Reviewerly/exHarmony huggingface dataset:
Active Probability Backpropagation Neural Network Model for Monthly Prediction of Probabilistic Seismic Hazard Analysis in Taiwan

In this study, an active probability backpropagation neural network model (PBNNM) was built by training a backpropagation neural network (BPNN) to predict the probability distribution of the probabilistic seismic hazard analysis (PSHA) monthly.The four-layered BPNN framework was determined using training data that were obtained from an earthquake catalogue for the time period of 1990-2015 (Taiwan Standard Time, TST).The studied region was divided into 500 small grids, each 0.2 • × 0.2 • in size, which is approximately 20 × 20 km 2 .Each grid was assigned a predicted earthquake occurrence probability for a month between 2015 and 2018 by the PBNNM.The PBNNM successfully predicted the Tainan earthquake (2 February 2016 TST) and Hualien earthquake (2 February 2018 TST) with probabilities of 94% and 95%, respectively.A quantitative analysis of the reliability of the PBNNM, the standard error of the mean (SEM), and the normalised mean square error (NMSE) were used as statistical approaches to evaluate predicted probability errors of the PBNNM.The SEM (1.79) and NMSE (1.45) of 2015 were the inside test and within the time period of the training data.The SEM (2.11) and NMSE (2.03) of 2016, the low SEM (2.17) of 2017, the NMSE (2.13) and SEM (2.17) of 2017, and the SEM (1.71) and NMSE (1.32) of 2018 were the outside tests and not within the time period of the training data.These low SEM and NMSE values confirmed the accuracy of the PBNNM.In addition, the PBNNM does not consider the prerequisite conditions of past studies (e.g., the return period and assumed probability density model).Therefore, the PBNNM can be commercialised with relatively low cost and minimal resources and equipment compared with the methods presented in previous studies; only earthquake catalogues would be necessary. INDEX TERMSActive probability backpropagation neural network model (PBNNM), probabilistic seismic hazard analysis (PSHA), standard error of the mean (SEM), normalized mean square error (NMSE).
FUBImethod: Strategies to engage children in the co-design of Full-Body interactive experiences

In this paper, we present the FUBImethod, a method based on six stages to co-design interactive experiences based on Full-Body Interaction. The FUBImethod aims specifically to engage children in co-design processes and to benefit from their natural playfulness and expertise in movement. This approach allows designers to go beyond the surface level of content-driven ideas by raising awareness of the body and space, and by proposing techniques that help the design team to understand and incorporate the specific qualities that constitute Full-Body Interaction. We also propose strategies to strengthen the children's perspective in the design process in reaching a common agreement in the design goals and the selection of adequate design choices within the design team. We ground our theoretical discussions on the outcomes of the research project “Evaluation-Driven Design”, the goal of which was the exploration and definition of adequate research and design methods in Full-Body Interaction.
A Type Theory for Defining Logics and Proofs

We describe a Martin-Lof-style dependent type theory, called Cocon, that allows us to mix the intensional function space that is used to represent higher-order abstract syntax (HOAS) trees with the extensional function space that describes (recursive) computations. We mediate between HOAS representations and computations using contextual modal types. Our type theory also supports an infinite hierarchy of universes and hence supports type-level computation thereby providing metaprogramming and (small-scale) reflection. Our main contribution is the development of a Kripke-style model for Cocon that allows us to prove normalization. From the normalization proof, we derive subject reduction and consistency. Our work lays the foundation to incorporate the methodology of logical frameworks into systems such as Agda and bridges the longstanding gap between these two worlds.
Chan–Vese Reformulation for Selective Image Segmentation

Selective segmentation involves incorporating user input to partition an image into foreground and background, by discriminating between objects of a similar type. Typically, such methods involve introducing additional constraints to generic segmentation approaches. However, we show that this is often inconsistent with respect to common assumptions about the image. The proposed method introduces a new fitting term that is more useful in practice than the Chan-Vese framework. In particular, the idea is to define a term that allows for the background to consist of multiple regions of inhomogeneity. We provide comparative experimental results to alternative approaches to demonstrate the advantages of the proposed method, broadening the possible application of these methods.
Analysis of Reaction and Timing Attacks Against Cryptosystems Based on Sparse Parity-Check Codes

