Reviewerly/exHarmony huggingface dataset:
An Interactive Multimodal Retrieval System for Memory Assistant and Life Organized Support

Lifelogging is known as the new trend of writing diary digitally where both the surrounding environment and personal physiological data and cognition are collected at the same time under the first perspective. Exploring and exploiting these lifelog (i.e., data created by lifelogging) can provide useful insights for human beings, including healthcare, work, entertainment, and family, to name a few. Unfortunately, having a valuable tool working on lifelog to discover these insights is still a tough challenge. To meet this requirement, we introduce an interactive multimodal retrieval system that aims to provide people with two functions, memory assistant and life organized support, with a friendly and easy-to-use web UI. The output of the former function is a video with footages expressing all instances of events people want to recall. The latter function generates a statistical report of each event so that people can have more information to balance their lifestyle. The system relies on two major algorithms that try to match keywords/phrases to images and to run a cluster-based query using a watershed-based approach.
A decentralized consensus application using blockchain ecosystem

The consensus is a critical operation of any decision-making process. &#x0D; It involves a set of eligible members; whose decision need to be honored by taking their acknowledgment before making any decision. The traditional consensus process follows centralized architecture, the members need to rely on and trust this architecture. The proposed system aims to develop a secure decentralized consensus application in the untrusted environment by making use of blockchain technology along with smart contract and interplanetary file system (IPFS).
Selective recruitment designs for improving observational studies using electronic health records

Large‐scale electronic health records (EHRs) present an opportunity to quickly identify suitable individuals in order to directly invite them to participate in an observational study. EHRs can contain data from millions of individuals, raising the question of how to optimally select a cohort of size n from a larger pool of size N . In this article, we propose a simple selective recruitment protocol that selects a cohort in which covariates of interest tend to have a uniform distribution. We show that selectively recruited cohorts potentially offer greater statistical power and more accurate parameter estimates than randomly selected cohorts. Our protocol can be applied to studies with multiple categorical and continuous covariates. We apply our protocol to a numerically simulated prospective observational study using an EHR database of stable acute coronary disease patients from 82 089 individuals in the U.K. Selective recruitment designs require a smaller sample size, leading to more efficient and cost‐effective studies.
A Study on Development of PMS Logic Diagram for Performance Management of Coal-fired Power Plant

Thermal performance, one of the important performance determinants of a power plant, is the result of periodic performance measurements and analysis that require replacement, maintenance, or longevity of obsolete equipment. For this purpose, the performance management system (PMS) is used at all times in real time. In the conventional PMS, only the final calculation result is shown, so it is difficult to verify the reliability of the result and to analyze the cause of the anomaly. In order to solve this problem, this paper presents the equation of thermal efficiency in Logic Diagram, shows the whole process of calculation in real time, and secures the reliability of the result value. Developed to facilitate verification.
Fuzzy Neighborhood Semantics for Multi-agent Probabilistic Reasoning in Games

In this contribution we apply fuzzy neighborhood semantics to multiple agents’ reasoning about each other’s subjective probabilities, especially in game-theoretic situations. The semantic model enables representing various game-theoretic notions such as payoff matrices or Nash equilibria, as well as higher-order probabilistic beliefs of the players about each other’s choice of strategy. In the proposed framework, belief-dependent concepts such as the strategy with the best expected value are formally derivable in higher-order fuzzy logic for any finite matrix game with rational payoffs.
The Design and Implementation of Low-Latency Prediction Serving Systems

Author(s): Crankshaw, Daniel | Advisor(s): Gonzalez, Joseph E | Abstract: Machine learning is being deployed in a growing number of applications which demand real- time, accurate, and cost-efficient predictions under heavy query load. These applications employ a variety of machine learning frameworks and models, often composing several models within the same application. However, most machine learning frameworks and systems are optimized for model training and not deployment.In this thesis, I discuss three prediction serving systems designed to meet the needs of modern interactive machine learning applications. The key idea in this work is to utilize a decoupled, layered design that interposes systems on top of training frameworks to build low-latency, scalable serving systems. Velox introduced this decoupled architecture to enable fast online learning and model personalization in response to feedback. Clipper generalized this system architecture to be framework-agnostic and introduced a set of optimizations to reduce and bound prediction latency and improve prediction throughput, accuracy, and robustness without modifying the underlying machine learning frameworks. And InferLine provisions and manages the individual stages of prediction pipelines to minimize cost while meeting end-to-end tail latency constraints.
Discriminating L2 Writing Proficiency through the Use of Computational Tool Coh-Metrix

