Reviewerly/exHarmony huggingface dataset:
Productivity of Software Development Teams Based on Social and Human Factors: A Dynamic Conceptual Model

Social and human factors are relevant aspects for software project management because of their direct relationship with people, their interactions, and their skills and experience in the software development process. The influence of these aspects on the productivity of the software development team is evident and key to the success of projects. This article presents a causal diagram to understand the dynamic nature of software development team productivity as affected by the social and human aspects inherent in the process. Under the system dynamics theory, a causal diagram based on a goal-seeking archetype with delays is proposed. The proposed causal diagram is a conceptual model that aims to facilitate software project management, analyzing productivity from the influence of social and human factors in software development teams.
English Automatic Translation Platform Based on BP Neural Network for Phrase Translation Combination

In order to further improve the intelligent level of English translation system and enhance the accuracy of English machine translation, this paper designs an intelligent English automatic translation system for phrase translation combination. The system includes two parts: machine algorithm and software design. Through semantic feature analysis and phrase translation combination mode, the automatic translation algorithm is optimized, the translation algorithm program is loaded, and the automatic translation system software is designed based on embedded environment. On this basis, the automatic design of translation system is carried out by means of cross compiling and multi thread phrase translation loading. The system test results show that the system has high translation accuracy and good intelligence.
Text Revision by On-the-Fly Representation Optimization

share moderate resemblance in surface form but differentiate in attributes, such as text style transfer (
Encryption and Decryption of Secure Data for Diverse Genomes

In all respects, information management is the most important challenge in the present situation, since “DATA IS THE NEXT FUEL”. Data preservation is a set of actions meant to protect data from unauthorized access or changes. To safeguard data, there are many techniques available, and encryption is one among them. Protecting genomic data is a critical concern in genomics as it includes personal data. In this paper, we propose a secure encryption and decryption for diverse genomic data which is in FASTA/FASTQ format. Since we know the sequenced data is massive in bulk, the raw sequenced data is broken into sections and compressed. To encrypt the content, we propose a specific Advanced Encryption Standard (AES) key, and Galois/Counter Mode (GCM) to decrypt the encrypted data. Using this suggested method, the disk capacity needed for data disk can be minimized, and the data can be preserved.
Exploiting Language Model Prompts Using Similarity Measures: A Case Study on the Word-in-Context Task

As a recent development in few-shot learning, prompt-based techniques have demonstrated promising potential in a variety of natural language processing tasks.However, despite proving competitive on most tasks in the GLUE and SuperGLUE benchmarks, existing prompt-based techniques fail on the semantic distinction task of the Word-in-Context (WiC) dataset.Specifically, none of the existing few-shot approaches (including the incontext learning of GPT-3) can attain a performance that is meaningfully different from the random baseline.Trying to fill this gap, we propose a new prompting technique, based on similarity metrics, which boosts few-shot performance to the level of fully supervised methods.Our simple adaptation shows that the failure of existing prompt-based techniques in semantic distinction is due to their improper configuration, rather than lack of relevant knowledge in the representations.We also show that this approach can be effectively extended to other downstream tasks for which a single prompt is sufficient.
Agile Transformation for Capstone Projects: Preparing Graduates for the Job Market

Preparing graduates for the job market is a key objective of higher education. The Information Technology (IT) department at King Saud University has adopted a strategy of program alignment with industry to ensure that program outcomes are in line with the market needs and requirements. Graduates in the field of Information Technology should be equipped with software development skills needed by industry to drive business value and deliver high quality software products and services. To this end, the IT department undertook the decision to adopt an agile transformation strategy for the final year capstone project course converting it from a waterfall software development process model to an agile approach in response to the job market need. In this paper, we present the transformation strategy, the design of the course, and discuss opportunities and challenges. Reporting our transformation experience will provide insights and guidance to those who want to undergo a similar transformation.
Recommending Code Understandability Improvements Based on Code Reviews

Developers spend 70% of their time understanding code. Code that is easy to read can save time, while hard-to-read code can lead to the introduction of bugs. However, it is difficult to establish what makes code more understandable. Although there are guides and directives on improving code understandability, in some contexts, these practices can have a detrimental effect. Practical software development projects often employ code review to improve code quality, including understandability. Reviewers are often senior developers who have contributed extensively to projects and have an in-depth understanding of the impacts of different solutions on code understandability. This paper is an early research proposal to recommend code understandability improvements based on code reviewer knowledge. The core of the proposal comprises a dataset of code understandability improvements extracted from code reviews. This dataset will serve as a basis to train machine learning systems to recommend understandability improvements.
Detecting Fraud in Banking Transactions with Random Forest Models

