Reviewerly/exHarmony huggingface dataset:
Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware

With the proliferation of specialized neural network processors that operate on low-precision integers, the performance of Deep Neural Network inference becomes increasingly dependent on the result of quantization. Despite plenty of prior work on the quantization of weights or activations for neural networks, there is still a wide gap between the software quantizers and the low-precision accelerator implementation, which degrades either the efficiency of networks or that of the hardware for the lack of software and hardware coordination at design-phase. In this paper, we propose a learned linear symmetric quantizer for integer neural network processors, which not only quantizes neural parameters and activations to low-bit integer but also accelerates hardware inference by using batch normalization fusion and low-precision accumulators (e.g., 16-bit) and multipliers (e.g., 4-bit). We use a unified way to quantize weights and activations, and the results outperform many previous approaches for various networks such as AlexNet, ResNet, and lightweight models like MobileNet while keeping friendly to the accelerator architecture. Additional, we also apply the method to object detection models and witness high performance and accuracy in YOLO-v2. Finally, we deploy the quantized models on our specialized integer-arithmetic-only DNN accelerator to show the effectiveness of the proposed quantizer. We show that even with linear symmetric quantization, the results can be better than asymmetric or non-linear methods in 4-bit networks. In evaluation, the proposed quantizer induces less than 0.4\% accuracy drop in ResNet18, ResNet34, and AlexNet when quantizing the whole network as required by the integer processors.
Deep Recurrent Neural Networks for Edge Monitoring of Personal Risk and Warning Situations

Accidental falls are the main cause of fatal and nonfatal injuries, which typically lead to hospital admissions among elderly people. A wearable system capable of detecting unintentional falls and sending remote notifications will clearly improve the quality of the life of such subjects and also helps to reduce public health costs. In this paper, we describe an edge computing wearable system based on deep learning techniques. In particular, we give special attention to the description of the classification and communication modules, which have been developed by keeping in mind the limits in terms of computational power, memory occupancy, and power consumption of the designed wearable device. The system thus developed is capable of classifying 3D-accelerometer signals in real-time and to issue remote alerts while keeping power consumption low and improving on the present state-of-the-art solutions in the literature.
Deep Learning Architectures for Computer Vision Applications: A Study

Deep learning has becomeBagi, Randheer one ofDutta, Tanima theGupta, Hari Prabhat most preferred solution for many complex problems. It shows outstanding performance in the field of computer vision to perform tasks like, image classification, object detection, and image generation. Recently, many research efforts are focused on changing the deep learning architecture for widespread application domain. In this paper, we present a comprehensive survey on the various issues and challenges faced by deep learning techniques. Furthermore, we analyze different deep learning architectures to provide the solution for the computer vision tasks along with their importance.
Naive Bayes Classifier on Twitter Sentiment Analysis BPJS of HEALTH

Public health insurance is one indicator of the success of the government's active role in managing and facilitating its citizens. Health media and excellent facilities undoubtedly read a positive impact on the development of society, especially at this time. BPJS, as a government health media for the people of Indonesia, of course, must bring change and be a solution to the imbalance of health services for small and medium people. Sentiment analysis of BPJS products is one solution to get information on the active role of the community as the primary user of their health products. Sentiment analysis is carried out by utilizing social media as the primary basis for data collection. In this study, the initial stage taken was data collection and continued to do Post Tagging on community tweet data. Furthermore, these data are classified again using the Naïve Bayes model to obtain optimal results. The results of the study note that BPJS health services get an accuracy rate of 70% negative for payment topics and 72% positive for information topics, and get a 65% score likely from users in using BPJS services as their health service.
Arabic Sign Language Recognition System on Smartphone

Deaf And other verbally challenged people face challenges most of the time communicating with the society, sign language is what they commonly use between them to represent what they want to say to each other for example numbers, words or phrase. To bridge this communication Barrier between them and the society an automated system to stand as a translator between them and the society is needed, recently many researches are performed, but most of the developed Systems are only executable on computers, which are difficult and impractical to take around. We are proposing the use of smartphone as a platform, a client server system is implemented, sign image background is detected and removed under HSV color space, features extracted from the frame are the binary pixels, SVM is used to classify the features, we are able to classify 10 Arabic Sign Language with an experimental accuracy result of 92.5%.
Segmentation of Plasmodium using Saturation Channel of HSV Color Space

