Reviewerly/exHarmony huggingface dataset:
Extending the SAGA Survey (xSAGA) I: Satellite Radial Profiles as a  Function of Host Galaxy Properties

We present "Extending the Satellites Around Galactic Analogs Survey" (xSAGA), a method for identifying low-$z$ galaxies on the basis of optical imaging, and results on the spatial distributions of xSAGA satellites around host galaxies. Using spectroscopic redshift catalogs from the SAGA Survey as a training data set, we have optimized a convolutional neural network (CNN) to identify $z < 0.03$ galaxies from more distant objects using image cutouts from the DESI Legacy Imaging Surveys. From the sample of $> 100,000$ CNN-selected low-$z$ galaxies, we identify $>20,000$ probable satellites located between 36-300 projected kpc from NASA-Sloan Atlas central galaxies in the stellar mass range $9.5 < \log(M_\star/M_\odot) < 11$. We characterize the incompleteness and contamination for CNN-selected samples, and apply corrections in order to estimate the true number of satellites as a function of projected radial distance from their hosts. Satellite richness depends strongly on host stellar mass, such that more massive host galaxies have more satellites, and on host morphology, such that elliptical hosts have more satellites than disky hosts with comparable stellar masses. We also find a strong inverse correlation between satellite richness and the magnitude gap between a host and its brightest satellite. The normalized satellite radial distribution between 36-300 kpc does not depend strongly on host stellar mass, morphology, or magnitude gap. The satellite abundances and radial distributions we measure are in reasonable agreement with predictions from hydrodynamic simulations. Our results deliver unprecedented statistical power for studying satellite galaxy populations, and highlight the promise of using machine learning for extending galaxy samples of wide-area surveys.
Combining Design Thinking and Software Requirements Engineering to Create Human-Centered Software-Intensive Systems

Effective Requirements Engineering is a crucial activity in software-intensive development projects. The human-centric working mode of Design Thinking is considered a powerful way to complement such activities when designing innovative systems. Research has already made great strides to illustrate the benefits of using Design Thinking for Requirements Engineering. However, it has remained mostly unclear how to actually realize a combination of both. In this chapter, we contribute an artifact-based model that integrates Design Thinking and Requirements Engineering for innovative software-intensive systems. Drawing from our research and project experiences, we suggest three strategies for tailoring and integrating Design Thinking and Requirements Engineering with complementary synergies.
AES Encrypted Real-Time Video Stream and Image Transmission from ESP32-CAM

Download This Paper Open PDF in Browser Add Paper to My Library Share: Permalink Using these links will ensure access to this page indefinitely Copy URL AES Encrypted Real-Time Video Stream and Image Transmission from ESP32-CAM 6 Pages Posted: 11 Aug 2022 See all articles by Pınar SavaştürkPınar SavaştürkGebze Technical UniversityÖmer AydınManisa Celal Bayar UniversityGökhan DalkılıçDokuz Eylul University Date Written: 2021 Abstract With the development of technology, the demand for security has increased. Security comes to the forefront due to the increase in data produced and transmitted in real time. In this study, improvements on the security of video data transmitted in real time are proposed. Data encrypted with AES was transferred to the local server from ESP32-Cam module. The encrypted data transferred was decrypted on the local server so the user was able to access the video. In this way, the security level of the data transmitted live between the parties has been increased. Our motivation in this study is that the number of samples proposed on real-time systems is not too much. Keywords: ESP32-CAM, secure video transmission, encrypted image transmission, Nodejs, Arduino, AES, security Suggested Citation: Suggested Citation Savaştürk, Pınar and Aydın, Ömer and Dalkılıç, Gökhan, AES Encrypted Real-Time Video Stream and Image Transmission from ESP32-CAM (2021). Available at SSRN: https://ssrn.com/abstract=4171323 Pınar Savaştürk Gebze Technical University Ömer Aydın (Contact Author) Manisa Celal Bayar University ( email ) ManisaTurkey Gökhan Dalkılıç Dokuz Eylul University Download This Paper Open PDF in Browser Do you have a job opening that you would like to promote on SSRN? Place Job Opening Paper statistics Downloads 1 Abstract Views 5 PlumX Metrics Feedback Feedback to SSRN Feedback (required) Email (required) Submit If you need immediate assistance, call 877-SSRNHelp (877 777 6435) in the United States, or +1 212 448 2500 outside of the United States, 8:30AM to 6:00PM U.S. Eastern, Monday - Friday. Submit a Paper Section 508 Text Only Pages SSRN Quick Links SSRN Solutions Research Paper Series Conference Papers Partners in Publishing Jobs & Announcements Newsletter Sign Up SSRN Rankings Top Papers Top Authors Top Organizations About SSRN SSRN Objectives Network Directors Presidential Letter Announcements Contact us FAQs Copyright Terms and Conditions Privacy Policy We use cookies to help provide and enhance our service and tailor content. To learn more, visit Cookie Settings. This page was processed by aws-apollo4 in 0.173 seconds
Time-Traveling Debugging Queries: Faster Program Exploration

