Reviewerly/exHarmony huggingface dataset:
CAFFNet: Channel Attention and Feature Fusion Network for Multi-target Traffic Sign Detection

The fact that the existing traffic sign images are easily affected by external factors, and the traffic signs are generally small targets on the images at different scales, has made it difficult in feature extraction when doing traffic sign detection. To achieve better detection results, a multi-target traffic sign detection method with channel attention and feature fusion network (CAFFNet in short) is proposed. This method effectively learns the correlation between feature channels through a lightweight channel attention network, realizes local cross-channel interaction without dimensionality reduction, and enhances the representation ability of the network. The feature pyramid network is used to achieve feature fusion and generate high-resolution multiscale semantic information. The dilated convolution is utilized to capture the multiscale context information to narrow the difference between features and improve the detection effect of the model. The experimental results show that the proposed method on the two datasets GTSDB and CTSD has achieved superior performance in the evaluation criteria compared with the existing detection algorithms.
Undergraduate Students’ Views on the Use of Online Learning Platform during COVID-19 Pandemic

Technology-based online learning platforms have supported and helped Indonesian students to succeed the online learning education amid the novel coronavirus. They are allowed to access learning materials, learning activities, and learning practice for free. The purpose of this paper is to investigate the students’ paradigms on how effective is online learning platforms for their learning process. A qualitative method in form of a case study design was applied to this present study which had a hundred and eighty-five undergraduate students as the subjects. A questionnaire consisting of twenty-five statements was distributed to them through Google Forms. A follow-up interview via WhatsApp video calls was also conducted with the forty students who were chosen randomly to strengthen the findings. The results of this study show that most students agreed that the online learning platform might provide some beneficial effects for them. They mentioned that online activities through this platform were easily followed and challenge them to strive for keeping their balance motivation and interest to accomplish the online learning process. They were comfortable communicating and interacting with classmates and lecturers electronically. Besides, they might also be able to manage their study time and self-disciplined. Therefore, an online learning platform is considered to be a beneficial tool to support online learning environments.
Revisiting Few-sample BERT Fine-tuning

This paper is a study of fine-tuning of BERT contextual representations, with focus on commonly observed instabilities in few-sample scenarios. We identify several factors that cause this instability: the common use of a non-standard optimization method with biased gradient estimation; the limited applicability of significant parts of the BERT network for down-stream tasks; and the prevalent practice of using a pre-determined, and small number of training iterations. We empirically test the impact of these factors, and identify alternative practices that resolve the commonly observed instability of the process. In light of these observations, we re-visit recently proposed methods to improve few-sample fine-tuning with BERT and re-evaluate their effectiveness. Generally, we observe the impact of these methods diminishes significantly with our modified process.
Hybrid Service Recovery: Design for Seamless Inquiry Handovers between Conversational Agents and Human Service Agents

The effort of companies to deploy conversational agents (CAs) for customer self-service has been renewed due to their recent technological improvements.Despite their efficiency in processing recurring simple customer inquiries, limited capabilities of CAs to handle complex inquiries still lead to service failure and unsatisfied customers.Therefore, we propose a hybrid service recovery strategy with real-time handovers of inquiries from CAs to human service agents (HSAs), if CAs' capabilities are exceeded.Following a Design Science Research (DSR) approach, we present design principles (DPs) for the inquiry handover scenario, based on meta-requirements (MRs) derived from literature and expert interviews.By evaluating the DPs via prototype instantiation and process modulation, the suitability and interdependence of CAs' information collection activities and information presentation for handover could be verified.
Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth

A key factor in the success of deep neural networks is the ability to scale models to improve performance by varying the architecture depth and width. This simple property of neural network design has resulted in highly effective architectures for a variety of tasks. Nevertheless, there is limited understanding of effects of depth and width on the learned representations. In this paper, we study this fundamental question. We begin by investigating how varying depth and width affects model hidden representations, finding a characteristic block structure in the hidden representations of larger capacity (wider or deeper) models. We demonstrate that this block structure arises when model capacity is large relative to the size of the training set, and is indicative of the underlying layers preserving and propagating the dominant principal component of their representations. This discovery has important ramifications for features learned by different models, namely, representations outside the block structure are often similar across architectures with varying widths and depths, but the block structure is unique to each model. We analyze the output predictions of different model architectures, finding that even when the overall accuracy is similar, wide and deep models exhibit distinctive error patterns and variations across classes.
Ensemble Model Based on NNAR and SVR for Predicting Influenza Incidences

