Reviewerly/exHarmony huggingface dataset:
Lightweight Key Encapsulation Using LDPC Codes on FPGAs

In this paper, we present a lightweight hardware design for a recently proposed quantum-safe key encapsulation mechanism based on QC-LDPC codes called LEDAkem, which has been admitted as a round-2 candidate to the NIST post-quantum standardization project. Existing implementations focus on high speed while few of them take into account area or power efficiency, which are particularly decisive for low-cost or power constrained IoT applications. The solution we propose aims at maximizing the metric of area efficiency by rotating the QC-LDPC code representations amongst the block RAMs in digit level. Moreover, optimized parallelized computing techniques, lazy accumulation and block partition are exploited to improve key decapsulation in terms of area and timing efficiency. We show for instance that our area-optimized implementation for 128-bit security requires 6.82 x 105 cycles and 2.26 x 106 cycles to encapsulate and decapsulate a shared secret, respectively. The area-optimized design uses only 39 slices (3 percent of the available logic) and 809 slices (39 percent of the available logic) for key encapsulation and key decapsulation respectively, on a small-size low-end Xilinx Spartan-6 FPGA.
ADARC: An anomaly detection algorithm based on relative outlier distance and biseries correlation

Summary The application of anomaly detection to data monitoring is a fundamental requirement of the public service systems of a smart city. Many detection methods have been proposed for identifying anomalous situations, including methods based on periodicity or biseries correlations. However, the detection results of these methods are not ideal. Thus, we present a new anomaly detection algorithm for time series based on the relative outlier distance (ROD) and biseries correlations. The proposed algorithm detects outliers based on the ROD and identifies abnormal points and change points based on biseries correlations. Experimental results show that our method achieves better recall and F1‐measure scores than various time series–based techniques while maintaining a high level of precision.
Deep semantic-Based Feature Envy Identification

Code smells regularly cause potential software quality problems in software development. Thus, code smell detection has attracted the attention of many researchers. A number of approaches have been suggested in order to improve the accuracy of code smell detection. Most of these approaches rely solely on structural information (code metrics) extracted from source code and heuristic rules designed by people. In this paper, We propose a method-representation based model to represent the methods in textual code, which can effectively reflect the semantic relationships embedded in textual code. We also propose a deep learning based approach that combines method-representation and a CNN model to detect feature envy. The proposed approach can automatically extract semantic and features from textual code and code metrics, and can also automatically build complex mapping between these features and predictions. Evaluation results on open-source projects demonstrate that our proposed approach achieves better performance than the state-of-the-art in detecting feature envy.
Adversarial Machine Learning Attack on Modulation Classification

Modulation classification is an important component of cognitive self-driving networks. Recently many ML-based modulation classification methods have been proposed. We have evaluated the robustness of 9 ML-based modulation classifiers against the powerful Carlini & Wagner (C-W) attack and showed that the current ML-based modulation classifiers do not provide any deterrence against adversarial ML examples. To the best of our knowledge, we are the first to report the results of the application of the C-W attack for creating adversarial examples against various ML models for modulation classification.
Touchless interaction with medical images based on 3D hand cursors supported by single-foot input: A case study in dentistry

Feet input can support mid-air hand gestures for touchless medical image manipulation to prevent unintended activations, especially in sterile contexts. However, foot interaction has yet to be investigated in dental settings. In this paper, we conducted a mixed methods research study with medical dentistry professionals. To this end, we developed a touchless medical image system in either sitting or standing configurations. Clinicians could use both hands as 3D cursors and a minimalist single-foot gesture vocabulary to activate manipulations. First, we performed a qualitative evaluation with 18 medical dentists to assess the utility and usability of our system. Second, we used quantitative methods to compare pedal foot-supported hand interaction and hands-only conditions next to 22 medical dentists. We expand on previous work by characterizing a range of potential limitations of foot-supported touchless 3D interaction in the dental domain. Our findings suggest that clinicians are open to use their foot for simple, fast and easy access to image data during surgical procedures, such as dental implant placement. Furthermore, 3D hand cursors, supported by foot gestures for activation events, were considered useful and easy to employ for medical image manipulation. Even though most clinicians preferred hands-only manipulation for pragmatic purposes, feet-supported interaction was found to provide more precise control and, most importantly, to decrease the number of unintended activations during manipulation. Finally, we provide design considerations for future work exploring foot-supported touchless interfaces for sterile settings in Dental Medicine, regarding: interaction design, foot input devices, the learning process and camera occlusions.
A Multi-Authority Attribute-Based Signcryption Scheme with Efficient Revocation for Smart Grid Downlink Communication