Efficiently debugging a program requires program comprehension. To acquire it, developers explore the program execution, a task often performed using interactive debuggers. Unfortunately, exploring a program execution through standard interactive debuggers is a tedious and costly task. In this paper, we propose Time-Traveling Queries (TTQs) to ease program exploration. TTQs is a mechanism that automatically explores program executions to collect execution data. This data is used to time-travel through execution states, facilitating the exploration of program executions. We built a set of key TTQs based on typical questions developers ask when trying to understand programs. We conducted a user study with 34 participants to evaluate the impact of our queries on program comprehension activities. Results show that, compared to traditional debugging tools, TTQs significantly improve developers' precision, while reducing required time and efforts when performing program comprehension tasks.
Construct informative triplet with two-stage hard-sample generation

In this paper, we propose a robust sample generation scheme to construct informative triplets. The proposed hard sample generation is a two-stage synthesis framework that produces hard samples through effective positive and negative sample generators in two stages, respectively. The first stage stretches the anchor-positive pairs with piecewise linear manipulation and enhances the quality of generated samples by skillfully designing a conditional generative adversarial network to lower the risk of mode collapse. The second stage utilizes an adaptive reverse metric constraint to generate the final hard samples. Extensive experiments on several benchmark datasets verify that our method achieves superior performance than the existing hard-sample generation algorithms. Besides, we also find that our proposed hard sample generation method combining the existing triplet mining strategies can further boost the deep metric learning performance.
Automatic tumour segmentation in H&amp;E-stained whole-slide images of the pancreas.

Pancreatic cancer will soon be the second leading cause of cancer-related death in Western society. Imaging techniques such as CT, MRI and ultrasound typically help providing the initial diagnosis, but histopathological assessment is still the gold standard for final confirmation of disease presence and prognosis. In recent years machine learning approaches and pathomics pipelines have shown potential in improving diagnostics and prognostics in other cancerous entities, such as breast and prostate cancer. A crucial first step in these pipelines is typically identification and segmentation of the tumour area. Ideally this step is done automatically to prevent time consuming manual annotation. In this paper, we propose a multi-task convolutional neural network to balance disease detection and segmentation accuracy. We validated our approach on a dataset of 29 patients (for a total of 58 slides) at different resolutions. The best single task segmentation network achieved a median Dice of 0.885 (0.122) IQR at a resolution of 15.56 &mu;m. Our multi-task network improved on that with a median Dice score of 0.934 (0.077) IQR.
Solving the Families In the Wild Kinship Verification Challenge by Program Synthesis

Kinship verification is the task of determining whether a parent-child, sibling, or grandparent-grandchild relationship exists between two people and is important in social media applications, forensic investigations, finding missing children, and reuniting families. We demonstrate high quality kinship verification by participating in the 2021 Recognizing Families in the Wild challenge which provides the largest publicly available dataset in the field. Our approach is among the top 3 winning entries in the competition. We ensemble models written by both human experts and a foundation model, OpenAI Codex, trained on text and code. We use Codex to generate model variants, and also demonstrate its ability to generate entire running programs for kinship verification tasks of specific relationships.
Traversing Time with Multi-Resolution Gaussian Process State-Space  Models

Gaussian Process state-space models capture complex temporal dependencies in a principled manner by placing a Gaussian Process prior on the transition function. These models have a natural interpretation as discretized stochastic differential equations, but inference for long sequences with fast and slow transitions is difficult. Fast transitions need tight discretizations whereas slow transitions require backpropagating the gradients over long subtrajectories. We propose a novel Gaussian process state-space architecture composed of multiple components, each trained on a different resolution, to model effects on different timescales. The combined model allows traversing time on adaptive scales, providing efficient inference for arbitrarily long sequences with complex dynamics. We benchmark our novel method on semi-synthetic data and on an engine modeling task. In both experiments, our approach compares favorably against its state-of-the-art alternatives that operate on a single time-scale only.
Quantum time dynamics of 1D-Heisenberg models employing the Yang-Baxter  equation for circuit compression

Quantum time dynamics (QTD) is considered a promising problem for quantum supremacy on near-term quantum computers. However, QTD quantum circuits grow with increasing time simulations. This study focuses on simulating the time dynamics of 1-D integrable spin chains with nearest neighbor interactions. We show how the quantum Yang-Baxter equation can be exploited to compress and produce a shallow quantum circuit. With this compression scheme, the depth of the quantum circuit becomes independent of step size and only depends on the number of spins. We show that the compressed circuit scales quadratically with system size, which allows for the simulations of time dynamics of very large 1-D spin chains. We derive the compressed circuit representations for different special cases of the Heisenberg Hamiltonian. We compare and demonstrate the effectiveness of this approach by performing simulations on quantum computers.
There exist infinitely many kinds of partial separability/entanglement

