Reviewerly/exHarmony huggingface dataset:
Comparing research trends through author-provided keywords with machine extracted terms: A ML algorithm approach using publications data on neurological disorders

Objective. This study aimed to identify the primary research areas, countries, and organizational involvement in publications on neurological disorders through an analysis of human-assigned keywords. These results were then compared with unsupervised and machine-algorithm-based extracted terms from the title and abstract of the publications to gain knowledge about deficiencies of both techniques. This has enabled us to understand how far machine-derived terms through titles and abstracts can be a substitute for human-assigned keywords of scientific research articles. Design/Methodology/Approach. While significant research areas on neurological disorders were identified from the author-provided keywords of downloaded publications of Web of Science and PubMed, these results were compared by the terms extracted from titles and abstracts through unsupervised based models like VOSviewer and machine-algorithm-based techniques like YAKE and CounterVectorizer. Results/Discussion. We observed that the post-covid-19 era witnessed more research on various neurological disorders, but authors still chose more generic terms in the keyword list than specific ones. The unsupervised extraction tool, like VOSviewer, identified many other extraneous and insignificant terms along with significant ones. However, our self-developed machine learning algorithm using CountVectorizer and YAKE provided precise results subject to adding more stop-words in the dictionary of the stop-word list of the NLTK tool kit. Conclusion. We observed that although author provided keywords play a vital role as they are assigned in a broader sense by the author to increase readability, these concept terms lacked specificity for in-depth analysis. We suggested that the ML algorithm being more compatible with unstructured data was a valid alternative to the author-generated keywords for more accurate results. Originality/Value. To our knowledge, this is the first-ever study that compared the results of author-provided keywords with machine-extracted terms with real datasets, which may be an essential lead in the machine learning domain. Replicating these techniques with large datasets from different fields may be a valuable knowledge resource for experts and stakeholders.
What will the education of the future look like? How have Metaverse and Extended Reality affected the higher education systems?

Education is one of the essential foundations of the sustainable development of societies, in particular, the objectives SDG4 of the UN General Assembly. Extended Reality (XR, so-called Metaverse) enables multisensory interaction with virtual environments, computer-created objects, and avatars. Also, the dynamic development of Head Mounted Displays (HDMs) allows for an increasingly deeper experience of the virtual world, especially through the development of depth perception, including the rendering of several modalities like vision, touch, and hearing. This creates a unique opportunity to revolutionize the higher education system by adding a new dimension of cognition and making it accessible to more people, especially those living in hard-to-reach areas. It is also a perfect complement to the process of educating students during a pandemic, such as the recent COVID-19 pandemic. In this paper, based on the literature and our experience, we provided an overview of the possibility of the Metaverse application in higher education taking into account the advantages and limitations of the systems. It turned out that XR-based solutions can be successfully applied in medical education, chemistry courses as well as in Science, Technology, Engineering, and Mathematics (STEM) education. Moreover, the XR-based systems are useful for learning spatial skills such as navigation, spatial reasoning, and perception. In the case of remote learning, XR enables easier adaptation to the educational formula. Also, during lockdowns, an XR-based application can be considered a tool to promote socialization in the event. Thus, it enables to implementation of open and inclusive learning and teaching space, namely Edu-Metaverse. In the current social context, the obtained results provided valuable insights into factors affecting the users during the application of Metaverse in education processes, including remote learning. Finally, this paper suggests a research direction for the development of effective Metaverse-based educational solutions.
Artificial bee colony algorithm: A component-wise analysis using diversity measurement

A swarm-based metaheuristic algorithm, like artificial bee colony (ABC), embraces four key elements of collective intelligence: positive feedback, negative feedback, multiple interactions, and fluctuation. Fluctuation refers to population diversity which can be measured using dimension-wise diversity. This paper performed component-wise analysis of ABC algorithm using diversity measurement. The analysis revealed scout bees component as counterproductive and onlooker bees component with poor global search ability. Subsequently, an ABC algorithm without scout bees component and modified onlooker bees component is proposed in this paper. The effectiveness and efficiency of the proposed ScoutlessABC is validated on test suite of a dozen of benchmark functions. To further evaluate the performance, ScoutlessABC is employed on the parameter training problem of fuzzy neural network for solving eight classification problems. The experimental results show that ScoutlessABC maintains strong convergence ability than the original ABC algorithm. Overall, this study has two major contributions: (a) an effective component-wise analysis approach using diversity measurement and (b) a simplified and modified ABC variant with enhanced search efficiency.
Entanglement of indistinguishable particles: A comparative study

