Reviewerly/exHarmony huggingface dataset:
Word Embedding based News Classification by using CNN

In this era of information technology, the number of online news portal is increasing day by day. These online news portals make a good profit by advertising different consumer products to their reader. However, due to the lack of intelligence, traditional news portals cannot identify what types of news are preferred by the users. As a consequence, these news portals most of the time show irrelevant advertisements to the readers and incur a great economic loss to the advertisers. If these news portals can identify what type of news a user is reading, then they can provide contextual advertisements (showing advertisements of news-related products) and gain more profit. Therefore, in this paper, we proposed a method integrating word embedding with Convolutional Neural Network (CNN) for the classification of English news into four different categories: Sports, Business, National and International. The performance of the proposed method is evaluated on our prepared dataset in terms of macro- f1 and micro-f1 scores. The experimental result shows that our proposed method achieved macro-f1 and micro-f1 scores of 0.90 and 0.89, respectively which are significantly higher than that of all the baseline methods.
Numerical Simulation for Wind Shear Detection With a Glide Path Scanning Algorithm Based on Wind LiDAR

To study the performance of the glide path scan wind shear alerting algorithm (GLYGA) for wind shear detection with wind LiDAR sensors, this paper constructed two types of simulated wind fields for wind shear detection experiments based on a MATLAB toolbox: uniform wind field and shear wind field. The uniform wind field is used to study the influence of the relative position between the wind LiDAR system and the glide path on the wind detection results; the shear wind field is used to study the effectiveness of the GLYGA. The shear wind field consists of four types: frontal, downburst, low-level jet and turbulence. The results show that both the distance from the wind LiDAR system to the runway and the angle between the laser beam and the glide path should be minimized to ensure better wind detection results while avoiding the scenario in which the laser beam runs perpendicular to the glide path; otherwise, the wind speed measured by LiDAR is 0, which will introduce great error. At the same time, the GLYGA can adequately identify the position and intensity of wind shear along the glide path of aircraft.
Towards cloud‐based unobtrusive monitoring in remote multi‐vendor environments

Abstract Nowadays, many complex multi‐vendor production environments, such as telecom infrastructures in smart cities or on‐board passenger information systems in trains, are based on micro‐services and deployed in the cloud. From a service integrator viewpoint, building new solutions for these environments, which can host a large number of externally designed and developed micro‐services, is often complex and error‐prone. This is in part due to undocumented behaviour or undocumented architectural specifications of such systems. Advanced service monitoring can offer a solution to quickly detect anomalies or unexpected service interaction behaviour during on‐site integration. However, the monitoring service should not have an impact on the production environment itself. Therefore, this article proposes an agent‐based unobtrusive monitoring platform, capable of monitoring both internally developed and externally developed services through the use of sidecar containers. It monitors state, metrics and network traffic at micro‐service level and the research was conducted as part of the DynAMo research project, a collaboration with various industry partners. Prototype evaluation proves that our solution has a negligible impact (below 0.02% CPU usage on average) on an existing micro‐service environment just as other monitoring systems like Prometheus while offering additional functionality focused on multi‐vendor service integration. This makes it suitable to be deployed in complex production domains to further aid on‐site integration and quickly find potential new anomalies.
XR Carousel

Recent years have seen a new uptake in immersive media and eXtended Reality (XR). And due to a global pandemic, computer-mediated communication over video conferencing tools became a new normal of everyday remote collaboration and virtual meetings. Social XR leverages XR technologies for remote communication and collaboration. But in order for XR to facilitate a high level of (social) presence and thus high-quality mediated social contact between users, we need high-quality 3D representation of users. One approach to providing detailed 3D user representations as new immersive media is to use point clouds or meshes, but these representation formats come with complexity on compression bitrate and processing time. In the example of virtual meetings, compression has to fulfill stringent requirements such as low latency and high quality. As the compression techniques for 3D immersive media steadily advance, it is important to be able to easily compare different compression techniques on their technical and visual merits in an easy way. The proposed demonstrator in this paper is a visualization tool that helps assessing the visual quality of a 3D representation employing various coding schemes. The complete end-to-end rendering/encoding chain can be easily assessed, allowing for subjective testing by showing the differences between the selected encoding parameters. The tool presented in this demo paper offers an improved and easy visual process for the comparison of encoders of immersive media.
Pervasive Futuristic Healthcare and Blockchain enabled Digital Identities-Challenges and Future Intensions

