Reviewerly/exHarmony huggingface dataset:
Working technology with psychosomatic symptoms and conversion disorder in psychotherapy and counseling

Background. Overcoming accusations of unproven soul-body connection can be demonstrated by a streamlined system of unloading the psychological layers behind the physical symptom. Purpose of the article. To structure in detail and algorithmize psychological work with psychosomatic symptoms and conversion disorder, and to present it in the form of a well-thought-out and proven technological process Results. The subject of technological influence in psychotherapeutic work with a conversion symptom is the body - as an area of the unconscious, represented by a dream process (A. Mindel "Dreaming body"). The hidden "logic" of conversion can be established by working with three systems of representative experience - proprioceptive, iconic, and symbolic (J. Sandler, J. Bastiaans, G. Murphy). In Technological means of work with the psychosomatic symptom, such working technologies are 1) Tracking illness technologies; 2) Technologies for finding the trigger of psychosomatic disorder; 3) Technologies for the formation of conditions for access to processes in the body; 4) Technologies of conscious communication with the body; 5) Processing technologies; 6) Technologies of transition to the symbolic level of reading and elaboration of representatives of experience. The tools for working with the physical, emotional, and symbolic-figurative components of the experience of psychosomatic disorders are offered. A standardized algorithm of psychological elaboration of the conversion symptom, objectification of its hidden content is presented. Conclusion. The findings provide practical guidance for a psychotherapist working with conversion and psychosomatic disorders.
Penerapan Metode Linear Regression untuk Memprediksi Peserta Training pada PT. NetSolution

Along with the rapid development of technology, the use of information technology is also very much needed by many companies as well as yourself. For this reason, many people have an interest in doing IT training, in addition to increasing knowledge, IT training is also good for improving careers. PT Netsolution is one of the companies that provides IT training services. In its business, it must have a very large set of training participant data, therefore the data must be processed properly and neatly. This research was conducted in order to help the Sales &amp; Marketing team to predict the number of new training participants, so that the admin can prepare a training schedule that is predicted to increase in each year. In this case the Linear Regression method is a method that can help estimate or predict new training participants in the future. The software that will be used to apply the Linear Regression method is RapidMiner and produces Performance with an RMSE value of 0.503 which shows good performance and fairly accurate prediction results.
Prediksi Hasil Tangkap Ikan Laut Di Kota Tanjungpinang Menggunakan Metode SARIMA (Seasonal Autoregressive Integrated Moving Average)

Tanjungpinang is an archipelago. Fish is a food that is often consumed by local people. In 2019 the fish consumption rate is 66 kg/year. and in 2020 it will reach 68 kg/year. Meanwhile, the volume of fish caught in the Riau Islands is 322 kg/year and in 2021 it will decrease to 303 kg/year. Tanjungpinang is rich in marine potential so that the local government takes advantage of this by exporting fish abroad. With fish consumption rates increasing every year, the volume of fish caught is uncertain every year and there is demand for fish exports abroad. So it is necessary to have fish catch predictions to find out the availability of fish the following month. This prediction helps the government to develop a strategy to increase fish catches in Tanjungpinang City. Therefore a fish catch prediction system was built using the SARIMA method. The stages in this study were identifying seasonal data patterns, dividing training data and test data, designing the SARIMA method. The prediction results show that the best model for predicting marine fish catches is the model (0,2,1)(1,2,0)2 with a training data MAPE of 10% which is a very good prediction category and is suitable for use as an analysis by the government to regulate budget estimation and future work program strategy.
Transport Inspired Particle Filters with Poisson-Sampled Observations in Gaussian Setting

Motivated by the need for developing computationally efficient solutions to filtering problem with limited information, this article develops particle filtering algorithms for continuous-time stochastic processes with time-sampled observation process. The state process is modeled by a continuous-time linear stochastic differential equation driven by Wiener process, and the observation process is a linear mapping of the state with additive Gaussian noise. For practical reasons, we assume that the observations are time-sampled and the underlying sampling process is a Poisson counter. With the aim of developing particle filters for this system, we first propose a mean-field type process which is an observation-driven stochastic differential equation such that the conditional distribution of this process given the observations coincides with the optimal filtering distribution. This model is then used to simulate a collection of particles which are driven only by the sample mean and sample covariance, without simulating the differential equation for the covariance matrix. It is shown that the dynamics of the sample mean and the sample covariance coincide with the optimal ones. An academic example is included for illustration.
Energy-Optimal Trajectory-Based Traveling Salesman Problem for Multi-Rotor Unmanned Aerial Vehicles

