Reviewerly/exHarmony huggingface dataset:
Fog Native Architecture: Intent-Based Workflows to Take Cloud Native toward the Edge

The cloud native approach is rapidly transforming how applications are developed and operated, turning monolithic applications into microservice applications, allowing teams to release faster, increase reliability, and expedite operations by taking full advantage of cloud resources and their elasticity. At the same time, “fog computing” is emerging, bringing the cloud toward the edge, near the end user, in order to increase privacy, improve resource efficiency, and reduce latency. Combining these two trends, however, proves difficult because of four fundamental disconnects between the cloud native paradigm and fog computing. This article identifies these disconnects and proposes a fog native architecture along with a set of design patterns to take full advantage of the fog. Central to this approach is turning microservice applications into microservice workflows, constructed dynamically by the system using an intent-based approach taking into account a number of factors such as user requirements, request location, and available infrastructure and microservices. The architecture introduces a novel softwarized fog mesh facilitating both inter-microservice connectivity, external communication, and end-user aggregation. Our evaluation analyzes the impact of distributing microservice-based applications over a fog ecosystem, illustrating the impact of CPU and network latency and application metrics on perceived quality of service of fog native workflows compared to the cloud. The results show the fog can offer superior application performance given the right conditions.
Binary Signal Perfect Recovery From Partial DFT Coefficients

How to perfectly recover a binary signal from its discrete Fourier transform (DFT) coefficients is studied. The theoretic lower bound and a practical recovery strategy are derived and developed. The concept of ambiguity pair is introduced. This pair of signals has almost the same DFT coefficients except for some positions. It can prove that when the signal length is <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula>, then at least <inline-formula><tex-math notation="LaTeX">$\tau (N)$</tex-math></inline-formula> DFT coefficients must be sampled, where <inline-formula><tex-math notation="LaTeX">$\tau (N)$</tex-math></inline-formula> is the number of factors of the signal length <inline-formula><tex-math notation="LaTeX">$N$</tex-math></inline-formula>. A recovery algorithm is proposed and implemented. It can achieve the lowed bound for length <inline-formula><tex-math notation="LaTeX">$N=2^{m}, m \leq 6$</tex-math></inline-formula>. To overcome the length limitation problem, a more practical recovery method is also proposed and implemented for <inline-formula><tex-math notation="LaTeX">$N=2^{m}, m&gt;6$</tex-math></inline-formula>. We can sample 11&#x0025; of the total DFT coefficients to perfectly recover the binary signal. We also extend the concept of ambiguity pair to other discrete transforms (DCT and WHT) and two-dimensional DFT cases.
Multivision Sensor Extrinsic Calibration Method With Non-Overlapping Fields of View Using Encoded 1D Target

Multiple vision sensors (multi-VSs) are difficult to calibrate in large fields of view vision measurement owing to the lack of overlapping areas. Thus, this study proposes a novel method for the external parameter calibration of multi-VSs by using an encoded 1D target. The quick response code is used in this study to uniquely identify the feature circle. The Hough transform method and ellipse center solving method are jointly applied to optimize the feature points and further improve the collinearity of the feature points. As the cross-ratio of the collinearity feature points involves a projection invariance, the rotation and translation vectors between the multi-VSs are solved by the inverse process of projection and rigid transformation. The accuracy and advantages of the proposed method are verified by simulation and real experiments. The proposed target has a simple structure, the operation is convenient and flexible, and the method can be applied to the global calibration of multi-VSs with non-overlapping fields of view.
Blockchains Through Ontologies: The Case Study of the Ethereum ERC721 Standard in oasis

Blockchains are gaining momentum due to the interest of industries and people in decentralized applications (Dapps), particularly in those for trading assets through digital certificates secured on blockchain, called tokens. As a consequence, providing a clear unambiguous description of any activities carried out on blockchains has become crucial, and we feel the urgency to achieve that description at least for trading. This paper reports on how to leverage the Ontology for Agents, Systems, and Integration of Services ("OASIS") as a general means for the semantic representation of smart contracts stored on blockchain as software agents. Special attention is paid to non-fungible tokens (NFTs), whose management through the ERC721 standard is presented as a case study.
Updating Prediction Models for Predictive Process Monitoring

Predictive monitoring is a key activity in some Process-Aware Information Systems (PAIS) such as information systems for operational management support. Unforeseen circumstances like COVID can introduce changes in human behaviour, processes, or computing resources, which lead the owner of the process or information system to consider whether the quality of the predictions made by the system (e.g., mean time to solution) is still good enough, and if not, which amount of data and how often the system should be trained to maintain the quality of the predictions. To answer these questions, we propose, compare, and evaluate different strategies for selecting the amount of information required to update the predictive model in a context of offline learning. We performed an empirical evaluation using three real-world datasets that span between 2 and 13 years to validate the different strategies which show a significant enhancement in the prediction accuracy with respect to a non-update strategy.
Path-Constrained and Collision-Free Optimal Trajectory Planning for Robot Manipulators

