Reviewerly/exHarmony huggingface dataset:
Neural Representation Learning for Scribal Hands of Linear B

In this work, we present an investigation into the use of neural feature extraction in performing scribal hand analysis of the Linear B writing system. While prior work has demonstrated the usefulness of strategies such as phylogenetic systematics in tracing Linear B’s history, these approaches have relied on manually extracted features which can be very time consuming to define by hand. Instead we propose learning features using a fully unsupervised neural network that does not require any human annotation. Specifically our model assigns each glyph written by the same scribal hand a shared vector embedding to represent that author’s stylistic patterns, and each glyph representing the same syllabic sign a shared vector embedding to represent the identifying shape of that character. Thus the properties of each image in our dataset are represented as the combination of a scribe embedding and a sign embedding. We train this model using both a reconstructive loss governed by a decoder that seeks to reproduce glyphs from their corresponding embeddings, and a discriminative loss which measures the model’s ability to predict whether or not an embedding corresponds to a given image. Among the key contributions of this work we (1) present a new dataset of Linear B glyphs, annotated by scribal hand and sign type, (2) propose a neural model for disentangling properties of scribal hands from glyph shape, and (3) quantitatively evaluate the learned embeddings on findplace prediction and similarity to manually extracted features, showing improvements over simpler baseline methods.
Solving Rubik’s cube via quantum mechanics and deep reinforcement learning

Rubik's Cube is one of the most famous combinatorial puzzles involving nearly $4.3 \times 10^{19}$ possible configurations. Its mathematical description is expressed by the Rubik's group, whose elements define how its layers rotate. We develop a unitary representation of such group and a quantum formalism to describe the Cube from its geometrical constraints. Cubies are describedby single particle states which turn out to behave like bosons for corners and fermions for edges, respectively. When in its solved configuration, the Cube, as a geometrical object, shows symmetrieswhich are broken when driven away from this configuration. For each of such symmetries, we build a Hamiltonian operator. When a Hamiltonian lies in its ground state, the respective symmetry of the Cube is preserved. When all such symmetries are preserved, the configuration of the Cube matches the solution of the game. To reach the ground state of all the Hamiltonian operators, we make use of a Deep Reinforcement Learning algorithm based on a Hamiltonian reward. The Cube is solved in four phases, all based on a respective Hamiltonian reward based on its spectrum, inspired by the Ising model. Embedding combinatorial problems into the quantum mechanics formalism suggests new possible algorithms and future implementations on quantum hardware.
IAE-ClusterGAN: A new Inverse autoencoder for Generative Adversarial Attention Clustering network

Clustering is a challenging and crucial task in unsupervised learning. Recently, though many clustering algorithms combined with deep learning have been proposed, we observe that the existing deep clustering algorithms do not considerably preserve the clustering structure and information of raw data in the learned latent space. To address this issue, we propose a Generative Adversarial Attention Clustering network Based on Inverse autoencoder (IAE-ClusterGAN), which can control the distribution type of the learned latent code without additional constraints so that unsupervised clustering tasks can be done efficiently. Meanwhile, we integrate the attention mechanism into the network to make the latent code contain more useful clustering information. Moreover, we utilize hyperspherical mapping in the discriminator to improve the stability of model training and reduce the training parameters. Experimental results demonstrate that IAE-ClusterGAN achieves competitive results compared to the state-of-the-art models on five benchmark datasets.
NA-Aware Machine Reading Comprehension for Document-Level Relation Extraction

Document-level relation extraction aims to identify semantic relations between target entities from the document.Most of the existing work roughly treats the document as a long sequence and produces target-agnostic representation for relation prediction, limiting the model’s ability to focus on the relevant context of target entities. In this paper, we reformulate the document-level relation extraction task and propose a NA-aware machine Reading Comprehension (NARC) model to tackle this problem. Specifically, the input sequence formulated as the concatenation of a head entity and a document is fed into the encoder to obtain comprehensive target-aware representations for each entity. In this way, the relation extraction task is converted into a reading comprehension problem by taking all the tail entities as candidate answers. Then, we add an artificial answer \(\texttt {NO-ANSWER}\) (NA) for each query and dynamically generate a NA score based on the decomposition and composition of all candidate tail entity features, which finally weighs the prediction results to alleviate the negative effect of having too many no-answer instances after task reformulation. Experimental results on DocRED with extensive analysis demonstrate the effectiveness of NARC.
Informing the Design of a News Chatbot

