Reviewerly/exHarmony huggingface dataset:
Fast Face Features Extraction Based on Deep Neural Networks for Mobile Robotic Platforms

The concept of Smart Environment (SE) provides a great benefit to its users: interactive informational services (corporate TV, video communication, navigation and localization services) and mobile autonomous entities: mobile robotic platforms, quadcopters, anthropomorphic robots etc. It is important to take into account all the personal details of the user's behavior so to provide him a personalized most useful data. So, the task of a person identification based on an image of his face is urgent. One of the most famous approach to recognize a client is by their face. It's important to have as huge dataset as it's conceivable to prepare an exact classifier, particularly if profound neural systems are being used. It's expensive to compose delegate dataset physically – to snap a picture of each individual from each conceivable edge with each conceivable light condition. This is the reason why generation of a synthetic data for training a classifier, utilizing least genuine information is so urgent. At this paper several face features extractors were tested based on deep learning models in order to find its advantages and disadvantages in the context of training a classifier for a facial recognition task and a clustering for a tracking unique people in SE.
Effects of Architecture and Training on Embedding Geometry and Feature Discriminability in BERT

Natural language processing has improved substantially in the last few years due to the increased computational power and availability of text data. Bidirectional Encoder Representations from Transformers (BERT) have further improved the performance by using an auto-encoding model that incorporates larger bidirectional contexts. However, the underlying mechanisms of BERT for its effectiveness are not well understood. In the paper we investigate how the BERT architecture and its pretraining protocol affect the geometry of its embeddings and the effectiveness of its features for classification tasks. As an autoencoding model, during pre-training, it produces representations that are context dependent and at the same time must be able to "reconstruct" the original input sentences. The complex interactions of the two via transformers lead to interesting geometric properties of the embeddings and subsequently affect the inherent discriminability of the resulting representations. Our experimental results illustrate that the BERT models do not produce "effective" contextualized representations for words and their improved performance may mainly be due to fine-tuning or classifiers that model the dependencies explicitly by encoding syntactic patterns in the training data.
VoiceMessage++: Augmented Voice Recordings for Mobile Instant Messaging

Media (e.g. videos, images, and text) shared on social platforms such as Facebook and WeChat are often visually enriched through digital content (e.g. emojis, stickers, animal faces) increasing joy, personalization, and expressiveness. While voice messages (VMs) are experiencing a high frequent usage, they currently lack any form of digital augmentation. This work is the first to present and explore the concept of augmented VMs. Inspired by visual augmentations we designed and implemented an editor, allowing users to enhance VMs with background sounds, voice changers, and sound stickers. In a first evaluation (N = 15) we found that participants used augmentations frequently (2.73 per message on average) and rated augmented VMs to be expressive, personal and more fun than ordinary VMs. In a consecutive step, we analyzed the 45 augmented VMs recorded during the study and identified three distinct message types (decoration, composition and integrated) that inform about potential usage.
Mining Exceptional Mediation Models

In statistics, mediation models aim to identify and explain the direct and indirect effects of an independent variable on a dependent variable. In heterogeneous data, the observed effects might vary for parts of the data. In this paper, we develop an approach for identifying interpretable data subgroups that induce exceptionally different effects in a mediation model. For that purpose, we introduce mediation models as a novel model class for the exceptional model mining framework, introduce suitable interestingness measures for several subtasks, and demonstrate the benefits of our approach on synthetic and empirical datasets.
Explaining the interactions of humans and artifacts in insider security behaviors: The mangle of practice perspective

Recent decades have seen numerous studies of individual information security behaviors and compliance within organizational contexts. One regular finding is that individuals continue to fail at compliance; however the explanations for why this occurs typically fail to consider the temporal, emergent and dynamic nature of security encounters between human users information security-related artifacts when they come together in the practice of security. In addition, existing research typically does not consider the inherent tension that exists between information systems (IS) use and security controls where the former is used to perform tasks and the latter may constrain the accomplishment of those tasks. This paper offers a new perspective that may better explain how the human and the security-related artifacts they create interact to both afford and constrain the enactment of information security. Our perspective builds on Pickering's mangle of practice metaphor to consider the constitutive entanglement of humans and artifacts and explain information security behaviors. This perspective is applied and discussed in the context of insider compliance with information security policies but could apply to a wide range of other information security and information systems behavioral contexts.
Deep Distance Map Regression Network with Shape-Aware Loss for Imbalanced Medical Image Segmentation

