Reviewerly/exHarmony huggingface dataset:
Leveraging Semantic Embeddings for Safety-Critical Applications

Semantic Embeddings are a popular way to represent knowledge in the field of zero-shot learning. We observe their interpretability and discuss their potential utility in a safety-critical context. Concretely, we propose to use them to add introspection and error detection capabilities to neural network classifiers. First, we show how to create embeddings from symbolic domain knowledge. We discuss how to use them for interpreting mispredictions and propose a simple error detection scheme. We then introduce the concept of semantic distance: a real-valued score that measures confidence in the semantic space. We evaluate this score on a traffic sign classifier and find that it achieves near state-of-the-art performance, while being significantly faster to compute than other confidence scores. Our approach requires no changes to the original network and is thus applicable to any task for which domain knowledge is available.
Ontology-Based Recommender System for Sport Events

Sport event participation has changed much recently with effective support of technology. Advanced developments in recommender systems and World Wide Web bring chances such as efficiently distantly booking to alone travelers which allows them to enjoy sport events without being dependent on expensive tourist agencies as in the past. However, currently, popular information sources for such a recommender system are isolated and mainly relied on Web 2.0 formats which are difficult to stored and processed, especially among different platforms and communities. To utilize huge resources of Web 2.0 as well as apply cutting-edge features of Web 3.0 and under-developing Web 4.0, the authors propose an implementation of a hybrid system which collects data from different sources in the Internet (Mashup), apply machine learning to process raw information (Natural Language Processing and Unsupervised Clustering), add semantics to the processed data and make it compatible to latest web generation (Ontology), and provide recommendations based on smart content-based filtering and social-network-based user profiles for sport events. Empirical results show promising applications of such a framework to the market portion of alone travelers and also set an example as a demonstration for the authors’ expectation toward Web 4.0 applications in the future.
An Improved Multithreshold Segmentation Algorithm Based on Graph Cuts Applicable for Irregular Image

In order to realize the multithreshold segmentation of images, an improved segmentation algorithm based on graph cut theory using artificial bee colony is proposed. A new weight function based on gray level and the location of pixels is constructed in this paper to calculate the probability that each pixel belongs to the same region. On this basis, a new cost function is reconstructed that can use both square and nonsquare images. Then the optimal threshold of the image is obtained through searching for the minimum value of the cost function using artificial bee colony algorithm. In this paper, public dataset for segmentation and widely used images were measured separately. Experimental results show that the algorithm proposed in this paper can achieve larger Information Entropy (IE), higher Peak Signal to Noise Ratio (PSNR), higher Structural Similarity Index (SSIM), smaller Root Mean Squared Error (RMSE), and shorter time than other image segmentation algorithms.
Experimental Implementation and Verification of Scalar Field Ridge, Trench, and Saddle Point Maneuvers Using Multirobot Adaptive Navigation

Adaptive navigation of scalar fields is a compelling capability in which mobile robotic systems make real-time navigation decisions based on sensed measurements of the environment. This capability can enable efficient identification and location of specific features of interest within the field of interest, potentially saving time and energy while also being responsive to changing conditions. Applications can include finding the sources and impact zones of a pollutant, establishing hazard perimeters, finding safe zones, and safe paths of travel. This paper presents new work that experimentally verifies several adaptive navigation control policies for moving to/along critical scalar field features with a group of mobile robots. Specifically, we demonstrate the use of a five robot cluster of sensor-equipped mobile robots to descend ridges within a scalar field, to ascend trenches, and to move to and hold a position at saddle points. This is done through the use of differential measurements across the cluster's formation baselines and control laws that have been previously demonstrated in simulation. This paper also incorporates a new state machine within the adaptive navigation control architecture in order to monitor the performance of the individual control primitives and to respond to conditions such as losing track of the feature of interest. Finally, this paper is the first in which we have experimentally demonstrated control of a five robot group of robots using our cluster space control methodology. The experiments were conducted using a novel indoor multi-robot testbed with the ability to establish customizable scalar fields printed in greyscale on large sheets that are actively sensed by the robots to enable controlled experimental evaluation. Four different fields are used in this study in order to demonstrate the new capabilities of interest.
Comparing Performance of Commercial Cloud Storage Systems: The Case of Dropbox and One Drive

