Reviewerly/exHarmony huggingface dataset:
Real-time processing of stabilizer measurements in a bit-flip code

Abstract Although qubit coherence times and gate fidelities are continuously improving, logical encoding is essential to achieve fault tolerance in quantum computing. In most encoding schemes, correcting or tracking errors throughout the computation is necessary to implement a universal gate set without adding significant delays in the processor. Here, we realize a classical control architecture for the fast extraction of errors based on multiple cycles of stabilizer measurements and subsequent correction. We demonstrate its application on a minimal bit-flip code with five transmon qubits, showing that real-time decoding and correction based on multiple stabilizers is superior in both speed and fidelity to repeated correction based on individual cycles. Furthermore, the encoded qubit can be rapidly measured, thus enabling conditional operations that rely on feed forward, such as logical gates. This co-processing of classical and quantum information will be crucial in running a logical circuit at its full speed to outpace error accumulation.
Redundancy, Context, and Preference: An Empirical Study of Duplicate Pull Requests in OSS Projects

OSS projects are being developed by globally distributed contributors, who often collaborate through the pull-based model today. While this model lowers the barrier to entry for OSS developers by synthesizing, automating and optimizing the contribution process, coordination among an increasing number of contributors remains as a challenge due to the asynchronous and self-organized nature of distributed development. In particular, duplicate contributions, where multiple different contributors unintentionally submit duplicate pull requests to achieve the same goal, are an elusive problem that may waste effort in automated testing, code review and software maintenance. While the issue of duplicate pull requests has been highlighted, to what extent duplicate pull requests affect the development in OSS communities has not been well investigated. In this paper, we conduct a mixed-approach study to bridge this gap. Based on a comprehensive dataset constructed from 26 popular GitHub projects, we obtain the following findings: (a) Duplicate pull requests result in redundant human and computing resources, exerting a significant impact on the contribution and evaluation process. (b) Contributors’ inappropriate working patterns and the drawbacks of their collaborating environment might result in duplicate pull requests. (c) Compared to non-duplicate pull requests, duplicate pull requests have significantly different features, e.g., being submitted by inexperienced contributors, being fixing bugs, touching cold files, and solving tracked issues. (d) Integrators choosing between duplicate pull requests prefer to accept those with early submission time, accurate and high-quality implementation, broad coverage, test code, high maturity, deep discussion, and active response. Finally, actionable suggestions and implications are proposed for OSS practitioners.
Unsupervised Resolution of Histomorphologic Heterogeneity in Renal Cell Carcinoma Using a Brain Tumor–Educated Neural Network

PURPOSE Applications of deep learning to histopathology have proven capable of expert-level performance, but approaches have largely focused on supervised classification tasks requiring context-specific training and deployment. More generalizable workflows that can be easily shared across subspecialties could help accelerate and broaden adoption. Here, we hypothesized that histology-optimized feature representations, generated by a convolutional neural network (CNN) during supervised learning, are transferable and can resolve meaningful differences in large-scale, discovery-type unsupervised analyses. METHODS We used a CNN, previously trained to recognize brain tumor histomorphologies, to extract 512 feature representations from &gt; 550 digital whole-slide images (WSIs) of renal cell carcinomas (RCCs) from The Cancer Genome Atlas and other previously unencountered tumors. We use these extracted feature vectors to conduct unsupervised image-set clustering and analyze the clinical and biologic relevance of the intra- and interpatient subgroups generated. RESULTS Within individual WSIs, feature-based clustering could reliably segment tumor regions and other relevant histopathologic subpatterns (eg, adenosquamous and poorly differentiated regions). Across the larger RCC cohorts, clustering extracted features generated subgroups enriched for clinically relevant subtypes (eg, papillary RCC) and outcomes (eg, survival). Importantly, individual feature activation mapping highlighted salient subtype-specific patterns and features of malignancies (eg, nuclear grade, sarcomatous change) contributing to subgroupings. Moreover, some proposed clusters were enriched for recurring, human-based RCC-subtype misclassifications. CONCLUSION Our data support that CNNs, pretrained on large histologic datasets, can extend learned representations to novel scenarios and resolve clinically relevant intra- and interpatient tissue-pattern differences without explicit instruction or additional optimization. Repositioning of existing histology-educated networks could provide scalable approaches for image classification, quality assurance, and discovery of unappreciated patterns and subgroups of disease.
Evaluation of Agile Team Work Quality

