Reviewerly/exHarmony huggingface dataset:
PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning

Detecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data machine learning methods have already found widespread adoption. However, deep learning approaches are not yet commonly applied to ocean bottom data due to a lack of appropriate training data and models. Here, we compiled an extensive and labeled ocean bottom seismometer dataset from 15 deployments in different tectonic settings, comprising ~90,000 P and ~63,000 S manual picks from 13,190 events and 355 stations. We propose PickBlue, an adaptation ot the two popular deep learning networks EQTransformer and PhaseNet. PickBlue joint processes three seismometer recordings in conjunction with a hydrophone component and is trained with the waveforms in the new database. The performance is enhanced by employing transfer learning, where initial weights are derived from models trained with land earthquake data. PickBlue significantly outperforms neural networks trained with land stations and models trained without hydrophone data. The model achieves a mean absolute deviation (MAD) of 0.05 s for P waves and 0.12 s for S waves. We integrate our dataset and trained models into SeisBench to enable an easy and direct application in future deployments.
Advanced Features of Wireshark

In this chapter, we will review the capabilities of Wireshark that are in the Advanced features category and as such not referenced in many of the different documents on Wireshark. We will review the Kerberos protocol communication in more detail. Following this, we will review dissectors that allow us to extract different types of network traffic.
Overdose di storie. La narrazione senza fine dei social media

Stories are now a distinctive and established genre in social media. From Snapchat to WhatsApp, via Facebook and Instagram, more than half a billion authors (amateurs, but not only) interact with apps by composing and consuming stories that configure new literature in which alphabetic writing coexists with the growing dominance of visual language. Centered on the narrativization of the lives of the users, invited to tell and retell themselves seamlessly, the hardware architecture and software interfaces of digital devices and media seem to generate a form of addiction to narratives, a need induced in both writing and reading. In the face of such an overdose, the question remains whether those of social media are still "stories that heal" or, rather, stories that poison.
Efficient Network Representation Learning via Cluster Similarity

Network representation learning is a de-facto tool for graph analytics. The mainstream of the previous approaches is to factorize the proximity matrix between nodes. However, if n is the number of nodes, since the size of the proximity matrix is $$n \times n$$ , it needs $$O(n^3)$$ time and $$O(n^2)$$ space to perform network representation learning. The proposed approach computes the representations of the clusters from similarities between clusters and computes the representations of nodes by referring to them. If l is the number of clusters, since $$l \ll n$$ , we can efficiently obtain the representations of clusters from a small $$l \times l$$ similarity matrix. Experiments show that our approach can perform network representation learning more efficiently and effectively than existing approaches.
Location-Aware Recommender System: A review of Application Domains and Current Developmental Processes

Recommender systems (RS) have been widely used to extract relevant and meaningful information from a vast body of data, to make appropriate suggestions to users with different preferences in various domains of applications. However, despite the success of the early recommendation systems, they suffer from two major challenges of cold start and data sparsity. Traditional RS consider an interaction between user and item (2D), neglecting contextual information such as location, until fairly recently. The contexts extend traditional RS to multi-dimension interaction and provides a useful information that allow recommendations to be more personalized. Surprisingly, taking these contexts such as location, into consideration eliminates the challenges of traditional RS. Location-Aware Recommender System (LARS) takes user's location into account as an additional context. The combination allows the prediction of spatial items, items closest to the users, to reduce information overload and was proved to be more effective than earlier RS. In this research, we provide a systematic literature of the existing literature in LARS from 2010 to 2021, focusing on the state-of-the-art methodologies, the domain of applications, and trends of publications in LARS. The paper proposed several models of LARS based on the traditional RS methodologies, providing future directions to researchers. Despite numerous reviews available on LARS, a review that proposed several LARS techniques were not found in the literature. The results indicated that the trend of publication in LARS is growing exponentially and that the field is getting attention rapidly with the number of publications on the rise every year.
Proof-Carrying Data from Arithmetized Random Oracles

