Reviewerly/exHarmony huggingface dataset:
Security Analysis and the Collection of Hard and Soft Information

We use minute-by-minute Bloomberg online status microdata during 2017-2021 to directly study how hard and soft information collection affects equity analyst performance. Collection of hard information, proxied by office workday length, is positively associated with the quantity and timeliness of analyst reports. Soft information collection, as proxied by propensity to travel, is positively correlated with the market’s reaction to recommendation changes and the likelihood of becoming a star analyst. Both hard and soft information collection improve forecast precision, a causal result that we confirm using the COVID lockdown as an instrument.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Riesz transform phase-shifting technique from a single fringe pattern

A computational four-step phase-shifting method based on the Riesz transform is proposed for optical phase extraction from a single fringe pattern. It provides a robust solution in interferometry and can replace the conventional phase-shifting techniques.
A More Flexible And Robust Feature Selection Algorithm

With the rising number of actual data, the challenges of huge model operations and poor generalization capacity arise, making the selection of the right feature set a significant issue. This study proposes ImprovedRFECV, an enhanced feature selection approach for cross-validated recursive feature elimination (RFECV). The algorithm first increases the robustness of the optimal feature subset by randomly sampling different data, building multiple models, and comparing the scores. Simultaneously, L1 regularization term and L2 regularization term scores are introduced to thoroughly evaluate the value of each feature, reducing the influence on the anti-interference term and further improving the algorithm’s accuracy and stability. Additionally, a multi-model ensemble learning framework is utilized to promote generalization ability and effectively prevent overfitting. Finally, a both-end expansion removal strategy is adopted to solve the problem of strong covariance of features while enhancing the flexibility of the algorithm. The experimental results show that, when compared to the RFECV algorithm, the ImprovedRFECV algorithm obtains fewer optimal average features and performs better on the optimal feature subset on five datasets from five different domains, demonstrating the algorithm’s high robustness and generalization ability.
A-MAD: An Adaptive-Microseismic Activity Detector based on Gaussian Mixture Models and Spectral Subtraction

Continuous monitoring of active volcanoes is essential for understanding their behavior and providing accurate forecasts and warnings. Automating the detection of volcano microseismic events plays a crucial role in large-scale analysis, early warning systems, and efficient handling of large-scale data. This paper presents a novel approach for the automated detection of microseismic events associated with volcanic activity, focusing on the Cotopaxi volcano in Ecuador. Inspired by voice activity detection (VAD) systems used in speech processing, we propose an Adaptive-Microseismic Activity Detector (A-MAD) that modifies VAD techniques to identify frames within seismic signals containing volcanic activity. The A-MAD system incorporates a spectral subtraction stage to mitigate the impact of environmental noise and employs Gaussian Mixture Models (GMMs) to model the probabilistic distribution of microseismic events. Mel Frequency Cepstral Coefficients (MFCCs) are used as features to describe the seismic signals, enabling adaptability for varying noise levels. Experimental results on signals from Cotopaxi Volcano in Ecuador demonstrate the effectiveness of the A-MAD system, achieving high accuracy (96.39% for discrete events and 98.45% for continuous signals) while meeting the efficiency requirements of volcano monitoring institutions. This work contributes to the advancement of early warning systems for volcanic eruptions, providing a robust and adaptive approach for microseismic activity detection.
Perturbation AUTOVC: Voice Conversion from Perturbation and Autoencoder Loss

AUTOVC is a voice-conversion method that performs self-reconstruction using an autoencoder structure for zero-shot voice conversion. AUTOVC has the advantage of being easy and simple to learn because it only uses the autoencoder loss for learning. However, it performs voice conversion by disentangling speech information from speakers and linguistic information by adjusting the bottleneck dimension; this requires highly meticulous fine tuning of the bottleneck dimension and involves a tradeoff between speech quality and speaker similarity. To address these issues, neural analysis and synthesis (NANSY)—a fully self-supervised learning system that uses perturbations to extract speech features—is proposed. NANSY solves the problem of the adjustment of the bottleneck dimension by utilizing perturbation and exhibits high-reconstruction performance. In this study, we propose perturbation AUTOVC, a voice conversion method that utilizes the structure of AUTOVC and the perturbation of NANSY. The proposed method applies perturbations to speech signals (such as NANSY signals) to solve the problem of the voice conversion method using bottleneck dimensions. Perturbation is applied to remove the speaker-dependent information present in the speech, leaving only the linguistic information, which is then passed through a content encoder and modeled as a content embedding containing only the linguistic information. To obtain speaker information, we used x-vectors, which are extensively used in pretrained speaker recognition. The concatenated linguistic and speaker information extracted from the encoder and additional energy information is used as input to the decoder to perform self-reconstruction. Similar to AUTOVC, it is easy and simple to learn using only the autoencoder loss. For the evaluation, we measured three objective evaluation metrics: character error rate (%), cosine similarity, and short-time objective intelligibility, as well as a subjective evaluation metric: mean opinion score. The experimental results demonstrate that our proposed method outperforms other voice conversion techniques and demonstrated robust performance in zero-shot conversion.
How Does a Transformer Learn Compression? An Attention Study on Huffman and LZ4