Cloud storage systems allows users to access their data from anywhere. They can download or upload their files or share them with others through the internet. Companies are competing to provide cloud storage services to users with varying features. This paper introduces criteria to compare cloud storage systems. It demonstrates the comparison between Dropbox and OneDrive in terms of their security, download/upload speed, application integration, pricing and storage, synchronization performance and focus on differences between them. The comparison conducts depend on six criteria that are: security, pricing and storage, integration of application, content sharing and synchronization. Dropbox shows the best performance in security, pricing and storage, synchronization and the download and upload of small files with small sizes. However, OneDrive shows a good performance in speed of download and upload big size of file. The comparison is intended to guide users and organizations when choose between the two services.
Quantification of the Disturbances of Phosphate Series Using the Box-Counting Method on Geoelectrical Images (Sidi Chennane, Morocco)

The phosphate series of Sidi Chennane (Morocco) is frequently affected by sterile bodies called “disturbances” causing many problems during the mining operation. The quantification of these bodies is thus considered as a crucial step for the OCP mining engineers. In following this research, we propose the fractal dimension as a new and simple efficient tool to analyze the disturbed areas. The work was carried out on geoelectrical maps of a study area of 50 hectares located in the northern part of the Sidi Chennane deposit. Fractal dimension was used as a representative parameter for examining the disturbed areas by using the box-counting method. The end result shows a strong linkage between the rate of the disturbances and the corresponding fractal dimension. This may have an important implication to quantify the disturbances and get a precise phosphate reserves estimate.
Fuzzy Logic Based Dynamic Plotting of Mood Swings from Tweets

Twitter is one the most popular social media platforms. Users express their feelings easily on social media regarding any trending event. In this paper, we propose a fuzzy logic based approach for dynamic plotting of mood swings from tweets. The novelty of the paper is use of linguistic hedges with fuzzy logic to compute the sentiment of tweet. Comparison of our approach with existing methods, on real-time tweets extracted from online website confirms the superiority and efficiency of our method. The tweets used in our experiments are extracted from the timeline of the India Vs Pakistan final ICC world-cup match in June 2017. They reflect the moods of the twitter users as the match progresses. Using our fuzzy logic based approach, we successfully plot the dynamic mood vs time and compute the polarity of the sentiment at each time instant.
A New Cryptography Algorithm Based on ASCII Code

Cryptography is the science of converting messages to coded messages to be recognized only by the intended receiver. It is considered as the key enabling technology for protecting distributed systems. The need to protect user's data and infrastructures becomes more crucial than ever as the use of internet is growing very rapidly. In spite of the great number of cryptography algorithms reported in the literature, important aspects have, to date been given little attention. One of these aspects is how to link each character in the plaintext with its previous character. This paper proposes a new cryptography algorithm which considers linking each character in the plaintext with its previous one during encryption and decryption. The proposed algorithm can be used to handle different situations in cryptography applications. The simulations results show the effectiveness of the proposed algorithm.
An efficient eCK secure certificateless authenticated key agreement scheme with security against public key replacement attacks

Authenticated Key Agreement (AKA) protocol enables two communicating entities to mutually establish a session key, in such a way that each entity is assured of the authenticity of its peer. Certificateless AKA (CL-AKA) protocols are widely researched, since, it eliminates the key escrow problem in Identity based systems and the cumbersome certificate management overheads associated with the Public Key Infrastructure (PKI) model. However, AKA protocols in the Certificateless setting suffer from public key replacement attacks, since, the user public keys cannot be validated explicitly by certificates. Our investigations reveal that the existing eCK- secure CL-AKA protocols are vulnerable to basic impersonation attacks and Man-in-the-middle attacks by the Key Generation Center (KGC). Moreover, public key replacement attacks by other malicious users can prevent communicating entities from ever agreeing upon a valid session key. In order to mitigate the aforementioned vulnerabilities, we propose the first eCK secure CL-AKA scheme with security against public key replacement attacks by the KGC, as well as, the external adversaries. Contrary to the conventional CL-AKA protocols, the proposed scheme does not have a partial private key escrow at the KGC and does not require secure authenticated channels for the transmission of the partial private keys from the KGC to individual users. Furthermore, we cryptanalyze two of the most recently proposed CL-AKA protocols, to illustrate their lack of eCK security and discuss the flaws in their security proofs. Comparative analysis of the proposed scheme with other CL-AKA protocols in the literature reveals that the proposed scheme offers better efficiency and security.
Towards skill recognition using eye-hand coordination in industrial production