In tri-partite systems, there are three basic biseparability, $A$-$BC$, $B$-$CA$ and $C$-$AB$ biseparability according to bipartitions of local systems. We begin with three convex sets consisting of these basic biseparable states in the three qubit system, and consider arbitrary iterations of intersections and/or convex hulls of them to get convex cones. One natural way to classify tri-partite states is to consider those convex sets to which they belong or do not belong. This is especially useful to classify partial entanglement of mixed states. We show that the lattice generated by those three basic convex sets with respect to convex hull and intersection has infinitely many mutually distinct members, to see that there are infinitely many kinds of three qubit partial entanglement. To do this, we consider an increasing chain of convex sets in the lattice and exhibit three qubit Greenberger-Horne-Zeilinger diagonal states distinguishing those convex sets in the chain.
Semantic cut elimination for the logic of bunched implications, formalized in Coq

The logic of bunched implications (BI) is a substructural logic that forms the backbone of separation logic, the much studied logic for reasoning about heap-manipulating programs. Although the proof theory and metatheory of BI are mathematically involved, the formalization of important metatheoretical results is still incipient. In this paper we present a self-contained formalized, in the Coq proof assistant, proof of a central metatheoretical property of BI: cut elimination for its sequent calculus.
PAN-DOMAIN: Privacy-preserving Sharing and Auditing of Infection Identifier Matching

The spread of COVID-19 has highlighted the need for a robust contact tracing infrastructure that enables infected individuals to have their contacts traced, and followed up with a test. The key entities involved within a contact tracing infrastructure may include the Citizen, a Testing Centre (TC), a Health Authority (HA), and a Government Authority (GA). Typically, these different domains need to communicate with each other about an individual. A common approach is when a citizen discloses his personally identifiable information to both the HA a TC, if the test result comes positive, the information is used by the TC to alert the HA. Along with this, there can be other trusted entities that have other key elements of data related to the citizen. However, the existing approaches comprise severe flaws in terms of privacy and security. Additionally, the aforementioned approaches are not transparent and often being questioned for the efficacy of the implementations. In order to overcome the challenges, this paper outlines the PAN-DOMAIN infrastructure that allows for citizen identifiers to be matched amongst the TA, the HA and the GA. PAN-DOMAIN ensures that the citizen can keep control of the mapping between the trusted entities using a trusted converter, and has access to an audit log.
Everything at Once -- Multi-modal Fusion Transformer for Video Retrieval

Multi-modal learning from video data has seen increased attention recently as it allows to train semantically meaningful embeddings without human annotation enabling tasks like zero-shot retrieval and classification. In this work, we present a multi-modal, modality agnostic fusion transformer approach that learns to exchange information between multiple modalities, such as video, audio, and text, and integrate them into a joined multi-modal representation to obtain an embedding that aggregates multi-modal temporal information. We propose to train the system with a combinatorial loss on everything at once, single modalities as well as pairs of modalities, explicitly leaving out any add-ons such as position or modality encoding. At test time, the resulting model can process and fuse any number of input modalities. Moreover, the implicit properties of the transformer allow to process inputs of different lengths. To evaluate the proposed approach, we train the model on the large scale HowTo100M dataset and evaluate the resulting embedding space on four challenging benchmark datasets obtaining state-of-the-art results in zero-shot video retrieval and zero-shot video action localization.
Numerical optimization of amplitude-modulated pulses in microwave-driven entanglement generation

Abstract Microwave control of trapped ions can provide an implementation of high-fidelity two-qubit gates free from errors induced by photon scattering. Furthermore, microwave conductors may be embedded into a scalable trap structure, providing the chip-level integration of control that is desirable for scaling. Recent developments have demonstrated how amplitude modulation of the gate drive can permit a two-qubit entangling operation to become robust against motional mode noise and other experimental imperfections. Here, we discuss a method for the numerical optimization of the microwave pulse envelope to produce gate pulses with noise resilience, considerably faster operation and high energy efficiency.
Construction de variables \`a l'aide de classifieurs comme aide \`a la  r\'egression

This paper proposes a method for the automatic creation of variables (in the case of regression) that complement the information contained in the initial input vector. The method works as a pre-processing step in which the continuous values of the variable to be regressed are discretized into a set of intervals which are then used to define value thresholds. Then classifiers are trained to predict whether the value to be regressed is less than or equal to each of these thresholds. The different outputs of the classifiers are then concatenated in the form of an additional vector of variables that enriches the initial vector of the regression problem. The implemented system can thus be considered as a generic pre-processing tool. We tested the proposed enrichment method with 5 types of regressors and evaluated it in 33 regression datasets. Our experimental results confirm the interest of the approach.
Evaluation of the Effect of Smartphone-based Virtual Social Networking on Academic Performance of Undergraduate Nursing Students

