Reviewerly/exHarmony huggingface dataset:
Unraveling Representations in Scene-selective Brain Regions Using Scene-Parsing Deep Neural Networks

Abstract Visual scene perception is mediated by a set of cortical regions that respond preferentially to images of scenes, including the occipital place area (OPA) and parahippocampal place area (PPA). However, the differential contribution of OPA and PPA to scene perception remains an open research question. In this study, we take a deep neural network (DNN)-based computational approach to investigate the differences in OPA and PPA function. In a first step, we search for a computational model that predicts fMRI responses to scenes in OPA and PPA well. We find that DNNs trained to predict scene components (e.g., wall, ceiling, floor) explain higher variance uniquely in OPA and PPA than a DNN trained to predict scene category (e.g., bathroom, kitchen, office). This result is robust across several DNN architectures. On this basis, we then determine whether particular scene components predicted by DNNs differentially account for unique variance in OPA and PPA. We find that variance in OPA responses uniquely explained by the navigation-related floor component is higher compared to the variance explained by the wall and ceiling components. In contrast, PPA responses are better explained by the combination of wall and floor, that is, scene components that together contain the structure and texture of the scene. This differential sensitivity to scene components suggests differential functions of OPA and PPA in scene processing. Moreover, our results further highlight the potential of the proposed computational approach as a general tool in the investigation of the neural basis of human scene perception.
Linguistic Accommodation in Teenagers’ Social Media Writing: Convergence Patterns in Mixed-gender Conversations

The present study analyzes the phenomenon of linguistic accommodation, i.e. the adaptation of one's language use to that of one's conversation partner. In a large corpus of private social media messages, we compare Flemish teenagers' writing in two conversational settings: same-gender (including only boys or only girls) and mixed-gender conversations (including at least one girl and one boy). We examine whether boys adopt a more 'female' and girls a more 'male' writing style in mixed-gender talks, i.e. whether teenagers converge towards their conversation partner with respect to gendered writing. The analyses focus on two sets of prototypical markers of informal online writing, for which a clear gender divide has been attested in previous research: expressive typographic markers (e.g., emoticons), which can be considered more 'female' features, and 'oral', speech-like markers (e.g., regional language features), which are generally more popular among boys. Using generalized linear-mixed models, we examine the frequency of these features in boys' and girls' writing in same- versus mixed-gender conversations.Patterns of convergence emerge from the data: they reveal that girls and boys adopt a more similar style in mixed-gender talks. Strikingly, the convergence is asymmetrical and only significant for a particular group of online language features.
Latent Feature Decentralization Loss for One-Class Anomaly Detection

Anomaly detection is essential for many real-world applications, such as video surveillance, disease diagnosis, and visual inspection. With the development of neural networks, many neural networks have been used for anomaly detection by learning the distribution of normal data. However, they are vulnerable to distinguishing abnormalities when the normal and abnormal images are not significantly different. To mitigate this problem, we propose a novel loss function for one-class anomaly detection: decentralization loss. The main goal of the proposed method is to cause the latent feature of the encoder to disperse over the manifold space, such that the decoder can generate images similar to those in a normal class for any input. To this end, a decentralization term designed based on the dispersion measure for latent vectors is also added to the existing mean-squared error loss. To design a general solution for various datasets, we restrict the latent space by designing a decentralization loss term-based upper bound of the dispersion measure. As intended, a model trained with the proposed decentralization loss function disperses vectors on the manifold space and generates constant images. Consequently, the reconstruction error increases when the given test image is unknown. Experiments conducted on various datasets verify that the proposed function improves detection performance improved by about 1% while reducing training time by 48%, without any structural changes in the conventional autoencoder.
FSLM: An Intelligent Few-Shot Learning Model Based on Siamese Networks for IoT Technology

As an important application of the Internet of Things (IoT) devices, sentiment analysis has been paid more attention with the rapid development of artificial intelligence. As a widely used method in artificial intelligence applications, traditional deep learning methods need massive data for training. However, due to the limitations of hardware, IoT devices have deficiencies in processing big data. In the case of insufficient sample size, how to carry out a machine learning method for IoT devices has become a common concern of the industry. In order to perform sentiment analysis on text with few data samples from the IoT devices, we propose FSLM, which is an intelligent few-shot learning model based on Siamese networks. The FSLM model consists of two self-attention models with the same parameters, which are divided into two parts. First, for two input texts, a self-attention model is used to extract sentiment features, and then the Mahalanobis distance is adopted to measure the similarity between two feature vectors to determine whether they belong to the same category. The FSLM is tested on the Amazon Review Sentiment Classification (ARSC) data set. The extensive experimental results on this data set demonstrate that the FSLM model has better accuracy and robustness for text sentiment analysis than other main existing models with a small number of samples.
Joint detection, tracking and classification of multiple extended objects based on the JDTC-GIW-MeMBer filter