These days, the usage of healthcare applications in the secure and authenticate blockchain network has been growing progressively. The distributed healthcare applications can store and shared data with another node without any centralized authority by exploiting the blockchain technology. However, existing proof of work inside blockchain did not consider the anomaly detection in healthcare networks and mobility of the workload widely ignored in the literature studies. In this paper, the study investigates the travelling salesman problem with the scheduling, threshold, and anomaly detection constraints in distributed fog nodes. The fog nodes are local servers and implementing inside the hospital to facilitate the users from different healthcare services. The study devises the mobility scheduler anomaly detection (MSAD) schemes which consist of two phases, e.g., the initial assignment of healthcare workloads to optimal fog nodes and anomaly detection and validation in the healthcare blockchain network. Simulation results show that MSAD outperformed in terms of scheduling, threshold, anomaly detection in the healthcare blockchain network as compared to baseline studies.
A Shortest Smooth-path Motion Planning for a Mobile Robot with Nonholonomic Constraints

This paper presents how to plan the shortest motion for a mobile robot with nonholonomic constraints. The proposed method uses the RRT* algorithm, the Potential Field algorithm, and Dubin Curves to create a path that can solve path planning problems. Firstly, the RRT* algorithm is used to find the shortest path by expanding the Tree in the global environment until reaching the goal region. Then, the resultant path is improved with Repulsive Force from the Potential Field algorithms that calculate the influence of static obstacles on the path for better obstacle avoidance. Finally, Dubins Curves is used to smooth the planned path with curves having the same radius, which helps handle nonholonomic constraints problems for Car-like mobile robot. The test method using simulation with MATLAB software is applied to prove that the proposed method can solve some typical path planning problems.
Toward a physical model of the clavichord

String excitation by the tangent in the clavichord is a unique mechanism. The tangent, keeping in contact with the string after the initial strike, continuously controls the string tension. Four main flexible subsystems are considered in the clavichord: the tangent/key subsystem, the string subsystem, the bridge-soundboard subsystem, and the string damper subsystem. A modal description of the dynamics of these subsystems is proposed. Parameters of the subsystems are estimated on a copy of a historical instrument by Hubert (1784). The different subsystems and their couplings are modeled using a modal Udwadia-Kalaba formulation. The string-tangent interaction is modeled via the intermittent contact dynamics, using the Kirchoff-Carrier string model. Realistic string, soundboard, and tangent motions are obtained using a time-domain synthesis scheme that computes the dynamics of the uncoupled subsystems and the constraints resulting from coupling between them. Simulated motions of the model in response to a driving force on the key are analyzed. The results are consistent with experimental measurements and published data on the dynamics of the clavichord. The model is able to reproduce the main acoustic features of the instrument: force on the key for intonation control, key velocity for dynamic nuances control, and constant spectral slope for varying dynamic nuances.
Framework of Visual Literacy Competences for Engineering Education Discussed in the Scope of DigComp framework with Examples from Educational R&amp;D Projects

Abstract The European Union began research on Learning and Skills for the Digital Era in 2005. The result is the Digital Competence Framework for Citizens (DigComp 2.1), which is built on five dimensions, each of which has eight proficiency levels. Nowadays DigComp has become a main standard of competencies in Europe, if not in the world. In this article we want to match DigComp with the Framework of Visual Literacy Competences for Engineering Education, which is the result of VLEE projects. The matching result will be illustrated with examples from different educational R&D projects.
A Hybrid Supervised/Unsupervised Machine Learning Approach to Classify Web Services

Reusing software is a promising way to reduce software development costs. Nowadays, applications compose available web services to build new software products. In this context, service composition faces the challenge of proper service selection. This paper presents a model for classifying web services. The service dataset has been collected from the well-known public service registry called ProgrammableWeb. The results were obtained by breaking service classification into a two-step process. First, Natural Language Processing(NLP) pre-processed web service data have been clustered by the Agglomerative hierarchical clustering algorithm. Second, several supervised learning algorithms have been applied to determine service categories. The findings show that the hybrid approach using the combination of hierarchical clustering and SVM provides acceptable results in comparison with other unsupervised/supervised combinations.
An Agent-based Model for Resource Provisioning and Task Scheduling in Cloud Computing Using DRL

