Reviewerly/exHarmony huggingface dataset:
This Time Is Different: Bitcoin Has More Reasons to Reach the Price of $100,000

Since its debut in January 2009 as the first successful cryptocurrency, Bitcoin has been associated with both extreme volatility and illicit activities (i.e. the black market website Silk Road) linked to terrorism-financing, money-laundering, illegal drugs and human trafficking. But this time is different and Bitcoin has more reasons to reach an unmatched price of $100,000 and $1,000,000 afterwards if governments of advanced nations along with that of China stops running headlong into backlash to the alpha Bitcoin and its prodigies (altcoins). Aside from the regulatory uncertainty, Bitcoin must improve design related flaws of its blockchain; as such, electric-intensive mining process; low scalability (i.e. block size limit of 1 megabyte and frequency block creation time of 10 minutes); proof-of-work - PoW (i.e. high latency, very costly and inefficient way of using resources); low transaction processing speed (i.e. between 3.3 and 7 transactions per second, compared with Ethereum’s 25, Ripple’s 1,500, OES’ 3,000, and Visa’s 25,000); and low security (i.e. the majority of mining is controlled by a handful of mining pools known as bitcoin cartels, which can rewrite an alternative financial history if 51% of the computing power is controlled by them). During the past decade (since 2013), the price of bitcoin has witnessed erotic price movements; when bitcoin passed the $1,000 mark (first bubble), the price rose from $196 in October 2013 to $1,155 in November 2013 and then plunged back to $227 in January 2015; company executives, hedge fund managers, media figures, and some celebrities have said the following about Bitcoin; “it is worse than tulip bulb”, “avoid Bitcoin like a plague”, “unlike stocks and bonds, has nothing to offer”, “will make central banks lose control”, “very iffy”, “Ponzi scheme”, “an unfounded fad (or even a pyramid scheme)”, “does not meet functions of money such as medium of exchange, store of value, and unit of account”, “a lot of froth and fraud.” Bitcoin phenomenon is nothing like the dot.com bubble or the 17th century Dutch tulipmania; while Bitcoin is still around and stronger than before, the antecedents that it is often compared with ceased to exist. Similar criticism keeps emerging every time Bitcoin breaks new price records, i.e. $20,089 on 17 December 2017, $32,052 on 3 January 2021, $40,846 on 6 February 2021, $50,341 on 16 February 2021, $61,221 on 14 March 2021, and finally $64,863 on 14 April 2021 (dropped to $28,893 on June 22, 2021). Conventional money will not disappear anytime soon, but at the same time, Bitcoin will continue to forge ahead unabated regardless of ongoing efforts to kill it off.
PaleoCodage—Enhancing machine-readable cuneiform descriptions using a machine-readable paleographic encoding

Abstract This publication introduces PaleoCodage, a new machine-readable way to capture cuneiform paleographic shapes. Many different systems of listing and describing cuneiform signs exist, but none of the systems can be used to describe the shape, i.e. paleographic features of cuneiform signs. PaleoCodage aims to fill this missing link of description which can benefit the Semantic Web community of linked open data dictionaries, the assyrologist community in documenting signs and their variants and the cuneiform image recognition community in providing a gold standard encoding to match against. The publication evaluates the encoding on more than 200 Unicode cuneiform signs, describes already available application cases, a linked data vocabulary for paleography and concludes by describing future work on further validating PaleoCodage and applying the paleographic vocabulary to more languages.
Seeds of SEED: Characterizing Enclave-level Parallelism in Secure Multicore Processors

Secure processor technologies incorporating some form of enclave-based isolation are being deployed in remote cloud computing environments. However, commercial enclave-based systems, such as Intel SGX, incur performance penalties due to architectural limitations arising from enclave interactions with the operating system (OS), encryption and attestation checks for data accesses to main memory, and limitations on the enclave memory size. Enclave software development frameworks like Graphene-SGX aim to improve these limitations with performance enhancements such as exitless calling that offset the latency of expensive enclave interactions with the OS. However, to the best of our knowledge, prior works have not presented a thorough characterization of enclave performance in the presence of increased enclave-level parallelism. In this paper, we characterize how enclave overheads trade off exploitable parallelism on an Intel SGX-enabled multicore CPU for a set of parallelized workloads. We develop a microbenchmark to study the effects of threading as a function of application characteristics, such as the intensity of memory operations and system calls to the OS. We extend our characterization to realistic parallelized enclave workloads from the database and web server domains. We find that application performance scaling with threading is tightly correlated to system call and memory-bound activities in applications. The real world applications stress these constraints, while the underlying system calling implementations deliver competing performance at different thread counts.
A Visualisation Analysis Using the VOSviewer of Literature on Virtual Reality Technology Application in Healthcare

