Reviewerly/exHarmony huggingface dataset:
ScarfNet: Multi-scale Features with Deeply Fused and Redistributed Semantics for Enhanced Object Detection

Convolutional neural networks (CNNs) have led us to achieve significant progress in object detection research. To detect objects of various sizes, object detectors often exploit the hierarchy of the multiscale feature maps called feature pyramids, which are readily obtained by the CNN architecture. However, the performance of these object detectors is limited because the bottom-level feature maps, which experience fewer convolutional layers, lack the semantic information needed to capture the characteristics of the small objects. To address such problems, various methods have been proposed to increase the depth for the bottom-level features used for object detection. While most approaches are based on the generation of additional features through the top-down pathway with lateral connections, our approach directly fuses multi-scale feature maps using bidirectional long short-term memory (biLSTM) in an effort to leverage the gating functions and parameter-sharing in generating deeply fused semantics. The resulting semantic information is redistributed to the individual pyramidal feature at each scale through the channel-wise attention model. We integrate our semantic combining and attentive redistribution feature network (ScarfNet) with the baseline object detectors, i.e., Faster R-CNN, single-shot multibox detector (SSD), and RetinaNet. Experimental results show that our method offers a significant performance gain over the baseline detectors and outperforms the competing multiscale fusion methods in the PASCAL VOC and COCO detection benchmarks.
Classify, Detect and Tell: Real-Time American Sign Language

Communication is an essential means of life, which is the main tool for people to express their ideas and feelings to others. It not only helps in sharing information but also helps in developing relationships. Verbal expression is one of the most effective communication methods for ordinary people, but for people with speech and hearing disabilities, sign language is their primary way of communication. They face a hard time communicating with people who do not understand sign language. Those who rely on sign language deserve to be engaged in the community and to be understood. Therefore, it is vital to develop a system that would enable real-time communication between the two groups of people. This paper addresses the issue of recognizing the hand gestures of American Sign Language utilizing deep learning artificial intelligence techniques. The recognition rates of sample data were measured after sample data was prepared in one of three ways: normalizing the image, converting to binary black and white, and color with background removed. Of the three methods, colored images with the background removed achieved the best results.
Clustering Techniques for Big Data Mining

This paper introduces the Clustering method as an unsupervised machine learning where the input and the output data are unlabeled. Many algorithms are designed to solve clustering problems and many approaches were developed to enhance deficiency or to seek efficiency and effectiveness. These approaches are partitioning-based, hierarchical-based, density-based, grid-based, and model-based. With the evolution of data amounts in every second, we become faced to deal with what is called big data that compelled researchers to develop the algorithms based on these approaches in order to adjust them to manage warehouses in a fast way. Our main purpose is the comparative of representative algorithms of each approach that respect most of the big data criterions which are called the 4Vs. The comparison aims to figure out which algorithms could mine efficiently information by clustering big data. The studied algorithms are FCM, CURE, OPTICS, BANG, and EM respectively from each approach aforementioned. Assessing these algorithms based on the 4Vs big data criterions which are Volume, Variety, Velocity and Value shows some deficiency in some of them. All trained algorithms clusters well large datasets but exclusively FCM and OPTICS algorithms suffer from the curse of dimensionality. FCM and EM algorithms are very sensitive to outliers which affect badly the results. FCM, CURE, and EM algorithms require the number of clusters as input which plays a deficiency if the optimal one wasn’t chosen. FCM and EM algorithms give spherical shapes of clusters unlike CURE, OPTICS, and BANG algorithms which give arbitrary ones that play an advantage for cluster quality. FCM algorithm is the fastest in performing big data, unlike EM algorithm that takes the longest time in training. For diversity in types of data CURE algorithm trains both numerical and categorical data types. Consequently, the analysis leads us to conclude that both CURE and BANG are efficient in clustering big data but we noticed that CURE lacks a bit of accuracy in data assignment. Therefore we infer to qualify the BANG algorithm to be the appropriate one to cluster a large dataset with high dimensionality and noise within it. BANG algorithm is based on a grid structure but comprises implicitly partitioning, hierarchical and density approaches the reason behind its efficiency in giving good accurate results. But even so, the ultimate accuracy in clustering isn’t reached yet but almost close. The conclusion we observe from the BANG algorithm should be applied to more algorithms by mixing approaches in order to attain the ultimate accuracy and effectiveness that lead consequently to accurate future decisions.
Publishing Base Registries as Linked Data Event Streams

