Miracle-dz/UniFAR-FTUs huggingface dataset:
PAPR Reduction in OFDM Signal Using Machine Learning Base Tone Reservation

The orthogonal frequency division multiplexing (OFDM) is a wireless communication modulation and multiplexing technology that has been widely used in practice due to its salient advantages, i.e., high spectral efficiency and multi-path fading resistance. However, OFDM signals is known to suffer from High Peak-to-Average Power Ratio. PAPR reduction techniques are subject of extensive research. Among them, Tone Reservation technique is considered a viable solution, as it is capable of reducing the PAPR of OFDM signals without scarifying the BER performance; this is achieved by reserving some subcarriers to control the peaks. Assigning appropriate signals to these subcarriers is required to maximize the PAPR reduction performance. In this paper, we apply two machine leaning algorithms, namely Support Vector Machine (SVM) and Artificial Neural Network (ANN) to help determine appropriate peak canceling signals for the Tone Reservation PAPR reduction technique. Numerical results on a relatively small subcarrier OFDM system have shown that both SVM and ANN offer good performance in assigning peak canceling signals as compared to the solution obtained by the exhaustive search, with ANN being a slightly better than SVM.
Genetic variability of two populations of Pseudoplatystoma reticulatum from the Upper Paraguay River Basin

Catfishes of the genus Pseudoplatystoma are very important species due to both their high commercial value and their ecological role as voracious predators. They undertake lengthy migratory movements during their life-cycle, this including reproductive migration which occurs from October to December in the rainy season. In the present study, seven microsatellite loci were analyzed to access genetic variability in two samples of P. reticulatum from the Upper Paraguay Basin. The loci were highly polymorphic (mean = 7.28). According to all analysis, the two samples of P. reticulatum revealed pronounced genetic differentiation. Fst value was 0.2290, Rst value 0.1067 and AMOVA 22.90% (Fst ) and 10.67% (Rst ), all being highly significant (p < 0.001). The division of the fishes into two groups was confirmed by microsatellite multi-locus Bayesian assignment testing. The results obtained present evidence of genetic structuring in a P. reticulatum population.
Merkel Cell Carcinoma in Patients with Long‐Term Ingestion of Arsenic

Merkel Cell Carcinoma in Patients with Long‐Term Ingestion of Arsenic: Sheng‐Yow HO, et al. Departments of Radiation Oncology, Sin‐Lau Christian Hospital, Taiwan—Merkel cell carcinoma (MCC) is a rare primary neuroendocrine carcinoma of the skin, mostly occurring late in life on sun‐exposed body parts. Little is known about the specific etiological factors in the pathogenesis of MCC. A previous report indicated that arsenic exposure might cause MCC, which might be another specific type of skin cancer associated with arsenic exposure. On the southwest coast of Taiwan, high arsenic levels in artesian well water have been documented, and various diseases associated with arsenic exposure have been found to be prevalent in this area. We report two pathologically confirmed cases of MCC in patients who had histories of long‐term ingestion of arsenic from drinking water. The tumors were on the anterior chest wall, an area less exposed to the sun, in both cases. The literature on the dose‐response relationship between arsenic exposure and MCC is limited. We estimated that the total arsenic ingested by these two cases was around 14.7 and 2.6 gm, respectively. We also tried to assess the cancer risk on the basis of the estimated doses of arsenic exposure and the cancer risk model developed by the U.S. Environmental Protection Agency (EPA). The estimated lifetime target cancer risk was 1.3 × 10–2 in Case 1 and 2.3 × 10–3 in Case 2. Both are much higher than the 10–6 upper limit on lifetime cancer risk put forth by the U.S. EPA health protection standard. We believe that arsenic intoxication played an important role in the carcinogenic process of MCC in our cases.
Unravelling the Landscape of Mental Accounting Research Using Machine Learning: A Topic Modelling Approach

