Reviewerly/exHarmony huggingface dataset:
Ad-Hoc Architecture of Systems for Disaster Risk Management

The essence of functioning of present-day systems of disasters risk management is information processing for decision making in target domain of risk management. Risk management is the specific field of situational management. Disasters is a result of evolution of situations concerned with different environment natural, technogenic, and human activity processes. The main purpose of disasters risks management (DRM) systems is to provide organizational and technological services to participants in risk management processes to perform the functions assigned to them. DRM system is a complex organizational and technical system. The result of its configuration should be accorded with the architectural model of the organization and cover all levels of organizational and technical means that ensure its operation. Ad-hoc architectural views should reflect point of views concerted with situation context (semantics), stakeholders' positions and available required management means. Organization of DRM system based on ad-hoc architecture approach is proposed in the paper.
A Gesture Recognition Approach Using Multimodal Neural Network

Abstract Gesture recognition based on visual modal often encounters the problem of reduced recognition rate in some extreme environments such as in a dim or near-skinned background. When human beings make judgments, they will integrate various modal information. There should also be some connections between human gestures and speech. Based on this, we propose a multimodal gesture recognition network. We use 3D CNN to extract visual features, GRU to extract speech features, and fuse them at late stage to make the final judgment. At the same time, we use a two-stage structure, a shallow network as detector and a deep network as classifier to reduce the memory usage and energy consumption. We make a gesture dataset recorded in a dim environment, named DarkGesture. In this dataset, people say the gesture’s name when they make a gesture. Then, the network proposed in this paper is compared with the single-modal recognition network based on DarkGesture. The results show that the multi-modal recognition network proposed in this paper has better recognition effect.
FPGA based convolution and memory architecture for Convolutional Neural Network

Convolutional Neural Networks(CNNs) are widely used in vision based applications to increase the performance but at the cost of higher storage and increase in computation. Hardware implementations of CNN are limited by the computational complexity and bandwidth while accessing off-chip memory. In this work a novel FPGA based hardware architecture for 2D convolution operation with reduced computational complexity using Winograd’s 2D minimal filtering algorithm and a memory architecture to reduce on-chip read operations to access adjacent input data tiles for convolution operations is proposed to accelerate CNNs. An on-chip memory bank reuse architecture is also utilized to reduce the number of memory read and write operations to off-chip memory. The proposed architecture for convolution operation achieves lower computational complexity by reducing the number of multiplication operations without proportionate increase in number of addition operations compared to prior implementations. The number of data read operations from on-chip memory is reduced by 4 times and using the on-chip memory bank reuse scheme latency associated with accessing intermediate data is reduced. The implemented uses 16-bit fixed point representation which could reduce bit width to save area and energy. Virtex Ultra scale+ VCU118 Evaluation Board 2.0 populated with XCVU9P-L2FLGA2104 is used as the platform for implementing the design. VGG Net based CNN is used for the implementation. The computation time for individual convolutional layer is also estimated and it is found to be reduced. For a 3x3 kernel the number of multiplications is reduced to 4 from 9 compared to standard convolution operation and the number of addition operations reduced to 12 from 14 compared to prior hardware implementations of Winograd’s 2D minimal filtering algorithm.
Data Post-Processing Strategies for the Application of Short-Term PV Forecasting in Microgrids

It is essential to eliminate the uncertainties in the variability of the output power of the solar photovoltaics (PV) as much as possible to improve the penetration level of such systems in microgrids. Hence, employing PV forecasting tools can be very helpful in this aspect. This paper has used a battery energy storage system and short-term PV forecasting together to assess the potential of fuel-saving in diesel generator-PV-battery hybrid microgrids. An innovative framework has been proposed to address the problems of forecast error for short-time horizon, realizing the persistence of the forecast's performance variation for various sky and cloud conditions throughout the year. This approach helps to build a confidence level for utilizing the PV forecast method as short as 1-min ahead. The studies have shown that PV forecasting mechanism yields in promising results when innovative strategies comprising of the post-processing of forecast data is effectively utilized.
Safety control in robotic manipulator visual servoing system

Industrial tasks where cooperation between human operators and robots is required in order to execute a task is still a challenging research area. In most cases the industrial robots are isolated in areas where the human operator is not allowed to enter. This is to avoid the possible collision and this type of configuration does not allow the development of more flexible industrial tasks achieved by the cooperation between humans and robots. Such a collaboration would have several advantages since complementary features of both entities could be employed in the task execution. Industrial robots can perform repetitive tasks (which can be tedious and exhausting for a human operator) while human operator can execute specialized tasks which demand intelligence and dexterity. To ensure a good collaboration between robots and humans safety control has to be included in the control algorithms. In this paper an overview of the image based control techniques and how including safety in the scheme will improve the performance of task execution.
A minimum barrier distance for multivariate images with applications