There is a debate in course about the quantification of entanglement of indistinguishable particles and recently a new method due to Lo Franco and Compagno (LFC) [Sci. Rep. \textbf{6}, 20603 (2016)] appeared with the aim of settling the controversies of this debate. In this work we show that the LFC approach can be recovered using the second quantization formalism and a local single-particle basis to perform the trace over one particle state. The use of the second quantization formalism has the advantage of being well known in many-body physics. We also compare the LFC and second quantization approaches, or equivalently, the effect of the partial trace over one particle depending on the local and nonlocal caracter of the single-particle basis. Additionally, we study the role played by the exchange correlations in the quantification of entanglement of indistinguishable particles.
Activity-Aware Wearable System for Power-Efficient Prediction of Physiological Responses

Wearable health monitoring has emerged as a promising solution to the growing need for remote health assessment and growing demand for personalized preventative care and wellness management. Vital signs can be monitored and alerts can be made when anomalies are detected, potentially improving patient outcomes. One major challenge for the use of wearable health devices is their energy efficiency and battery-lifetime, which motivates the recent efforts towards the development of self-powered wearable devices. This article proposes a method for context aware dynamic sensor selection for power optimized physiological prediction using multi-modal wearable data streams. We first cluster the data by physical activity using the accelerometer data, and then fit a group lasso model to each activity cluster. We find the optimal reduced set of groups of sensor features, in turn reducing power usage by duty cycling these and optimizing prediction accuracy. We show that using activity state-based contextual information increases accuracy while decreasing power usage. We also show that the reduced feature set can be used in other regression models increasing accuracy and decreasing energy burden. We demonstrate the potential reduction in power usage using a custom-designed multi-modal wearable system prototype.
Cryptocurrencies and Investment Diversification: Empirical Evidence from Seven Largest Cryptocurrencies

The study examines the diversification capability of seven cryptocurrencies with the largest market size against risks from economic factors as oil price, gold price, interest rate, USD strength, and S&P500. Using the weekly data of Bitcoin, Litecoin, Ripple, Stellar, Monero, Dash, and Bytecoin in the period Aug/2014-Jun/2018, the study finds that there are structural breaks and ARCH disturbance in each cryptocurrency, suggesting a systematic risk within the cryptocurrency market. However, the causality between cryptocurrencies and economic factors is undirected. Interestingly, our findings show that cryptocurrencies are insignificant correlations with economic factors. The result implies that cryptocurrencies can not be assumed as financial assets to hedge systematic risks from economic factors.
From Collisions to Chosen-Prefix Collisions Application to Full SHA-1

A chosen-prefix collision attack is a stronger variant of a collision attack, where an arbitrary pair of challenge prefixes are turned into a collision. Chosen-prefix collisions are usually significantly harder to produce than (identical-prefix) collisions, but the practical impact of such an attack is much larger. While many cryptographic constructions rely on collision-resistance for their security proofs, collision attacks are hard to turn into break of concrete protocols, because the adversary has a limited control over the colliding messages. On the other hand, chosen-prefix collisions have been shown to break certificates (by creating a rogue CA) and many internet protocols (TLS, SSH, IPsec). In this article, we propose new techniques to turn collision attacks into chosen-prefix collision attacks. Our strategy is composed of two phases: first a birthday search that aims at taking the random chaining variable difference (due to the chosen-prefix model) to a set of pre-defined target differences. Then, using a multi-block approach, carefully analysing the clustering effect, we map this new chaining variable difference to a colliding pair of states using techniques developed for collision attacks. We apply those techniques to MD5 and SHA-1, and obtain improved attacks. In particular, we have a chosen-prefix collision attack against SHA-1 with complexity between $$2^{66.9}$$ and $$2^{69.4}$$ (depending on assumptions about the cost of finding near-collision blocks), while the best-known attack has complexity $$2^{77.1}$$ . This is within a small factor of the complexity of the classical collision attack on SHA-1 (estimated as $$2^{64.7}$$ ). This represents yet another warning that industries and users have to move away from using SHA-1 as soon as possible.
Acute Lymphoblastic Leukemia Classification from Microscopic Images Using Convolutional Neural Networks

Examining blood microscopic images for leukemia is necessary when expensive equipment for flow cytometry is unavailable. Automated systems can ease the burden on medical experts for performing this examination and may be especially helpful to quickly screen a large number of patients. We present a simple, yet effective classification approach using a ResNeXt convolutional neural network with Squeeze-and-Excitation modules. The approach was evaluated in the C-NMC online challenge and achieves a weighted F1-score of 88.91 % on the test set. Code is available at https://github.com/jprellberg/isbi2019cancer .
Data Augmentation with Atomic Templates for Spoken Language Understanding