The methods developed for fraud detection can be applicable for use in modern banking systems that give users the ability to conduct online transactions. With the help of the developed algorithms, such systems would be able to detect fraudulent or anomalous transactions in a timely manner and with great precision, which would allow them to warn users of the system (bank account owners) about the anomalies detected in their transactions and block potentially dangerous transactions with the goal of avoiding unwanted financial losses. The subject of this paper is the analysis and comparison of available methods of banking fraud detection with the methods developed in this paper, as well as the development of the methods themselves and their optimization with the help of various approaches. Also, the subject of research includes the analysis of these optimizations and comparison of them with each other, runtime and numerical analysis of the success of the developed methods and their comparison with the accuracy and speed of methods, approaches and algorithms which had been developed earlier.
The High-Performance Solution with Federated Learning in Supply Chain System

Supply chain is an important commercial pattern which used in industry, agriculture and service widely. However different parties have different information even though they are all in the same supply chain. That would results in information silo and the extra increasing on cost. On one hand, information could not transfer to other parties for itself benefits or intellectual property. On the other hand, other parties want to obtain supplementary information in order to make equitable trade and lower trade risk. A skillful method would be designed and employed for solve above problems in this paper. Federated Learning technology was introduced in order to solve the puzzle between information privacy and information sharing. In the concretely implement process, Vertical Federated Learning (VFL) model was constructed and trained for resolving several problems specially because the overlap of parties are more but the features are small in supply chain forward. Gradient descent methods and loss computation ways were also used in training process in order to advance the performance. A series of experiments were used to evaluate VFL in supply chain. Experimental results revealed that VFL used in supply chain was effective.
Feature Fusion Based Adversarial Example Detection against Second-Round Adversarial Attacks

Convolutional Neural Networks (CNNs) achieve remarkable performances in various areas. However, adversarial examples threaten their security. They are designed to mislead CNNs to output incorrect results. Many methods are proposed to detect adversarial examples. Unfortunately, most detection-based defense methods are vulnerable to second-round adversarial attacks, which can simultaneously deceive the base model and the detector. To resist such second-round adversarial attacks, handcrafted steganalysis features are introduced to detect adversarial examples, while such a method receives low accuracy at detecting sparse perturbations. In this paper, we propose to combine handcrafted features with deep features via a fusion scheme to increase the detection accuracy and defend against second-round adversarial attacks. To avoid deep features being overwhelmed by high-dimensional handcrafted features, we propose an expansion-then-reduction process to compress the dimensionality of handcrafted features. Experimental results show that the proposed model outperforms the state-of-the-art adversarial example detection methods and remains robust under various second-round adversarial attacks.
Alaska focus area definition for data acquisition for potential domestic sources of critical minerals in Alaska for antimony, barite, beryllium, chromium, fluorspar, hafnium, magnesium, manganese, uranium, vanadium, and zirconium

First posted July 14, 2022 For additional information, contact: Director, Alaska Science CenterU.S. Geological Survey4210 University DriveAnchorage, Alaska 99508Contact Pubs Warehouse Phase 3 of the Earth Mapping Resources Initiative (Earth MRI) focuses on geologic belts that are favorable for hosting mineral systems that could contain the critical minerals antimony, barite, beryllium, chromium, fluorspar, hafnium, magnesium, manganese, uranium, vanadium, and zirconium. Prior phases of the Earth MRI program in Alaska focused only on rare earth elements, aluminum, cobalt, graphite, lithium, niobium, platinum-group metals, tantalum, tin, titanium, and tungsten. An additional 11 critical minerals planed for future phases of Earth MRI (As, Bi, Cs, Ga, Ge, In, Re, Rb, Sc, Sr, Te) are considered prospective in these focus areas. Together, Alaska focus areas address 22 of the 35 minerals or mineral material groups presently deemed critical. This report describes the methodology and techniques utilized to define focus areas for future data acquisition in Alaska; the conterminous United States are covered in a separate report.Focus areas are identified using a mineral systems framework, which accounts for all the possible tectonic and geologic settings where co-genetic mineral deposits may form. These deposits contain many commodities, including byproduct and critical minerals. Large system-scale processes may be evaluated using such a framework to determine the influence they play on critical mineral endowment within the deposits. Analyzing larger mineral systems provides an integrated and broad context to determine how and where critical minerals are sourced, transported, and deposited in geologic systems.Statewide geological, geochemical, geophysical, and mineral occurrence datasets informed the delineation of focus areas in Alaska. For some mineral systems, previously published data-driven prospectivity analyses for critical mineral-bearing deposit types provided the basis for focus areas. We report a total of 22 new focus areas that are prospective for phase 3 critical minerals. These new focus areas represent four different mineral systems that are known or suspected to occur in Alaska. An additional 55 focus areas that were previously identified for phase 1 and phase 2 commodities were also identified as being prospective for phase 3 critical minerals. Collectively, 102 focus areas in Alaska have known or suspected potential for hosting phase 1, phase 2, and (or) phase 3 critical minerals. These focus areas represent 17 different mineral systems also containing critical minerals that are planned for consideration in future Earth MRI phases. Thus, the focus areas delineated herein, and in previous reports for Alaska, are comprehensive for all critical minerals as presently defined and may be used to guide the collection of new geologic, geochemical, and geophysical data in the region.
Tick Tock Break the Clock: Breaking CAPTCHAs on the Darkweb

