Reviewerly/exHarmony huggingface dataset:
Diagnostic Accuracy of Sonomammography in Diagnosis of BIRADS 4 Suspicious Breast Lesions Keeping Histopathology as Gold Standard

Objective: To assess the diagnostic accuracy of sonomammography in diagnosis of BIRADS ≥4 suspicious breast lesions taking histopathology as gold standard. Design of the Study: It was a descriptive cross-sectional study. Study Settings: This study was carried out at Department Of Diagnostic Radiology And Diagnostic Imaging Combined Military Hospital, Lahore from January 2017 to June 2017. Material and Methods: A total of 190 patients of age 30-70 years, presenting with palpable breast lesions with BIRAD ≥4 undergoing biopsy under local anesthesia were included. Patients with already diagnosed with breast cancer and recurrent breast lesion were excluded. Then all patients were undergone USG by using a high-resolution unit (PLM703AT) with a linear array probe centered at 7, 5 MHz, along with mammography using mamomet 3000 NOVA SIEMENS, General Electric Medical Systems, Milwaukee. Patients were labeled as positive or negative. Sonomammography findings were correlated with histopathological findings. Results of the Study: In 117 sonomammography positive patients, 107 (True Positive) had malignant breast lesions and 08 (False Positive) had benign on histopathology findings. Among, 75 sonomammography negative patients, 06 (False Negative) had malignant breast lesions on histopathology whereas 69 (True Negative) had benign lesions on histopathology. Overall sensitivity, specificity, positive predictive value, negative predictive value and diagnostic accuracy of sonomammography in diagnosis of BIRADS ≥4 suspicious breast lesions taking histopathology as gold standard was 94.69%, 89.61%, 93.04%, 92.0% and 92.63% respectively. Conclusion: This study concluded that diagnostic accuracy of sonomammography in diagnosis of BIRADS ≥4 suspicious breast lesions is quite high. Keywords: Breast lesions, malignant, sonomammography, sensitivity.
Convergence of GANs Training: A Game and Stochastic Control Methodology

Training generative adversarial networks (GANs) is known to be difficult, especially for financial time series. This paper first analyzes the well-posedness problem in GANs minimax games and the convexity issue in GANs objective functions. It then proposes a stochastic control framework for hyper-parameters tuning in GANs training. The weak form of dynamic programming principle and the uniqueness and the existence of the value function in the viscosity sense for the corresponding minimax game are established. In particular, explicit forms for the optimal adaptive learning rate and batch size are derived and are shown to depend on the convexity of the objective function, revealing a relation between improper choices of learning rate and explosion in GANs training. Finally, empirical studies demonstrate that training algorithms incorporating this adaptive control approach outperform the standard ADAM method in terms of convergence and robustness. From GANs training perspective, the analysis in this paper provides analytical support for the popular practice of ``clipping'', and suggests that the convexity and well-posedness issues in GANs may be tackled through appropriate choices of hyper-parameters.
DIGITAL RESOURCES FOR LEARNING UNDER LOCKDOWN FOR STUDYING THE TOPIC “SHADOWING EFFECTS” IN THE DISCIPLINE “MOBILE CELLULAR NETWORKS”

Last year’s lockdowns, resulting from the COVID-19 pandemic, caught everyone off guard. No contingency measures were prepared. For education, the solution was to implement remote learning. The primary requirement for this approach is an abundance of digital resources. Doing lectures this way was already widely tested but for the exercise portion of the curriculum the lecturers had to think outside the box, so they can prepare entirely new tasks that simulate the normal hands-on experience with measuring equipment. In the discipline “Mobile cellular networks” students learn about different effects in these networks, not only the components that they consist of. With preparing digital resources to learn, lecturers decide to use software for the exercises. One major problem in mobile cellular networks is the shadowing effect. This effect cannot be avoided, but need to be investigated and possibly reduced. To investigate this process students need to perform a number of simulations in Matlab that will provide an idea of the magnitude of the shadowing attenuation caused by obstacles in the radio path.This paper presents the tasks that students need to perform when learning about shadowing effects in mobile cellular networks and the active learning methods that are used in the classes.The learning process is complemented by the introduction of active learning methods to increase the levels of the students’ engagement and to ensure long-term knowledge retention. Applying active learning methods also allows the teacher to gauge everyone’s level of understanding, receive valuable feedback, and focus on the parts of the topic that may require additional explanation, clarification, or more practice exercises.
The Effect of Computer-Assisted Phonics Games on Thai Kindergarten Students’ Listening Proficiency and English Phonics Achievement

