Reviewerly/exHarmony huggingface dataset:
On multiple media representations and CDN performance

This paper proposes a mathematical model describing the effects of using multiple media representations on CDN performance in HTTP-based streaming systems. Specifically, we look at cases of using multiple versions of the same content packaged differently and derive an asymptotic formula for CDN cache-miss probability considering parameters of the content's distribution and the distribution of formats used for packaging and delivery. We then study the validity of this proposed formula by considering statistics collected for several streaming deployments using mixed HLS and DASH packaging and show that it predicts the experimentally observed data reasonably well. We further discuss several possible extensions and applications of this proposed model.
Improving lesion detection algorithm in digital breast tomosynthesis leveraging ensemble cross-validation models with multi-depth levels

We report an improved algorithm for detecting biopsy-proven breast lesions on digital breast tomosynthesis (DBT) where the given positive samples in the training set were limited. Instead of using a large scale inhouse dataset, our original algorithm used false positive findings (FPs) from non-biopsied (actionable) images to tackle the problem of a limited number of trainable samples. In this study, we further improved our algorithm by fusing multiple weak lesion detection models by using an ensemble approach. We used cross-validation (CV) to develop multiple lesion detection models. We first constructed baseline detection algorithms by varying the depth levels (medium and large) of the convolutional layers in the YOLOv5 algorithm using biopsied samples. We detected actionable FPs in non-biopsied images using a medium baseline model. We fine-tuned the baseline algorithms using the identified actionable FPs and the biopsied samples. For lesion detection, we processed the DBT volume slice-by-slice, then combined the estimated lesion of each slice along the depth of the DBT volume using volumetric morphological closing. Using 5-fold CV, we developed different multi-depth detection models for each depth level. Finally, we developed an ensemble algorithm by combining CV models with different depth levels. Our new algorithm achieved a mean sensitivity of 0.84 per DBT volume in the independent validation set from the DBTex challenge set, close to one of the top performing algorithms utilizing large inhouse data. These results show that our ensemble approach on different CV models is useful for improving the performance of the lesion detection algorithms.
Utilizing Core-Query for Context-Sensitive Ad Generation Based on Dialogue

In this work, we present a system that sequentially generates advertisements within the context of a dialogue. Advertisements tailored to the user have long been displayed on the digital signage in stores, on web pages, and on smartphone applications. Advertisements will work more effectively if they are aware of the context of the dialogue between the users. Creating an advertising sentence as a query and searching the web by using that query is one way to present a variety of advertisements, but there is currently no method to create an appropriate search query for the search in accordance with the dialogue context. Therefore, we developed a method called the Conversational Context-sensitive Advertisement generator (CoCoA). The novelty of CoCoA is that advertisers simply need to prepare a few abstract phrases, called Core-Queries, and then CoCoA dynamically transforms the Core-Queries into complete search queries in accordance with the dialogue context. Here, "transforms" means to add words related to the context in the dialogue to the prepared Core-Queries. The transformation is enabled by a masked word prediction technique that predicts a word that is hidden in a sentence. Our attempt is the first to apply masked word prediction to a web information retrieval framework that takes into account the dialogue context. We asked users to evaluate the search query presented by CoCoA against the dialogue text of multiple domains prepared in advance and found that CoCoA could present more contextual and effective advertisements than Google Suggest or a method without the query transformation. In addition, we found that CoCoA generated high-quality advertisements that advertisers had not expected when they created the Core-Queries.
Laplacian face blending

Abstract Designing realistic tridimensional facial models is a challenging task, not only due to the effort and artistic abilities required but also because human visual perception is very tuned to the processing of facial features. For this reason, rather than creating face models from scratch, artists usually start from a scanned model of a real person. In this work, we present a novel method for blending human faces in order to create a new one. In a nutshell, our proposal uses Laplacian smoothing to segregate layers of details from one or more faces, which are then integrated into a base face with the help of an interactive and visual editor. In particular, our method supports blending multiple faces and multiple sub‐regions in those faces. Since our approach is intuitive and relatively easy to implement, it can be integrated into artistic pipelines aiming at designing human face models from preexisting ones.
A Dynamic Strategy for Classifying Sentiment From Bengali Text by Utilizing Word2vector Model