In this paper, we propose a multi-authority attribute-based signcryption scheme with efficient revocation for smart grid downlink communications. The proposed scheme allows grid operators and electricity vendors to securely send multicast messages to different groups of consumers. Such secure multicast communication is required in several applications such as firmware update distribution, sending direct load control messages, and sending energy trading requests. Our scheme can ensure the confidentiality and the integrity of the multicasted messages, allows consumers to authenticate the senders of the multicasted messages, achieves non-repudiation property, and allows prompt attribute revocation, simultaneously which are the security requirements of the smart grid downlink communications. Our security analysis demonstrates that the proposed scheme is secure and can thwart various security threats to the smart grid. Our experiments conducted on an advanced metering infrastructure (AMI) testbed confirm that the proposed scheme has low computational overhead.
Smartphone-Based Lifelogging: An Investigation of Data Volume Generation Strength of Smartphone Sensors

The lifelogging enable people to digitally record information about their daily life events for a variety of purposes including human memory augmentation. However, the lifelogging systems have several challenges regarding capturing, managing, semantic analyses, indexing, and retrieval of error-prone and noisy data produced by the sensors. The ubiquitous nature and technological developments makes smartphone as de-facto lifelogging device. The smartphone integrates a rich set of sensors, which provide unique opportunities for capturing contents and contextual information into a comprehensive lifelog archive. However, the continuous use of sensors can generate large amount of data that could raise problems for smartphone-based lifelogging systems. In addition, insight understanding of smartphone sensors data generation strength is needed for effective smartphone-based lifelogging systems development. These estimations will also help in understanding of smartphone sensors capability of fulfilling lifelogging systems objectives. To fulfill objective of this paper, an Android based application namely Sensors dAta Volume Estimator (SAVE) is developed using a proposed architecture. The SAVE utilizes smartphone sensors to capture and estimate sensors data from different real world scenarios. The results indicated that smartphone sensors can generate significant amount of data that can create storage, retrieval, and battery power issues for smartphone-based lifelogging systems.
Survey of Deep Learning Based Object Detection

The main tasks of computer vision are image classification/location, target detection, target tracking, semantic segmentation and instance segmentation. The task of target detection is to output the borders and labels of a single target from the image. Object detection is an important issue in the field of computer vision. It has important research significance and application value in video monitoring, autonomous driving and human-computer interaction. In recent years, deep learning has made a breakthrough in the research of image classification and led to the rapid development of object vision detection. This paper briefly introduces the object detection algorithm based on deep learning. First, the basic process of object detection is introduced, then several current algorithms of object detection are introduced, and finally the future development trend is prospected.
BitLat

With the growing popularity and prosperity of living streaming applications, it is naturally confronting users' quality of experience (QoE) degradation issues especially under dynamic environments arised from nonnegligible factors such as high latency and intermittent bitrate. In this paper, we propose an efficient adaptive bitrate (ABR) algorithm called BitLat to achieve both bitrate-control and latency-control. BitLat is based on reinforcement learning to get strong adaptability for dealing with the complex and changing network conditions. More specifically, in our work, we determine the specific value of latency threshold with the help of current advanced algorithm, and design the structure of the neural network in reinforcement learning, the features used in the training process, and the corresponding reward function. Additional, we use the Dynamic Reward Method to further enhance the performance. Comprehensive experiments are conducted to demonstrate BitLat outperforms the state-of-the-art ABR algorithms, with improvements in average QoE of 20%-62%.
Exploiting background divergence and foreground compactness for salient object detection

In this paper, we propose an efficient and discriminative saliency method that takes advantage of background divergence and foreground compactness. Concretely, a graph is first constructed by introducing the concept of virtual node to effectively enhance the distinction between nodes along object boundaries and the similarity among object regions. A reasonable edge weight is defined by incorporating low-level features as well as deep features extracted from deep networks to measure the relationship between different regions. To remove incorrect outputs, two computational mechanisms are then developed to extract reliable background seeds and compact foreground regions, respectively. The saliency value of a node is calculated by fully considering the relationship between the corresponding node and the virtual background (foreground) node. As a result, two types of saliency maps are obtained and integrated into a uniform map. In order to achieve significant performance improvement consistently, we propose a robust saliency optimization mechanism, which subtly combine suppressed/active (SA) nodes and mid-level structure information based on manifold ranking. Extensive experimental results demonstrate that the proposed algorithm performs favorably against the state-of-art saliency detection methods in terms of different evaluation metrics on several benchmark datasets.
A novel approach for solving all-pairs shortest path problem in an interval-valued fuzzy network