Transformers have excelled in natural language processing and vision domains. This leads to the intriguing proposition: can Transformers be adapted to a more generalized framework, such as understanding general finite state machines? To explore this, we trained a Transformer network on compression algorithms like Huffman and LZ4, viewing them as stepping stones towards mastering general finite state machines. Our analysis indicates that Transformers can adeptly internalize these methods and replicate essential states, echoing human-like interpretation via their attention mechanisms. This provides evidence of their capability to decipher practical finite state machines. Examining the attention maps offers insights into the Transformer’s methodology when engaging with these compression techniques. In Huffman encodings, the encoder predominantly focuses on input statistics to define the present state, which the decoder subsequently leverages to produce output bits. Remarkably, with a 2nd-order Markov input, the encoder’s attention is prominently directed at the previous two symbols, underscoring its proficiency in summarizing input statistics. The cross-attention maps further elucidate the exact association between input symbols and output bits. For the more complex LZ4 compression, the attention maps vividly portray the Transformer’s processing of input sequences and the close linkage between the input and resulting output bit stream. This study underscores the Transformer’s proficiency in comprehending compression algorithms and its keen ability to grasp input statistics, implying that its mechanisms, as illustrated by attention maps, provide profound interpretability.
MediBioDeBERTa: Biomedical Language Model with Continuous Learning and Intermediate Fine-Tuning

The emergence of large language models (LLMs) has marked a significant milestone in the evolution of natural language processing. With the expanded use of LLMs in multiple fields, the development of domain-specific pre-trained language models (PLMs) has become a natural progression and requirement. Developing domain-specific PLMs requires careful design, considering not only differences in training methods but also various factors such as the type of training data and hyperparameters. This paper proposes MediBioDeBERTa, a specialized language model (LM) for biomedical applications. First, we present several practical analyses and methods for improving the performance of LMs in specialized domains. As the initial step, we developed SciDeBERTa v2, an LM specialized in the scientific domain. In the SciERC dataset evaluation, SciDeBERTa v2 achieves the state-of-the-art model performance in the named entity recognition (NER) task. We then provide an in-depth analysis of the datasets and training methods used in the biomedical field. Based on these analyses, MediBioDeBERTa, was continually trained on SciDeBERTa v2 to specialize in the biomedical domain. Utilizing the biomedical language understanding and reasoning benchmark (BLURB), we analyzed factors that degrade task performance and proposed additional improvement methods based on intermediate fine-tuning. The results demonstrate improved performance in three categories: named entity recognition (NER), semantic similarity (SS), and question-answering (QnA), as well as in the ChemProt relation extraction (RE) task on BLURB, compared with existing state-of-the-art LMs.
Enhanced Deep Deterministic Policy Gradient Algorithm using Grey Wolf Optimizer for continuous Control Tasks

Deep Reinforcement Learning (DRL) allows agents to make decisions in a specific environment based on a reward function, without prior knowledge. Adapting hyperparameters significantly impacts the learning process and time. Precise estimation of hyperparameters during DRL training poses a major challenge. To tackle this problem, this study utilizes Grey Wolf Optimization (GWO), a metaheuristic algorithm, to optimize the hyperparameters of the Deep Deterministic Policy Gradient (DDPG) algorithm for achieving optimal control strategy in two simulated Gymnasium environments provided by OpenAI. The ability to adapt hyperparameters accurately contributes to faster convergence and enhanced learning, ultimately leading to more efficient control strategies. The proposed DDPG-GWO algorithm is evaluated in the 2DRobot and MountainCarContinuous simulation environments, chosen for their ease of implementation. Our experimental results reveal that optimizing the hyperparameters of the DDPG using theGWO algorithm in the Gymnasium environments maximizes the total rewards during testing episodes while ensuring the stability of the learning policy. This is evident in comparing our proposed DDPG-GWO agent with optimized hyperparameters and the original DDPG. In the 2DRobot environment, the original DDPG had rewards ranging from -150 to -50, whereas, in the proposed DDPG-GWO, they ranged from -100 to 100 with a running average between 1 and 800 across 892 episodes. In the MountainCarContinuous environment, the original DDPG struggled with negative rewards, while the proposed DDPG-GWO achieved rewards between 20 and 80 over 218 episodes with a total of 490 timesteps.
An Oracle-Based Framework for Implementing a Quantum Parallel Decoder/Multiplexer