The trajectory-based traveling salesman problem represents an extension of the classical traveling salesman problem, aimed at determining the most optimal trajectory that passes through a designated set of points. This paper introduces a novel formulation, termed the Energy-Optimal Trajectory-Based Traveling Salesman Problem (EOTB-TSP), which is grounded in an innovative energy assessment model. This model takes into account the intricate dynamics of unmanned aerial vehicles (UAVs). In addition, the EOTB-TSP is cast as a bilevel optimization challenge. To tackle this complex problem, we introduce a modified genetic algorithm specifically tailored for its resolution. To validate the effectiveness of our proposed approach, we conduct a series of experiments and apply it to real-world scenarios. Our evaluation and comparative analyses unequivocally demonstrate the high efficiency of our method in minimizing energy consumption.
Federated Learning in Wireless Networks via Over-the-Air Computations

In a multi-agent system, agents can cooperatively learn a model from data by exchanging their estimated model parameters, without the need to exchange the locally available data used by the agents. This strategy, often called federated learning, is mainly employed for two reasons: (i) improving resource-efficiency by avoiding to share potentially large data sets and (ii) guaranteeing privacy of local agents' data. Efficiency can be further increased by adopting a beyond-5G communication strategy that goes under the name of Over-the-Air Computation. This strategy exploits the interference property of the wireless channel. Standard communication schemes prevent interference by enabling transmissions of signals from different agents at distinct time or frequency slots, which is not required with Over-the-Air Computation, thus saving resources. In this case, the received signal is a weighted sum of transmitted signals, with unknown weights (fading channel coefficients). State of the art papers in the field aim at reconstructing those unknown coefficients. In contrast, the approach presented here does not require reconstructing channel coefficients by complex encoding-decoding schemes. This improves both efficiency and privacy.
Motion Planning for The Estimation of Functions*

We consider the problem of estimation of an unknown real valued function with real valued input by an agent. The agent exists in 3D Euclidean space. It is able to traverse in a 2D plane while the function is depicted in a 2D plane perpendicular to the plane of traversal. By viewing the function from a given position, the agent is able to collect a data point lying on the function. By traversing through the plane while paying a control cost, the agent collects a finite set of data points. The set of data points are used by the agent to estimate the function. The objective of the agent is to find a control law which minimizes the control cost while estimating the function optimally. We formulate a control problem for the agent incorporating an inference cost and the control cost. The control problem is relaxed by finding a lower bound for the cost function. We present a kernel based linear regression model to approximate the cost-to-go and use the same in a control algorithm to solve the relaxed optimization problem. We present simulation results comparing the proposed approach with greedy algorithm based exploration.
Computer Generated Imagery Animation and Visual Effects Education in India during the Covid-19 pandemic: A significant assessment

The COVID-19 outbreak has been designated an epidemic in India. Lockdown was implemented on March 25, 2020 to fight COVID-19, affecting the country's school system. It has transformed conventional education into an Ed Tech paradigm where teaching and evaluation are done online. This research tries to highlight the challenges Animation and Visual Effects (VFX) educators experience while teaching and assessing online in India. This study used interpretive phenomenological analysis (IPA). Educators from public and private universities were surveyed. Animation and VFX Online instructors and evaluators were semi-structured. The research identified different types of difficulties experience while teaching and assessing online. Lack of basic amenities, external distractions, family disruptions during instruction and evaluation were key difficulties mentioned. Institutional support hurdles included training, technical assistance, and clarity, lack of understanding of online teaching platforms, In addition to a lack of technical understanding was came out from the research. That's good news for institutions that plan to frequently utilize online Computer-Generated Imagery (CGI) Animation and Visual Effects (VFX) education and evaluation in the future.
Intelligent Personalized Nutrition Guidance System Using IoT and Machine Learning Algorithm

Nowadays, dietary issues are increasing around the world. Numerous problems, such as weight gain, obesity, diabetes, etc., can arise from an unbalanced diet. By integrating image processing, the system can assess food images in new ways to provide personalized solutions for better eating habits. The system uses machine learning, IoT, and image processing techniques to evaluate food image data and extract useful information. IoT devices like smartphones or specialized cameras take images of food and send to the cloud for analysis. The Support Vector Machine (SVM) is a machine learning technique that is integrated with Internet of Things (IoT) technology in this study to propose a novel method for creating a system that uses personalized nutritional suggestions. The SVM algorithm is then used to analyze and analyze this data to find patterns, correlations, and individual dietary requirements. A cloud database is used to process and store all the data related to food consumption. The system analyzes food images and determines the nutrition and caloric content of the food using image processing and segmentation. The system takes up new abilities using an extensive collection of dietary records that have been labeled and include details about the sorts of foods, portion sizes, and nutritional value. The system can predict and identify users' nutritional needs, deficits, and personalized dietary objectives by using this trained SVM model to their nutritional data.
Data integrity validation using host write counts in Solid State Drive (SSD) in digital forensic investigation.

