Reviewerly/exHarmony huggingface dataset:
Gesture Commands for Controlling High-Level UAV Behavior

In this paper, an accelerometer and gyroscope are used to sense gesture commands, which are then classified using a logistic regression model. Seven gestures were chosen and mapped to specific behaviors that a fixed wing unmanned air vehicle could accomplish. These behaviors specified various searching, following, and tracking patterns that could be used in a dynamic environment. The system was trained to recognize the seven gestures and then tested in a hardware-in-the-loop simulation. The system was able to identify all gestures with an overall accuracy of 90% and with five of the seven gestures being accurately identified at least 94% of the time. Each of the behaviors associated with the gestures was tested in simulation and the ability to dynamically switch between behaviors was proven. The results show that the system can be used as a natural interface to assist an operator in directing an unmanned air vehicle's behaviors.
A secure inter-domain communication for IoT devices

Nowadays, a multitude of sensors are used to gather data in several fields from smart buildings, to industries, to cars, etc.. These sensor data are instrumental in making smart decisions. In order to send data to end users, these sensors are connected to the Internet of Things (IoT) devices. Usually, the intra-domain data transmission is secure, indeed sensors and consumers of data can belong to the same Virtual Private Network (VPN). Security problems can be raised in the inter-domain data transmission because the transmitting channel is not ciphered nor is the identity of devices certain. Therefore, in case of attack, for consumers of data is not possible to recognize real data gathered from devices from fake data sent by attackers. In order to address this challenge, in this paper we present a novel method to secure data acquired from sensors connected to IoT devices. In particular, utilizing a Public Key Infrastructure (PKI) and the ESP32 microcontroller, we can send data privately to each recipient. In order to validate the system, we performed specific analysis considering different levels of security (512, 1024, 2048 bits key length) and increasing number of connected sensors (0, 1, 5, 10, 20). In particular, we considered the time to set up the IoT device and to cipher packets. Experiments have shown that the time required for the setup increases with the increase of the key length. Considering the 512 and 1024 bits keys, the time required to cipher data coming from sensors increase with the increasing of sensors. Instead, for the 2048 bits key length the ciphering time is almost constant, this because packet size and key length are comparable.
Assessment of Graduate Students' Resumes Using Short Text Searching Method

Among the fast progress of business domain all enterprises are undergoing a dramatic shift to help themselves improve their performance. Decision making in any organization should firstly rely on choosing the perfect candidates for any job opportunity they have, therefore linking an employer to the perfect graduate is a hard mission when having to search between thousands of resumes. In general, some key points or particular information found in a specific resume may help accomplishing this mission. For this reason, finding a reliable way to search for some data through the resumes is a perfect solution that saves time, effort and complexity. This paper proposes a method that helps employers, orientation units in any academic institution, training centers or job fairs to search for a specific job description in a system flooded with resumes in order to find the correct query answer, even if the resume of the candidate is written with some fuzzy mistakes.
Ultra-High Resolution Video Distributed Transcoding System Using Memory-based High-speed Data Distribution Method

Delivering ultra-high resolution video in realtime over a public network is a challenging task. There are two possible solutions: 1) tiled encoding and streaming technology can effectively transmit the video with an extremely large bit rate; and 2) distributed transcoding can encode ultra-high resolution videos in real-time. Distributed transcoder-based real-time encoding of ultra-high resolution videos has latency due to input stream distribution and output stream integration. The existing disk-based distributed transcoding system has a limitation on the data transfer speed between the master and the slaves due to the read/write speed of the disk. In this paper, we propose a tiled distributed transcoding system using memory-based distribution method. The proposed system supports High-efficiency Video Coding (HEVC) or AOMedia Video 1 (AV1) transcoding on Spark on Yarn platform. Experimental results show a transcoding speed improvement of about 21% compared to the existing system.
Looking Under the Hood: Visualizing What LSTMs Learn

Recurrent Neural Networks are a state of the art method for modeling sequential data. Unfortunately, the practice of RNNs is ahead of the theory. We lack any method for summarizing or analyzing what a network has learned, once it’s trained. This paper presents two methods for visualizing concepts learned by RNNs in the domain of action recognition. The first method shows the sensitivity of joints over time. The second generates synthetic videos that maximize the responses of a class label or hidden unit given a set of anatomical constraints. These techniques are combined in a visualization tool called SkeletonVis to help developers and users gain insights into models embedded in RNNs for action recognition.
Pay attention and you won’t lose it: a deep learning approach to sequence imputation