In today's world, around 230 million people used the Bengali or Bangla language to communicate. These individuals are progressively associated with online exercises on famous micro-blogging and long-range interpersonal communication locales, imparting insights what's more, musings, and also the vast majority of articles are in the Bengali language. Thus, Bengali people express their emotions using the Bangla language by reviewing, commenting, or recommendations. Sentiment analysis helps determine the people's emotions expressed on social media or several online platforms. Therefore, this study focused on extracting their emotion from a Bengali text by utilizing Word2vector, Skip-Gram, and Continuous Bag of Words (CBOW) with a new Word to Index model by focusing on three individual classes happy, angry, and excited. The authors achieved the highest accuracy of 75% by utilizing the skip-gram model to classify those three types of emotions. This study also outperformed other existing works with LSTM, CNN model with existing datasets.
Causal discovery with a mixture of DAGs

Real causal processes may contain cycles, evolve over time or differ between populations. However, many graphical models cannot accommodate these conditions. We propose to model causation using a mixture of directed cyclic graphs (DAGs); each sample follows a joint distribution that factorizes according to a DAG, but the DAG may differ between samples due to multiple independent factors. We then introduce an algorithm called Causal Inference over Mixtures that uses longitudinal data to infer a graph summarizing the causal relations generated from a mixture of DAGs even when cycles, non-stationarity, latent variables or selection bias exist. Experiments demonstrate improved performance in inferring ancestral relations as compared to prior approaches. R code is available at https://github.com/ericstrobl/CIM.
Cell-Based Target Localization and Tracking with an Active Camera

This paper proposes a new method of target localization and tracking. The method consists of four parts. The first part is to divide the scene into multiple cells based on the camera’s parameters and calibrate the position and error of each vertex. The second part mainly uses the bounding box detection algorithm, YOLOv4, based on deep learning to detect and recognize the scene image sequence and obtain the type, length, width, and position of the target to be tracked. The third part is to match each vertex of the cell in the image and the cell in the scene, generate a homography matrix, and then use the PnP model to calculate the precise world coordinates of the target in the image. In this process, a cell-based accuracy positioning method is proposed for the first time. The fourth part uses the proposed PTH model to convert the obtained world coordinates into P, T, and H values for the purpose of actively tracking and observing the target in the scene with a PTZ camera. The proposed method achieved precise target positioning and tracking in a 50 cm ∗ 250 cm horizontal channel and a vertical channel. The experimental results show that the method can accurately identify the target to be tracked in the scene, can actively track the moving target in the observation scene, and can obtain a clear image and accurate trajectory of the target. It is verified that the maximum positioning error of the proposed cell-based positioning method is 2.31 cm, and the average positioning error is 1.245 cm. The maximum error of the proposed tracking method based on the PTZ camera is 1.78 degrees, and the average error is 0.656 degrees.
One4all—A New SCADA Approach

The main purpose of this paper is to introduce a new concept, named "one4all" in the realm of SCADA (Supervisory Control and Data Acquisition) systems, used by a regional company (particularly a water supplying company) for managing the different views of its users. As a secondary purpose, the paper presents an integration of such an SCADA system with a GIS (Geographical Information System) system. All the regional water supply companies in Romania manage water and wastewater networks, many sensors and actuators, dozens of water pump plants, several water treatment and wastewater plants, tanks and many hydrophores in different parts of their operating range. Due to the wide geographical operating range, an SCADA system needs to be put in place, but the management of such a system in a traditional way is hard to implement, especially when the human resource is low. The methodology presented in this paper, involving adding helper tables and dynamic template windows within an SCADA tool ("one4all" tool), will show how efficiently the human resource can be used. Additionally, the paper shows that companies as described above, can use a single SCADA system that generates different views for all the managed sub regions instead of different systems for every sub region. Implementing only one SCADA system built with the concept "one4all" in mind, and integrating it with a GIS system that is built on the same principle, represents a new approach that will bring value to any organization willing to adopt it. The concept of "one4all", implemented as a software tool for an SCADA system, is a new concept that will help any developer to easily build applications that generate different views for different users based on their permissions and their operating range. Finally, the paper presents some examples of the same concept, implemented in a different vertical (GIS) and programming language, thus presenting that a "one4all" concept is viable and helpful, bringing value to the information technology industry.
Digital Forensic Investigation of Healthcare Data in Cloud Computing Environment