Solid-state drives (SSD) are currently replacing hard disc drives (HDD) in most computing systems. In terms of performance, SSDs have proven to be superior to HDDs, which has increased the need for SSDs to replace HDDs. However, SSD design is not forensically sound because data integrity is questionable. Due to TRIM, Garbage Collection, and Wear Leveling, the data in SSD constantly changes, causing problems with data integrity validation in digital forensics. Validation of data integrity using hash algorithms is essential for proving the authenticity of evidence collected from a suspect system. To explain this issue, an experiment has been conducted to collect the data while on an active as well as idle state of SSD to analyze the write count parameters of SMART (Self-Monitoring, Analysis, and Reporting Technology) as well as the power on count parameter to determine the data integrity that can assist in digital forensic investigation process.
Application Interface Methods and Structures for Using Natural Language Requests

Software developers have to connect various application interface projects in their area of work. One of the challenges during the development phase is understanding the documentation and test requests, the methods of a particular application interface. Sometimes libraries are very difficult to understand, implement and build into a product due to the presence of different structures and logic. In this work, we have analyzed various cloud systems to implement communication between systems and queries through natural language processing. This study proposes approaches to building solutions for using natural language processing for application interfaces and provides a table of general level sentence model templates, as well as tables of projections and conditions of the relationship between the request and the application interface framework. The possibility of creating such an interface for using natural language processing will simplify and improve the development of software that uses the application interface.
Lightweight Anonymous Key Sharing Authentication Protocol for Resource-Constrained IoT Devices

The Internet of Things (IoT), as a new type of communication network, is involved in developing and applying secure transmission and storing a large amount of sensitive information. Due to the IoT devices' constrained resources, high connection costs, sluggish transmission speeds, and the inapplicability of security fundamentals like public key methods and digital signatures for simple device authentication, this paper presents a lightweight anonymous key-sharing authentication protocol for IoT devices. The protocol utilizes the tamper-resistant and anti-cloning properties of Hardware Physical Unclonable Functions (PUFs) to generate shared keys. In addition, it incorporates security primitives such as the MASK algorithm and Hash functions. The security analysis and verification using Ban logic and the proper tool ProVerif demonstrate that the protocol can defend against attacks such as man-in-the-middle, de-synchronization, impersonation, and modeling attacks. Compared with other protocols, this protocol exhibits the characteristics of low computational cost, minimal communication overhead and storage capacity, and high-security performance, making it suitable for secure communication and transmission in resource-constrained devices.
Emotion-Enhanced Content Recommendation in IoT-Connected Entertainment Environments for Personalized Streaming Experiences

The Internet of Things (IoT) and content recommendation systems are converging, which provides a new paradigm for improving the personalization of streaming experiences. This convergence occurs within the context of the shifting landscape of entertainment consumption. This study investigates the topic of "Emotion-Enhanced Content Recommendation in IoT-Connected Entertainment Environments." A ground-breaking framework for identifying the emotional states of viewers while streaming content has been established by integrating Internet of Things devices with technology for emotion identification. This system uses real-time data from the Internet of Things devices, such as wearables and ambient sensors, to pick up on subtle emotional indicators such as facial expressions and physiological reactions. These insights into users' feelings are then used to dynamically alter content suggestions so that the people align with the predominant emotional contexts of individual users. The use of contextual emotional intelligence not only boosts user engagement and happiness but also plots a road toward a streaming ecology that is more empathic and immersive. This research investigates ethical problems, highlights technical hurdles, and highlights the possibility of individualized emotional resonance in entertainment powered by the IoT.
Optimal Active Sensing Control for Two-Frame Systems