Abstract The maturity of organizations is measured with process assessment models like the ISO/IEC 33001. The product quality is aligned with internal and external product quality charactersitics based on models like the ISO/IEC 25010. With the shift from the Tailorism-driven process orientation to a more people centric organization, the two dimensions process and product quality have to be extened by the people or team quality dimension. The presented approach offers aspects for agile Team Work Quality (aTWQ), as well as related measurement indicators. The approach is evaluated in the large enterprise context of the Volkswagen AG. The indicators of aTWQ have been integrated and established in the agile tool box for a sustainable agile transition of the company.
Assessment of the Capability of Sentinel-2 Imagery for Iron-Bearing Minerals Mapping: A Case Study in the Cuprite Area, Nevada

With several bands covering iron-bearing mineral spectral features, Sentinel-2 has advantages for iron mapping. However, due to the inconsistent spatial resolution, the sensitivity of Sentinel-2 data to detect iron-bearing minerals may be decreased by excluding the 60 m bands and neglecting the 20 m vegetation red-edge bands. Hence, the capability of Sentinel-2 for iron-bearing minerals mapping were assessed by applying a multivariate (MV) method to pansharpen Sentinel-2 data. Firstly, the Sentinel-2 bands with spatial resolution 20 m and 60 m (except band 10) were pansharpened to 10 m. Then, extraction of iron-bearing minerals from the MV-fused image was explored in the Cuprite area, Nevada, USA. With the complete set of 12 bands with a fine spatial resolution, three band ratios (6/1, 6/8A and (6 + 7)/8A) of the fused image were proposed for the extraction of hematite + goethite, hematite + jarosite and the mixture of iron-bearing minerals, respectively. Additionally, band ratios of Sentinel-2 data for iron-bearing minerals in previous studies were modified with substitution of narrow near infrared band 8A for band 8. Results demonstrated that the capability for detection of iron-bearing minerals using Sentinel-2 data was improved by consideration of two extra bands and the unified fine spatial resolution.
Mobile Traffic Classification Through Physical Control Channel Fingerprinting: A Deep Learning Approach

The automatic classification of applications and services is an invaluable feature for new generation mobile networks. Here, we propose and validate algorithms to perform this task, at <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">runtime</i> , from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">raw physical control channel</i> of an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">operative mobile network</i> , without having to decode and/or decrypt the transmitted flows. Towards this, we decode Downlink Control Information (DCI) messages carried within the LTE Physical Downlink Control CHannel (PDCCH). DCI messages are sent by the radio cell in clear text and, in this article, are utilized to classify the applications and services executed at the connected mobile terminals. Two datasets are collected through a large measurement campaign: one labeled, used to train the classification algorithms, and one unlabeled, collected from four radio cells in the metropolitan area of Barcelona, in Spain. Among other approaches, our Convolutional Neural Network (CNN) classifier provides the highest classification accuracy of 98%. The CNN classifier is then augmented with the capability of rejecting sessions whose patterns do not conform to those learned during the training phase, and is subsequently utilized to attain a fine grained decomposition of the traffic for the four monitored radio cells, in an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">online</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unsupervised</i> fashion.
Efficacy of a student-led community contact tracing program partnered with an academic medical center during the coronavirus disease 2019 pandemic

Contact tracing has proven successful at controlling coronavirus 2019 (COVID-19) globally, and the Center for Health Security has recommended that the United States add 100,000 contact tracers to the current workforce. To address gaps in local contact tracing, health professional students partnered with their academic institution to conduct contact tracing for all COVID-19 cases diagnosed onsite, which included identifying and reaching their contacts, educating participants, and providing social resources to support effective quarantine and isolation. From March 24 to May 28, 536 laboratory-confirmed COVID-19 cases were contacted and reported an average of 2.6 contacts. Contacts were informed of their exposure, asked to quarantine, and monitored for the onset of symptoms. Callers reached 94% of cases and 84% of contacts. Seventy-four percent of cases reported at least one contact. Household members had higher rates of reporting symptoms (odds ratio, 1.65; 95% confidence interval, 1.19–2.28). The average test turnaround time decreased from 21.8 days for the first patients of this program to 2.3 days on the eleventh week. This provides evidence for the untapped potential of community contact tracing to respond to regional needs, confront barriers to effective quarantine, and mitigate the spread of COVID-19.
Do Language Embeddings capture Scales?