The heightened global recognition of mental accounting in the realm of behavioural finance research has attracted the attention of academics, scholars, industry professionals, financial institutions, and decision-makers. This study utilizes bibliometric analysis and structural topic modelling to investigate a dataset of 388 articles from Elsevier’s Scopus database over the past two decades (2002-2022), with a specific focus on the field of mental accounting. It analyses publication trends, influential countries, and notable journals. The United States leads with 102 articles, mainly single-country publications (85 SCPs and 17 MCPs), resulting in an MCP Ratio of 0.167. The "Journal of Economic Psychology" emerges as a prominent source, contributing 18 articles (4.6% of the total articles). Structural topic modelling results reveal five thematic clusters: Financial decision-making and investment biases; Consumer behaviour in purchases, spending, and payments; Consumption patterns and economic decisions; Accounting, taxation, and pricing effects; and Perceived value of mental accounting. The future research agenda for mental accounting research encompasses interdisciplinary approaches, innovative methodologies, and a strong focus on practical applications aimed at enhancing financial decision-making and overall financial well-being. These findings enhance understanding of key topics and keywords in mental accounting. These findings carry substantial significance for a diverse audience, including scholars, financial institutions, and professionals.
Complementary Radially Shaped Split Resonator Structure for Quality Assessment of Cooking Oils

A complementary radially shaped split resonator (CRSR) sensor structure with improved sensitivity is proposed in this work for the quality assessment of cooking oils. The developed sensor resonates at 3 GHz with a quality factor of 110 and quite a high uniform field concentration in the sensing region. The improved fractional sensitivity (8.7%), and the enhanced electric field confinement are some of the salient features of the proposed sensor as compared to those of a typical complementary split ring resonator (CSRR) of the same dimension. The structure is simulated using the CST MWS with its equivalent circuit developed in ADS. The fabricated high-sensitive RF sensor prototype is employed to differentiate the cold-pressed oils and their respective commercial versions, by recording the change in the insertion loss and the resonant frequency corresponding to these two versions of the oil. It is demonstrated that the proposed sensor due to its high sensitivity is able to provide a reasonable shift in the resonant frequency as well as the transmission magnitude notch for the pure and refined versions of both the coconut oil and the mustard oil.
ASPN: Adaptive Spatial Propagation Network for Depth Completion

Acquiring accurate pixel-wise scene depth maps is crucial to various visual applications such as autonomous driving and augmented reality. Most recent works are based on spatial propagation network (SPN). They generate initial dense depth maps and fixed affinity matrices for refining the former. However, the refinement process is rigid since the affinity matrices are fixed. For this purpose, we propose an adaptive spatial propagation network (ASPN) consisting of a state-aware (SA) module and a feature state-aware (FSA) module. SA can dynamically update the affinity matrices according to current depth completion states using a gated recurrent unit (GRU). SA improves the refinement flexibility and avoids the transmission of incorrect information in iterations. We further extend SA to feature levels as FSA, which expands the propagation receptive field and enhances the feature representation. Experiments on KITTI Depth Completion (DC) dataset and NYU Depth V2 dataset prove our ASPN balances volume and performance well. Specifically, ASPN requires only one-sixth of iterations and less than half the model parameters of our baseline method (NLSPN) to match or even exceed its performance.
Improved Facial Emotion Detection Using CoAtNet and Temperature Scaling

Facial emotion detection is a pivotal AI technology with broad applications in healthcare and entertainment, enhancing natural human-machine interactions. However, existing methods, based on traditional machine learning and deep learning, often suffer from accuracy and robustness limitations. This research introduces an emotion detection method based on the CoAtNet model. We compare the efficacy of CNN and transformer backbones and investigate the role of temperature scaling in emotion classification. Our approach retains pixel-level information by optimising downsampling layers, preserving critical facial nuances, and enhances training stability through temperature scaling. Experimental results, based on the RAF-DB dataset, reveal a 9% and 8% accuracy improvement over the ResNet baseline and the original CoAtNet method.
Machine Learning Based Framework for Human Recognition Using Gait Pattern Analysis