Chatbots use conversational interfaces to simulate human communication and recently have been applied to different domains due to advancing techniques of natural language understanding and generation. In particular, in the domain of digital journalism, chatbots provide a new channel for audiences to engage with news. However, most chatbots operated by news organizations have so far failed to achieve business growth. In this paper, we have conducted a qualitative user study to better understand users' attitudes towards news chatbots. Specifically, 15 participants were asked to interact with several news chatbots implemented on Facebook Messenger by large international news organizations (e.g., ABC News, NBC News, and BBC News), by issuing a set of 22 sample questions covering various search and recommendation goals related to COVID-19. Then the participants expressed their expectations of news chatbots and the advantages/disadvantages perceived in using them. From these findings, we derive several design guidelines on effectiveness, informativeness, efficiency, humanization, and facility, suggesting developments to news chatbots that may better serve users' needs.
Few shot object detection in remote sensing images

Object detection in remote sensing images has far-reaching significance. However, compared with object detection tasks in the field of natural images, there are still some challenge problems that need to be improved in remote sensing, due to the similarity and unbalanced scale between objects in remote sensing images. Currently, object detection algorithms based on deep learning, i.e. Faster-RCNN, YOLO, SSD, etc., have reached an incredible grade with the concerted efforts of researchers, and have been used in various aspects such as remote sensing, face recognition, pedestrian detection and so on. However, these methods always need a lot of labeled data, while collecting a dataset is labor intensive and time consuming. In order to overcome these difficulties, we introduce a few shot learning algorithm with attention mechanism for object detection in remote sensing images, aiming to detect objects of unknown classes with only a few labeled remote sensing images. We use the structure of Siamese-network to extract the features of the target from support images and query images, and then use the features of support images as a kernel to do a depth-wise convolution on the feature map of query image. By this way, we can enhance the characteristics of the target category and weaken the characteristics of other categories and the background. For unbalanced scale problem, our method takes care of various objects of different scales in the process of extracting features. We integrate the information of multi-layer feature maps and make predictions on three feature maps of different scales. Experiments on HRSC-2016 dataset validate the effectiveness of our method.
Investigation of Applicability of Impact Factors to Estimate Solar Irradiance: Comparative Analysis Using Machine Learning Algorithms

This study explores investigation of applicability of impact factors to estimate solar irradiance by four machine learning algorithms using climatic elements as comparative analysis: linear regression, support vector machines (SVM), a multi-layer neural network (MLNN), and a long short-term memory (LSTM) neural network. The methods show how actual climate factors impact on solar irradiation, and the possibility of estimating one year local solar irradiance using machine learning methodologies with four different algorithms. This study conducted readily accessible local weather data including temperature, wind velocity and direction, air pressure, the amount of total cloud cover, the amount of middle and low-layer cloud cover, and humidity. The results show that the artificial neural network (ANN) models provided more close information on solar irradiance than the conventional techniques (linear regression and SVM). Between the two ANN models, the LSTM model achieved better performance, improving accuracy by 31.7% compared to the MLNN model. Impact factor analysis also revealed that temperature and the amount of total cloud cover are the dominant factors affecting solar irradiance, and the amount of middle and low-layer cloud cover is also an important factor. The results from this work demonstrate that ANN models, especially ones based on LSTM, can provide accurate information of local solar irradiance using weather data without installing and maintaining on-site solar irradiance sensors.
Website Aesthetics and Functional User States as Factors of Web Usability

In web usability studies, a 'contextual fidelity' model identifies four factors that significantly affects testing accuracy: web aesthetics; testing environment and circumstances; participants' personality traits and user states; and the nature of test task. In most works, these factors are considered independent; however, this has never been properly tested. To partly cover this gap, we experimentally test the one-directional effects of web aesthetics and task nature upon user states. Of dysfunctional user states described in literature, we have chosen monotony and anxiety, both equally damaging for user experience but differing by cognitive strategy. By combining high/low-quality web aesthetics and tasks directed to monotony/anxiety, we show that these two factors condition each other in users' action. To detect monotony and anxiety, we measured cognitive efficiency and emotional stress. 48 student assessors divided into 4 groups were involved in the experiment. Our results shown that, counter-intuitively, the impact of website aesthetics on user states differs in cognitive performance and not in emotional stress. In the case of anxiety, high quality of design provokes hyper-lability of attention, does not contribute to its concentration, and reduces productivity. In the case of monotony, the aesthetic layout dramatically reduces the rate of fatigue and increases productivity. Our results prove that web aesthetics has a non-negligible impact upon more rational decision-making of users, which was overlooked in previous studies.
Structured Ensembles: An approach to reduce the memory footprint of ensemble methods

