Miracle-dz/UniFAR-FTUs huggingface dataset:
The Skewed Privacy Concerns of Bystanders in Smart Environments

As ubiquitous computing brings sensors and actuators directly into our homes, they introduce privacy concerns for the owners and bystanders. However, privacy concerns may vary among devices and depend on the bystanders' social relation to the owner. In this work, we hypothesize 1) that bystanders assign more privacy concerns to smart home devices than personal computing devices, such as smartphones, even though they have the same capabilities, and 2) that a stronger social relationship mitigates some of the bystanders' privacy concerns. By conducting an online survey (n=170), we found that personal computing devices are perceived as significantly less privacy-concerning than smart home devices while having equal capabilities. By varying the assumed social relationship, we further found that a stronger connection to the owner reduces privacy concerns. Thus, as bystanders underestimate the risk of personal computing devices and are generally concerned about smart home devices, it is essential to alert the user about the presence of both. We argue that bystanders have to be informed about the privacy risks while entering a new space, in the best case, already in the entrance area.
Astrophysical neutrino flavor ratios in the presence of sterile neutrinos

Astrophysical objects such as active-galactic nuclei (AGN) and gamma-ray bursts (GRBs) can be sources of high energy, astrophysical neutrinos. The decay of charged pions produces electron and muon-flavor neutrinos from the primary decay of the pion, and from the secondary decay of the resulting charged lepton. At low energies we expect the flavor ratio $\Phi_{\nu_e}:\Phi_{\nu_\mu}:\Phi_{\nu_\tau}$ to be $1:2:0$ at the source. We are interested in the flavor ratios as measured on Earth after the neutrinos propagate over cosmic distance scales from the source. If we only consider vacuum flavor transition probabilities between the three active flavors then we expect a measured flavor ratio of $1:1:1$ up to small corrections from $\theta_{13}$ and non-maximal $\theta_{23}$. When we include mixing with two additional flavors of sterile neutrinos then we see corrections to this ratio up to $\sim 30%$. Furthermore, if we consider energy-loss of the charged leptons involved in the pion decay from cosmic sources then these flux ratios depend on the neutrino energy. We examine these energy-dependent flavor ratios using a specific model for sterile neutrino mixing, and compare to expected ratios when only three neutrino flavors are considered. At energies $E_\nu >$ 1 TeV the flavor ratios observed in experiments such as IceCube can probe the existence of sterile neutrinos.
Curriculum Incremental Deep Learning on BreakHis DataSet

This paper examines methodological aspects of the training procedure of neural networks for medical image classification. The research question concerns the conjecture that: feeding a network with datasets of increasing magnification leverages high-level knowledge and helps the network to better classify. This study confirms this hypothesis by an experiment carried out on a dataset of breast cancer histopathological images. Results are presented that underline the importance of the order in which data is introduced to the neural network during the training phase. Extensive experiments done on the BreakHis dataset demonstrate that curriculum incremental learning reaches 98.76% accuracy for binary classification while the best state of the art approach only reaches 96.78. %. Concerning multi-class classification, curriculum incremental learning reaches 95.93% while the state of the art approaches only reaches 95.49%. Moreover both the computational time and the stabilization time of the learning process of the incremental curriculum learning approach are reduced (respectively by 6% and by more than 20%) wrt a non curriculum learning approach.
TEMPY: Test Smell Detector for Python

Automated software testing is a process in software development that aims to extend software quality through test code. Thus, we can avoid manual and repetitive rework and have fast test runs. When writing test code, testers may execute bad practices, known as test smells, which are coding patterns that can negatively impact test quality in terms of maintainability, understandability, and defect detection effectiveness. Python became the most widely used programming language in the world in 2020, however, most research on test code quality is conducted for the Java language. In a previous work, we analyze occurrences of test smells in Python, so we developed TEMPY, an open-source test smells detection tool for Python, which detects 10 types of test smells, 5 of them not yet present in other Python detection tools. TEMPY achieved 100% accuracy on validation with our oracle. We evaluated TEMPY through interviews with 10 Python developers, obtaining 99% agreement on the detections pointed out by TEMPY. We hope TEMPY can support Python developers to improve test code quality, thereby supporting software testing activities. Tool Demonstration: https://youtu.be/NxuQOJmQnKA
Naval device applications of relaxor piezoelectric single crystals

