Reviewerly/exHarmony huggingface dataset:
Online Video Summarization: Predicting Future to Better Summarize Present

Automatically generating the summary of a video is a challenging problem due to its subjective nature. Most of the previous works in the field consider the entire video to extract out the important frames. Unlike them, our paper presents MerryGoRoundNet, a supervised learning approach to solve this problem in an online fashion. We observe that to effectively summarize a video, one needs to take into account both the spatial and temporal relations between video frames. MerryGoRoundNet utilizes encoder-decoder style architecture and convolutional LSTM to establish spatiotemporal relationship and generates the summary on the fly, thereby being more efficient than non-autoregressive counterparts in terms of time and memory. In order to make summary more diverse and complete, we augment our network with unsupervised task of next frame prediction and a supervised task of scene start detection and propose a loss function that explicitly focuses on achieving the right balance between continuity and diversity in the produced summary. Ablation study performed affirms the architecture and learning objective of our approach. Evaluation of MerryGoRoundNet on different datasets demonstrates superior performance among online summarization approaches and competitive performance when compared with offline approaches as well.
Towards Optimized and Constant-Time CSIDH on Embedded Devices

We present an optimized, constant-time software library for commutative supersingular isogeny Diffie-Hellman key exchange (CSIDH) proposed by Castryck et al. which targets 64-bit ARM processors. The proposed library is implemented based on highly-optimized field arithmetic operations and computes the entire key exchange in constant-time. The proposed implementation is resistant to timing attacks. We adopt optimization techniques to evaluate the highest performance CSIDH on ARM-powered embedded devices such as cellphones, analyzing the possibility of using such a scheme in the quantum era. To the best of our knowledge, the proposed implementation is the first constant-time implementation of CSIDH and the first evaluation of this scheme on embedded devices. The benchmark result on a Google Pixel 2 smartphone equipped with 64-bit high-performance ARM Cortex-A72 core shows that it takes almost 12 s for each party to compute a commutative action operation in constant-time over the 511-bit finite field proposed by Castryck et al. However, using uniform but variable-time Montgomery ladder with security considerations improves these results significantly.
Image Decolorization Combining Local Features and Exposure Features

Image decolorization is a task aiming to transform a color image to a grayscale one and is a dimension reduction process which inevitably suffers from information loss. The general goal of image decolorization is to preserve the color contrast of the color image. According to human visual study, exposure affects the human visual perception, and low-exposure areas or over-exposure areas will first attract the sense of sight. In addition, exposure also affects the contrast of the image, the contrast of low-exposure areas and over-exposure areas often cannot be well shown. Thus, the exposure should be taken into account in the process of image decolorization. Traditional local methods are not accurate enough to process local pixel blocks which may tend to cause local artifacts, while traditional global methods cannot greatly deal with local color blocks, which are usually time consuming too. Besides, the traditional image decolorization method usually uses the low-level features of an image. In this paper, the convolutional neural network is used to learn high-level abstract features of the image. We design a new convolutional neural-network framework with a local feature network and a rough classifier, which can learn the local semantic features and distinguish the different exposure conditions of color images. It is possible to learn the mapping model between input-output image pairs, which can generate better results in terms of color contrast preservation and exposure adjustment. Experiments indicate that our method does better in terms of color contrast preservation and exposure adjustment than the state of the art.
Locality domains and morphological rules

Korean subject honorification and Korean negation have both affixal and suppletive exponents. In addition, Korean negation has a periphrastic realization involving an auxiliary verb. By examining their interaction, we motivate several hypotheses concerning locality constraints on the conditioning of suppletion and the insertion of dissociated morphemes (‘node-sprouting’). At the same time, we come to a better understanding of the nature of Korean subject honorification. We show that Korean honorific morphemes are ‘dissociated’ or ‘sprouted,’ i.e., introduced by morphosyntactic rule in accordance with morphological well-formedness constraints, like many other agreement morphemes. We argue that the conditioning domain for node-sprouting is the syntactic phase. In contrast, our data suggest that the conditioning domain for suppletion is the complex X0, as proposed by Bobaljik (2012). We show that the ‘spanning’ hypotheses concerning exponence (Merchant 2015; Svenonius 2012), the ‘linear adjacency’ hypotheses (Embick 2010), and ‘accessibility domain’ hypothesis (Moskal 2014, 2015a, 2015b; Moskal and Smith 2016) make incorrect predictions for Korean suppletion. Finally, we argue that competition between honorific and negative suppletive exponents reveals a root-outwards effect in allomorphic conditioning, supporting the idea that insertion of vocabulary items proceeds root-outwards (Bobaljik 2000).
Equivalent mechanism: Releasing location data with errors through differential privacy

