Reviewerly/exHarmony huggingface dataset:
Efficient FPGA Implementation of an ANN-Based Demapper Using Cross-Layer Analysis

In the field of communication, autoencoder (AE) refers to a system that replaces parts of the traditional transmitter and receiver with artificial neural networks (ANNs). To meet the system performance requirements, it is necessary for the AE to adapt to the changing wireless-channel conditions at runtime. Thus, online fine-tuning in the form of ANN-retraining is of great importance. Many algorithms on the ANN layer are developed to improve the AE’s performance at the communication layer. Yet, the link of the system performance and the ANN topology to the hardware layer is not fully explored. In this paper, we analyze the relations between the design layers and present a hardware implementation of an AE-based demapper that enables fine-tuning to adapt to varying channel conditions. As a platform, we selected field-programmable gate arrays (FPGAs) which provide high flexibility and allow to satisfy the low-power and low-latency requirements of embedded communication systems. Furthermore, our cross-layer approach leverages the flexibility of FPGAs to dynamically adapt the degree of parallelism (DOP) to satisfy the system-level requirements and to ensure environmental adaptation. Our solution achieves 2000× higher throughput than a high-performance graphics processor unit (GPU), draws 5× less power than an embedded central processing unit (CPU) and is 5800× more energy efficient compared to an embedded GPU for small batch size. To the best of our knowledge, such a cross-layer design approach combined with FPGA implementation is unprecedented.
Home / Archives / No. 2 (2022) / Philosophy and Science Evolutionary Approach to Consciousness in Modern Philosophy and Interdisciplinary Studies

Experiencing, awareness, and being conscious of the subjective experience are familiar to everyone and invariably bear the stamp of subjectivity. This makes the concept of consciousness one of the most problematic in modern science; insofar as there is no satisfactory definition of human consciousness that has been developed yet. This article reviews the most popular approaches to the definition of consciousness in modern philosophy and cognitive sciences. Our study continues the tradition of the Russian activity-based approach in studying consciousness, thus the greatest attention is paid to studies that consider con­sciousness in the context of interaction with the world and the culture. The au­thors consider the latest data of anthropology and neuroscience in order to find the features that distinguish human consciousness from the other forms of sub­jective reality, which are present in the other species. Based on the provided analysis, the authors propose to define human consciousness as a characteristic ability of a human to control their actions at the level of intentions, which should be mastered during the life. Therefore, it becomes possible to distinguish particular features of the human consciousness from other forms of subjective reality and to show the logic of the evolutionary development of subjective re­ality. This result can become a foundation for further research on how to iden­tify most common subjective states of human with the corresponding activity of the neural system.
CMAP-LAP: Configurable Massively Parallel Solver for Lattice Problems

Lattice problems are a class of optimization problems that are notably hard. There are no classical or quantum algorithms known to solve these problems efficiently. Their hardness has made lattices a major cryptographic primitive for post-quantum cryptography. Several different approaches have been used for lattice problems with different computational profiles; some suffer from super-exponential time, and others require exponential space. This motivated us to develop a novel lattice problem solver, CMAP-LAP, based on the clever coordination of different algorithms that run massively in parallel. With our flexible framework, heterogeneous modules run asynchronously in parallel on a large-scale distributed system while exchanging information, which drastically boosts the overall performance. We also implement full checkpoint-and-restart functionality, which is vital to high-dimensional lattice problems. CMAP-LAP facilitates the implementation of large-scale parallel strategies for lattice problems since all the functions are designed to be customizable and abstract. Through numerical experiments with up to 103,680 cores, we evaluated the performance and stability of our system and demonstrated its high capability for future massive-scale experiments.
Kernel PCA Based Non-Local Means Method for Speckle Reduction in Medical Ultrasound Images

The speckle noise is considered one of the main causes of degradation in ultrasound image quality.Many despeckling filters have been proposed, which are always making a trade-off between noise suppression and loss of information.A class of despeckling methods based Non-Local Means (NLM) algorithm is known to efficiently preserve the edges and all fine details of an image while reducing the noise.The core idea of NLM filter is to estimate the denoised pixel by performing a weighted average of similar patches in the neighborhood around the noisy pixel.However, the presence of noise degrades the similarity measurement process of the NLM and thereby decreases its efficiency.In this work, a novel despeckling scheme for ultrasound images is proposed, by introducing the kernel principal component analysis (PCA) to the NLM and computing the similarity in a high dimension kernel PCA subspace.The kernel representation is robust to the presence of noise and it can give better performance even under high noisy conditions.And it takes into account higher-order statistics of the pixels which can lead to accurate edge preservation.In this work, a novel despeckling scheme for ultrasound images is proposed using the kernel PCA-NLM extended to speckle noise model.The visual inspection and image metrics will show that the proposed filter is very competitive with respect to one of state-of-the-art methods, the Optimized Bayesian Non Local Means filter (OBNLM), in terms of low contrast object detectability, speckle noise suppression, edge's preservation.
Computationally efficient hybrid differential evolution with learning for engineering application