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An Automated Method for Biometric Handwritten Signature Authentication Employing Neural Networks

Handwriting biometrics applications in e-Security and e-Health are addressed in the course of the conducted research. An automated analysis method for the dynamic electronic representation of handwritten signature authentication was researched. The developed algorithms are based on the dynamic analysis of electronically handwritten signatures employing neural networks. The signatures were acquired with the use of the designed electronic pen described in the paper. The triplet loss method was used to train a neural network suitable for writer-invariant signature verification. For each signature, the same neural network calculates a fixed-length latent space representation. The hand-corrected dataset containing 10,622 signatures was used in order to train and evaluate the proposed neural network. After learning, the network was tested and evaluated based on a comparison with the results found in the literature. The use of the triplet loss algorithm to teach the neural network to generate embeddings has proven to give good results in aggregating similar signatures and separating them from signatures representing different people.
Affordance-Based Mobile Robot Navigation Among Movable Obstacles

Avoiding obstacles in the perceived world has been the classical approach to autonomous mobile robot navigation. However, this usually leads to unnatural and inefficient motions that significantly differ from the way humans move in tight and dynamic spaces, as we do not refrain interacting with the environment around us when necessary. Inspired by this observation, we propose a framework for autonomous robot navigation among movable obstacles (NAMO) that is based on the theory of affordances and contact-implicit motion planning. We consider a realistic scenario in which a mobile service robot negotiates unknown obstacles in the environment while navigating to a goal state. An affordance extraction procedure is performed for novel obstacles to detect their movability, and a contact-implicit trajectory optimization method is used to enable the robot to interact with movable obstacles to improve the task performance or to complete an otherwise infeasible task. We demonstrate the performance of the proposed framework by hardware experiments with Toyota's Human Support Robot.
Weak measurements of the waist of an arbitrarily polarized beam via in-plane spin splitting

As an important parameter to determine the transmission characteristics of a Gaussian beam, the beam waist holds a huge impact in laser technology and imaging systems. Although it is necessary to clearly measure the specific value of the beam waist, the traditional measurement steps are complex and easily introduce error in the measurement process. In this work, we propose an effective method using the in-plane spin splitting (IPSS) generated by the photonic spin Hall effect (PSHE) to precisely estimate the beam waist. We establish a highly sensitive propagation model to describe the relationship between the IPSS shifts and the beam waist of an arbitrary linearly polarized light and then combine with the quantum weak measurement system to amplify the IPSS shifts. We reveal that the IPSS shifts are sensitive to the variation of beam waists when the beam is reflected near the Brewster angle. With the huge amplified IPSS shifts (maximum of 1500 microns), the variation of beam waist can be accurately detected, even by propagation amplification alone. Prospectively, our scheme may provide an effective method for accurately determining the Gaussian beam waist of arbitrary polarization.
Blockchain Security as “People Security”: Applying Sociotechnical Security to Blockchain Technology

The notion that blockchains offer decentralized, “trustless” guarantees of security through technology is a fundamental misconception held by many advocates. This misconception hampers participants from understanding the security differences between public and private blockchains and adopting blockchain technology in suitable contexts. This paper introduces the notion of “people security” to argue that blockchains hold inherent limitations in offering accurate security guarantees to people as participants in blockchain-based infrastructure, due to the differing nature of the threats to participants reliant on blockchain as secure digital infrastructure, as well as the technical limitations between different types of blockchain architecture. This paper applies a sociotechnical security framework to assess the social, software, and infrastructural layers of blockchain applications to reconceptualize “blockchain security” as “people security.” A sociotechnical security analysis of existing macrosocial level blockchain systems surfaces discrepancies between the social, technical, and infrastructural layers of a blockchain network, the technical and governance decisions that characterize the network, and the expectations of, and threats to, participants using the network. The results identify a number of security and trust assumptions against various blockchain architectures, participants, and applications. Findings indicate that private blockchains have serious limitations for securing the interests of users in macrosocial contexts, due to their centralized nature. In contrast, public blockchains reveal trust and security shortcomings at the micro and meso-organizational levels, yet there is a lack of suitable desktop case studies by which to analyze sociotechnical security at the macrosocial level. These assumptions need to be further investigated and addressed in order for blockchain security to more accurately provide “people security”.
Analysis of Sentiment on Movie Reviews Using Word Embedding Self-Attentive LSTM

