Reviewerly/exHarmony huggingface dataset:
Towards Ultra-Efficient DNN Inference Acceleration on Edge Devices for Wellbeing Applications

Various Deep Neural Networks (DNN) have served as the fundamental building blocks of a broad spectrum of machine learning applications due to its superior performance. However, it is not an easy task to apply or deploy deep learning techniques on the rapidly increasing edge devices such as wearable Internet of Things (IoT), smartphones or smart health devices with embedded sensors due to the limited computation and memory resources. It is desirable to develop efficient systems design and algorithms for the wide deployment of deep learning inferences on edge devices. To address this problem, we propose a set of hardware-friendly structured model pruning and compiler optimization techniques to accelerate DNN executions on edge devices. The structured model pruning is adopted to satisfy the limited computation and memory resource constraints and enable potential hardware acceleration. In the meantime, the compile optimization is utilized to further implement superior DNN inference acceleration performance on edge devices. With the proposed techniques, we are able to achieve real-time DNN inferences on edge devices as shown in the demo with various DNN applications deployed on mobile devices. These techniques enable us to explore impactful solutions with deep learning algorithms on cheaper affordable wearable IoTs to help in the well beings of users. Specifically, better personalization of health related solutions can help to care for the health and enhance users' experience with the superior performance of deep learning on smart health devices.
Faster general parsing through context-free memoization

We present a novel parsing algorithm for all context-free languages. The algorithm features a clean mathematical formulation: parsing is expressed as a series of standard operations on regular languages and relations. Parsing complexity w.r.t. input length matches the state of the art: it is worst-case cubic, quadratic for unambiguous grammars, and linear for LR-regular grammars. What distinguishes our approach is that parsing can be implemented using only immutable, acyclic data structures. We also propose a parsing optimization technique called context-free memoization. It allows handling an overwhelming majority of input symbols using a simple stack and a lookup table, similarly to the operation of a deterministic LR(1) parser. This allows our proof-of-concept implementation to outperform the best current implementations of common generalized parsing algorithms (Earley, GLR, and GLL). Tested on a large Java source corpus, parsing is 3–5 times faster, while recognition—35 times faster.
Language Modeling for Formal Mathematics

We examine whether language modeling applied to mathematical formulas enables logical reasoning. We suggest several logical reasoning tasks that can be used to evaluate language models trained on formal mathematical statements, such as type inference, suggesting missing assumptions and completing equalities. To train language models for formal mathematics, we propose a novel skip-tree task, which outperforms standard language modeling tasks on our reasoning benchmarks. We also analyze the models' ability to formulate new conjectures by measuring how often the predictions that do not fit the ground truth or any training data turn out to be true and useful statements.
State Space Expectation Propagation: Efficient Inference Schemes for Temporal Gaussian Processes

We formulate approximate Bayesian inference in non-conjugate temporal and spatio-temporal Gaussian process models as a simple parameter update rule applied during Kalman smoothing. This viewpoint encompasses most inference schemes, including expectation propagation (EP), the classical (Extended, Unscented, etc.) Kalman smoothers, and variational inference. We provide a unifying perspective on these algorithms, showing how replacing the power EP moment matching step with linearisation recovers the classical smoothers. EP provides some benefits over the traditional methods via introduction of the so-called cavity distribution, and we combine these benefits with the computational efficiency of linearisation, providing extensive empirical analysis demonstrating the efficacy of various algorithms under this unifying framework. We provide a fast implementation of all methods in JAX.
How do you Propose Your Code Changes? Empirical Analysis of Affect Metrics of Pull Requests on GitHub

Software engineering methodologies rely on version control systems such as git to store source code artifacts and manage changes to the codebase.Pull requests include chunks of source code, history of changes, log messages around a proposed change of the mainstream codebase, and much discussion on whether to integrate such changes or not.A better understanding of what contributes to a pull request fate and latency will allow us to build predictive models of what is going to happen and when.Several factors can influence the acceptance of pull requests, many of which are related to the individual aspects of software developers.In this study, we aim to understand how the affect (e.g., sentiment, discrete emotions, and valence-arousal-dominance dimensions) expressed in the discussion of pull request issues influence the acceptance of pull requests.We conducted a mining study of large git software repositories and analyzed more than 150,000 issues with more than 1,000,000 comments in them.We built a model to understand whether the affect and the politeness have an impact on the chance of issues and pull requests to be merged-i.e., the code which fixes the issue is integrated in the codebase.We built two logistic classifiers, one without affect metrics and one with them.By comparing the two classifiers, we show that the affect metrics improve the prediction performance.Our results show that valence (expressed in comments received and posted by a reporter) and joy expressed in the comments written by a reporter are linked to a higher likelihood of issues to be merged.On the contrary, sadness, anger, and arousal expressed in the comments written by a reporter, and anger, arousal, and dominance expressed in the comments received by a reporter, are linked to a lower likelihood of a pull request to be merged.
Computer-aided diagnosis system of lung carcinoma using Convolutional Neural Networks

