Reviewerly/exHarmony huggingface dataset:
Comparison of intraday probabilistic forecasting of solar power using time series models

There is a growing interest to estimate the inherent uncertainty of photovoltaic (PV) power forecasts with probability forecasting methods to enable a robust operational planning of energy systems. The aim of this paper is to present a detailed comparison of probabilistic extensions for time series models regarding their practical applicability, as these are still widely used in the field. For this purpose, five probabilistic forecasting methods are analyzed for intraday PV power forecast with respect to their reliability, sharpness and computational complexity. There may be limited data sets available for the initial commissioning in industrial applications. Hence, the analyses are performed using sliding windows of respectively one, three, five and seven weeks of training data. The investigations are carried out with three roof-mounted PV systems from Germany and Austria. An adaptive ARX model is applied as the underlying point forecast, which uses decomposed past PV power measurements and an external weather forecast for the next day as input features. Results suggest that with the methods presented in this paper, already seven days of training data are sufficient to produce significant improvements of up to 45 % in forecasting quality compared to the reference case. Additionally, the quantile regression approaches outperform the bootstrapping approaches slightly in terms of forecast quality and significantly in terms of required computation time.
A study on machine learning approaches to detect credit card fraud

Credit card usage has increased a lot nowadays. Due to online banking services and mobile banking applications, payments done through credit cards are growing year by year. As usage increased, frauds on them increased, which are resulting in severe consequences. Nowadays, most of the authors are using Machine Learning models to detect credit card frauds and produce noticeable results. In this paper, we gave a detailed study of those models with exciting insights. These are useful for researchers in finding the gaps in this domain and proposing novel solutions for their contributions.
Communication networks as the basis for functioning of the Internet

In the times of fast development of the Internet and use of an increasing range of online services, it seems increasingly important to make these services accessible for everyone. In this regard, a major role is played by communication networks. Their proper development and improvement depend largely on activity of telecommunication operators. Due to a strong relationship between telecommunications and the Internet, new market opportunities emerge for telecommunication operators, as well as new threats. The aim of this article is to present the current status and to analyze the role of communication networks in functioning of the Internet, as well as to present the directions of improvement of telecommunication networks, which are desirable from the perspective of telecommunication operators.
Computing CQ Lower-Bounds over OWL 2 Through Approximation to RSA

Conjunctive query (CQ) answering over knowledge bases is an important reasoning task. However, with expressive ontology languages such as OWL, query answering is computationally very expensive. The PAGOdA system addresses this issue by using a tractable reasoner to compute lower and upper-bound approximations, falling back to a fully-fledged OWL reasoner only when these bounds don’t coincide. The effectiveness of this approach critically depends on the quality of the approximations, and in this paper we explore a technique for computing closer approximations via RSA, an ontology language that subsumes all the OWL 2 profiles while still maintaining tractability. We present a novel approximation of OWL 2 ontologies into RSA, and an algorithm to compute a closer (than PAGOdA) lower bound approximation using the RSA combined approach. We have implemented these algorithms in a prototypical CQ answering system, and we present a preliminary evaluation of our system that shows significant performance improvements w.r.t. PAGOdA.
River Flow Path Control With Reinforcement Learning

In this study, a cyber-physical system (CPS) for river flow path control is proposed using reinforcement learning. Recently, there has been a frequent occurrence of river flooding due to heavy rains, resulting in serious economic losses and victims. One cause of river flooding is the meandering due to the river bed growing and flow path change. As a mean of avoiding the meandering, river groynes can be used to regularize the flow. However, the mechanism of the flow path growing, and its optimal control is unclear. Therefore, in this study, a dynamic flow path control system is proposed using a data-driven approach to solve the problem at once. As a data-driven approach, reinforcement learning is adopted. The proposed system is designed to control meandering by adaptively deforming and moving the groynes with the reward of the flow path health. The effectiveness of the proposed flow path control system is verified through a simulation of the river model.
Temporal Coordination in Piano Duet Networked Music Performance (NMP): Interactions Between Acoustic Transmission Latency and Musical Role Asymmetries