Proof-carrying data (PCD) is a powerful cryptographic primitive that allows mutually distrustful parties to perform distributed computation in an efficiently verifiable manner. Known constructions of PCD are obtained by recursively-composing SNARKs or related primitives. SNARKs with desirable properties such as transparent setup are constructed in the random oracle model. However, using such SNARKs to construct PCD requires heuristically instantiating the oracle and using it in a non-black-box way. [CCS22] constructed SNARKs in the low-degree random oracle model, circumventing this issue, but instantiating their model in the real world appears difficult. In this paper, we introduce a new model: the arithmetized random oracle model (AROM). We provide a plausible standard-model (software-only) instantiation of the AROM, and we construct PCD in the AROM, given only a standard-model collision-resistant hash function. Furthermore, our PCD construction is for arbitrary-depth compliance predicates. We obtain our PCD construction by showing how to construct SNARKs in the AROM for computations that query the oracle, given an accumulation scheme for oracle queries in the AROM. We then construct such an accumulation scheme for the AROM. We give an efficient “lazy sampling” algorithm (an emulator) for the ARO up to some error. Our emulator enables us to prove the security of cryptographic constructs in the AROM and that zkSNARKs in the ROM also satisfy zero-knowledge in the AROM. The algorithm is non-trivial, and relies on results in algebraic query complexity and the combinatorial nullstellensatz.
The Black-Scholes Model in QuantLib

Any remaining errors are our fault, not theirs.
Biostratigraphy of Yazdanshahr No.2 section in Kerman area, Central Iran, based on conodont communities

Yazdanshahr No.2 section, which consists mostly of Bahram and Sibzar formations is located in the Zarand city, northern Kerman province (Central Iran). Five biozones have been identified based on the conodont community. The first biozone, which includes Padehat (15 meter) and Sibzar formations (75 meter), was distinguished based on genera Bipennatus and Icriodus and the probable corresponding age was considered to span Emsian to Eifelian. The second biozone is recognized by the first appearance of index taxon Polygnathus varcus (varcus Zone: early to middle Givetian). The third biozone starts with the firth appearance of the index genus Icriodus expansus (expansus zone: middle Givetian). The base of the Fourth biozone is recognized by the first appearance of the genus I. subterminus (subterminus Zone: middle to late Givetian) and the last biozone was distinguished with the first appearance of genera Ancyrodella and Polygnathus incompletes in the Givetain-Frasnian boundary (falsiovalis to crepida Zone?: Frasnian to Famenian?). Bahram Formation (94 meter) consists of Fossiliferous limestone (varcus to subterminus Zone) at the lower part, and alternation of dolomite and sandstone (falsiovalis to crepida Zone?) at the upper part. Sibzar Formation consists of Middle Devonian dolomite and limy dolomite containing intercalation of sandstone and limestone layers.
Fine-Grained Non-interactive Key-Exchange: Constructions and Lower Bounds

In this work, we initiate a study of K-NIKE protocols in the fine-grained setting, in which there is a polynomial gap between the running time of the honest parties and that of the adversary. Our goal is to show the possibility, or impossibility, of basing such protocols on weaker assumptions than those of K-NIKE for $$K \ge 3$$ . Our contribution is threefold.
A Customized Web Spider for Why-QA Pairs Corpus Preparation

Considering the growth of researches on improving the performance of non-factoid question answering system, there is a need of an open-domain non-factoid dataset.There are some datasets available for non-factoid and even how-type questions but no appropriate dataset available which comprises only open-domain why-type questions that can cover all range of questions format.Why-questions play a significant role and are usually asked in every domain.They are more complex and difficult to get automatically answered by the system as why-questions seek reasoning for the task involved.They are prevalent and asked in curiosity by real users and thus their answering depends on the users" need, knowledge, context and their experience.The paper develops a customized web crawler for gathering a set of why-questions from five popular question answering websites viz.Answers.com,Yahoo!Answers, Suzan Verberne"s open-source dataset, Quora and Ask.com available on Web irrespective of any domain.Along with the questions, their category, document title and appropriate answer candidates are also maintained in the dataset.With this, distribution of why-questions according to their type and category are illustrated.To the best of our knowledge, it is the first large enough dataset of 2000 open-domain why-questions with their relevant answers that will further help in stimulating researches focusing to improve the performance of non-factoid type why-QAS.
Developing An E-Commerce Web Application with ReactJS and Firebase

bstract: With the rise of e-commerce platforms, shopping is now possible anytime, anywhere. Web application development is a great solution for the shopping needs of people who want to avoid the hassle of physical stores. The web application will provide users with a user-friendly interface that will allow them to easily purchase a wide range of products. This app will include various features like product search, filtering, sorting, shopping cart management, payment gateway integration. In contemporary times, most people use technology to live their lives and meet their daily needs. In this generation, most of us use e-commerce sites to buy clothes, groceries, and electronics. Additionally, this app will provide users with personalized recommendations based on their purchase history, search history, and preferences. The application is designed to provide users with stable and fast engagement. All in all, it will provide a convenient and a safe platform for users to purchase products online. With its powerful features and modern technology, the app will help users save time and effort while providing a hassle-free shopping experience.Loading time and minimalist design. The ambition of this article is to discuss the development of an app that provides users with a seamless shopping experience while ensuring security and comfort.
Impact of Varying Strokes on Recognition Rate: A Case Study on Handwritten Mathematical Expressions