Small object segmentation, like tumor segmentation, is a difficult and critical task in the field of medical image analysis. Although deep learning based methods have achieved promising performance, they are restricted to the use of binary segmentation mask and suffer from the imbalance problem. In this research, we aim to tackle this limitation by adopting distance map as a novel ground truth and employing distance map regression as a proxy of the existing segmentation framework. Specially, we propose a new segmentation framework that incorporates the existing binary segmentation network and a light weight regression network (dubbed as LR-Net). Thus, the LR-Net can convert the conventional classification-based segmentation into a regression task and leverage the rich information of distance maps. Additionally, we derive a shape-aware loss by employing distance maps as penalty map to capture the complete shape of an object. We evaluated our approach on MICCAI 2017 Liver Tumor Segmentation (LiTS) Challenge dataset and a clinical dataset. Experimental results show that our approach outperforms the classification-based methods as well as other existing state-of-the-arts. Code is available at https://github.com/Huiyu-Li/Deep-Distance-Map-Regression.
Attention-Enhanced And More Balanced R-CNN For Object Detection

Attention mechanisms have been widely used in deep neural convolution networks and different fields, such as object detection and instance segmentation. Many attention mechanisms will cost too much calculation, so in this paper, we incorporate a kind of light attention mechanism, the attended residual module, into our object detection backbone to get an accuracy-efficiency trade-off. Besides, to solve the imbalance problem in region sample level, we use the cascade region proposal network (RPN) module to gain anchors of higher quality resulting in higher average recall (AR). Furthermore, we replace the non-local attention module in feature fusion level with the criss-cross attention module to reduce computation and improve performance. With them all, our method significantly improves the detection performance and achieves 43.6 AP in COCO test-dev.
Deep Learning And Interactivity For Video Rotoscoping

In this work we extend the idea of object co-segmentation [10] to perform interactive video segmentation. Our framework predicts the coordinates of vertices along the boundary of an object for two frames of a video simultaneously. The predicted vertices are interactive in nature and a user interaction on one frame assists the network to correct the predictions for both frames. We employ attention mechanism at the encoder stage and a simple combination network at the decoder stage which allows the network to perform this simultaneous correction efficiently. The framework is also robust to the distance between the two input frames as it can handle a distance of up to 50 frames in between the two inputs.We train our model on professional dataset, which consists pixel accurate annotations given by professional Roto artists. We test our model on DAVIS [15] and achieve state of the art results in both automatic and interactive mode surpassing Curve-GCN [11] and PolyRNN++ [1].
Candidate Classification and Skill Recommendation in a CV Recommender System

In this paper, we describe a CV recommender system with a focus on two properties. The first property is the ability to classify candidates into roles based on automatic processing of their CV documents. The second property is the ability to recommend skills to a candidate which are not listed in their CV, but the candidate is likely to have them. Both features are based on skills extraction from a textual CV document. A spectral skill clustering is precomputed for the purpose of candidate classification, while skill recommendation is based on various similarity-based strategies. Experimental results include both automatic experiments and an empirical study, both of which demonstrate the effectiveness of the presented methods.
New Word Detection Using BiLSTM+CRF Model with Features

With the widespread popularity of a large number of social platforms, an increasing number of new words gradually appear. However, such new words have made some NLP tasks like word segmentation more challenging. Therefore, new word detection is always an important and tough task in NLP. This paper aims to extract new words using the BiLSTM+CRF model which added some features selected by us. These features include word length, part of speech (POS), contextual entropy and degree of word coagulation. Comparing to the traditional new word detection methods, our method can use both the features extracted by the model and the features we select to find new words. Experimental results demonstrate that our model can perform better compared to the benchmark models.
Case-Based Gesture Interface for Multiagent Formation Control

Current multiagent systems require human operators to communicate in real-time with one another. A better option would be to have a single human operator control a swarm of agents using a natural interface, such as something worn by the operator. This includes being able to control the shape and movement of the agents in a dynamically shifting environment using an intuitive interface. Prior research has focused on moving the collective of agents as a whole, and has not used a case-based approach for this purpose. We show that a swarm can be shaped into different formations via a case-based gesture control strategy using a wearable (smart watch) interface. This approach enables a swarm to complete a task by taking high-level commands from an operator/user. We describe our approach, our initial results and demonstrations, and discuss next steps.
PHMNet: A Deep Super Resolution Network using Parallel and Hierarchical Multi-Scale Residual Blocks