The Resource Provisioning (RP) and Task Scheduling (TS) issues has become an attractive paradigms in cloud industry, this is due to the increasing demand for the services provided by virtual machines that are structured by physical servers owned by the data centers of cloud service providers (CSPs). In this paper, we propose a new model based on multi-agent system for the RP and TS reducing the cost of energy using Deep Reinforcement Learning DRL. A Quantile Regression Deep Q Network (QR-DQN) algorithm generates an appropriate policy and the optimal long-term decisions. A set of experiments show the efficiency of our proposed scheduling approach and the performance of our task allocation method..
Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks

In contrast to paraffin sections, frozen sections can be quickly generated during surgical interventions. This procedure allows surgeons to wait for histological findings during the intervention to base intra-operative decisions on the outcome of the histology. However, compared to paraffin sections, the quality of frozen sections is typically lower, leading to a higher ratio of miss-classification. In this work, we investigated the effect of the section type on automated decision support approaches for classification of thyroid cancer. This was enabled by a data set consisting of pairs of sections for individual patients. Moreover, we investigated, whether a frozen-to-paraffin translation could help to optimize classification scores. Finally, we propose a specific data augmentation strategy to deal with a small amount of training data and to increase classification accuracy even further.
Information Hiding using Steganography

The process of hiding data and information is known to be steganography it is done to provide secure communication, in present world there is a demand of sending and displaying data in a hidden format especially when the exchange of information and data is taking place publically, and this is the reason because of which many methods have been proposed for data and information hiding. In this paper data and information are being hidden in digital image format, as it is mostly in demand on the internet. For data hiding there are so many techniques developed some are easy, some are bit tedious as compared to the other and all of these techniques have their own benefits, use and limitation. This paper mainly focuses to present Steganography overview, its demand, advantages and the techniques involved in it. In this paper there is also an attempt to identify which Steganography techniques are more useful and what are their requirements and also it shows which application will have more compatibility with which steganography technique.
On the extremal points of the Lambda polytopes and classical simulation of quantum computation with magic states

We investigate the $\Lambda$-polytopes, a convex-linear structure recently defined and applied to the classical simulation of quantum computation with magic states by sampling. There is one such polytope, $\Lambda_n$, for every number $n$ of qubits. We establish two properties of the family $\{\Lambda_n, n\in \mathbb{N}\}$, namely (i) Any extremal point (vertex) $A_\alpha \in \Lambda_m$ can be used to construct vertices in $\Lambda_n$, for all $n>m$. (ii) For vertices obtained through this mapping, the classical simulation of quantum computation with magic states can be efficiently reduced to the classical simulation based on the preimage $A_\alpha$. In addition, we describe a new class of vertices in $\Lambda_2$ which is outside the known classification. While the hardness of classical simulation remains an open problem for most extremal points of $\Lambda_n$, the above results extend efficient classical simulation of quantum computations beyond the presently known range.
A facial recognition using a combination of a novel one dimension deep CNN and LDA

Face recognition is one of the useful tasks and can be used for many applications as security systems, it is necessary to find effective and low complexity facial classifier methods. In this paper, we proposed a new one-dimensional CNN deep convolutional neural network (1D-DCNN) classifier was combined with linear discriminative analysis (LDA) techniques to produce a new face recognition methodology. The contribution of this paper is generated of one-dimensional face feature set by LDA from original image database to training of 1D-DCNN classifier, that it contributed in the improvement of facial recognition performance. The model has been tested on MCUT dataset consisted of 3755 images for 276 classes. The results of the implementation of face recognition were accuracy of 100%, precision of 100%, recall of 100%, and F-measure of 100%.
Onion Routing with Replies

Onion routing (OR) protocols are a crucial tool for providing anonymous internet communication. An OR protocol enables a user to anonymously send requests to a server. A fundamental problem of OR protocols is how to deal with replies: ideally, we would want the server to be able to send a reply back to the anonymous user without knowing or disclosing the user’s identity.Existing OR protocols do allow for such replies, but do not provably protect the payload (i.e., message) of replies against manipulation. Kuhn et al. (IEEE S&P 2020) show that such manipulations can in fact be leveraged to break anonymity of the whole protocol.In this work, we close this gap and provide the first framework and protocols for OR with protected replies. We define security in the sense of an ideal functionality in the universal composability model, and provide corresponding (less complex) game-based security notions for the individual properties.We also provide two secure instantiations of our framework: one based on updatable encryption, and one based on succinct non-interactive arguments (SNARGs) to authenticate payloads both in requests and replies. In both cases, our central technical handle is an implicit authentication of the transmitted payload data, as opposed to an explicit, but insufficient authentication (with MACs) in previous solutions. Our results exhibit a new and surprising application of updatable encryption outside of long-term data storage.
Validation of Side-Channel Models via Observation Refinement