Researchers have studied several different types of directed shortest path (SP) problems under fuzzy environment. However, few researchers have focused on solving this problem in an interval-valued fuzzy network. Thus, in order to light these, we investigate a generalized kind of the SP problem und er interval-valued fuzzy environment namely all pairs shortest path (APSP) problem. The main contributions of the present study are fivefold: (1) In the interval-valued fuzzy network under consideration, each arc weight is represented in terms of interval-valued fuzzy number. (2) We seek the shortest weights between every pair of nodes in a given interval-valued fuzzy network based on a dynamic approach. (3) In contrast to most existing approaches, which provide the shortest path between two predetermined nodes, the proposed approach provides the interval-valued fuzzy shortest path between every pair of nodes. (4) Similarly to the competing methods in the literature, the proposed approach not only gives the interval-valued fuzzy weights of all pairs shortest path but also provides the corresponding interval-valued fuzzy APSP. (5) The efficiency of the proposed approach is illustrated through two applications of APSP problems in wireless sensor networks and robot path planning problems.
The Surrogate Body in Play

Games research within the Human-Computer Interaction (HCI) community currently draws most of its understanding of immersion, engagement and player experience from Psychology. However, these phenomena are also studied by the humanities, i.e., Media Theory, a field that conceptualises these parameters as affective and situated in specific contexts. Here, we draw from surrogate body (Leihkörper) theory proposed by Voss for cinematic experiences, and apply it to a variety of different contexts in which digital games are played. Doing so, we not only refine the theory but also introduce a socio-technical, cultural and affective understanding of play contexts that can contribute to how we examine players' experiences. Further, we provide a case detailing how Games Research can engage with humanistic theories, and explore their relevance for the design and evaluation of digital games.
Adaptive Multi-Path Aggregation for Human DensePose Estimation in the Wild

Dense human pose "in the wild'' task aims to map all 2D pixels of the detected human body to a 3D surface by establishing surface correspondences, i.e., surface patch index and part-specific UV coordinates. It remains challenging especially under the condition of "in the wild'', where RGB images capture complex, real-world scenes with background, occlusions, scale variations, and postural diversity. In this paper, we propose an end-to-end deep Adaptive Multi-path Aggregation network (AMA-net) for Dense Human Pose Estimation. In the proposed framework, we address two main problems: 1) how to design a simple yet effective pipeline for supporting distinct sub-tasks (e.g., instance segmentation, body part segmentation, and UV estimation); and 2) how to equip this pipeline with the ability of handling "in the wild''. To solve these problems, we first extend FPN by adding a branch for mapping 2D pixels to a 3D surface in parallel with the existing branch for bounding box detection. Then, in AMA-net, we extract variable-sized object-level feature maps (e.g., 7×7, 14×14, and 28×28), named multi-path, from multi-layer feature maps, which capture rich information of objects and are then adaptively utilized in different tasks. AMA-net is simple to train and adds only a small overhead to FPN. We discover that aside from the deep feature map, Adaptive Multi-path Aggregation is of particular importance for improving the accuracy of dense human pose estimation "in the wild''. The experimental results on the challenging Dense-COCO dataset demonstrate that our approach sets a new record for Dense Human Pose Estimation task, and it significantly outperforms the state-of-the-art methods. Our code: \urlhttps://github.com/nobody-g/AMA-net.
Beauty Product Retrieval Based on Regional Maximum Activation of Convolutions with Generalized Attention

Beauty and Personal care product retrieval has attracted more and more research attention for its value in real life. However, suffering from data variants and complex background, this task has been very challenging. In this paper, we propose a novel Generalized-attention Regional Maximal Activation of Convolutions (GRMAC) descriptor which helps to generate image features for retrieval. This method introduces attention mechanism to reduce the influence of clustered background and highlight the target, and thus contributes to enhancing the effectiveness of features and boosting the retrieval performance. Different from other attention-based methods, our method supports adjusting mask with a hyperparameter p, which is more flexible and accurate in real application. To demonstrate its effectiveness, we conduct experiments on the dataset containing more than half million personal care products (Perfect-500K) and obtain remarkable results. Furthermore, we try to fuse multiple features from different models for more improvements. And finally, our team (USTC_NELSLIP) ranked 1st in the Grand Challenge of AI Meets Beauty in ACM Multimedia 2019 with a MAP score of 0.408614. Our code is available at: https://github.com/gniknoil/Perfect500K-Beauty-and-Personal-Care-Products-Retrieval-Challenge
Hydrothermal listvenitization and associated mineralizations in Zagros Ophiolites: Implications for mineral exploration in Iraqi Kurdistan