With the number of lung cancers' morbidity and mortality is showing a trend of increasing year by year, the demand for pathologists is increasing rapidly, so in this study, we aimed to design a medical pathologically assistant diagnostic system to help pathologists complete diagnostic analysis tasks. A Deep Convolutional Neural Network(DCNN) is adopted to automatically distinguish tumor tissues from normal tissues in digitized hematoxylin and eosin (H&E) stained lung cell pathological slides that collected from The Cancer Genome Atlas (TCGA) and collaborate hospitals, we trained and evaluate WSIs(the whole slide images) captured at 10x magnification and other higher magnification, results show the difference are negligible. Moreover, we also compared the training effect of different models on same level magnification WSIs, the results show that performance of Resnet-18 network model and Resnet-50 network model is nearly consistent. Actually processing time based on Resnet-18 model is shorter than Resnet-50 model, so we don't need deeper network for study. Our system was shown to enormous advantages in accuracy, sensitivity and efficiency, could reduce the burden on pathologists, enable them to spend more time on advanced decision-making tasks, would be widely applied to pathological diagnosis, clinical practice, scientific research and so on.
Annotated Knowledge Graphs for Teaching in Higher Education

Digital systems that enable so-called intelligent, adaptive or personalized learning are thought to bear great potential for the future of education. Research and development towards such innovative learning systems has therefore evolved into an expanding field. Two of the key challenges are (1) to automate the extraction of expert knowledge and (2) the development of an advanced domain model, on which the system can draw. To tackle these challenges, our interdisciplinary contribution is to suggest adopting a novel approach to creating educational knowledge graphs of texts (1), which can then be further annotated and supplemented by instructors and students (2). In particular, we will outline practical use cases for blended learning scenarios in Higher Education.
Retico: An incremental framework for spoken dialogue systems

In this paper we present the newest version of retico - a python-based incremental dialogue framework to create state-of-the-art spoken dialogue systems and simulations. Retico provides a range of incremental modules that are based on services like Google ASR, Google TTS and Rasa NLU. Incremental networks can be created either in code or with a graphical user interface. In this demo we present three use cases that are implemented in retico: a spoken translation tool that translates speech in real-time, a conversation simulation that models turn-taking and a spoken dialogue restaurant information service.
On the determination of coherent solar climates over a tropical island with a complex topography

Many tropical islands aim at developing a greener self-sufficient energy production systems based on renewable energy, notably solar-generated electricity. This work explores the mean diurnal and annual solar cycles over La Réunion island (southwest Indian Ocean: 21°S, 55.5°E), and their spatial behavior, using the Solar surfAce RAdiation Heliosat – East (SARAH-E) satellite-derived data at high spatial (0.05°×0.05°) and time (hourly) resolutions over period 1999–2016. Comparisons of the SARAH-E data with ground-based measurements over the period 2011–2015 show differences of ~15% for diurnal-seasonal variations. The solar resource over the island displays strong spatial variability, with differences larger than 100 Wm-2 between coastal and mountainous zones. The mean solar resource is lower on the island than on the nearby sea by ~20%. The strongest interactions between the diurnal and annual cycles are found at the windward mid-slopes and near the active volcano, in line with the well-known cloud processes encountered there. A clustering of solar zones, based on diurnal-seasonal cycles, structures the island into a dipole that opposes the western to the eastern side of the island.
A Fragile Watermarking Algorithm for Speech Authentication by Modifying Least Significant Digits

Speech content is often faced with malicious forgery and tampering in transit. Therefore, we introduce a novel fragile watermarking algorithm by modifying the least significant digits for speech content authentication and tampering recovery. In this letter, the proposed authentication scheme consists of two processes: the first one is watermark generation and embedding, the second one is content authentication and recovery. In the former process, the compressed speech signal, frame number and check information are generated based on speech content to form a watermark. Subsequently, the generated watermark is embedded into the least significant digits of speech sampling points using the proposed watermarking algorithm. In the latter process, the embedded watermark is utilized to locate the tampered area and recover that tampered content synchronously. Experimental results show that the proposed watermarking algorithm is fragile to common signal processing. Additionally, it has favorable inaudibility, accurate location ability and high recovery quality.
A Convolutional Result Sharing Approach for Binarized Neural Network Inference

