Reviewerly/exHarmony huggingface dataset:
MotionRFCN: Motion Segmentation Using Consecutive Dense Depth Maps

It is important to enable autonomous robots to detect or segment moving objects in dynamic scenes as they must perform collision-free navigation. Motion segmentation from a moving platform is challenging due to the dual motion caused by the background and the moving objects. Existing approaches for motion segmentation either have long multistage pipelines which are inefficient for real-time application or utilize optical flow which is sensitive to environment. In this paper, this challenging task is tackled by constructing spatiotemporal features from two consecutive dense depth maps. Depth maps can be generated either by LiDaR scans data or stereo vision algorithms. The core of the proposed approach is a fully convolutional network with inserted Gated-Recurrent-Units, denoted as MotionRFCN. We also create a publicly available dataset (KITTI-MoSeg) which contains more than 2000 frames with motion annotations. Qualitative and quantitative evaluation of MotionRFCN are presented to demonstrate its state-of-the-art performance on the KITTI dataset. The basic MotionRFCN can run in real time and segment moving objects whether the platform is stationary or moving. To the best of our knowledge, the proposed method is the first to implement motion segmentation with only dense depth maps inputs.
Towards a New Architecture for Data Multilevels Interactive Visualization in Big Data Domains

The progress of the Big Data technology in the different domains continues to reveal, day after day, so many issues affecting this field. The data presentation is a part of these issues, because it is directly related to the user that requires visualization to facilitate the data collection as well as the analysis and the utilization of results as needed. However, implementing visualization requires a low latency. Therefore, many problems regarding the Big Data major characteristics, namely volume, variety and velocity arise and should be addressed. Knowing that each Big Data domain has specific peculiarities, it remains far from thinking of a generic solution to the visualization problem. This latter is manifested in different kinds. One of these kinds that is prevalent in Big Data domains is hierarchical visualization, which is based on presenting data by levels, while respecting the relations between them. In this paper, we propose an architecture composed of modules that cover the Big Data visualization process, taking into account the hierarchical relations between data. In order to valorise our proposal, we implement a prototype that supports the modules composing our architecture which are developed using different tools such as Scrapy and D3js libraries. The experimentation, which was carried out in the educational domain, showed its applicability to real systems.
Analysis and countermeasures to side-channel attacks: a hardware design perspective

With the Internet-of-Things revolution, the security assessment against implementation attacks has become a critical concern not only for hardware accelerators but also for embedded CPUs executing cryptographic primitives. Starting from an FPGA implementation of the open-hardware ORPSoC system-on-chip running a software version of the AES, this paper explores two implications of the side-channel information leakage that impact the hardware design of general purpose computing platforms. First, we explore the mapping between the side-channel information leakage and the microarchitectural components that are contributing to it. Second, we discuss the variations in the observability of the side-channel information leakage at post-synthesis and post-implementation levels of the FPGA hardware design flow.
Image Target Detection Based on Deep Convolutional Neural Network

Aiming at the problem of poor foreground extraction in dynamic background images, an image foreground target detection method based on deep convolutional neural network is proposed. The VGG16-NET-based network model is used to extract the feature map. Using the deconvolution method and the pyramid pooling method ameliorates the defect that VGG16-Net can only classify the entire image, and the model can only receive fixed-size images.
Research on Intelligent Fuzzy Control Algorithm for Moving Path of Handling Robot

In order to solve the problem of poor path control effect of traditional robots, the intelligent fuzzy control algorithm for mobile path of mobile robots was studied. Through the design of the mobile environment perception system of the mobile robot, the mobile environment of the robot is effectively collected, and the optimal mobile path is planned after the environmental characteristics are collected. On this basis, the intelligent control of the mobile path of the robot is carried out by combining the fuzzy algorithm, and the path deviation and deviation correction parameters are calculated to complete the intelligent control of the mobile path of the robot. Finally, the experiment proves that the intelligent fuzzy control algorithm of the mobile path of the mobile robot is more practical than the traditional path control method of the robot.
Smart Meters Fault Prediction Technology Based on Cost-sensitive XGBoost Algorithm for Imbalanced Dat

Building a multi-class prediction model by using machine learning algorithms is an effective way to achieve accurate judgment of diversified fault types of smart meters. After screening, duplication eliminating, missing value complementation, dualization and discretization of smart meter history fault data, this paper presents a hierarchical clustering algorithm based on attribute fault type distributions, which merges attributes with similar fault type distributions to reduce the data dimension after the binarization process, and compresses the processed sparse matrix to improve data quality. Aiming at the imbalance among sample data from different fault classes of smart meters, we select the XGBoost algorithm to build the fault prediction model, and design a cost-sensitive function to improve the multi-class imbalance classification ability of the model. Finally, a comparative experiment is designed on the actual collected history fault data of smart meters. The results show that the proposed method has higher accuracy for fault type prediction.
Dynamic Epistemic Logic with Communication Actions