Gait analysis has become a promising biometric method for identifying people, having special benefits including non-intrusiveness and potential long-range identification. The proposed methodology involves 2 different approaches: Temporal and Spatial. In the temporal approach, we use sequential machine learning models on the width, mass and width/mass vectors. In the spatial approach, we use convolutional neural networks on Gait Energy Image, Skeleton Gait Signature and Dynamic Image. To validate our method, we have used a standard gait dataset, CASIA-A. We achieve a best accuracy of 100% using the width vector on 0-degree view of CASIA-A. The results add to the literature in biometrics by offering insightful information and laying the groundwork for future developments in gait-based identification systems.
Privacy-Focused Edge AI-on-IoT with Federated Learning

Artificial Intelligence (AI) running locally on the Internet-of-Things (IoT) Edge devices is referred to as Edge AI. Edge AI can be used to augment IoT applications prioritizing data privacy, low latency, and high availability. Federated Learning (FL) is a Machine Learning technique based on the concept of collaborative model training. With FL, a privacy-focused, flexible Edge AI framework can be created for IoT applications. This paper proposes a FL-based Edge AI, using a Convolutional Neural Network (CNN) for image classification. The primary research objective is to create a flexible, privacy-focused Edge AI framework capable of catering to a variety of IoT Edge devices. To improve accessibility of the Edge AI, optimization has been attempted in terms of finding optimal training parameters for FL and implementing model optimization. Our proposed Edge AI framework is able to accommodate several types of IoT Edge devices, and is capable of being trained via FL. Experimental results showed an accuracy improvement of 19.59% from modifying training parameters, although a balance was needed to prevent overfitting. Implementing model optimization reduced computing resource usage during inference. A CPU time reduction of 59.93% was observed, with only a 3.60% corresponding drop in model accuracy. Furthermore, model optimization also enabled a low-power IoT Edge device to run a complex CNN model, a task it was previously incapable of doing.
SentiWordNet and LDA Analysis of Foreign Tourist

This research aims to explore and understand the sentiments and concerns of foreign tourists visiting Borobudur, Indonesia, through the application of sentiment analysis and LDA modeling on textual reviews. The sentiment analysis using SentiWordNet, and LDA topic modeling to identify key topics. Initial results highlight five prominent negative topics, including concerns about overcrowding, animal welfare, temple preservation, differentiated ticket prices, and interactions with sellers.
Review of Face Emotion Recognition Using Feature Extraction Techniques

Face Emotion Recognition is a much-needed technique in a number of business, community and government applications. It involves a number of processes including feature extraction and selection. The present paper provides a thorough review of existing literature on the topic. The review provides various pointers for future research on Face Emotion Recognition. Some of the pointers are features types, feature extraction methods, feature selection methods and emotion classifying techniques. The study shows promising attributes for face emotion recognition like color, shape, texture of the face image. It also provides a comparative study of different techniques used for emotion classification techniques such as support vector machine, neural networks, Gabor Filters etc.
Exploring the Effectiveness of Various Deep Learning Techniques for Text Generation in Natural Language Processing

Natural Language Processing (NLP) demands the generation of text that exhibits cohesion, fluidity, and semantic coherence. Text generation plays a pivotal role in achieving this objective. Over time, the evolution of Deep Learning (DL) techniques has led to the emergence of several methods for generating text, including Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformers. This study undertakes a comprehensive examination of DL methods for text generation within the realm of NLP.After providing a general overview of text generation and its inherent challenges, an extensive exploration of the various deep learning models and their adaptations is conducted. The strengths and limitations of these models are meticulously assessed, while their performance relative to more traditional approaches is also examined. To conclude, current trends are illuminated, and unanswered questions within this domain are posed. Beyond simply identifying areas ripe for further investigation, this review aims to equip both scholars and practitioners with a comprehensive understanding of the latest developments in DL-based text generation.
Production of 3‐geranyl‐4‐hydroxybenzoate acid in yeast, an important intermediate of shikonin biosynthesis pathway

