Reviewerly/exHarmony huggingface dataset:
Investigating the impact of data quality on the energy yield forecast using data mining techniques

Integration of renewable energy resources (RES) in the power system is increasing. However, the variability of PV output poses several challenges in maintaining reliable grid operation. This variability is a function of several meteorological variables. An accurate PV forecasting can tackle this issue in maintaining and scheduling stable grid operation. In this paper, we conduct a study to analyze the impact of using optimum combination (feature selection and extraction) of meteorological features and low dimensional subspace (dimensionality reduction) on the forecasting accuracy. We also assess and compare the output of the forecasting model when it is fed with all the input features present in the dataset with the case when we use low subspace of the dataset as an input to the model.
Enhancing developers’ support on pull requests activities with software bots

Software bots are employed to support developers' activities, serving as conduits between developers and other tools. Due to their focus on task automation, bots have become particularly relevant for Open Source Software (OSS) projects hosted on GitHub. While bots are adopted to save development cost, time, and effort, the bots' presence can be disruptive to the community. My research goal is two-fold: (i) identify problems caused by bots that interact in pull requests, and (ii) help bot designers enhance existing bots. Toward this end, we are interviewing maintainers, contributors, and bot developers to understand the problems in the human-bot interaction and how they affect the collaboration in a project. Afterward, we will employ Design Fiction to capture the developers' vision of bots' capabilities, in order to define guidelines for the design of bots on social coding platforms, and derive requirements for a meta-bot to deal with the problems. This work contributes more broadly to the design and use of software bots to enhance developers' collaboration and interaction.
Asymptotic Majorization of Finite Probability Distributions

This paper studies majorization of high tensor powers of finitely supported probability distributions. Viewing probability distributions as a resource with majorization as a means of transformation corresponds to the resource theory of pure bipartite quantum states under LOCC transformations vis-\`a-vis Nielsen's Theorem. In [T. Fritz (2017)] a formula for the asymptotic exchange rate between any two finitely supported probability distributions was conjectured. The main result of the present paper is Theorem 3.11, which resolves this conjecture.
Fast, sub-pixel accurate, displacement measurement method: optical and terahertz systems

A novel (to the best of our knowledge), fast method to measure in-plane object motion in 1D with sub-pixel accuracy which complements the correlation technique is proposed. The method is verified experimentally using both visible and terahertz images. The absolute sum of grey level accumulated change is used to quantify object motion. The method requires calibration for each target, but only addition and subtraction operations. This results in a decrease of two orders of magnitude in the computation time.
Data Security inside Optical Communication Networks through Encoding and Steganography

Abstract This paper provides securing flowing of data in optical systems through encoding and steganography. Encoding (Cryptography) and steganography are major elements for secure the data. Suggested communication system aids for transporting huge volumes of information of quickly and reliably. This communication system has previously displayed unrivalled characteristics of most secure. Secure connection requires the integration of cryptography and steganography, so it is very difficult to penetrate it. The system program uses cryptography and steganography to merge any type from files and encrypted text that are effectually hidden for human eye with a carrier image. Also, the extra purpose from this paper is to procure the threshold losses of the optical system and introduce a new approach to create an ideal system that can effectively secrete a message that includes important information, if and only if the optical cable losses increase by its threshold magnitude.
Two-Stage Training of Graph Neural Networks for Graph Classification

Graph neural networks (GNNs) have received massive attention in the field of machine learning on graphs. Inspired by the success of neural networks, a line of research has been conducted to train GNNs to deal with various tasks, such as node classification, graph classification, and link prediction. In this work, our task of interest is graph classification. Several GNN models have been proposed and shown great accuracy in this task. However, the question is whether usual training methods fully realize the capacity of the GNN models. In this work, we propose a two-stage training framework based on triplet loss. In the first stage, GNN is trained to map each graph to a Euclidean-space vector so that graphs of the same class are close while those of different classes are mapped far apart. Once graphs are well-separated based on labels, a classifier is trained to distinguish between different classes. This method is generic in the sense that it is compatible with any GNN model. By adapting five GNN models to our method, we demonstrate the consistent improvement in accuracy and utilization of each GNN's allocated capacity over the original training method of each model up to $$5.4\%$$ points in 12 datasets.
Reservoir computing approaches to microsleep detection

