Reviewerly/exHarmony huggingface dataset:
Adaptive tetrahedral interpolation for reconstruction of uneven freehand 3D ultrasound

Abstract Objective. Freehand 3D ultrasound volume reconstruction has received considerable attention in medical research because it can freely perform spatial imaging at a low cost. However, the uneven distribution of the original ultrasound images in space reduces the reconstruction effect of the traditional method. Approach. An adaptive tetrahedral interpolation algorithm is proposed to reconstruct 3D ultrasound volume data. The algorithm adaptively divides the unevenly distributed images into numerous tetrahedrons and interpolates the voxel value in each tetrahedron to reconstruct 3D ultrasound volume data. Main results. Extensive experiments on simulated and clinical data confirm that the proposed method can achieve more accurate reconstruction than six benchmark methods. Specifically, the averaged interpolation error at the gray level can be reduced by 0.22–0.82, and the peak signal-to-noise ratio and the mean structure similarity can be improved by 0.32–1.83 dB and 0.01–0.05, respectively. Significance. With the parallel implementation of the algorithm, one 3D ultrasound volume data with size 279 × 279 × 276 can be reconstructed from 100 slices 2D ultrasound images with size 200 × 200 at 1.04 s. Such a quick and accurate approach has practical value in medical research.
Membership Privacy for Asynchronous Group Messaging

The Signal protocol is a secure messaging protocol providing end-to-end encrypted asynchronous communication. In this paper, we focus on a method capable of hiding membership information from the viewpoint of non group members in a secure group messaging (SGM) protocol, which we call “membership privacy”. Although Chase et al. (ACM CCS 2020) have considered the same notion, their proposal is an extension of Signal so called “Pairwise Signal” where a group message is repeatedly sent over individual Signal channels. Thus, for the number of group users n, their protocol is not scalable where each user is required O(n) computational and communication costs for updating keys. In this work, we extend the Cohn-Gordon et al. SGM protocol (ACM CCS 2018), which we call the Asynchronous Ratcheting Trees (ART) protocol, to add membership privacy. The ART protocol is scalable where each user is required $$O(\log n)$$ computational and communication costs for updating keys. We employ a key-private and robust public-key encryption (Abdalla et al., TCC2010/JoC2018) for hiding membership-related values in the setup phase. Furthermore, we concentrate on the fact that a group common key provides anonymity. This fact is used to encrypt membership information in the key update phase. Our extension does not affect the forward secrecy and post-compromise security of the original ART protocol. Our modification achieves asymptotically the same efficiency of the ART protocol in the setup phase. Any additional cost for key update does not depend on the number of group members (specifically, one encryption and decryption of a symmetric-key encryption scheme and one execution of a key-derivation function for each key update are employed). Therefore, the proposed protocol can add membership privacy to the ART protocol with a quite small overhead.
Cognitive Engagement Involvement as a Basis for Designing the Educational Process in the Preparation of Students of Pedagogical Directions

The article presents the rationale and definition of the parameters of cognitive engagement of students in pedagogical areas of training that optimize the design of educational activities at the university, the results of assessing the quality of the educational process in the preparation of bachelors in the direction of “Pedagogical education” at the Tambov State University named after G.R. Derzhavin and Moscow State Regional University based on the application of the integral criterion of students’ cognitive engagement.The article analyzes foreign and domestic research in the field of “cognitive engagement”, the best practices for monitoring the engagement of students in other areas at the Higher School of Economics, MEPhI, at the universities of the USA, identifies the main indicators and defines an integral criterion for the cognitive engagement of students in pedagogical areas based on their coordination with the cognitive mode learning activities and cognitive strategies of students, as well as the experience of diagnosing the cognitive engagement of student teachers.The results of the study presented in the article are the basis for improving the efficiency and effectiveness of the activities of university departments that provide training of teaching staff by means of designing the educational process with high cognitive engagement of students. The developed diagnostic and technological tools can be used to manage, design, assess the quality of pedagogical activity, the educational result of a student and develop priority educational modes of training, learning technologies in higher pedagogical education, and can also serve as the basis for monitoring conducted by quality assessment units, auditing educational innovation at the university.
Interval Type-2 Fuzzy Logic Control of Mobile Wheeled Inverted Pendulums

In this chapter, an integrated IT2 FLS that simultaneously models and controls an MWIP was proposed. By analyzing the characteristics of the system dynamics and the requirements of balance, position and direction control, the proposed method implemented four interval type-2 fuzzy logic systems, including two Takagi-Sugeno model for system modeling and position control and two Mamdani model for position and direction control. The stability of the balance controller was ensured by introducing a linear matrix inequality, and simulation and experimental results verified the effectiveness of the proposed method.
Ontology-based Data Federation