In this paper, the problem of joint detection, tracking and classification (JDTC) of multiple extended objects (EOs) is addressed using the random finite set (RFS) theory. In the proposed method, the shape of an EO is restricted to an ellipse that can be easily modelled by a random matrix. The classification of an EO is estimated on the basis of size information with respect to its extended part. Furthermore, the extended object RFS is proposed to be modelled as a multi-Bernoulli (MeMBer) process, then the MeMBer filter is naturally adopted to propagate the EO posteriors. In this paper, in order to achieve the closed-form solution, we model the spatial probability density function (SPDF) of each Bernoulli component as the mixture Gaussian inverse Wishart (GIW) process for the JDTC problem. Compared to the existing JDTC strategies, the proposed algorithm, named as JDTC-GIW-MeMBer filter, is demonstrated via simulations to achieve better performance to the existing algorithms on detection and tracking of the EO centers, the estimation accuracy of the extended states, and EO classifications.
Gesture Recognition Based on 3D Human Pose Estimation and Body Part Segmentation for RGB Data Input

This paper presents a novel approach for dynamic gesture recognition using multi-features extracted from RGB data input. Most of the challenges in gesture recognition revolve around the axis of the presence of multiple actors in the scene, occlusions, and viewpoint variations. In this paper, we develop a gesture recognition approach by hybrid deep learning where RGB frames, 3D skeleton joint information, and body part segmentation are used to overcome such problems. Extracted from the RGB images are the multimodal input observations, which are combined by multi-modal stream networks suited to different input modalities: residual 3D convolutional neural networks based on ResNet architecture (3DCNN_ResNet) for RGB images and color body part segmentation modalities; long short-term memory network (LSTM) for 3D skeleton joint modality. We evaluated the proposed model on four public datasets: UTD multimodal human action dataset, gaming 3D dataset, NTU RGB+D dataset, and MSRDailyActivity3D dataset and the experimental results on these datasets proves the effectiveness of our approach.
Attention-based label consistency for semi-supervised deep learning based image classification

Semi-supervised deep learning, which aims to effectively use the available unlabeled data to aid the model in learning from labeled data, is a hot topic recently. To effectively employ the abundant unlabeled data and handle the imbalance in labeled data, we propose a novel attention-based label consistency (ALC) model for semi-supervised deep learning. The relationships between different samples are well exploited by the proposed scheme of channel and sample attention; meanwhile, the class estimations are required to be smooth for nearby unlabeled data. The proposed ALC is further extended to the imbalanced case by developing a label-imbalance ALC model. We have implemented the proposed ALC model in the semi-supervised frameworks of Π model and MeanTeacher, and the experimental results on four benchmark datasets, (e.g., Fashion-MNIST, CIFAR-10, SVHN, and ImageNet) clearly show the advantages of our proposed method.
Video captioning with boundary-aware hierarchical language decoding and joint video prediction

The explosion of video data on the Internet requires effective and efficient technology to generate captions automatically for people, especially those who are visually impaired. Despite the great progress of video captioning research, particularly in video feature encoding, the language decoder is still largely based on the prevailing recurrent structure such as LSTM, which tends to prefer frequent words that align with the video and do not generalize well to new videos. In this paper, we propose a boundary-aware hierarchical language decoder for video captioning, which consists of a high-level decoder, working as a global (caption-level) language model, and a low-level decoder, working as a local (phrase-level) language model. Most importantly, we introduce a binary gate into the low-level language decoder to detect the phrasal boundaries. Together with other advanced components including a joint video prediction module, a shared soft attention, and a boundary-aware video encoding module, our integrated video captioning framework can discover hierarchical language information and distinguish the subjects from the objects of the verbs in a sentence, which are usually confusing during caption generation. Extensive experiments on two widely-used video captioning datasets, MSR-Video-to-Text (MSR-VTT) and YouTube-to-Text (MSVD), show that our method is highly competitive, compared with the state-of-the-art methods.
Optimal minimal linear codes from posets

Recently, some infinite families of minimal and optimal binary linear codes were constructed from simplicial complexes by Hyun et al. We extend this construction method to arbitrary posets. Especially, anti-chains are corresponded to simplicial complexes. In this paper, we present two constructions of binary linear codes from hierarchical posets of two levels. In particular, we determine the weight distributions of binary linear codes associated with hierarchical posets with two levels. Based on these results, we also obtain some optimal and minimal binary linear codes not satisfying the condition of Ashikhmin–Barg.
Application of Lightweight Railway Transit Object Detector