The binary-weight-binary-input binarized neural network (BNN) allows a much more efficient way to implement convolutional neural networks (CNNs) on mobile platforms. During inference, the multiply-accumulate operations in BNNs can be reduced to XNOR-popcount operations. Thus, the XNOR-popcount operations dominate most of the computation in BNNs. To reduce the number of required operations in convolution layers of BNNs, we decompose 3-D filters into 2-D filters and exploit the repeated filters, inverse filters, and similar filters to share results. By sharing the results, the number of operations in convolution layers of BNNs can be reduced effectively. Experimental results show that the number of operations can be reduced by about 60% for CIFAR-10 on BNNs while keeping the accuracy loss within 1% of originally trained network.
Prediction Vulnerability Level of Dengue Fever Using KNN and Random Forest

Indonesia is a country that is prone to Dengue Fever, this happens because Indonesia is a country with a tropical climate. More than 50 years after Indonesia contracted the dengue virus, dengue fever cases have not been resolved, currently the cases that occur are greatly increased over time this happens because of factors that cause dengue fever. By considering this serious problem, the authors created a system that can predict the vulnerability level in Bandung and looks for the factors that most influence from all factors of Dengue Fever using the KNN Algorithm and Random Forest. The results of the system show the results of the best model is KNN algorithm with RMSE 29,26, and from the model shows the most influencing factors are population density, growth rate population mobility, rainfall, wind speed. by utilizing the results of the study, the government can adjust actions to each level of sub-district vulnerability and pay more attention to the factors that most influence dengue fever according to the results of the study.
Stable positioning for mobile targets using distributed fusion correction strategy of heterogeneous data

Positioning technology of mobile target has become the key of navigation tracking and multi-machine cooperation. Considering the interference of noise and initial error in the process of speed measurement, there are accumulated errors in the long voyage of mobile target dead reckoning. Besides, there exists interference of time-varying signal propagation and line of sight occlusion in wireless sensor network, and there is wrong positioning in wireless positioning of mobile target in local area. Therefore, only using a single positioning method leads to abnormal sensor data, which makes the positioning untrustworthy. In this paper, the synergistic effect of multi-sensor measurement is applied to study the distributed fusion correction of heterogeneous data for mobile target stable positioning. Firstly, the error propagation characteristics including speed and yaw angle and the position calculation model are established. Secondly, the dual mapping between wireless parameters including wireless pseudo range and anchor node reference coordinate and network topology is established. Finally, based on multi-source data, the real-time position output tight coupling mechanism of mobile target is adopted by fusion correction strategy. Through the design of fusion filter based on adaptive correction, positioning accuracy keeps moderate when heterogeneous data is normal or even abnormal, and the feasibility and effectiveness of the proposed distributed fusion correction strategy of abnormal data are verified. The research in this paper can help to reveal the multi-parameters coupling mechanism of mobile target, improve the accuracy and stability of mobile target positioning performance in whole space and long time, and also provide theoretical and experimental basis for the research of robot navigation tracking and multi-machine cooperation in the same environment.
An E-Voting Framework with Enterprise Blockchain

Electronic voting or E-voting system is being widely used in both corporate and government voting system for its ease of use and efficiency. It has proved some significant advantages over the paper-based one, but vital key elements of voting remain within the later system like privacy, anonymity, etc. The evolution of Blockchain technology with advanced cryptographic techniques paved the way to solve the existing drawbacks of the E-voting system with additional new features. In this paper, we proposed a scheme of the E-voting system based on permissioned or Enterprise Blockchain. The solution proposed here is suitable for online voting of small to medium-scale enterprise, student representative election in universities, or conducting an election for professional bodies. The Hyperledger Fabric (HLF) platform was used in the core of the framework along with blind signature and zero-knowledge proof (ZKP) through Idemix to establish security, un-reusability, transparency, and privacy or receipt-freeness of the voting system.
Neural Decomposition: Functional ANOVA with Variational Autoencoders

Variational Autoencoders (VAEs) have become a popular approach for dimensionality reduction. However, despite their ability to identify latent low-dimensional structures embedded within high-dimensional data, these latent representations are typically hard to interpret on their own. Due to the black-box nature of VAEs, their utility for healthcare and genomics applications has been limited. In this paper, we focus on characterising the sources of variation in Conditional VAEs. Our goal is to provide a feature-level variance decomposition, i.e. to decompose variation in the data by separating out the marginal additive effects of latent variables z and fixed inputs c from their non-linear interactions. We propose to achieve this through what we call Neural Decomposition - an adaptation of the well-known concept of functional ANOVA variance decomposition from classical statistics to deep learning models. We show how identifiability can be achieved by training models subject to constraints on the marginal properties of the decoder networks. We demonstrate the utility of our Neural Decomposition on a series of synthetic examples as well as high-dimensional genomics data.
Algebraic and Analytic Approaches for Parameter Learning in Mixture Models.