This paper provides a complete characterization of the trajectories that maximize the information collected by a moving vehicle, through sensors' measurements, for the recently introduced class of nonlinear “two-frame systems”. The information is quantified in terms of the trace of the observability Gramian (OG) along a trajectory. In general, this quantity nontrivially depends on the control inputs and the state trajectory, resulting in a difficult optimal control problem. Herein, we leverage the property of invariant filtering that Jacobians are state-trajectory independent, that is, only depend on the control inputs, which enables us to mathematically derive optimal trajectories in closed form. We illustrate the results numerically on problems from robotics such as 3D robot localization, and 2D simultaneous localization and mapping.
Oblique Logistic Function for the Rank-Frequency Distribution of Letters

The rank-frequency distribution of smaller subword units do not usually exhibit a linear behavior on the logarithmic scale in contrast to word n-grams. While the latter has been extensively studied in the literature since the seminal work by Zipf, relatively less attention has been paid to the distributional properties of letters both from an analytical and empirical point of view. In this paper, we propose a continuous function that can operate akin to a double pareto distribution with two regimes to fit rank-frequency data of letters. For evaluation, we compare it to the beta rank function, which has been consistently found to be the most accurate representation of such distributions by previous studies. Applied on a hundred different languages from a parallel corpus, it was shown that the proposed function yields a lower root mean square error on average outperforming the parameterized beta rank function for the majority of languages.
Securing Pedestrian Crosswalks in Smart Cities: An Embedded Vision System for Pedestrian Detection and Safety Enhancement

One of the most severe issues in the context of rapidly growing intelligent cities is the safety of pedestrians at urban crosswalks. To address this issue, the system developed an embedded vision system for real-time pedestrian detection and to enhance safety at crosswalks. A camera module captures real-time video, an image processor analyzes the captured data using a pedestrian detection algorithm, and a microcontroller manages the system's other functions. Our system can accurately identify pedestrians inside the crossing area using deep learning-based object detection models. If pedestrians are detected, an LED warning system alerts drivers to either slow down or stop, drastically decreasing the possibility of an accident. A real-time pedestrian count and any applicable safety warnings are also shown on an LCD screen. By connecting with existing systems, this smart technology may significantly improve the security of smart cities for pedestrians. Preliminary testing has demonstrated the technology has great potential for deployment in real-world urban environments.
Exploring Effective Distillation of Self-Supervised Speech Models for Automatic Speech Recognition

Self-supervised learning (SSL) has achieved great success in speech processing, but always with a large model size to increase the modeling capacity. This may limit its potential applications due to the expensive computation and memory costs introduced by the oversize model. Compression for SSL models has become an important research direction of practical value. To this end, we explore the effective distillation of HuBERT-based SSL models for automatic speech recognition. First, a comprehensive study of different student model structures is conducted. On top of this, as a supplement to the regression loss widely adopted in previous works, a discriminative loss is introduced for HuBERT to enhance the distillation performance, especially in low-resource scenarios. In addition, we design a simple and effective algorithm to distill the front-end input from waveform to Fbank feature, resulting in 17% parameter reduction and doubling inference speed, at marginal performance degradation.
Detection of Vowel Errors in Children’s Speech using Synthetic Phonetic Transcripts

The analysis of phonological processes is crucial in evaluating speech development disorders in children, but encounters challenges due to limited children audio data. This work focuses on automatic vowel error detection using a two-stage pipeline. The first stage uses a fine-tuned cross-lingual phone recognizer (wav2vec 2.0) to extract phone sequences from audio. The second stage employs a language model (BERT) for classification from a phone sequence, entirely trained on synthetic transcripts, to counteract the very broad range of potential mistakes. We evaluate the system on nonword audio recordings recited by preschool children from a speech development test. The results show that the classifier trained on synthetic data performs well, but its efficacy relies on the quality of the phone recognizer. The best classifier achieves an 94.7% F1 score when evaluated against phonetic ground truths, whereas the F1 score is 76.2% when using automatically recognized phone sequences.
Visual Concept Formation Using a Dynamic System

Spatial signal processing algorithms often use pre-given coordinate systems to label pixel positions. These processing algorithms are thus burdened by an external reference grid, making the acquisition of relative, intrinsic features difficult. This is in contrast to animal vision and cognition: animals recognize features without an external coordinate system. We show that a visual signal processing algorithm with content-based addressing is not only important for animal vision, but also fundamental for concept formation. In this paper we start with a visual object deformation transfer experiment. We then formulate an algorithm that achieves deformation-invariance with relative coordinates. The paper concludes with the implications for general concept formation.