Reviewerly/exHarmony huggingface dataset:
The Novel Method for Prediction of Neurodegenerative Diseases Using Human GAIT Analysis

Abstract: Analyzing human motion is essential for diagnosing movement disorders and guiding neurodegenerative diseases like Parkinson’s diseases and Alzheimer’s diseases. Optical motion capture systems are the standard for estimating diseases, but the equipment is expensive and requires a predefined space, while wearable sensors systems can estimate in any environment and the use of these technological instruments can be of great support in both clinical diagnosis and severity assessment of these pathologies. Here the sensors, features and processing methodologies have been reviewed in order to provide a highly consistent work that explores the issues related to human gait analysis. First, the phase of human gait cycle are briefly explained, along with some non-normal gait patterns (gait abnormalities) typical of some neurodegenerative diseases. Then the most common processing techniques for both feature selection and extraction and for classification and clustering. Finally, a conclusive discussion on current open problem and future directions is outlined.
Changing role of millennial accountants in the information revolution era (Industry 4.0) and challenges in the society generation scope (Society 5.0)

This study aims to analyze and test the influence of Revolution information (Industry 4.0) on the role of Accountants millennials with challenges on the scope generation community (society 5.0) as a moderating variable. This research uses approach descriptive and associative quantitative. The population in this study are Accountants and student candidates Accountants in Medan City with the selected sample by random sampling amounting to 110 people. Data analysis techniques using Structural Equation Model (SEM) with tools help Smart PLS V 3.2. Research Results show that there is an influence of Revolution Industry 4.0 towards changing the role of the millennial accountant. And challenge on the scope generation community (society 5.0) moderates the changing role of Accountants millennials in the era of industry 4.0.
Analysis of Single Photon Detectors in Differential Phase Shift Quantum Key Distribution

Abstract In the current research work, an analysis of differential phase shift quantum key distribution using InGaAs/InP and Silicon-APD (avalanche photodiode) as single photon detectors is performed. Various performance parameters of interest such as shifted key rate, secure key rate, and secure communication distance obtained are investigated. In this optical fiber-based differential phase shift quantum key distribution, it is observed that Si-APD under frequency conversion method at telecommunication window outperforms the InGaAs/InP APD.
Social learning with a grain of salt

Humans are remarkably effective social learners, with several recent studies formalizing this capacity using computational models. However, previous research has often been limited to tasks where observer and demonstrator share the same reward function. In contrast, humans can learn from others who have different preferences, skills, or goals. To study social learning under individual differences, we introduce the socially correlated bandit, where participants have personalized rewards, which are correlated with but not identical to those of others. Social information can still be useful, but not when used verbatim. We present a model of Social Generalization that integrates individual and social information into the generalization process, but assumes social information to be noisier and thus less informative. This model out-competes previous models, with it being the dominant strategy in evolutionary simulations. Our findings expand on previous models of social learning, showing humans can integrate social information more flexibly than previously assumed.
Second quantization of open quantum systems in Liouville space

We present a theoretical framework based on second quantization in Liouville space to treat open quantum systems. We consider an ensemble of identical quantum emitters characterized by a discrete set of quantum states. The second quantization is performed directly at the level of density matrices, thereby significantly reducing the size of the Liouville space. In contrast to conventional Hilbert space techniques, statistically mixed states and dissipation are naturally incorporated. As a particular example of application, we study the effect of incoherent processes and statistical mixing of emitters initial states in the interaction with quantum light. Moreover, we link our framework to a phase-space description of the dynamics, which can overcome the computational limitations of our method with the increasing number of particles.
CUTS+: High-dimensional Causal Discovery from Irregular Time-series

Causal discovery in time-series is a fundamental problem in the machine learning community, enabling causal reasoning and decision-making in complex scenarios. Recently, researchers successfully discover causality by combining neural networks with Granger causality, but their performances degrade largely when encountering high-dimensional data because of the highly redundant network design and huge causal graphs. Moreover, the missing entries in the observations further hamper the causal structural learning. To overcome these limitations, We propose CUTS+, which is built on the Granger-causality-based causal discovery method CUTS and raises the scalability by introducing a technique called Coarse-to-fine-discovery (C2FD) and leveraging a message-passing-based graph neural network (MPGNN). Compared to previous methods on simulated, quasi-real, and real datasets, we show that CUTS+ largely improves the causal discovery performance on high-dimensional data with different types of irregular sampling.
A Classification of Feedback Loops and Their Relation to Biases in Automated Decision-Making Systems