Intelligent traffic systems for railway object detection have become the focus of research in recent years. Accurate and fast object detection using a camera is an important but challenging problem in the railway industry. In this article, we propose an object detector with low power yet robust detection for collision warning in a train safety system. The proposed object detector comprises three modules. First, a stable sampling module is used to reduce the dimensions of the feature map and image information loss. Second, a lightweight feature extraction module utilizes a dynamic bottleneck structure to control the calculation load and enhance the expressive ability of the model. Third, a feature-fusion module combines high- and low-level features to enhance the semantic information and improve the accuracy of detection of multiscale and small objects. Experimental results demonstrate that the proposed network achieves reasonable results for railway object detection and outperforms the current state-of-the-art detectors. Finally, we design an obstacle-avoidance device that can be installed at the front of the train for real-time security warning in real-world conditions.
Expected vs. Unexpected: Selecting Right Measures of Interestingness

Measuring interestingness in between data items is one of the key steps in association rule mining. To assess interestingness, after the introduction of the classical measures (support, confidence and lift), over 40 different measures have been published in the literature. Out of the large variety of proposed measures, it is very difficult to select the appropriate measures in a concrete decision support scenario. In this paper, based on the diversity of measures proposed to date, we conduct a preliminary study to identify the most typical and useful roles of the measures of interestingness. The research on selecting useful measures of interestingness according to their roles will not only help to decide on optimal measures of interestingness, but can also be a key factor in proposing new measures of interestingness in association rule mining.
Palisade: A framework for anomaly detection in embedded systems

In this article, we propose Palisade, a distributed framework for streaming anomaly detection. Palisade is motivated by the need to apply multiple detection algorithms for distinct anomalies in the same scenario. Our solution blends low latency detection with deployment flexibility and ease-of-modification. This work includes a thorough description of the choices made in designing Palisade and the reasons for making those choices. We carefully define symptoms of anomalies that may be detected, and we use this taxonomy in characterizing our work. The article includes two case studies using a variety of anomaly detectors on streaming data to demonstrate the effectiveness of our approach in an embedded setting.
Automatic Breast Cancer Classification from Histopathological Images

Breast cancer (BC) is a common health problem of major significance, as it is the most widely kind of cancer among women which leads to morbidity and mortality. Pathological diagnosis is considered as the golden standard of BC detection. However, the investigation of histopathology images is a challenging task. Automatic diagnosis of BC could lower the death rate by constructing a computer aided diagnosis (CAD) system capable of accurately diagnosing BC and reducing the time consumed by pathologists during examinations. This paper presents a CAD system to classify BC to benign and malignant. The proposed CAD method consists of 4 stages; image pre-processing, feature extraction and fusion, feature reduction, and classification. The CAD is based on fusion features extracted with ResNet Deep Convolution Neural Network (DCNN) with features of wavelets packet decomposition (WPD) and histograms of oriented gradient (HOG). Next, the feature data were reduced by utilizing principle component analysis (PCA). Finally, the reduced features are used to train different individual classifiers. Results show that the highest accuracy of 97.1% is achieved. The results were compared with recent related CAD systems. The comparison showed that the proposed CAD system is capable of accurately classifying BC to benign and malignant compared to other work. Thus, it can be used to help medical experiments in investigation procedures.
Feature-Based Model Difference Identification for Aerospace Sheet Metal Parts

A semantic model difference identification (MDI) solution can be defined as an MDI solution that identifies and represents the differences between two compared models in terms of meaningful engineering information.Semantic representation of the differences between 3D CAD models is especially challenging due to the variety of modeling solutions used across industries, the non-uniqueness of modeling sequences and the use of low-level information, e.g.B-rep STEP files, in engineering communications.This work proposes an MDI method that represents the differences between 3D CAD models based on engineering semantics through features.Brake-and hydro-formed aerospace sheet metal parts are used as the application domain in which to propose and illustrate the proposed method.This method consists mainly of a pose registration stage and a difference identification stage.The pose registration method considers that all the features of a part serve specific functions, some of which are fundamental to the part's essential functionality, and that they are intertwined with the design intent of the part, which is particularly true for aerospace sheet metal parts.This provides the opportunity to semantically register feature-based 3D CAD models according to the unique purpose of the features in this specific domain of application.Difference identification is approached by primarily identifying and segregating the commonality between the compared 3D CAD models and then identifying the differences.The differences between 3D CAD models are classified as added, removed or differed features.Differed features are those features that are of the same type, but whose definition varies.As an outcome, the proposed method describes a way to fully pose-register 3D CAD models and identify their differences semantically based solely on their features, and, by extension, their design intent.
Safe Motion Planning Based on a New Encoding Technique for Tree Expansion Using Particle Swarm Optimization