Today’s audio, visual, and internet technologies allow people to interact despite physical distances, for casual conversation, group workouts, or musical performance. Musical ensemble performance is unique because interaction integrity critically depends on the timing between each performer’s actions and when their acoustic outcomes arrive. Acoustic transmission latency (ATL) between players is substantially longer for networked music performance (NMP) compared to traditional in-person spaces where musicians can easily adapt. Previous work has shown that longer ATLs slow the average tempo in ensemble performance, and that asymmetric co-actor roles and empathy-related traits affect coordination patterns in joint action. Thus, we are interested in how musicians collectively adapt to a given latency and how such adaptation patterns vary with their task-related and person-related asymmetries. Here, we examined how two pianists performed duets while hearing each other’s auditory outcomes with an ATL of 10, 20, or 40 ms. To test the hypotheses regarding task-related asymmetries, we designed duets such that pianists had: (1) a starting or joining role and (2) a similar or dissimilar musical part compared to their co-performer, with respect to pitch range and melodic contour. Results replicated previous clapping-duet findings showing that longer ATLs are associated with greater temporal asynchrony between partners and increased average tempo slowing. While co-performer asynchronies were not affected by performer role or part similarity, at the longer ATLs starting performers displayed slower tempos and smaller tempo variability than joining performers. This asymmetry of stability vs. flexibility between starters and joiners may sustain coordination, consistent with recent joint action findings. Our data also suggest that relative independence in musical parts may mitigate ATL-related challenges. Additionally, there may be a relationship between co-performer differences in empathy-related personality traits such as locus of control and coordination during performance under the influence of ATL. Incorporating the emergent coordinative dynamics between performers could help further innovation of music technologies and composition techniques for NMP.
PROJECT-BASED LEARNING AT A UNIVERSITY: ORGANIZATION AND COMMON MISTAKES

According to the Concept of long-term social and economic development of Russia, one of the priorities of the higher education is the integration of educational process and scientific activities. To that end it is planned to broaden traditional teaching practices by introducing a project-based learning (PBL). Since mechanisms for its implementation are new for some domestic institutions of higher education, numerous of the current attempts to introduce the PBL have failed. Along with the common mistakes, strengths and weaknesses, successful practices of its introduction and use at a university are described in the paper. Conclusions on the feasibility of using the PBL are drawn and recommendations for its implementation are given.
Analysis Of Various Object Detection Techniques for Self-Driving Cars

Image Processing techniques like object detection, recognition and segmentation are being integrated into a wide range of applications. With the rise in integration, there is a rise in the number of different approaches to these techniques. Some techniques provide high accuracy by requiring more computation time (train or test), while some are very efficient for low end computational devices with a compromise on accuracy. There are pre-trained models that can be further trained for a specific domain using transfer learning. Other approaches include using trained models from scratch or a combination of pre-trained models along with custom models on top of it. The purpose of this paper is to test various approaches (CenterNet, EfficientNet, custom Deep-Learning models - FRCNN, SSD) in order to find a model with the accuracy-computation trade-off. The models will be trained on a customized dataset for the purpose of self-driving cars.
Multi-scale Pedestrian Detection Based on Receptive Field Matching

Pedestrian detection is a hot topic in both academia and industry. However, the pedestrian in the image presents different scales due to the different distance from the fixed cameras. Thus, how to detect pedestrians of different scales has become an attractive problem in pedestrian detection field. In this paper, we propose a simple and compact multi-scale pedestrian detection architecture based on receptive field matching (denoted as RFMNet), which can cover continuous multi-scale pedestrians with 100% in theory. In this model, the receptive field is regarded as an invisible “anchor”, so that the feature points with different scale receptive field can detect different scale pedestrian targets without any bells and whistles. Based on the above analysis, the proposed approach has an anchor-free setting. The extensive experiments on Caltech-USA benchmark demonstrate that our method outperforms the state-of-the-art pedestrian detection algorithms.
A Template-Based Approach to Code Generation Within an Agent Paradigm

Today, the paradigm of multi-agent systems has earned a place in the field of software engineering thanks to its versatility to adapt to various domains. However, the construction of these systems is complex, which leads to additional costs in the implementation process. In recent years, however, several frameworks have emerged to simplify this task by providing functionalities that these systems need as a basis, or even tools to generate code related to this paradigm. These tools are based on a single framework, protocol and language, which sets many limits to the code generation capacity of these tools. Therefore, this paper proposes a tool for code generation of complete multi-agent systems, focused on the elimination of the restrictions of programming language, framework, communication protocol, etc. through the use of model-driven and template-driven development.
A Survey on Applications of H-Technique: Revisiting Security Analysis of PRP and PRF

The Coefficients H technique (also called the H-technique), developed by Patarin circa 1991, is a tool used to obtain the upper bounds on distinguishing advantages. This tool is known to provide relatively simple and (in some cases) tight bound proofs in comparison to some other well-known tools, such as the game-playing technique and random systems methodology. In this systematization of knowledge (SoK) paper, we aim to provide a brief survey on the H-technique. The SoK is presented in four parts. First, we redevelop the necessary nomenclature and tools required to study the security of any symmetric-key design, especially in the H-technique setting. Second, we provide a full description of the H-technique and some related tools. Third, we present (simple) H-technique-based proofs for some popular symmetric-key designs, across different paradigms. Finally, we show that the H-technique can actually provide optimal bounds on distinguishing advantages.
Reasoning in Large Games with Unboundedly Many Players