Prediction-based decision-making systems are becoming increasingly prevalent in various domains. Previous studies have demonstrated that such systems are vulnerable to runaway feedback loops, e.g., when police are repeatedly sent back to the same neighborhoods regardless of the actual rate of criminal activity, which exacerbate existing biases. In practice, the automated decisions have dynamic feedback effects on the system itself that can perpetuate over time, making it difficult for short-sighted design choices to control the system's evolution. While researchers started proposing longer-term solutions to prevent adverse outcomes (such as bias towards certain groups), these interventions largely depend on ad hoc modeling assumptions and a rigorous theoretical understanding of the feedback dynamics in ML-based decision-making systems is currently missing. In this paper, we use the language of dynamical systems theory, a branch of applied mathematics that deals with the analysis of the interconnection of systems with dynamic behaviors, to rigorously classify the different types of feedback loops in the ML-based decision-making pipeline. By reviewing existing scholarly work, we show that this classification covers many examples discussed in the algorithmic fairness community, thereby providing a unifying and principled framework to study feedback loops. By qualitative analysis, and through a simulation example of recommender systems, we show which specific types of ML biases are affected by each type of feedback loop. We find that the existence of feedback loops in the ML-based decision-making pipeline can perpetuate, reinforce, or even reduce ML biases.
ChatGPT as a Text Simplification Tool to Remove Bias

The presence of specific linguistic signals particular to a certain sub-group of people can be picked up by language models during training. If the model begins to associate specific language with a distinct group, any decisions made based upon this language would hold a strong correlation to a decision based upon their protected characteristic, leading to possible discrimination. We explore a potential technique for bias mitigation in the form of simplification of text. The driving force of this idea is that simplifying text should standardise language between different sub-groups to one way of speaking while keeping the same meaning. The experiment shows promising results as the classifier accuracy for predicting the sensitive attribute drops by up to 17% for the simplified data.
Is Bitcoin the 'Digit Gold'A Potential Safe-haven Asset?

In this paper, we examine whether bitcoin has the potential to become safe-haven asset that can rival gold in the future. We observed, compared and analyzed and the performance of bitcoin and gold in face of a falling market and inflation pressure. We can see if investors can rely on bitcoin to reduce risk exposure significantly through empirical tests. At the end of our research, we found that bitcoin did not perform as well as gold did when faced with market crash and inflation. Therefore, we conclude that bitcoin does not yet show the potential to possess risk-proof merits as gold, the traditional high-quality hedge asset. Gold would probably remain the preferred hedge asset against cryptocurrency for now.
Duopoly insurers’ incentives for data quality under a mandatory cyber data sharing regime

We study the impact of data sharing policies on cyber insurance markets. These policies have been proposed to address the scarcity of data about cyber threats, which is essential to manage cyber risks. We propose a Cournot duopoly competition model in which two insurers choose the number of policies they offer (i.e., their production level) and also the resources they invest to ensure the quality of data regarding the cost of claims (i.e., the data quality of their production cost). We find that enacting mandatory data sharing sometimes creates situations in which at most one of the two insurers invests in data quality, whereas both insurers would invest when information sharing is not mandatory. This raises concerns about the merits of making data sharing mandatory.
FEATURES OF THE DEVELOPMENT OF HIGHER PEDAGOGICAL EDUCATION IN THE CONTEXT OF PROFESSIONAL TRAINING OF FUTURE EDUCATORS TO WORK WITH PRESCHOOL CHILDREN IN THE USA

This article explores the specific characteristics of the development of higher pedagogical education in the United States with a focus on preparing future educators who work with preschool children. The article outlines the historical development of the American education system and describes the changes that have taken place in the process of preparing future teachers to work with preschool children.The concept of “professional training”, “early childhood educator”, “collaborative learning” in the context of the professional training of future preschool teachers is revealed. The features of the development of higher predagogical education in the context of professional training of future educators to work with preschool children in the USA are detailed described in the article. The author also focuses on key features of the professional training of future preschool teachers. The qualifications including Associateʼs, Bachelor’s, and Master’s degrees, and certificates that can be obtained are specified in the article.The annotation suggests that the paper examines the various approaches, methods, and strategies used to train educators in the United States, particularly in the context of working with young children. Furthermore, the author emphasizes on the various approaches, methods, and strategies used to train educators in the United States, particularly when it comes to working with preschoolers. It also examines the key competencies and skills that future educators need to develop to work effectively with preschoolers and the challenges that they may encounter in the process.The authors note that the professional training of future teachers should be focused on the requirements of the modern world, combining the cooperation of high-quality theoretical knowledge and practical skills necessary for the effective work of teachers with preschool children. Therefore, the article reveals the current state of the higher education system in the United States, focusing on the professional training program for future educators. Overall, the topic seeks to shed light on the current state of higher pedagogical education in the United States and its relevance to the needs of the early childhood education sector.&#x0D; Keywords: professional training; educator; future educators; early childhood education; higher pedagogical education; preschool children; preschool; STEM education; educational program; collaborative learning.
Action Identification Characteristics and Priming Effects in ChatGPT