In most areas of machine learning, it is assumed that data quality is fairly consistent between training and inference. Unfortunately, in real systems, data are plagued by noise, loss, and various other quality reducing factors. While a number of deep learning algorithms solve end-stage problems of prediction and classification, very few aim to solve the intermediate problems of data pre-processing, cleaning, and restoration. Long Short-Term Memory (LSTM) networks have previously been proposed as a solution for data restoration, but they suffer from a major bottleneck: a large number of sequential operations. We propose using attention mechanisms to entirely replace the recurrent components of these data-restoration networks. We demonstrate that such an approach leads to reduced model sizes by as many as two orders of magnitude, a 2-fold to 4-fold reduction in training times, and 95% accuracy for automotive data restoration. We also show in a case study that this approach improves the performance of downstream algorithms reliant on clean data.
Light Field Reconstruction Using Shearlet Transform in TensorFlow

Shearlet Transform (ST) is one of the most effective approaches for light field reconstruction from Sparsely-Sampled Light Fields (SSLFs). This demo paper presents a comprehensive implementation of ST for light field reconstruction using one of the most popular machine learning libraries, i.e. Tensor Flow. The flexible architecture of TensorFlow allows for the easy deployment of ST across different platforms (CPUs, GPUs, TPUs) running varying operating systems with high efficiency and accuracy.
Information verification improvement by textual entailment methods

Information verification is significant because the rate of information generation is high and growing every day, generally in social networks. This also causes social networks to be invoked as a news agency for most of the people. Accordingly, information verification in social networks becomes more significant. Therefore, in this paper, a method for information verification on Twitter is proposed. The proposed method employs textual entailment methods for enhancement of verification methods on Twitter. Aggregating the results of entailment methods in addition to the state-of-the-art methods can enhance the outcomes of tweet verification. In addition, as writing style of tweets is not perfect and formal enough for textual entailment, we used the language model to supplement tweets with more formal and proper texts for textual entailment. Although singly utilizing entailment methods for information verification may result in acceptable results, it is not possible to provide relevant and valid sources for all of the tweets, especially in early times by posting tweets. Therefore, we utilized other sources as a user conversational tree (UCT) besides utilizing entailment methods for tweet information verification. The analysis of UCT is based on the pattern extraction from the UCT. Experimental results indicate that using entailment methods enhances tweet verification.
HEHLKAPPE

Image analysis and manipulation have always been active topics, both in practice and academia. Driven by the progress in the field of deep learning, significant advances have been achieved in recent years. This causes even complex image manipulation and analysis tasks to be easily applicable by a wide audience. Combined with traditional attack methods, this results in new attack vectors. In this paper, we present HEHLKAPPE, an application to hide persons from real-time video streams while keeping other movement untouched. The application is fully automated and does not require any domain knowledge in deep learning, image manipulation or other related areas in order to use it. In addition, we present 2 attack vectors to access the video stream to enable the manipulation. Our evaluation shows that HEHLKAPPE works well with static camera positions, is able to adapt to background changes and therefore is suitable to deceive observers. The discussion of the results discovers potential for improvement by using even more sophisticated techniques. We are confident that these techniques will be applicable in real-time in the near future. Appropriate countermeasures to mitigate our attack include improving the state of IoT security and verifying the authenticity of each frame using a blockchain-linke structure.
Enhancing credibility of digital evidence through provenance-based incident response handling

Digital forensics are becoming increasingly important for the investigation of computer-related crimes, white-collar crimes and massive hacker attacks. After an incident has been detected an appropriate incident response is usually initiated with the aim to mitigate the attack and ensure the recovery of the IT systems. Digital Forensics pursues the goal of acquiring evidence that will stand up in court for sentencing and sometimes opposes contradicting objectives of incident response approaches. The concept presented here provides a solution to strengthen the credibility of digital evidence during actions related to incident response. It adapts an approach for data provenance to accurately track the transformation of digital evidence. For this purpose, the affected system and the incident response systems are equipped with a whole system data provenance capturing mechanism and then data provenance is captured simultaneously during an incident response. Context information about the incident response is also documented. An adapted algorithm for sub-graph detection is used to identify similarities between two provenance graphs. By applying the proposed concept to a use case, the advantages are demonstrated and possibilities for further development are presented.
Field Environment Intelligent Navigation System for Tomato Transportation Robot Based on Dijkstra