Malaria is a vector-borne disease that is spread throughout all regions, especially in the tropical countries. Early malaria detection is necessary to reduce the mortality rate. Microscopic imaging technique is still considered as gold standard for diagnosing malaria. However, due to numerous number of patients with limited parasitological experts, it is necessary to have such an automated system for diagnosing malaria. To obtain more accurate diagnosis, it is essential to have an accurate segmentation of Plasmodium. This study aims to develop a scheme to segment Plasmodium in digital thin blood smear images with different characteristics. There are three main steps consisting of preprocessing, grouping of image characteristics and segmenting Plasmodium. Saturation channel is extracted from HSV in the preprocessing. Difference of image standard deviation is keys for grouping the image characteristics. There are three groups of image that is segmented in different ways. Otsu thresholding combined with morphological image processing is used to segment Plasmodium. The data used contains 124 cropped images. The evaluation results achieve 97.99%, 82.23% and 99.33% of accuracy, sensitivity and specificity, respectively. These high evaluation values indicate that the proposed method is suitable to support the development of CAD in malaria diagnosing.
Detecting File Types Using Machine Learning Algorithms

In this study, experiments have been carried out to analyze the file content and classify the type of file using machine learning algorithms. First a new benchmark dataset was created including popular file types used as malicious software. Then the proposed system was trained using different machine learning algorithms. The accuracy and runtime performance comparison of models were evaluated. As a result, the highest accuracy rate was obtained as 97.83% by XGBoost algorithm.
Quantifying parallelism of vectors is the quantification of distributed<i>n</i>-party entanglement

The three way distributive entanglement is shown to be related to the parallelism of vectors. Using a measurement based approach we form a set of 2-dimensional vectors, representing the post measurement states of one of the parties. These vectors originate at the same point and have an angular distance between them. The area spanned by a pair of such vectors is a measure of the entanglement of formation. This leads to a geometrical manifestation of the 3-tangle in 2-dimensions, from an inequality in area which generalizes for n-qubits to reveal that the n-tangle also has a planar structure. Quantifying the genuine n-party entanglement in every bi-partition, we show that the genuine n-way entanglement does not manifest in n-tangle. A new quantity geometrically similar to 3-tangle is then introduced that represent the genuine n-way entanglement. Extending our formalism to 3-qutrits, we show that the non locality without entanglement arises from a condition under which the post measurement state vectors of a separable state show parallelism. A connection to non trivial sum uncertainty relation analogous to Maccone and Pati uncertainty relation (Maccone and Pati 2014 Phys. Rev. Lett. 113 260401) is then shown using decomposition of post measurement state vectors along parallel and perpendicular direction of the pre-measurement state vectors.
Programmable liquid crystal display based noise reduced dynamic synthetic coded aperture imaging camera (NoRDS-CAIC)

Besides traditional lens-based imaging techniques, coded aperture imaging (CAI) can also provide target images but without using any optical lenses, therefore it is another solution in imaging applications. Most CAI methods reconstruct target image only from a single-shot coded image using a fixed coding mask; however, the collected partial information inevitably deteriorates the reconstruction quality. Though multi-exposure CAI methods are designed, these existed algorithms can hardly improve reconstruction signal-to-noise ratio (SNR) and spatial resolution simultaneously; additionally, dynamic coding mask display still requires expensive devices and complicated systems. In order to reconstruct target image with both enhanced spatial resolution and SNR but using cost-effective devices and a simple system, we design a noise reduced dynamic synthetic coded aperture imaging camera (NoRDS-CAIC) in this paper. The NoRDS-CAIC only consists of a programmable liquid crystal display (LCD) and an image recorder, and both of them are integrated with a three-dimensional printed shell with the compact size of 19 cm × 15 cm × 16 cm and controlled by our designed software to automatically realize coding mask display, coded image recording and target image reconstruction. When using the NoRDS-CAIC, the optimized coding mask is first sent to the programmable LCD and displayed, then the corresponding coded image is automatically captured using the image recorder. Next, cycle the above procedures to capture enough coded images with previously known coding masks and measured point spread functions (PSFs), and the target image can be finally reconstructed using our designed NoRDS-CAIC decoding algorithm, which is shown with better noise suppression capability and higher reconstruction resolution compared to other classical CAI algorithms. According to the experimental verifications, the NoRDS-CAIC can reach the high resolution of 99.2 µm and the high SNR of 19.43 dB, proving that the designed NoRDS-CAIC can be potentially used for lensless imaging in practical applications.
Implementation of Role-Based Access Control on OAuth 2.0 as Authentication and Authorization System