Ontology-based data access (OBDA) is a well-established approach to information management which facilitates the access to a (single) relational data source through the mediation of a high-level ontology, and the use of a declarative mapping linking the data layer to the ontology. We formally introduce here the notion of ontology-based data federation (OBDF) to denote a framework that combines OBDA with a data federation layer where multiple, possibly heterogeneous sources are virtually exposed as a single relational database. We discuss opportunities and challenges of OBDF, and provide techniques to deliver efficient query answering in an OBDF setting. Such techniques are validated through an extensive experimental evaluation based on the Berlin SPARQL Benchmark.
Blind Text Image Deblurring Algorithm Based on Multi-Scale Fusion and Sparse Priors

The goal of blind text image deblurring is to obtain a clean text image from the given blurry text image without knowing the blur kernel.Sparsity-based methods have been shown their effectiveness in various blind text image deblurring models.However, the blur kernel estimation methods based on sparse priors lack of the consideration for the brightness information about the blur kernel, which will affect the restoration effect of the blur kernel.Besides, previous methods seldom apply sparse priors to both spatial domain and transform domain information.We propose a novel blind text image deblurring model based on multi-scale fusion and sparse priors.Besides the sparse gradient prior on the latent clean text image, we add the sparse prior on the high-frequency wavelet coefficients of the latent text image, which will better constrain the solution space and obtain good clean images.The semi-quadratic splitting method is used to alternately optimize the blur kernel and the latent clean image.Meanwhile, we consider the influence of the brightness feature of the restored blur kernel.By multi-scale fusion technique on the basis of Laplacian weight and saliency weight, we fuse the computed blur kernels in three channels to improve the quality of blur kernel.The experimental results show that our algorithm has good results in the restoration of blur kernels and text images.
Does training with blurred images bring convolutional neural networks closer to humans with respect to robust object recognition and internal representations?

It has been suggested that perceiving blurry images in addition to sharp images contributes to the development of robust human visual processing. To computationally investigate the effect of exposure to blurry images, we trained convolutional neural networks (CNNs) on ImageNet object recognition with a variety of combinations of sharp and blurred images. In agreement with recent reports, mixed training on blurred and sharp images (B+S training) brings CNNs closer to humans with respect to robust object recognition against a change in image blur. B+S training also slightly reduces the texture bias of CNNs in recognition of shape-texture cue conflict images, but the effect is not strong enough to achieve human-level shape bias. Other tests also suggest that B+S training cannot produce robust human-like object recognition based on global configuration features. Using representational similarity analysis and zero-shot transfer learning, we also show that B+S-Net does not facilitate blur-robust object recognition through separate specialized sub-networks, one network for sharp images and another for blurry images, but through a single network analyzing image features common across sharp and blurry images. However, blur training alone does not automatically create a mechanism like the human brain in which sub-band information is integrated into a common representation. Our analysis suggests that experience with blurred images may help the human brain recognize objects in blurred images, but that alone does not lead to robust, human-like object recognition.
Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review

Abstract Objective We conducted a systematic review to characterize and critically appraise developed prediction models based on structured electronic health record (EHR) data for adverse drug event (ADE) diagnosis and prognosis in adult hospitalized patients. Materials and Methods We searched the Embase and Medline databases (from January 1, 1999, to July 4, 2022) for articles utilizing structured EHR data to develop ADE prediction models for adult inpatients. For our systematic evidence synthesis and critical appraisal, we applied the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS). Results Twenty-five articles were included. Studies often did not report crucial information such as patient characteristics or the method for handling missing data. In addition, studies frequently applied inappropriate methods, such as univariable screening for predictor selection. Furthermore, the majority of the studies utilized ADE labels that only described an adverse symptom while not assessing causality or utilizing a causal model. None of the models were externally validated. Conclusions Several challenges should be addressed before the models can be widely implemented, including the adherence to reporting standards and the adoption of best practice methods for model development and validation. In addition, we propose a reorientation of the ADE prediction modeling domain to include causality as a fundamental challenge that needs to be addressed in future studies, either through acquiring ADE labels via formal causality assessments or the usage of adverse event labels in combination with causal prediction modeling.
Email Spam Detection Using Naive Bayes and Random Forest Classifiers

