Miracle-dz/UniFAR-FTUs huggingface dataset:
Impacts on the Grid-Side Voltage Due to the Current Unbalance Amplification Caused by Induction Motors

It is well known that supply voltage unbalance is detrimental to induction motors as it increases the losses, reduces the output torque and the overall efficiency, and increases stress on the insulation which can lead to machine failure. It is also well documented that current unbalance in induction motors is several times greater than the voltage unbalance. While induction motors are known to be victims of voltage unbalance, there has been little exploration of the converse. What impacts do IMs operating under unbalanced voltage have on the power system? This paper focuses on analyzing these effects via simulation using the Electromagnetic Transients Study Program (EMTP) by utilizing the model of a test power system with preexisting supply voltage unbalance and several induction motors. The distribution system under test is the IEEE 34-node test feeder with a nominal voltage of 24.9 kV and unbalanced loads. Induction motors are connected at nodes with highest unbalance to observe effects throughout the system and results pertaining to change in voltage and current unbalance are presented and analyzed.
Relative information capacity of simple relational database schemata

Fundamental notions of relative information capacity between database structures are studied in the context of the relational model. Four progressively less restrictive formal definitions of "dominance" between pairs of relational database schemata are given. Each of these is shown to capture intuitively appealing, semantically meaningful properties which are natural for measures of relative information capacity between schemata. Relational schemata, both with and without key dependencies, are studied using these notions. A significant intuitive conclusion concerns the informal notion of relative information capacity often suggested in the conceptual database literature, which is based on accessability of data via queries. Results here indicate that this notion is too general to accurately measure whether an underlying semantic connection exists between database schemata. Another important result of the paper shows that under any natural notion of information capacity equivalence, two relational schemata (with no dependencies) are equivalent if and only if they are identical (up to re-ordering of the attributes and relations). The approach and definitions used here can form part of the foundation for a rigorous investigation of a variety of important database problems involving data relativism, including those of schema integration and schema translation.
Scale‐Invariant Directional Alignment of Surface Parametrizations

Various applications of global surface parametrization benefit from the alignment of parametrization isolines with principal curvature directions. This is particularly true for recent parametrization‐based meshing approaches, where this directly translates into a shape‐aware edge flow, better approximation quality, and reduced meshing artifacts. Existing methods to influence a parametrization based on principal curvature directions suffer from scale‐dependence, which implies the necessity of parameter variation, or try to capture complex directional shape features using simple 1D curves. Especially for non‐sharp features, such as chamfers, fillets, blends, and even more for organic variants thereof, these abstractions can be unfit. We present a novel approach which respects and exploits the 2D nature of such directional feature regions, detects them based on coherence and homogeneity properties, and controls the parametrization process accordingly. This approach enables us to provide an intuitive, scale‐invariant control parameter to the user. It also allows us to consider non‐local aspects like the topology of a feature, enabling further improvements. We demonstrate that, compared to previous approaches, global parametrizations of higher quality can be generated without user intervention.
A Novel Hybrid Fingerprint-Based Indoor Localization Scheme with Hierarchical Training

Due to its numerous applications, indoor localization is gaining relevance in industrial and general-purpose fields. Although the usage of machine learning-based methods has been proposed previously for indoor localization, most of the solutions provide generally unsatisfactory results when localization is performed with only one access point (AP) and Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) propagation conditions are combined. In order to account for this problem, a novel hybrid fingerprints-based indoor localization scheme with hierarchical training is proposed in this paper. Various types of representative large-scale and small-scale channel characteristics are used to define a novel hybrid fingerprint database, which allows for improving the localization performance. The proposed scheme is capable of: (i) achieving high localization accuracy when only one AP is used to transmit signals; (ii) performing LoSINLoS environmental sensing through the least-squares support vector classifiers (LS-SV C) model; and (iii) by using different methods to perform localization in the LoS and NLoS environments, enable optimal localization accuracy for each of them. More specifically, for the localization in NLoS environments, a hierarchical-SVC-support vector regressors (H-SVC-SVR) model is developed, based on hierarchical training. Simulation results are provided to prove that the proposed scheme exhibits superior advantages in localization accuracy compared to standard support vector machine (SVM)-based fingerprint indoor localization approaches. Furthermore, the method is proven to be robust to environmental changes, such as moving pedestrians. The designed approach represents a breakthrough in the field of machine learning-based indoor positioning methodologies.
When Unsupervised Domain Adaptation Meets Tensor Representations