Location-based services are raising remarkable convenience to our daily life while seriously threatening the location privacy of individuals. Differential privacy provides a promising privacy definition for location data. It is enforced by injecting random noise into each location such that the level of privacy and utility provided by this sanitization when querying an LBS is quantified and controlled. However, the primitive differential privacy overlooks data errors, which constantly exist in real-life location data, thereby potentially deviating a specified indistinguishability. Therefore, we determine the impact of data errors on the indistinguishability to address the abovementioned issue. Then, we design an equivalent mechanism to enforce differential privacy and analyze its privacy and utility. Extensive experimental evaluation on real-world datasets demonstrates that our proposed equivalent mechanism consistently outperforms several state-of-the-art mechanisms in data utility at the same privacy level.
A roadmap for semi-automatically extracting predictive and clinically meaningful temporal features from medical data for predictive modeling

Predictive modeling based on machine learning with medical data has great potential to improve healthcare and reduce costs. However, two hurdles, among others, impede its widespread adoption in hdealthcare. First, medical data are by nature longitudinal. Pre-processing them, particularly for feature engineering, is labor intensive and often takes 50-80% of the model building effort. Predictive temporal features are the basis of building accurate models, but are difficult to identify. This is problematic. Healthcare systems have limited resources for model building, while inaccurate models produce sub-optimal outcomes and are often useless. Second, most machine learning models provide no explanation of their prediction results. However, offering such explanations is essential for a model to be used in usual clinical practice. To address these two hurdles, this paper outlines: 1) a data-driven method for semi-automatically extracting predictive and clinically meaningful temporal features from medical data for predictive modeling; and 2) a method of using these features to automatically explain machine learning prediction results and suggest tailored interventions. This provides a roadmap for future research.
The Application of Blockchain Technology in Financial markets

In recent years, blockchain technology has been developing rapidly in social economy. As blockchain technology has many excellent properties, it is used for the transaction of Bitcoin at first. With the rise of Bitcoin, the connection between blockchain technology and financial market will be closer more integrative. And then more and more financial institutions have recognized the importance of blockchain technology and started to try to use this technology in financial operations, such as R3CEV, Hyperledger and Qiwi. Many financial institutions have started trying to apply blockchain technology into financial transactions in order to decrease transaction costs and increase operational efficiencies, especially in financial note, cross-border payment and asset-backed securitization. It can be seen that blockchain technology will have widely application prospects in financial fields in the future.
An activity vigilance system for elderly based on fuzzy probability transformations

Housing around 17% of the earth's total population, the Republic of India is a place where 91.6 million of its total population is above 60 years old. Elderly people in India has always been taken care of by their children; however, due to the rampant urbanization and employment constraints, child ren along with their own families move to the places of their employment leaving old parents alone in their houses, unattended. In this paper, a fuzzy probability transformation based system is proposed to ensure that the elderly are safe in their houses. The scope of this work is limited to learn the bathroom usage pattern of an elderly individual. If an unusual pattern is detected, the system will inform the caregivers immediately. To deal with the ambiguities, the concept of fuzzy numbers is used in the system. The proposed system utilizes the idea of the Internet of Things (IoT) to collect, transmit and present data to the stakeholders. Bayesian reasoning is used as a rider to the proposed methodology for robust decision making in cases of deep uncertainties. An alternative analysis is also performed on the same data using logistic regression and time-series analysis. The results show that the proposed method learns well with time than the other methods and provides superior accuracy.
Detection of Delamination in Polymer Composites by Digital Image Correlation—Experimental Test

Fiber-reinforced polymer composite structures are frequently used in industries where personal safety is critical; therefore, it is important to periodically estimate or monitor the condition of high value, load bearing structures. The digital image correlation (DIC) is well known as an effective method to obtain full field surface strains; in this paper, it was used to detect artificial damage inside the structures. Carbon or glass fabric reinforced epoxy specimens were produced and tested. All specimens contained an artificial through-delamination which was created by the insertion of different foils of a mould release agent during production. Tensile and compression tests were done while the camera system collected the images of the deformed surface to be analyzed posteriorly. In most cases the approximate locations of delaminations could be effectively detected from strain maps by the localization of zones showing different strain values than intact zones.
A Unified Entropy-Based Distance Metric for Ordinal-and-Nominal-Attribute Data Clustering