Email is used in business and education and almost everywhere for communications. Email is categorized into many categories based on its content like primary, social, promotional, and spam. So spams are also a sub-category under all the categories. Spam may be in the form of text messages,web messages, images and others also. In this paper, we are discussing Email spam. Email spam, sometimes termed junk emails or undesired emails that consumes computing resources, users time and information. These emails are also involved in many cyber crimes like phishing, vishing, identity stolen, data stolen and more. Spam detection and filtration are major issues for email providers and users. Email filtering is a key tool for detecting and combating spam. In our research work, Machine learning algorithms Naive Bayes and random forest are utilized to achieve the objective of spam detection. To evaluate performance, we use accuracy, precision, recall, and f score metrics and discussed the results. We found that these classifiers performed well. In the future, we can utilize deep learning to achieve better results in both text and email categories.
Barriers and enabling factors for error analysis in NLG research

Earlier research has shown that few studies in Natural Language Generation (NLG) evaluate their system outputs using an error analysis, despite known limitations of automatic evaluation metrics and human ratings. This position paper takes the stance that error analyses should be encouraged, and discusses several ways to do so. This paper is not just based on our shared experience as authors, but we also distributed a survey as a means of public consultation. We provide an overview of existing barriers to carry out error analyses, and proposes changes to improve error reporting in the NLG literature.
Towards Universal Cross-Domain Recommendation

In industry, web platforms such as Alibaba and Amazon often provide diverse services for users. Unsurprisingly, some developed services are data-rich, while some newly started services are data-scarce accompanied by severe data sparsity and cold-start problems. To alleviate the above problems and incubate new services easily, cross-domain recommendation (CDR) has attracted much attention from industrial and academic researchers. Generally, CDR aims to transfer rich user-item interaction information from related source domains (e.g., developed services) to boost recommendation quality of target domains (e.g., newly started services). For different scenarios, previous CDR methods can be roughly divided into two branches: (1) Data sparsity CDR fulfills user preference aided by other domain data to make intra-domain recommendations for users with few interactions, (2) Cold-start CDR projects user preference from other domain to make inter-domain recommendations for users with none interactions. In the past years, many outstanding CDR methods are emerged, however, to the best of our knowledge, none of them attempts to solve the two branches simultaneously. In this paper, we provide a unified framework, namely UniCDR, which can universally model different CDR scenarios by transferring the domain-shared information. Extensive experiments under the above 2 branches on 4 CDR scenarios and 6 public and large-scale industrial datasets demonstrate the effectiveness and universal ability of our UniCDR.
Weakly Supervised Entity Alignment with Positional Inspiration

The current success of entity alignment (EA) is still mainly based on large-scale labeled anchor links. However, the refined annotation of anchor links still consumes a lot of manpower and material resources. As a result, an increasing number of works based on active learning, few-shot learning, or other deep network learning techniques have been developed to address the performance bottleneck caused by a lack of labeled data. These works focus either on the strategy of choosing more informative labeled data or on the strategy of model training, while it remains opaque why existing popular EA models (e.g., GNN-based models) fail the EA task with limited labeled data. To overcome this issue, this paper analyzes the problem of weakly supervised EA from the perspective of model design and proposes a novel weakly supervised learning framework, Position Enhanced Entity Alignment (PEEA). Besides absorbing structural and relational information, PEEA aims to increase the connections between far-away entities and labeled ones by incorporating positional information into the representation learning with a Position Attention Layer (PAL). To fully utilize the limited anchor links, we further introduce a novel position encoding method that considers both anchor links and relational information from a global view. The proposed position encoding will be fed into PEEA as additional entity features. Extensive experiments on public datasets demonstrate the effectiveness of PEEA.
Information Rates With Non Ideal Photon Detectors in Time-Entanglement Based QKD

We develop new methods of quantifying the impact of photon detector imperfections on possible secret key rates in Time-Entanglement based Quantum Key Distribution (QKD). We address photon detection timing jitter, detector downtime, and dark photon counts and show how each may decrease the maximum achievable secret key rate differently. We begin with a standard Discrete Memoryless Channel (DMC) model to get a good bound on the mutual information lost due to the timing jitter, then introduce a novel Markov Chain (MC) based model to characterize the effect of detector downtime and show how it introduces memory to the key generation process. Finally, we propose a new method of including dark counts in the analysis that shows how dark counts can be especially detrimental when using the common Pulse Position Modulation (PPM) for key generation. Our results show that these three imperfections can significantly reduce the achievable secret key rate when using PPM for QKD. One of our main results is providing tooling for experimentalists to predict their systems’ achievable secret key rate given the detector specifications.
One step surgical scene restoration for robot assisted minimally invasive surgery