As today's technology transition from monolithic towards microservices architecture, the authentication and authorization system also becomes a new concern because of the difference between monolithic and microservices pattern. Monolithic mostly uses role-based access control while microservices uses scope with OAuth 2.0. With this in mind, there is a need for a model that can integrate OAuth 2.0 with role-based access control. With role-based access control implemented on OAuth 2.0, we expect a simpler authorization process and a more secure authentication and authorization system for microservices backend architecture. This paper proposes a model to implement role-based access control on OAuth 2.0 using Laravel framework, we also test the performance of the system following by response time, data transferred and throughput. From the performance test, this approach has a good performance and can handle certain requests with simulated users even with limited resources.
Topological data analysis in computer vision

The paper will provide examples of computer vision tasks in which topological data analysis gave new effective solutions. Ideas underlying topological data analysis and its basic methods will be briefly described and illustrated with examples of computer vision problems. No prior knowledge in topological data analysis and computational geometry is assumed, a brief introduction to subject is given throughout the text.
Generation of hyper-entangled photons in a hot atomic vapor

A source of hyper-entangled photons plays a vital role in quantum information processing, owing to its high information capacity. In this Letter, we demonstrate a convenient method to generate polarization and orbital angular momentum (OAM) hyper-entangled photon pairs via spontaneous four-wave mixing (SFWM) in a hot <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:msup> <mml:mi /> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>87</mml:mn> </mml:mrow> </mml:msup> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">R</mml:mi> <mml:mi mathvariant="normal">b</mml:mi> </mml:mrow> </mml:math> atomic vapor. The polarization entanglement is achieved by coherently combining two SFWM paths with the aid of two beam displacers that constitute a phase self-stabilized interferometer, and OAM entanglement is realized by taking advantage of the OAM conservation condition during the SFWM process. Our hyper-entangled biphoton source possesses high brightness and high nonclassicality and may have broad applications in atom–photon-interaction-based quantum networks.
Quantum scheme for N-level atom interacting with a two two-level atom: Atomic Fisher information and entropy squeezing

In this work, we introduce a model of a two-atom interacting with a multi-level atom governed by su(2) Lie algebra in the presence of external classical field. The influence of the classical field on the system is discussed in detail for certain values for the classical terms. The atomic density matrix of the proposed system is obtained. The dynamical behavior of the atomic Fisher information as an indicator of the nonlocal correlation between a two-atom and su(2) field is discussed. Moreover, we examine the effect of classical field on the evolution of entropy squeezing and the geometric phase induced between the initial and final state of the proposed system. The results outlined some important phenomena as sudden death and sudden birth of entanglement in presence of the classical terms is observed through the dynamics of atomic Fisher information in the presence of classical field for the large number of levels.
Embedding Gesture Prior to Joint Shape Optimization Based Real-Time 3D Hand Tracking

In this paper, we present a novel approach for 3D hand tracking in real-time from a set of depth images. In each frame, our approach initializes hand pose with learning and then jointly optimizes the hand pose and shape. For pose initialization, we propose a gesture classification and root location network (GCRL), which can capture the meaningful topological structure of the hand to estimate the gesture and root location of the hand. With the per-frame initialization, our approach can rapidly recover from tracking failures. For optimization, unlike most existing methods that have been using a fixed-size hand model or manual calibration, we propose a hand gesture-guided optimization strategy to estimate pose and shape iteratively, which makes the tracking results more accuracy. Experiments on three challenging datasets show that our proposed approach achieves similar accuracy as state-of-the-art approaches, while runs on a low computational resource (without GPU).
The SQL File Evaluation (SQLFE) Tool

The SQL File Evaluation (SQLFE) tool is a GUI-based, flexible, and extendible software system for evaluating the correctness of files of student SQL queries as compared to specified SQL query answers. SQLFE can be configured per question to weight any of over 30 different tests in judging correctness of a submitted answer for a particular question. These tests include successful DBMS interpretation of the query, same result set (as specified answer), the use count of particular keywords, simple formatting style, and partial credit based on simple structural format. SQLFE currently works for databases constructed under Oracle and MySQL database management systems (DBMSs), and can be extended to more DBMSs. SQLFE is available from GitHub.
Deconstructing the garbage-first collector

Garbage-First is among today's most widely used garbage collectors. It is used in the HotSpot and OpenJDK virtual machines, and shares algorithmic foundations with three other important contemporary collectors: Shenandoah, C4, and ZGC. However, the design of the core algorithms and the performance tradeoffs they manifest have not been carefully analyzed in the literature. In this work, we deconstruct the G1 algorithm and re-implement it from first principles. We retrospectively develop a concurrent, region-based evacuating collector, CRE, which captures the principal design elements shared by G1, Shenandoah, C4, and ZGC. We then evaluate the impact of each of the major elements of G1 on performance, including pause time, remembered set footprint and barrier overheads. We find that G1's concurrent marking and generational collection reduces the 95-percentile GC pauses by 64% and 93% respectively. We find that the space overhead of G1's remembered sets is very low, typically under 1%. We also independently measure the barriers used by G1 and find that they have an overhead of around 12% with respect to total performance. This analysis gives users and collector designers insights into the garbage-first collector and the other fixed-size region-based concurrent evacuating collectors, which we hope will lead to better use of the collectors and provoke future improvements.
M-SQL: Multi-Task Representation Learning for Single-Table Text2sql Generation

