Reviewerly/exHarmony huggingface dataset:
Leveraging Unstructured Data to Improve Customer Engagement and Revenue in Financial Institutions: A Deep Reinforcement Learning Approach to Personalized Transaction Recommendations

Personalized customer transaction insights have become increasingly important for financial institutions to increase customer engagement and revenue. In this paper, we propose a novel methodology that combines deep learning and reinforcement learning techniques to extract features from customer demographics and capture temporal dynamics of customer transactions to provide personalized credit card recommendations. Our methodology is designed to learn a policy that maximizes the expected reward for providing personalized recommendations to customers. To gain additional insights about customer preferences and sentiments, we utilize both structured and unstructured data, including the transaction descriptions. To process the unstructured transaction descriptions, we use natural language processing techniques to extract relevant features and insights. Incorporating unstructured data into the proposed methodology can improve the accuracy and effectiveness of the customer transaction insights, and can provide a more comprehensive understanding of customer preferences and behaviors. We test our proposed methodology on the publicly available IEEE-CIS Fraud Detection dataset, which contains over 590,000 transactions with information such as transaction amount, transaction date and time, and transaction description. For the test set, we achieve an AUC-ROC score of 0.9485, outperforming existing techniques for customer transaction insights. Our findings show that delivering customised credit card suggestions to customers has the potential to increase revenue for financial institutions. Overall, the use of deep learning and reinforcement learning techniques, as well as structured and unstructured data, can provide a more thorough understanding of consumer transaction data, allowing for more accurate and effective personalized suggestions. The use of unstructured data is a crucial component of our technique since it can give financial institutions with new insights and value. It is critical to appropriately anonymize and secure the customers whose data is used in the study.
SLNER: Chinese Few-Shot Named Entity Recognition with Enhanced Span and Label Semantics

Few-shot named entity recognition requires sufficient prior knowledge to transfer valuable knowledge to the target domain with only a few labeled examples. Existing Chinese few-shot named entity recognition methods suffer from inadequate prior knowledge and limitations in feature representation. In this paper, we utilize enhanced Span and Label semantic representations for Chinese few-shot Named Entity Recognition (SLNER) to address the problem. Specifically, SLNER utilizes two encoders. One encoder is used to encode the text and its spans, and we employ the biaffine attention mechanism and self-attention to obtain enhanced span representations. This approach fully leverages the internal composition of entity mentions, leading to more accurate feature representations. The other encoder encodes the full label names to obtain label representations. Label names are broad representations of specific entity categories and share similar semantic meanings with entities. This similarity allows label names to offer valuable prior knowledge in few-shot scenarios. Finally, our model learns to match span representations with label representations. We conducted extensive experiments on three sampling benchmark Chinese datasets and a self-built food safety risk domain dataset. The experimental results show that our model outperforms the F1 scores of 0.20–6.57% of previous state-of-the-art methods in few-shot settings.
A stable control method for planar robot with underactuated constraints via motion planning and intelligent optimization

For the planar robot with underactuated constraints, a stable control method is presented on the foundation of motion planning method and intelligent optimization, which includes two stages. (1) Designing the controllers to control the actuated manipulators to given target states. (2) Planning the motion trajectory combined with the underactuated constraints between all links, using the intelligent algorithm to find the adaptable trajectory parameters, and tracking such planned trajectories to control full manipulators to the given states simultaneously. At last, multigroup simulations demonstrate the validity of the proposed method.
Focused Crawling for Automated IsiXhosa Corpus Building

IsiXhosa is a low-resource language, which means that it does not have many large, high-quality corpora. This makes it difficult to perform many kinds of research with the language. This paper examines the use of focused Web crawling for automatic corpus generation. The resulting corpus is characterised using statistical methods: its vocabulary growth has been found to fit Heaps’ Law, and its word frequency has been found to be heavy-tailed. In addition, as expected, the corpus statistics did not match expectations from non-agglutinative languages.
SReResNet: A stage recursive residual network for suppressing semantic redundancy during feature extraction