Differential evolution (DE) is popular as an optimisation technique for its ability to achieve a globally optimal solution in a non-convex fitness landscape. However, convergence rate of DE is still unsuitable for real-time applications. Hence, a new leader-centric learning algorithm based on DE named hybrid differential evolution with learning (HDEL) is proposed in this work to improve the convergence performance of DE. In the proposed scheme, the mutation and crossover strategy is supervised by the learning knowledge of the global best and global worst individual as well as the personal best of an individual in its current generation. Extensive simulations have been performed on a set of 44 mathematical benchmark functions and a novel unmanned aerial vehicle (UAV) planning problem. The results of the performance comparison of the proposed HDEL with other state-of-the-art algorithms confirm the significant improvement of HDEL in terms of optimum value, convergence speed, and computational complexity.
Non-cost proposal to reduce educational-technological inequity during confinement in Chile

Introduction: In Chile, social inequity becomes a critical factor that prevents the successful implementation of an online curriculum due to the unbalanced distribution of technological resources. Even though during Covid-19 confinement there is a multichannel approach from the Ministry of Education to provide families with educational material, e-learning stands as the only available option for formal instruction. Objective: This study aimed to reduce educational-technological inequity during 2020 confinement in Chile through a non-cost feasible proposal. Material and Methods: The study was applied research under a mixed approach paradigm. In order to reach the main objective, two specific objectives were set: (1) To determine the contextual challenges related to technology that prevent online implementation of courses in Chile (2) To determine the popular technological resources in Chile that can be used for educational purposes. A checklist based on Aldowah's questionnaire, and an analytic matrix were used to accomplish these objectives. Conclusions: A feasible contribution to educational-technological equity during 2020 confinement in Chile seems to be to use Facebook® for collaborative learning, lectures, oral presentations, reading comprehension, written production, discussion, creative learning, and feedback, since it can support all these types of activities, in a free-internet-access mode, supported for multi-device, which increases the opportunities of participation of all Chilean students in online classes.
Fusing pre-trained convolutional neural networks features for multi-differentiated subtypes of liver cancer on histopathological images

Abstract Liver cancer is a malignant tumor with high morbidity and mortality, which has a tremendous negative impact on human survival. However, it is a challenging task to recognize tens of thousands of histopathological images of liver cancer by naked eye, which poses numerous challenges to inexperienced clinicians. In addition, factors such as long time-consuming, tedious work and huge number of images impose a great burden on clinical diagnosis. Therefore, our study combines convolutional neural networks with histopathology images and adopts a feature fusion approach to help clinicians efficiently discriminate the differentiation types of primary hepatocellular carcinoma histopathology images, thus improving their diagnostic efficiency and relieving their work pressure. In this study, for the first time, 73 patients with different differentiation types of primary liver cancer tumors were classified. We performed an adequate classification evaluation of liver cancer differentiation types using four pre-trained deep convolutional neural networks and nine different machine learning (ML) classifiers on a dataset of liver cancer histopathology images with multiple differentiation types. And the test set accuracy, validation set accuracy, running time with different strategies, precision, recall and F1 value were used for adequate comparative evaluation. Proved by experimental results, fusion networks (FuNet) structure is a good choice, which covers both channel attention and spatial attention, and suppresses channel interference with less information. Meanwhile, it can clarify the importance of each spatial location by learning the weights of different locations in space, then apply it to the study of classification of multi-differentiated types of liver cancer. In addition, in most cases, the Stacking-based integrated learning classifier outperforms other ML classifiers in the classification task of multi-differentiation types of liver cancer with the FuNet fusion strategy after dimensionality reduction of the fused features by principle component analysis (PCA) features, and a satisfactory result of 72.46% is achieved in the test set, which has certain practicality.
The Future Psychologists’ Motivation to Work in the System of Inclusive Education

