Reviewerly/exHarmony huggingface dataset:
Anemia Prediction Based on Rule Classification

Iron or vitamin lack on the human body can be avoided to prevent some types of diseases like anemia. Anemia causes severe health problems like pregnancy complications or even heart real issues if not a predicted at earlier stages. In this paper, data is collected of 539 participants with 10 related features to each one. Three based rule classification techniques are applied to design on a curate anemia prediction system: ZeroR, OneR and PART to extract relevant of anemia dataset associated to "If" and "Then" a procedure. Among applied techniques, PART provided 85% accuracy higher than ZeroR and OneR. These techniques provided a benchmark of other applied technique to clarify sufficient knowledge of anemia data rules.
Radar Emitter Identification Based on Auto-Correlation Function and Bispectrum via Convolutional Neural Network

This article proposes to apply the auto-correlation function (ACF), bispectrum analysis, and convolutional neural networks (CNN) to implement radar emitter identification (REI) based on intrapulse features. In this work, we combine ACF with bispectrum for signal feature extraction. We first calculate the ACF of each emitter signal, and then the bispectrum of the ACF and obtain the spectrograms. The spectrum images are taken as the feature maps of the radar emitters and fed into the CNN classifier to realize automatic identification. We simulate signal samples of different modulation types in experiments. We also consider the feature extraction method directly using bispectrum analysis for comparison. The simulation results demonstrate that by combining ACF with bispectrum analysis, the proposed scheme can attain stronger robustness to noise, the spectrograms of our approach have more pronounced features, and our approach can achieve better identification performance at low signal-to-noise ratios.
EaSe: A Diagnostic Tool for VQA based on Answer Diversity

We propose EASE, a simple diagnostic tool for Visual Question Answering (VQA) which quantifies the difficulty of an image, question sample.EASE is based on the pattern of answers provided by multiple annotators to a given question.In particular, it considers two aspects of the answers: (i) their Entropy; (ii) their Semantic content.First, we prove the validity of our diagnostic to identify samples that are easy/hard for state-of-art VQA models.Second, we show that EASE can be successfully used to select the most-informative samples for training/fine-tuning. Crucially, only information that is readily available in any VQA dataset is used to compute its scores. 1 1 Code at: github.com/shailzajolly/
Surface air temperature trends in Kathmandu Valley for 2011-2017

In this study, we reviewed the maximum and minimum temperature trends of the Kathmandu valley over the period of 2011-2017. In addition, the average monthly temperature trends were studied annually for the same period, with the data made available from the Department of Hydrology and Meteorology (DHM) of Nepal. The annual temperature trended in the same direction, with winter temperatures being lower and summer temperatures being higher. The annual average minimum and maximum air temperature trends were found to be slightly rising at 0.097˚C/year and 0.04˚C/year, respectively. The mean air temperature in Kathmandu valley is increasing at a rate of 0.06 degrees Celsius per year, with 2016 being the warmest year and 2012 being the least warm, with annual mean temperatures of 19.82˚C and 19.32˚C, respectively. The temperature difference is much smaller in the summer (less than ~12˚C) than in the winter. &#x0D; BIBECHANA 18 (2) (2021) 95-104
Probabilistic Label Tree for Streaming Multi-Label Learning

Multi-label learning aims to predict a subset of relevant labels for each instance, which has many real-world applications. Most extant multi-label learning studies focus on a fixed size of label space. However, in many cases, the environment is open and changes gradually and new labels emerge, which is coined as streaming multi-label learning (SMLL). SMLL poses great challenges in twofolds: (1) the target output space expands dynamically; (2) new labels emerge frequently and can reach a significantly large number. Previous attempts on SMLL leverage label correlations between past and emerging labels to improve the performance, while they are inefficient when deal with large-scale problems. To cope with this challenge, in this paper, we present a new learning framework, i.e., the probabilistic streaming label tree(Pslt). In particular, each non-leaf node of the tree corresponding to a subset of labels, and a binary classifier is learned at each leaf node. Initially, Pslt is learned on partially observed labels, both tree structure and node classifiers are updated while new labels emerge. Using carefully designed updating mechanism, Psltcan seamlessly incorporate new labels by first passing them down from the root to leaf nodes and then update node classifiers accordingly. We provide theoretical bounds for the iteration complexity of tree update procedure and the estimation error on newly arrived labels. Experiments show that the proposed approach improves the performance in comparison with eleven baselines in terms of multiple evaluation metrics. The source code is available at https://gitee.com/pslt-kdd2021/pslt.
Hierarchical Encoders for Modeling and Interpreting Screenplays