Objective.The detection of microsleeps in a wide range of professionals working in high-risk occupations is very important to workplace safety. A microsleep classifier is presented that employs a reservoir computing (RC) methodology. Specifically, echo state networks (ESN) are used to enhance previous benchmark performances on microsleep detection.Approach.A clustered design using a novel ESN-based leaky integrator is presented. The effectiveness of this design lies with the simplicity of using a fine-grained architecture, containing up to 8 neurons per cluster, to capture individualized state dynamics and achieve optimal performance. This is the first study to have implemented and evaluated EEG-based microsleep detection using RC models for the detection of microsleeps from the EEG.Main results.Microsleep state detection was achieved using a cascaded ESN classifier with leaky-integrator neurons employing 60 principal components from 544 power spectral features. This resulted in a leave-one-subject-out average detection in performance ofφ = 0.51 ± 0.07 (mean ± SE), AUC- ROC = 0.88 ± 0.03, and AUC- PR = 0.44 ± 0.09.Significance.Although performance of EEG-based microsleep detection systems is still considered modest, this refined method achieved a new benchmark in microsleep detection.
Temporal quantum noise reduction acquired by an electron-multiplying charge-coupled-device camera

Electron-multiplying charge-coupled-device cameras (EMCCDs) have been used to observe quantum noise reductions in beams of light in the transverse spatial degree of freedom. For the quantum noise reduction in the temporal domain, "bucket detectors," usually composed of photodiodes with operational amplifiers, are used to register the intensity fluctuations in beams of light within the bandwidth of the detectors. Spatial information, however, is inevitably washed off by the detector. In this paper, we report on measurements of the temporal quantum noise reduction in bright twin beams using an EMCCD camera. The four-wave mixing process in an atomic rubidium vapor cell is used to generate the bright twin beams of light. We observe more than 25% of temporal quantum noise reduction with respect to the shot-noise limit in images captured by the EMCCD camera. Compared with bucket detectors, EMCCD makes it possible to take advantage of the spatial and temporal quantum properties of light simultaneously, which would greatly benefit many applications using quantum protocols.
High-resolution Thermopile Array Sensor-based System for Human Detection and Tracking in Indoor Environment

This paper proposes an indoor multiple human detection and tracking system based on a high-resolution thermopile array sensor. The sensor is deployed at the height of 3m with a vertical downward view. The infrared data of the detection area collected by the sensor is called thermal distribution. The sensor obtains 24×32 pixels thermal distribution. The thermal distribution data is first preprocessed by interpolation and filtering. Then, the background is removed by an adaptive threshold. The high temperature regions and their center points of human targets are obtained by a weighted meanshift method. The thermal feature of a high temperature region is the sum of temperature in the region. Finally, through the space distance and the thermal feature, the center points of high temperature regions are associated with the corresponding human trajectories. Due to the high resolution of thermopile array sensor, the tracking system has a good accuracy, and it can handle the detection and tracking of multiple humans close to each other as well.
Heteroscedastic Uncertainty for Robust Generative Latent Dynamics

Learning or identifying dynamics from a sequence of high-dimensional observations is a difficult challenge in many domains, including reinforcement learning and control. The problem has recently been studied from a generative perspective through latent dynamics: high-dimensional observations are embedded into a lower-dimensional space in which the dynamics can be learned. Despite some successes, latent dynamics models have not yet been applied to real-world robotic systems where learned representations must be robust to a variety of perceptual confounds and noise sources not seen during training. In this paper, we present a method to jointly learn a latent state representation and the associated dynamics that is amenable for long-term planning and closed-loop control under perceptually difficult conditions. As our main contribution, we describe how our representation is able to capture a notion of heteroscedastic or input-specific uncertainty at test time by detecting novel or out-of-distribution (OOD) inputs. We present results from prediction and control experiments on two image-based tasks: a simulated pendulum balancing task and a real-world robotic manipulator reaching task. We demonstrate that our model produces significantly more accurate predictions and exhibits improved control performance, compared to a model that assumes homoscedastic uncertainty only, in the presence of varying degrees of input degradation.
Fast image clustering based on compressed camera fingerprints

Every camera sensor leaves unique traces on the acquired images that can be thought of as a camera fingerprint. This work presents an efficient algorithm for clustering images based on their camera fingerprints. The algorithm performs a fast preliminary clustering based on a compressed representation of the camera fingerprints, then it refines the initial clusters using full-size fingerprints. The efficiency of the method is further improved by scanning the images according to a ranking index that depends on fingerprint estimation quality. The results confirm that the proposed method achieves a performance comparable to the state of the art approaches, with a significantly lower computational complexity, especially on large datasets. The method can also handle cases in which the number of clusters is much larger than the average size of the clusters.
A Spatial-Frequency Domain Associated Image-Optimization Method for Illumination-Robust Image Matching