High frequency sonar is becoming ever more important to the Navy through expanded use of unmanned underwater vehicles (UUV). Proposed missions for many UUV's involve shallow water operation where broad bandwidth is required making these applications ideal candidates to use single crystal piezoelectrics. In addition, many UUV sonar systems have commercial uses including oceanographic research, oil and mineral prospecting, salvage, and undersea equipment inspection and maintenance. The properties of single crystal piezoelectrics were exploited for broad bandwidth, high frequency sonar. Crystal sonar investigations based on Tonpilz transducers utilizing the "33" resonance mode have shown limitations on bandwidth due to less than ideal resonator aspect ratio. This is a result of the crystals' low elastic stiffness, which leads to short resonators with large lateral dimensions. To address this issue an alternative design was proposed utilizing the "32" resonance mode with the resonating length oriented along a special crystallographic cut. Crystals with this orientation are known to have high properties; d/sub 32/ values as high as 1600 pC/N have been observed. Since prestress for such a design is applied perpendicular to the poling direction, "32" mode Tonpilz elements exhibit lower loss and higher reliability than "33" mode designs. The feasibility of such "32" mode Tonpilz. resonators was presented as determined through property measurements and finite element analysis. A prototype single element Tonpilz will be constructed and tested in future research based on these results. The targeted application for this work is broadband (>100%), high frequency (45 kHz) synthetic aperture arrays for unmanned underwater vehicles.
BEATS: Search Engine for Architectural Strategies for Security

The software system’s architecture is an important artifact for addressing, at design time, quality attributes requirements, e.g., performance, reliability, scalability, and security, i.e., focus of this work. Some design strategies, such the application of architectural patterns or tactics, are available in literature and industry. Their reuse is considered a good practice, since they are proven solutions in real industry scenarios, in a diversity of problems, for instance, software vulnerabilities mitigation. Specifically, architectural strategies for security are widespread through different information sources, mostly closed to community contributions, impeding the consolidation of sustainable body of knowledge for security-by-design strategies. We propose BEATS, a web browser of architectural strategies for security. This tool aims to assist the unification and centralization of architectural knowledge for security reported in diverse information sources. BEATS is a more complete repository, with a friendly user interface. It also allows the security-by-design community contributions, adding new strategies and maintaining all knowledge updated over time. Video Link: https://youtu.be/76oQaX9tNuY
Model of effective cost management in the time of corona crisis: a PLS-SEM approach

Companies all over the world are affected by the Corona crisis. It is especially important for companies to keep the key business parameters under control in difficult economic times. This paper allocates the factors that influence cost management effectiveness during the corona crisis. The proposed influence model was evaluated in an empirical study involving Austrian companies and validated with Partial Least Squares - Structural Equation Modelling (PLS-SEM) approach. The results show, that allocated factors have a positive effect on the effectiveness of cost management and explain about half of the variance of the corresponding variable, giving moderate explanatory power to the proposed model. The results demonstrate to what extent the use of cost management tools can be a helpful instrument for crisis management.
Split Feature Space Ensemble Method using Deep Reinforcement Learning for Algorithmic Trading

In the financial sector, machine learning is a promising tool, which can be utilized in stock trading as well. The aim of the research was to develop and refine deep reinforcement learning models to execute stock trading that maximizes revenue and minimizes investment risk. The main focus of this paper is on a new ensemble technique based on splitting the feature space and the optimization of decision-making by agents learning in parallel. As the consequence of the splitting, the space formed by all input features is replaced by subspaces, where each subspace is covered by a functional group of technical indicators. Based on the feature space splitting idea, we proposed a new ensemble method, called the Split Feature Space Ensemble Method, and developed three model variants of it. The experimental results show that the proposed method outperforms the standard ensemble approach.
TeamPlus: A Decision Support System for Software Team Formation

The literature presents several search and optimization-based solutions for the software team formation problem. However, the existing solutions do not reflect the real needs of practitioners. In general, they do not systematically address the problem, provide adequate visual resources, or integrate with data from enterprise systems. Therefore, the existing solutions are unaware of the actual dynamics of projects and miss important historical information from team members. This paper introduces TeamPlus, a Decision Support System capable of communicating with the people management environment and applying quantitative measures to data to build profiles, suggest teams based on entry criteria and allow the manager to manipulate the suggestions and adjust the preferences before the best decision is made. We developed TeamPlus through a qualitative study with industry practitioners and validated it, given its practical utility and effectiveness. This paper presents an overview of its main features, a comparison with other existing solutions, and preliminary evaluation results. Link to video: https://youtu.be/Uhf_aYCjh9o License type: Proprietary software (no public license)
The Reappropriation of Instant Messaging

Instant messengers (IMs) have created an unprecedented shift in interpersonal communication. This study explores how users reappropriate instant messengers, their motivations, and the associated factors behind this behavior. We conducted a survey, with 81 responses, and interviewed 14 participants to understand the unexpected ways instant messengers are used for purposes beyond communication, focusing on how they are used for Personal Information Management (PIM). We used an iterative process of open-coding and affinity diagramming to reveal three key themes: users' reappropriation of messaging technologies for saving, remembering, and transferring information; users' struggles and strategies to find information buried in conversations; and users' emotional attachments and attitudes about keeping and deleting this data. Our study reveals informality and accessibility as the primary motivators for reappropriating IMs and examines how this repurposing behavior connects to privacy, digital possessions, and lifelogging.
An Out-of-sample Extension of Sparse Subspace Clustering and Low Rank Representation for Clustering Large Scale Data Sets