Nowadays, almost all major websites employ CAPTCHAs.This prevents website scraping, fake account creation as well as DDoS or bruteforce attacks.For anonymity reasons, mainstream CAPTCHAs such as Google's reCAPTCHA cannot be used on the darkweb.Due to the evolution of machine learning and computer vision, the CAPTCHA challenges used there, such as the clock CAPTCHA, are usually more arduous than those found on the clearweb.This paper presents an automated system that uses machine learning to break clock CAPTCHA challenges with a high success rate.We evaluate our system in a real world setting against 725 clock challenges from live darkweb marketplaces.Our results show an accuracy of 96.83% while maintaining low time requirements while analyzing, predicting and submitting the CAPTCHA solution.
The COVID-19 Pandemic as a Catalyst for Digital Entrepreneurship Education: Reflections on a Rapid Transformation of an Educational Ecosystem

This chapter explores the implications of the COVID-19 crisis on entrepreneurship education at higher education institutions. Different challenges for university management, students and entrepreneurship instructors will be considered from a stakeholder-based, holistic perspective on transforming entrepreneurship towards digital education in the crisis. This view allows integrating a range of education management challenges that ultimately impact on the interaction between students and lecturers in digital entrepreneurship teaching. In addressing these challenges, the chapter also highlights possible approaches and remedies suggested in entrepreneurship literature and from university examples (e.g. structured professional development and institutionalization of student support in online entrepreneurship education; use of balanced synchronous/asynchronous as well as on-site/online components; adding crisis and resilience management to the entrepreneurship teaching agenda). Beyond the technological dimension of the digital transition of entrepreneurship education, the chapter also reflects on some managerial and psychological aspects and issues that came with the COVID-19 crisis for entrepreneurship students and start-up founders alike and derives ideas for future research.
Bar Code Decoding in a Camera-Based Scanner: Analysis and Algorithm

The goal of this work is to provide a foundational understanding of the resolution requirement for camera-based bar code scanning systems and to develop an effective algorithm for decoding. The main theoretical question we wish to address is how well resolved must the bar code image be for the system to be able to decode it. To facilitate the analysis we consider the UPC bar code, which is widely used in retail and commerce. We further assume that the pixels of the camera are aligned with the bar code, making the problem one-dimensional. We find that if the narrowest bar in the bar code is larger than two-thirds the size of the camera pixel, it is possible to uniquely determine the encoded message in the bar code. The result, which exploits the symbology of the UPC bar code, shows that under-resolved bar code images can be decoded. To further gain insight into the robustness of the decoding process, we perform numerical experiments.
Improved Deep Multi-Patch Hierarchical Network For Handling Saturation In Image Deblurring

The most active study topic in the field of computer vision is dealing with saturation in image deblurring. The Deep Multi Patch Framework (DMPHN) is primarily utilized in dynamic scene deblurring, and its fine-to-coarse hierarchical representation results in lower processing costs. The contribution of the framework in gathering and processing local residual information of blur at the courser levels in the hierarchy makes the dynamic scene deblurring successful. The existing framework uses the traditional CNN with the convolutional filter, which is a generalized linear model (GLM) that goes along with a nonlinear activation function to scan the input. When the samples of latent concepts are not linearly separable then the linear CNN cannot abstract the good representations. Because saturation in an image contradicts the linear properties of the blur model, the present Deep Multi Patch architecture with linear CNN is unable to collect the necessary local information of blur in an image. In CNN, the higher-level layer maps to the larger regions of the input image by combining the lower-level concepts from each local patch. Due to the framework’s incapacity to retain information at lower levels, noticeable ringing artifacts and poor deblurring performance resulted. We designed a nonlinear function approximator for this section, which was influenced by the good abstraction capabilities of ”Network-in-Network”. The nonlinear features of saturated pixels and blur are successfully captured by this upgraded DMPHN with a nonlinear function approximator. Compared to the current DMPHN model, the suggested approach greatly reduced the number of trainable parameters while improving the time cost. On the GoPro, Synthetic, and Natural Saturated Blurred Image datasets, the upgraded DMPHN network achieves superior deblurring performance with better PSNR and SSIM than the state-of-the-art.
Structured and unstructured data processing