Image matching forms an essential means of data association for computer vision, photogrammetry and remote sensing. The quality of image matching is heavily dependent on image details and naturalness. However, complex illuminations, denoting extreme and changing illuminations, are inevitable in real scenarios, and seriously deteriorate image matching performance due to their significant influence on the image naturalness and details. In this paper, a spatial-frequency domain associated image-optimization method, comprising two main models, is specially designed for improving image matching with complex illuminations. First, an adaptive luminance equalization is implemented in the spatial domain to reduce radiometric variations, instead of removing all illumination components. Second, a frequency domain analysis-based feature-enhancement model is proposed to enhance image features while preserving image naturalness and restraining over-enhancement. The proposed method associates the advantages of the spatial and frequency domain analyses to complete illumination equalization, feature enhancement and naturalness preservation, and thus acquiring the optimized images that are robust to the complex illuminations. More importantly, our method is generic and can be embedded in most image-matching schemes to improve image matching. The proposed method was evaluated on two different datasets and compared with four other state-of-the-art methods. The experimental results indicate that the proposed method outperforms other methods under complex illuminations, in both matching performances and practical applications such as structure from motion and multi-view stereo.
Learning from similarity and information extraction from structured documents

The automation of document processing is gaining recent attention due to the great potential to reduce manual work through improved methods and hardware. Neural networks have been successfully applied before - even though they have been trained only on relatively small datasets with hundreds of documents so far. To successfully explore deep learning techniques and improve the information extraction results, a dataset with more than twenty-five thousand documents has been compiled, anonymized and is published as a part of this work. We will expand our previous work where we proved that convolutions, graph convolutions and self-attention can work together and exploit all the information present in a structured document. Taking the fully trainable method one step further, we will now design and examine various approaches to using siamese networks, concepts of similarity, one-shot learning and context/memory awareness. The aim is to improve micro F1 of per-word classification on the huge real-world document dataset. The results verify the hypothesis that trainable access to a similar (yet still different) page together with its already known target information improves the information extraction. Furthermore, the experiments confirm that all proposed architecture parts are all required to beat the previous results. The best model improves the previous state-of-the-art results by an 8.25 gain in F1 score. Qualitative analysis is provided to verify that the new model performs better for all target classes. Additionally, multiple structural observations about the causes of the underperformance of some architectures are revealed. All the source codes, parameters and implementation details are published together with the dataset in the hope to push the research boundaries since all the techniques used in this work are not problem-specific and can be generalized for other tasks and contexts.
High-Performance Computing with TeraStat

In the last years we are witnessing a progressive shift toward the adoption of the cloud-computing model for a large variety of application domains. High-performance computing (HPC) is not an exception to this trend, with a steadily increasing amount of performance-heavy scientific experiments that are conducted on the cloud. However, this approach may pose some serious security, operational and economical issues. In this paper we report about our experience with this scenario, that culminated with the development of a small-scale on-premise HPC facility called TeraStat, available through our Department. This facility has been purposely deployed and customized so to simplify and accelerate the execution of performance-heavy computational experiments. The success of TeraStat is confirmed by the large number of experiments conducted in these years. Here, we focus on two of the most significant case studies, outlining the positive role played by TeraStat in their solution. The first is a large scale analysis of climate data with annual cycles. The second is a study about the advantages of using compression techniques while processing large genomic data by means of a distributed approach.
Research on Passive Detection and Location by Fixed Single Observer

The fixed single station can realize the passive location of the moving target by measuring the angle, angular velocity and Doppler frequency change rate, but this method requires high angular velocity measurement accuracy. For this reason, a fixed mono-station passive detection and location method based on space-frequency domain information is proposed, which uses direction of arrival, phase difference rate-of-change, carrier frequency and Doppler frequency rate-of change to realize passive location of moving targets. The analysis and simulation show that this method avoids the high precision measurement of diagonal velocity, reduces the requirement of parameter measurement technology, and the ranging error is less affected by angle and frequency measurement error. It is greatly affected by the measurement error of phase difference rate-of-change and Doppler frequency rate-of-change.
SOME OBSERVATIONS ABOUT GENERALIZED QUANTIFIERS IN LOGICS OF IMPERFECT INFORMATION