In this paper, we propose a general framework to address two problems in Sparse Subspace Clustering (SSC) and Low Rank Representation (LRR), i.e., scalability issue and out-of-sample problem. SSC and LRR are two recently proposed subspace clustering algorithms which construct a similarity graph for spectral clustering by using the sparsest and the lowest-rank coefficients, respectively. SSC and LRR have achieved state-of-the-art results in data clustering. However, their time complexities are very high so that it is inefficient to apply them to large scale data set. Moreover, SSC and LRR cannot cope with out-of-sample data that are not used to construct the similarity graph. For each new datum, they must recalculate the sparsest/lowest-rank coefficients and membership assignment matrix of the whole data set. This makes SSC and LRR not competitive to fast online learning algorithm. To overcome these problems, we propose a simple but effective method which makes SSC and LRR feasible to grouping new data and large scale data. The solution of our method adopts a "sampling, clustering, coding, and classifying" strategy. Specifically, we split the data into two parts, in-sample data and out-of-sample data, where out-of-sample data are located at the subspaces spanned by in-sample data; and obtain the cluster membership of in-sample data; after that, assign each out-of-sample datum to the nearest subspace that produces the minimal reconstruction error. Both theoretical analysis and experimental results show the efficacy of our methods.
Automatic Risks Detection and Comparison Techniques for General Conditions of Technical Documents in Purchasing Order

This research is to develop a technique that recognizes the technical documents as part of purchasing order (PO) exchanged between the owner (buyer) and the supplier (seller) in capital investment, such as maintenance and replacement of equipment, automatically detect specific potential risk contained clauses and shows the comparison results. This research has selected the proof of concept (PoC) technology to (1) the performance guarantee clauses for the purchasing equipment and (2) the delivery schedule requirement clauses to be checked and compared with the utmost cares when reviewing technical documents by the plant owner. The PoC research was implemented based on the Python programming language in conjunction with the spaCy libraries, and further was developed to a cloud-platform-based application for user implementation. This technique preprocesses technical documents of PO in PDF format and, after recognizing and converts into the entire text, detects and extracts the risks-related sentences with logic created by analyzing the patterns of PoC clauses. This research also built a database of all units and formats that can be used in PoC clauses and developed knowledge-based rules to normalize PoC clauses expressed differently in two (buyer's and seller's) and documents. Finally, the result of comparing PoC clauses unified in the same unit and format is output to an Excel or CSV file. Also, these techniques and comparison results were verified through the confusion matrix and accuracy-check. This study is expected to reduce the workload and improve practitioners' productivity in engineering procurement processes for capital investment projects.
Analysis of Misclassified Cases in a Metabolic Syndrome Prediction Model: Explain misclassification of a prediction model

Metabolic syndrome (MetS) refers to a phenomenon in which dangerous adult diseases, such as arteriosclerosis, hypertension, obesity, diabetes, and hyperlipidemia, occur simultaneously in one person. It has become one of the most common diseases. Recently, machine learning prediction models for MetS have been proposed to this end. Their prediction accuracies are under 0.9 and need to be improved further for a practical use in the medical field. In this study, we propose an analysis of misclassified cases in a MetS prediction model as a point of feature importance and interaction. We adopt a case-based feature importance/interaction approach for the analysis. The results help us understand the roles of features in the prediction model. Furthermore, this study can be used to improve the performance of prediction models.
The transcriptional and epigenetic reprogramming associated with chronic IL1β-mediated expansion and myeloid priming in TET2 deficient stem and progenitor cells

Clonal hematopoiesis (CH) increases the risk for the development of hematological malignancy and cardiovascular disease. IL1β is elevated in patients with CH and its inhibition mitigates cardiovascular risk in murine models with Tet2 loss-of-function. How IL1β alters population dynamics of hematopoietic cells upon Tet2 deletion (Tet2-KO) is not well understood. We demonstrated IL1β expands Tet2-KO monocytes/macrophages, and long-term hematopoietic stem cells. IL1β promoted myeloid bias over other lineages of Tet2-KO HSPCs coinciding with the failure to demethylate lineage-associated enhancer and transcription factor binding sites. IL1β enhanced the self-renewal ability of Tet2-KO HSPCs by upregulating genes associated with self-renewal and by resisting the demethylation of binding sites of transcription factors promoting terminal differentiation. The IL1β-mediated premalignant phenotype is suppressed by the IL1β antagonist or deletion of the IL1 receptor-1, in vivo in aged mice. Our results demonstrate that targeting IL1 signaling could be an efficient early intervention strategy in preleukemic disorders. STATEMENT OF SIGNIFICANCE IL1β promoted myeloid expansion and self-renewal capacity of TET2-null pre-leukemic cells. Lineage bias occurred early within progenitors towards pro-inflammatory macrophages. Genes specific to aging and with roles in promoting self-renewal capacity, and myeloid bias were upregulated. Hypermethylation occurred within lymphoid and erythroid lineage-specific regulatory elements. Targeting IL1R1 reduced aberrant myeloid bias and premalignant phenotype.
Regenerative hallmarks of aging: Insights through the lens of Pleurodeles waltl