Ordinal data are common in many data mining and machine learning tasks. Compared to nominal data, the possible values (also called categories interchangeably) of an ordinal attribute are naturally ordered. Nevertheless, since the data values are not quantitative, the distance between two categories of an ordinal attribute is generally not well defined, which surely has a serious impact on the result of the quantitative analysis if an inappropriate distance metric is utilized. From the practical perspective, ordinal-and-nominal-attribute categorical data, i.e., categorical data associated with a mixture of nominal and ordinal attributes, is common, but the distance metric for such data has yet to be well explored in the literature. In this paper, within the framework of clustering analysis, we therefore first propose an entropy-based distance metric for ordinal attributes, which exploits the underlying order information among categories of an ordinal attribute for the distance measurement. Then, we generalize this distance metric and propose a unified one accordingly, which is applicable to ordinal-and-nominal-attribute categorical data. Compared with the existing metrics proposed for categorical data, the proposed metric is simple to use and nonparametric. More importantly, it reasonably exploits the underlying order information of ordinal attributes and statistical information of nominal attributes for distance measurement. Extensive experiments show that the proposed metric outperforms the existing counterparts on both the real and benchmark data sets.
μDPM: Dynamic Power Management for the Microsecond Era

The complex, distributed nature of data centers have spawned the adoption of distributed, multi-tiered software architectures, consisting of many inter-connected microservices. These microservices exhibit extremely short request service times, often less than 250μs. We show that these “killer microsecond” service times can cause state-of-the-art dynamic power management techniques to break down, due to short idle period length and low power state transition overheads. In this paper, we propose μDPM, a dynamic power management scheme for the microsecond era that coordinates request delaying, per-core sleep states, and voltage frequency scaling. The idea is to postpone the wake up of a CPU as long as possible and then adjust the frequency so that the tail latency constraint of requests are satisfied just-in-time. μDPM reduces processor energy consumption by up to 32% and consistently outperforms state-of-the-art techniques by 2×.
Faster polynomial multiplication over finite fields using cyclotomic coefficient rings

We prove that for a fixed prime p, polynomials in Fp[X] of degree n may be multiplied in O(nlogn4log∗n) bit operations. Previously, the best known bound was O(nlogn8log∗n).
Context-Aware Cross-Lingual Mapping

Hanan Aldarmaki, Mona Diab. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Deep Learning-Aided OCR Techniques for Chinese Uppercase Characters in the Application of Internet of Things

Optical character recognition (OCR) has become one of the most important techniques in computer vision, given that it can easily obtain digital information from various images on the Internet of Things (IoT). However, existing OCR techniques pose a big challenge in the recognition of the Chinese uppercase characters due to their poor performance. In order to solve the problem, this paper proposes a deep learning-aided OCR technique for improving recognition accuracy. First, we generate a database of the Chinese uppercase characters to train four neural networks: a convolution neural network (CNN), a visual geometry group, a capsule network, and a residual network. Second, the four networks are tested on the generated dataset in terms of accuracy, network weight, and test time. Finally, in order to reduce test time and save computational resources, we also develop a lightweight CNN method to prune the network weight by 96.5% while reducing accuracy by no more than 1.26%.
Second-Order Fault Tolerant Extended Kalman Filter for Discrete Time Nonlinear Systems

As missing sensor data may severely degrade the overall system performance and stability, reliable state estimation is of great importance in modern data-intensive control, computing, and power systems applications. Aiming at providing a more robust and resilient state estimation technique, this paper presents a novel second-order fault-tolerant extended Kalman filter estimation framework for discrete-time stochastic nonlinear systems under sensor failures, bounded observer-gain perturbation, extraneous noise, and external disturbances condition. The failure mechanism of multiple sensors is assumed to be independent of each other with various malfunction rates. The proposed approach is a locally unbiased, minimum estimation error covariance based nonlinear observer designed for dynamic state estimation under these conditions. It has been successfully applied to a benchmark target-trajectory tracking application. Computer simulation studies have demonstrated that the proposed second-order fault-tolerant extended Kalman filter provides more accurate estimation results, in comparison with traditional first- and second-order extended Kalman filter. Experimental results have demonstrated that the proposed second-order fault-tolerant extended Kalman filter can serve as a powerful alternative to the existing nonlinear estimation approaches.
Face recognition in low-quality images using adaptive sparse representations