A recent geological field work in the Northeastern part of Iraqi Kurdistan and subsequent analytical studies showed that the previously reported metamorphosed limestone outcrops around Rayat area represent actually listvenite bodies. Field mapping and remote sensing applications were used to understand the spatial distribution of the listvenite; additionally petrographical, mineralogical and geochemical studies were applied in order to define the features of the listvenite and to provide an initial genetic model, as well as to assess potential mineralizations related to the listvenitization. Listvenitization occurred as a product of hydrothermal alteration of the Cr-rich serpentinised peridotite that extensively occurs in the area, and resulted in the formation of silica and carbonate listvenites, as well as transitional phases, with fuchsite mineralization being predominant along the sheared zones. The results of this study upgrade the broad area of Rayat as potential hydrothermal ore-forming system and provide a basis for future detail prospecting of the area.
Image registration using machine and deep learning

Image registration is a crucial and fundamental procedure in medical image analysis. Although many registration methods have been proposed, it is still a challenging task in some scenarios, such as images with large anatomical variations, multimodal registration, etc. Additionally, the scale and diversity of model imaging data have significantly increased, which pose more challenges for the registration algorithm. Machine learning techniques applied to image registration tasks can help address the aforementioned issues. Specifically, different machine learning techniques can be employed to learn from prior registration results to improve the registration performance in some challenging tasks. For instance, they can be employed for learning an appearance mapping model, learning an effective initialization for the optimization, etc. Recent studies have also demonstrated the potential of deep learning methods in addressing challenging registration problems. This chapter will be dedicated to summarizing state-of-the-art learning-based registration algorithms.
Design Immersion and Virtual Presence

The recent emergence of virtual reality (VR) as a next-generation human-computer interface followed a strong industry push for manufacturing affordable consumer-based hardware. Advances in optics, ...
iQIYI Celebrity Video Identification Challenge

We held the iQIYI Celebrity Video Identification Challenge in ACMMULTIMEDIA 2019. The purpose was to encourage the research on video-based person identification. We released the iQIYI-VID-2019 dataset, which contains 200K videos of 10K celebrities. In this paper, we introduce the organization of the challenge, the dataset, the evaluation process, and the results.
Identification of Bicycling Periods Using the MicroPEM Personal Exposure Monitor

Exposure assessment studies are the primary means for understanding links between exposure to chemical and physical agents and adverse health effects. Recently, researchers have proposed using wearable monitors during exposure assessment studies to obtain higher fidelity readings of exposures actually experienced by subjects. However, limited research has been conducted to link a wearer’s actions to periods of exposure, a necessary step for estimating inhaled dosage. To aid researchers in these settings, we developed a machine learning model for identifying periods of bicycling activity using passively collected data from the RTI MicroPEM wearable exposure monitor, a lightweight device capable of continuously sampling both air pollution levels and accelerometry parameters. Our best performing model identifies biking activity with a mean leave-one-session-out (LOSO) cross-validation F1 score of 0.832 (unweighted) and 0.979 (weighted). Accelerometer derived features contributed greatly to the model performance, as well as temporal smoothing of the predicted activities. Additionally, we found competitive activity recognition can occur with even relatively low sampling rates, suggesting suitability for exposure assessment studies where continuous data collection for long periods (without recharge) are needed to capture realistic daily routines and exposures.
Creating a Library-Wide Collection Management Cycle: One Academic Library’s Approach to Continuous Collection Assessment

Collections assessment in academic libraries has experienced a shift in recent years from project-based models to continuous models that aim improve the responsivity of library collections to campus needs. This article reports on how one university library used a library committee with representation from across library departments to develop a holistic, on-going Collection Management Cycle. This newly created process evaluates the entire library collection over a five-year period and aligns with library reporting requirements for university program review to minimize additional workload and improve the impact of external reporting activities.
Instance-Level Future Motion Estimation in a Single Image Based on Ordinal Regression

A novel algorithm to estimate instance-level future motion in a single image is proposed in this paper. We first represent the future motion of an instance with its direction, speed, and action classes. Then, we develop a deep neural network that exploits different levels of semantic information to perform the future motion estimation. For effective future motion classification, we adopt ordinal regression. Especially, we develop the cyclic ordinal regression scheme using binary classifiers. Experiments demonstrate that the proposed algorithm provides reliable performance and thus can be used effectively for vision applications, including single and multi object tracking. Furthermore, we release the future motion (FM) dataset, collected from diverse sources and annotated manually, as a benchmark for single-image future motion estimation.