While natural language understanding of long-form documents remains an open challenge, such documents often contain structural information that can inform the design of models encoding them. Movie scripts are an example of such richly structured text – scripts are segmented into scenes, which decompose into dialogue and descriptive components. In this work, we propose a neural architecture to encode this structure, which performs robustly on two multi-label tag classification tasks without using handcrafted features. We add a layer of insight by augmenting the encoder with an unsupervised ‘interpretability’ module, which can be used to extract and visualize narrative trajectories. Though this work specifically tackles screenplays, we discuss how the underlying approach can be generalized to a range of structured documents.
Privacy and Security Evaluation of Amazon Echo Voice Assistant

The number of devices connected to the Internet across the world is above eight billion according to IoT Analytics statistics. There has been a 56% increase after 2016 and is estimated to keep increasing in the subsequent years. Considering the expansive reach of the Internet of Things (IoT) and smart homes, it has transformed how people interact with technology and has generated a debate on subjects such as integrity and privacy. Even though there are many reports reviewing the Amazon Echo dot (which is a device used to control smart home via verbal commands), a few of which have literature review supporting. This study aims to examine Echo Dot (second generation), one of Amazon's most popular IoT devices, by conducting a behavioural evaluation as well as a security and privacy analysis. Moreover, certain relevant security and privacy threats will be examined. Echo Dot's behaviour was analysed to ensure an effective analysis as per the network traffic rate (bandwidth), while its consumption of power was monitored over three days in various circumstances such as normal, silent, and noisy days. These behaviours were then compared to determine any abnormal behaviour. The study also subjected the device to two types of attacks which are sound-based attack and network-based attack. It was observed that the behaviour of the device in silent and normal modes was as per the expectations, whereas in a noisy environment, it consumed more power and had a higher number of transmitted packets compared to the average ideal mode. It should be noted that a major advantage of the Echo Dot is that the majority of the logic takes place on the Amazon cloud servers behind the scenes. The study's analysis showed that the device offers less satisfactory security and privacy. It is estimated that though sound-based attacks have potential, further understanding regarding the system's inner workings is necessary. It was found that the device persevered against the network-based attack and encrypted all packets, and thus, it was not easy to obtain information from them. Moreover, it was ensured that this study was scalable and could be implemented for other smart home or VAs IoT devices for identifying security or privacy issues.
Effects of Number and Position of Auxiliary Networks used in Inception Convolutional Neural Network on Object Recognition

This paper focuses on effects of number and position of auxiliary networks used in inception convolutional neural networks (ICNNs) on object recognition. The ICNNs, using six modules of inception- block having two-auxiliary network (2-ICNN), one- auxiliary-network-after-first-inception-block (1F- ICNN), one-auxiliary-network-before-last-inception- block, (1L-ICNN) are experimented here. According to 2- ICNN, two auxiliary networks are inserted after the first inception-block and before the last one; whilst the auxiliary networks before the last inception-block and after the first one are removed according to 1F-ICNN and 1L-ICNN, consecutively. The experiments rely on Oxford-17 and Oxford-102 flower datasets. Recognition performance assessments depend on averages of F1 and accuracy scores, based on 10-fold cross validation for bias reduction purpose. The results indicate that 1F-ICNN yields 81.88% and 86.39% best recognition performance for Oxford-17, containing 17 classes of flower species; whereas, 2-ICNN provides 70.16% and 79.29% best performance for Oxford-102 with 102 classes of species, based on 70 × 70 and 140 × 140 pixels resized images.
Appearance Guidance Attention for Multi-Object Tracking

Appearance information is one of the most important matching indicators for multi-object data association. In tracking by detection model, appearance information and detection information are usually integrated in the same sub-network for learning and output. This phenomenon will result in the appearance embedding vectors to be coupled to the network inference method during learning stage. As a result, the appearance embedding vectors contains too much background information and affects the accuracy of data association. Based on the non-end-to-end tracking model, we design an appearance guidance attention module for the appearance extraction branch. This module can effectively strengthen the network's learning of the object visual appearance features and reduce the attention in the learning of background features. Finally, we utilize the appearance embedding vectors that decoupled from the inference method as the input of the back-end tracker and perform data association. The proposed method is tested on the MOT16 and MOT17 datasets. Experiments show that the proposed method provides more high-quality appearance representation information for the back-end tracker and the tracking performance on two datasets is better than other comparison models. At the same time, our model can reach 24.9FPS on a single 1080Ti GPU. The code is available at: https://github.com/JoJoliking/AGM
Equivalence Checking of Dynamic Quantum Circuits