In large anonymous games, payoffs are determined by strategy distributions rather than strategy profiles. If half the players choose a strategy a, all of them get a certain payoff, whereas if only one-third of the players choose that strategy, the players choosing may get a different payoff. Strategizing in such a game by a player involves reasoning about not who does what but what fraction of the population makes the same choice as that player.We present a simple modal logic to reason about such strategization in large games. Since actual numbers are irrelevant, a player need not even know how many others are in the game, thus leading to the consideration of games with unboundedly many players. The logic we consider is the propositional modal fragment of a first order modal logic. We show that it has a bounded agent property, giving us a decision procedure for satisfiability. We also present a complete axiomatization of the valid formulas. The logic admits a natural model checking algorithm and bisimulation characterization. The logic with quantification over players is more appropriate, but is undecidable.
Pluto: High-Performance IoT-Aware Stream Processing

Nowadays, large numbers of small IoT stream queries are created from diverse IoT applications and executed on cloud backend servers. However, existing distributed stream processing systems such as Storm and Flink do not efficiently handle the large numbers of IoT stream queries because of their tightly-coupled query/code submission layer and inefficient query execution layer. In this paper, we propose Pluto, a new IoT-aware stream processing system. As a first step for IoT stream processing, this paper focuses on optimizing the execution of many IoT stream queries on a node. Pluto optimizes the end-to-end query processing with a three-phase execution, harnessing IoT-query characteristics. First, Pluto minimizes bottlenecks in the IoT query submission by decoupling the code registration from the query submission process with new APIs, which eliminates duplicate code registration and enables code sharing across queries. Second, in the execution phase, Pluto shares system resources as much as possible and minimizes resource bottlenecks in a machine by exploiting commonalities among IoT stream queries and information exposed in the API. Our evaluations show that Pluto improves the throughput by an order of magnitude compared to other stream processing systems on a 24-core machine, keeping P99 latency less than one second.
The First Workshop on Multiple Input Modalities and Sensations for VR/AR Interactions (MIMSVAI)

With the advance of VR/AR technology, more and more VR/AR applications are emerging and have been popular among new users. Users interact with both physical or virtual worlds through multiple input modalities and sensations to create an immersive VR/AR experience. However, a lack of robust and intuitive interaction interfaces and realistic sensations hinders users’ experience for achieving a fascinating acceptance in various application areas of VR/AR interactions. This workshop discusses the challenges and applications of designing a higher coherence between different input modalities and sensations to offer more engaging VR/AR experiences, which can create opportunities for the researchers from both UbiComp and VR/AR fields to jointly discuss and brainstorm new directories of designing new input modalities and sensations for VR/AR interactions.
BGR to HSV based Text Extraction from Manuscripts Using Slidebars

Text extraction is a very important step in language processing and pattern recognition. The method proposed here first converts the images to the HSV color space and then their Hue(H), Value(V), and Saturation(S) is varied manually with the help of the slide-bars introduced. The experimental results show the algorithm used to extract the text of a palm leaf manuscript performs fairly well.
Online Transaction Fraud Detection System Based on Machine Learning

Abstract With the rapid development of Internet technology, the scale of online transactions is constantly expanding. At the same time, the related network transaction fraud problem has become more significant. Compared with the credit card transaction, the network transaction has the characteristics of low cost, wide coverage and high frequency, which makes the detection of fraud more complex. Aiming at the problem of difficult fraud detection in network transactions, this paper designed two fraud detection algorithms based on Fully Connected Neural Network and XGBoost, whose AUC values can achieve 0.912 and 0.969 respectively. Meanwhile, we designed an interactive online transaction fraud detection system based on XGBoost model, which can automatically analyze the transaction data uploaded and return the fraud detection results to users.
Algorithmic Correspondence for Relevance Logics I. The Algorithm $$\mathsf {PEARL}$$