In this paper, we propose a novel ensembling technique for deep neural networks, which is able to drastically reduce the required memory compared to alternative approaches. In particular, we propose to extract multiple sub-networks from a single, untrained neural network by solving an end-to-end optimization task combining differentiable scaling over the original architecture, with multiple regularization terms favouring the diversity of the ensemble. Since our proposal aims to detect and extract sub-structures, we call it Structured Ensemble. On a large experimental evaluation, we show that our method can achieve higher or comparable accuracy to competing methods while requiring significantly less storage. In addition, we evaluate our ensembles in terms of predictive calibration and uncertainty, showing they compare favourably with the state-of-the-art. Finally, we draw a link with the continual learning literature, and we propose a modification of our framework to handle continuous streams of tasks with a sub-linear memory cost. We compare with a number of alternative strategies to mitigate catastrophic forgetting, highlighting advantages in terms of average accuracy and memory.
Keyword Extraction Algorithm for Classifying Smoking Status from Unstructured Bilingual Electronic Health Records Based on Natural Language Processing

Smoking is an important variable for clinical research, but there are few studies regarding automatic obtainment of smoking classification from unstructured bilingual electronic health records (EHR). We aim to develop an algorithm to classify smoking status based on unstructured EHRs using natural language processing (NLP). With acronym replacement and Python package Soynlp, we normalize 4711 bilingual clinical notes. Each EHR notes was classified into 4 categories: current smokers, past smokers, never smokers, and unknown. Subsequently, SPPMI (Shifted Positive Point Mutual Information) is used to vectorize words in the notes. By calculating cosine similarity between these word vectors, keywords denoting the same smoking status are identified. Compared to other keyword extraction methods (word co-occurrence-, PMI-, and NPMI-based methods), our proposed approach improves keyword extraction precision by as much as 20.0%. These extracted keywords are used in classifying 4 smoking statuses from our bilingual EHRs. Given an identical SVM classifier, the F1 score is improved by as much as 1.8% compared to those of the unigram and bigram Bag of Words. Our study shows the potential of SPPMI in classifying smoking status from bilingual, unstructured EHRs. Our current findings show how smoking information can be easily acquired for clinical practice and research.
Exploiting transient dynamics of a time-multiplexed reservoir to boost the system performance

Delay-based reservoir computing is an unconventional information processing method that allows the implementation of recurrent neural networks on different kinds of hardware substrates. It facilitates machine learning based on the transient dynamics of a single nonlinear node through time-multiplexing. Here, we explore the interplay of the driving strength of the nonlinear node and the modulation rate of the time-multiplexing. We find two contrasting combinations of input gain and node separation, each yielding the best performance in a different prediction task, respectively. A weak input gain and large node separation is superior in a near-future prediction of a chaotic Mackey-Glass system, while a high input gain and short node separation leads to the best performance in a far-future prediction. Furthermore, for increasing input gains, we obtain that the node separation yielding the best performance decreases significantly below the characteristic time scale of the underlying delay system. This allows the realization of large networks with up to one thousand nodes even for high processing rates. We investigate the parameter's relation further by analyzing the average state entropy and computing the information processing capacity in a time-multiplexed reservoir for one input mask. This supports an in-depth understanding of the interplay between node separation and input gain.
A Design Space for User Interface Elements using Finger Orientation Input

Despite touchscreens being used by billions of people every day, today’s touch-based interactions are limited in their expressiveness as they mostly reduce the rich information of the finger down to a single 2D point. Researchers have proposed using finger orientation as input to overcome these limitations, adding two extra dimensions – the finger’s pitch and yaw angles. While finger orientation has been studied in-depth over the last decade, we describe an updated design space. Therefore, we present expert interviews combined with a literature review to describe the wide range of finger orientation input opportunities. First, we present a comprehensive set of finger orientation input enhanced user interface elements supported by expert interviews. Second, we extract design implications as a result of the additional input parameters. Finally, we introduce a design space for finger orientation input.
Using the Theory of Interpersonal Behavior to predict Information Security Policy Compliance

Employees’ compliance with information security policies (ISP) which may minimize the information security threats has always been a major concern for organizations. Numerous research and theoretical models had been investigated in the related field of study to identify factors that influence ISP compliance behavior. The study presented in this paper is the first to apply the Theory of Interpersonal Behavior (TIB) for predicting ISP compliance, despite a few studies suggested its strong explanatory power. Taking on the prior results of the literature review, we adopt the TIB and aim to further the theoretical advancement in this field of study. Besides, previous studies had only focused on individuals as well as organizations in which the role of government, from the aspect of its effectiveness in enforcing data protection regulation, so far has not been tested on its influence on individuals’ intention to comply with ISP. Hence, we propose an exploratory study to integrate government effectiveness with TIB to explain ISP compliance in a Malaysian context. Our results show a significant influence of government effectiveness in ISP compliance, and the TIB is a promising model as well as posing strong explanatory power in predicting ISP compliance.
Hash Access in Blockchain Radio Access Networks: Characterization and Optimization