Tomato fields are large and complex. The picked tomatoes cannot be transported to the storage area in time, which will affect the quality of tomato. Transport tomato not only cost time but also need many labors. So, it is very important to use the tomato transport robot. This paper designed a navigation system for tomato transport robot based on Dijkstra algorithm. The system improved the robustness of the algorithm. The improved algorithm can plan the path for any two points in the field environment. Realized a function that can transport the tomatoes with a best and fastest route. The navigation system defining the route and the intersection. The routes and intersections of the filed can be added and modified flexibly that adapt to changeable field environment. Achieving the human-computer interaction interface with Java, which make the operation easier and more flexible. It is very important that the real-time performance of the robot. The average calculate time of this navigation system is about 0.53 s, which conform to real-time requirements of transportation robot.
Hierarchical Rules for Knowledge Representation and Learning

Definitions of predicates by hierarchical rules are introduced. These rules extend Horn clauses to full first-order logic with functions. This paper describes how to learn representations of hierarchically definable predicates. The representations are used to predict formula validity probabilities. Validity probability scoring can be combined with inference.
YUN: A Fast Ground-to-air Cloud Image Recognition Framework

The recognition of cloud maps plays an important role in the field of meteorological prediction. The mainstream recognition of cloud maps mainly focuses on satellite cloud maps, while research on ground-to-air cloud maps is less. In this paper, we propose an analysis framework - YUN - to quickly locate and identify clouds in these maps. A Quick Cloud region Proposal(QCP) was designed to segment cloud maps to the greatest extent. Then we use the convolutional neural network to extract features of the cloud maps and use fully connected network to predict the type of cloud. In addition, a voting mechanism with a variety of methods has achieved good prediction results.
Face recognition under varying lighting conditions: improving the recognition accuracy for local descriptors based on weber-face followed by difference of Gaussians

Illumination variation is among the several bottlenecks in a face recognition system because it can greatly affect the appearance of a face image, which causes a reduction in the face matching performance of the system. Using illumination preprocessing methods is an effective approach to overcome this problem. Despite the achievements made, however, each method still has its own demerits. In this paper, an efficient representation method insensitive to varying illumination based on a combination of Weber-face (WF) and the difference of Gaussians (DoG) methods is proposed for human face recognition using local descriptors. After processing by our method, the obtained image will preserve more facial features and edge information while shading effects are eliminated. To demonstrate the potential of the proposed method, two systems using nearest-neighbor and support vector machine classifiers were implemented. Experimental results for the CMU-PIE and extended Yale B face databases showed that our method could effectively eliminate the effect of uneven illumination and achieved better recognition accuracies in comparison to other state-of-the-art methods (DoG, gradient face, histogram equalization, and WF).
RESDEC

Software product lines (SPL) management is one of the most important activities for the software engineer and it represents one of the key pieces of software product line engineering. When a software system grow fast, configuring a product becomes a costly and error-prone activity due to the amount of features available for configuration. This process becomes more complex when for each feature, there is more than one component that implements it. Currently the tools available for configuration management do not have automated mechanisms to facilitate the optimal components selection that meet the functions required by a given product. In this paper, we introduce a prototype component-based recommender system called RESDEC (REcommender System that suggest implementation Components from selecteD fEatures) designed to manage the best implementation components alternatives. Our tool is validated using WordPress-based websites where the implementation components are represented by plugins and the recommendations generated by RESDEC help interested parties in the search and efficient plugins selection to configure websites.
Alphanumeric Test Paper Checker Through Intelligent Character Recognition Using OpenCV and Support Vector Machine

This paper presents the development of a test paper checker that will recognize a handwritten text using Intelligent Character Recognition (ICR) for Alphanumeric Characters. An examination can be conducted in two ways—digital and manual—and each way has a different approach in checking. In this study, the main objective is to recognize alphanumeric handwritten characters accurately using intelligent character recognition. OpenCV is used in the Python programming language and Support Vector Machine as a tool in machine learning for ICR. Answer sheet was designed with 120 items for MCQ and problem-solving questions. Item analysis and printing of results are included in the device. Experiments were conducted by giving an actual examination from the 131 participants in Technological University of the Philippines for testing the accuracy of the device. The results obtained from comparing manual and machine checking had an accuracy of 93.0769%. Thus, the proposed method is applicable for the development of handwritten character recognition.
Learning local feature representation from matching, clustering and spatial transform