This study was aimed at comparing the Kindergarten 2 students’ listening proficiency and English Phonics achievement before and after using computer-assisted phonics games in English Phonics class at an international school in Bangkok during the academic year 2020-2021. This research followed a one group pre-test post-test pre-experimental research design. One conveniently chosen class of 13 Kindergarten 2 students participated in this 8-week study. An instructional intervention was implemented in English Phonics class two sessions per week and 40 minutes per session. Statistical data analysis indicated that the difference between participants’ listening proficiency before and after using computer-assisted phonics games in English Phonics class was significant, favoring the latter condition. Moreover, the participants’ listening proficiency improved from good before using computer-assisted phonics games, to excellent after using such games. It was also found that there was a significant difference in participants’ English Phonics achievement before and after using computer-assisted phonics games in English Phonics class, favoring the latter condition. Results from the data analysis also showed that Kindergarten 2 students’ English Phonics achievement improved from a recommendation for English Phonics class support before using computer-assisted phonics games, to an excellent achievement after using such games. Based on the research findings, recommendations for students, teachers, administrators, curriculum developers and future researchers are provided.
Error-immune quantum communication

Environmental effects on the transmission of a state result, in general, in a change in the information carried by it. To mitigate this, many techniques such as quantum error--correcting codes, decoherence--free--subspaces [Rev Mod Phys, 88(4):041001, 2016] are employed. The basic idea underlying them is to protect/recover the state. These techniques require multi-party entanglement, whose generation is a difficult task. Further, retrieval of information would require complete tomography, which inevitably requires a large number of copies. Taking this into account, in this work, a formalism has been laid down which does not require recovery of a state. The formalism employs scaling laws to obtain quantities that remain invariant under a noisy evolution of a state. The information encoded in these invariant quantities can be transmitted in an error-immune manner. Since multiparty entanglement and error detection/correction will not be required, the proposed scheme would be cost-effective and may be reliably employed for error-free information transfer. Employing the formalism, we have obtained invariant quantities for various noisy channels of a qubit and a quNit.
The Capability of Media Students to Interact with Three- Dimensional Augmented Reality in Virtual Broadcast News Studios

The latest developments in technology are constantly affecting all fields, including through the implementation of Augmented Reality (AR) in media broadcasting. Using AR would improve the public image of broadcasting and allow international, national, and regional information and stories to be presented impressively and effectively, allow presenters to interact with television stories through a three-dimensional virtual world, and provide the viewer with rich information about the world. However, the undergraduate media departments at universities in Jordan are still teaching the presentation of the news in basic newsrooms. Therefore, this paper presents an experimental collaboration between the Television Department and the Animation/Multimedia Department at the University of Petra involving the use of an AR virtual studio and three-dimensional computer graphics animation to teach performance skills to students who aim to become television news anchors after graduation.
HoughCL: Finding Better Positive Pairs in Dense Self-supervised Learning

Recently, self-supervised methods show remarkable achievements in image-level representation learning. Nevertheless, their image-level self-supervisions lead the learned representation to sub-optimal for dense prediction tasks, such as object detection, instance segmentation, etc. To tackle this issue, several recent self-supervised learning methods have extended image-level single embedding to pixel-level dense embeddings. Unlike image-level representation learning, due to the spatial deformation of augmentation, it is difficult to sample pixel-level positive pairs. Previous studies have sampled pixel-level positive pairs using the winner-takes-all among similarity or thresholding warped distance between dense embeddings. However, these naive methods can be struggled by background clutter and outliers problems. In this paper, we introduce Hough Contrastive Learning (HoughCL), a Hough space based method that enforces geometric consistency between two dense features. HoughCL achieves robustness against background clutter and outliers. Furthermore, compared to baseline, our dense positive pairing method has no additional learnable parameters and has a small extra computation cost. Compared to previous works, our method shows better or comparable performance on dense prediction fine-tuning tasks.
Approximate Inference via Clustering