Domain adaption (DA) allows machine learning methods trained on data sampled from one distribution to be applied to data sampled from another. It is thus of great practical importance to the application of such methods. Despite the fact that tensor representations are widely used in Computer Vision to capture multi-linear relationships that affect the data, most existing DA methods are applicable to vectors only. This renders them incapable of reflecting and preserving important structure in many problems. We thus propose here a learning-based method to adapt the source and target tensor representations directly, without vectorization. In particular, a set of alignment matrices is introduced to align the tensor representations from both domains into the invariant tensor subspace. These alignment matrices and the tensor subspace are modeled as a joint optimization problem and can be learned adaptively from the data using the proposed alternative minimization scheme. Extensive experiments show that our approach is capable of preserving the discriminative power of the source domain, of resisting the effects of label noise, and works effectively for small sample sizes, and even one-shot DA. We show that our method outperforms the state-of-the-art on the task of cross-domain visual recognition in both efficacy and efficiency, and particularly that it outperforms all comparators when applied to DA of the convolutional activations of deep convolutional networks.
Low Power and Area-Efficient Hybrid Adder for ALU Operation

This study describes a new low-power, area-efficient hybrid adder architecture for arithmetic logic unit (ALU) operations in digital systems. The goal is to integrate multiple adder architectures to reduce power consumption while increasing speed. The proposed hybrid adder combines pass transistor logic (PTL), gate diffusion input (GDI) logic, and modified GDI (MGDI) logic to provide carry and sum outputs, that reduces power consumption and improve circuit performance, with less number of transistor count. Cadence Virtuoso is used for evaluation using a 45 nm generic process design kit (GPDK) and the power supply voltage is set at 1.2 V. A comparative analysis with the existing adders has been performed considering parameters like propagation delay (PD), average power (AP), and power delay product (PDP). By balancing power consumption, circuit performance, and space utilization, the research adds to adder design. Designers and researchers of efficient adders in portable and battery-powered devices will find this information useful.
EMPATHIC: Emulating Human-Like Multimodal Personality Architecture Through Thoughtful Human-AI Conversation

In the evolving landscape of human-computer interaction, this paper introduces an innovative framework poised to revolutionize chatbot systems. Our framework, meticulously designed for emotionally aware multimodal chatbots, seamlessly integrates real-time face and emotion recognition with natural language processing. Currently in the developmental phase, the framework exhibits a distinctive proficiency in executing a diverse array of human instructions. These instructions span tasks such as generating detailed captions, object counting, and responding to general queries. Leveraging parameter-efficient fine-tuning from OpenFlamingo, augmented by the inclusion of the Low-rank Adapter (LoRA), our framework strategically prioritizes versatility and operational efficiency. The core of our approach lies in the establishment of instruction templates that intricately merge both vision and language data. This strategic integration forms the backbone of our multi-modality instruction tuning methodology, enhancing the adaptability of the chatbot. Recognizing the pivotal role of high-quality training data, we emphasize its significance in shaping the dialogue performance of the chatbot. As the framework progresses through its developmental stages, this research lays a robust foundation for the creation of a versatile conversational agent. The envisioned applications of this agent range from enhancing customer service experiences to providing valuable support in mental health scenarios.
Comparing the Effects of Oral Contraceptives Containing Levonorgestrel With Products Containing Antiandrogenic Progestins on Clinical, Hormonal, and Metabolic Parameters and Quality of Life in Women With Polycystic Ovary Syndrome: Crossover Randomized Controlled Trial Protocol

Background Oral contraceptives (OCs) have been used as a first-line option for medical treatment in women with polycystic ovary syndrome (PCOS). Despite theoretical superiority of products containing antiandrogenic progestins compared to OCs containing levonorgestrel (LNG), the clinical advantage of these compounds remains unclear. Objective The aim of this study was to compare the effects of OCs containing LNG with products containing antiandrogenic progestins including cyproterone acetate, drospirenone, and desogestrel on clinical, hormonal, and metabolic parameters and quality of life in women with PCOS. Methods We conducted a 6-arm crossover randomized controlled trial with each arm including OCs containing LNG and one of those 3 OCs containing antiandrogenic progestins. The anthropometric and clinical manifestations and hormonal and biochemical parameters of participants were assessed at 6 time points including baseline, after washout period, and 3 and 6 months after intervention. Results The study is ongoing and follow-up of recruited women will continue until 2018. Conclusions This study will provide scientific evidence on comparability of OCs with the various progesterones that will assist in decision making taking into account cost effectiveness. Trial Registration Iranian Registry of Clinical Trials IRCT201702071281N2; http://www.irct. ir/searchresult.php? keyword=&id=1281&number=2&prt=12869&total=10&m=1 (Archived by WebCite at http://www.webcitation.org/6tSP8FNWo)
Memer: News Meme Generator Using Gpt-3: Attention Of Young Generations To Current Affairs