Although many previous studies have examined how differences in writing proficiency are related to certain linguistic features, much more research is needed to provide a coherent understanding and more reliable predictors of L2 writing proficiency. In this vein, this study tried to fill this gap by identifying linguistic features to represent different proficiency levels of Korean EFL learners
Effect of Outlier Removal on Grid Search and Distance Between Two Classes (The Techniques to Find Hyperparameter, Sigma of Support Vector Machine)

Support Vector Machine is widely used for classification in machine learning due to the flexibility offered by the various kernels. One of the kernels popularly used is based on the radial basis function (RBF). However, for using this kernel the parameter such as sigma needs to be selected properly by various techniques. Often this parameter is selected by the grid search technique using cross validation in machine learning. Although this technique gives accurate parameter, it is very time consuming. Distance between two Classes (DBTC) is another promising technique to find sigma, which is preferred after the grid search technique when accuracy is concerned. If execution time is considered then it is preferred than grid search. Outliers are point, which appear to markedly deviate from other observations, and they are removed from the data to have more uniformity. The study in this paper deals with the effect of outlier removal on grid search and DBTC. The outliers are detected by the Local Outlier Factor (LOF) technique. Outliers ranging from 1% to 10% are removed from the data and grid search and DBTC techniques are carried out to find the optimal hyperparameter sigma. This sigma is then used to find the machine learning accuracy, to conclude which technique is better. The findings reveal that the behavior of accuracy with outliers removal is random for grid search of SVM and there is no definite pattern observed for 4 out of 7 datasets. Similarly, for DBTC, the behavior of accuracy is random with outlier removal for 5 out of 7 datasets. In addition, outlier removal does not affect DBTC for 6 out of 7 datasets for the predefined sigma values considered in this study.
Classical and Modern Methods in Item Analysis of Test Tools

Item analysis is a process which examines the examinee’s responses to individual test item (questions) in order to assess the characteristics of those items and of the test as a whole. Item analysis is especially valuable in improving items which will be used again in later tests, but it can also be used to eliminate ambiguous or misleading items in a single test administration. There are different approaches in item analysis. In all approaches general goal is to arrive at tests having minimum items that will yield necessary degree of reliability. Classical Test Theory (CTT) and Item Response Theory (IRT) are the two broad methodology of test theory. In CTT framework, using the selected sample, some indices like item difficulty, item discriminations are calculated for each item. The quality of item will be decided on the basis of these values. In IRT, which is also known as modern test theory, the item characteristics are decided based on values taken by the parameters of the model chosen for the item response. The parameters are estimated from the samples chosen for the item analysis. Based on the values taken by the parameters for each item, the quality of the item will be decided. This paper tries to explain the item analysis procedure in both classical and Item Response Theory frameworks
Model Literasi Informasi Pengajar Dalam Mengembangkan Model Kecerdasan Ruang Visual (Spatial Intelligence): Studi pada para peserta bimbingan belajar Villa Merah Bandung

This study aims to determine the information literacy patterns of the instructors in developing models of visual space intelligence. This research was conducted at the learning center “Villa Merah” Bandung. The focus of this study includes the views of the teacher’s views on information literacy and the teacher’s literacy pattern in the information seeking process. The method used in this study uses qualitative research with a type of case study research. The technique of collecting data is done through observation, interviews and through library research. Based on the results of the study, it is known that the instructors in tutoring at Villa Merah Bandung are still inaccurate in understanding what is really meant by literacy. Whereas regarding the pattern of literacy if it is associated with the stages of information retrieval based on the big six model such as the stages of problem formulation and identification, strategy formulation, determination of location and access to information, stages of use, selection and synthesis stages and evaluation stages in general the instructors in seeking information has carried out the stages of information retrieval.
Peerlock: Flexsealing BGP

BGP route leaks frequently precipitate serious disruptions to interdomain routing. These incidents have plagued the Internet for decades while deployment and usability issues cripple efforts to mitigate the problem. Peerlock, introduced in 2016, addresses route leaks with a new approach. Peerlock enables filtering agreements between transit providers to protect their own networks without the need for broad cooperation or a trust infrastructure. We outline the Peerlock system and one variant, Peerlock-lite, and conduct live Internet experiments to measure their deployment on the control plane. Our measurements find evidence for significant Peerlock protection between Tier 1 networks in the peering clique, where 48% of potential Peerlock filters are deployed, and reveal that many other networks also deploy filters against Tier 1 leaks. To guide further deployment, we also quantify Peerlock's impact on route leaks both at currently observed levels and under hypothetical future deployment scenarios via BGP simulation. These experiments reveal present Peerlock deployment restricts Tier 1 leak export to 10% or fewer networks for 40% of simulated leaks. Strategic additional Peerlock-lite deployment at all large ISPs (fewer than 1% of all networks), in tandem with Peerlock within the peering clique as deployed, completely mitigates 80% of simulated Tier 1 route leaks.
Amortised Learning by Wake-Sleep