Observational models enable the analysis of information flow properties against side channels. Relational testing has been used to validate the soundness of these models by measuring the side channel on states that the model considers indistinguishable. However, unguided search can generate test states that are too similar to each other to invalidate the model. To address this we introduce observation refinement, a technique to guide the exploration of the state space to focus on hardware features of interest. We refine observational models to include fine-grained observations that characterize behavior that we want to exclude. States that yield equivalent refined observations are then ruled out, reducing the size of the space. We have extended an existing model validation framework, Scam-V, to support refinement. We have evaluated the usefulness of refinement for search guidance by analyzing cache coloring and speculative leakage in the ARMv8-A architecture. As a surprising result, we have exposed SiSCLoak, a new vulnerability linked to speculative execution in Cortex-A53.
Rewire-then-Probe: A Contrastive Recipe for Probing Biomedical Knowledge of Pre-trained Language Models

Knowledge probing is crucial for understanding the knowledge transfer mechanism behind the pre-trained language models (PLMs).Despite the growing progress of probing knowledge for PLMs in the general domain, specialised areas such as biomedical domain are vastly under-explored.To facilitate this, we release a well-curated biomedical knowledge probing benchmark, MedLAMA, constructed based on the Unified Medical Language System (UMLS) Metathesaurus.We test a wide spectrum of state-of-the-art PLMs and probing approaches on our benchmark, reaching at most 3% of acc@10.While highlighting various sources of domain-specific challenges that amount to this underwhelming performance, we illustrate that the underlying PLMs have a higher potential for probing tasks.To achieve this, we propose Contrastive-Probe, a novel self-supervised contrastive probing approach, that adjusts the underlying PLMs without using any probing data.While Contrastive-Probe pushes the acc@10 to 24%, the performance gap remains notable.Our human expert evaluation suggests that the probing performance of our Contrastive-Probe is underestimated as UMLS does not comprehensively cover all existing factual knowledge.We hope MedLAMA and Contrastive-Probe facilitate further developments of more suited probing techniques for this domain. 1
The impact of cloud-based human resource and supply chain management systems on the performance of multinational organizations

Purpose One of the significant dimensions in organizations is the human resource discussion and its related procedures. Human beings have a special place and particular importance in modern management to focus on organizational behavior and human resource management. The cloud-based human resource (CBHR) can be converted into human capital and impressive contributions to health, education and moral valence. Also, one of the most robust operational paradigms is the cloud-based supply chain management (CBSCM) for developing the competitive pros of service and manufacturing organizations to give the same attention to those who manage the supply chain or those operating in it. The present investigation's target is to check out whether CBHR and CBSCM enhance the performance of multinational organizations. Design/methodology/approach Multinational organizations consist of numerous integrated applications, including manufacturing, logistics, marketing, accounting, distribution, human resources, finance, supply chain, etc. This paper proposes using CBHR and CBSCM to improve financial, marketing and collaborative performances. We focus on the applications in multinational organizations. This study has utilized the SEM to estimate the measurement model's validity and reliability and assess the causal model. The offered model and the questionnaires were analyzed using SPSS and LISREL. Findings The research results showed that CBSCM influences the companies' performance. Additionally, the outcomes showed that CBHRM affects the performance of companies. The results support the proposition that CBSCM and CBHRM are both necessary and good for financial performance, marketing performance and collaborative performance. Research limitations/implications Using a questionnaire to mentally measure some of the variable dimensions of a firm's financial performance and market performance for which objective data are available can be helpful. However, since such information is considered confidential in companies, it is not possible to access it. Originality/value Our innovation is the primary attempt of applying CBHR and CBSCM to elevate performance in multinational organizations.
Franchised Quantum Money

The construction of public key quantum money based on standard cryptographic assumptions is a longstanding open question. Here we introduce franchised quantum money, an alternative form of quantum money that is easier to construct. Franchised quantum money retains the features of a useful quantum money scheme, namely unforgeability and local verification: anyone can verify banknotes without communicating with the bank. In franchised quantum money, every user gets a unique secret verification key, and the scheme is secure against counterfeiting and sabotage, a new security notion that appears in the franchised model. Finally, we construct franchised quantum money and prove security assuming one-way functions.