Although unconstrained face recognition has been widely studied over the recent years, state-of-the-art algorithms still result in an unsatisfactory performance for low-quality images. In this paper, we make two contributions to this field: the first one is the release of a new dataset called ‘AR-LQ’ that can be used in conjunction with the well-known ‘AR’ dataset to evaluate face recognition algorithms on blurred and low-resolution face images. The proposed dataset contains five new blurred faces (at five different levels, from low to severe blurriness) and five new low-resolution images (at five different levels, from 66 × 48 to 7 × 5 pixels) for each of the hundred subjects of the ‘AR’ dataset. The new blurred images were acquired by using a DLSR camera with manual focus that takes an out-of-focus photograph of a monitor that displays a sharp face image. In the same way, the low-resolution images were acquired from the monitor by a DLSR at different distances. Thus, an attempt is made to acquire low-quality images that have been degraded by a real degradation process. Our second contribution is an extension of a known face recognition technique based on sparse representations (ASR) that takes into account low-resolution face images. The proposed method, called blur-ASR or bASR, was designed to recognize faces using dictionaries with different levels of blurriness. These were obtained by digitally blurring the training images, and a sharpness metric for matching blurriness between the query image and the dictionaries. These two main adjustments made the algorithm more robust with respect to low-quality images. In our experiments, bASR consistently outperforms other state-of-the-art methods including hand-crafted features, sparse representations, and seven well-known deep learning face recognition techniques with and without super resolution techniques. On average, bASR obtained 88.8% of accuracy, whereas the rest obtained less than 78.4%.
Human-Engaged Computing: the future of Human–Computer Interaction

Debates regarding the nature and role of Human-Computer Interaction (HCI) have become increasingly common. This is because HCI lacks a clear philosophical foundation from which to derive a coherent vision and consistent aims and goals. This paper proposes a conceptual framework for ongoing discussion that can give more meaningful and pertinent direction to the future of HCI; we call the proposed approach Human-Engaged Computing (HEC). HEC is a focused yet adaptable philosophical approach which aims to establish “synergized interactions between engaged humans and engaging computers for high level wisdom which enhances our human survival probability and our full potential as humans”. In this paper, HEC is described through five perspectives—definition, components, principles, case studies and benefits. The paper concludes by suggesting future directions for HEC.
Cost efficient resource allocation for real-time tasks in embedded systems

Various application classes are being deployed to the cloud these days making use of a pay-as-you-go policy. However, existing cloud technologies are still at an early stage of maturity for applications with real-time constraints. With the emergence of Internet of Things (IoT) deployments and embedded systems in smart infrastructure, requirements for off-loading computation to cloud are increasing. In real-time systems, the resource allocation problem is NP-hard, especially when these systems are deployed in cloud computing environments where task execution involves deadline constraints. As a solution, hybrid approaches provide the opportunities to investigate efficient resource allocation for task scheduling problems. We propose a hybridized form of cuckoo search and genetic algorithms known as HGCS (hybrid genetic and cuckoo search) by embedding genetic operators that optimize makespan and cost of real-time tasks scheduled on cloud virtual machines. The inclusion of genetic operators in the cuckoo search algorithm leads to a rigorous search of the solution space, finding the best feasible schedule that can execute tasks in the lowest time, which in turn reduces the total resources usage cost. The performance of the proposed algorithm is tested by using real-time tasks that need data files for successful completion. The HGCS algorithm is evaluated by comparing the results with genetic and cuckoo search algorithms individually. The experimental results favor HGCS over the other two counterparts in providing a schedule respecting the time constraints of the system with reduced makespan and execution cost.
Enhancing Server Efficiency in the Face of Killer Microseconds

We are entering an era of “killer microseconds” in data center applications. Killer microseconds refer to μs-scale “holes” in CPU schedules caused by stalls to access fast I/O devices or brief idle times between requests in high throughput microservices. Whereas modern computing platforms can efficiently hide ns-scale and ms-scale stalls through micro-architectural techniques and OS context switching, they lack efficient support to hide the latency of μs-scale stalls. Simultaneous Multithreading (SMT) is an efficient way to improve core utilization and increase server performance density. Unfortunately, scaling SMT to provision enough threads to hide frequent μs-scale stalls is prohibitive and SMT co-location can often drastically increase the tail latency of cloud microservices. In this paper, we propose Duplexity, a heterogeneous server architecture that employs aggressive multithreading to hide the latency of killer microseconds, without sacrificing the Quality-of-Service (QoS) of latency-sensitive microservices. Duplexity provisions dyads (pairs) of two kinds of cores: master-cores, which each primarily executes a single latency-critical master-thread, and lender-cores, which multiplex latency-insensitive throughput threads. When the master-thread stalls, the master-core borrows filler-threads from the lender-core, filling μs-scale utilization holes of the microservice. We propose critical mechanisms, including separate memory paths for the master-thread and filler-threads, to enable master-cores to borrow filler-threads while protecting master-threads' state from disruption. Duplexity facilitates fast master-thread restart when stalls resolve and minimizes the microservice's QoS violation. Our evaluation demonstrates that Duplexity is able to achieve 1.9× higher core utilization and 2.7× lower iso-throughput 99th-percentile tail latency over an SMT-based server design, on average.