Pretrained Language Models (LMs) have been shown to possess significant linguistic, common sense and factual knowledge.One form of knowledge that has not been studied yet in this context is information about the scalar magnitudes of objects.We show that pretrained language models capture a significant amount of this information but are short of the capability required for general common-sense reasoning.We identify contextual information in pre-training and numeracy as two key factors affecting their performance, and show that a simple method of canonicalizing numbers can have a significant effect on the results.
Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations

We investigate the problem of generating informative questions in information-asymmetric conversations.Unlike previous work on question generation which largely assumes knowledge of what the answer might be, we are interested in the scenario where the questioner is not given the context from which answers are drawn, but must reason pragmatically about how to acquire new information, given the shared conversation history.We identify two core challenges: (1) formally defining the informativeness of potential questions, and (2) exploring the prohibitively large space of potential questions to find the good candidates.To generate pragmatic questions, we use reinforcement learning to optimize an informativeness metric we propose, combined with a reward function designed to promote more specific questions.We demonstrate that the resulting pragmatic questioner substantially improves the informativeness and specificity of questions generated over a baseline model, as evaluated by our metrics as well as humans.
Unraveling ML Models of Emotion With NOVA: Multi-Level Explainable AI for Non-Experts

In this article, we introduce a next-generation annotation tool called NOVA for emotional behaviour analysis, which implements a workflow that interactively incorporates the 'human in the loop'.A main aspect of NOVA is the possibility of applying semisupervised active learning where Machine Learning techniques are used already during the annotation process by giving the possibility to pre-label data automatically.Furthermore, NOVA implements recent eXplainable AI (XAI) techniques to provide users with both, a confidence value of the automatically predicted annotations, as well as visual explanations.We investigate how such techniques can assist non-experts in terms of trust, perceived self-efficacy, cognitive workload as well as creating correct mental models about the system by conducting a user study with 53 participants.The results show that NOVA can easily be used by non-experts and lead to a high computer self-efficacy.Furthermore, the results indicate that XAI visualisations help users to create more correct mental models about the machine learning system compared to the baseline condition.Nevertheless, we suggest that explanations in the field of AI have to be more focused on user-needs as well as on the classification task and the model they want to explain.
TheoryOn: A Design Framework and System for Unlocking Behavioral Knowledge Through Ontology Learning

The scholarly information-seeking process for behavioral research consists of three phases: searching, accessing, and processing of past research. Existing IT artifacts, such as Google Scholar, have in part addressed the searching and accessing phases,
Human Language: from genes and brains to behavior

Drawing on developments in neuroimaging, genetic sequencing, and computational modeling, Human Language: from genes and brains to behavior provides a comprehensive overview of human language in all...
Skills for a Working Future: How to Bring about Professional Success from the Educational Setting

Globalization, digitalization, and the permanent alteration of information have led to important changes in the world of work. This demands a realignment of essential skills in order to access job positions in the coming years. In order to face up to the digitalization process, education is one of the ambits that has most come to a tipping point. This adds an urgency to the need to bring skills in line with the new demands of the job market and the challenges posed in the 21st Century. The methodology was based on a systematic review of the most commonly-used databases. It analyzed and synthesized the existing information on the skills required for the future job market and educational proposals to facilitate their acquisition. At the beginning of the search, 2045 records were selected. However, following the application of the exclusion criteria, a total of 63 records were included. From this in-depth analysis, it was uncovered that the most in-demand skill for the job market relates to the management of technology. Different proposals were located which targeted these skills in educational settings. The majority of these pertained to innovative projects emanating from digital and technological phenomena.
The Long-Run Impact of Information Security Breach Announcements on Investors’ Confidence: The Context of Efficient Market Hypothesis

Information and communication technologies (ICTs) are the cornerstone for sustainable development, but if they are not appropriately managed, they will impede progress towards the United Nations Global Sustainable Development Goals. Among undesirable impacts, emphasis must be put on the risk of information security (ISec) breaches, as they pose a potential threat to businesses there. Especially for publicly traded firms, they could create a long-lasting influence on their financial performance and, thus, stock investors’ confidence. Following the efficient market hypothesis’s footsteps, previous studies have examined only the short-run impact on investors’ confidence ensuing to ISec breach announcements. Therefore, this study investigates the long-run impact of ISec breach announcements on investors’ confidence. Based on a sample of 73 ISec breach announcements during 2011–2019, this paper examines the impact on investors’ confidence, as demonstrated by long-run abnormal returns and equity risk of those firms. Using a one-to-one matched sampling approach, each firm’s performance is analyzed with its control firm over eighteen months, starting six months before the announcement, through twelve months after the announcement. Firms experienced a significant negative abnormal return of 15% to 18% during the twelve months following the breach announcement. In comparison, equity risk increased by 11% within six months before and after an announcement. This study can help investors, managers, and researchers better understand a long-term relationship between ISec breaches and investor confidence in the context of efficient market hypothesis.
Machine learning for mobile network payment security evaluation system

