Reviewerly/exHarmony huggingface dataset:
Methodology to design ontologies from organizational models: application to creativity workshops

Abstract Innovation and creativity are a mandatory for companies who wish to stay competitive. In order to promote an inventive dynamic, it implies to set up tools, habits, and an adapted environment to foster creativity. Creativity is the wealth of companies that should be valorized. To promote creativity, companies implement creativity workshops that gather people with various roles and expertise exchange and create knowledge to solve collectively open-ended engineering problems. However, group dynamics or facilitation can make the wrong decision and make the creative problem-solving unfruitful. The aim of our research project is to create a digital system to manage and valorize knowledge during creativity workshops. To design this system, we need to formalize the knowledge domain of creative workshops. The ontologies are used for decades to structure and manage information and knowledge in different domains. However, methodologies to design these ontologies are either hardly reproducible or not oriented to extract knowledge from organization. This article describes a methodology based on an organizational modeling to build ontologies. We will illustrate our approach by designing an ontology that models knowledge of creativity workshops.
Rethinking IoT Network Reliability in the Era of Machine Learning

In the Internet of Things (IoT), wireless sensor networks are often paired with machine learning frameworks to deliver applications of high societal impact and support critical infrastructures. In this context, this paper investigates the relationship between network reliability and the reliability of the machine learning framework in terms of prediction accuracy. Our experimental analysis leverages six data sets of various degrees of information redundancy and considers four machine learning algorithms that are commonly used for classification. In turn, packet loss is inserted in the raw input data, emulating various networking loss patterns in terms of burstiness. The experimental results consistently demonstrate a non-linear relationship between the reliability of the network and the accuracy of the machine learning classifier, indicating that not all data packets are equally valuable to the application performance. We conclude with recommendations for IoT practitioners and IoT system designers.
Depth image restoration based on bimodal joint sequential filling

In this paper, we propose a depth image restoration algorithm based on the bimodal joint sequential filling. Basically, the types, causes, and characteristics of invalid points in the depth image are thoroughly analyzed. Besides, using the off-line calibration, accurate registration between the depth and the color image is effectively achieved. Finally, filling priority estimation based on the bimodal joint conditional entropy and the depth value prediction depended on the hypothesis testing are put forward and integrated to realize the depth image restoration under the framework of sequential filling. In the experiments, a comparison between different settings is carried out under the framework of sequential filling. Then the quantitative and qualitative comparisons between other competing methods and ours are implemented. The results demonstrate that our algorithm can restore various types of invalid points in the depth images, and it has significant improvements over several popular competing algorithms in terms of both robustness and preciseness.
Adversarial Robustness of Streaming Algorithms through Importance  Sampling

In this paper, we introduce adversarially robust streaming algorithms for central machine learning and algorithmic tasks, such as regression and clustering, as well as their more general counterparts, subspace embedding, low-rank approximation, and coreset construction. For regression and other numerical linear algebra related tasks, we consider the row arrival streaming model. Our results are based on a simple, but powerful, observation that many importance sampling-based algorithms give rise to adversarial robustness which is in contrast to sketching based algorithms, which are very prevalent in the streaming literature but suffer from adversarial attacks. In addition, we show that the well-known merge and reduce paradigm in streaming is adversarially robust. Since the merge and reduce paradigm allows coreset constructions in the streaming setting, we thus obtain robust algorithms for $k$-means, $k$-median, $k$-center, Bregman clustering, projective clustering, principal component analysis (PCA) and non-negative matrix factorization. To the best of our knowledge, these are the first adversarially robust results for these problems yet require no new algorithmic implementations. Finally, we empirically confirm the robustness of our algorithms on various adversarial attacks and demonstrate that by contrast, some common existing algorithms are not robust. (Abstract shortened to meet arXiv limits)
A New Adaptive Visual Tracking Scheme for Robotic System Without Image-Space Velocity Information