Distance transforms and the saliency maps they induce are widely used in image processing, computer vision, and pattern recognition. The minimum barrier distance (MBD) has proved to provide accurate results in this context. Recently, Géraud et al. have presented a fast-to-compute alternative definition of this distance, called the Dahu pseudo-distance. This distance is efficient, powerful, and have many important applications. However, it is restricted to grayscale images. In this article we revisit this pseudo-distance. First, we offer an extension to multivariate image. We call this extension the vectorial Dahu pseudo-distance . We provide an efficient way to compute it. This new version is not only able to deal with color images but also multi-spectral and multi-modal ones. Besides, through our benchmarks, we demonstrate how robust and competitive the vectorial Dahu pseudo-distance is, compared to other MB-based distances. This shows that this distance is promising for salient object detection, shortest path finding, and object segmentation. Secondly, we combine the Dahu pseudo-distance with the geodesic distance to take into account spatial information from the image. This combination of distances provides efficient results in many applications such as segmentation of thin elements or path finding in images.
Development of Intelligent Human Flow Density Detection System based on Sensor Fusion

Crowd counting and human flow estimation is the critical technique for modern sociality safety. In this study, the intelligent human flow density detection system based on sensor fusion technique are developed. The system includes three kinds of subsystems. The image subsystem employs SSD neural network and CSRNet to estimate human counting and crowd density map. The LiDAR subsystem uses Voxel Grid filter, RANSAC estimation, KD tree background subtraction and Kalman filter to detect and track human flow. Through using Image and LiDAR chessboard calibration algorithm, the extrinsic calibration parameters of the 3D-2D transformation matrix are estimated. The system can re-project distance information of point clouds to the image space and display the human density map in point cloud space. Three kinds of different crowd environmental conditions were selected to verify the performance of the proposed system. The results of experiment testify the proposed system achieved human counting and human flow detection in the scan field.
Super-Resolution Coding Defense Against Adversarial Examples

Deep neural networks have achieved state-of-the-art performance in many fields including image classification. However, recent studies show these models are vulnerable to adversarial examples formed by adding small but intentional perturbations to clean examples. In this paper, we introduce a significant defense method against adversarial examples. The key idea is to leverage a super-resolution coding (SR-coding) network to eliminate noise from adversarial examples. Furthermore, to boost the effect of defending noise, we propose a novel hybrid approach that incorporates SR-coding and adversarial training to train robust neural networks. Experiments on benchmark datasets demonstrate the effectiveness of our method against both the state-of-the-art white-box attacks and black-box attacks. The proposed approach significantly improves defense performance and achieves up to 41.26% improvement based on the accuracy by ResNet18 on PGD white-box attack.
A signal detection method based on matrix information geometric dimensionality reduction

Abstract Traditional signal detection methods have achieved satisfactory performance in many contexts of signal processing. However, these methods based on mathematical statistics show a drawback in dealing with the low SNR cases, which limits their practicability. To this end, inspired by image processing techniques, we first make use of the short time Fourier transform to generate sufficient 2-D spectrograms of the received data. Then we extract high dimensional features of these spectrogram to construct high dimensional covariance matrices, transforming into a binary classification problem lying on a symmetric positive definite (SPD) manifold. In addition, by reducing the dimensionality directly on the SPD manifold, these spectrograms are mapped into a more discriminative SPD manifold, which improves the separability between the two classes. The simulation experiment results demonstrate that our method achieve satisfactory signal detection performance in the task of signal detection under K distribution data, even in the case of SNR = -10.
Analysis and improvement of image segmentation algorithm based on fuzzy edge compensation

In order to solve the problems of mosaics and fuzzy details in traditional image interpolation and magnification algorithms, an image magnification algorithm based on compensation item edge segmentation is proposed. According to the boundary degree and pixel distance, the boundary line is extracted from the image, and then the plane area, the adjacent boundary line and the boundary line are respectively enlarged by using their own adaptive interpolation amplification algorithm. The linear interpolation algorithm based on the one-dimensional compensation term is adopted to obtain the new interpolation edge points between each point, and retain the boundary information and texture details information, so as to enhance the image magnification It can improve the resolution and definition of the image. Experiments show that the algorithm can effectively improve the magnification effect of the image, retain the details of the image, and is conducive to image processing.
AFFLOG: A Logic Based Affective Tutoring System