This work proposes a Dynamic Epistemic Logic with Communication Actions that can be performed concurrently. Unlike Concurrent Epistemic Action Logic introduced by Ditmarsch, Hoek and Kooi [van Ditmarsch, H., W. van der Hoek and B. Kooi, "Concurrent Dynamic Epistemic Logic," Kluwer, Ed. V.F. Hendricks et al., vol. 322, 2003], where the concurrency mechanism is the so called true concurrency, here we use an approach based on process calculus, like CCS and CSP, and Action Models Logic. Our approach makes possible the proof of soundness, completeness and decidability, different from the others approaches. We present an axiomatisation and show that the proof of soundness, completeness and decidability can be done using a reduction method.
Enabling Training in Orthodontics Through Mobile Augmented Reality

Orthodontic education, which currently emphasizes a didactic and apprenticeship approach, is facing numerous pedagogical challenges that affect knowledge delivery and instruction. This chapter discusses the challenges and limiting factors that affect orthodontic training and proposes the use of mobile augmented reality (MAR) to create a platform for effective learning, visualization, deliberate practice, effective feedback, and a personalized learning environment. MAR, with its visually enriched clinical simulations and ubiquitous learning, can effectively reduce cognitive dissonance and improve overall retention and skill gain by students. However, MAR has its limitations, as the technology is still new and limited evidence is available to back up the claims of knowledge and skill gain in the health professional's education. This chapter also provides future directions for exploring and enabling MAR so that it can become an efficient tool for learning and instruction across all faculties of education.
Adaptive Database’s Performance Tuning Based on Reinforcement Learning

Database (DB) performance tuning is a difficult task that requires a vast amount of skill, experience and efforts in tweaking a DB for optimum results. With the hundreds of parameters to be considered under the diverse application configurations, business logic and software technology, getting a true global optimum setting is difficult for a DB administrator. We propose a novel approach based on Reinforcement Learning to tune a DB adaptively with minimum risk to the production setup. It results in a new set of parameters tailored to the production DB. Empirical results show that there is a significant gain in performance for the DB in its overall efficiency while reducing the IO overheads, based on a set of key performance statistics collected before and after the optimization process.
Language identification at word level in Sinhala-English code-mixed social media text

Automatic analyzing and extracting useful information from the noisy social media content are currently getting attention from the research community. It is common to find people easily mixing their native language along with the English language to express their thoughts in social media, using Unicode characters or the Unicode characters written in Roman Scripts. Thus these types of noisy code-mixed text are characterized by a high percentage of spelling mistakes with phonetic typing, wordplay, creative spelling, abbreviations, Meta tags, and so on. Identification of languages at word level become a necessary part for analyzing the noisy content in social media. It would be used as an intimidate language identifier for chatbot application by using the native languages. For this study we used Sinhala- English code-mixed text from social media. Natural Language Processing (NLP) and Machine Learning (ML) technologies are used to identify the language tags at the word level. A novel approach proposed for this system implemented is machine learning classifier based on features such as Sinhala Unicode characters written in Roman scripts, dictionaries, and term frequency. Different machine learning classifiers such as Support Vector Machines (SVM), Naive Bayes, Logistic Regression, Random Forest and Decision Trees were used in the evaluation process. Among them, the highest accuracy of 90.5% was obtained when using Random Forest classifier.
Programming with Flow-Limited Authorization: Coarser is Better

Applications that handle sensitive information need to express and reason about the trust relationships between security principals. Such reasoning is difficult because the trust relationships are dynamic, and must thus be reasoned about at runtime when discovering and reasoning about trust relationships might inadvertently reveal confidential information and be subject to manipulation by untrusted principals. The Flow-Limited Authorization Model (FLAM) by Arden et al. exactly meets these needs. However, previous attempts to use FLAM in a programming language have not reaped the full benefits of the model. We present Flamio, an instantiation of FLAM in a language with coarse-grained dynamic information-flow control (IFC) which naturally lends itself to dynamic enforcement techniques. In our implementation of Flamio, the FLAM proof search rules for deriving trust relationships are implemented as regular Flamio computations: the IFC requirements for FLAM proof rules are a natural fit with coarse-grained information-flow mechanisms. Flamio even supports remote procedure calls, and thus seamlessly supports FLAM's distributed proof search. We have implemented Flamio as a Haskell library, and proved that a calculus based on Flamio enforces a noninterference-based security guarantee. We have implemented several case studies, demonstrating the expressiveness and usefulness of Flamio in distributed settings, as well as our novel approach to control label creep during proof search.
LSTM-Based Facial Performance Capture Using Embedding Between Expressions