Companies are re-focusing and making use of human labor [4, 21] in order to create individualized lot-size-1 products and not produce the exact same mass product again and again. While human workers can produce with a least with the same quality as machines do, they are not that consistent, so it's better to combine strengths of both men and machines [14]. In this work, we investigate how we can utilize how humans behave in relation to task-required skills levels. To do so we investigate hand-eye coordination on precision tasks, its relation to fine and gross motor skills, in an unconstrained industrial setting. This setting consists of an up to 22 tasks assembly processes of two variants of a high quality product. We establish that there is a high correlation between expected task required skill level and the captured hand eye coordination of expert factory workers and that hand eye coordination can be used to distinguish between fine and gross motor skills. In addition we provide insights how this can be exploited in future work.
A Novel Meta-Information Management System for SaaS

Cloud hosts numerous services with various underlying business logic and data stores. In this multifaceted and distributed environment locating or updating SaaS services is a crucial task. Researchers proposed various mechanism for this purpose, which involves accessing or altering the meta-information of the services, its underlying business logics. Thus, management of meta-information is crucial for SaaS services. It demands efficient categorization and cataloguing mechanism by preserving semantic-relationships among interrelated services, business processes and data sources. This article proposes a flexible and scalable meta-information management system for SaaS, capable of maintaining dependencies among various SaaS resources. It is useful towards identification of interrelated business processes, services and data sources and facilitates incremental update of meta-information. The proposed system is implemented using Hadoop and a series of experiments have been carried out, which shows that it can efficiently scale and effectively categorize and catalogue different SaaS resources.
Multi-state objects depth acquisition based on binocular structured light

Three-dimensional depth perception using structured light has been widely researched and developed due to its robustness, high precision, and density. An open research issue exists with current structured light technology to obtain depth maps of scenes with objects in different motion states, while ensuring high accuracy and resolution. In this paper, a new algorithm for depth acquisition of multi-state targets is designed. A novel projection pattern is designed based on sinusoidal, speckle and line patterns. The foreground targets are separated according to the background segmentation technology, and regions with different motion states are divided into static, slow motion or fast motion regions. Then, the different motion states are used to select different pattern decoding schemes. In the experiments, the depth of multi-state objects are successfully calculated by our technique.
Machine learning using template matching applied to object tracking in video data

This paper presents the algorithms for tracking a moving object through video data using template matching. As the object translates and rotates, the template is adaptively updated so that the object is never lost while in frame. The algorithms were developed in MATLAB and applied to a video of a quadcopter in flight in both visible and infrared imagery. The normalized cross-correlation algorithm is the core of the research, providing an invariant of scale method to perform the template match. Then a bounding box is applied to the matched area and center of mass centroiding allow the object to be tracked frame-to-frame.
Common Self-polar Triangle of Concentric Conics for Light Field Camera Calibration

Accurate light field camera calibration plays an important role in various applications. Instead of a planar checkerboard, we propose to calibrate light field camera using a concentric conics pattern. In this paper, we explore the property and reconstruction of common self-polar triangle with respect to concentric circle and ellipse. A light field projection model is formulated to compute out an effective linear initial solution for both intrinsic and extrinsic parameters. In addition, a 4-parameter radial distortion model is presented considering different view points in light field. Finally, we establish a cost function based on Sampson error for non-linear optimization. Experimental results on both synthetic data and real light field have verified the effectiveness and robustness of the proposed algorithm.
BASIC: Towards a Blockchained Agent-Based SImulator for Cities

Autonomous Vehicles (AVs), drones and robots will revolutionize our way of travelling and understanding urban space. In order to operate, all of these devices are expected to collect and analyze a lot of sensitive data about our daily activities. However, current operational models for these devices have extensively relied on centralized models of managing these data. The security of these models unveiled significant issues. This paper proposes BASIC, the Blockchained Agent-based Simulator for Cities. This tool aims to verify the feasibility of the use of blockchain in simulated urban scenarios by considering the communication between agents through smart contracts. In order to test the proposed tool, we implemented a car-sharing model within the city of Cambridge (Massachusetts, USA). In this research, the relevant literature was explored, new methods were developed and different solutions were designed and tested. Finally, conclusions about the feasibility of the combination between blockchain technology and agent-based simulations were drawn.
DGR-Net: Deep Groupwise Registration of Multispectral Images

Groupwise registration of multispectral images (MSI) is clinically essential to facilitate accurate information fusion across different modalities. However, the groupwise registration of multispectral images is a challenging task because multiple different imaging modalities makes it difficult to jointly optimize the deformation. In this work, we propose an unbiased deep groupwise registration framework, DGR-Net, which takes a complete consideration of the information aggregated by calculating the deformation of the sequence image. Our framwork guided by principal component analysis (PCA) image. Network optimization is accelerated by combining internal smoothing and external correlation of the deformation fields. Experimental results have shown that, the proposed method can achieve promising accuracy and efficiency for the challenging multi-modality groupwise registration task and also outperforms the state-of-the-art approaches.
Progressive information filtering fusion for multi-sensor nonlinear systems