In the contemporary world, people share their thoughts rapidly in social media. Mining and extracting knowledge from this information for performing sentiment analysis is a complex task. Even though automated machine learning algorithms and techniques are available, and extraction of semantic and relevant key terms from a sparse representation of the review is difficult. Word embedding improves the text classification by solving the problem of sparse matrix and semantics of the word. In this paper, a novel architecture is proposed by combining long short-term memory (LSTM) with word embedding to extract the semantic relationship between the neighboring words and also a weighted self-attention is applied to extract the key terms from the reviews. Based on the experimental analysis on the IMDB dataset, the authors have shown that the proposed architecture word-embedding self-attention LSTM architecture achieved an F1 score of 88.67%, while LSTM and word embedding LSTM-based models resulted in an F1 score of 84.42% and 85.69%, respectively.
Facilitating Terminology Translation with Target Lemma Annotations

Most of the recent work on terminology integration in machine translation has assumed that terminology translations are given already inflected in forms that are suitable for the target language sentence.In day-to-day work of professional translators, however, it is seldom the case as translators work with bilingual glossaries where terms are given in their dictionary forms; finding the right target language form is part of the translation process.We argue that the requirement for apriori specified target language forms is unrealistic and impedes the practical applicability of previous work.In this work, we propose to train machine translation systems using a source-side data augmentation method 1 that annotates randomly selected source language words with their target language lemmas.We show that systems trained on such augmented data are readily usable for terminology integration in real-life translation scenarios.Our experiments on terminology translation into the morphologically complex Baltic and Uralic languages show an improvement of up to 7 BLEU points over baseline systems with no means for terminology integration and an average improvement of 4 BLEU points over the previous work.Results of the human evaluation indicate a 47.7% absolute improvement over the previous work in term translation accuracy when translating into Latvian.
A Multi-Tiered Framework for Insider Threat Prevention

As technologies are rapidly evolving and becoming a crucial part of our lives, security and privacy issues have been increasing significantly. Public and private organizations have highly confidential data, such as bank accounts, military and business secrets, etc. Currently, the competition between organizations is significantly higher than before, which triggers sensitive organizations to spend an excessive volume of their budget to keep their assets secured from potential threats. Insider threats are more dangerous than external ones, as insiders have a legitimate access to their organization’s assets. Thus, previous approaches focused on some individual factors to address insider threat problems (e.g., technical profiling), but a broader integrative perspective is needed. In this paper, we propose a unified framework that incorporates various factors of the insider threat context (technical, psychological, behavioral and cognitive). The framework is based on a multi-tiered approach that encompasses pre, in and post-countermeasures to address insider threats in an all-encompassing perspective. It considers multiple factors that surround the lifespan of insiders’ employment, from the pre-joining of insiders to an organization until after they leave. The framework is utilized on real-world insider threat cases. It is also compared with previous work to highlight how our framework extends and complements the existing frameworks. The real value of our framework is that it brings together the various aspects of insider threat problems based on real-world cases and relevant literature. This can therefore act as a platform for general understanding of insider threat problems, and pave the way to model a holistic insider threat prevention system.
Framing Word Sense Disambiguation as a Multi-Label Problem for Model-Agnostic Knowledge Integration

Recent studies treat Word Sense Disambiguation (WSD) as a single-label classification problem in which one is asked to choose only the best-fitting sense for a target word, given its context.However, gold data labelled by expert annotators suggest that maximizing the probability of a single sense may not be the most suitable training objective for WSD, especially if the sense inventory of choice is finegrained.In this paper, we approach WSD as a multi-label classification problem in which multiple senses can be assigned to each target word.Not only does our simple method bear a closer resemblance to how human annotators disambiguate text, but it can also be extended seamlessly to exploit structured knowledge from semantic networks to achieve stateof-the-art results in English all-words WSD.
Recurrent Multi-Frame Deraining: Combining Physics Guidance and Adversarial Learning

In this paper, we build a more comprehensive rain model with several degradation factors and construct a novel two-stage video rain removal method that combines the power of synthetic videos and real data. Specifically, a novel two-stage progressive network is proposed: recovery guided by a physics model, and further restoration by adversarial learning. The first stage performs an inverse recovery process guided by our proposed rain model. An initially estimated background frame is obtained based on the input rain frame. The second stage employs adversarial learning to refine the result, i.e. recovering the overall color and illumination distributions of the frame, the background details that are failed to be recovered in the first stage, and removing the artifacts generated in the first stage. Furthermore, we also introduce a more comprehensive rain model that includes degradation factors, e.g. occlusion and rain accumulation, which appear in real scenes yet ignored by existing methods. This model, which generates more realistic rain images, will train and evaluate our models better. Extensive evaluations on synthetic and real videos show the effectiveness of our method in comparisons to the state-of-the-art methods.
Design and FPGA Implementation of New Multidimensional Chaotic Map for Secure Communication