Current virtual reality technology applications in healthcare perform potential abilities in clinical and nursing practices. This review aims to analyse the use and the development direction of virtual reality technology in the whole healthcare field. Researchers searched (n = 5,209) English-language literature related to the application of virtual reality in healthcare on the basis of the Web of Science online database and used VOSviewer 1.6.17 to visualise and analyse the time trend co-authorship, co-occurrence keywords, and country distribution of the literature. Furthermore, they found that the application of virtual reality technology in healthcare shows an overall fragmentation and a relatively concentrated trend, focusing on medical education, rehabilitation therapy, and psychological interventions. Augmented reality and COVID-19 are present research hotspots.
MetaPix: Domain transfer for semantic segmentation by meta pixel weighting

Training a deep neural model for semantic segmentation requires collecting a large amount of pixel-level labeled data. To alleviate the data scarcity problem presented in the real world, one could utilize synthetic data whose label is easy to obtain. Previous work has shown that the performance of a semantic segmentation model can be improved by training jointly with real and synthetic examples with a proper weighting on the synthetic data. Such weighting was learned by a heuristic to maximize the similarity between synthetic and real examples. In our work, we instead learn a pixel-level weighting of the synthetic data by meta-learning, i.e., the learning of weighting should only be minimizing the loss on the target task. We achieve this by gradient-on-gradient technique to propagate the target loss back into the parameters of the weighting model. The experiments show that our method with only one single meta module can outperform a complicated combination of an adversarial feature alignment, a reconstruction loss, plus a hierarchical heuristic weighting at pixel, region and image levels.
Fair and accurate age prediction using distribution aware data curation and augmentation

Deep learning-based facial recognition systems have experienced increased media attention due to exhibiting unfair behavior. Large enterprises, such as IBM, shut down their facial recognition and age prediction systems as a consequence. Age prediction is an especially difficult application with the issue of fairness remaining an open research problem (e.g., predicting age for different ethnicity equally accurate). One of the main causes of unfair behavior in age prediction methods lies in the distribution and diversity of the training data. In this work, we present two novel approaches for dataset curation and data augmentation in order to increase fairness through balanced feature curation and increase diversity through distribution aware augmentation. To achieve this, we introduce out-of-distribution detection to the facial recognition domain which is used to select the data most relevant to the deep neural network’s (DNN) task when balancing the data among age, ethnicity, and gender. Our approach shows promising results. Our best-trained DNN model outperformed all academic and industrial baselines in terms of fairness by up to 4.92 times and also enhanced the DNN’s ability to generalize outperforming Amazon AWS and Microsoft Azure public cloud systems by 31.88% and 10.95%, respectively.
A combination of learning and non-learning based method for enhancement, compression and reconstruction of underwater images

Complex nature of underwater environment poses biggest challenge towards image acquisition and transmission of underwater images. This paper proposes an integrated approach which consists of a non-learning enhancement method with deep Convolutional Neural Networks (CNN) for compression and reconstruction of the image. The proposed method does color and contrast correction for image enhancement. The enhanced images are down-sampled using 9-layer CNN followed by Discrete Wavelet Transform (DWT). The decompression is done by using Inverse DWT. Further, the sub-pixel up-sampled image is de-blurred using a three-layer CNN. Residual Dense CNN (RD-CNN) is used to improve the quality of the reconstructed image after deblurring. The quality of the reconstructed images is measured using Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index Metric (SSIM). The proposed model provides better image enhancement, compression, and reconstruction quality than the existing state-of-the-art methods and Super Resolution CNN (SRCNN) respectively.
Normalizing Flow as a Flexible Fidelity Objective for Photo-Realistic Super-resolution

Super-resolution is an ill-posed problem, where a ground-truth high-resolution image represents only one possibility in the space of plausible solutions. Yet, the dominant paradigm is to employ pixel-wise losses, such as L<inf>1</inf>, which drive the prediction towards a blurry average. This leads to fundamentally conflicting objectives when combined with adversarial losses, which degrades the final quality. We address this issue by revisiting the L<inf>1</inf> loss and show that it corresponds to a one-layer conditional flow. Inspired by this relation, we explore general flows as a fidelity-based alternative to the L<inf>1</inf> objective. We demonstrate that the flexibility of deeper flows leads to better visual quality and consistency when combined with adversarial losses. We conduct extensive user studies for three datasets and scale factors, where our approach is shown to outperform state-of-the-art methods for photo-realistic super-resolution. Code and trained models: git.io/AdFlow
Training Acceleration for Deep Neural Networks: A Hybrid Parallelization Strategy