Quantum computers have already shown high potential in solving complex computational problems much more efficiently compared to the typical conventional supercomputers. One major challenge in the field of quantum computing is to implement the major operations of the conventional computers using the available quantum hardware (quantum gates). Such operations are the arithmetic operations as well as decoding and multiplexing. An obvious burden is the absence of quantum gates that directly implement the fundamental Boolean operations like AND and OR. Moreover, care should be taken in order to reduce the costs involved when implementing a quantum circuit. In this paper, we present an oracle-based approach to implement quantum decoding and multiplexing in a joint circuit. The use of oracles necessarily implies that some type of quantum programming will be used. A quantum program is a process organized in a sequence of computations whose data elements are selected from the set of the known quantum gates. In our approach, the oracles are used as pre-programmed black boxes that determine if a datum should be directed from the input to the output. The important idea behind our scheme is that the oracles operate in parallel, therefore their selection decisions are taken simultaneously, while the approaches found in the literature implement sequential circuits with feedbacks from the outputs to inputs that burden the overall performance. We have used Qiskit open-source software development kit to verify the correctness of our design through extensive simulations.
The Analysis of Music Emotion and Visualization Fusing Long Short-Term Memory Networks under the Internet of Things

This paper aims to provide an automatic analysis of music emotion, enhancing users’ ability to perceive and comprehend the emotional nuances conveyed in music intuitively. It first reviews the limitations of traditional music emotion analysis methods, and highlights the transformative potential of Internet of Things (IoT) technology in addressing these limitations. Subsequently, the paper employs the Long Short-Term Memory (LSTM) network to model time series data in music, integrating it with the Sequence-to-Sequence (STS) framework to construct an advanced music emotion analysis model. Finally, several machine learning algorithms are adopted to train and evaluate the system. The findings underscore the efficacy of the music emotion analysis model based on the LSTM network fused with STS, demonstrating notable success in the task of music emotion prediction. Specifically, the fusion model achieves an average absolute error of Arousal value of 0.921, a root mean square error (RMSE) of 0.534, and an R square of 0.498. The average absolute error in the Valence value is 0.902, the RMSE is 0.575, and the R square is 0.478. This paper holds significant implications for a deeper understanding of music emotion and provides guidance for music recommendation and creation.
An Edge-Directed Diffusion Equation-based Image Restoration Approach for Font Generation

The intelligent algorithms-based font generation has been a prevalent online application. In existing researches, visual communication effect of font generation results is usually neglected. To deal with this issue, this paper combines sparse representation and convex set projection restoration algorithm, and proposes a font generation image restoration method based on edge Diffusion equation. Firstly, the image is modeled using sparse representation methods, and sparsity constraints are introduced to make the solution sparse and convenient for computation. Then, the Inverse problem is transformed into a Convex optimization problem, and the global optimal solution is obtained by using the properties of Convex optimization. Finally, image restoration and reconstruction are realized by edge Diffusion equation. In this paper, we propose an image restoration method for font generation based on edge Diffusion equation. By combining sparse representation and convex set projection restoration algorithms, fast image restoration and high-quality reconstruction have been achieved. And performance evaluation was conducted using the peak signal-to-noise ratio (PSNR) metric. The experimental results have well verified the correctness of this method. The experimental results show that this method can effectively solve the ill posed problem in the Inverse problem, and achieve fast image restoration and high-quality reconstruction. Compared with traditional image restoration methods, this method has higher restoration quality and faster computational speed.
Incrementally Learned Angular Representations for Few-Shot Class-Incremental Learning