Young people are paying less attention to current affairs and using social media most of the time in the information explosion. Recently, memes have been widely used by the young for entertainment. For current affairs, we investigated whether memes could be used for current affairs using an automatic meme generator. The meme generator was integrated into a social system on websites to increase the younger generation's attention to current affairs.
Evaluating CNN-BiLSTM Models for LOD Predictions

The term “Earth Orientation Parameters” (EOPs) refers to parameters that indicate variations in how the Earth rotates. Numerous real-life applications, such as spacecraft navigation, weather forecasting, and disaster management, depend on correct, short-term forecasts of Earth orientation parameters (EOP). The hardest component of the EOP to forecast is the LOD (Length Of Day), which is a measure of variations in Earth's rotation rate. This study aims to evaluate CNN-BiLSTM to forecast and model the time series of LOD. The methodology can be described as follows: The trend components are first removed using polynomial curve fitting and least-square methods. After that, the obtained residuals are used in the CNN-BiLSTM model for predictions. The predictions are then evaluated for mean absolute error using different input sizes.
Beyond Key-Based Authentication: A Novel Continuous Authentication Paradigm for IoTs

We propose a secure and lightweight continuous authentication scheme for IoT devices as an alternative to key-based authentication based on physical channels. Existing physical-channel-based authentication methods have limitations such as poor robustness, high reconciliation overhead, and a lack of secrecy for authentication keys. Our proposed authentication scheme utilizes the IoT devices' grant-free non-orthogonal multiple access transmission model for seed generation and authentication, providing continuous authentication by examining access time slots and spreading sequences. Thus, the proposed authentication scheme eliminates the need for coherent keys, thereby concealing seed information from attackers. Our scheme outperforms benchmark schemes, showing reduced misdetection of illegitimate devices and near-zero false alarms. It also has significantly lower computational complexity compared to other benchmark schemes. These results confirm the practicality and effectiveness of our proposed authentication scheme for IoT devices.
Optimizing Advantage Actor-Critic with Policy Gradient and Deep Q-learning to Maximize Profit in Forex Trading Prediction

Financial markets, such as forex markets, are inherently complex, with high volatility, noise, trends, and market shocks. Therefore, designing effective trading strategies is crucial for maximizing investment returns. The implementation of deep reinforcement learning (DRL) in financial trading has become an attractive research topic in recent years. This study proposes Advantage Actor-Critic-based algorithms (A2C) algorithms to learn forex trading strategies using deep reinforcement schemes. The ensemble trading strategy was obtained by combining the best features of the two algorithms' Policy Gradient as Actor and Q-learning as Critic, resulting in a robust strategy that adapts to different market situations. Our experiment on the EURUSD currency on the 4 hour candlestick timeframe using five years of data history was divided into three parts for training, validating, and testing. The A2C agent learns to buy, sell, and hold a trade with the goals of maximizing profits and reducing losses. Our proposed method provides an effective and robust solution to various financial trading problems using deep reinforcement learning. Our proposed methods provide effective and robust solutions to various financial trading problems using deep reinforcement learning. The evaluation results improved from each training, where the Cumulative Return evaluation increased from 29.65 to 112.74, the Maximum Drawdown reduced from 36.18% to 4.48%, the Sharp Ratio increased from 0.94 to 2.47%, and the AHPR increased from 0.19% to 3.56%. This indicates that longer training data result in higher cumulative rewards and an increase in total profit.
Research on the Application of BP Algorithm of Neural Network in Teaching Quality Evaluation

Teaching quality evaluation is an important link in the field of education, which not only relates to the improvement of teachers' teaching and students' learning effect, but also directly affects the reputation and development of schools. Traditional teaching quality evaluation methods mainly rely on expert review, questionnaire survey and other qualitative analysis, which has some problems such as strong subjectivity, low efficiency and insufficient use of data. With the development of information technology, especially the emergence of neural network technology, it provides a new solution to improve the accuracy and objectivity of teaching quality evaluation
Robust Beamforming for Covert Communications Aided by Reconfigurable Dual-Functional Surface

In this paper, we propose a robust beamforming scheme for the simultaneous transmitting and reflecting intel-ligent surface (STAR-RIS)-aided covert communication system with imperfect channel state information. Specifically, STAR-RIS is exploited to facilitate the covert transmission when the legitimate receiver and the warden are located in the different sides of the surface. To maximize the covert rate, we formulate an optimization problem to jointly design the active and passive beamforming, which is non-convex due to the CSI error-incurred semi-infinite constraints and the coupled optimization variables. To solve the problem, we employ the techniques of matrix lifting, S-procedure, and successive convex approximation, and de-velop an alternating optimization framework. Numerical results show that the proposed STAR-RIS-aided robust beamforming scheme significantly improves the covert rate, compared to its transmitting-only counterpart and the existing baseline.
Explainable AI for Breast Cancer Detection: A LIME-Driven Approach