Deep Neural Networks (DNNs) are widely investigated due to their striking performance in various applications of artificial intelligence. However, with DNNs becoming larger and deeper, the computing resource of a single hardware accelerator is insufficient to meet the training requirements of popular DNNs. Hence, it is required to train them using multiple accelerators in a distributed setting. For a better utilization of the accelerators and a faster training, it is necessary to partition the whole process into segments that can run in parallel. However, in this context, intra-layer parallelization techniques (i.e., data and model parallelization) often face communication and memory bottlenecks, while the performance and resource utilization of inter-layer parallelization techniques (i.e., using pipelining) depend on the partitioning possibilities of the model. We present EffTra, a synchronous hybrid parallelization strategy, that uses a combination of intra-layer and inter-layer parallelism to realize a distributed training of DNNs. EffTra employs the idea of dynamic programming to try to search for the optimal partitioning of a DNN model and assigns devices to the obtained partitions. Our evaluation shows that EffTra accelerates training by up to 2.0x and 1.78x compared to state-of-the-art inter-layer (i.e., GPipe) and intra-layer (i.e., data parallelism) parallelization techniques respectively.
A Canonical Algebra of Open Transition Systems

Feedback and state are closely interrelated concepts. Categories with feedback, originally proposed by Katis, Sabadini and Walters, are a weakening of the notion of traced monoidal categories, with several pertinent applications in computer science. The construction of the free such categories has appeared in several different contexts, and can be considered as state bootstrapping. We show that a categorical algebra for open transition systems, \(\mathbf {Span}(\mathbf {Graph})_*\), also due to Katis, Sabadini and Walters, is the free category with feedback over \(\mathbf {Span}(\mathbf {Set})\). This algebra of transition systems is obtained by adding state to an algebra of predicates, and therefore \(\mathbf {Span}(\mathbf {Graph})_*\) is the canonical such algebra.
Advanced Double Layered Multi-Agent Systems Based on A3C in Real-Time Path Planning

In this paper, we propose an advanced double layered multi-agent system to reduce learning time, expressing a state space using a 2D grid. This system is based on asynchronous advantage actor-critic systems (A3C) and reduces the state space that agents need to consider by hierarchically expressing a 2D grid space and determining actions. Specifically, the state space is expressed in the upper and lower layers. Based on the learning results using A3C in the lower layer, the upper layer makes decisions without additional learning, and accordingly, the total learning time can be reduced. Our method was verified experimentally using a virtual autonomous surface vehicle simulator. It reduced the learning time required to reach a 90% goal achievement rate by 7.1% compared to the conventional double layered A3C. In addition, the goal achievement by the proposed method was 18.86% higher than that of the traditional double layered A3C over 20,000 learning episodes.
Class token and knowledge distillation for multi-head self-attention speaker verification systems

This paper explores three novel approaches to improve the performance of speaker verification (SV) systems based on deep neural networks (DNN) using Multi-head Self-Attention (MSA) mechanisms and memory layers. Firstly, we propose the use of a learnable vector called Class token to replace the average global pooling mechanism to extract the embeddings. Unlike global average pooling, our proposal takes into account the temporal structure of the input what is relevant for the text-dependent SV task. The class token is concatenated to the input before the first MSA layer, and its state at the output is used to predict the classes. To gain additional robustness, we introduce two approaches. First, we have developed a new sampling estimation of the class token. In this approach, the class token is obtained by sampling from a list of several trainable vectors. This strategy introduces uncertainty that helps to generalize better compared to a single initialization as it is shown in the experiments. Second, we have added a distilled representation token for training a teacher-student pair of networks using the Knowledge Distillation (KD) philosophy, which is combined with the class token. This distillation token is trained to mimic the predictions from the teacher network, while the class token replicates the true label. All the strategies have been tested on the RSR2015-Part II and DeepMine-Part 1 databases for text-dependent SV, providing competitive results compared to the same architecture using the average pooling mechanism to extract average embeddings.
Deep neural models for automated multi-task diagnostic scan management—quality enhancement, view classification and report generation