Strokes constitute major units that form the contents of any handwritten text.When every user feeds or writes any handwriting sample, we archive strokes depending upon the context.In this study, the authors have experimented with recognizing handwritten mathematical expressions on datasets with varying strokes.The case study on mathematical expressions has been conducted where distinct datasets are analyzed, identified, and compared.A deep neural network-based recognizer has been deployed to test the formulated hypothesis around stroke characteristics.One experimented dataset had handwritten samples that had uniformly thin width of the stroke, whereas the other had strokes of varying width.After the experimentation, it has been observed that the dataset with thin stroke width (up to 1px) results in a significantly less effective recognition rate.The one achieved on experimenting with a dataset with varying strokes outcomes a jaw dropping recognition rate compared to the previous case.A comparative analysis has been performed to infer and affirm the hypothesis about stroke characteristics and their impact on the recognition rate.
Recommendation Systems: Types, Applications, and Challenges

Recommendation Systems help users select appropriate products or services from a wide range of choices.Thus, It solves the problem of information overload upto a remarkable extent.Specifically, It is highly applicable in certain industries that sell the product online or provide the services online.Recommendation Systems are very relevant in such a domain because they can grow their business by putting it in the practice.In this review article, we offer an overview of the Recommendation Systems and their variations and extension.We address the numerous techniques used for Recommendation Systems, including content-based filtering, collaborative filtering, sequential, session-based, etc.A comparison has been given for each technique for detailed analysis.It extends the review for the variety of dataset domains, such as movies, music, jobs, products, books, etc.Besides datasets, We have discussed various applications of the recommendation Systems across multiple domains in the industry.We survey various evaluation metrics used in a wide range of Recommendation Systems.In the end, we summarized the different challenges posed by the recommendation Systems, which helps make them more accurate and reliable.
A Comprehensive Analysis of Signature Schemes: Towards Pairing and Non-pairing, Taxonomy and Future Scopes

The notion of a "Signature scheme" carries possibilities to solve the message and key security problems.A signature scheme aims to secure the channels, IoT nodes, and Blockchain to use public resources and provide high-quality services.The Information and communication system acquires a prominent role in IoT and Blockchain applications.These signature schemes provide trust-free transparency, pseudo-anonymity, equality, motorization, decentralization, and protection.The article contributes a pervasive analysis of the literature pairing, and the non-pairing scheme provides high Security, cost-effectiveness, high service, and several keys for lightweight components.Our proposed approach analyzes the security schemes and differentiates the different security levels.The schemes introduced research contribution and research motivation.Finally, the article presents a well-organized fundamental for future work, segregation analysis of security models and schemes.This article benefits the new researcher with detailed information about signatures and critical security analysis.
DL-based Generation of facial portraits from diverse data sources

Artificial intelligence has a tremendous potential to reinforce and revolutionize law enforcement.By automating the tedious and time-consuming tasks of data collection and analysis, AI can help police departments become more effective.In this paper, we provide an overview of possible deep learning models that can be utilized by law enforcement to reconstruct the suspect's face, through generating real and sketch facial portraits.To accomplish this, we collect four types of data from the crime scene, handwritten text and audio from an officer's note, images from a smartphone, and video from a surveillance camera.Then, employ two pre-trained models: ABM-CNN for attribute multi-label classification and Google Speech API for speech-to-text conversion.In addition, we train three other models, the first of which, a handwritten model trained on the IAM Handwriting dataset, reads and digitizes handwritten notes with an accuracy of 76%, outperforming state-of-the-art results.Second, we train YoloV5 with the Wider Face dataset to detect one or multiple faces on images or videos with an average precision of 93%, a recall of 90%, and a precision of 88%.In the third model, we adapt the Zero-Shot Text-to-Image Generation technology to generate real faces and sketch.Our resulting model outperforms existing models from literature regards to high-quality, and the training reach a loss of 37.4%.
KNOWING YOUR ROOTS: ENHANCING ORAL PROFICIENCY OF ELEMENTARY STUDENTS IN KURDISTAN USING ORAL PRESENTATION AS AN ALTERNATIVE ASSESSMENT IN THE ‘FAMILY TREE’ TASK