Models that employ latent variables to capture structure in observed data lie at the heart of many current unsupervised learning algorithms, but exact maximum-likelihood learning for powerful and flexible latent-variable models is almost always intractable. Thus, state-of-the-art approaches either abandon the maximum-likelihood framework entirely, or else rely on a variety of variational approximations to the posterior distribution over the latents. Here, we propose an alternative approach that we call amortised learning. Rather than computing an approximation to the posterior over latents, we use a wake-sleep Monte-Carlo strategy to learn a function that directly estimates the maximum-likelihood parameter updates. Amortised learning is possible whenever samples of latents and observations can be simulated from the generative model, treating the model as a black box. We demonstrate its effectiveness on a wide range of complex models, including those with latents that are discrete or supported on non-Euclidean spaces.
Efficient Policy Learning from Surrogate-Loss Classification Reductions

Recent work on policy learning from observational data has highlighted the importance of efficient policy evaluation and has proposed reductions to weighted (cost-sensitive) classification. But, efficient policy evaluation need not yield efficient estimation of policy parameters. We consider the estimation problem given by a weighted surrogate-loss classification reduction of policy learning with any score function, either direct, inverse-propensity weighted, or doubly robust. We show that, under a correct specification assumption, the weighted classification formulation need not be efficient for policy parameters. We draw a contrast to actual (possibly weighted) binary classification, where correct specification implies a parametric model, while for policy learning it only implies a semiparametric model. In light of this, we instead propose an estimation approach based on generalized method of moments, which is efficient for the policy parameters. We propose a particular method based on recent developments on solving moment problems using neural networks and demonstrate the efficiency and regret benefits of this method empirically.
Medical diagnosis decision support: online system

This project designs and implements an online medical diagnosis decision support system. The challenges faced by patients in accessing medical consultation, necessitated the developer to develop a user-friendly system to tackle these challenges. The system provides support on diagnosis decisions for the medically inclined. The system allows the study of the influence of the various symptoms that affect diagnosis. The evaluation of the system has been carried out by comparing the automated diagnostic algorithm with known case examples of various diseases. An exhaustive evaluation of the system by comparison with published cases is presented in this project. The overall result shows that the system is functional and reliable. The system was developed using Nodejs and MYSQL technologies implementing fuzzy logic.Keywords: Patience, Diagnosis, Preponderance, Practitioner, Fuzzy InferenceVol. 26 No 1 June 2019
Fast & Accurate Method for Bounding the Singular Values of Convolutional Layers with Application to Lipschitz Regularization.

This paper tackles the problem of Lipschitz regularization of Convolutional Neural Networks. Lipschitz regularity is now established as a key property of modern deep learning with implications in training stability, generalization, robustness against adversarial examples, etc. However, computing the exact value of the Lipschitz constant of a neural network is known to be NP-hard. Recent attempts from the literature introduce upper bounds to approximate this constant that are either efficient but loose or accurate but computationally expensive. In this work, by leveraging the theory of Toeplitz matrices, we introduce a new upper bound for convolutional layers that is both tight and easy to compute. Based on this result we devise an algorithm to train Lipschitz regularized Convolutional Neural Networks.
Spark and Databricks

Apache Spark has taken the world by storm the last few years. In this chapter, we’ll go through why it’s so and what Apache Spark actually is. We’ll also take a look at what Databricks brings to the table.
Blockchain: A future technology for registrars?

Higher education continues to face economic pressure to deliver a student‐centered education to meet the needs of today's employers. Throughout the higher education ecosystem, some institutions have shifted strategic priorities to focus on the application of new technologies and use data to drive business processes. Additionally, some higher education institutions have shifted their attention to alternative, modular‐based programming to address skills identified by employers. But if employers seek workers with these competencies, how can employers verify these skills assertions by education providers are reliable? How can employers affirm these skills were overseen by legitimate education providers? Some of these concerns may be addressed by the use of blockchain technology.
Trigger Word Recognition using LSTM