Feature extraction neural networks are essential components of computer vision systems. As the most famous one, ResNet has been widely used in engineering. Although many modern networks have outperformed ResNet, the costly pretraining and hyper-parameter optimization processes prevent their application of them in industrial computer vision systems. To avoid these costly processes, a Stage Recursive Residual Network (SReResNet) is proposed, which merely adjusts the forward propagation pipeline without changing the parameter architecture of ResNet. Thus, it can inherit existing model parameters of ResNet and replace ResNet through simple fine-tuning. Moreover, SReResNet is the first network to improve accuracy by utilizing the semantic trend during feature extraction instead of well-designed modules. It models a series of cascaded modules as a semantic unit and feeds the high-level feature maps back to the low-level modules for further semantic redundancy suppression through one feedback connection within the unit based on the semantic trend and the mechanism of looking and thinking twice. For object detection tasks, a Stage Recursive Feature Pyramid Network (SReFPN) is also proposed to rethink and suppress semantic redundancy further. Experiments demonstrate that SReResNet outperforms its counterparts in object detection and image classification tasks. On the MS COCO 2017 dataset, SReResNet outperforms ResNet with 1.2 Box AP improvement, and SReResNet with SReFPN further achieves 2.5 Box AP improvement without bells and whistles. On the CIFAR-100 dataset, SReResNet outperforms its counterparts, such as ResNet, DenseNet, and ConvNeXt, with at least 2.33% top-1 acc improvement. The code is available at https://github.com/unbelieboomboom/SReResNet.
Examining the co-integrating relationship between financial development and economic growth

This study investigated the nexus between financial development and economic growth, exploring the short-run and long-run impact of financial development on economic growth in the Sothern African Development Community (SADC). The SADC was fragmented into middle-income countries and low-income countries so as to ascertain the income effect on the nature of the relationship, an exploration not considered by previous studies. The study used panel data covering the period 1980 to 2020. It employed the Autoregressive Distributed Lag (ARDL) Bounds and the Toda and Yamamoto and Dolado and Lütkepohl (TYDL) models to examine the relationship and the direction of causality respectively. In the short run, financial development, through domestic credit to the private sector, spurs economic growth and in the long run financial development focus should be on promoting bank deposits. Causality tests between economic growth and financial development tests showed mixed results depending on the variable used for measuring financial development. In addition, the SADC countries are encouraged to strengthen their financial sector legislation, strategies, and banking system supervision so as to enhance the financial sector efficiency as well as to realise the benefits of financial innovation.
The Perennial Exit of Teachers From Alaska and the Inadequacy of Herzberg Theory: A Follow-Up Qualitative Study

&lt;p style="text-align:justify"&gt;The researcher assessed the subject teachers’ levels of satisfaction and/or dissatisfaction using the Herzberg Two-Factor Theory. The study assesses employee motivation and hygiene, as discussed in the first part of the study about exploring the socio-demographic profile and job satisfaction level among teachers in Alaska. Frederick Herzberg (1923–2000) was a well-known author, and his work was praised by the Harvard Business Review. His theory has been widely accepted by many different groups and studies, with many people adapting and applying the theory to business management. For many years, teachers have been leaving school districts, particularly in Alaska, and this project seeks to investigate the causes of this phenomenon. Running a school is like running a business, except your clients or customers are students and parents. You must be able to choose what type of education they require. Schools have a system structure that is like a business. A school district that oversees several schools is analogous to a conglomerate that oversees a group of businesses. The study was conducted in the Bering Strait School District, in northwest Alaska, which serves fifteen isolated villages and two islands. Various methods including survey questionnaires, interviews and data gathering, were used and implemented. Out of 30 targeted respondents, 13 had high hygiene and high motivator factor (HHHM) scores and were further analyzed through interviews, with the data gathered subjected to thematic analysis. It was noted that some of the factors that affected the respondents in leaving their school are professional-related reasons, professional growth, job stability, physiological growth, no consistent reliable childcare options or childcare, very cold weather, and emotional reasons. Lack of these factors can influence the participants to seek such services elsewhere—from another workplace that can provide them with these services, and thus give them comfort, and peace of mind as well.&lt;/p&gt;
Secure EVMs Against Tampering with AES Encryption and Hashing

Conducting elections in a country where population is increasing rapidly like wildfire, is the most challenging aspect for the government. Some people take advantage of this aspect and create disturbances by involving in tampering the Electronic Voting Machines (EVMs). This is a situation the authorities face in conducting elections, which is emerging as a problem to be taken care with an utmost priority. This paper presents a method to know if the EVMs have tampered or not and when the EVM is tampered if they are found to be tampered. This method uses the powerful AES Algorithm, an advanced symmetric key cryptographic technique that provides better security and efficiency. It is paired with various hashing algorithms (MD5, SHA-1, SHA-256, and Whirlpool) for validation. Hence, the proposed model defines a unique way to secure the EVMs from tampering. In addition, the method is experimented with various key sizes and hashing algorithms.
Noise resistant steam generator water level reconstruction for nuclear power plant based on deep residual shrinkage network