Fostering interoperability, Public Sector Bodies (PSBs) maintain datasets that should become queryable as an integrated Knowledge Graph (KG). While some PSBs allow to query a part of the KG on their servers, others favor publishing data dumps allowing the querying to happen on third party servers. As the budget of a PSB to publish their dataset on the Web is finite, PSBs need guidance on what interface to offer first. A core API can be designed that covers the core tasks of Base Registries, which is a well-defined term in Flanders for the management of authoritative datasets. This core API should be the basis on which an ecosystem of data services can be built. In this paper, we introduce the concept of a Linked Data Event Stream (LDES) for datasets like air quality sensors and observations or a registry of officially registered addresses. We show that extra ecosystem requirements can be built on top of the LDES using a generic fragmenter. By using hypermedia for describing the LDES as well as the derived datasets, agents can dynamically discover their best way through the KG, and server administrators can dynamically add or remove functionality based on costs and needs. This way, we allow PSBs to prioritize API functionality based on three tiers: (i) the LDES, (ii) intermediary indexes and (iii) querying interfaces. While the ecosystem will never be feature-complete, based on the market needs, PSBs as well as market players can fill in gaps as requirements evolve.
Few-Shot Visual Grounding for Natural Human-Robot Interaction

Natural Human-Robot Interaction (HRI) is one of the key components for service robots to be able to work in human-centric environments. In such dynamic environments, the robot needs to understand the intention of the user to accomplish a task successfully. Towards addressing this point, we propose a software architecture that segments a target object from a crowded scene, indicated verbally by a human user. At the core of our system, we employ a multi-modal deep neural network for visual grounding. Unlike most grounding methods that tackle the challenge using pre-trained object detectors via a two-stepped process, we develop a single stage zero-shot model that is able to provide predictions in unseen data. We evaluate the performance of the proposed model on real RGB-D data collected from public scene datasets. Experimental results showed that the proposed model performs well in terms of accuracy and speed, while showcasing robustness to variation in the natural language input.
Blockchain storage middleware based on external database

Because of the anti-tampering, decentralization, anonymous transaction, and other characteristics of the blockchain, it is considered to be a technology that can bring huge changes to the Internet field, especially the financial Internet. As a huge distributed ledger, the blockchain has obvious deficiencies in query processing module design and client query performance. In addition, the current blockchain storage engine cannot provide version control and multi-person collaboration functions. This article proposes to solve the problem of query performance and query semantics not rich by linking other databases, the version control function is realized by separately designing the version control semantics, and the function of deleting the duplicate data by the Pos-tree can be effectively used to solve the problem of waste of storage space during version control. Experiments show that under normal network delay $(\lt 80$ ms), the middleware system proposed in this paper has a slight improvement in query performance, and can also save storage space as much as possible during version control.
Joint Face Alignment and 3D Face Reconstruction with Efficient Convolution Neural Networks

3D face reconstruction from a single 2D facial image is a challenging and concerned problem. Recent methods based on CNN typically aim to learn parameters of 3D Morphable Model (3DMM) from 2D images to render face alignment and 3D face reconstruction. Most algorithms are designed for faces with small, medium yaw angles, which is extremely challenging to align faces in large poses. At the same time, they are not efficient usually. The main challenge is that it takes time to determine the parameters accurately. In order to address this challenge with the goal of improving performance, this paper proposes a novel and efficient end-to-end framework. We design an efficient and lightweight network model combined with Depthwise Separable Convolution and Muti-scale Representation, Lightweight Attention Mechanism, named Mobile-FRNet. Simultaneously, different loss functions are used to constrain and optimize 3DMM parameters and 3D vertices during training to improve the performance of the network. Meanwhile, extensive experiments on the challenging datasets show that our method significantly improves the accuracy of face alignment and 3D face reconstruction. Model parameters and complexity of our method are also improved greatly.
A Framework of Blockchain-Based Security for WBANs

Purposed to collect and store human physiological data, WBANs as a telemedicine system play a significant role in sensor networks. The complex network environment presents a formidable challenge to the stability of the system and the security of the database. The traditional centralized architecture is to store the registration data of the sensor node and the patient's physiological data in the hub node. When the hub node is subjected to Denial of Service attacks (DoS) and Distributed Denial of Service attacks (DDoS), there will be a single point of failure, so that the sensor node cannot establish a communication connection with the hub node. In this paper, a cloud server layer architecture based on blockchain technology is proposed for WBNAs to ensure system stability and patient data security. Meanwhile, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme is used to ensure patient physiological data safety.
Adversarially Robust Classification Based on GLRT