We analyse the two definitions of generalized quantifiers for logics of dependence and independence that have been proposed by F. Engström, comparing them with a more general, higher-order definition of team quantifier. We show that Engström's definitions (and other quantifiers from the literature) can be identified, by means of appropriate lifts, with special classes of team quantifiers. We point out that the new team quantifiers express a quantitative and a qualitative component, while Engström's quantifiers only range over the latter. We further argue that Engström's definitions are just embeddings of the first-order generalized quantifiers into team semantics, and fail to capture an adequate notion of team-theoretical generalized quantifier, save for the special cases in which the quantifiers are applied to flat formulas. We also raise several doubts concerning the meaningfulness of the monotone/nonmonotone distinction in this context. In the appendix we develop some proof theory for Engström's quantifiers.
Blockchain Towards Supply Chain Management

Blockchain technology has come into our business market with the potential to ensure safer and more transparent trading. This technology allows the optimization of supply chain management processes, while also ensuring control over it. Adaptation, innovation, and sustainability are the pillars of a company in a period of a constant digital transform. Adopting Blockchain signifies to control transparency between suppliers and customers, the veracity of the life cycle of a product, certify sustainability and optimize supply chain processes trough automation. This paper will come up with an outline of the blockchain technology, bring into focus the advantages and the main challenges faced, into the supply chain management of the textile sector.
Tool Support for Validation of Formal System Models: Interactive Visualization and Requirements Traceability

Development processes in various engineering disciplines are incorporating formal models to ensure safety properties of critical systems. The use of these formal models requires to reason about their adequacy, i.e., to validate that a model mirrors the structure of the system sufficiently that properties established for the model indeed carry over to the real system. Model validation itself is non-formal, as adequacy is not a formal (i.e., mathematical) property. Instead it must be carried out by the modeler to justify the modeling to the certification agency or other stakeholders. In this paper we argue that model validation can be seen as a special form of requirements engineering, and that interactive visualization and concepts from requirements traceability can help to advance tool support for formal modeling by lowering the cognitive burden needed for validation. We present the VisualisierbaR tool, which supports the formal modeling of railway operations and describe how it uses interactive visualization and requirements traceability concepts to validate a formal model.
Construction of a database for speech recognition of isolated Arabic words

Automatic speech recognition for the Arabic language is a field that is in a current remarkable development and still attracts many researchers who try to improve year after year the recognition rate. Many works, nowadays, have been focused on automatic speech recognition (ASR) for the Arabic language. The paper presents the significance of the ASR systems built in the past few years. This work also aims to introduce a new Arabic database for isolated word by defining a new concept of phonetic units: semi-syllable units. Thus, the corpus contains a collection of semi-syllable audio files as well as their corresponding transcription files. This database will help us in future works.
Improving Target-side Lexical Transfer in Multilingual Neural Machine Translation

To improve the performance of Neural Machine Translation (NMT) for low-resource languages (LRL), one effective strategy is to leverage parallel data from a related high-resource language (HRL).However, multilingual data has been found more beneficial for NMT models that translate from the LRL to a target language than the ones that translate into the LRLs.In this paper, we aim to improve the effectiveness of multilingual transfer for NMT models that translate into the LRL, by designing a better decoder word embedding.Extending upon a general-purpose multilingual encoding method Soft Decoupled Encoding (Wang et al., 2019), we propose DecSDE, an efficient character n-gram based embedding specifically designed for the NMT decoder.Our experiments show that DecSDE leads to consistent gains of up to 1.8 BLEU on translation from English to four different languages. 1
Electrophysiological Changes in the Virtual Reality Sickness

This paper proposes a specific brain wave in which changes in virtual reality (VR) sickness are noticeably observed by comparing stages before and during the watching VR video. Although VR sickness generally induces physical symptoms such as headache, dizzy, and nausea and causes difficulties in using VR devices, there were still insufficient methods to quantify the degree of VR sickness. We divided the experiment into three conditions: baseline, VR video, and recovery. Each condition was measured by electroencephalography (EEG) to examine which frequency band responded prominently. The absolute power of alpha wave decreased during VR video condition compared to the baseline, and after the VR sickness was relieved, the absolute power of alpha wave increase during recovery condition compared to the VR video condition.