In recent years, large-scale Bayesian learning draws a great deal of attention. However, in big-data era, the amount of data we face is growing much faster than our ability to deal with it. Fortunately, it is observed that large-scale datasets usually own rich internal structure and is somewhat redundant. In this paper, we attempt to simplify the Bayesian posterior via exploiting this structure. Specifically, we restrict our interest to the so-called well-clustered datasets and construct an \emph{approximate posterior} according to the clustering information. Fortunately, the clustering structure can be efficiently obtained via a particular clustering algorithm. When constructing the approximate posterior, the data points in the same cluster are all replaced by the centroid of the cluster. As a result, the posterior can be significantly simplified. Theoretically, we show that under certain conditions the approximate posterior we construct is close (measured by KL divergence) to the exact posterior. Furthermore, thorough experiments are conducted to validate the fact that the constructed posterior is a good approximation to the true posterior and much easier to sample from.
Communicating Science: Reform Model of the Gates Open Research Platform

The EU’s scientific potential is increasingly flowing into the world of new scientific knowledge. The object of this paper is the communication interpretation of the Open Science policy, covering not only access and storage of scientific information and preservation of scientific information, but communication aspects also. Purpose of the study: Establish modern trends in the scientific ecosystem oriented towards facilitating the publication and communication of scientific results. Tasks of the study: Compare new solutions in science communication models in the most popular platforms, and explore what is the alternative to traditional scientific journals. Methodology/approach: The qualitative systematic review (qualitative evidence synthesis),scientific criticism of sociological surveys, methods of analytic and synthetic processing of primary and secondary resources, secondary data analysis and overview of scientific publications available in the libraries worldwide, have been used to obtain data about the impact of new EU solutions: the European Road Map for development of the European Research Area (ERA),the European Strategy Forum for Research Infrastructures (ESFRI), the Organization for Economic Cooperation and Development, etc. A comparative analysis of innovation in publishing platforms was conducted with special attention to the Bill and Melinda Gates Foundation’s Gates Open Research platform. Results: The creators of the Gates Open Research platform defend the view of the rapid and socially beneficial effect of new and publicly-accepted scientific knowledge.The cutting-edge solutions are: transfer power from the hands of editors to the hands of the authors; minimize barriers or gatekeepers on the path of the new scientific outcome for society; assessment of the research not in view of the venue of publication but on the basis of the intrinsic value of the completed study; minimize the funds invested in publishing and dissemination. Implications: The conclusions can be important in identifying technological and ideological regularities for optimizing the model of scientific publications and increasing the speed and visibility of any scientific news.
The vulnerability of North African stock markets to international monetary policy

North African economic markets are particularly vulnerable to the consequences of international monetary policies. The impact that rapid changes have had on the financial systems of economies in transformation has consequences on the social level in particular: populations are now manifesting their discontent.
Holographic photonic neuron

Abstract The promise of artificial intelligence (AI) to process complex datasets has brought about innovative computing paradigms. While recent developments in quantum-photonic computing have reached significant feats, mimicking our brain’s ability to recognize images are poorly integrated in these ventures. Here, I incorporate orbital angular momentum (OAM) states in a classical Vander Lugt optical correlator to create the holographic photonic neuron (HoloPheuron). The HoloPheuron can memorize an array of matched filters in a single phase-hologram, which is derived by linking OAM states with elements in the array. Successful correlation is independent of intensity and yields photons with OAM states of lℏ , which can be used as a transmission protocol or qudits for quantum computing. The unique OAM identifier establishes the HoloPheuron as a fundamental AI device for pattern recognition that can be scaled and integrated with other computing platforms to build-up a robust neuromorphic quantum-photonic processor.
DIGITAL TRANSFORMATION OF THE UNIVERSITY AS A RESPONSE TO GLOBAL CHALLENGES: EXPERIENCE AND PROSPECTS OF RUSSIA