Deep image super resolution networks use a nonlinear end-to-end mapping between the low and high resolution versions of an image and therefore, provide a good performance. As the different parts of a single image appear in different scales, developing a deep learning based image super resolution scheme that is capable of generating features at different scales and levels is essential. In this paper, a new residual block is proposed with a view of generating a rich set of features extracted at different scales and levels. The development of the proposed block is carried out using two distinct strategies, the first one focussing on generating features directly in two different scales, whereas the second one aims at generating multi-scale features indirectly by extracting them from two different hierarchical levels of abstraction. It is shown through experimental results that the proposed scheme of designing the residual block results in a network that provides a superior performance with reduced number of parameters than that provided by the light-weight networks using other types of residual blocks.
IF-Net: An Illumination-invariant Feature Network

Feature descriptor matching is a critical step is many computer vision applications such as image stitching, image retrieval and visual localization. However, it is often affected by many practical factors which will degrade its performance. Among these factors, illumination variations are the most influential one, and especially no previous descriptor learning works focus on dealing with this problem. In this paper, we propose IF-Net, aimed to generate a robust and generic descriptor under crucial illumination changes conditions. We find out not only the kind of training data important but also the order it is presented. To this end, we investigate several dataset scheduling methods and propose a separation training scheme to improve the matching accuracy. Further, we propose a ROI loss and hard-positive mining strategy along with the training scheme, which can strengthen the ability of generated descriptor dealing with large illumination change conditions. We evaluate our approach on public patch matching benchmark and achieve the best results compared with several state-of-the-arts methods. To show the practicality, we further evaluate IF-Net on the task of visual localization under large illumination changes scenes, and achieves the best localization accuracy.
Dynamic Gestures Recognition through a Low-cost Data Glove

This paper presents some results towards the realization of a commercial wearable device for sign language translation. In particular, we propose an entire workflow for recognizing dynamic gestures. Different techniques are analysed and compared, both to realize a database of gestures' features and to classify a performed gesture. We test the proposed method on dataset of 10 dynamic gestures, acquiring data of two IMUs embedded in a data-glove. Different classifiers achieve a 100% of accuracy, using spline fitting as feature extraction technique.
Index Matrices Based Modelling of a DC-DC Buck Converter with PID Controller and GUI on It

This paper presents an approach for electronic circuits modeling based on index matrices. The last one is developed by the authors as more general tool, but it is applied mostly for electrical circuits representation as well as for evaluation of voltage and amperage in time. Basic electronic components and their junction are modeled in a simple manner. The presented index matrices on a PID controller shows that this approach can be performed for more complex components. The proposed graphic user interface handles various parameters and visualizes time dependences of voltage and amperage values.
A pattern system for sound processes

This article reports on a new library for the ScalaCollider and Sound Processes computer music environments, a translation and adaptation of the patterns subsystem known from SuperCollider. From the perspective of electroacoustic music, patterns can easily be overlooked by reducing their meaning to the production of "notes" in the manner of "algorithmic composition". However, we show that they can be understood as a particular kind of programming language, considering them as a domain specific language for structures inspired by collection processing. Using examples from SuperCollider created by Ron Kuivila during an artistic research residency embedded in our project Algorithms that Matter, we show the challenges in translating this system from one programming language with a particular set of paradigms to another. If this process is studied as a reconfiguration of an algorithmic ensemble, the translated system produces new usage scenarios hitherto not possible.
Simulation of learning effects of adaptive learning

In this research, we propose a teaching simulation on the complex doubly structural network with adaptive learning and verify the effect of the adaptive learning environment. The teaching simulation on the complex doubly structural network is a method to verify the learning effect in the learning environment. The simulation model has an internal network and a social network. As a result of experiments, we found that the adaptive learning model has a significantly lower average number of teachings than the lecture model. Lessons tailored to individual’s understanding lead to improved learning effects. And adding the support of teacher to adaptive learning contributes to improving learning efficiency. It is important to properly design the entire learning environment while combining various mechanisms. And there are cases where the placement effect is large and cases where the placement effect is small depending on the learning environment. The learning effect of the mechanism varies depending on the learning environment.
Image Completion And Extrapolation With Contextual Cycle Consistency