Action Identification Theory (Act ID) refers to the traits and mental processes behind how people think about and describe actions in more concrete terms, called Lower-Level Action Identities, or more abstract terms, called Higher-Level Action Identities. Optimal Act ID use is associated with a variety of communicational and conversational goals and outcomes, and is deeply embedded in the structure of human language. ChatGPT is a Large Large Model (LLM)-driven Conversational AI Chatbot, trained on patterns of language and conversational dynamics, including actions and descriptions of actions at different levels of concreteness and abstraction. ChatGPT is therefore a suitable subject for novel forms of Act ID research. Across 3 pre-registered studies, the Behavior Identification Form was used to assess: 1) ChatGPT’s ability to Apply Act ID (Study 1); To measure its default Act ID level (Study 2); and to evaluate its susceptibility to priming-like effects (Study 3). Results revealed that ChatGPT: 1) Can learn and apply Act ID (r = 1.0, p &amp;lt; .000); 2) Displays a high-level default Act ID (M = 20.72, SD = .79); and 3) Can be experimentally induced to select more or fewer lower- or higher-level identities via simple tasks inspired by human research. These findings hold significant implications for optimal human-AI interactions, raise critical questions about the intelligence and safety of these virtual agents, help further our understanding of how Psychological theories and models can improve the study and development of these applications, and may contribute to the broader study of the human mind and consciousness.
Leading Technological Change – Case Study of Emirates Airlines

Purpose: Emirates Airlines is one of the leading airlines with over 100 international destinations. The airline sector is continually evolving, bringing both difficulties and possibilities. Emirates Airlines has reacted to these changes by effecting an organizational change plan. Implementing a Resource Management System (RMS) in the line maintenance department is the primary subject of this study since it was the main change in the Emirates. Based on specified criteria and flight updates, the RMS software program produces jobs for technicians and engineers. Therefore the study aims to evaluate how the organization handles the change process after introducing new technological applications in the emirates airlines.&#x0D; Methodology: This study employs various qualitative research techniques, including interviews.&#x0D; Findings: The study reveals that critical leadership traits for implementing change include self-awareness, self-regulation, empathy, self-motivation, and social skills. Embracing change enables organizations to enhance their services and provide a distinct competitive edge compared to resistance, which can render the firm irrelevant. Resistance to change mainly occurs due to inadequate information, poor comprehension, and varying visions (Elgohary &amp; Abdelazyz, 2020).&#x0D; Unique Contribution to Theory, Practice and Policy: The study was anchored on change model. The study recommends that the RMS should benefits the department by improving labor utilization, lowering maintenance costs, and raising customer satisfaction.
Analisis dan Model Dispersi Kualitas Udara di TPA Klotok Kediri

Klotok landfill is a final waste processing site in the City of Kediri that has been operating since 1992 to manage waste in the Kediri City and its surroundings. Waste management activities at the Klotok TPA generate emissions from the waste pile and heavy equipment activities. This study aims to determine the air quality at the Klotok Landfill through direct measurements to obtain results with a more accurate approach. The measurement results will then be modeled with AERMOD software to determine the distribution of these pollutants to the area around the TPA. In this study, air quality measurements were carried out at three points in the Klotok TPA to determine the level of pollution that occurred. Air quality is measured directly using a High Volume Air Sampler (HVAS), Impinger, and Sound Level Meter. Air pollutant parameters measured included Nitrogen Dioxide (NO2), Total Suspended Particulate (TSP), Sulfur Dioxide (SO2), Oxidants (O3), Carbon Monoxide (CO), Lead (Pb), Non-Methane Hydrocarbons (NMHC), Particulate Matter 2.5 (PM2.5), PM10, Ammonia (NH3), and noise. The measurement results show that the CO parameter exceeds theambient air quality standards of PP No. 22 Year 2021 at the weighbridge and the landfill 3 with values of 13,750 and 18,750 µg/Nm3 respectively,while other parameters still meet the quality standard. The model with AERMOD software shows the dispersion of CO nearby the landfill area occurs quickly so that since the first 100 meters the CO concentration has been dispersed to below 10 µg/Nm3. CO concentrations at a distance of more than 1 km from the Klotok TPA are generally below 1 µg/Nm3.
The role of diminutive suffixes in the Italian Time Nouns constructions: From approximation to focus?