We present a novel end-to-end framework for facial performance capture given a monocular video of an actor’s face. Our framework are comprised of 2 parts. First, we optimize a triplet loss to learn the embedding space which ensures the semantically closer facial expressions are closer in the embedding space and the model can be transferred to distinguish the expressions that are not presented in the training dataset. Second, the embeddings are fed into an LSTM network to learn the deformation between frames. In the experiments, we demonstrated that compared to other methods, our method can distinguish the delicate motion around lips and significantly reduce artifacts between the tracked meshes.
Partitioning 2D Images into Prototypes of Slope Region

A gray scale digital image can be represented as a 2.5D surface where the height of the surface corresponds to the gray value of the respective pixel. Analysis of the gray scale image can be efficiently done by exploiting the properties of the plane graph embedded in the 2.5D surface. The vertices of the graph can be easily categorized into critical and non-critical points by use of Local Binary Patterns (LBPs). Well defined graph operations such as contraction and removal of edges are used to eliminate the non-critical points and preserve the critical points thereby reducing the size of graph. In this process, it is important to preserve the structural and topological properties of the regions of a gray scale image. After analysing the topological properties of a well composed image, we provide two prototypes of the slope region and the necessary conditions for their existence. Also we prove that every slope region conforms to either of the two prototype. Conversely the prototypes may be used to generate an image with a required topological properties.
Multi-modulation Recognition Using Convolution Gated Recurrent Unit Networks

Abstract To solve the problems of long modulation and recognition time and complexity of convolutional neural networks, a modulation method based on CNN-GRU (Convolutional Neural Networks and Gated Recurrent Unit) is proposed. Firstly, the spatial features of the signal are extracted by CNN convolution operation, then the timing correlation of the signal is extracted by GRU and finally the recognition probability is output by using the softmax layer to achieve the purpose of multi-modulation recognition. The experimental results show that the recognition performance of the method is further improved under the condition of no prior information such as channel and noise, and 11 modulation categories such as 16QAM and 64QAM can be effectively identified, and the complexity of the method is low, which greatly saves. The training identification time has good engineering application value.
Image-to-image translation using a relativistic generative adversarial network

The image-to-image translation tasks have improved a lot since the utilization of conditional generative adversarial network. However, GAN itself have the shortcoming in the training stability. Recent years, there are many new techniques to solve this problem and the relativistic generative adversarial network has modified a key element missing from standard GAN. In this paper, we will propose an improved image-to-image translation using a relativistic generative adversarial network which is easy to converge. This discriminator will not aim to find the fake data from the real directly, but to detect the faker ones. In the implementation, we take experiments in three image translation tasks which indicate the relativistic generative adversarial network have a universal applicability for this task. And our framework has achieved successful in the label-to-photo and photo-to-map tasks in experiments.
Parts per trillion (ppt) gold in groundwater: can we believe it, what is anomalous and how do we use it?

There is a pressing need for new exploration tools to target and vector towards mineralization in covered terrains. Groundwater provides a valuable and under-utilized geochemical sampling medium, and represents an important and cost-effective tool to expose covered terrains to systematic exploration. For Au exploration, researchers agree the best hydrogeochemistry pathfinder is dissolved Au itself, with additional potential from other pathfinders (albeit non-unique) such as As, Ag, W and Mo. Despite Au9s relatively low solubility, with rigorous field protocols and appropriate analytical methods, explorers can respond to dissolved Au directly with robust parts per trillion (ppt)-level analyses. Even with ppt-level analyses, a practical implication of Au9s low solubility is that a deposit9s dissolved Au signature is generally weaker than seen in other more mobile pathfinders, producing a smaller detectable footprint, which must be considered when designing exploration programmes. Using purpose-drilled groundwater sampling bores, explorers can collect groundwater samples at the density required to respond to dissolved Au where existing borehole coverage is otherwise insufficient. In addition to its use at the regional scale, with even tighter sample density, hydrogeochemistry also shows promise at the project scale, allowing the 3D modelling of pathfinder dispersion. For hydrogeochemistry to be widely adopted for Au exploration, explorers need confidence in ppt-level dissolved Au analyses, and the context to understand their significance. This paper aims to address these topics and provide a straightforward starting point for Au explorers interested in applying hydrogeochemistry by: (i) summarizing examples of regional sampling programmes and more focused case studies to illustrate how covered Au deposits create measurable dissolved Au footprints distinguishable from background; and (ii) sharing examples of dissolved Au analyses that are being integrated into exploration at the regional and project scales. As seen in the results, the distributions of dissolved Au in the regional- and project-scale programmes show remarkably similar and easy to interpret high-contrast, low-frequency anomalies against relatively low backgrounds. These are desirable attributes of any geochemical pathfinder. When combined with the benefits of hydrogeochemistry v. other geochemical exploration tools (e.g. groundwater can create larger footprints requiring fewer samples to detect, and groundwater can recharge from depth to reflect deeper mineralization), dissolved Au is a powerful pathfinder ideally suited for Au exploration in covered terrains. While this paper focuses on the use of dissolved Au, additional pathfinders can provide valuable information, including indications of lithological changes, hydrothermal alteration and different styles of mineralization, as well as opportunities to use secondary pathfinders when sample density or local conditions may not result in detectable dissolved Au signatures.
Numerical Simulation Technology Study on Automatic Translation of Foreign Language Images Based on Tesseract-ORC