In this article, we investigate the adaptive visual tracking problem for uncertain robotic system with time-varying depth, in the case of no image-space velocity information. Existing works dealing with this problem either assume the estimated time-varying depth is invertible or require the priori information of the system kinematics. This case potentially leads to the singularity of the control scheme and requirement for a high gain feedback. In this brief, a new image-space observer which does not involve the inversion of the estimated depth is proposed, upon which, we design an adaptive visual tracking controller without using the image-space velocity information. More importantly, the observer employs a passive image Jacobian matrix-based constant feedback instead of high gain feedback and the priori information is no longer demanded. Additionally, the proposed control scheme enjoys a desirable separation property for the robotic dynamics and kinematics which makes it applicable to most industrial/commercial robots having a closed torque control loop and only permitting the joint velocity command. By using the Lyapunov stability analysis, the asymptotical convergence of both image-space tracking errors and observation errors are obtained. Numerical simulations and implementation issues concerning the application to industrial/commercial robots are presented to verify the efficacy of the control scheme.
Indistinguishable photons

Quantum mechanics allows identical particles to lose their individuality completely and become truly indistinguishable. This property of indistinguishability gives rise to exclusive quantum phenomena such as two particle interference. Photon indistinguishability is crucial in realizing many quantum information protocols. This manuscript covers the concepts and applications related to indistinguishable photons.
Improving transferability of adversarial examples by saliency distribution and data augmentation

Although deep neural networks (DNNs) have advanced performance in many application scenarios, they are vulnerable to the attacks of adversarial examples that are crafted by adding imperceptible perturbations. Most of the existing adversarial attacks rely on the structure and parameters information of the attacked network. Thus, the generated adversarial examples are poor in transferability to attack black-box defense models, which makes them difficult to be used in real-world applications. In this paper, we propose an approach based on saliency distribution and data augmentation to generate transferable adversarial examples against the defense models. By optimizing perturbations over non-saliency regions, the generated adversarial examples are less sensitive to the attacked source models and have better transferability. Further, by utilizing data augmentation in generating adversarial examples, the overfitting problem on source models is alleviated in targeted attacks. Extensive experiments show that the proposed approach can generate adversarial examples with higher transferability. The source code is available at https://github.com/dongysxd/SDM-FGSM-Attack.
Real-Time Dynamic Map With Crowdsourcing Vehicles in Edge Computing

Autonomous driving perceives surroundings with line-of-sight sensors that are compromised under environmental uncertainties. To achieve real time global information in high definition map, we investigate to share perception information among connected and automated vehicles. However, it is challenging to achieve real time perception sharing under varying network dynamics in automotive edge computing. In this paper, we propose a novel real time dynamic map, named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LiveMap</i> to detect, match, and track objects on the road. We design the data plane of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LiveMap</i> to efficiently process individual vehicle data with multiple sequential computation components, including detection, projection, extraction, matching and combination. We design the control plane of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LiveMap</i> to achieve adaptive vehicular offloading with two new algorithms (central and distributed) to balance the latency and coverage performance based on deep reinforcement learning techniques. We conduct extensive evaluation through both realistic experiments on a small-scale physical testbed and network simulations on an edge network simulator. The results suggest that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LiveMap</i> significantly outperforms existing solutions in terms of latency, coverage, and accuracy.
LYRICS: a General Interface Layer to Integrate AI and Deep Learning.

In spite of the amazing results obtained by deep learning in many applications, a real intelligent behavior of an agent acting in a complex environment is likely to require some kind of higher-level symbolic inference. Therefore, there is a clear need for the definition of a general and tight integration between low-level tasks, processing sensorial data that can be effectively elaborated using deep learning techniques, and the logic reasoning that allows humans to take decisions in complex environments. This paper presents LYRICS, a generic interface layer for AI, which is implemented in TersorFlow (TF). LYRICS provides an input language that allows to define arbitrary First Order Logic (FOL) background knowledge. The predicates and functions of the FOL knowledge can be bound to any TF computational graph, and the formulas are converted into a set of real-valued constraints, which participate to the overall optimization problem. This allows to learn the weights of the learners, under the constraints imposed by the prior knowledge. The framework is extremely general as it imposes no restrictions in terms of which models or knowledge can be integrated. In this paper, we show the generality of the approach showing some use cases of the presented language, including generative models, logic reasoning, model checking and supervised learning.
Evolution of Quantum Computing Based on Grover's Search Algorithm