SUMMARY Robots are now among us and even though they compete with human beings in terms of performance and efficiency, they still fail to meet the challenge of performing a task optimally while providing strict motion safety guarantees. It is therefore necessary that the future generation of robots evolves in this direction. Generally, in robotics state-of-the-art approaches, the trajectory optimization and the motion safety issues have been addressed separately. An important contribution of this paper is to propose a motion planning method intended to simultaneously solve these two problems in a formal way. This motion planner is dubbed PassPMP-PSO. It is based on a periodic process that interleaves planning and execution for a regular update of the environment’s information. At each cycle, PassPMP-PSO computes a safe near-optimal partial trajectory using a new tree encoding technique based on particle swarm optimization (PSO). The performances of the proposed approach are firstly highlighted in simulation environments in the presence of moving objects that travel at high speed with arbitrary trajectories, while dealing with sensors field-of-view limits and occlusions. The PassPMP-PSO algorithm is tested for different tree expansions going from 13 to more than 200 nodes. The results show that for a population between 20 and 100 particles, the frequency of obtaining optimal trajectory is 100% with a rapid convergence of the algorithm to this solution. Furthermore, an experiment-based comparison demonstrates the performances of PassPMP-PSO over two other motion planning methods (the PassPMP , a previous variant of PassPMP-PSO , and the input space sampling). Finally, PassPMP-PSO algorithm is assessed through experimental tests performed on a real robotic platform using robot operating system in order to confirm simulation results and to prove its efficiency in real experiments.
How Experts Adapt Their Gaze Behavior When Modeling a Task to Novices

Abstract Domain experts regularly teach novice students how to perform a task. This often requires them to adjust their behavior to the less knowledgeable audience and, hence, to behave in a more didactic manner. Eye movement modeling examples (EMMEs) are a contemporary educational tool for displaying experts’ (natural or didactic) problem‐solving behavior as well as their eye movements to learners. While research on expert‐novice communication mainly focused on experts’ changes in explicit, verbal communication behavior, it is as yet unclear whether and how exactly experts adjust their nonverbal behavior. This study first investigated whether and how experts change their eye movements and mouse clicks (that are displayed in EMMEs) when they perform a task naturally versus teach a task didactically. Programming experts and novices initially debugged short computer codes in a natural manner. We first characterized experts’ natural problem‐solving behavior by contrasting it with that of novices. Then, we explored the changes in experts’ behavior when being subsequently instructed to model their task solution didactically. Experts became more similar to novices on measures associated with experts’ automatized processes (i.e., shorter fixation durations, fewer transitions between code and output per click on the run button when behaving didactically). This adaptation might make it easier for novices to follow or imitate the expert behavior. In contrast, experts became less similar to novices for measures associated with more strategic behavior (i.e., code reading linearity, clicks on run button) when behaving didactically.
Active Set Complexity of the Away-Step Frank--Wolfe Algorithm

\bfA \bfb \bfs \bft \bfr \bfa \bfc \bft .In this paper, we study active set identification results for the away-step Frank--Wolfe algorithm in different settings.We first prove a local identification property that we apply, in combination with a convergence hypothesis, to get an active set identification result.We then prove, for nonconvex objectives, a novel O(1/ \surd k) convergence rate result and active set identification for different step sizes (under suitable assumptions on the set of stationary points).By exploiting those results, we also give explicit active set complexity bounds for both strongly convex and nonconvex objectives.While we initially consider the probability simplex as feasible set, in an appendix we show how to adapt some of our results to generic polytopes.\bfK \bfe \bfy \bfw \bfo \bfr \bfd \bfs .surface identification, manifold identification, active set complexity \bfA \bfM \bfS \bfs \bfu \bfb \bfj \bfe \bfc \bft \bfc \bfl \bfa \bfs \bfs \bfi fi\bfc \bfa \bft \bfi \bfo \bfn \bfs .65K05, 90C06, 90C30
Do the best cloud configurations grow on trees?

Cloud configuration optimization is the procedure to determine the number and the type of instances to use when deploying an application in cloud environments, given a cost or performance objective. In the absence of a performance model for the distributed application, black-box optimization can be used to perform automatic cloud configuration. Numerous black-box optimization algorithms have been developed; however, their comparative evaluation has so far been limited to the hyper-parameter optimization setting, which differs significantly from the cloud configuration problem. In this paper, we evaluate 8 commonly used black-box optimization algorithms to determine their applicability for the cloud configuration problem. Our evaluation, using 23 different workloads, shows that in several cases Bayesian optimization with Gradient boosted regression trees performs better than methods chosen by prior work.