In this paper we study reaction and timing attacks against cryptosystems based on sparse parity-check codes, which encompass low-density parity-check (LDPC) codes and moderate-density parity-check (MDPC) codes. We show that the feasibility of these attacks is not strictly associated to the quasi-cyclic (QC) structure of the code but is related to the intrinsically probabilistic decoding of any sparse parity-check code. So, these attacks not only work against QC codes, but can be generalized to broader classes of codes. We provide a novel algorithm that, in the case of a QC code, allows recovering a larger amount of information than that retrievable through existing attacks and we use this algorithm to characterize new side-channel information leakages. We devise a theoretical model for the decoder that describes and justifies our results. Numerical simulations are provided that confirm the effectiveness of our approach.
Random Finite Set-Based Anomaly Detection for Safety Monitoring in Construction Sites

Low visibility hazard detection in construction sites is a crucial task for prevention of fatal accidents.Manual monitoring of construction workers to ensure they follow the safety rules (e.g., wear high-visibility vests) is a cumbersome task and practically infeasible in many applications.Therefore, an automated monitoring system is of both fundamental and practical interest.This paper proposes an intelligent solution that uses live camera images to detect workers who breach safety rules by not wearing high-visibility vests.The proposed solution is formulated in the form of an anomaly detection algorithm developed in the random finite set (RFS) framework.The proposed system is comprised of three steps: 1) applying a deep neural network to extract people in the image; 2) extracting particularly engineered features from each blob returned by the deep neural network; and 3) applying the RFS-based anomaly detection algorithm to each set of detected features.The experimental results demonstrate that in terms of F1-score, the proposed solution (as the combination of the newly engineered features and RFS-based anomaly detection algorithm) significantly outperforms various combinations of common and the state-ofthe-art features and anomaly detection algorithms employed in machine vision applications.
Meta Learning for Task-Driven Video Summarization

Existing video summarization approaches mainly concentrate on sequential or structural characteristic of video data.However, they do not pay enough attention to the video summarization task itself.In this paper, we propose a meta learning method for performing task-driven video summarization, denoted by MetaL-TDVS, to explicitly explore the video summarization mechanism among summarizing processes on different videos.
VORNet: Spatio-Temporally Consistent Video Inpainting for Object Removal

Video object removal is a challenging task in video processing that often requires massive human efforts. Given the mask of the foreground object in each frame, the goal is to complete (inpaint) the object region and generate a video without the target object. While recently deep learning based methods have achieved great success on the image inpainting task, they often lead to inconsistent results between frames when applied to videos. In this work, we propose a novel learning-based Video Object Removal Network (VORNet) to solve the video object removal task in a spatio-temporally consistent manner, by combining the optical flow warping and image-based inpainting model. Experiments are done on our Synthesized Video Object Removal (SVOR) dataset based on the YouTube-VOS video segmentation dataset, and both the objective and subjective evaluation demonstrate that our VORNet generates more spatially and temporally consistent videos compared with existing methods.
Guessing Attacks on Distributed-Storage Systems

The secrecy of a distributed-storage system for passwords is studied. The encoder, Alice, observes a length- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> password and describes it using two hints, which she stores in different locations. The legitimate receiver, Bob, observes both hints and the eavesdropper, Eve, only one. In one scenario—the “guessing version”—we require that the expected number of guesses it takes Bob to guess the password approach one as <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> tends to infinity, and in the second—the “listsize version”—that the expected size of the shortest list that Bob must form to guarantee that it contain the password approach one. Assuming that Alice cannot control which hint Eve observes, the largest normalized (by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> ) exponent that can be guaranteed for the expected number of guesses it takes Eve to guess the password is characterized for each scenario. Key to the proof are new results on Massey–Arikan guessing, Bunte–Lapidoth task-encoding, and the close relation between them. A generalization that allows for Alice to produce <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> (not necessarily two) hints, for Bob to observe <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\nu $ </tex-math></inline-formula> (not necessarily two) of the hints, and for Eve to observe <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\eta $ </tex-math></inline-formula> (not necessarily one) of the hints is also discussed. This models scenarios where hints are stored on fail-prone disks.
Robust visual tracking based on global-and-local search with confidence reliability estimation

