Reviewerly/exHarmony huggingface dataset:
Tackling Loneliness and Isolation in Older Adults With Virtual Reality: How do We Move Forward?

Current trends in gerontology conceptualize Virtual Reality (VR) as a tool for rehabilitation, lauding its potential for cognitive rehabilitation or as an intervention to reduce cognitive function decline. However, we must take a critical stance and identify not just the potential positive impact, but also how things may go wrong without appropriate guidelines, and the need for careful design around the interaction affordances of the technology. We conducted co-discovery and co-design workshops involving expert stakeholders and older adults ( N = 25) over a period of 6 months, involving practical activities including user personas and focus groups to understand the complexities of loneliness and identify possible solutions with VR. Based on our findings we focus our argument on two key factors in the conceptualization of loneliness: spaces, and activities which may take place within said spaces. We present our reconceptualization of VR as a tool for group activities instead of passive consumption of content and make suggestions to the community for reducing feelings of loneliness with VR.
A Challenge in A(G)I, Cybernetics Revived in the Ouroboros Model as One Algorithm for All Thinking

A topical challenge for algorithms in general and for automatic image categorization and generation in particular is presented with the aim of highlighting strengths and deficiencies of current Artificial Intelligence approaches while sketching a way forward. A general lack of encompassing symbol-embedding and (not only) -grounding in some bodily basis is made responsible for current deficiencies. A concomitant dearth of hierarchical organization of concepts follows suite. As a remedy for these shortcomings, it is proposed to take a wide step back and to newly incorporate aspects of cybernetics and analog control processes. It is claimed that a promising overarching perspective is provided by the Ouroboros Model with a valid and versatile algorithmic backbone for general cognition at all accessible levels of abstraction and capabilities.
Equivalence in Argumentation Frameworks with a Claim-centric View: Classical Results with Novel Ingredients

A common feature of non-monotonic logics is that the classical notion of equivalence does not preserve the intended meaning in light of additional information. Consequently, the term strong equivalence was coined in the literature and thoroughly investigated. In the present paper, the knowledge representation formalism under consideration is claimaugmented argumentation frameworks (CAFs) which provide a formal basis to analyze conclusion-oriented problems in argumentation by adapting a claim-focused perspective. CAFs extend Dung AFs by associating a claim to each argument representing its conclusion. In this paper, we investigate both ordinary and strong equivalence in CAFs. Thereby, we take the fact into account that one might either be interested in the actual arguments or their claims only. The former point of view naturally yields an extension of strong equivalence for AFs to the claim-based setting while the latter gives rise to a novel equivalence notion which is genuine for CAFs. We tailor, examine and compare these notions and obtain a comprehensive study of this matter for CAFs. We conclude by investigating the computational complexity of naturally arising decision problems.
An exponentially-growing family of universal quantum circuits

Abstract Quantum machine learning has become an area of growing interest but has certain theoretical and hardware-specific limitations. Notably, the problem of vanishing gradients, or barren plateaus, renders the training impossible for circuits with high qubit counts, imposing a limit on the number of qubits that data scientists can use for solving problems. Independently, angle-embedded supervised quantum neural networks were shown to produce truncated Fourier series with a degree directly dependent on two factors: the depth of the encoding and the number of parallel qubits the encoding applied to. The degree of the Fourier series limits the model expressivity. This work introduces two new architectures whose Fourier degrees grow exponentially: the sequential and parallel exponential quantum machine learning architectures. This is done by efficiently using the available Hilbert space when encoding, increasing the expressivity of the quantum encoding. Therefore, the exponential growth allows staying at the low-qubit limit to create highly expressive circuits avoiding barren plateaus. Practically, parallel exponential architecture was shown to outperform the existing linear architectures by reducing their final mean square error value by up to 44.7% in a one-dimensional test problem. Furthermore, the feasibility of this technique was also shown on a trapped ion quantum processing unit.
Using optical sensors for industrial robot-human interactions in a Gazebo environment.

This paper presents an overview of a robot operating system based architecture for human-industrial robot interactions using peripheral optical sensors for real-time object detection and collision avoidance with an industrial robot in the virtual world of the Gazebo simulator.Machine vision plays a huge role in production automation, and develop a system based on Kuka KR3 industrial robot for detecting and tracking a human and other objects in a working area using optical sensors.The ability to work in low light and crowded conditions, as well as the ability to reconstruct a method of execution of a task, while maintaining control of a robot.This work considers several optical sensors and a comparative analysis.
Modeling of Human Actions in a Collaborative Robotic Space Using AR601M Humanoid Robot: Pilot Experiments in the Gazebo Simulator