This paper focuses on learning the local image region representation via deep neural networks. Existing works mainly learn from matched corresponding image patches, with which the learned feature is too sensitive to the individual local patch matching result and cannot handle aggregation based tasks such as image level retrieval. Thus, we propose to use both the matched corresponding image patches and the clustering result as labels for the network training. To resolve the inconsistency between the matched correspondences and clustering results, we propose a semi-supervised iterative training scheme together with a dual margins loss. Moreover, a jointly learned spatial transform prediction network is utilized to obtain better spatial transform invariance of the learned local features. Using SIFT as the label initializer, experimental results show the comparable or even better performance than the hand-crafted feature, which sheds lights on learning local feature representation in an unsupervised or weakly supervised manner.
[DC] Immersive Gamified Environments (IGE) as an Approach to Assess Subjective Qualities of Daylighting in Architectural Spaces

Daylighting performance is a major factor in shaping the quality of any architectural space. Therefore, visualization and assessment of daylighting are some of the important applications of Virtual Reality (VR) in architecture. However, many of the related current methodologies show limitations in terms of realism, user-engagement, and locomotion. This research introduces and validates the term “Immersive Gamified Environment” (IGE) as a human-oriented, more engaging approach to evaluate and optimize the subjective qualities of daylighting in early stages of the design process. The initial project of this research creates a user-oriented VR game for the assessment of daylighting performance in “Kimbell Art Museum” by Louis Kahn. The methodology is based on fulfilling in-game objectives and a follow-up questionnaire. The outcomes of this study show potentials of the proposed system in terms of realism, light perception, and interaction, despite some limitations regarding scale perception and motion sickness.
An Efficient Elitist Covariance Matrix Adaptation for Continuous Local Search in High Dimension

In this paper, we propose a computationally efficient variant of elitist covariance matrix evolution strategy for continuous local search in high dimensional space. It focuses on searching in a low-dimensional subspace expanded by a small number of promising search directions. This leads to the linear internal computational complexity of each iteration, which enables the algorithm to scale to high dimensional problems. We conduct comprehensive experiments to evaluate the parameter sensitivity and the algorithm’s performance. The experimental results validate that the proposed algorithm reduces the running time by a factor of ten, and it can be easily scaled up to n>1000 on a set of commonly used test functions.
Graphene: A Secure Cloud Communication Architecture

Due to ubiquitous-elastic computing mechanism, platform independence and sustainable architecture, cloud computing emerged as the most dominant technology. However, security threats become the most blazing issue in adopting such a diversified and innovative approach. To address some of the shortcomings of traditional security protocols (e.g., SSL/TLS), we propose a cloud communication architecture (Graphene) that can provide security for data-in-transit and authenticity of cloud users (CUs) and cloud service providers (CSPs). Graphene also protects the communication channel against some most common attacks such as man-in-the-middle (MITM) (including eavesdropping, sniffing, identity spoofing, data tampering), sensitive information disclosure, replay, compromised-key, repudiation and session hijacking attacks. This work also involves the designing of a novel high-performance cloud focused security protocol. This protocol efficiently utilizes the strength and speed of symmetric block encryption with Galois/Counter mode (GCM), cryptographic hash, public key cryptography and ephemeral key-exchange. It provides faster reconnection facility for supporting frequent connectivity and dealing with connection trade-offs. The security analysis of Graphene shows promising protection against the above discussed attacks. Graphene also outperforms TLSv1.3 (the latest stable version among the SSL successors) in performance and bandwidth consumption significantly and shows reasonable memory usage at the server-side.
Improved Manipulation Detection with Convolutional Neural Network for JPEG Images

JPEG images are ubiquitously used in most real-world online and mobile applications, where uncompressed images are not available from the very beginning when digital images are generated by digital camera or smartphone. In this paper, an improved manipulation detection scheme for JPEG images with convolutional neural network is proposed which works better in practical scenarios. No uncompressed or lossless compressed images are used for network training and testing. All images are stored in JPEG format before and after any kind of manipulation. The proposed scheme is also able to detect manipulation even if the manipulated images are compressed with different JPEG quality factors from the training images. Experimental results demonstrate that the proposed scheme significantly outperforms the existing method under practical conditions of real-world applications.
Gaze and Motion-aware Real-Time Dome Projection System

We present the ICG Dome, a research facility to explore human visual perception in a high-resolution virtual environment. Current state-of-the-art VR devices still suffer from some technical limitations, like limited field of view, screen-door effect or so-called god-rays. These issues are not present or at least strongly reduced in our system, by design. Latest technology for real-time motion capture and eye tracking open up a wide range of applications.