In this work, the Affective Logic (AFFLOG) Tutor is presented. An Affective Tutoring System that uses knowledge representation and reasoning tools such as Answer Set Programming and the Event Calculus (EC) in order to represent the main components of the tutor. AI Planning is used to select individual parts of a given course material (tutorials) in order to build a specific course tailored to the needs of each user according to the user’s learning preferences. This course can dynamically change during the teaching session responding to the user’s mental and emotional states, providing affective support by offering praise, consolation or encouragement depending on the current emotion of the user. The design and a functioning implementation of the system is presented. As a proof of concept, a course on how to play the Settlers of Catan(c) board game was designed and implemented.
Dynamic contol structure in gameplay mechanism as a solution to gaming addiction : Tartarus – Conceptual approach using a simple FSM pattern

A game by the simplest meaning is a challenge to the cognitive abilities of an individual aimed at entertaining or refreshing the mental state. With rapid technological advancements, this concept has emerged as an industry with enormous market. Modern day video games do not confine themselves to the leisure vested in hobbies. Businesses and careers run at stake with the on-going trends. The common attention has now shifted to enhancing the gaming experiences of the users and generating profits. These trends have led to less concerns over the trivial problems raised by gaming addiction. To overcome these drawbacks and assert conventional meaning to video games we move on to the proposed conceptual game. Tartarus revolves around the idea of providing dynamicity to the control mechanics. The video game features a preliminary control schema for the users which switches via randomness over a function of time. The various finite machine states are triggered as the game progresses. Successive stages keep recursing over the controls at a lower threshold than former ones providing least possible time for neural activity patterns in the brain to emerge with learning sequences. As a result, the ideology of proficiency in a video game by spending time mastering its structure and mechanics is hampered. The conceptual game proposes to test as well as enhance the visual spatial and cognitive skills and reduce effects of gaming addition on impulsiveness, self-control, executive functioning, and cognitive control.
Explaining bug provenance with trace witnesses

Bug finders are mainstream tools used during software development that significantly improve the productivity of software engineers and lower maintenance costs. These tools search for software anomalies by scrutinising the program's code using static program analysis techniques, i.e., without executing the code. However, current bug finders do not explain why bugs were found, primarily due to coarse-grain abstractions that abstract away large portions of the operational semantics of programming languages. To further improve the utility of bug finders, it is paramount to explain reported bugs to the end-users.
Eye-tracking for Sense of Immersion and Linguistic Complexity in the Skyrim Game: Issues and Perspectives

As part of an experimental study aimed at evaluating the linguistic and paralinguistic factors that can influence the sense of immersion in an open-world video game, we have partially opted for an eye-tracking data collection protocol. In doing so, various problems emerged in the course of the research and we therefore propose to report and analyze them in this article in order to provide useful feedback for further research. The first set of problems is of a technical nature and relates to the difficulty of collecting reliable eye tracking data in an open and complex game environment. Our second concern is about the difficulties that may appear depending on the morphological characteristics of the players. The third issue is about player’s familiarity with the game and the experimental parameters. And lastly, we discuss some post-processing issues for the analysis. The reflections raised from these few difficulties allow us to discuss some challenges for future oculometric research in complex video game environments.
Robust video source recognition in presence of motion stabilization

Video source attribution is getting a growing interest from researchers, law enforcement officers and forensic analysts. The capability of linking a video recording with its source device enables to search out who has generated a video recording. Such a feature finds immediate application in fighting against technology enabled crimes such as digital piracy and child abuse online. Currently, the most powerful techniques rely on the unique noise traces left by each camera sensor within any visual content, widely known as Photo Response NonUniformity. However, in the case of videos, the increasing adoption of digital motion stabilization interferes with the extraction of reliable noise patterns. In such a context, this paper describes a novel methodology for creating a robust reference video PRNU from still images for source camera recognition. Moreover, we provide a novel optimized strategy to compare two different PRNUs extracted from videos in presence of motion stabilization. The conducted experimental evaluation highlights the strength of the proposed methods.
Adaptive Filtering Approach With Forgetting Factor for Stochastic Signals Applied to EEG