Visual object tracking is an open and challenging problem, an online tracker must be able to keep track of the target object for a long time period even in complex scenarios, such as target drift and background occlusion. Discriminative correlation filters (DCF) have shown excellent performance in short-term target tracking problems thanks to their circular dense sampling mechanism and fast computation with a discrete Fourier transform. However, they tend to drift from the target when the target encounters drastic deformation, fast motion, or background occlusion. This can result in a bad model update since the tracker searches the target in a local region centered at the position where target was located in the previous frame. There is no recovery mechanism for target re-identification and re-location. To handle this issue, this paper proposes a global-and-local-search technique that applies a DCF-based tracking model with a novel target-aware detector in a collaborative way. Our tracking model performs the local search process with high tracking confidence, and the target-aware detector is executed to re-identify and locate the target via global search from the entire frame when the model instability and confidence fluctuation are detected by proposed tracking system. Additionally, we designed an enhanced peak-to-sidelobe ratio (EPSR) for confidence estimation, which indicates system instability and fluctuation degree. Thus, the local tracking model and target-aware detector are collaboratively applied for both final target state estimation and online model updates. This not only avoids model corruption from bad updates, but also prevents our tracker from drifting problems for long-term tracking. Experiments on OTB-100 and VOT2016 benchmarks demonstrate that the proposed tracking method achieves state-of-the-art tracking performance in terms of accuracy and robustness, with 22 fps tracking speed (close to realtime) run on a single GPU.
Virtualization of Tissue Staining in Digital Pathology Using an Unsupervised Deep Learning Approach

Histopathological evaluation of tissue samples is a key practice in patient diagnosis and drug development, especially in oncology. Historically, Hematoxylin and Eosin (H&E) has been used by pathologists as a gold standard staining. However, in many cases, various target specific stains, including immunohistochemistry (IHC), are needed in order to highlight specific structures in the tissue. As tissue is scarce and staining procedures are tedious, it would be beneficial to generate images of stained tissue virtually. Virtual staining could also generate in-silico multiplexing of different stains on the same tissue segment. In this paper, we present a sample application that generates FAP-CK virtual IHC images from Ki67-CD8 real IHC images using an unsupervised deep learning approach based on CycleGAN. We also propose a method to deal with tiling artifacts caused by normalization layers and we validate our approach by comparing the results of tissue analysis algorithms for virtual and real images.
Using machine learning for cryptocurrency trading

In this study, we use random forest to predict several cryptocurrencies' prices by using part of factors in Alpha101 [1] to represent features from the history of cryptocurrencies' market data on Binance and Bitfinex. The result shows our strategy with some factors from Alpha101 is effective in cryptocurrency trading.
Responding to a TOEFL iBT integrated speaking task: Mapping task demands and test takers’ use of stimulus content

Integrated speaking tasks requiring test takers to read and/or listen to stimulus texts and to incorporate their content into oral performances are now used in large-scale, high-stakes tests, including the TOEFL iBT. These tasks require test takers to identify, select, and combine relevant source text information to recognize key relationships between source text ideas, and to organize and transform information. Despite being central to evaluations of validity, relationships between stimulus content, task demands, and the oral discourse produced by test takers are yet to be empirically scrutinized to an adequate degree. In this study, we focus on a TOEFL iBT reading–listening–speaking task, applying discourse analytic measures developed by Frost, Elder and Wigglesworth (2012) to 120 oral performances to examine (a) the integration of source text ideas by test takers across three proficiency levels, and (b) the appropriateness of content-related criteria in the TOEFL integrated speaking rubric. We then combine analyses of these aspects of performances with a qualitative analysis of the generic structure and semantic profiles of stimulus texts to explore relationships between stimulus text properties and oral performances. Findings suggest that the extent to which content-related rating scale criteria distinguish between proficiency levels is contingent on stimulus text properties, with important implications for construct definitions and task design.
Novel Weighted Interest Similarity Measurement for Recommender Systems Using Rating Timestamp

This paper proposes a novel similarity measurement for recommender systems that uses weighted user interests and rate timestamps. Although some works were proposed previously to include the time factor in the recommendation process, these works were based on the use of the time factor with user rates. In this work, we show that using user rates could be misleading in some cases, and we propose the use of the time factor with the hidden user interest(s) instead of user rates. The user interests are weighted according to the time factor so that recent interests are given more weight than the older ones as they are more important. Experimental results proved that our proposed similarity measurement is efficient in terms of accuracy and recommendation time.
Seamless Tracking of Apparent Point and Extended Targets Using Gaussian Process PMHT

In practical target tracking scenarios, targets of different sizes (or extents) may be near or far away from the sensor, which may result in targets appearing as point sources or as extended targets spanning one or more resolution cells, respectively, depending on distance and sensor resolution. In this paper, a new Gaussian Process (GP) measurement model is proposed to explicitly describe the observation about each basis point of GP by an individual dynamic Poisson measurement rate. By employing this model, a novel algorithm to track multiple point targets and extended targets, simultaneously and seamlessly, in the presence of clutter and missed detections is proposed within the Probabilistic Multi-Hypothesis Tracker (PMHT) framework. The proposed algorithm can adapt to spatio-temporally varying target sizes or extents of extended targets and temporally varying target cardinality. In addition, the posterior Cramer-Rao lower bound (PCRLB) for extended targets, which quantifies the accuracies of estimates of multiple extended target states in scenarios with clutter, is derived. Simulations with a scenario consisting of multiple extended targets and point targets are used to verify the effectiveness of the proposed algorithm and to compare its performance with the extended target PCRLB and with those of existing extended target tracking algorithms.
Interacting Multiple Model-Based Human Pose Estimation Using a Distributed 3D Camera Network