Abstract Minimally invasive surgery (MIS) offers several advantages to patients including minimum blood loss and quick recovery time. However, lack of tactile or haptic feedback and poor visualization of the surgical site often result in some unintentional tissue damage. Visualization aspects further limits the collection of imaged frame contextual details, therefore the utility of computational methods such as tracking of tissue and tools, scene segmentation, and depth estimation are of paramount interest. Here, we discuss an online preprocessing framework that overcomes routinely encountered visualization challenges associated with the MIS. We resolve three pivotal surgical scene reconstruction tasks in a single step; namely, (i) denoise, (ii) deblur, and (iii) color correction. Our proposed method provides a latent clean and sharp image in the standard RGB color space from its noisy, blurred, and raw inputs in a single preprocessing step (end-to-end in one step). The proposed approach is compared against current state-of-the-art methods that perform each of the image restoration tasks separately. Results from knee arthroscopy show that our method outperforms existing solutions in tackling high-level vision tasks at a significantly reduced computation time.
Matrix Quantization and LPC Vocoder Based Linear Predictive for Low-Resource Speech Recognition system

Over the last ten years, there has been significant progress in the use of low-rate speech coders in voice applications for computers, military communications, and civil communications. This advancement has been made possible by the development of new speech coders that can generate high-quality speech at low data rates. The majority of existing coders include spectral representation of speech, speech waveform matching, and ”optimization” of the coder’s performance for human hearing. The goal of this paper is to provide a thorough evaluation of voice coding methods for educational purposes, with a particular emphasis on the algorithms used in low-rate cellular communication standards. The algorithm we developed using a voice-excited LPC vocoder produces clear, low-distortion results. Ordinary LPCs, on the other hand, fall short of vocoders because they can handle signals other than speech, such as music. To improve quality, additional bandwidth is used to reduce the bit rate. To improve the quality, we tried two approaches. The first was to increase the number of bits required to quantize the DCT coefficients. This coefficient would outperform the inverse DCT in closer error rearrangements. The second possibility is to increase the total number of quantized coefficients. As a result, error array rearrangements would be more accurate. The goal is to identify the point at which a method improvement outperforms the previous, better result. Other coding methods become more complex, but this vocoder suffices.
EL-Rec: Efficient Large-Scale Recommendation Model Training via Tensor-Train Embedding Table

Deep learning Recommendation Models (DLRMs) plays an important role in various application domains. However, existing DLRM training systems require a large number of GPUs due to the memory-intensive embedding tables. To this end, we propose EL-Rec, an efficient computing framework harnessing the Tensor-train (TT) technique to democratize the training of large-scale DLRMs with limited GPU resources. Specifically, EL-Rec optimizes TT decomposition based on key computation primitives of embedding tables and implements a high-performance compressed embedding table which is a drop-in replacement of Pytorch API. EL-Rec introduces an index reordering technique to harvest the performance gains from both local and global information of training inputs. EL-Rec also highlights a pipeline training paradigm to eliminate the communication overhead between the host memory and the training worker. Comprehensive experiments demonstrate that EL-Rec can handle the largest publicly available DLRM dataset with a single GPU and achieves 3× speedup over the state-of-the-art DLRM frameworks.
EVALUATION OF VIRTUAL REALITY-BASED APPLICATIONS IN EDUCATION

Interactive virtual reality (VR) applications are experiencing a great development nowadays. This development is supported by the constant emergence of virtual reality devices (HTC VIVE, Oculus Rift, etc.) that are becoming more robust and reliable. Currently, there are virtual reality applications for increasingly diverse fields, from VR games and movies to tutorials and virtual labs. The educational field is no exception, at present there are many VR applications that try to increase the interest for learning the specific knowledge of different school subjects. On the other hand, the experience of users with virtual reality educational devices and applications is influenced by a number of factors. Some of these factors are related to the technical features and facilities of VR devices, another category of factors is to the accuracy and correctness of the educational content of VR applications and there are also factors related to infrastructure (Internet connectivity and workspace).From this perspective, there is a need for reliable tools for evaluating the pedagogical effectiveness of virtual reality applications for the educational environment. The purpose of this study is to test the first version of a tool for evaluating virtual reality educational applications. The evaluation tool is structured on several dimensions related to both the ergonomics of the VR platform and the pedagogical effectiveness of the tested applications. For testing, the Edmersive application was used, a virtual laboratory that offers an experimentation and learning environment for several disciplines (geography, chemistry, biology, history, etc.). In total, 52 students from the Faculty of Psychology and Education Sciences, from the University of Bucharest participated in the study and tested the Edmersive virtual laboratory, using HTC VIVE devices. The results of the study show that students with specialization in the pedagogical field find it useful to use the Edmersive virtual laboratory in the teaching process. At the same time, the evaluation tool has been revised and future releases will focus on refining the scale and increasing validity.