Evaluation of the Effect of Smartphone-based Virtual Social Networking on Academic Performance of Undergraduate Nursing Students
Mining Shape Expressions with ShapeIt

We present ShapeIt, a tool for mining specifications of cyber-physical systems (CPS) from their real-valued behaviors. The learned specifications are in the form of linear shape expressions, a declarative formal specification language suitable to express behavioral properties over real-valued signals. A linear shape expression is a regular expression composed of parameterized lines as atomic symbols with symbolic constraints on the line parameters. We present here the architecture of our tool along with the different steps of the specification mining algorithm. We also describe the usage of the tool demonstrating its applicability on several case studies from different application domains.
The Construction Plan Of The Chinese and Korean Colloquial Corpus

The construction of the Korean corpus has always been an effective way to analyze and explore the Korean language. Due to cultural differences and translation errors, the study and learning of the Korean language, especially the colloquial category, is in the stage of blind reading, which has caused a lot of understanding and research on its original meaning. In view of the impact of Korean colloquialism, this article proposes a corpus based on Korean colloquialism and a corpus model suitable for Korean colloquialism analysis. And the corpus model is verified and analyzed. The experimental results show that the core of the colloquialism can be clearly analyzed based on the Korean colloquialism corpus model, which provides an ideal solution for the learning of Korean colloquialism.
Forgery Detection in Images Using Zernike Moments and Gabor Filter Banks

Images are the major wellspring of data in different areas like military, news-casting, online media, business, and so forth to pass on the data. The credibility and honesty of images are in trouble because of different effectively worked and minimal expense editors. Image hashing is a way to produce a compact digest of an image to represent the data of the image. Here image hash is generated by feature extraction using zernike moments and gabor filter banks which represents the luminance/chrominance characteristics and texture characteristics respectively. Then a secret key is used to generate the hash value. Besides, the evaluation of key-dependent security ensures that any third person cannot manipulate or evaluate the right hash value without a secret key. While the hash is resilient in terms of content-preserving image processing, it is vulnerable to malicious alteration and thus important for images verification. The hash of test image hash is compared to a reference image hash. If the hash distance exceeds the threshold, the image is determined to be a fake.Experimental outcomes are introduced to show viability of the technique.
A Generation Method of Synthetic Images with Reduced Domain Gap for Car Detection

Deep learning has become the main way of the object detection task for autonomous vehicles. Meanwhile, this method typically requires vast amounts of training data to reach their full potential. However, collecting the data from real world and labeling manually is an expensive, time-consuming and error-prone process. Synthetic image has the potential to replace real image for training neural networks, because image creation and labeling annotations are free in this way. For the network trained by synthetic images, the reality gap between real and synthetic images is the main obstacle to use it in the real world. And most previous works are only devoted to generate synthetic images with a good performance on model training, but lack of analysis of the domain gap that affects the performance. This work designs a method of generating the real and synthetic images to analyze and reduce the reality gap between synthetic and real images for car detection. Firstly, this work put one single car with no-background in a random background image to generate real single car and synthetic single car images. In order to further reduce the domain gap in content level, this method keeps the car distribution in synthetic images is similar with the distribution of car in real world. For the purpose of reducing the domain gap in appearance level, the parameters of camera model are same as the camera parameters of image collecting cars and the image is rendered by using the PBRT(Physically Based Ray Tracing) when we generated the synthetic images. Secondly, by training the neural network of instance segmentation with different datasets, the across validation result proves that the reality gap between synthetic and real images is no more than the domain gap between real images. Thirdly, the training results of datasets with different samples diversity show that the diversity of the samples yields better generalization between different datasets for car detection which can effectively reduce the domain gap.
Two-stage Parallax Correction and Multi-stage Cross-view Fusion Network Based Stereo Image Super-Resolution

Stereo image super-resolution (SR) has achieved great progress in recent years. However, the two major problems of the existing methods are that the parallax correction is insufficient and the cross-view information fusion only occurs in the beginning of the network. To address these problems, we propose a two-stage parallax correction and a multi-stage cross-view fusion network for better stereo image SR results. Specially, the two-stage parallax correction module consists of horizontal parallax correction and refined parallax correction. The first stage corrects horizontal parallax by parallax attention. The second stage is based on deformable convolution to refine horizontal parallax and correct vertical parallax simultaneously. Then, multiple cascaded enhanced residual spatial feature transform blocks are developed to fuse cross-view information at multiple stages. Extensive experiments show that our method achieves state-of-the-art performance on the KITTI2012, KITTI2015, Middlebury and Flickr1024 datasets.