Abstract Shikonin and its derivatives are the main active components in the medicinal plant Arnebia euchroma and possess extensive pharmaceutical properties. In this study, we developed an optimized yeast system to obtain high‐level production of 3‐geranyl‐4‐hydroxybenzoate acid (GBA), an important intermediate involved in shikonin biosynthesis pathway. For host selection, recombinant expression of p‐hydroxybenzoate:geranyltransferase (PGT) derived from A. euchroma was performed in Saccharomyces cerevisiae WAT11U strain and high yield monoterpene strain. In shake flask culture with 1 mM p‐hydroxybenzoate acid (PHBA), they could yield GBA at 0.1567 and 20.8624 mg L−1, respectively. Additionally, AePGT6 showed higher enzymatic activity than its homologs. Moreover, by combining improvement in the homologous mevalonate pathway with reconstruction in the heterologous shikimic pathway, a de novo GBA synthesis pathway was constructed in StHP6tHC with co‐overexpressed SctHMG1, optimized EcUbiC and AePGT6. A high titer of 179.29 mg L−1 GBA was achieved in StHP6tHC under shake flask fermentation with 1 mM PHBA. These results suggest that yeast could be engineered systematically to enable an efficient monoterpene‐quinone or naphthoquinone production.
3D Semantic Mapping based on RGB-D Camera and LiDAR Sensor in Beach Environment

The increasing amount of sea garbage proving to be a huge problem not only for marine life but also causing a tremendous number of troubles to humans living along the coastlines. Especially Japan, that is surrounded by the ocean which has a long coastline that spans thousands of kilometers, and this provides ample opportunities for marine debris to accumulate. This paper proposes a 3D mapping system utilizing the semantic information from the RGBD sensor extracted from a specifically trained state-of-art deep learning neural network to merge with the spatial information from the LiDAR sensor using Max Fusion Technique. This paper also aims to contribute to the implementation of deep learning and autonomous driving in beach cleaning-related robots and spark interest and raise awareness of the public in beach clean-up activities. It is also shown that the proposed system can achieve a mean accuracy of 98% approximately with the uniquely trained deep learning model.
E Ve Nt -re La T Ed Po T En Ti a Ls an D M Ag Ne T Ic Fi E Ld S I N Th E H U Ma N B Ra In

To uncover the neural bases of a cognitive process it is important both to identify the participating brain regions and determine the precise time course of information transmission within and among those regions. Although neu-roimaging techniques based on cerebral blood flow or metabolism (e.g., positron emission tomography [PET] and functional magnetic resonance imaging [fMRI]) are providing increasingly detailed pictures of the anatomic regions activated during cognitive activity, these methods lack the temporal resolution to reveal the rapid-fire patterning of neuronal communication. Noninvasive recordings of the electrical and magnetic fields generated by active neuronal populations, however, can reveal the timing of brain activity related to cognition with a very high, msec-level resolution. This chapter gives an overview of how these temporally precise recording techniques have been used to analyze perceptual and cognitive mechanisms in the human brain. The changes in field potentials that are time-locked to sensory, motor, or cognitive events are known as event-related potentials (ERPs) and the corresponding magnetic field changes are termed event-related fields (ERFs). Both ERPs and ERFs consist of precisely timed sequences of waves of varying field strength and polarity (Fig. 32.1). These observed peaks and troughs in the waveform are often referred to as ''components. '' Some authors, however, prefer to use the term component to refer to portions of the wave-form that originate from particular neural structures, whereas others consider ERP/ERF components to be those waveform features that are associated with a particular cog-nitive process or manipulation (2). Both ERPs and ERFs are generated primarily by the flow of ionic currents in elongated nerve cells during synaptic activity. Whereas syn-aptic currents flowing across nerve cell membranes into the extracellular fluid produce ERPs, the flow of synaptic current through neuronal processes produce ERFs, thereby giving rise to concentric magnetic fields surrounding the cell. When a sufficient number of neurons having a similar ana-tomic configuration are synchronously active, their sum-mated fields may be strong enough to be detectable at the scalp. When ERPs or ERFs are recorded from the surface of the head, the locations of the active neural generators can only be estimated rather than visualized directly. The calculation of generator locations from surface field distributions is known as the inverse problem, which typically has no unique solution. However, the validity of inverse source estimations can be considerably improved by using algorithms and models that take into account the geometry of the cortical surface, biophysical properties …
Self-Service Business Intelligence: Meeting User Needs in a Data-Rich World