Machine learning models are vulnerable to adversarial attacks that can often cause misclassification by introducing small but well designed perturbations. In this paper, we explore, in the setting of classical composite hypothesis testing, a defense strategy based on the generalized likelihood ratio test (GLRT), which jointly estimates the class of interest and the adversarial perturbation. We evaluate the GLRT approach for the special case of binary hypothesis testing in white Gaussian noise under ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</inf> norm-bounded adversarial perturbations, a setting for which a minimax strategy optimizing for the worst-case attack is known. We show that the GLRT approach yields performance competitive with that of the minimax approach under the worst-case attack, while yielding a better robustness-accuracy trade-off under weaker attacks. The GLRT defense is applicable in multi-class settings and generalizes naturally to more complex models for which optimal minimax classifiers are not known.
Relationship between Selected Major, Minor, and Trace Elements in Iron Oxide–Copper–Gold Deposits, an Example from the Unique Sin Quyen Deposit (Lào Cai Province, North Vietnam)

Abstract —We study the relations between several selected elements present in the Sin Quyen IOCG deposit, Lào Cai, North Vietnam, and interpret the obtained correlations, especially with a coefficient higher than 0.7. The correlations with high coefficients are mainly observed for the elements belonging to the chalcophile group (Cu, Ag, Au, Te, and Bi) and for the relation between uranium and Ag, Au, Cu, Pb, and Bi. Although the S-, Fe-, and REE-bearing minerals are predominant in the studied deposit, no strong correlation between them and the other elements was observed, even with Cu. The phenomena are primarily explained based on the geochemical properties of the mentioned elements and the characteristics of IOCG deposits.
‘Hello, this is Martha’: Interaction dynamics of live scambaiting on Twitch

The growth of the Internet has seen the emergence of elaborate examples of cybercrime in the form of ‘scams’. Alongside this, a resistance has also developed, with ‘scambaiters’ engaging in complex and deceptive scenarios to waste scammers’ time and educate others about online scams. This has been facilitated by the evolution of new media platforms inclusive of the live stream video-sharing site Twitch, where scambaiters take part in interactions with scammers in real time in front of large audiences. Such platforms present significant potential for diverse interaction and participation roles that move beyond those of other sites and audiovisual media. Scambaiting texts thus present a valuable opportunity to explore the complex interactive affordances of live stream video-sharing platforms. In this article, we aim to map an interactional framework for live-streamed video-sharing platforms by analysing a scambait call by the scambaiter ‘Kitboga’ on Twitch. The results show that the platform of Twitch and the novel context of a live-streamed scambait call offer new insights into online interaction within the context of emerging digital genres.
Improving Memory Banks for Unsupervised Learning with Large Mini-Batch, Consistency and Hard Negative Mining

An important component of unsupervised learning by instance-based discrimination is a memory bank for storing a feature representation for each training sample in the dataset. In this paper, we introduce 3 improvements to the vanilla memory bank-based formulation which brings massive accuracy gains: (a) Large mini-batch: we pull multiple augmentations for each sample within the same batch and show that this leads to better models and enhanced memory bank updates. (b) Consistency: we enforce the logits obtained by different augmentations of the same sample to be close without trying to enforce discrimination with respect to negative samples as proposed by previous approaches. (c) Hard negative mining: since instance discrimination is not meaningful for samples that are too visually similar, we devise a novel nearest neighbour approach for improving the memory bank that gradually merges extremely similar data samples that were previously forced to be apart by the instance level classification loss. Overall, our approach greatly improves the vanilla memory-bank based instance discrimination and outperforms all existing methods for both seen and unseen testing categories with cosine similarity.
Investigating the Effect of Chatbot-to-User Questions and Directives on Student Participation

Pedagogical chatbots are used to elicit information from students. Yet, how the amount of student-generated content differs according to the type of chatbot-delivered questions is unknown. To this end, 19 South Korean English majors completed six chatbot assignments through an in-house developed Facebook Messenger chatbot. The chatbot activity entailed creating original stories for class presentations. In addition to directives requesting plot details, the chatbot used closed-ended button reply questions, open-ended questions, and fill-in-the-blank template statements to help students create stories. Results indicated that button reply questions allowed for pacing, recall and content assessment and required low levels of critical thinking. Next, open-ended questions and fill-in-the-blank template statements resulted in similar word count production but different levels of creativity and critical thinking. Lastly, directives requesting user input resulted in 35% more output, indicating students took more action when told to do something than when asked. Regarding the novelty effect, fewer students volunteered to do the sixth and final chatbot activity, but those who did produced word count on par with their initial chatbot activity.
Estimation of correlations and nonseparability in quantum channels via unitarity benchmarking