Cloud computing is widely used in various sectors such as finance, health care, and education. Factors such as cost optimization, interoperability, data analysis, and data ownership functionalities are attracting healthcare industry to use cloud services. Security and forensic concerns are associated in cloud environments as sensitive healthcare data can attract the outside attacker and inside malicious events. Storage is the most used service in cloud computing environments. Data stored in iCloud (Apple Inc. Cloud Service Provider) is accessible via a Web browser, cloud client application, or mobile application. Apple Inc. provides iCloud service to synchronize data from MacBook, iPhone, iPad, etc. Core applications such as Mail, Contacts, Calendar, Photos, Notes, Reminders, and Keynote are synced with iCloud. Various operations can be performed on cloud data, including editing, deleting, uploading, and downloading data, as well as synchronizing data between devices. These operations generate log files and directories that are essential from an investigative perspective. This paper presents a taxonomy of iCloud forensic tools that provides a searchable catalog for forensic practitioners to identify the tools that meet their technical requirements. A case study involving healthcare data storage on iCloud service demonstrates that artifacts related to environmental information, browser activities (history, cookies, cache), synchronization activities, log files, directories, data content, and iCloud user activities are stored on a MacBook system. A GUI-based dashboard is developed to support iCloud forensics, specifically the collection of artifacts from a MacBook system.
Are emojis processed visuo-spatially or verbally? Evidence for dual codes

While some argue that emojis are processed like words, opponents note dissimilarities. We used a divided attention (DA) technique to examine whether memory for emojis, relative to words, engages primarily verbal or visuo-spatial cognitive representations. We compared the decline in memory output experienced when participants freely recalled a list of studied words or emojis under dual-task conditions with a concurrently performed distracting task. Participants encoded either words or emojis (between-subjects) under full attention (FA), and later recalled them under FA or while concurrently performing a 1-back task to words (DA Words), emojis (DA Emojis), or stars (DA Stars), manipulated within-subject. Emoji memory was higher overall compared to words. Word recall was unaffected by the DA Stars condition, but significantly worse in the DA Words-and to a lesser degree DA Emojis-condition, relative to FA. Results suggest that memory for words relies primarily on reactivation of verbal representations, which is hampered when the distracting task also requires verbal, but not visuo-spatial, processing. In contrast, small but significant declines in recall were observed for emojis across all DA conditions relative to FA. Results suggest that unlike words, representing and retrieving emojis engages both verbal and visuo-spatial processing.
Cross‐domain opinion classification via aspect analysis and attention sharing mechanism

Abstract The purpose of cross‐domain opinion classification is to leverage useful information acquired from the source domain to train a classifier for opinion classification in the target domain, which has a huge amount of unlabeled data. An opinion classifier trained on a specific domain usually acts poorly, when directly employed to another domain. Annotating the data for all the domains is a laborious and costly process. The majority of available approaches are centered on identifying invariant features among domains. Unluckily, they are unable to properly capture the context within the sentences and better utilization of unlabeled data. To properly address this issue, we propose an aspect‐based attention model for cross‐domain opinion classification. By incorporating knowledge of aspects and sentences, the proposed model provides a transfer mechanism for better‐transferring opinions among domains. We introduce two learning networks, first learning network aims to recognize the shared features between domains, while the purpose of the second learning network is to extract the information from the aspects by utilizing shared words as a bridge. We benefit from BERT and bidirectional gated recurrent unit to get a deep understanding and deep level semantic information of the text. Further, the joint attention learning mechanism is performed for these two learning modules so that the aspects and sentences can impact the resulting opinion expression. In addition, we introduce a gradient reversal layer to obtain invariance features. The comprehensive experiments are performed on Amazon multidomain product datasets and show the effectiveness and significance of the proposed model over state‐of‐the‐art techniques.
Testing and discovery: Responding to challenges to digital philosophy of science

For all that digital methods—including network visualization, text analysis, and others—have begun to show extensive promise in philosophical contexts, a tension remains between two uses of those tools that have often been taken to be incompatible, or at least to engage in a kind of trade‐off: the discovery of new hypotheses and the testing of already‐formulated positions. This paper presents this basic distinction, then explores ways to resolve this tension with the help of two interdisciplinary case studies, taken from preregistration in contemporary science and the debate over whig history in the history of science. These case studies, the paper argues, refocus our attention from a mutually exclusive testing/discovery binary to the relationship between our background data or philosophical views and the empirical generalizations that we might draw from the data. Finally, it develops a set of three challenges for philosophers and corresponding avenues for future work that will, it is hoped, allow us to better justify our use of these methods.
Pattern-Based Augmented Reality Authoring Using Different Degrees of Immersion: A Learning Nugget Approach