This paper presents a new stochastic adaptive estimation-identification technique for nonstationary systems. The proposed method enhances the initial results from an on average estimation, and its identification, through a generalized adaptable function based on the Exponential Forgetting Factor (EFF), and the Sliding Mode (SM) regarding the error identification. In this form, the presented process includes the function implementation in three stages-estimation, adaptive estimation, and adaptive estimation-identification, allowing us to observe the gradual convergence to a nonstationary reference signal. Simulations first introduce convergence level checks obtained from the estimation and identification of artificial signals. After that, the algorithm is applied for real references, considering the Electroencephalogram (EEG) signals taken from a public database, finding their internal nonstationary gains, indirectly. Finally, the results include a performance comparison between the proposed strategy concerning the Recursive Least Square (RLS), the Least Mean Square (LMS), and the Kalman Filter (KF).
Interactive Cake Decoration with Whipped Cream

We present a three-dimensional whipped cream printing technique for decorating cakes. Conventional cake decorating patterns are produced by specially trained professionals because substantial practice is required. In this paper, we introduce a printer that can stack whipped-cream voxels on a cake sponge using a robot arm. An interactive graphical user interface enables users to draw their favorite patterns on demand. By optimizing the motion of the nozzle, the robot can dispense and stack the cream accurately.
Garbage collection using a finite liveness domain

Functional languages manage heap data through garbage collection. Since static analysis of heap data is difficult, garbage collectors conservatively approximate the liveness of heap objects by reachability i.e. every object that is reachable from the root set is considered live. Consequently, a large amount of memory that is reachable but not used further during execution is left uncollected by the collector.
Blockchain based Security System for the Devices in Internet of Things

Internet of Things (IoT) is a network of the devices which are capable of receiving the data from the environment and communicating through the internet. The IoT devices generate a significant amount of sensitive information which may be prone to the several security attacks. The evolution of the IoT demands a high degree of security in terms of confidentiality, integrity, reliability and other factors. Because of the hardware restrictions, the security mechanisms used for the normal networks cannot be applied. For such reasons, instead of a centralized approach, the distributed record based technique called the blockchain (BC) approach is used. This produces better results in terms of security consideration such as security, privacy etc. when compared with the existing security mechanisms.
Ceramic Jewelry with Texture and Ornament Islamic Pattern and Batik Indonesia—Design, Manufacturing, and Fabrication

The development of science and technology has become important to grow the manufacturing industry in Indonesia and this is characterized by high consumer demand for customized product designs that are precise, accurate, and detailed on complex ornaments or textures and have high selling value in the industrial market. This paper discusses and demonstrates the application of modern computer-aided engineering system (CARESystem) technology in the process of designing—manufacturing ceramics jewelry at the ceramic company PT. Indonesian Nuanza porcelain. Batik and Islamic patterns are chosen as the basic texture for pendant products. The design process uses Zbrush to get 3D CAD models for ceramics jewelry. The process of manufacturing optimization on CNC machines using computer-aided manufacturing (CAM) software PowerMill 2016 and Rhinoceros 4.0. The results of the research show that the optimal processing time and quality of machining in CNC machines is the use of a toolpath strategy horizontal roughing, and parallel finishing in Rhinoceros 4.0 for roughening and semi-finish processes. Toolpath strategy model area clearance, optimize constant z, and step and shallow finishing at PowerMill 2016 for the finishing process. Production time can be increased to twice compared to manual technology.
Inference Acceleration: Adding Brawn to the Brains

Much attention in artificial intelligence (AI) is focused on creating and training smarter models. As these models grow in accuracy, they increase size and inefficiency. Inference acceleration enables AI to solve ever-larger problems on ever-smaller devices.
CVNodes: A Visual Programming Paradigm for Developing Computer Vision Algorithms

Advances in machine learning have led to a rapid pace of innovation in Computer vision and deep learning classification algorithms. Deep learning classification models are often limited in flexibility due to their fixed pre-processing steps embedded into the algorithm and lack ways to easily iterate, debug, and analyze developed algorithms without programming knowledge. The lack of high-level tools for developing vision algorithms leads to longer development times that require significant knowledge of underlying algorithms. What about individuals without this deep knowledge of machine learning and vision yet wish to develop algorithms to prototype ideas? What about nonprogrammers such as designers and artists that wish to utilize the state-of-the-art in computer vision in their work? To address this under-served community, we propose a visual-programming solution akin to those found in modern game engines geared towards computer vision algorithm development. These results in a new prototyping tool to empower researchers and nonprogrammers to easily iterate algorithm development, use pretrained classification models, and provide statistical post-analysis tools.