The time nouns (TNs), minuto (‘minute’), momento (‘moment’), attimo (‘instant’) can be part of constructions in which they keep all their categorial and syntactic properties, but also in constructions which seem to undergo a process of grammaticalisation: loss of categorial property of nominal inflection; the sequence DET+TN (un attimo, un momento, un minuto) can no longer be interrupted by modifiers nor have postnominal modifiers; semantic shift from the original temporal meaning to the more general meaning of a ‘(small) indefinite amount’, to continue towards more properly grammatical values of quantifiers to pragmatic values of hedges, alerter and textual marker of focus. Interestingly, while the original temporal constructions do not occur in diminutive forms, the departure from temporal meaning makes the use of diminutive forms possible. The article explores in which cases they are allowed and the semantic and textual factors which contribute to their presence.
SemEval-2023 Task 2: Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2)

We present the findings of SemEval-2023 Task 2 on Fine-grained Multilingual Named Entity Recognition (MultiCoNER 2). Divided into 13 tracks, the task focused on methods to identify complex fine-grained named entities (like WRITTENWORK, VEHICLE, MUSICALGRP) across 12 languages, in both monolingual and multilingual scenarios, as well as noisy settings. The task used the MultiCoNER V2 dataset, composed of 2.2 million instances in Bangla, Chinese, English, Farsi, French, German, Hindi, Italian., Portuguese, Spanish, Swedish, and Ukrainian. MultiCoNER 2 was one of the most popular tasks of SemEval-2023. It attracted 842 submissions from 47 teams, and 34 teams submitted system papers. Results showed that complex entity types such as media titles and product names were the most challenging. Methods fusing external knowledge into transformer models achieved the best performance, and the largest gains were on the Creative Work and Group classes, which are still challenging even with external knowledge. Some fine-grained classes proved to be more challenging than others, such as SCIENTIST, ARTWORK, and PRIVATECORP. We also observed that noisy data has a significant impact on model performance, with an average drop of 10% on the noisy subset. The task highlights the need for future research on improving NER robustness on noisy data containing complex entities.
Segment and Track Anything

This report presents a framework called Segment And Track Anything (SAMTrack) that allows users to precisely and effectively segment and track any object in a video. Additionally, SAM-Track employs multimodal interaction methods that enable users to select multiple objects in videos for tracking, corresponding to their specific requirements. These interaction methods comprise click, stroke, and text, each possessing unique benefits and capable of being employed in combination. As a result, SAM-Track can be used across an array of fields, ranging from drone technology, autonomous driving, medical imaging, augmented reality, to biological analysis. SAM-Track amalgamates Segment Anything Model (SAM), an interactive key-frame segmentation model, with our proposed AOT-based tracking model (DeAOT), which secured 1st place in four tracks of the VOT 2022 challenge, to facilitate object tracking in video. In addition, SAM-Track incorporates Grounding-DINO, which enables the framework to support text-based interaction. We have demonstrated the remarkable capabilities of SAM-Track on DAVIS-2016 Val (92.0%), DAVIS-2017 Test (79.2%)and its practicability in diverse applications. The project page is available at: https://github.com/z-x-yang/Segment-and-Track-Anything.
Evaluating Open-Domain Question Answering in the Era of Large Language Models

Lexical matching remains the de facto evaluation method for open-domain question answering (QA). Unfortunately, lexical matching fails completely when a plausible candidate answer does not appear in the list of gold answers, which is increasingly the case as we shift from extractive to generative models. The recent success of large language models (LLMs) for QA aggravates lexical matching failures since candidate answers become longer, thereby making matching with the gold answers even more challenging. Without accurate evaluation, the true progress in open-domain QA remains unknown. In this paper, we conduct a thorough analysis of various open-domain QA models, including LLMs, by manually evaluating their answers on a subset of NQ-open, a popular benchmark. Our assessments reveal that while the true performance of all models is significantly underestimated, the performance of the InstructGPT (zero-shot) LLM increases by nearly +60%, making it on par with existing top models, and the InstructGPT (few-shot) model actually achieves a new state-of-the-art on NQ-open. We also find that more than 50% of lexical matching failures are attributed to semantically equivalent answers. We further demonstrate that regex matching ranks QA models consistent with human judgments, although still suffering from unnecessary strictness. Finally, we demonstrate that automated evaluation models are a reasonable surrogate for lexical matching in some circumstances, but not for long-form answers generated by LLMs. The automated models struggle in detecting hallucinations in LLM answers and are thus unable to evaluate LLMs. At this time, there appears to be no substitute for human evaluation.