Nuclear power plant (NPP) steam generator (SG) water level has strong nonlinear pattern and lagging characteristic under control system and manual adjustment. SG water level signal accidental missing and frequent abnormity may lead to incorrect operational intervention or even emergency shutdown. Deep learning SG water level signal reconstruction method trained with historical sensor data can provide supportive information for operators’ decision-making to decrease the financial cost of maintenance and improve the safety of NPP assets. However, the sensor data transferred by electronic devices can be mixed up with noise in transients or complex conditions. This study proposed a noise resistant SG water level signal reconstruction method based on deep residual shrinkage network (DRSN). The dataset in this study is collected from a power-down process collected from a NPP in China. Results from the experiments under different levels of noise are conducted to illustrate the efficacy and robustness of the proposed approaches compared with other deep learning signal reconstruction methods.
AInvR: Adaptive Learning Rewards for Knowledge Graph Reasoning Using Agent Trajectories

Multi-hop reasoning for incomplete Knowledge Graphs (KGs) demonstrates excellent interpretability with decent performance.Reinforcement Learning (RL) based approaches formulate multi-hop reasoning as a typical sequential decision problem.An intractable shortcoming of multi-hop reasoning with RL is that sparse reward signals make performance unstable.Current mainstream methods apply heuristic reward functions to counter this challenge.However, the inaccurate rewards caused by heuristic functions guide the agent to improper inference paths and unrelated object entities.To this end, we propose a novel adaptive Inverse Reinforcement Learning (IRL) framework for multi-hop reasoning, called AInvR.(1) To counter the missing and spurious paths, we replace the heuristic rule rewards with an adaptive rule reward learning mechanism based on agent's inference trajectories; (2) to alleviate the impact of over-rewarded object entities misled by inaccurate reward shaping and rules, we propose an adaptive negative hit reward learning mechanism based on agent's sampling strategy; (3) to further explore diverse paths and mitigate the influence of missing facts, we design a reward dropout mechanism to randomly mask and perturb reward parameters for the reward learning process.Experimental results on several benchmark knowledge graphs demonstrate that our method is more effective than existing multi-hop approaches.
Efficient Algorithm for the $k$-Means Problem with Must-Link and Cannot-Link Constraints

Constrained clustering, such as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -means with instance-level Must-Link (ML) and Cannot-Link (CL) auxiliary information as the constraints, has been extensively studied recently, due to its broad applications in data science and AI. Despite some heuristic approaches, there has not been any algorithm providing a non-trivial approximation ratio to the constrained <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -means problem. To address this issue, we propose an algorithm with a provable approximation ratio of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O(\log k)$</tex> when only ML constraints are considered. We also empirically evaluate the performance of our algorithm on real-world datasets having artificial ML and disjoint CL constraints. The experimental results show that our algorithm outperforms the existing greedy-based heuristic methods in clustering accuracy.
Foreword

Extract Modern crypto currencies like Bitcoin rely on blockchain technology and they make use of various cryptographic algorithms. The average user of a crypto currency has little knowledge how the underlying system works and what assumptions have to be satisfied which guarantee that a user’s wallet cannot be accessed by an un-authorized person. Even if the content of a wallet is safeguarded carefully and transactions are done in an intended manner one might wonder to what degree cryptography guarantees anonymity and security of the funds. The book by Andreas Bolfing explains in detail how a crypto-currency like Bitcoin is constructed and what underlying cryptographic algorithms are used. Not only this but Bolfing explains also in details the mathematical structure of the cryptographic algorithms. For this he develops in an elementary way the necessary mathematical tools from group theory, ring theory and number theory. He also gives an introduction into complexity theory and the theory of elliptic curves. A reader who had some mathematics courses at university level should be well prepared to follow the content of the book.
Dedication

Extract This book is dedicated to my parents, Lisbeth and Kurt Bolfing, who were always standing by me, even in difficult times. Mom and Dad, you are the reason why I am what I am today. Thank you for your unbiased support and your unfailing love. The second part of the dedication is for a good friend. The generated SHA256 value is given by 425b583f1d6e31af2b18a46f4e7342eb4ae8da03bd7bd7363d2b68a04aa0b79b. Perhaps someday someone will be able to reverse the hash value back into the original message. If so, please let her know.
Unveiling Thyroid Disease Associations: An Exceptionality-Based Data Mining Technique