Despite the rapid development of quantum computing these years, state-of-the-art quantum devices still contain only a limited number of qubits. One possible way to execute more realistic algorithms in near-term quantum devices is to employ dynamic quantum circuits (DQCs). In DQCs, measurements can happen during the circuit, and their outcomes can be processed with classical computers and used to control other parts of the circuit. This technique can help significantly reduce the qubit resources required to implement a quantum algorithm. In this paper, we give a formal definition of DQCs and then characterise their functionality in terms of ensembles of linear operators, following the Kraus representation of superoperators. We further interpret DQCs as tensor networks, implement their functionality as tensor decision diagrams (TDDs), and reduce the equivalence of two DQCs to checking if they have the same TDD representation. Experiments show that embedding classical logic into conventional quantum circuits does not incur a significant time and space burden.
An Electrical Equipment Image Enhancement Approach Based on Zero-DCE Model for Power IoTs Edge Service

Electrical equipment intelligent inspection technology has been effectively used in the applications of electrical equipment operation and maintenance for reducing labor efforts and efficiently monitoring equipment status. However, inspecting electrical equipment is often affected by weather, equipment diversity and other factors, which makes the equipment images captured by the IOT edge services are difficult to be used for efficiently electrical equipment inspection. In this paper, we propose a framework of electrical equipment image enhancement (EEIE) for electrical equipment intelligent inspection where the Zero-DCE deep network model is adopted to enhance the image quality of electrical equipment. Firstly, the original images of electrical equipment are collected and further filtered to construct the electrical equipment image data set. Then, low-light electrical equipment images can be enhanced by the Zero-DCE based technology. At last, the related experiments were made and the experimental results show that the proposed EEIE framework can effectively enhance the image quality of electrical equipment in comparison with the popular Retinex-Net method and LOL dataset for satisfying the requirements of power IoTs edge services.
Vacuum-induced correlations in superconducting microwave cavity under multiple pump tones

Quantum correlations are an essential resource in advanced information processing based on quantum phenomena. Remarkably, the vacuum state of a quantum field may act as a key element for generation of strong quantum correlations. Besides, superconducting microwave cavities offer an excellent platform for experimental studies of such quantum effects. In this work, we numerically investigate vacuum correlations in a flux-tunable superconducting cavity under multiple pump tones. We consider double and triple pumping cases and explore pairwise correlations between three frequency bands specified within a single cavity resonance. Our work shows that three pumps produce more correlations than two pumps; thus, quantum resources facilitated by the triple pump scheme offers enhanced prospects for quantum data processing using parametric microwave cavities.
A Secure Digital E-Voting Using Blockchain Technology

Abstract In digital electronic voting security is always the biggest anxiety. In existing system, the EVM system used, when compare to the conventional paper ballot system EVM reduces the time for casting vote and result announcement, but still having many issues there is the risk that the election authorities can able to change or remove the vote therefore chances for violating its secrecy. The entire system may rigged by any third party. Blockchain technology provide secure electronic voting platform, which is a decentralized, peer-to-peer transaction ledger enables every vote that is casted will be consider as an individual transaction which creates transparent and secure environment for elections, where the users will be able to cast their votes only once and will be able to view the total votes casted in real time without having the permission to edit the same after election gets over. These votes will be counted and the results will then be announced. This work achieves by solving the issues of digital voting systems and a mechanism to boost the number of voters and their trust in the electoral process.
Image Demoiréing with a Dual-Domain Distilling Network

Due to slight discrepancy of spatial frequency between the camera sensor array and sub-pixel layout of LCD monitor, moiré pattern artifacts appear in various shapes and colours which seriously degrade the quality of captured images. It is challenging yet practically crucial to remove moiré artifacts from a single camera-captured screen image. In this paper, we propose a dual-domain distilling network (3DNet for short) to tackle this problem in an end-to-end manner. The 3DNet consists of a dual-branch student network (a.k.a. demoiréing network), and two teacher networks. The two branches of student network exploit knowledge in both spatial-domain and frequency-domain for the sake of removing moiré artifacts, based on the observation that rich image details can be discovered in the frequency-domain while structure information can be well kept in the spatial-domain. The demoiréing process of two branches is supervised by the knowledge distilled from two teacher networks trained for reconstructing clear images in the spatial and frequency domains respectively. Comprehensive experimental results are conducted to demonstrate the efficacy of our design, and reveal its superiority over state-of-the-art alternatives.
Breast Lesions Detection Using FADHECAL and Multilevel Otsu Thresholding Segmentation in Digital Mammograms