Blockchain radio access network (B-RAN) is a decentralized, trustworthy wireless networking paradigm spurred by distributed ledger technologies (DLTs). In B-RAN, even though the blockchain builds trust in upper layers, the absence of trust between client devices still causes the problem with open access, or the so-called Rogue’s dilemma, and degrades the network performance. Therefore, Hash Access was proposed for B-RAN to address the trust issue between clients and enforce client devices to obey the grant-free access rule. However, the characteristics and performance of Hash Access in B-RAN remain unclear. In this work, we dive deep into the Rogue’s dilemma from a game-theoretic model to emphasize the necessity of Hash Access. We establish an analytical model to comprehensively evaluate the performance of B-RAN using Hash Access regarding transmission success probability, access delay, and network throughput. Based on the analytical model, we further optimize the Hash Access protocol for network throughput and provide useful practical guidelines. Simulation results are presented to validate our proposed model and insights.
Exploiting Activation Sparsity for Fast CNN Inference on Mobile GPUs

Over the past several years, the need for on-device deep learning has been rapidly increasing, and efficient CNN inference on mobile platforms has been actively researched. Sparsity exploitation has been one of the most active research themes, but the studies mostly focus on weight sparsity by weight pruning. Activation sparsity, on the contrary, requires compression at runtime for every input tensor. Hence, the research on activation sparsity mainly targets NPUs that can efficiently process this with their own hardware logic. In this paper, we observe that it is difficult to accelerate CNN inference on mobile GPUs with natural activation sparsity and that the widely used CSR-based sparse convolution is not sufficiently effective due to the compression overhead. We propose several novel sparsification methods that can boost activation sparsity without harming accuracy. In particular, we selectively sparsify some layers with an extremely high sparsity and adopt sparse convolution or dense convolution depending on the layers. Further, we present an efficient sparse convolution method without compression and demonstrate that it can be faster than the CSR implementation. With ResNet-50, we achieved 1.88 <?TeX $\times$?> speedup compared to TFLite on a Mali-G76 GPU.
Detection of Railroad Anomalies using Machine Learning Approach

Maintenance of assets owned by an organization or company is an activity that will never stop. From the time of implementation, maintenance will be more optimal if it is carried out before the asset is in a damaged condition or cannot operate. Pro-active repair model is proven to reduce 15-60% of operational costs. The existence of technology and computing models currently supports big data processing, both in the form of transactional data, historical data and statistical data. The asset maintenance cycle transformed into an autonomous and integrated system, will assist in the decision-making process. A machine learning approach that is supported by big data analysis is one solution that can realize the predictive maintenance process. To accurately predict the condition of critical components, it can be started with data collection, followed by detecting normal and abnormal behavior, and continued by training algorithms to make predictions. Detection of railroad anomalies is used as the initial process in the predictive maintenance of railroads. The process of detecting railroad anomalies can be done by comparing the lateral, longitudinal and vertical acceleration from the sensing results through the accelerometers on both sides of the train wheels. Differences will pay attention to the data acceleration draft rail geometry either angkatan or listringan. The results of rail anomaly detection will indicate the rail maintenance process that can be carried out immediately.
AlexNet-CNN Based Feature Extraction and Classification of Multiclass ASL Hand Gestures

In computer vision, accurate recognition and classification of hand gestures play a vital part for developing gesture recognition systems for human–computer interaction (HCI). Error-free HCI can considerably improve systems that can recognize diverse class of hand gesture characters. In this manuscript, we propose a modified deep learning architecture based on AlexNet for recognizing American Sign Language (ASL) gestures. The objective of static gesture recognition is to characterize the data given by the hand gesture into predefined gesture classes. The ASL dataset taken in our methodology is composed of five different individuals. In contrast to most of the prior works, where data is split randomly for training and testing, we have trained the network on samples of four individuals and tested on the fifth individual. We have taken the original AlexNet architecture and modified the final fully connected layers to classify the ASL dataset. The modified network is trained using ASL dataset and is compared to the state-of-the-art AlexNet architecture to demonstrate the work's competence. The paper also demonstrates the training testing performance of the modified network with the ASL dataset.