Artificial Intelligence is transforming the healthcare industry due to the increasing accessibility of organized and unorganized information and the rapid development of analytical techniques. As artificial intelligence becomes more significant in healthcare, concerns are arising regarding the lack of transparency, explainability, and the possibility of bias in model predictions. The goal of this paper is to utilize interpretable machine learning to provide a better understanding of breast cancer using the Local Interpretable Model-agnostic Method (LIME). This study uses Local Interpretable Model-Agnostic Explanations to explain how the machine-learning model accurately classifies breast cancer cases as either Benign or Malignant. It provides a LIME plot for Benign cases, highlighting the significant role of "Bare Nuclei" where lower values strongly suggest Benign predictions. Other features like Normal Nucleoli, Marginal adhesion, single adhesion, Mitoses, and uniformity of cell size also contribute to the Benign class prediction when they fall below a particular threshold. The study also presents a LIME plot for Malignant cases, emphasizing the importance of "Bare Nuclei" and Clump thickness, where higher values indicate a higher likelihood of Malignant predictions. Other features like Normal Nucleoli, Marginal adhesion, Bland chromatin, uniformity of cell size, and Mitoses also contribute to Malignant predictions when their values exceed specific thresholds. The feature "Concave points_worst" influences the model’s benign predictions when it is below 0.07, while the "texture" feature affects predictions when it exceeds 29.41. Furthermore, the study provides further explanations for Malignant predictions based on "Concave points_worst" and "Texture. " These findings provide valuable insights into the decision-making process of the model, making it more interpretable and useful for breast cancer diagnosis.
CS 224 W Project Report : Finding Top UI / UX Design Talent on Adobe Behance

The Behance social network allows professionals of diverse artistic disciplines to exhibit their work and connect among each other. We investigate the network properties of the UX/UI designer subgraph. Considering the subgraph is motivated by the idea that professionals in the same discipline are more likely to give a realistic assessment of a colleagues work. We therefore developed a metric to assess the influence and importance of a specific member of the community based on structural properties of the subgraph and other measures of popularity. Furthermore, we investigated spacial properties of the social graph. We identified appreciations as a useful measure to include in a weighted PageRank algorithm, as it adds a measure of prestige to the ranking that is not contained in the structural information of the graph. With this pagerank, we identified locations that have a high density of influential UX/UI designers. Investigating the geographic distribution of the Behance social graph further, we found that there are two separate processes that generate connections, a geographically dense circle of presumably persons from the direct professional environment of the artist and a geography independent network of connections.
EmoDetect: A Learning-Centred Affective Database for Detecting Student Frustration in Online Learning

The role of facial expressions in human interaction is essential, with significant strides being made in the development of systems designed to automatically recognise these expressions in the context of human-computer interaction (HCI). However, there is a noticeable lack of databases dedicated to training these automatic detectors for learning-centric affect detection in online learning. Existing annotated databases often draw primarily on images sourced from movies, the internet, or simulated expressions in controlled laboratory environments, and they fail to capture learners’ facial expressions at different stages and in varying contexts of genuine learning experiences. To address this gap, we present EmoDetect, a novel database comprising video recordings of 30 students’ facial expressions and screen-captured interactions with an online learning system as they engage in online learning tasks. By analysing these videos, we have identified facial action units related to frustration and discovered new action units and body language specific to learning that can aid in detecting learning-centred frustration. These findings enhance our understanding of automatic recognition of students’ affective states in online learning and offer valuable data for future research.
Can we extend local sea-ice measurements to satellite scale? An example from the N-ICE2015 expedition

ABSTRACT Knowledge of Arctic sea-ice conditions is of great interest for Arctic residents, as well as for commercial usage, and to study the effects of climate change. Information gained from analysis of satellite data contributes to this understanding. In the course of using in situ data in combination with remotely sensed data, the question of how representative local scale measurements are of a wider region may arise. We compare in situ total sea-ice thickness measurements from the Norwegian young sea ICE expedition in the area north of Svalbard with airborne-derived total sea-ice thickness from electromagnetic soundings. A segmented and classified synthetic aperture radar (SAR) quad-pol ALOS-2 Palsar-2 satellite scene was grouped into three simplified ice classes. The area fractions of the three classes are: 11.2% ‘thin’, 74.4% ‘level’, and 14.4% ‘deformed’. The area fractions of the simplified classes from ground- and helicopter-based measurements are comparable with those achieved from the SAR data. Thus, this study shows that there is potential for a stepwise upscaling from in situ, to airborne, to satellite data, which allow us to assess whether in situ data collected are representative of a wider region as observed by satellites.