The ability to transfer coherent quantum information between systems is a fundamental component of quantum technologies and leads to coherent correlations within the global quantum process. However correlation structures in quantum channels are less studied than those in quantum states. Motivated by recent techniques in randomized benchmarking, we develop a range of results for efficient estimation of correlations within a bipartite quantum channel. We introduce sub-unitarity measures that are invariant under local changes of basis, generalize the unitarity of a channel, and allow for the analysis of coherent information exchange within channels. Using these, we show that unitarity is monogamous, and provide a novel information-disturbance relation. We then define a notion of correlated unitarity that quantifies the correlations within a given channel. Crucially, we show that this measure is strictly bounded on the set of separable channels and therefore provides a witness of non-separability. Finally, we describe how such measures for effective noise channels can be efficiently estimated within different randomized benchmarking protocols. We find that the correlated unitarity can be estimated in a SPAM-robust manner for any separable quantum channel, and show that a benchmarking/tomography protocol with mid-circuit resets can reliably witness non-separability for sufficiently small reset errors. The tools we develop provide information beyond that obtained via simultaneous randomized benchmarking and so could find application in the analysis of cross-talk and coherent errors in quantum devices.
Leveraging semantic segmentation for hybrid image retrieval methods

Content-based image retrieval (CBIR) is the task of finding images in a database that are the most similar to the input query based on its visual characteristics. Several methods from the state of the art based on visual methods (bag of visual words, VLAD, etc.) or recent deep leaning methods try to solve the CBIR problem. In particular, deep learning is a new field and used for several vision applications including CBIR. But, even with the increase in the performance of deep learning algorithms, this problem is still a challenge in computer vision. In this work, we propose three different methodologies combining deep learning-based semantic segmentation and visual features. We show experimentally that by exploiting semantic information in the CBIR context leads to an increase in the retrieval accuracy. We study the performance of the proposed approach on eight different datasets (Wang, Corel-10k, Corel-5k, GHIM-10K, MSRC V1, MSRC V2, Linnaeus and NUS-WIDE)
Video Quality Prediction Using Voxel-Wise fMRI Models of the Visual Cortex

In this work, we address the problem of full-reference video quality prediction. To address this problem, we rely on deep learning based spatio-temporal representations of natural videos. Specifically, we use feature representations derived from a per-voxel deep learning regression model. This model predicts the functional Magnetic Resonance Imaging (fMRI) responses of the visual cortical regions to natural video stimuli. We construct a rudimentary full-reference spatio-temporal quality feature that is simply the L1-norm of the error between the voxel model’s response to the reference and test video stimuli. This feature is shown to correlate well with subjective quality scores. Additionally, we rely on the Multi-Scale Structural Similarity (MS-SSIM) index as the spatial quality feature. We show that the combination of the proposed spatio-temporal feature and the spatial (MS-SSIM) feature delivers competitive performance for both Quality of Experience (QoE) prediction and Video Quality Assessment (VQA) tasks. This finding not only provides corroborative evidence to previous results based on electroencephalograph (EEG) signals on the role of the visual cortex in quality prediction but also opens up interesting directions for perceptually inspired design of objective video quality metrics.
Lightning Network Economics: Channels

Compared with existing payment systems, Bitcoin’s throughput is low. Designed to address Bitcoin’s scalability challenge, the Lightning Network (LN) is a protocol allowing two parties to secure bitcoin payments and escrow holdings between them. In a lightning channel, each party commits collateral towards future payments to the counterparty and payments are cryptographically secured updates of collaterals. The network of channels increases transaction speed and reduces blockchain congestion. This paper (i) identifies conditions for two parties to optimally establish a channel, (ii) finds explicit formulas for channel costs, (iii) obtains the optimal collaterals and savings entailed, and (iv) derives the implied reduction in congestion of the blockchain. Unidirectional channels costs grow with the square-root of payment rates, while symmetric bidirectional channels with their cubic root. Asymmetric bidirectional channels are akin to unidirectional when payment rates are significantly different, otherwise to symmetric bidirectional.
Ontology-Based Design Pattern Selection

The software design phase is important and challenging due to its high impact on other phases of the software development life cycle. Design patterns are proven solutions based on software developers' experience to solve recurring problems, which used to acquire quality software design. However, selecting an appropriate design pattern is quite difficult. Hence, many studies have been done to automate the design pattern selection process. The existing automated design pattern selection methodologies have certain issues such as the need to have a large sample size, user restrictions on selecting preset concepts, time-consuming, and incomprehensiveness. To address these issues in this paper, a two-phase method for selecting an appropriate design pattern is presented. The proposed method is based on an ontology approach that enables domain knowledge to be modeled in a simple and abstract way and enables queries to be evaluated against a knowledge base. The concepts of ontology are then linked to WordNet. Subsequently, a dataset includes use cases that can be satisfied with GOF design patterns is provided. The set of use cases is then processed in such a way as to make it easy and fast to select the concept-constraint pair to query the ontology. The experimental shows promising and effective results of the proposed method.