This paper aims at presenting the specifics of designing oral presentations as an alternative assessment for elementary students in Kurdistan. Being non-native speakers of English, oral proficiency is an increasingly significant aspect of language teaching among young Kurdish learners. Thus, the purpose of oral presentation in the ‘Family Tree’ task is to build students’ confidence in speaking a foreign language (English language) among young non-native speakers in a creative and comfortable learning environment. It also increases communication among students and the teacher by encouraging students to easily share what they have learned in class. However, previous research has criticized oral examinations for lack of reliability and validity. Consequently, the current research seeks to design a reliable and valid alternative assessment by incorporating the Family Tree task into an oral presentation. To measure students’ oral communication skills, a “Task-Specific” scoring rubric was designed to evaluate students’ performances with four essential dimensions; (1) Vocabulary, (2) Pronunciation, (3) Task, and (4) Fluency. Data were obtained from two elementary classes of a public school located in Dohuk in Kurdistan among 13 boys and 14 girls. The student consists of fourth and fifth-grade students within the range of 9 to 10 years old. The result shows that assessing students using the oral presentation as an alternative assessment escalates students’ confidence and motivation to speak publicly as the examination setting becomes less threatening and that oral presentation using a rubric allows detailed identification of students’ areas of improvement for elementary school students.
Demystifying and Mitigating Bias for Node Representation Learning

Node representation learning has attracted increasing attention due to its efficacy for various applications on graphs. However, fairness is a largely under-explored territory within the field, although it is shown that the use of graph structure in learning amplifies bias. To this end, this work theoretically explains the sources of bias in node representations obtained via graph neural networks (GNNs). It is revealed that both nodal features and graph structure lead to bias in the obtained representations. Building upon the analysis, fairness-aware data augmentation frameworks are developed to reduce the intrinsic bias. Our theoretical analysis and proposed schemes can be readily employed in understanding and mitigating bias for various GNN-based learning mechanisms. Extensive experiments on node classification and link prediction over multiple real networks are carried out, and it is shown that the proposed augmentation strategies can improve fairness while providing comparable utility to state-of-the-art methods.
Determinants of Marketing Performance and Company Performance: The Role of Employee Creativity As Moderating Variable

The company performance can also reflect the quality of products and or services provided by the company's customers.Several previous studies have found several strategies that are widely used and considered effective in encouraging company performance to be able to compete with companies that have been around for a long time.However, different from various previous studies, this study tries to relate not only one or two but the various variables previously mentioned, such as firm performance, marketing performance, innovation, network capability, and customer relationship.Therefore, this research aims to determine factors that can affect firm performance.This research is conducted with a quantitative approach with the subject being the service users of newcomer IT manufactures companies as many as 75 companies with 200 respondents.The analysis technique uses a Structural Equation Model (SEM) approach with smart PLS.The results in this study indicate that digital innovation, network capability, and marketing performance affect firm performance but customer relationship management does not.In addition, innovation and customer relationship management affect marketing performance but network capability does not.Other than that, marketing performance can moderate digital innovation and firm performance, customer relationship management and firm performance but cannot on network capability and firm performance.It means that digital innovation, network capability, and marketing performance need maintaining in order to increase the firm performance.Therefore, any company needs to determine the significant factors to reach the advantages.In addition, because this research only focuses on several factors such as digital innovation, network capability, marketing performance, and customer relationship, other research that involves several factors that can increase firm performance needs to be conducted.
AlleRT: Food Recommender Web Application with Allergy Filtration

An allergy is a person's reaction when exposed to chemicals, typically harmless for other people in an environment.Allergens are believed to occur in dust mites, animals, pollen, flies, ticks, molds and some medicine.As 20% of household middle incomes go to restaurants to go for a bite.One in a family of five will likely have allergies to the food they eat.Being exposed to substances is a daily occurrence for an individual and there is always a chance to be exposed to unknown chemicals that can harm a person without knowing the contents of the substance.This paper focuses on helping users navigate safely in the menu of a restaurant with their allergies considered.The developed system followed a software methodology system that includes gathering of data, requirements analysis, system design, implementation, and testing.A user acceptance testing was conducted with a satisfactory result.