In this paper, we develop a novel path-constrained and collision-free optimal trajectory planning algorithm for robot manipulators in the presence of obstacles for the following problem: Given a desired sequence of discrete waypoints of robot configurations, a set of robot kinematic and dynamic constraints, and a set of obstacles, determine a time and jerk optimal and collision-free trajectory for the robot passing through the given waypoints. Our approach in developing the robot path through the waypoints relies on the orthogonal collocation method where the states are represented with Legendre polynomials in the Barycentric form; the transcription process efficiently converts the continuous-time formulation of the optimal control problem into a discrete non-linear program. In addition, we provide an efficient method for avoiding robot self-collisions (of joints and links) and collisions with workspace obstacles by modeling them as the union of spheres and cylinders in the workspace. The resulting collision free optimal trajectory provides smooth and constrained motion for the robot passing through all the waypoints in the given prescribed sequence with a constant speed. The proposed method is validated using numerical simulations and experiments on a six degree-of-freedom robot. Note to Practitioners—This paper is motivated by planning collision-free optimal trajectories with constant Cartesian speed (norm of translation velocity) along a given list of waypoints. The primary applications include developing constant Cartesian speed trajectories for robotic surface finishing operations, spray painting operations and robot endurance testing. Sampling-based motion planning algorithms have been widely used for their high efficiency and robustness. However, those methods in general do not take into account the joint level constraints, motion jerk, robot dynamic model and kinematic constraints together. In addition, with these algorithms, it is difficult to generate a trajectory along a list of waypoints while maintaining a constant Cartesian speed. We provide an efficient robot trajectory planning algorithm for articulated robots that is capable of achieving time and jerk optimality while avoiding obstacles and satisfying robot kinematic and dynamic constraints. The scope of this work is limited to considering only static obstacles and pre-defined Cartesian waypoints; potential extensions include consideration of dynamic obstacles and incorporating tighter bounds for objects modeled by cylinders and spheres so that a larger workspace is available for trajectory planning, etc.
HybridBERT4Rec: a hybrid (content-based filtering and collaborative filtering) recommender system based on BERT

Because a user&#x2019;s behavior depends mainly on the user&#x2019;s current interests, which can change over time, the sequential recommendation approach has become more prevalent in recommender systems. Many methods have been proposed that aim to model sequential recommendations. One of these is BERT4Rec, which applies the bidirectional-encoder-representations-from-transformers (BERT) technique to model user behavior sequences by considering the target user&#x2019;s historical data, i.e., a content-based filtering (CBF) approach. Despite BERT4Rec&#x2019;s effectiveness, we argue that considering only this historical data is insufficient to provide the most accurate recommendation. We believe that if BERT were to consider other users&#x2019; interactions in its analysis, it would increase the model accuracy. Therefore, we propose a new method called HybridBERT4Rec, which applies BERT to both CBF and collaborative filtering (CF). For CBF, we want to extract the characteristics of the target user&#x2019;s interactions with purchased items. (We implement this in the same way as in BERT4Rec, with our model generating a target user profile.) For CF, we want to find neighboring users who are similar to the target user. Here, we extract the target item&#x2019;s characteristics using all other users who rated the target item as a second input to BERT. This generates a target item profile. After obtaining both profiles, we use them to predict a rating score. We experimented with three datasets, finding that our model was more accurate than the original BERT4Rec.
Pre-Trained Multilingual Sequence-to-Sequence Models: A Hope for Low-Resource Language Translation?

What can pre-trained multilingual sequenceto-sequence models like mBART contribute to translating low-resource languages?We conduct a thorough empirical experiment in 10 languages to ascertain this, considering five factors: (1) the amount of fine-tuning data, (2) the noise in the fine-tuning data, (3) the amount of pre-training data in the model, (4) the impact of domain mismatch, and (5) language typology.In addition to yielding several heuristics, the experiments form a framework for evaluating the data sensitivities of machine translation systems.While mBART is robust to domain differences, its translations for unseen and typologically distant languages remain below 3.0 BLEU.In answer to our title's question, mBART is not a low-resource panacea; we therefore encourage shifting the emphasis from new models to new data 1 .
Refined Optimal Control Problem and Its Solution Using Symbolic Regression