Spoken Language Understanding (SLU) converts user utterances into structured semantic representations.Data sparsity is one of the main obstacles of SLU due to the high cost of human annotation, especially when domain changes or a new domain comes.In this work, we propose a data augmentation method with atomic templates for SLU, which involves minimum human efforts.The atomic templates produce exemplars for fine-grained constituents of semantic representations.We propose an encoder-decoder model to generate the whole utterance from atomic exemplars.Moreover, the generator could be transferred from source domains to help a new domain which has little data.Experimental results show that our method achieves significant improvements on DSTC 2&3 dataset which is a domain adaptation setting of SLU.
Animated Stickies: Fast Video Projection Mapping onto a Markerless Plane through a Direct Closed-Loop Alignment

This paper presents a fast projection mapping method for moving image content projected onto a markerless planar surface using a low-latency Digital Micromirror Device (DMD) projector. By adopting a closed-loop alignment approach, in which not only the surface texture but also the projected image is tracked by a camera, the proposed method is free from a calibration or position adjustment between the camera and projector. We designed fiducial patterns to be inserted into a fast flapping sequence of binary frames of the DMD projector, which allows the simultaneous tracking of the surface texture and a fiducial geometry separate from a single image captured by the camera. The proposed method implemented on a CPU runs at 400 fps and enables arbitrary video contents to be "stuck" onto a variety of textured surfaces.
An Investigation into On-Device Personalization of End-to-End Automatic Speech Recognition Models

Speaker-independent speech recognition systems trained with data from many users are generally robust against speaker variability and work well for a large population of speakers. However, these systems do not always generalize well for users with very different speech characteristics. This issue can be addressed by building personalized systems that are designed to work well for each specific user. In this paper, we investigate the idea of securely training personalized end-to-end speech recognition models on mobile devices so that user data and models never leave the device and are never stored on a server. We study how the mobile training environment impacts performance by simulating on-device data consumption. We conduct experiments using data collected from speech impaired users for personalization. Our results show that personalization achieved 63.7\% relative word error rate reduction when trained in a server environment and 58.1% in a mobile environment. Moving to on-device personalization resulted in 18.7% performance degradation, in exchange for improved scalability and data privacy. To train the model on device, we split the gradient computation into two and achieved 45% memory reduction at the expense of 42% increase in training time.
A Highly Efficient Distributed Deep Learning System for Automatic Speech Recognition

Modern Automatic Speech Recognition (ASR) systems rely on distributed deep learning to for quick training completion. To enable efficient distributed training, it is imperative that the training algorithms can converge with a large mini-batch size. In this work, we discovered that Asynchronous Decentralized Parallel Stochastic Gradient Descent (ADPSGD) can work with much larger batch size than commonly used Synchronous SGD (SSGD) algorithm. On commonly used public SWB-300 and SWB-2000 ASR datasets, ADPSGD can converge with a batch size 3X as large as the one used in SSGD, thus enable training at a much larger scale. Further, we proposed a Hierarchical-ADPSGD (H-ADPSGD) system in which learners on the same computing node construct a super learner via a fast allreduce implementation, and super learners deploy ADPSGD algorithm among themselves. On a 64 Nvidia V100 GPU cluster connected via a 100Gb/s Ethernet network, our system is able to train SWB-2000 to reach a 7.6% WER on the Hub5-2000 Switchboard (SWB) test-set and a 13.2% WER on the Call-home (CH) test-set in 5.2 hours. To the best of our knowledge, this is the fastest ASR training system that attains this level of model accuracy for SWB-2000 task to be ever reported in the literature.
Machines Learning Trends, Perspectives and Prospects in Education Sector

In the contemporary exam-driven domain of education, each time a new technology transpires, societies want to know how it can be used to make kids get superior grades, how it can expedite teaching and cut the expenditure of learning, and could it be used to substitute teachers altogether? For a considerable length of time, training technophiles have imagined a future wherein gee-whiz gadgets and drawing in advanced applications whisk students from the stagnations of conventional study hall guidance and into a fun universe of signaling PCs, self-managed exercises, and cloud-based coordinated effort. Machine learning can possibly strengthen parts of educating and learning that are as of now tedious and hard to oversee. Machine learning is tremendously affecting the education industry. Moving forward into year 2020, it is not the technology itself that needs to change. In most aspects of our lives, technology has made significant changes for good and bad, but in education, predominantly schools and universities, there is still persistent resistance. Subsequently, students were compelled to attempt to alter their style of learning to the exercise plan, instead of a different way. As society eyes, arranged innovation with both fervor and doubt, universities the nation over are developing frameworks that gather and examine immense measures of understudy information to foresee and reinforce understudy achievement and achieve other institutional objectives.
Subcarrier wave continuous variable quantum key distribution with discrete modulation: mathematical model and finite-key analysis