Quantum computing introduces an efficient way for unfolding complex problems on computing systems. It uses concept of superposition and entanglement, & hence supports intrinsic parallelism for obtaining the speedup in context of time over classical computing. Quantum algorithms can be efficiently implemented over quantum computing system. Most popular quantum algorithms like Grover's and Shor's provide a great foundation to specifically design the algorithms. These algorithms work on improving the lower runtime bounds of classical algorithms. Grover's algorithm exhibits a quadratic polynomial speedup while searching an element among `N' elements unstructured database. The algorithmic strategy can be applied over various computer science related applications and some important are being considered in this article i.e. pattern matching, data mining and machine learning. This article explores quantum fundamentals; history based evolution followed by Grover's search algorithm in detail with cascading advantages, limitations & applications and also finally concludes with the significance of quantum computing & quantum algorithmic domains.
Deep Leaning Using Keras

A branch of artificial intelligence (AI) known as deep learning consists of statistical analysis algorithms known as artificial neural networks (ANN) inspired by the structure and function of the brain. The accuracy of predicting a task has tremendously improved with the implementation of deep neural networks, which in turn incorporate deep layers into the model allowing the system to learn complex data. This chapter intends to give a straightforward manual for the complexities of Google's Keras framework that is easy to understand. The basic steps for the installation of Anaconda, CUDA, along with deep learning libraries, specifically Keras and Tensorflow, are discussed. A practical approach towards solving deep learning problems in order to identify objects in CIFAR 10 dataset is explained in detail. This will help the audience in understanding deep learning through substantial practical examples to perceive algorithms instead of theory discussions.
Trustful ad hoc cross‐organizational data exchanges based on the Hyperledger Fabric framework

Summary Organizations share data in a cross‐organizational context when they have the goal to derive additional knowledge by aggregating different data sources. The collaborations considered in this article are short‐lived and ad hoc, that is, they should be set up in a few minutes at most (e.g., in emergency scenarios). The data sources are located in different domains and are not publicly accessible. When a collaboration is finished, it is however unclear which exchanges happened. This could lead to possible disputes when dishonest organizations are present. The receipt of requests/responses could be falsely denied or their content could be point of discussion. In order to prevent such disputes afterwards, a logging mechanism is needed which generates a replicated irrefutable proof of which exchanges have happened during a single collaboration. Distributed database solutions can be taken from third parties to store the generated logs, but it can be difficult to find a party which is trusted by all participating organizations. Permissioned blockchains provide a solution for this as each organization can act as a consensus participant. Although the consensus mechanism of the permissioned blockchain Hyperledger Fabric (versions 1.0–1.4) is not fully decentralized, which clashes with the fundamental principle of blockchain, the framework is used in this article as an enabler to set up a distributed database, and a proposal for a logging mechanism is presented which does not require the third party to be fully trusted. A proof of concept is implemented which can be used to experiment with different data exchange setups. It makes use of generic web APIs and behaves according to a Markov chain in order to create a fully automated data exchange scenario where the participants explore their APIs dynamically. The resulting mechanism allows a data‐delivering organization to detect missing logs and to take action, for example, (temporarily) suspend collaboration. Furthermore, each organization is incentivized to follow the steps of the logging mechanism as it may lose access to data of others, otherwise. The created proof of concept is scaled to 10 organizations, which autonomously exchange different data types for 10 min, and evaluation results are presented accordingly.
Performance of Speckle Filters for COSMO-SkyMed Images From the Brazilian Amazon

Speckle filtering is an important step for target detection in SAR images since this effect makes it difficult or even impossible to extract information from these images. There are several filters available in the literature although evaluating their performances is not a trivial task since it requires comparing the filtered images with a speckle-free image, which is generally unknown. This evaluation is even more complex when the features in the images are heterogeneous, for example, from tropical forests. The objective of this study is to evaluate the performance of the Lee, deGrandi, GammaMAP, single Anisotropic Nonlinear Diffusion (ANLD), multitemporal ANLD, Fast Adaptive Nonlocal SAR (FANS), and Fast GPU-Based Enhanced Wiener filters to reduce the speckle present in the COSMO-SkyMed Stripmap X-band images from the Brazilian Amazon forest region. The evaluation was conducted qualitatively through the visual inspection of the ratio image and the edge detection in the ratio images and quantitatively through the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\alpha \beta $ </tex-math></inline-formula> estimator and other statistical parameters of the filtered images. The GammaMAP filter showed the best performances, both qualitatively and quantitatively, and the FANS filter only qualitative.
A test to compare interval time series