The new refined setting of the optimal control problem is presented. Now a solution of the new problem can be realized in control object directly. Previously, the solution of the classical statement of the optimal control problem could not be realized on the object directly because this led to an open control system. The new refined setting of the problem includes additional conditions. The optimal trajectory in the state space must have an attraction property in some neighborhood. To solve the new refined problem the synthesized control method is proposed. According to the method initially the control synthesis problem is solved. The control object becomes a stable in some equilibrium point in the state space. Secondly the optimal control problem is solved by moving positions of stable equilibrium point. An example of solving the new optimal control problem with complex phase constraints for mobile robot is presented. For comparison this problem was solved by directly method without new additional conditions. Both solutions are simulated with perturbations of the model. Experiments shows that the solution of the new problem is less sensitivity to perturbations than the solution of the classical one.
A Dataset and BERT-based Models for Targeted Sentiment Analysis on Turkish Texts

Targeted Sentiment Analysis aims to extract sentiment towards a particular target from a given text.It is a field that is attracting attention due to the increasing accessibility of the Internet, which leads people to generate an enormous amount of data.Sentiment analysis, which in general requires annotated data for training, is a well-researched area for widely studied languages such as English.For lowresource languages such as Turkish, there is a lack of such annotated data.We present an annotated Turkish dataset suitable for targeted sentiment analysis.We also propose BERTbased models with different architectures to accomplish the task of targeted sentiment analysis.The results demonstrate that the proposed models outperform the traditional sentiment analysis models for the targeted sentiment analysis task.
Selection and Modeling of a Formal Heuristic Evaluation Process Through Comparative Analysis

Due to the importance of usability, multiple usability evaluation methods have been proposed that help Human-Computer Interaction (HCI) specialists determine whether the interfaces of a software product are usable, easy to use, understandable, and attractive. Among these methods, the heuristic evaluation proposed by Jakob Nielsen is the one that stands out. Although Nielsen offers general guidelines for the execution of heuristic evaluations, very few authors of different studies present a formal agreement or process on how the evaluations should be carried out, which leads us to the problem of the absence of a comparative analysis that allows determining the most appropriate formal evaluation process to carry out heuristic inspections. To complement, some proposals found in the literature compare the results of the execution of heuristic evaluations, the definition of new heuristics, qualification forms, and the formalization of the complete process in 5 phases. Although these proposals contribute to the formalization of the heuristic evaluation process, the literature review has not provided a comparative analysis that allows determining which is the most appropriate, which could cause usability evaluators to interpret and carry out their own procedure, which would lead to inaccuracies in the results and increase the probability of improperly executing the inspection. The purpose of this study was to elaborate a comparative table of the formal processes; this allowed the grouping of the various studies found in the literature, to select a process, and thus, and to model by a BPMN tool the whole process. Finally, this process modeled was validated by expert judgement of HCI specialists.
An FPGA-Based General-Purpose Feature Detection Algorithm for Space Applications

Spacecraft takes images for applications/missions like attitude determination, astronomy, and space situational awareness. Feature detection is a primary operation in vision-based processing systems. Although different feature detection algorithms are used for specific purposes, they need to achieve invariance by scaling, rotation, and other interference, and output robust results. This article develops a general-purpose feature detection hardware architecture based on the speeded-up robust features (SURF) algorithm. On the other hand, the efficiency of the algorithm can affect the overall system performance to a large extent, especially in real-time operations. This article presents an field-programmable gate array (FPGA)-based implementation of a modified SURF algorithm. The advantages of FPGAs including parallel and pipelining, fixed-point arithmetic, and bitwise operations are fully applied to improve the performance and efficiency of the system in terms of power consumption, and resource utilization.
Multi-person Collaborative Augmented Reality Assembly Process Evaluation System Based on HoloLens

With the development of products in the direction of products in complexity and sophistication, assembly process planning becomes a complex and time-consuming issue in manufacturing. Augmented reality (AR), especially collaborative augmented reality, can be used to aid the design of assembly processes, help efficiently evaluate and improve the designed assembly process from multiple perspectives. This paper presents a methodology and the associated system for enhancing assembly process scheme evaluation in assembly process plan stage using collaborative augmented reality. It first describes the overall scheme of the system that based on a role-based multi-person collaboration method. Subsequently, system features like: AR collaborative environment (synchronization of multiple AR devices), AR assembly simulation (virtual model manipulation, collision detection), AR assistant evaluation tools (AR observation tool, visualize performance data, AR evaluation table) are described. Finally, this paper illustrates possible usages and potential benefits of how collaborative AR helps evaluate the assembly process and helps find the bottleneck of assembly step through a use case study.
ASSESSMENT OF RELIABILITY ENHANCEMENT IN HIGH-POWER CPUs AND GPUs USING DYNAMIC DIRECT-TO-CHIP LIQUID COOLING