The main challenge of FSCIL is the trade-off between underfitting to a new session task and preventing forgetting the knowledge for earlier sessions. In this paper, we reveal that the angular space occupied by the features within the embedded area is relatively narrow. Consequently, after the base session training with the converged feature extractor, adding features of new classes easily overlaps with the previously occupied space of previous classes. Furthermore, in contrast to the base session classes, whose features are relatively well aggregated, the features of new session classes are dispersed over large regions. Thus, we propose the Incrementally Learned Angular Representation (ILAR) learning structure to address these issues. During the base session, ILAR attempts to increase the marginal spacing between the distributions of features for each class. In incremental sessions, modifications of features and classifiers corresponding to the previous session classes are limited to preserve the past knowledge while the features of new classes are readjusted. Furthermore, we generate additional features according to the statistics learned from the base session to reduce the variance caused by data shortage. Experiments proceeded on three popular benchmark datasets, including CIFAR100, miniImageNet, and CUB200. We demonstrate that the proposed method achieves state-of-the-art performances by effectively enhancing the new session classification ability while preserving the knowledge of the past sessions.
Cnnformer: Transformer-Based Semantic Information Enhancement Framework for Behavior Recognition

Behavior recognition is a vital task in computer vision. While, semantic information extraction is still insufficient in behavior recognition models. In this paper, we propose a improved behavior recognition model, which is called Cnnformer, to alleviate the problem of inadequate semantic information extraction. Cnnformer is transformer-based semantic information enhancement model for behavior recognition. In Cnnformer, a new attention mechanism is designed and introduced into the encoder module. This attention mechanism uses dilated convolution to capture static context information, trigger mining dynamic context information, and obtain the final fused dynamic and static context information. In addition, four layers of convolution are added in front of the encoder module, which has a strong induction bias to extract the superficial feature representation (such as color, geometry, texture, etc.). Finally, Cnnformer combines the convolution module and the attention module into the encoder module to learn simultaneously both local and global features, so as to enhance visual representation. Experimental results show that Cnnformer has higher performance in behavior recognition, and the accuracy of Top-1 is 3.4% higher than that of the basic model in the Kinetics-400 dataset.
Deep Learning Meets Blockchain for Automated and Secure Access Control

Access control is a critical component of computer security, governing access to system resources. How- ever, designing policies and roles in traditional access control can be challenging and difficult to maintain in dynamic and complex systems, which is particularly problematic for organizations with numerous resources. Furthermore, traditional methods suffer from issues such as third-party involvement, inefficiency, and privacy gaps, making transparent and dynamic access control an ongoing research problem. Moreover detecting malicious activities and identifying users who are not behaving appropriately can present notable difficulties. To address these challenges, we propose DLACB, a Deep Learning Based Access Control Using Blockchain, as a solution to decentralized access control. DLACB uses blockchain to provide transparency, traceability, and reliability in various domains such as medicine, finance, and government while taking advantage of deep learning to not rely on predefined policies and eventually automate access control. With the integration of blockchain and deep learning for access control, DLACB can provide a general frame- work applicable to various domains, enabling transparent and reliable logging of all transactions. As all data is recorded on the blockchain, we have the capability to identify malicious activities. To expedite this process, we store a list of malicious activities in the storage system and employ a verification algorithm to cross-reference it with the blockchain. We conduct measurements and comparisons of the smart contract processing time for the deployed access control system in contrast to traditional access control methods, determining the time overhead involved. The processing time of DLBAC demonstrates remarkable stability when exposed to increased request volumes. This approach is particularly useful for organizations seeking to automate access control while simultaneously detecting and preventing data breaches to enhance security.
Automatic Scratch Detection in Sports Net Using IoT

The proposed system IOT sports net TOUCH detecting sensor is a low cost alternative which can help to sense the player whether they touch the net or not. This can effectively calculate the basic 3 parameters of the sport coaching system. Parameters are strength, technical and skills. K036 humanic sensors are used to detect the foul. Furthermore, the user can sense the individual player continuously and the location status is updated in real-time. Therefore, this proposed system can overcome the traditional method of a single coach system. At the same time, it offers cost savings and reliability which convince the users all the time. Finding the player’s foul point is quit helpful. In a humanic touch sensor, a module works by detecting a user's touch. When a user touches the sensor, the module typically uses capacitive sensing technology to detect the change in capacitance caused by the user's touch. This change in capacitance is then used to trigger an action or output from the module, such as a change in light or sound. The module can also be programmed to detect more complex interactions, such as swipe gesture or a tap. The module is typically connected to a microcontroller, which can be programmed to interpret the user's input and trigger a specific action.