We compare two interval time series (ITS) by testing whether their underlying distributions are significantly different or not. To perform hypothesis testing, we make use of the discrete wavelet transform (DWT) which decomposes a time series into a set of coefficients over a number of frequency bands or scales. We obtain the DWT of the radius and centre of each of the two ITS at different scales, and perform randomisation tests. In order to use a randomisation test, the observations must be uncorrelated; this condition is more or less satisfied since at each scale, the DWT coefficients are approximately uncorrelated with each other. Our proposed test statistic is the ratio of the determinants of the covariance matrix of radius and centre DWTs of the two ITS, at each scale. This test statistic ensures that the variability between the upper and lower bounds of each ITS is encompassed. Simulation studies conducted to evaluate the performance of the test show reasonably good estimates of size and power under most conditions, and applications to real interval time series reveal the practical usefulness of this test.
On interleaved rank metric codes

Horizontal and vertical interleavings of arbitrary matrix or vector rank metric codes are considered. A necessary and sufficient condition for an interleaved code to be maximum rank distance (MRO) code is obtained. In particular, horizontal interleaving of Gabidulin vector codes over the field F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> m gives an example of a non-Gabidulin MRD code of length more than m. It is shown that interleaving of Gabidulin codes with different sets of locators can be decoded directly by syndrome decoders. Any other decoder can also be used in the case of different locators after making proposed modification of the received word.
Polycyclic codes associated with trinomials: good codes and open questions

Polycyclic codes are a generalization of cyclic and constacyclic codes. Even though they have been known since 1972 and received some attention more recently, there have not been many studies on polycyclic codes. This paper presents an in-depth investigation of polycyclic codes associated with trinomials. Our results include a number of facts about trinomials, some properties of polycyclic codes, and many new quantum codes derived from polycyclic codes. We also state several conjectures about polynomials and polycyclic codes. Hence, we show useful features of polycyclic codes and present some open problems related to them.
Survey on the Design and Development of Indian Language Chatbots

A chatbot is an intelligent system which can hold a conversation in real-time with a human being using natural language. This paper describes a multitude of chatbots that communicate in Indian Languages. Extensive research has been done on chatbots in English before, but there is little study on Chatbots in Indian languages. This survey was conducted as a part of our project on an Intelligent Chatbot using Deep Reinforcement Learning. The paper is based on the firm belief that chatbots in Indian languages can be used to bridge the communication gap amongst Indian users. For the chatbot to work resourcefully, it needs to be provided vast logical resources. The peculiarities of natural language require special care, especially in the processing of synonyms, word forms, context, etc. This survey paper deeply studies the methodologies and behaviours of various Indian chatbots, and a comparison is drawn based on their accuracy.
JomFacial Recognition Attendance Systems

Nowadays, most universities as well as offices need to keep the attendance of their students and employees. As technology is growing rapidly, machines are designed to reduce the daily workload of mankind. An automatic facial recognition system can make it easier for institutes to keep track of attendance. Introducing a facial recognition attendance system to a university will promote its branding, make them more efficient, and cost-effective. A mixed-method of data collection is used in this research. In this paper, we will be proposing a facial recognition attendance system to replace the traditional manual attendance system. Theoretical and management implication are discussed.
Multi-Authority Fine-Grained Data Sharing and Search Scheme for Cloud Banking Systems

The migration of the banking system to the cloud seems inevitable in near future like any other industry. By leveraging cloud technology, the personal and financial data of any customer can be accessed and controlled by third-party service providers. However, in order to maintain confidentiality, this information should be kept in an encrypted format, which has an impact on the usefulness and flexibility of fundamental operations like search. Moreover, in a financial institution, a data owner may want to provide the searching capability to the users from diverse domains. Therefore, to provide such flexibility, a system of multi-authority fine-grained search is introduced where each authority manages a single but entirely disjoint domain of attributes. As a result, the proposed system is more scalable, it can handle a large number of users from diverse domains and manage their credentials effectively. While most of the schemes in the literature lack this feature, and have a performance bottleneck because of a single centralized trusted authority.