The article is devoted to the research of the future psychologists’ readiness to work with children with psychophysical disorders and exist in the conditions of inclusive education, according to the professional and motivational component. The average level of formation of the specified component is appropriate for 50,0%. These investigated persons demonstrate mostly positive, but in some cases an uncertain attitude towards various aspects of the future professional activity; in general, they are focused on self-development. Not all students entering university, are guided by the desire to become a psychologist, just prevailing motivation to obtain higher education. The low level is appropriate for 9,5%. The mostly negative or uncertain attitude towards various aspects of future professional activity and insufficient level of motivation by choosing a profession, its prestige and competitiveness is discovered. It’s determined, that one of the main tasks of the practical psychologists’ professional training should be development of scientific bases of their professionally oriented training to work in the system of inclusive education by eliminating small training courses and by creating integrative trans- and interdisciplinary complexes. Activation of the professional and motivational component should take place by introducing into the curriculum a special course "Fundamentals of Axiology" aimed in the professional formation of the system of principles, norms, canons, ideals that regulate interaction in the inclusive educational environment
Global Currencies and Domestic Regulation

This chapter identifies cryptocurrencies and other virtual currencies as global currencies that could have a major impact on national jurisdictions. Regulation concerning cryptocurrencies can be described in the terms of the ‘double movement’ that Karl Polanyi identified for the expansion of the market society in the nineteenth and twentieth centuries. Cryptocurrencies have been developed by anti-establishment individuals and groups, and other opponents of the global financial system that—in Polanyi’s terms—belong to a collectivist counter-movement. The effect they have produced, though, is rather to expand global markets and the market system. This has spurred a counter-movement to the counter-movement, or what could be called the ‘anti-countermovement’. The response of the anti-countermovement to the expansion and influence of the global currencies is paradoxical, if not schizophrenic. The anti-countermovement treats global currencies both as currencies and as a technology. This has led to various regulatory measures in different jurisdictions. When viewed as currency, cryptocurrencies are regulated both as money and commodities, leading to an indifferent approach to their regulation or a command-and-control approach or various intermediate approaches. When viewed as a technology, different jurisdictions have taken an enabling approach to the regulation of cryptocurrencies by establishing ‘innovation hubs’ and ‘regulatory sandboxes’ for FinTech companies. This chapter concludes by discussing the dangers of embedding cryptocurrencies through enabling them, namely the problem of more finance, and possibly an internal clash of domestic agencies. The way to mitigate the dangers of embedding through enabling is by regulating the new cryptocurrency intermediaries.
Histopathological cells segmentation using exponential grasshopper optimisation algorithm-based fuzzy clustering method

Automated cell segmentation in histopathological images is a challenging problem due to the complexities of these images. In this paper, a new exponential grasshopper optimisation algorithm is presented which is further used to find the optimal fuzzy clusters for segmenting the cells in histopathological images. For better cluster quality, compactness is considered as the objective function. The performance of the proposed method is validated in terms of F1 score and aggregated Jaccard index value on two standard histopathological image datasets, namely TNBC patients cancer dataset and UCSB bio segmentation images dataset. The simulation results show the effectiveness of the proposed method over other state-of-the-art clustering segmentation methods such as K-means and fuzzy c-means.
Bi-GRU model based on pooling and attention for text classification

Aiming at the problems that most of the text classification models based on neural network are easy to overfit and ignore key words in sentences in the training process, an improved text classification model is proposed. The proposed model is a Bi-direction Gated Recurrent Unit (Bi-GRU) model based on pooling and attention mechanism. The model solves the above problems in the following ways. First, the bidirectional gated recurrent unit is used as the hidden layer to learn the deep semantic representation. Second, the max-pooling is adopted to extract text features and the self-attention mechanism is used to obtain information about the influence of words and sentences for text classification. Finally, the model uses the splicing results of the two to classify texts. The experimental results show that the proposed model is better than the Text-CNN model and Bi-GRU_CNN model such as precision, recall rate and F-score. Compared with the optimal model, the precision, recall rate and F-score are respectively increased by 5.9%, 5.8% and 4.6% for Fudan Set that is the longer text data set.
A novel oversampling technique based on the manifold distance for class imbalance learning