In this paper we report a continuous-variable quantum key distribution protocol using multimode coherent states generated on subcarrier frequencies of the optical spectrum. To detect the quadrature components of bosonic field we propose a coherent detection scheme where power from a carrier wave is used as a local oscillator. We compose a mathematical model of the proposed scheme and perform its security analysis in the finite-size regime using fully quantum asymptotic equipartition property technique. We calculate a lower bound on the secret key rate for the system under the assumption that the quantum channel noise is negligible compared to detector dark counts, and an eavesdropper is restricted to collective attacks. Our calculation shows that the current realistic system implementation would allow distributing secret keys over channels with losses up to 9 dB.
Person Tube Retrieval via Language Description

This paper focuses on the problem of person tube (a sequence of bounding boxes which encloses a person in a video) retrieval using a natural language query. Different from images in person re-identification (re-ID) or person search, besides appearance, person tube contains abundant action and information. We exploit a 2D and a 3D residual networks (ResNets) to extract the appearance and action representation, respectively. To transform tubes and descriptions into a shared latent space where data from the two different modalities can be compared directly, we propose a Multi-Scale Structure Preservation (MSSP) approach. MSSP splits a person tube into several element-tubes on average, whose features are extracted by the two ResNets. Any number of consecutive element-tubes forms a sub-tube. MSSP considers the following constraints for sub-tubes and descriptions in the shared space. 1) Bidirectional ranking. Matching sub-tubes (resp. descriptions) should get ranked higher than incorrect ones for each description (resp. sub-tube). 2) External structure preservation. Sub-tubes (resp. descriptions) from different persons should stay away from each other. 3) Internal structure preservation. Sub-tubes (resp. descriptions) from the same person should be close to each other. Experimental results on person tube retrieval via language description and other two related tasks demonstrate the efficacy of MSSP.
CUT ELIMINATION AND NORMALIZATION FOR GENERALIZED SINGLE AND MULTI-CONCLUSION SEQUENT AND NATURAL DEDUCTION CALCULI

Any set of truth-functional connectives has sequent calculus rules that can be generated systematically from the truth tables of the connectives. Such a sequent calculus gives rise to a multi-conclusion natural deduction system and to a version of Parigot's free deduction. The elimination rules are "general," but can be systematically simplified. Cut-elimination and normalization hold. Restriction to a single formula in the succedent yields intuitionistic versions of these systems. The rules also yield generalized lambda calculi providing proof terms for natural deduction proofs as in the Curry-Howard isomorphism. Addition of an indirect proof rule yields classical single-conclusion versions of these systems. Gentzen's standard systems arise as special cases.
Quantum computing cryptography: Finding cryptographic Boolean functions with quantum annealing by a 2000 qubit D-wave quantum computer

As the building block in symmetric cryptography, designing Boolean functions satisfying multiple properties is an important problem in sequence ciphers, block ciphers, and hash functions. However, the search of n-variable Boolean functions fulfilling global cryptographic constraints is computationally hard due to the super-exponential size O(22n) of the space. Here, we introduce a codification of the cryptographically relevant constraints in the ground state of an Ising Hamiltonian, allowing us to naturally encode it in a quantum annealer, which seems to provide a quantum speedup. Additionally, we benchmark small n cases in a D-Wave machine, showing its capacity of devising cryptographic Boolean functions with certain relevant properties. We have complemented it with local search and chain repair to improve the D-Wave quantum annealer performance related to the low connectivity. This work shows how to codify super-exponential cryptographic problems into quantum annealers and paves the way for reaching quantum supremacy.
Reducing approximate Longest Common Subsequence to approximate Edit Distance

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2020 ACM-SIAM Symposium on Discrete Algorithms (SODA)Reducing approximate Longest Common Subsequence to approximate Edit DistanceAviad Rubinstein and Zhao SongAviad Rubinstein and Zhao Songpp.1591 - 1600Chapter DOI:https://doi.org/10.1137/1.9781611975994.98PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Given a pair of n-character strings, the problems of computing their Longest Common Subsequence and Edit Distance have been extensively studied for decades. For exact algorithms, LCS and Edit Distance (with character insertions and deletions) are equivalent; the state of the art running time is (almost) quadratic in n, and this is tight under plausible fine-grained complexity assumptions. But for approximation algorithms the picture is different: there is a long line of works with improved approximation factors for Edit Distance, but for LCS (with binary strings) only a trivial 1/2-approximation was known. In this work we give a reduction from approximate LCS to approximate Edit Distance, yielding the first efficient (1/2 + ϵ)-approximation algorithm for LCS for some constant ϵ > 0. Previous chapter Next chapter RelatedDetails Published:2020eISBN:978-1-61197-599-4 https://doi.org/10.1137/1.9781611975994Book Series Name:ProceedingsBook Code:PRDA20Book Pages:xxii + 3011