Distributed camera network for human pose estimation can solve the problem of limited view and occlusion of single view, which has great potential for wide area surveillance applications. To fuse different field of view information, we propose a distributed human pose estimation method by combining the interactive multiple model (IMM) algorithm with the distributed information fusion of human skeleton joints. Compared with the state-of-the-art works which often use a single motion model to depict the motion of human skeleton joints, e.g., constant velocity, the novelty of our work is that the maneuvering property of human action is handled by the IMM, i.e., the motion model of human skeleton joints in the filter is approximated using constant velocity, constant acceleration, and Singer motion models. Based on the advantages of IMM algorithm for maneuvering target tracking, our method can not only solve the single-view occlusion problem, but also solve the problem of joint point fluctuation caused by the estimation error of each sensor node after distributed information fusion. The final human action recognition experimental results show that the proposed method can improve the action recognition rate on the datasets captured by Kinect V2. In addition, we built a distributed camera network using embedded machine learning boards, such that deep learning-based human pose estimation methods can be employed in our framework to handle the limitations of original Kinect SDK.
On the relative expressiveness of Bayesian and neural networks

A neural network computes a function. A central property of neural networks is that they are "universal approximators:" for a given continuous function, there exists a neural network that can approximate it arbitrarily well, given enough neurons (and some additional assumptions). In contrast, a Bayesian network is a model, but each of its queries can be viewed as computing a function. In this paper, we identify some key distinctions between the functions computed by neural networks and those by marginal Bayesian network queries, showing that the former are more expressive than the latter. Moreover, we propose a simple augmentation to Bayesian networks (a testing operator), which enables their marginal queries to become "universal approximators."
Critical analysis of the effects of the digital technologies on reading and learning / Análisis crítico sobre los efectos de las tecnologías digitales en la lectura y el aprendizaje

In the digital age, the introduction of digital technologies (or information and communication technologies, ICT) in classrooms is a necessary, imperative reality. However, the educational results do not seem to match the expectations generated. For this reason, this revision seeks to make up for the scant attention paid to the possible harmful effects of the use of ICT based on research and educational practice. After providing a general overview of the impact of ICT on learning, we survey the results of studies that reveal potential negative consequences of their use inside and outside the school context. We analyse their impact on both academic performance and other more specific areas, such as reading comprehension, and we stress the negative effects on so-called ‘digital natives’. In short, what stands out is the importance of an evidence-based education practice which bears in mind the possible harmful effects of using ICT and bears in mind that its beneficial effects seem to rely on not only how much but also how they are used.
Hierarchical Memory System With STT-MRAM and SRAM to Support Transfer and Real-Time Reinforcement Learning in Autonomous Drones

This article presents a transfer learning (TL) followed by reinforcement learning (RL) algorithm mapped onto a hierarchical embedded memory system to meet the stringent power budgets of autonomous drones. The power reduction is achieved by 1. TL on meta-environments followed by online RL only on the last few layers of a deep convolutional neural network (CNN) instead of end-to-end (E2E) RL and 2. Mapping of the algorithm onto a memory hierarchy where the pretrained weights of all the conv layers and the first few fully connected (FC) layers are stored in dense, low standby leakage Spin Transfer Torque (STT) RAM eNVM arrays and the weights of the last few FC layers are stored in the on-die SRAM. This memory hierarchy enables real-time RL as the drone explores unknown territories and the system only reads the weights from eNVM (that are slow and power hungry to write otherwise) for inference and uses the on-die SRAM for low latency training through both write and read of the weights of the last few layers. The proposed system is extensively simulated on a virtual environment and dissipates 83.5% lower energy per image frame as well as 79.4% lower latency as compared to E2E RL without any loss of accuracy. The speed of the drone is improved by a factor of 3× due to higher frame rates as well.
Leveraging Change Intents for Characterizing and Identifying Large-Review-Effort Changes

Code changes to software occur due to various reasons such as bug fixing, new feature addition, and code refactoring. In most existing studies, the intent of the change is rarely leveraged to provide more specific, context aware analysis.