We apply and extend the theory and methods of algorithmic correspondence theory for modal logics, developed over the past 20 years, to the language \(\mathcal {L}_R\) of relevance logics with respect to their standard Routley–Meyer relational semantics. We develop the non-deterministic algorithmic procedure \(\mathsf {PEARL}\) for computing first-order equivalents of formulae of the language \(\mathcal {L}_R\), in terms of that semantics. \(\mathsf {PEARL}\) is an adaptation of the previously developed algorithmic procedures SQEMA (for normal modal logics) and ALBA (for distributive and non-distributive modal logics). We then identify a large syntactically defined class of inductive formulae in \(\mathcal {L}_R\), analogous to previously defined such classes in the classical, distributive and non-distributive modal logic settings, and show that \(\mathsf {PEARL}\) succeeds for every inductive formula and correctly computes a first-order definable condition which is equivalent to it with respect to frame validity. We also provide a detailed comparison with two earlier works, each extending the class of Sahlqvist formulae to relevance logics, and show that both are subsumed by simple subclasses of inductive formulae.
Stereolithographic additive manufacturing of acoustic devices with spatially modulated cavities

Abstract Stereolithographic additive manufacturing (STL‐AM) has been developed to create practical components with functional geometries. Nanoparticles are mechanically dispersed into photosensitive resins, and the resulting mixed paste is thinly spread on a flat substrate. One‐dimensional (1D) raster lines were cured via ultraviolet laser scanning. Two‐dimensional (2D) cross‐sections are formed by adjacent joining. Three‐dimensional (3D) solid structures are fabricated via lamination bonding. The composite precursors were dewaxed and sintered under optimized heat treatment conditions. Computer‐aided design, manufacture, and evaluation (CAD/CAM/CAE) processes of acoustic devices with rigid alumina structures will be progressively reviewed. Cylindrical cavities were divided into self‐similar volumes as harmonic instruments to perform overtone sounds. Spherical cavities were connected with steric junctions as noise suppressors to exhibit absorption bands. Qualitative improvements in social and industrial environments by using stereolithographic technologies are discussed and practically demonstrated.
Contrastive Representations for Continual Learning of Fine-Grained Histology Images

We show how a simple autoencoder based deep network with a contrastive loss can effectively learn representations in a continual/incremental manner with limited labelling. This is of particular interest to the biomedical imaging research community, for whom the visual task is often a binary decision (healthy vs. disease) with limited quantity data and costly labelling. For such applications, the proposed method provides a light-weight option of 1) representing patterns with relatively few training samples using a novel collaborative contrastive loss function 2) update the autoencoder based deep network in an unsupervised fashion for continual learning for new incoming data. We overcome the drawbacks of existing methods through planned technical design, and demonstrate the efficacy of the proposed method on three histology image classification tasks (lung, colon, breast cancer) with SOTA results.
A comparative study of Word Embedding Techniques to extract features from Text

Extract information from text into feature vectors is known as word embedding, which is used to represent the meaning of words into vector format. There have been no. of word embedding techniques developed that allow a computer to process natural language and compare the relationships between different words programmatically. In this paper, first, we introduce popular word embedding models and discuss desired properties of word model like similarity analysis, or the testing of words for synonymic relations, is used to compare several of these techniques to see which performs the best.
Predicting Group Work Performance from Physical Handwriting Features in a Smart English Classroom

Embodied cognition theory states that students thinking in a learning environment is embodied in physical activity. In this regard, recent research has shown that signal-level handwriting dynamics can distinguish learning performance. Although machine learning has been considered to detect how multimodal modalities correlate to specific learning processes, the use of deep learning has received insufficient attention. With this in mind, we build a Group Work Performance Prediction system from analysis of 3D (including strokes frequency) handwriting signals of students in a smart English classroom, with deep convolutional neuronal network (CNN) based regression models. For labelling of their proficiency level, their spoken language performance is being used. The students were working together in groups. A 3D (2D writing coordinates plus frequency) handwriting dataset (3D-Writing-DB) was collected through a collaboration platform known as ‘creative digital space’. We extracted the 3D handwriting signal from a table tablet during English discussion sessions. Afterwards, professional English teachers annotated the English speech (values vary from 0 - 5). Our experimental results indicate that group work performance can be successfully predicted from physical handwriting features by using deep learning, as shown by our best result, i. e., 0.32 in regression assessment by applying RMSE for evaluation.
Resilient Supervisory Multiagent Systems

Accidental or deliberate disruption of the coordination function in a multiagent system has been discussed and referred to in the social sciences literature as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">leader decapitation</i> ; this article outlines a methodology for making multiagent networks resilient to this type of failure, enabling a timely restoration of operation normalcy by leveraging machine learning techniques. The approach involves endowing the agents with a cascade of independent learning modules that enable them to discover over time their role in the overall system coordinating strategy, so that they are able to autonomously implement it when central coordination seizes to function. Through these machine learning algorithms, the agents incrementally identify the overall system’s task specification and simultaneously optimize their strategy to serve the common goal.