Image Completion refers to the task of filling in the missing regions of an image and Image Extrapolation refers to the task of extending an image at its boundaries while keeping it coherent. Many recent works based on GAN have shown progress in addressing these problem statements but lack adaptability for these two cases, i.e. the neural network trained for the completion of interior masked images does not generalize well for extrapolating over the boundaries and vice-versa. In this paper, we present a technique to train both completion and extrapolation networks concurrently while benefiting each other. We demonstrate our method's efficiency in completing large missing regions and we show the comparisons with the contemporary state of the art baseline.
Giving Motivation for Using Secure Credentials through User Authentication by Game

One of the issues in the knowledge-based user authentication is that users do not set and use a secure credential. Some methods exist to be able to resolve this issue, such as password policy, education, and password meter. However, these countermeasures impose a usability cost that is difficult for many users to accept. Therefore, these measures have not propelled users to use secure credentials in user authentication. We consider that motivating users is necessary to voluntarily accept the cost of using secure credentials. Thus, we attach a role-playing game function to pattern-based user authentication, and provide an incentive to users through user authentication. We conducted a small experiment with eight participants, and the result demonstrated that the prototype system has the potential to prompt users to use secure credentials.
Human motion recognition based on limit learning machine

With the development of technology, human motion capture data have been widely used in the fields of human–computer interaction, interactive entertainment, education, and medical treatment. As a problem in the field of computer vision, human motion recognition has become a key technology in somatosensory games, security protection, and multimedia information retrieval. Therefore, it is important to improve the recognition rate of human motion. Based on the above background, the purpose of this article is human motion recognition based on extreme learning machine. Based on the existing action feature descriptors, this article makes improvements to features and classifiers and performs experiments on the Microsoft model specific register (MSR)-Action3D data set and the Bonn University high density metal (HDM05) motion capture data set. Based on displacement covariance descriptor and direction histogram descriptor, this article described both combine to produce a new combination; the description can statically reflect the joint position relevant information and at the same time, the change information dynamically reflects the joint position, uses the extreme learning machine for classification, and gets better recognition result. The experimental results show that the combined descriptor and extreme learning machine recognition rate on these two data sets is significantly improved by about 3% compared with the existing methods.
Using Traditional Research Methods in Contemporary UX Surveying

In my paper I write about three methods which are generally used and accepted in social sciences and market research, and can be effectively applied – not in a standard way, but innovatively – during the development of online interfaces, too. At the beginning of the paper I present how user interface research has become an indispensable component of web development, considering popular demand for ergonomic, user-friendly web pages and applications. The topic is also relevant from a business point of view, considering how web developments involving users prove to yield superior results.
Exploiting object features in deep gaze prediction models

The human visual system analyzes the complex scenes rapidly. It devotes the limited perceptual resources to the most salient subsets and/or objects of scenes while ignoring their less salient parts. Gaze prediction models try to predict the human eye fixations (human gaze) under free-viewing conditions while imitating the attentive mechanism. Previous studies on saliency benchmark datasets have shown that visual attention is affected by the salient objects of the scenes and their features. These features include the identity, the location, and the visual features of objects in the scenes, beside to the context of the input image. Moreover, the human eye fixations often converge to the specific parts of salient objects in the scenes. In this paper, we propose a deep gaze prediction model using object detection via image segmentation. It uses some deep neural modules to find the identity, location, and visual features of the salient objects in the scenes. In addition, we introduce a deep module to capture the prior bias of human eye fixations. To evaluate our model, several challenging saliency benchmark datasets are used in the experiments. We also conduct an ablation study to show the effectiveness of our proposed modules and its architecture. Despite its fewer parameters, our model has comparable, or even better performance on some datasets, to the state-of-the-art saliency models.
Lima or cima?

Abstract This paper outlines the construction of the corpus Alpenwort, a large, genre-based corpus of German texts on alpinism. We report on issues related to building the corpus from the Austrian Alpine Club Journal (1869–2010). First, a general description of our data and the project phases from digitization and annotation to publication is given. We focus on the most interesting challenges that the diverse layouts and the extensive use of Fraktur typefacing posed for optical layout recognition and optical character recognition (OCR) as well as post correction. The corrected data was lemmatized and annotated with part-of-speech information including named entities as well as TEI-conformant metadata. The resulting 19.9-million-word corpus is designed to be queried using CQPweb and Hyperbase and can be accessed freely online. Lastly, we give a short roadmap of current and future expansions and improvements as corpus data has been and is being enhanced in follow-up projects.