Abstract In the recent past, different types of Mobile Network payments gateways have been explosively growing, which allows consumers to access services using various types of mobile devices. The demanding, challenging factors in the mobile network payment gateway security evaluation system includes malware detection, multi‐factor authentication, and fraudulent detection in payment systems. In this paper, Machine Learning‐Assisted Secure Mobile Electronic Payment Framework (ML‐SMEPF) is proposed to detect the presence of malware, authentication issues, and fraud detection in mobile transactions. Here, the Efficient Random Oracle Model is introduced to detect the presence of malware on a host system and multi‐factor authentication challenges posed during mobile payments. Mutual Mobile Authentication model is incorporated with ML‐SMEPF, to identify the type of fraud detection which ensures a safe and secure mobile payment platform. The simulation analysis is performed based on accuracy ratio, security factor, performance, and cost factor proves the reliability of the proposed framework.
HybrIDX: New Hybrid Index for Volume-hiding Range Queries in Data Outsourcing Services

An encrypted index is a data structure that assisting untrusted servers to provide various query functionalities in the ciphertext domain. Although traditional index designs can prevent servers from directly obtaining plaintexts, the confidentiality of outsourced data could still be compromised by observing the volume of different queries. Recent volume attacks have demonstrated the importance of sealing volume-pattern leakage. To this end, several works are made to design secure indexes with the volume-hiding property. However, prior designs only work for encrypted keyword search. Due to the unpredictable range query results, it is difficult to protect the volume-pattern leakage while achieving efficient range queries.In this paper, for the first time, we define and solve the challenging problem of volume-hiding range queries over encrypted data. Our proposed hybrid index framework, called HybrIDX, allows an untrusted server to efficiently search encrypted data based on order conditions without revealing the exact volume size. It resorts to the trusted hardware techniques to assist range query processing by moving the comparison algorithm to trusted SGX enclaves. To enable volume-hiding data retrieval, we propose to host encrypted results outside the enclave in an encrypted multimaps manner. Apart from this novel hybrid index design, we further customize a bulk refresh mechanism to enable accesspattern obfuscation. We formally analyze the security strengths and complete the prototype implementation. Evaluation results demonstrate the feasibility and practicability of our designs.
Quadbox: Quadrilateral Bounding Box Based Scene Text Detection Using Vector Regression

Scene text appears with a wide range of sizes and arbitrary orientations. For detecting such text in the scene image, the quadrilateral bounding boxes provide a much tight bounding box compared to the rotated rectangle. In this work, a vector regression method has been proposed for text detection in the wild to generate a quadrilateral bounding box. The bounding box prediction using direct regression requires predicting the vectors from each position inside the quadrilateral. It needs to predict four-vectors, and each varies drastically in its length and orientation. It makes the vector prediction a difficult problem. To overcome this, we have proposed a centroid-centric vector regression by utilizing the geometry of quadrilateral. In this work, we have added the philosophy of indirect regression to direct regression by shifting all points within the quadrilateral to the centroid and afterward performed vector regression from shifted points. The experimental results show the improvement of the quadrilateral approach over the existing direct regression approach. The proposed method shows good performance on many existing public datasets. The proposed method also demonstrates good results on the unseen dataset without getting trained on it, which validates the approach's generalization ability.
Sentimental analysis from imbalanced code-mixed data using machine learning approaches

Knowledge discovery from various perspectives has become a crucial asset in almost all fields. Sentimental analysis is a classification task used to classify the sentence based on the meaning of their context. This paper addresses class imbalance problem which is one of the important issues in sentimental analysis. Not much works focused on sentimental analysis with imbalanced class label distribution. The paper also focusses on another aspect of the problem which involves a concept called "Code Mixing". Code mixed data consists of text alternating between two or more languages. Class imbalance distribution is a commonly noted phenomenon in a code-mixed data. The existing works have focused more on analyzing the sentiments in a monolingual data but not in a code-mixed data. This paper addresses all these issues and comes up with a solution to analyze sentiments for a class imbalanced code-mixed data using sampling technique combined with levenshtein distance metrics. Furthermore, this paper compares the performances of various machine learning approaches namely, Random Forest Classifier, Logistic Regression, XGBoost classifier, Support Vector Machine and Naïve Bayes Classifier using F1- Score.