A Trigger word is a word that you use to wake up a virtual voice assistant, for example "Hey Siri" or "Hey Alexa".First, we need a good labelled training data to train our model.We could record 10 seconds audio clip of people saying positive ("activate" in this case) and negative (words that are not "activate") examples and label manually when the trigger words were spoken by the people.Labelling the data manually is complex and time consuming.Instead, the training data is generated artificially.We would then need 3 types of audio clips: 1. Positive examples of people saying the word "activate", 1 or 2 seconds each 2. Negative examples of people saying random words, 1 or 2 seconds each 3. Background noise, for example coffee shop or office, 10 seconds each The training data that we have generated need to be preprocessed before it is sent to a machine learning model.Due to the variation of air pressure sound can be produced.The input data to the model is the spectrogram for each generated audio due to which the target will be the labels created earlier.In recent years, Deep Learning (DL) has occupied increasing attention within the industry and academic world for its high performance in various domains.Today, Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are the most popular forms of DL architectures used.We are doing Trigger Word Recognition on speech data by using Long Short-Term Memory (LSTM).
Neural gradients are lognormally distributed: understanding sparse and quantized training.

Neural gradient compression remains a main bottleneck in improving training efficiency, as most existing neural network compression methods (e.g., pruning or quantization) focus on weights, activations, and weight gradients. However, these methods are not suitable for compressing neural gradients, which have a very different distribution. Specifically, we find that the neural gradients follow a lognormal distribution. Taking this into account, we suggest two methods to reduce the computational and memory burdens of neural gradients. The first one is stochastic gradient pruning, which can accurately set the sparsity level -- up to 85% gradient sparsity without hurting validation accuracy (ResNet18 on ImageNet). The second method determines the floating-point format for low numerical precision gradients (e.g., FP8). Our results shed light on previous findings related to local scaling, the optimal bit-allocation for the mantissa and exponent, and challenging workloads for which low-precision floating-point arithmetic has reported to fail. Reference implementation accompanies the paper.
Flickering Adversarial Attacks against Video Recognition Networks

Deep neural networks for video classification, just like image classification networks, may be subjected to adversarial manipulation. The main difference between image classifiers and video classifiers is that the latter usually use temporal information contained within the video. In this work we present a manipulation scheme for fooling video classifiers by introducing a flickering temporal perturbation that is practically unnoticeable by human observers and is implementable in the real world. After demonstrating the manipulation of action classification of single videos, we generalize the procedure to make universal adversarial perturbation by using only dozens of videos, achieving high fooling ratio. In addition, we generalize the universal perturbation and produce a temporal-invariant perturbation, which can be applied to the video without synchronizing the perturbation to the input. These properties allow us to bridge the gap between simulated environment and real-world application.
Implementation of Viola-Jones Algorithm for Object Detection Using FPGA

This research presents the design and implementation of hardware for face detection based on the system of the Viola-Jones Algorithm in the Field Programmable Gate Array (FPGA) using VHSIC Hardware Description Language (VHDL); VHSIC (Very High Speed Integrated Circuit). The data used in this study is based on real conditions and uses sample images where in this research we use the images sample with dimensions 450×375 pixels which is like the resolution of webcam that using for the Xilinx Zybo (Zynq Board). In this research, the proposed architecture is using VHDL and implemented in Xilinx Zybo Zynq-7000 ARM/FPGA SoC Trainer Board. To measure the performance of the FPGA-based face detection system from the Viola-Jones Algorithm, the number of frames processed per second (FPS) must be measured, where the processes must be designed which are divided into two types of partitioning, there are software and hardware designs. This is accomplished by measuring the time elapsed from capturing an image to displaying the detection results. More specifically this is the time elapsed from the start of a frame capture to the end of drawing face bounding boxes on to the captured image. An experiment was conducted to gather the minimum, maximum and average FPS of FPGA face detection system of the Viola-Jones Algorithm. From the process of this research we've been got hardware latency just for the 3 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rd</sup> modules implemented is 125,207 millisecond, whereas software implemented (run on ARM Cortex 9 / Hardcore Zybo Processor) is 3642,993 millisecond, so speedup by implementing these 3 modules as a hardware accelerator is 29,095 rounds.
Watermarking in Audio using Deep Learning

Watermarking is a technique used to used to mark the ownership in media such as audio or images by embedding a watermark, e.g. copyrights information, into the media. A good watermarking method sho ...
Single shot low-coherence digital interferometer with multi-reflection reference mirror for measuring long depth object

A new type of reference mirror constructed by a half mirror and a full reflective mirror was introduced for the lowcoherence interferometer. The interference pattern recorded by the camera consisted of the interference of the object with different reflectance orders of the reference mirror, it allowed reconstructing the profile of the object whose depth was many times longer than the coherence length of the light source in a single-short. Fast Fourier transform (FFT) of the recorded hologram and the spatial filters were applied to obtain the complex amplitude for each object parts those were then combined to obtain the completed 3D information of the object.