Due to their structure and complexity, chaotic systems have been introduced in several domains such as electronic circuits, commerce domain, encryption and network security. In this paper, we propose a novel multidimensional chaotic system with multiple parameters and nonlinear terms. Then, a two-phase algorithm is presented for investigating the chaotic behavior using bifurcation and Lyapunov exponent (LE) theories. Finally, we illustrate the performances of our proposal by constructing three (03) chaotic maps (3-D, 4-D and 5-D) and implementing the 3-D map on Field-Programmable-Gate-Array (FPGA) boards to generate random keys for securing a client–server communication purpose. Based on the achieved results, the proposed scheme is considered an ideal candidate for numerous resource-constrained devices and internet of the things (IoT) applications.
Automatic Data Clustering Framework Using Nature-Inspired Binary Optimization Algorithms

Cluster analysis using metaheuristic algorithms has earned increasing popularity over recent years due to the great success of these algorithms in finding high-quality clusters in complex real-world problems. This paper proposes a novel framework for automatic data clustering with the capability of generating clusters with approximately the same maximum distortion using nature-inspired binary optimization algorithms. The inherent problem with clustering using such algorithms is having a huge search space. Therefore, we have also proposed a binary encoding scheme for the particle representation to alleviate this problem. The proposed clustering solution requires no prior knowledge of the number of clusters and proceed with the process based on re-clustering, merging, and modifying the small clusters to compensate for the distortion gap between groups with different sizes. The proposed framework's performance has been evaluated over a wide range of synthetic, real-life, and higher dimensional datasets first by considering four different binary optimization algorithms for the optimizer module. Then, it has also been compared to multiple classical and new clustering solutions and two other automatic clustering techniques in continuous search space in terms of separation and compactness of the clusters by utilizing internal validity measures. The experimental results show the proposed solution is highly efficient in creating well-separated and compact clusters with approximately the same distortion in most datasets. Moreover, the application of the proposed framework to the correlated binary dataset is also reported as a case study. The presence of correlation in a dataset results from the similarity between data points in the same category, such as repeated measurements in remote sensing, crowdsourced multi-view video uploading, and augmented reality. Simplicity, customizability, and flexibility in adding extra conditions to the proposed solution and having a dynamic number of clusters are the advantages of the proposed framework.
Assessment of image generation by quantum annealer

Abstract Quantum annealing was originally proposed as an approach for solving combinatorial optimization problems using quantum effects. D-Wave Systems has released a production model of quantum annealing hardware. However, the inherent noise and various environmental factors in the hardware hamper the determination of optimal solutions. In addition, the freezing effect in regions with weak quantum fluctuations generates outputs approximately following a Gibbs–Boltzmann distribution at an extremely low temperature. Thus, a quantum annealer may also serve as a fast sampler for the Ising spin-glass problem, and several studies have investigated Boltzmann machine learning using a quantum annealer. Previous developments have focused on comparing the performance in the standard distance of the resulting distributions between conventional methods in classical computers and sampling by a quantum annealer. In this study, we focused on the performance of a quantum annealer as a generative model from a different aspect. To evaluate its performance, we prepared a discriminator given by a neural network trained on an a priori dataset. The evaluation results show a higher performance of quantum annealer compared with the classical approach for Boltzmann machine learning in training of the generative model. However the generation of the data suffers from the remanent quantum fluctuation in the quantum annealer. The quality of the generated images from the quantum annealer gets worse than the ideal case of the quantum annealing and the classical Monte-Carlo sampling.
Juice: A Julia Package for Logic and Probabilistic Circuits

Juice is an open-source Julia package providing tools for logic and probabilistic reasoning and learning based on logic circuits (LCs) and probabilistic circuits (PCs). It provides a range of efficient algorithms for probabilistic inference queries, such as computing marginal probabilities (MAR), as well as many more advanced queries. Certain structural circuit properties are needed to achieve this tractability, which Juice helps validate. Additionally, it supports several parameter and structure learning algorithms proposed in the recent literature. By leveraging parallelism (on both CPU and GPU), Juice provides a fast implementation of circuit-based algorithms, which makes it suitable for tackling large-scale datasets and models.