We present two different approaches for parameter learning in several mixture models in one dimension. Our first approach uses complex-analytic methods and applies to Gaussian mixtures with shared variance, binomial mixtures with shared success probability, and Poisson mixtures, among others. An example result is that $\exp(O(N^{1/3}))$ samples suffice to exactly learn a mixture of $k<N$ Poisson distributions, each with integral rate parameters bounded by $N$. Our second approach uses algebraic and combinatorial tools and applies to binomial mixtures with shared trial parameter $N$ and differing success parameters, as well as to mixtures of geometric distributions. Again, as an example, for binomial mixtures with $k$ components and success parameters discretized to resolution $\epsilon$, $O(k^2(N/\epsilon)^{8/\sqrt{\epsilon}})$ samples suffice to exactly recover the parameters. For some of these distributions, our results represent the first guarantees for parameter estimation.
NSTM: Real-Time Query-Driven News Overview Composition at Bloomberg

Millions of news articles from hundreds of thousands of sources around the globe appear in news aggregators every day.Consuming such a volume of news presents an almost insurmountable challenge.For example, a reader searching on Bloomberg's system for news about the U.K. would find 10,000 articles on a typical day.Apple Inc., the world's most journalistically covered company, garners around 1,800 news articles a day.We realized that a new kind of summarization engine was needed, one that would condense large volumes of news into short, easy to absorb points.The system would filter out noise and duplicates to identify and summarize key news about companies, countries or markets.When given a user query, Bloomberg's solution, Key News Themes (or NSTM), leverages state-of-the-art semantic clustering techniques and novel summarization methods to produce comprehensive, yet concise, digests to dramatically simplify the news consumption process.NSTM is available to hundreds of thousands of readers around the world and serves thousands of requests daily with sub-second latency.At ACL 2020, we will present a demo of NSTM.* Order reflects writing contributions; M.X., I.C.C., and J.B
Programming LEGO EV3 in Microsoft MakeCode

Jobs with Computer Science are very popular and requested in almost all companies. Companies cannot fill these positions, because there are not many IT professionals. The solution could be to motivate students on all levels of education to learn Computer Science, especially programming. There are many educational aids, which could be used for showing students, that programming is not so hard, as they think. The contribution describes our experience with using the online environment Microsoft MakeCode and the most popular educational robot Lego Mindstorms EV3 in an extracurricular activity for primary school children at Matej Bel University, Slovakia. In this paper, we present environment MakeCode and examples of tasks, which we are using for teaching programming in this environment.
Iterative Data Programming for Expanding Text Classification Corpora

Real-world text classification tasks often require many labeled training examples that are expensive to obtain. Recent advancements in machine teaching, specifically the data programming paradigm, facilitate the creation of training data sets quickly via a general framework for building weak models, also known as labeling functions, and denoising them through ensemble learning techniques. We present a fast, simple data programming method for augmenting text data sets by generating neighborhood-based weak models with minimal supervision. Furthermore, our method employs an iterative procedure to identify sparsely distributed examples from large volumes of unlabeled data. The iterative data programming techniques improve newer weak models as more labeled data is confirmed with human-in-loop. We show empirical results on sentence classification tasks, including those from a task of improving intent recognition in conversational agents.
Exploratory path planning for mobile robots in dynamic environments with ant colony optimization

In the path planning task for autonomous mobile robots, robots should be able to plan their trajectory to leave the start position and reach the goal, safely. There are several path planning approaches for mobile robots in the literature. Ant Colony Optimization algorithms have been investigated for this problem, giving promising results. In this paper, we propose the Max-Min Ant System for Dynamic Path Planning algorithm for the exploratory path planning task for autonomous mobile robots based on topological maps. A topological map is an environment representation whose focus is the main reference points of the environment and their connections. Based on this representation, the path can be composed by a sequence of state/actions pairs, which facilitates the navigability of the path, with no need to have the information of the complete map. The proposed algorithm was evaluated in static and dynamic environments, showing promising results in both of them. Experiments in dynamic environments show the adaptability of our proposal.
Research on the Detection Algorithm of Data Integrity Verification Results in Big Data Storage

Abstract As one of the most widely used big data computing technologies, data cloud storage technology brings great convenience to users. However, while sharing data, it also causes data corruption and missing data integrity problems. Today’s verification of the integrity of remote data is publicly performed by trusted third parties. This makes the verifier have the potential threat of providing false information. The resulting validation data is untrustworthy. In order to ensure the security and integrity of data, technicians need to establish a data authentication results detection algorithm. Moreover, this algorithm ensures the reliability and utilization of data by improving the limited verification results.