To guarantee a safe human-robot collaboration, a collaborative system development requires a significant amount of real world experiments.Yet, it is critical to avoid injury risks for participants of such experiments.The risks could be reduced by introducing a virtual experiments' stage to detect mistakes in a robot behavior prior to the real world experiments.This paper presents a virtual model of a humanoid robot AR601M in the Gazebo simulator.Unlike the standard human models in the Gazebo, this model allows to simulate the gross and fine motor skills of a human and could be used when performing various human actions in collaborative robotic cells.
A Kinect-based Augmented Reality Game for Arm Exercise

Augmented reality (A.R.) is the underlying technique where 3D virtual objects are integrated in real-time with a real environment.Augmented reality applications such as medical visualization, maintenance and repair, robot path planning, entertainment, military aircraft navigation, and targeting applications have been proposed.This paper introduces the development of an augmented reality game that allows the user to carry out arm exercises using a natural user interface based on Microsoft Kinect.The system has been designed as an augmented game in which the user's hands are in a world augmented with virtual objects generated by computer graphics.The player is sitting in a chair, just grasping the yellow stars that are displayed in the stage.It encourages the activities of a large number of arm muscles which will prevent decay.It is also suitable for rehabilitation.
Utilizing Blockchain Technology in Various Applications to Secure Data Flows. A Comprehensive Analysis

Blockchain technology has emerged as a disruptive force in the world of digital transactions, with applications across a wide range of industries. This paper provides a comprehensive review of the potential applications of blockchain technology, including finance, healthcare, supply chain management, and voting. The paper discusses the benefits and challenges of using blockchain technology, as well as real-world case studies of its implementation. The paper concludes with suggestions for future research, exploring new applications and addressing the challenges associated with the use of blockchain technology.
Defense and Attack Strategy Optimization for Reliability Systems with Virtual Components and Active Redundancy with a Game Theory Approach

Abstract Today, one of the most basic strategies for a government's survival is to defend sensitive places and resources, and to achieve this goal, strategic and efficient approaches are required. The goal of this research is to determine the best way to defend sensitive systems in which the defender tries to minimize the damage caused by the attacker while taking into account budget constraints and the space needed for defense equipment, and the attack tries to maximize the defender's injury. In this case, the defense creates a number of artificial targets in order to deceive the attacker, all while attempting to decrease the risk of damaging sensitive systems by not attacking them definitively. The goal's economic, human, and symbolic worth must be considered by both the defender and the attacker. The defender attempts to minimize projected harm and defense costs. The attacker aims to do as much damage as possible while keeping the attack's cost low. The attacker will "maybe" identify the targets because they are unable to identify the virtual targets. For the first time, a program model was developed that considers the possibilities for identifying virtual targets, reliability structures with mixed strategies (active and cold standby), non-homogeneous components in parallel series systems, and the game theory approach to finding equilibrium. A nonlinear layout is provided to optimize defense subsystems. The results are modeled and the results are evaluated in this research.
PENERAPAN METODE ASOSIASI DENGAN ALGORITMA APRIORI UNTUK MENDUKUNG STRATEGI PROMOSI

Business management in a building shop business is one way that can help business activities to get the best sales results. In business management, several strategies are needed, for example, the strategy of selling a collection of building goods into one type of product category, using this strategy buyers who usually only buy one item of goods can buy several items at once. This can increase sales patterns in the building shop. Therefore, the author uses one method, namely the association method with the a priori algorithm to find patterns in a building goods sales transaction every year, so that later the frequency of itemsets is found based on several determinant items, support and confidence values, to determine the association between combinations of product items. From the results, it will be used as a promotional media at Prima Jaya Baja Store so that it can support the level of sales of building goods products and can soon grow rapidly and become one of the reference stores for the sale of building goods in Central Lampung in particular and Lampung Province in general.&#x0D;
Type-Preserving Compilation of Class-Based Languages

The Dependent Object Type (DOT) calculus was designed to put Scala on a sound basis, but while DOT relies on structural subtyping, Scala is a fundamentally class-based language. This impedance mismatch means that a proof of DOT soundness by itself is not enough to declare a particular subset of the language as sound. While a few examples of Scala snippets have been manually translated into DOT, no systematic compilation scheme has been presented so far. In this thesis we develop a series of calculi of increasing complexity to model Scala and present a type-preserving compilation scheme from each of these calculus into DOT. Along the way, we develop some necessary extensions to DOT.
Pairs-trading System using Quantum-inspired Combinatorial Optimization Accelerator for Optimal Path Search in Market Graphs