Breast cancer is the most common cause of mortality among women. Early detection plays an important role to improve survival rates. Digital mammograms can be used to detect breast lesions within the breast tissue. However, digital mammograms have a limitation of low contrast images due to the low exposure factors used. As a result, the extraction of breast lesions using the region of interest (ROI) tool will be difficult and, thus, lead to misclassification. This paper presents a novel technique to detect breast lesions in digital mammograms, known as Fuzzy Anisotropic Diffusion Histogram Equalization Contrast Adaptive Limited (FADHECAL) incorporated with Multilevel Otsu Thresholding Segmentation. FADHECAL will enhance the breast lesions by reducing the image noise while preserving the details. Multilevel Otsu Thresholding Segmentation detects the breast lesions using the ROI tool at different intensity levels. The performance of FADHECAL incorporated with Multilevel Otsu Thresholding Segmentation has been tested on 115 digital mammograms from the Mammographic Image Analysis Society (MIAS) database with the abnormal conditions. The efficiency of the proposed technique is 94.8%, and the error rate is 5.2%. In conclusion, FADHECAL incorporated with the Multilevel Otsu Thresholding Segmentation has provided sufficient detection of breast lesions with the appropriate quality of the digital mammograms.
A Methodology for Tenant Migration in Legacy Shared-Table Multi-tenant Applications

Multi-tenancy enables cost-effective SaaS through resource consolidation. Multiple customers, or tenants, are served by a single application instance, and isolation is enforced at the application level. Service load for different tenants can vary over time, requiring applications to scale in and out. A large class of SaaS providers operates legacy applications structured around a relational (SQL) database. These applications achieve tenant isolation through dedicated fields in their relational schema and are not designed to support scaling operations. We present a novel solution for scaling in or out such applications through the migration of a tenant’s data to new application and database instances. Our solution requires no change to the application and incurs no service downtime for non-migrated tenants. It leverages external tables and foreign data wrappers, as supported by major relational databases. We evaluate the approach using two multi-tenant applications: Iomad, an extension of the Moodle Learning Management System, and Camunda, a business process management platform. Our results show the usability of the method, minimally impacting performance for other tenants during migration and leading to increased service capacity after migration.
Data-efficient Hindsight Off-policy Option Learning

Hierarchical approaches for reinforcement learning aim to improve data efficiency and accelerate learning by incorporating different abstractions. We introduce Hindsight Off-policy Options (HO2), an efficient off-policy option learning algorithm, and isolate the impact of action and temporal abstraction in the option framework by comparing flat policies, mixture policies without temporal abstraction, and finally option policies; all with comparable policy optimization. When aiming for data efficiency, we demonstrate the importance of off-policy optimization, as even flat policies trained off-policy can outperform on-policy option methods. In addition, off-policy training and backpropagation through a dynamic programming inference procedure -- through time and through the policy components for every time-step -- enable us to train all components' parameters independently of the data-generating behavior policy. We continue to illustrate challenges in off-policy option learning and the related importance of trust-region constraints. Experimentally, we demonstrate that HO2 outperforms existing option learning methods and that both action and temporal abstraction provide strong benefits in particular in more demanding simulated robot manipulation tasks from raw pixel inputs. Finally, we develop an intuitive extension to encourage temporal abstraction and investigate differences in its impact between learning from scratch and using pre-trained options.
Language-based Video Editing via Multi-Modal Multi-Level Transformer

Video editing tools are widely used nowadays for digital design. Although the demand for these tools is high, the prior knowledge required makes it difficult for novices to get started. Systems that could follow natural language instructions to perform automatic editing would significantly improve accessibility. This paper introduces the language-based video editing (LBVE) task, which allows the model to edit, guided by text instruction, a source video into a target video. LBVE contains two features: 1) the scenario of the source video is preserved instead of generating a completely different video; 2) the semantic is presented differently in the target video, and all changes are controlled by the given instruction. We propose a Multi-Modal Multi-Level Transformer (M$^3$L-Transformer) to carry out LBVE. The M$^3$L-Transformer dynamically learns the correspondence between video perception and language semantic at different levels, which benefits both the video understanding and video frame synthesis. We build three new datasets for evaluation, including two diagnostic and one from natural videos with human-labeled text. Extensive experimental results show that M$^3$L-Transformer is effective for video editing and that LBVE can lead to a new field toward vision-and-language research.
Data-Independent Structured Pruning of Neural Networks via Coresets

Model compression is crucial for the deployment of neural networks on devices with limited computational and memory resources. Many different methods show comparable accuracy of the compressed model and similar compression rates. However, the majority of the compression methods are based on heuristics and offer no worst case guarantees on the tradeoff between the compression rate and the approximation error for an arbitrarily new sample. We propose the first efficient structured pruning algorithm with a provable tradeoff between its compression rate and the approximation error for any future test sample. Our method is based on the coreset framework, and it approximates the output of a layer of neurons/filters by a coreset of neurons/filters in the previous layer and discards the rest. We apply this framework in a layer-by-layer fashion from the bottom to the top. Unlike previous works, our coreset is data-independent, meaning that it provably guarantees the accuracy of the function for any input [Formula: see text], including an adversarial one.