Text2SQL can help non-professionals connect with databases by turning natural languages into SQL.Although previous researches about Text2SQL have provided some workable solutions, most of them extract values based on column representation.If there are multiple values in the query and these values belong to different columns, the previous approaches based on column representation cannot accurately extract values.In this work, we propose a new neural network architecture based on the pre-trained BERT, called M-SQL.The column-based value extraction is divided into two modules, value extraction and valuecolumn matching.We evaluate M-SQL on a more complicated TableQA dataset, which comes from an AI competition.We rank first in this competition.Experimental results and competition ranking show that our proposed M-SQL achieves state-of-the-art results on TableQA.
Computational Approaches to Detect Illicit Drug Ads and Find Vendor Communities Within Social Media Platforms

The opioid abuse epidemic represents a major public health threat to global populations. The role social media may play in facilitating illicit drug trade is largely unknown due to limited research. However, it is known that social media use among adults in the US is widespread, there is vast capability for online promotion of illegal drugs with delayed or limited deterrence of such messaging, and further, general commercial sale applications provide safeguards for transactions; however, they do not discriminate between legal and illegal sale transactions. These characteristics of the social media environment present challenges to surveillance which is needed for advancing knowledge of online drug markets and the role they play in the drug abuse and overdose deaths. In this paper, we present a computational framework developed to automatically detect illicit drug ads and communities of vendors. The SVM- and CNN- based methods for detecting illicit drug ads, and a matrix factorization based method for discovering overlapping communities have been extensively validated on the large dataset collected from Google+, Flickr and Tumblr. Pilot test results demonstrate that our computational methods can effectively identify illicit drug ads and detect vendor-community with accuracy. These methods hold promise to advance scientific knowledge surrounding the role social media may play in perpetuating the drug abuse epidemic.
Classification of Digitized Documents Applying Neural Networks

The exponential increase of the information available in digital format during the last years and the expectations of future growth make it necessary for the organization of information in order to improve the search and access to relevant data. For this reason, it is important to research and implement an automatic text classification system that allows the organization of documents according to their corresponding category by using neural networks with supervised learning. In such a way, a faster process can be carried out in a timely and cost-efficient way. The criteria for classifying documents are based on the defined categories.
Comparative Analysis of Restricted Boltzmann Machine Models for Image Classification

Many applications for Restricted Boltzmann Machines (RBM) have been developed for a large variety of learning problems. Recent developments have demonstrated the capacity of RBM to be powerful generative models, able to extract useful features from input data or construct deep artificial neural networks. In this work, we propose a learning algorithm to find the optimal model complexity for the RBM by improving the hidden layer. We compare the classification performance of regular RBM use RBM() function, classification RBM use stackRBM() function and Deep Belief Network (DBN) use DBN() function with different hidden layer. As a result, Stacking RBM and DBN could improve our classification performance compare to regular RBM.
Why is cybersecurity not a human-scale problem anymore?

Examining the structure of the enterprise attack surface in view of the relative ease with which cyberdefenses can be subverted.
New loss functions for medical image registration based on VoxelMorph

Optimization of loss function is one of the research directions in medical image registration. A loss function of registration is the sum of two terms: a similarity term <i>L</i><sub>sim</sub> (<i>&Phi;</i>) and a smoothing term <i>L</i><i><sub>smooth</sub></i>(<i>&Phi;</i>). From variational method in differential geometry, control function is essential to generate better registration field <i>&Phi;</i>. Here, we propose a new registration loss function with novel smoothing terms using VoxelMorph based on control function and Laplacian operator. We divide the process into two steps. The first step is based on Laplacian operator. We replace the gradient of registration field <i>&Phi;</i> in <i>L<sub>smooth</sub></i> (<i>&Phi;</i>) by the Laplacian of <i>&Phi;</i>. In the second step, we add the term control function <i>F</i> to the <i>L<sub>smooth</sub></i> (<i>&Phi;</i>) in the first step, which is the key contribution of our method. We verify our method on two datasets including ADNI and IBSR, and obtain excellent improvement on MR image registration, with better convergence and gets higher average Dice and lower percentage of non-positive Jacobian locations compared with original loss function.