Thermal management of high-performance computing servers is becoming a prevalent challenge for the data center cooling industry due to increasing power densities at the server level and data center level. Efficient heat dissipation is also directly related to electronic package reliability. Improved cooling technologies like direct-to-chip liquid cooling can address the rising cooling demands due to the higher thermal performance of water-based coolants. A methodology to further enhance the efficiency of direct liquid cooling is experimentally investigated using the concept of dynamic cooling. A Flow Control Device (FCD) was developed to regulate the flow rate to four custom-made Thermal Test Vehicles using ceramic heaters. The TTV assembly was placed at four different levels in a standard 19-inch ITE (Information Technology Equipment) rack in test fixtures mounted with cold plates. The flow regulation to each of the TTVs was done based on the power dissipated by each TTV. The power dissipation of each TTV was varied for various non-homogeneous power distribution values in the entire rack. The influence of coolant inlet temperature and flow rate on the TTV temperature and rack pressure drops was analyzed. The results indicated more uniform temperatures on the TTVs and a reduction in the maximum temperature on the TTV with maximum power. The impact of the temperature uniformity on package level reliability was also analyzed by comparing the results obtained with the published literature.
Le competenze digitali degli insegnanti

Over the last years, the interest in teachers’ digital competence has become the object of increasing attention. The progressive digitalization of our societies led the European legislator, in 2006, to reformulate the framework of key competences, introducing digital competence among the new knowledge that school has to promote. Of course, both future and in-service teachers need to be prepared to favour students’ digital skills. This brought the European Commission to release in 2017 the DigCompEdu framework. In this chapter, we will first review some of the main conceptual models inspiring the reflection and research around teachers’ knowledge and digital skills. Then, we will focus on the DigComEdu framework, to illustrate its main areas of competences and the operational values.
Temporal Logic Usage in Control and Planning Models of Smart Systems

The successful feasibility of both control and planning models of smart systems depends on timing. The concept of this research is that planning can be viewed as the development of a proposed approach to achieve a set goal, corresponding to the specific conditions of its implementation and the resources available for this. Additionally, planning can be improved (including within the framework of current legislation) due to a different way of achieving the goal, more flexible consideration of the conditions for its implementation and additional resources. To implement this idea in the field of intelligent systems modeling, we should use quasi-fractal temporal logic which allows one to refine the assessment of the truth of decisions made taking into account the time factor, that is to monitor and refine the decision- making process.
Haze Elimination Model-Based Color Saturation Adjustment With Contrast Correction

In this paper, an effective contrast enhancement algorithm has been proposed to enhance the contrast of an image with preserved color details and visually similar to the undistorted reference image. The proposed algorithm is divided into two parts such as color saturation enhancement and brightness enhancement. The haze removal approach using the atmospheric scattering model is used to formulate a color saturation enhancement function for the low contrast image. The haze removal approach using the scattering model degrades the brightness in the output image. The brightness of the enhanced output using the haze removal model is restored using the proposed enhancement function, which maintains the natural brightness of the input image. The proposed algorithm manages to preserve the natural appearance and brightness of the image using the brightness function. The average computation time for the proposed method is very low and is suitable for real-time applications. The proposed method is evaluated over some of the popular image quality evaluation matrices on the images of the standard datasets. The qualitative, quantitative, and statistical analysis reflects that the proposed algorithm consistently enhances the low contrast images with preserved color and natural appearance.
Toward an Automatic Assistance Framework for the Selection and Configuration of Machine Learning Based Data Analytics Solutions in Industry 4.0

Machine Learning (ML) based data analytics provide methods to cope with the massive data amounts, generated by the various manufacturing processes. In this respect, the maintenance is among the most critical activities concerned by the industrial data analytics in the contexts of industry 4.0. We believe that the maintenance activities can be managed by the predictive processes dwelling on ML methods with the help of meta-learning based data analytics solutions. The challenge is then to facilitate the industry 4.0 actors, who are supposedly not AI specialists, with the application of machine learning. The automated machine learning seems to be the area dealing with this challenge. In this paper, we first show the problematic of assisting industry 4.0 actors to implement ML algorithms in the context of predictive maintenance. We then present a novel AutoML based framework. It aims to enable industry 4.0 actors and researchers, who presumably have limited competencies in machine learning, to generate ML-based data analytics solutions and their deployment in the manufacturing workflows. The framework implements primarily the approaches based on the meta-learning for this purpose. In the context of Industry 4.0 such approaches lead to the implementation of the smart factory concepts. It makes the factory processes more proactive on the basis of predictive knowledge extracted from the various manufacturing devices, sensors, and business processes.