Pairs-trading is a trading strategy that involves matching a long position with a short position in two stocks aiming at market-neutral profits. While a typical pairs-trading system monitors the prices of two statistically correlated stocks for detecting a temporary divergence, monitoring and analyzing the prices of more stocks would potentially lead to finding more trading opportunities. Here we report a stock pairs-trading system that finds trading opportunities for any two stocks in an $N$-stock universe using a combinatorial optimization accelerator based on a quantum-inspired algorithm called simulated bifurcation. The trading opportunities are detected through solving an optimal path search problem in an $N$-node directed graph with edge weights corresponding to the products of instantaneous price differences and statistical correlation factors between two stocks. The accelerator is one of Ising machines and operates consecutively to find multiple opportunities in a market situation with avoiding duplicate detections by a tabu search technique. It has been demonstrated in the Tokyo Stock Exchange that the FPGA (field-programmable gate array)-based trading system has a sufficiently low latency (33 $\mu$s for $N$=15 or 210 pairs) to execute the pairs-trading strategy based on optimal path search in market graphs.
Dynamics-informed deconvolutional neural networks for super-resolution identification of regime changes in epidemiological time series

The ability to infer the timing and amplitude of perturbations in epidemiological systems from their stochastically spread low-resolution outcomes is crucial for multiple applications. However, the general problem of connecting epidemiological curves with the underlying incidence lacks the highly effective methodology present in other inverse problems, such as super-resolution and dehazing from computer vision. Here, we develop an unsupervised physics-informed convolutional neural network approach in reverse to connect death records with incidence that allows the identification of regime changes at single-day resolution. Applied to COVID-19 data with proper regularization and model-selection criteria, the approach can identify the implementation and removal of lockdowns and other nonpharmaceutical interventions (NPIs) with 0.93-day accuracy over the time span of a year.
Patch-Transformer Network: A Wearable-Sensor-Based Fall Detection Method

Falls can easily cause major harm to the health of the elderly, and timely detection can avoid further injuries. To detect the occurrence of falls in time, we propose a new method called Patch-Transformer Network (PTN) wearable-sensor-based fall detection algorithm. The neural network includes a convolution layer, a Transformer encoding layer, and a linear classification layer. The convolution layer is used to extract local features and project them into feature matrices. After adding positional coding information, the global features of falls are learned through the multi-head self-attention mechanism in the Transformer encoding layer. Global average pooling (GAP) is used to strengthen the correlation between features and categories. The final classification results are provided by the linear layer. The accuracy of the model obtained on the public available datasets SisFall and UnMib SHAR is 99.86% and 99.14%, respectively. The network model has fewer parameters and lower complexity, with detection times of 0.004 s and 0.001 s on the two datasets. Therefore, our proposed method can timely and accurately detect the occurrence of falls, which is important for protecting the lives of the elderly.
Geochemical and multivariate statistical study to assess the salinisation origin of the Remila plain groundwater, Khenchela Algeria

The aquifer system of the Remila plain (Khenchela, Algeria), covering 250 km 2 , is one of the semiarid regions where groundwater is heavily exploited for urban supply and irrigation.An integrated hydrochemical and statistical analysis was performed on 70 water samples to identify the main processes and the origin of salinisation of our waters.Chemical analyses indicate salinity values (TDS) ranging from 568 to 1586 mg•dm -3 with an average of 869 mg•dm -3 , with sulphate being the dominant ions, especially in the north and northeastern parts of the region.The identified chemical facies are SO 4 -Cl-Ca in the northeastern part, SO 4 -Cl-Ca-Mg present in most waters, and HCO 3 -Ca-Mg in the southeastern part.We applied the statistical approach to group the waters into three categories using Principal Component Analysis (PCA) and Hierarchical Clustering Analysis (HCA); 1) saline waters (23%) (TDS > 1000 mg•dm -3 and SO 4 2dominance), 2) moderately saline waters (51%) with HCO 3 -dominance, 3) moderately saline waters (26%) with a mixed facies.The binary ion diagrams used suggest that the main hydrochemical processes are: evaporites dissolution and/or precipitation, accompanied by an exchange and/or reverse exchange of ions.Additionally, another process was detected in the northeastern part of the area; the saline intrusion of Sabkha waters, favoured by intensive aquifer exploitation.
Enhanced Adjacency Matrix-Based Lightweight Graph Convolution Network for Action Recognition

Graph convolutional networks (GCNs), which extend convolutional neural networks (CNNs) to non-Euclidean structures, have been utilized to promote skeleton-based human action recognition research and have made substantial progress in doing so. However, there are still some challenges in the construction of recognition models based on GCNs. In this paper, we propose an enhanced adjacency matrix-based graph convolutional network with a combinatorial attention mechanism (CA-EAMGCN) for skeleton-based action recognition. Firstly, an enhanced adjacency matrix is constructed to expand the model’s perceptive field of global node features. Secondly, a feature selection fusion module (FSFM) is designed to provide an optimal fusion ratio for multiple input features of the model. Finally, a combinatorial attention mechanism is devised. Specifically, our spatial-temporal (ST) attention module and limb attention module (LAM) are integrated into a multi-input branch and a mainstream network of the proposed model, respectively. Extensive experiments on three large-scale datasets, namely the NTU RGB+D 60, NTU RGB+D 120 and UAV-Human datasets, show that the proposed model takes into account both requirements of light weight and recognition accuracy. This demonstrates the effectiveness of our method.
Taylor Expansion as a Monad in Models of DiLL