Background: The prevalence of thyroid disease has seen a rapid increase in recent times, primarily attributed to the fast pace of lifestyles that often result in poor dietary choices, work-life imbalances, social stress, genetic mutations, and improved diagnostic capabilities. However, the precise contribution of these factors to thyroid disease remains a subject of controversy. Consequently, there is a pressing need to gain a comprehensive understanding of the related associations in order to potentially mitigate the associated morbidity and mortality rates. Methods: This study employed association rule mining techniques to reveal hidden correlations among complex and diverse epidemiological connections pertaining to thyroid disease associations. We proposed a framework which incorporates text mining and association rule mining algorithms with exceptionality measurement to simultaneously identify common and exception risk factors correlated with the disease through real-life digital health records. Two distinctive datasets were analyzed through two algorithms, and mutual factors were retained for interpretation. Results: The results confirmed that age, gender, and history of thyroid disease are risk factors positively related to subsequent thyroid cancer. Furthermore, it was observed that the absence of underlying chronic disease conditions, such as diabetes, hypertension, or obesity, are associated with reduced likelihood of being diagnosed with thyroid cancer. Conclusions: Collectively, the proposed framework demonstrates its sound feasibility and should be further recommended for different disease in-depth knowledge discovery.
A Variance Reducing Stochastic Proximal Method with Acceleration Techniques

We consider a fundamental problem in the field of machine learning-structural risk minimization, which can be represented as the average of a large number of smooth component functions plus a simple and convex (but possibly non-smooth) function. In this paper, we propose a novel proximal variance reducing stochastic method building on the introduced Point-SAGA. Our method achieves two proximal operator calculations by combining the fast Douglas–Rachford splitting and refers to the scheme of the FISTA algorithm in the choice of momentum factors. We show that the objective function value converges to the iteration point at the rate of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{O}(1/k)$</tex> when each loss function is convex and smooth. In addition, we prove that our method achieves a linear convergence rate for strongly convex and smooth loss functions. Experiments demonstrate the effectiveness of the proposed algorithm, especially when the loss function is ill-conditioned with good acceleration.
HOMOCHAR: A novel adversarial attack framework for exposing the vulnerability of text based neural sentiment classifiers

State-of-the-art deep learning algorithms have demonstrated remarkable proficiency in the task of text classification. Despite the widespread use of deep learning-based language models, there remains much work to be done in order to improve the security of these models. This is particularly concerning for their growing use in sensitive applications, such as sentiment analysis. This study demonstrates that language models possess inherent susceptibility to textual adversarial attacks, wherein a small number of words or characters are modified to produce an adversarial text that deceives the machine into producing erroneous predictions while maintaining its true meaning for human readers. The current study offers HOMOCHAR, a novel textual adversarial attack that operates within a black box setting. The proposed method generates more robust adversarial examples by considering the task of perturbing a text input with transformations at the character level. The objective is to deceive a target NLP model while adhering to specific linguistic constraints in a way such that the perturbations are imperceptible to humans. Comprehensive experiments are performed to assess the effectiveness of the proposed attack method against several popular models, including Word-CNN, Word-LSTM along with five powerful transformer models on two benchmark datasets, i.e., MR & IMDB utilized for sentiment analysis task. Empirical findings indicate that the proposed attack model consistently attains significantly greater attack success rates (ASR) and generates high-quality adversarial examples when compared to conventional methods. The results indicate that text-based sentiment prediction techniques can be circumvented, leading to potential consequences for existing policy measures.
An Improved Robust Sparse Convex Clustering

Convex clustering, turning clustering into a convex optimization problem, has drawn wide attention. It overcomes the shortcomings of traditional clustering methods such as K-means, Density-Based Spatial Clustring of Applications with Noise (DBSCAN) and hierarchical clustering that can easily fall into the local optimal solution. However, convex clustering is vulnerable to the occurrence of outlier features, as it uses the Frobenius norm to measure the distance between data points and their corresponding cluster centers and evaluate clusters. To accurately identify outlier features, this paper decomposes data into a clustering structure component and a normalized component that captures outlier features. Different from existing convex clustering evaluating features with the exact measurement, the proposed model can overcome the vast difference in the magnitude of different features and the outlier features can be efficiently identified and removed. To solve the proposed model, we design an efficient algorithm and prove the global convergence of the algorithm. Experiments on both synthetic datasets and UCI datasets demonstrate that the proposed method outperforms the compared approaches in convex clustering.