The digitalization of public relations, public policy and the economy sets new goals for the higher education system: to adapt to these conditions and continue sustainable development. The forced digital transformation became the lessons of the pandemic for many universities: the implementation of educational programs in online form, the exponential growth of digital educational products, etc. The purpose of this article is to consider the features of the digital transformation of Russian universities as a factor in the formation of their digital capital.Methodology and methods: The article is based on the theories of digital and social capital, information society, digital economy. The empirical study is based on the results of document analysis, analysis of statistical data, expert survey.Research results and discussion: The digital transformation of universities became a national priority in 2019, when 30 universities joined the Charter on the digitalization of the educational space. The digital transformation of universities in 2020 took place at an accelerated pace: 28% of educational programs of Russian universities in the spring semester were partially or completely implemented through online courses provided on various platforms. Artificial intelligence, big data processing, distributed ledgers and digital platforms have become everyday digital technologies for many universities in their main activities. Digital transformation is also carried out by increasing intangible assets, developing human potential, digital competencies and employee culture, as well as the correct organization of this process. At the end of 2020, 16 universities entered the Digital Universities Consortium, which also includes industrial partners in the IT field. The main tasks of the community are the unification of the technological standards of the IT infrastructure and services of the digital university
Object-Centric Unsupervised Image Captioning

Image captioning is a longstanding problem in the field of computer vision and natural language processing. To date, researchers have produced impressive state-of-the-art performance in the age of deep learning. Most of these state-of-the-art, however, requires large volume of annotated image-caption pairs in order to train their models. When given an image dataset of interests, practitioner needs to annotate the caption for each image in the training set and this process needs to happen for each newly collected image dataset. In this paper, we explore the task of unsupervised image captioning which utilizes unpaired images and texts to train the model so that the texts can come from different sources than the images. A main school of research on this topic that has been shown to be effective is to construct pairs from the images and texts in the training set according to their overlap of objects. Unlike in the supervised setting, these constructed pairings are however not guaranteed to have fully overlapping set of objects. Our work in this paper overcomes this by harvesting objects corresponding to a given sentence from the training set, even if they don't belong to the same image. When used as input to a transformer, such mixture of objects enables larger if not full object coverage, and when supervised by the corresponding sentence, produced results that outperform current state of the art unsupervised methods by a significant margin. Building upon this finding, we further show that (1) additional information on relationship between objects and attributes of objects also helps in boosting performance; and (2) our method also extends well to non-English image captioning, which usually suffers from a scarcer level of annotations. Our findings are supported by strong empirical results. Our code is available at https://github.com/zihangm/obj-centric-unsup-caption.
AUTOMATIC VERBAL AUTOPSY CLASSIFICATION USING HARD VOTING CLASSIFIER: RECURSIVE FEATURE ELIMINATION

Verbal autopsy is the medical method acknowledged to be as post-mortem which became carried out to become aware of the precise purpose of demise of people normally came about in poor and beneath developed countries wherein maximum of demise of human beings occurs at domestic in place of hospitals. Verbal post-mortem has many uses within the discipline of fitness and medical. In maximum instances health practitioner had been deputed to do the procedure of identification of purpose of death which have been usually guide in nature. The certification received via manually effects in controversies. Today Verbal Autopsy is completed through some trendy datasets which have been received in earlier manner and fashionable computer algorithms. In this paper we used an ensemble classifier known as Combination based voting classifier which selects the fine classifier among other classifiers. Widespread datasets like PHMRC and Matlab were used in this paper. We used three standard metrics like Sensitivity, Chance Corrected Concordance (CCC) and Cause-specific mortality fraction (CSMF) for comparison of our model with previous models like InsilicoVA, Tariff and InterVA-4. Computed results show that proposed model is better than the previous models.
Global Interaction Modelling in Vision Transformer via Super Tokens

With the popularity of Transformer architectures in computer vision, the research focus has shifted towards developing computationally efficient designs. Window-based local attention is one of the major techniques being adopted in recent works. These methods begin with very small patch size and small embedding dimensions and then perform strided convolution (patch merging) in order to reduce the feature map size and increase embedding dimensions, hence, forming a pyramidal Convolutional Neural Network (CNN) like design. In this work, we investigate local and global information modelling in transformers by presenting a novel isotropic architecture that adopts local windows and special tokens, called Super tokens, for self-attention. Specifically, a single Super token is assigned to each image window which captures the rich local details for that window. These tokens are then employed for cross-window communication and global representation learning. Hence, most of the learning is independent of the image patches $(N)$ in the higher layers, and the class embedding is learned solely based on the Super tokens $(N/M^2)$ where $M^2$ is the window size. In standard image classification on Imagenet-1K, the proposed Super tokens based transformer (STT-S25) achieves 83.5\% accuracy which is equivalent to Swin transformer (Swin-B) with circa half the number of parameters (49M) and double the inference time throughput. The proposed Super token transformer offers a lightweight and promising backbone for visual recognition tasks.