Creating Augmented Reality (AR) applications can be an arduous process. With most current authoring tools, authors must complete multiple authoring steps in a time-consuming process before they can try their AR application and get a first impression of it. Especially for laypersons, complex workflows set a high barrier to getting started with creating AR applications. This work presents a novel authoring approach for creating mobile AR applications. Our idea is to provide authors with small, ready-to-use AR applications that can be executed and tested directly as a starting point. Authors can then focus on customizing these AR applications to their needs without programming knowledge. We propose to use patterns from application domains to further facilitate the authoring process. Our idea is based on the learning nugget approach from the educational sciences, where a nugget is a small and self-contained learning unit. We transfer this approach to the field of AR authoring and introduce an AR nugget authoring tool. The authoring tool provides pattern-based self-contained AR applications, called AR nuggets. AR nuggets use simple geometric objects to give authors an impression of the AR application. By replacing these objects and further adaptions, authors can realize their AR applications. Our authoring tool draws from non-immersive desktop computers and AR devices. It synchronizes all changes to an AR nugget both to an AR device and a non-immersive device. This enables authors to use both devices, e.g., a desktop computer to type text and an AR device to place virtual objects in the 3D environment. We evaluate our proposed authoring approach and tool in a user study with 48 participants. Our users installed the AR nugget authoring tool on their own devices, worked with it for 3 weeks, and filled out a questionnaire. They were able to create AR applications and found the AR nugget approach supportive. The users mainly used the desktop computer for the authoring tasks but found the synchronization to the AR device helpful to experience the AR nuggets at any time. However, the users had difficulties with some interactions and rated the AR nugget authoring tool in a neutral field.
Synthesis method for S-boxes satisfying the criterion of correlation immunity of Boolean and 4-functions

The improvement of cryptanalysis methods, in particular, using the mathematical apparatus of many-valued logic functions, determines the need to create methods for the synthesis of cryptographic primitives, first of all, S-boxes that satisfy the criteria of cryptographic quality both in the sense of component Boolean functions, and in the sense of their possible representations by functions of many-valued logic. The most important criterion characterizing the ability of the S-box, and hence, of the entire cryptographic transformation in which it is used, to resist attacks of correlation cryptoanalysis, is the criterion of the correlation immunity of component functions. At the moment, there are no methods for the synthesis of S-boxes that satisfy the criterion of correlation immunity both in the sense of component Boolean functions and in the sense of component functions of many-valued logic, which complicates the further development of symmetric cryptographic algorithms, considering their possible representation using many-valued logic functions. In this paper, we propose a method for the synthesis of a full class of S-boxes of length N=16 corresponding to both the criterion of the correlation immunity of component Boolean functions and the criterion of the correlation immunity of component 4-functions. With the help of the developed method, it was possible to construct a set of 18 304 S-boxes that satisfies the criterion of correlation immunity both in the sense of component Boolean functions and in the sense of component 4-functions. The synthesized class of S-boxes can be used to increase the diffusion and confusion of modern symmetric cryptographic algorithms, as well as to increase their protection against promising cryptanalysis attacks based on many-valued logic functions.
Pauli Error Propagation-Based Gate Rescheduling for Quantum Circuit Error Mitigation

Noisy intermediate-scale quantum algorithms, which run on noisy quantum computers, should be carefully designed to boost the output state fidelity.While several compilation approaches have been proposed to minimize circuit errors, they often omit the detailed circuit structure information that does not affect the circuit depth or the gate count.In the presence of spatial variation in the error rate of the quantum gates, adjusting the circuit structure can play a major role in mitigating errors.In this article, we exploit the freedom of gate reordering based on the commutation rules to show the impact of gate error propagation paths on the output state fidelity of the quantum circuit, propose advanced predictive techniques to project the success rate of the circuit, and develop a new compilation phase postquantum circuit mapping to improve its reliability.Our proposed approaches have been validated using a variety of quantum circuits with different success metrics, which are executed on IBM quantum computers.Our results show that rescheduling quantum gates based on their error propagation paths can significantly improve the fidelity of the quantum circuit in the presence of variable gate error rates.
Modified nnU-Net for the MICCAI KiTS21 Challenge

KiTS21 Challenge is to develop the best system for automatic semantic segmentation of renal tumors and surrounding anatomy. The organizers provide a dataset of 300 cases and each case's CT scan is segmented to three semantic classes: Kidney, Tumor and Cyst. Compared with KiTS19 Challenge, cyst is a new semantic class, but these two tasks are quite close and that is why we choose nnUNet as our model and made some adjustments on it. Some important changes are made to the original nnUNet to adapt to this new task. Furthermore, we train models in 3 different ways and finally and merge them into one model by specific strategies. Detailed information is available in the part of Methods. The organizer uses an evaluation method called "Hierarchical Evaluation Classes" (HECs). The HEC scores of each model are showed in the following .