The detailed physiological perspectives captured by medical imaging provides actionable insights to doctors to manage comprehensive care of patients. However, the quality of such diagnostic image modalities is often affected by mismanagement of the image capturing process by poorly trained technicians and older/poorly maintained imaging equipment. Further, a patient is often subjected to scanning at different orientations to capture the frontal, lateral and sagittal views of the affected areas. Due to the large volume of diagnostic scans performed at a modern hospital, adequate documentation of such additional perspectives is mostly overlooked, which is also an essential key element of quality diagnostic systems and predictive analytics systems. Another crucial challenge affecting effective medical image data management is that the diagnostic scans are essentially stored as unstructured data, lacking a well-defined processing methodology for enabling intelligent image data management for supporting applications like similar patient retrieval , automated disease prediction etc. One solution is to incorporate automated diagnostic image descriptions of the observation/findings by leveraging computer vision and natural language processing. In this work, we present multi-task neural models capable of addressing these critical challenges. We propose ESRGAN, an image enhancement technique for improving the quality and visualization of medical chest x-ray images, thereby substantially improving the potential for accurate diagnosis, automatic detection and region-of-interest segmentation. We also propose a CNN-based model called ViewNet for predicting the view orientation of the x-ray image and generating a medical report using Xception net, thus facilitating a robust medical image management system for intelligent diagnosis applications. Experimental results are demonstrated using standard metrics like BRISQUE, PIQE and BLEU scores, indicating that the proposed models achieved excellent performance. Further, the proposed deep learning approaches enable diagnosis in a lesser time and their hybrid architecture shows significant potential for supporting many intelligent diagnosis applications.
Seeing the Unseen: Predicting the First-Person Camera Wearer’s Location and Pose in Third-Person Scenes

Our goal is to predict the camera wearer’s location and pose in his/her environment based on what’s captured by the camera wearer’s first-person wearable camera. Toward this goal, we first collect a new dataset in which the camera wearer performs various activities (e.g., opening a fridge, reading a book) in different scenes with time-synchronized first-person and stationary third-person cameras. We then propose a novel deep network architecture, which takes as input the first-person video frames and empty third-person scene image (without the camera wearer) to predict the location and pose of the camera wearer. We explore and compare our approach with several intuitive baselines and show initial promising results on this novel, challenging problem.
Mathematical Modeling of Electrical Conductivity of Anisotropic Nanocomposite with Periodic Structure

Composite materials consisting of a dielectric matrix with conductive inclusions are promising in the field of micro- and optoelectronics. The properties of a nanocomposite material are strongly influenced by the characteristics of the substances included in its composition, as well as the shape and size of inclusions and the orientation of particles in the matrix. The use of nanocomposite material has significantly expanded and covers various systems. The anisotropic form of inclusions is the main reason for the appearance of optical anisotropy. In this article, models and methods describing the electrical conductivity of a layered nanocomposite of a self-similar structure are proposed. The method of modeling the electrical conductivity of individual blocks, layers, and composite as a whole is carried out similarly to the method of determining the dielectric constant. The advantage of the method proposed in this paper is the removal of restrictions imposed on the theory of generalized conductivity associated with the need to set the dielectric constant.
Improvement of Teacher Qualification in the Field of Computer Animation: Training or Master Class?

The article addresses the issues of the need to learn throughout life and professional development of teachers in the field of information technology. In the article, the authors compare the effectiveness of training and master classes for teachers. The authors note the importance of visual support of educational activities, provide the results of the analysis of tools for creating computer animation and describe the results of the author's research. The authors compare the effectiveness of training and a master class in teaching teachers to create computer animations. The experiment was conducted at Makarenko Sumy State Pedagogical University (Ukraine). Number of study participants - 121. Both forms of training (training and master class) proved their effectiveness, but the authors noted differences for age groups of teachers: young teachers showed higher performance in the master class, and teachers with more than 10 years of experience - in the results of training. The authors also recorded the problem for young teachers not so much to create an animation, but to generate an idea for its construction (the idea of the animation itself).
Method of Using Data from Intelligent Machine Short-Term Memory in Fuzzy Logic System

Decision making based on the process’s history is the main challenge of the Internet of Things (IoT). Spatiotemporal datasets from a lot of heterogeneous sensors represent process’s history. Fuzzy logic systems (FLS) are not used in such conditions due to the large dimension of the problem. The two-stage Computing with Words (CWW) approach overcomes the dimensionality problem by using abstraction engine at the first stage, which maps the meaning of data from a lot of sensors to the meaning of a few words. In this article the CWW technology is extended by a Short-Term Memory (STM) model that stores a time sequence of data in the form of an ordered sequence of events. Fuzzy characteristics of words of a high level of abstraction represent the events. The STM model is supported by the footprint blur algorithm, in which calculations are performed considering the event aging rate. The preprocessing and postprocessing data models are presented, what allows integrate the STM model with FLS and using it for making decisions in IoT. An example of the use of STM data in the FLS when making decisions by an intelligent machine is given.