In the current data-driven era, businesses continuously generate vast quantities of data, underscoring the critical importance of deriving actionable insights from this data deluge to inform decision-making. Despite the potential of Business Intelligence (BI) tools, a noticeable gap persists. Most existing BI tools require extensive customization for non-technical users, posing barriers to accessing and comprehending essential business metrics. This paper introduces a novel self-service business intelligence (BI) system that utilizes the Python programming language. The system aims to address the existing gap in the field. The system places emphasis on several key factors, including ease of use, flexibility in data formatting, preprocessing capabilities, relevancy of data insights, and the inclusion of a user-friendly interface. The evaluation process dif-fers from the traditional approach by including a direct comparison with the widely recognized software Microsoft Power BI. Encouragingly, our findings indicate a significant enhancement in the user experience for non-technical users. This affirms the effectiveness and usability of our system in facilitating more informed decision-making. The Python-based solution we have developed enables individuals without technical expertise to effectively engage with, examine, and extract valuable insights from intricate datasets, while circumventing the generally challenging learning process associated with business intelligence (BI) technologies. As we collectively navigate the data-driven environment, our endeavors contribute to the improvement of the accessi-bility and usability gap within the domain of business intelligence.
Analysis of affective speech recordings using the superpositional intonation model

This paper presents an analysis of affective sentences spoken by a single speaker. The corpus was analyzed in terms of different acoustic and prosodic features, including features derived from the decomposition of pitch contours into phrase and accent curves. It was found that sentences spoken with a sad affect were most easily distinguishable from other affects as they were characterized by a lower F0, lower phrase and accent curves, lower overall energy and a higher spectral tilt. Fearful was also relatively easy to distinguish from angry and happy as it exhibited flatter phrase curves and lower accent curves. Angry and happy were more difficult to distinguish from each other, but angry was shown to exhibit a higher spectral tilt and a lower speaking rate. The analysis results provide informative clues for synthesizing affective speech using our proposed recombinant synthesis method.
Parallel Golomb-Rice Decoder with 8-bit Unary Decoding for Weight Compression in TinyML Applications

Due to the recent advances in AI, the requirement for Artificial Intelligence (AI) has increased exponentially in the domain of Internet of Things (IoT). Running Deep Neural Networks (DNNs) on edge devices gives the advantage of privacy, security, and lower latency. It is challenging to deploy them on embedded devices with constrained hardware resources since a lot of compute and memory resources are required. Memory access contributes to the majority of the energy requirements on edge devices. Although data compression plays a critical role in reducing storage and memory bandwidth requirements, most of the hardware decoders are inefficient in terms of power, area, and throughput. In this work, a hardware Parallel Golomb-Rice decoder is presented that can decode 8-bits of unary encoded data every cycle. The design has been integrated with a Neural Network (NN) accelerator and experimented with state-of-the-art benchmark models. Lossless compression is performed with an offline Golomb-Rice encoder. It encodes the weights of each layer with an optimum Golomb-Rice parameter. Applied to the benchmarks Anomaly Detection, Image Classification and Visual Wake Words the memory access during inference is reduced by 26.8%, 6.62% and 5.54% respectively. The decoder dissipates 0.4216 mW of power and delivers an average throughput of 860 MBps. The design has been synthesised with 40 nm technology and compared with state-of-the-art works.