Differential Linear Logic (DiLL) adds to Linear Logic (LL) a symmetrization of three out of the four exponential rules, which allows the expression of a natural notion of differentiation. In this paper, we introduce a codigging inference rule for DiLL and study the categorical semantics of DiLL with codigging using differential categories. The addition of codigging makes the rules of DiLL completely symmetrical. We will explain how codigging is interpreted thanks to the exponential function e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sup> , and in certain cases by the convolutional exponential. In a setting with codigging, every proof is equal to its Taylor series, which implies that every model of DiLL with codigging is quantitative. We provide examples of codigging in relational models, as well as models related to game logic and quantum programming. We also construct a graded model of DiLL with codigging in which the indices witness exponential growth. Codigging makes the exponential of-course connective ! in LL into a monad, where the monad axioms enforce Taylor expansion. As such, codigging opens the door to monadic reformulations of quantitative features in programming languages, as well as further categorical generalizations.
Finnish Internet Parsebank

Abstract We present a Finnish web corpus with multiple text sources and rich additional annotations. The corpus is based in large parts on a dedicated Internet crawl, supplementing data from the Common Crawl initiative and the Finnish Wikipedia. The size of the corpus is 6.2 billion tokens from 9.5 million source documents. The text is enriched with morphological analyses, word lemmas, dependency trees, named entities and text register (genre) identification. Paragraph-level scores of an n-gram language model, as well as paragraph duplication rate in each document are provided, allowing for further filtering of the dataset by the end user. Thanks to changes in the 2023 Finnish copyright legislation, the corpus is openly available for research purposes, and can also be accessed through the NoSketchEngine concordance tool and the dep search dependency tree query tool, all at https://turkunlp.org/finnish nlp.html.
Real-time Trading System based on Selections of Potentially Profitable, Uncorrelated, and Balanced Stocks by NP-hard Combinatorial Optimization

Financial portfolio construction problems are often formulated as quadratic and discrete (combinatorial) optimization that belong to the nondeterministic polynomial time (NP)-hard class in computational complexity theory. Ising machines are hardware devices that work in quantum-mechanical/quantum-inspired principles for quickly solving NP-hard optimization problems, which potentially enable making trading decisions based on NP-hard optimization in the time constraints for high-speed trading strategies. Here we report a real-time stock trading system that determines long(buying)/short(selling) positions through NP-hard portfolio optimization for improving the Sharpe ratio using an embedded Ising machine based on a quantum-inspired algorithm called simulated bifurcation. The Ising machine selects a balanced (delta-neutral) group of stocks from an $N$-stock universe according to an objective function involving maximizing instantaneous expected returns defined as deviations from volume-weighted average prices and minimizing the summation of statistical correlation factors (for diversification). It has been demonstrated in the Tokyo Stock Exchange that the trading strategy based on NP-hard portfolio optimization for $N$=128 is executable with the FPGA (field-programmable gate array)-based trading system with a response latency of 164 $\mu$s.
Contextuality, Coherences, and Quantum Cheshire Cats

We analyse the quantum Cheshire cat using contextuality theory, to see if this can tell us anything about how best to interpret this paradox. We show that this scenario can be analysed using the relation between three different measurements, which seem to result in a logical contradiction. We discuss how this contextual behaviour links to weak values, and coherences between prohibited states. Rather than showing a property of the particle is disembodied, the quantum Cheshire cat instead demonstrates the effects of these coherences, which are typically found in pre- and postselected systems.
Generating Benchmarks for Factuality Evaluation of Language Models

Before deploying a language model (LM) within a given domain, it is important to measure its tendency to generate factually incorrect information in that domain. Existing methods for factuality evaluation of LLM generation focus on facts sampled from the LM itself, and thus do not control the set of evaluated facts and might under-represent domain specific or rare facts. We propose FACTOR: Factual Assessment via Corpus TransfORmation, a scalable approach for evaluating LM factuality. FACTOR automatically transforms a factual corpus of interest into a benchmark evaluating an LM's propensity to generate true facts from the corpus vs. similar but incorrect statements. We use our framework to create three benchmarks: Wiki-FACTOR, News-FACTOR and Expert-FACTOR. We show that: (i) our benchmark scores increase with model size and improve when the LM is augmented with retrieval; (ii) benchmark score and perplexity do not always agree on model ranking; (iii) when perplexity and benchmark score disagree, the latter better reflects factuality in open-ended generation, as measured by human annotators. We make our data and code publicly available in https://github.com/AI21Labs/factor.