First of all, Tesseract OCR technology is used to automatically identify English words on the picture, including English words in the horizontal direction, vertical direction and a certain tilt Angle direction on the picture. Then, the automatic translation system is used to translate English words into Chinese words. Finally, OpenCV technology is used to automatically fill the translated Chinese words into the corresponding position in the English of the original picture, replace the original English words, and realize the automatic recognition and translation of foreign words in the picture. This research greatly improves the efficiency of machine translation. The research content of the project can also be applied to the automatic translation of images between any other languages.
On Applying Ambient Intelligence to Assist People with Profound Intellectual and Multiple Disabilities

Advances in ambient intelligence technologies achieved over recent years allow building ICT systems capable of supporting people in executing previously difficult tasks. This includes providing new opportunities to people with special needs, such as people with disabilities. In this paper we discuss our approach at designing and developing a smart platform that can assist people with profound intellectual and multiple disabilities (PIMD) in achieving a portion of independence. We apply this approach in the INSENSION project executed within the Horizon 2020 research and innovation programme of the European Commission. In this paper, we describe the characteristics of the disability in question, focusing on the main challenge, which is the inability of individuals with PIMD to use symbols in their interaction. Due to the fact that, to our best knowledge, the topic of constructing a system that could assist this interaction has not been undertaken so far, we were required to thoroughly analyze how individuals with PIMD interact using non-symbolic behaviors. We then defined requirements for the platform capable of supporting their interaction with other people and possibly living environment, designed the architecture and built the first version with the use of AI methods. The evaluation of this version confirmed the soundness of our approach, which enables us to continue our work towards successful implementation of the planned ambient intelligence system and its validation in real-life scenarios.
Design of Cricket Automatic Control System Based on Computer Vision Technology

This paper designs a cricket control system based on computer vision technology. This system is controlled by PID algorithm. The acquisition system is OpenMV camera, and the processing system is STM32 series MCU. The design is used to control the small ball to roll on the evenly distributed smooth square plate, so that the ball can be removed on the square plate without any external force except for the self-gravity, the support force of the plate and the friction force. Various specified requirements are fulfilled between circular areas of 3 cm in outer diameter. In this design, the image data is collected by the camera on the smooth flat panel range, and the collected data is transmitted to the STM32 core board through the serial port for processing, and the center coordinates of the hard ball are found through the processed data, and the coordinates are adjusted according to the set destination coordinates and the actual coordinates. PID (proportional-integral-derivative) controls the steering gear to rotate a certain angle to control the hard ball to achieve the relevant operational requirements.
First Steps Towards a Formalization of Forcing

We lay the ground for an Isabelle/ZF formalization of Cohen's technique of forcing. We formalize the definition of forcing notions as preorders with top, dense subsets, and generic filters. We formalize a version of the principle of Dependent Choices and using it we prove the Rasiowa-Sikorski lemma on the existence of generic filters. Given a transitive set M, we define its generic extension M[G], the canonical names for elements of M, and finally show that if M satisfies the axiom of pairing, then M[G] also does. We also prove that M[G] is transitive.
Improving Vietnamese WordNet using word embedding

This paper presents a simple but effective method to improve the quality of WordNet synsets and extract glosses for synsets. We translate the Princeton WordNet and other intermediate WordNets to a target language using a machine translator, then the correct candidates are selected by applying different ranking methods: occurrence count, cosine similarity between words, cosine similarity between word embeddings and cosine similarity between Doc2Vec of sentences. Our approaches may be applicable to build WordNets in any language which has some bilingual dictionaries and at least a monolingual corpus in the target language.