Oversampling is a popular problem-solver for class imbalance learning by generating more minority samples to balance the dataset size of different classes. However, resampling in original space is ineffective for the imbalance datasets with class overlapping or small disjunction. Based on this, a novel oversampling technique based on manifold distance is proposed, in which a new minority sample is produced in terms of the distances among neighbours in manifold space, rather than the Euclidean distance among them. After mapping the original data to its manifold structure, the overlapped majority and minority samples will lie in areas easily being partitioned. In addition, the new samples are generated based on the neighbours locating nearby in manifold space, avoiding the adverse effect of the disjoint minority classes. Following that, an adaptive adjustment method is presented to determine the number of the newly generated minority samples according to the distribution density of the matched-pair data. The experimental results on 48 imbalanced datasets indicate that the proposed oversampling technique has the better classification accuracy.
Enhancing data privacy through a decentralised predictive model with blockchain-based revenue

Federated learning (FL) permits a vast number of connected to construct deep learning models while keeping their private training data on the device. Rather than uploading the training data and model to the server, FL only sends the local gradients gradually. Hence, FL preserves data privacy by design. FL leverages a decentralised approach where the training data is no longer concentrated. Similarly, blockchain uses the same approach by providing a digital ledger that can cover the flaws in the centralised system. Motivated by the merits of a decentralised approach, we construct a collaborative model of simultaneous distributed learning by employing multiple computing devices over shared memory with blockchain smart contracts as a secure incentive mechanism. The collaborative model preserves a value-driven of distributed learning in enhancing users' privacy. It is supported by blockchain with a secure decentralised incentive technique without having a single point of failure. Furthermore, potential vulnerabilities and plausible defences are also outlined. The experimental results positively recommend that the collaborative model satisfies the design goals.
ENHANCED ONLINE BOOK STORE MODEL: ADOPTING THE PROGRESSIVE WEB APPLICATION (PWA) TECHNOLOGY

From the physical book store to the online bookstore, business owners find a way to meet the demands of their prospective customers. The daily advancement in technology has brought about a huge change the operation of e-commerce. The development of the Progressive Web Applications (PWA) by Google has caused a revolution in mobile development. Using an online bookstore as a case study, this research work presents a PWA architectural framework that can be adopted by any e-commerce applications. This was achieved after a systematic review of existing online bookstore models was carried out – identifying the gaps which will serve as strengths for the proposed model. Also, the emerging technology of PWA was critically reviewed to solidify the proposed model. Adoption of the model will avoid current issues faced the world of mobile development especially code fragmentation. However, exploring the payment gateways and modules will help solidify the model.
MyMUET: A Design of a Mobile based Crowdsourced Assessment for Malaysia University English Test with Non-Synchronous Participant Interaction

Malaysian University English Test is a compulsory exam for local Malaysian students and a prerequisite for graduation. In 2017, more than five hundred thousand local students were enrolled in Malaysian public universities and therefore will need to sit for this examination. This enrolment number is increasing every year. However, there number of competent TESL graduates or English teachers available to cater this volume of students yearly is unknown. Many of these students are not from English speaking background and there are limited resources and assistant available for tutors who need to cater the demand of these students. Even though there are many resources online, the resources for speaking module mainly consists of tips to score and question examples. Students are unable to get feedback of their speaking module even if they practiced the mock exam questions. Moreover, the MUET speaking exam consists of group speaking tasks which the student or user may not be able to fulfil the quorum for practice. This paper shall propose MyMUET, a design of a one stop mobile based crowdsourcing tutor service for MUET to enable students to be able to obtain real time feedback of their speaking exam with interaction with non-real time participants with recommended tutors. Other MUET modules such as writing, reading and listening is also available to be used. MyMUET is developed based on Android studio and enables tutors to list their services and students to select the respective tutors. This method saves time for tutors to develop their own exam questions, enable them to focus on evaluation. This shall also be able to standardize the MUET examination to students receive high quality mock exam questions. MyMUET is then evaluated to ensure its relevance based on subject matter expert feedback. The evaluated system shows that students and academic expert find the application helpful to improve students learning experience.
Text Selection

Text data is ultra-high dimensional, which makes machine learning techniques indispensable for textual analysis. Text is often selected—journalists, speechwriters, and others craft messages to target their audiences’ limited attention. We develop an economically motivated high dimensional selection model that improves learning from text (and from sparse counts data more generally). Our model is especially useful when the choice to include a phrase is more interesting than the choice of how frequently to repeat it. It allows for parallel estimation, making it computationally scalable. A first application revisits the partisanship of US congressional speech. We find that earlier spikes in partisanship manifested in increased repetition of different phrases, whereas the upward trend starting in the 1990s is due to entirely distinct phrase selection. Additional applications show how our model can backcast, nowcast, and forecast macroeconomic indicators using newspaper text, and that it substantially improves out-of-sample fit relative to alternative approaches.