Background Aging and regeneration are heavily linked processes. While it is generally accepted that regenerative capacity declines with age, some vertebrates, such as newts, can bypass the deleterious effects of aging and successfully regenerate a lens throughout their lifetime. Results Here, we used Optical Coherence Tomography (OCT) to monitor the lens regeneration process of larvae, juvenile, and adult newts. While all three life stages were able to regenerate a lens through transdifferentiation of the dorsal iris pigment epithelial cells (iPECs), an age-related decline in the kinetics of the regeneration process was observed. Consistent with these findings, iPECs from older animals exhibited a delay in cell cycle re-entry. Furthermore, it was observed that clearance of the extracellular matrix (ECM) was delayed in older organisms. Conclusions Collectively, our results suggest that although lens regeneration capacity does not decline throughout the lifespan of newts, the intrinsic and extrinsic cellular changes caused by aging alter the kinetics of this process. By understanding how aging affects lens regeneration in newts, we can gain important insights for restoring the age-related regeneration decline observed in most vertebrates.
A new technology for isolating organellar membranes provides fingerprints of lipid bilayer stress

Biological membranes have a stunning ability to adapt their composition in response to physiological stress and metabolic challenges. Little is known how such perturbations affect individual organelles in eukaryotic cells. Pioneering work provided insights into the subcellular distribution of lipids, but the composition of the endoplasmic reticulum (ER) membrane, which also crucially regulates lipid metabolism and the unfolded protein response, remained insufficiently characterized. Here we describe a method for purifying organellar membranes from yeast, MemPrep. We demonstrate the purity of our ER preparations by quantitative proteomics and document the general utility of MemPrep by isolating vacuolar membranes. Quantitative lipidomics establishes the lipid composition of the ER and the vacuolar membrane. Our findings have important implications for understanding the role of lipids in membrane protein insertion, folding, and their sorting along the secretory pathway. Application of the combined preparative and analytical platform to acutely stressed cells reveals dynamic ER membrane remodeling and establishes molecular fingerprints of lipid bilayer stress.
Deep6: Classification of Metatranscriptomic Sequences into Cellular Empires and Viral Realms Using Deep Learning Models

Deep6 is a deep learning model that classifies metatranscriptomic sequences as short as 250 nucleotides into prokaryotes, eukaryotes, or one of the four viral realms, using a reference-independent and alignment-free approach. Average accuracies range from 0.87 to 0.97, depending on sequence length. ABSTRACT Deep6 is a deep learning model that classifies metatranscriptomic sequences as short as 250 nucleotides into prokaryotes, eukaryotes, or one of the four viral realms, using a reference-independent and alignment-free approach. Average accuracies range from 0.87 to 0.97, depending on sequence length.
PCNA recruits cohesin loader Scc2/NIPBL to ensure sister chromatid cohesion

Sister chromatid cohesion is essential for faithful chromosome segregation and genome duplication during cell division. Failure to establish cohesion during S phase by the ring-shaped multiprotein complex cohesin leads to genomic instability1-4. Replisome-associated proteins are required to generate cohesion by two independent pathways5. One mediates conversion of cohesins bound to unreplicated DNA ahead of replication forks into cohesive entities behind them, while the second promotes cohesin de novo loading onto newly-replicated DNAs6. The latter process depends on the cohesin loader Scc2/NIPBL and the alternative PCNA loader CTF18-RFC. However, the precise mechanism of de novo cohesin loading during replication is unknown. Here we show that PCNA physically recruits yeast cohesin loader Scc2 via its C-terminal PCNA-interacting protein motif. Binding to PCNA is crucial, as scc2-pip mutant deficient in Scc2-PCNA interaction is defective in cohesion when combined with replisome mutants of the cohesin conversion pathway. Moreover, scc2-pip mutant becomes inviable without its partner Scc4/MAU2 that localizes cohesin loader to centromeres. Importantly, the role of NIPBL recruitment to PCNA for cohesion generation is conserved in vertebrate cells. Our results demonstrate that PCNA, the maestro of replication-linked functions, is also crucially involved in the cohesion establishment through de novo cohesin loading onto replicated DNA.