This paper studies the distributed fusion estimation problem for nonlinear systems. A common method for nonlinear filtering is to linearize the nonlinear state and measurement functions, and the linearization error is a major factor that affects the filtering accuracy. To reduce the impact of linearization error on the distributed fusion, a progressive information filter (PIF) is designed as local estimators by improving the working points of Taylor series expansion, thus the distributed fusion results of linear systems can be extended to nonlinear systems. Moreover, by introducing a termination condition for the progressive measurement update, the designed PIF can avoid over-estimation effectively. Finally, simulations of a target tracking example are presented to demonstrate the advantage and effectiveness of the proposed results.
aLeak: Context-Free Side-Channel from Your Smart Watch Leaks Your Typing Privacy

We revisit a crucial privacy problem in this paper - can the sensitive information, like the numeric passwords and personal data, frequently typed by user on mobile devices be inferred through the motion sensors of wearable device on user's wrist, e.g., smart watch or wrist band? Existing works have achieved the initial success under certain context-aware conditions, such as 1) the horizontal keypad plane, 2) the known keyboard size, and/or 3) the last keystroke on a fixed “enter” button. Taking one step further, the key contribution of this paper is to fully demonstrate, more importantly alarm people, the further risks of typing privacy leakage in much more generalized context-free scenarios, which are related to most of us for the daily usage of mobile devices. We validate this feasibility by addressing a series of unsolved challenges and developing a prototype system aLeak. Extensive experiments show the efficacy of aLeak, which achieves promising successful rates in the attack from more than 500 rounds of different users' typings on various mobile platforms without any context-related information.
A Distributed Approach for Mining Moroccan Hashtags using Twitter Platform

Twitter is a social networking service, on which users can share thoughts and interact with events. In this paper, the authors propose a distributed approach to combine the multilingualism analysis of hashtags generated by Moroccan users in the social network Twitter, to discover and understand the hot subjects that attract the community. The analysis of Moroccan twitter hashtags is a challenge for two main reasons. Firstly, since the Moroccan society is characterized by linguistic diversity, hashtags are expressed in several languages. Secondly, the hashtags of Moroccan users may include spelling errors and abbreviations and do not contain delimiters between words, which leads to misinterpretation. In this paper, we propose a distributed approach using Apache Hadoop Framework and Natural Language Processing Techniques for processing and mining Moroccan hashtags by a program we developed using open source libraries. The result is a clean corpus, which is stored in Apache Hive to allow applying analytic queries. Finally, we apply K-means algorithm to cluster all hashtags into general topics, and then plot them on the Moroccan map to specify their sources of publication by using the coordinates extracted from the tweets.
A framework for GPU‐accelerated exploration of massive time‐varying rectilinear scalar volumes

Abstract We introduce a novel flexible approach to spatiotemporal exploration of rectilinear scalar volumes. Our out‐of‐core representation, based on per‐frame levels of hierarchically tiled non‐redundant 3D grids, efficiently supports spatiotemporal random access and streaming to the GPU in compressed formats. A novel low‐bitrate codec able to store into fixed‐size pages a variable‐rate approximation based on sparse coding with learned dictionaries is exploited to meet stringent bandwidth constraint during time‐critical operations, while a near‐lossless representation is employed to support high‐quality static frame rendering. A flexible high‐speed GPU decoder and raycasting framework mixes and matches GPU kernels performing parallel object‐space and image‐space operations for seamless support, on fat and thin clients, of different exploration use cases, including animation and temporal browsing, dynamic exploration of single frames, and high‐quality snapshots generated from near‐lossless data. The quality and performance of our approach are demonstrated on large data sets with thousands of multi‐billion‐voxel frames.
Projection based dynamic point cloud compression using 3DTK toolkit and H.265/HEVC

This paper presents novel compression method for dynamic point clouds based on projections and the H.265/HEVC video coder. We used 3DTK - The 3D Toolkit to create equirect-angular projection images and x265 as H.265/HEVC coder, to compress and extract created projection images. Compression was introduced in 3DTK generated projection image size (different pixel size, but compatible with later video compression), as well as lossless/lossy video compression. Visual inspection shows better results for compression only using different projection resolution, with lossless video compression. Lossy video compression adds noise and creates additional points, resulting in lower visual quality.