the effectiveness, scalability, and interpretability of our approach.