The amount of data that are being created and stored on a global level is almost inconceivable, and it just keeps growing. That means there is even more potential to glean key insights from business information—yet only a small percentage of data is actually analyzed. What does that mean for businesses? How can they make better use of the raw information that flows into their organizations every day? With respect to the mass categorization that is central to most computer operations, there are two types of relevant data, which affect the speed of assimilation as well as information recall: structured data and unstructured data. Smart robots need both types of data in order to sort and process these data as fast they receive them to the level of trusted degree for their processing procedure and set assignment, known as service-level agreement. Please note that with minor editing and manipulation, the materials presented in this chapter have been borrowed from the book published from Zohuri and Moghaddam2 with permission from both authors and publisher as well.
Klasifikasi Profesi Berdasarkan Hobi Dengan Metode Minimum Spanning Tree

Application to classify the hobbies of final year students in determining their profession with the Minimum Spanning Tree is a method for classification. Tests carried out are to collect weighted data on hobbies obtained from users and later will be grouped based on the specified assessment table. There are 2 programming languages used to create this application, namely PHP and Python. PHP for user interface and python for calculations. Based on the tests carried out, it can be seen that the results of clustering testing of 21 sample data values for hobbies given by users who will be in the 7th cluster of intellectual intelligence can be concluded that the results of the cluster distance value are determined by referring to the 14 iterations carried out. After calculating the distance from each centroid, the cluster determination is carried out using the Minimum Spanning Tree which is represented by a graph, where the data points or vertices are taken from the minimum cluster distance in each iteration. Determination of clusters with the Minimum Spanning Tree is represented by a graph.
Action Recognition With Motion Diversification and Dynamic Selection

Motion modeling is crucial in modern action recognition methods. As motion dynamics like moving tempos and action amplitude may vary a lot in different video clips, it poses great challenge on adaptively covering proper motion information. To address this issue, we introduce a Motion Diversification and Selection (MoDS) module to generate diversified spatio-temporal motion features and then select the suitable motion representation dynamically for categorizing the input video. To be specific, we first propose a spatio-temporal motion generation (StMG) module to construct a bank of diversified motion features with varying spatial neighborhood and time range. Then, a dynamic motion selection (DMS) module is leveraged to choose the most discriminative motion feature both spatially and temporally from the feature bank. As a result, our proposed method can make full use of the diversified spatio-temporal motion information, while maintaining computational efficiency at the inference stage. Extensive experiments on five widely-used benchmarks, demonstrate the effectiveness of the method and we achieve state-of-the-art performance on Something-Something V1 & V2 that are of large motion variation.
PCVAE: Generating Prior Context for Dialogue Response Generation

Conditional Variational AutoEncoder (CVAE) is promising for modeling one-to-many relationships in dialogue generation, as it can naturally generate many responses from a given context. However, the conventional used continual latent variables in CVAE are more likely to generate generic rather than distinct and specific responses. To resolve this problem, we introduce a novel discrete variable called prior context which enables the generation of favorable responses. Specifically, we present Prior Context VAE (PCVAE), a hierarchical VAE that learns prior context from data automatically for dialogue generation. Meanwhile, we design Active Codeword Transport (ACT) to help the model actively discover potential prior context. Moreover, we propose Autoregressive Compatible Arrangement (ACA) that enables modeling prior context in autoregressive style, which is crucial for selecting appropriate prior context according to a given context. Extensive experiments demonstrate that PCVAE can generate distinct responses and significantly outperforms strong baselines.
Improving Inductive Link Prediction Using Hyper-Relational Facts (Extended Abstract)

For many years, link prediction on knowledge. graphs has been a purely transductive task, not allowing for reasoning on unseen entities. Recently, increasing efforts are put into exploring semi- and fully inductive scenarios, enabling inference over unseen and emerging entities. Still, all these approaches only consider triple-based KGs, whereas their richer counterparts, hyper-relational KGs (e.g., Wikidata), have not yet been properly studied. In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks. Our experiments on a novel set of benchmarks show that qualifiers over typed edges can lead to performance improvements of 6% of absolute gains (for the Hits@10 metric) compared to triple-only baselines. Our code is available at https://github.com/mali-git/hyper_relational_ilp.