Reviewerly/exHarmony huggingface dataset:
Evaluating the Performance of Some Local Optimizers for Variational Quantum Classifiers

Abstract In this paper, we have studied the performance and role of local optimizers in quantum variational circuits. We studied the performance of the two most popular optimizers and compared their results with some popular classical machine learning algorithms. The classical algorithms we used in our study are support vector machine (SVM), gradient boosting (GB) and random forest (RF). These were compared with a variational quantum classifier (VQC) using two sets of local optimizers viz AQGD and COBYLA. For experimenting with VQC, IBM Quantum Experience and IBM Qiskit was used while for classical machine learning models, scikit-learn was used. The results show that machine learning on noisy immediate scale quantum machines can produce comparable results as on classical machines. For our experiments, we have used a popular restaurant sentiment analysis dataset. The extracted features from this dataset and then after applying PCA reduced the feature set into 5 features. Quantum ML models were trained using 100 epochs and 150 epochs on using EfficientSU2 variational circuit. Overall, four Quantum ML models were trained and three Classical ML models were trained. The performance of the trained models was evaluated using standard evaluation measures viz, Accuracy, Precision, Recall, F-Score. In all the cases AQGD optimizer-based model with 100 Epochs performed better than all other models. It produced an accuracy of 77% and an F-Score of 0.785 which were highest across all the trained models.
Multi-level Graph Matching Networks for Deep and Robust Graph Similarity Learning

While the celebrated graph neural networks yield effective representations for individual nodes of a graph, there has been relatively less success in extending to graph similarity learning. Recent works have considered either global-level graph-graph interactions or low-level node-node interactions, ignoring the rich cross-level interactions (e.g., between nodes of a graph and the other whole graph). In this paper, we propose a Multi-level Graph Matching Network (MGMN) for computing the graph similarity between any pair of graph-structured objects in an end-to-end fashion. The proposed MGMN model consists of a node-graph matching network for effectively learning cross-level interactions between nodes of a graph and the other whole graph, and a siamese graph neural network to learn global-level interactions between two graphs. Furthermore, to bridge the gap of the lack of standard graph similarity learning benchmark, we have created and collected a set of datasets for both graph-graph classification and regression tasks with different sizes in order to evaluate the robustness of models. Our comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baselines on these graph similarity learning benchmarks.
4 Managing layers of risk: Uncertainty in large development programs combining agile software development and traditional project management

How risks are managed implicitly and explicitly at multiple levels of agile projects has not been extensively studied and there is a need to investigate how risk management can be used in large agile projects. This is the objective of this exploratory study which investigates the following research question: How does a large software/hardware development project using agile practices manage uncertainty at project/subproject and work package levels?
Tv series and their boundaries

In this paper I follow Ted Nannicelli in the project of establishing boundaries of television works. I focus on serialized television works pertaining to a particular genre and I set out to provide an account of their identity. My claim is that external identity of such works is determined by their specific genre-affiliation, given the way in which generic norms determine the content of the series, namely, its characteristic storylines and regular set of characters. From the internal perspective, a series’ identity is determined by the particular way in which it implements abstract generic norms. I describe the process of such implementation as creation of series-specific formulaic patterns, and I analyze different layers of works where such patterns are evident. My central claim is that series-specific formulaic patterns create a coherent set of norms which, through repeated instantiation in the series’ episodes, become associated with that particular series. The function of this set is to establish the series’ boundaries, and to maintain its identity when different modifications to the patterns are made.
Privacy-preserving and robust watermarking on sequential genome data using belief propagation and local differential privacy

Abstract Motivation Genome data is a subject of study for both biology and computer science since the start of the Human Genome Project in 1990. Since then, genome sequencing for medical and social purposes becomes more and more available and affordable. Genome data can be shared on public websites or with service providers (SPs). However, this sharing compromises the privacy of donors even under partial sharing conditions. We mainly focus on the liability aspect ensued by the unauthorized sharing of these genome data. One of the techniques to address the liability issues in data sharing is the watermarking mechanism. Results To detect malicious correspondents and SPs—whose aim is to share genome data without individuals’ consent and undetected—, we propose a novel watermarking method on sequential genome data using belief propagation algorithm. In our method, we have two criteria to satisfy. (i) Embedding robust watermarks so that the malicious adversaries cannot temper the watermark by modification and are identified with high probability. (ii) Achieving ϵ-local differential privacy in all data sharings with SPs. For the preservation of system robustness against single SP and collusion attacks, we consider publicly available genomic information like Minor Allele Frequency, Linkage Disequilibrium, Phenotype Information and Familial Information. Our proposed scheme achieves 100% detection rate against the single SP attacks with only 3% watermark length. For the worst case scenario of collusion attacks (50% of SPs are malicious), 80% detection is achieved with 5% watermark length and 90% detection is achieved with 10% watermark length. For all cases, the impact of ϵ on precision remained negligible and high privacy is ensured. Availability and implementation https://github.com/acoksuz/PPRW\_SGD\_BPLDP Supplementary information Supplementary data are available at Bioinformatics online.
The Implementation of CIA (Creative, Innovative and Active) Learning Approach as a Solution for the Monotonous of Online Learning System in the Covid-19 Pandemic Era

When COVID 19 attacked, the decision made by the government was to impose an online learning system.Unfortunately, various problems arose that required universities to take responsive steps to find the solutions to the monotonous of the online learning system which made students' enthusiasm for online lectures drop dramatically due to the ability of lecturers in utilizing online applications which was limited on giving face-to-face lectures virtually without any modification.This study aimed to elaborate the effectiveness of applying CIA learning approach as an effort to increase students' enthusiasm and interest in the midst of the monotonous of face-to-face lectures virtually during the Covid-19 pandemic.The researcher used descriptive qualitative research methods.The samples used were 100 students from various disciplines at Sawerigading University, Makassar who were chosen randomly.The data collection technique was through questionnaire and observation.In general.The results showed that students' interest in online learning systems increased from 12.7% which stated that the learning system applied by lecturers during the first period of the pandemic was less qualified to 61.8% of high quality in the second period so that the students felt very interested in joining the online system.The students response agreed that the online learning system by Sawerigading University lecturers was more creative, interactive and active after the CIA learning approach was applied through various activities in the virtual room.These results identified that the students gave positive responses to the application of the CIA learning approach as an effort by the University of Sawerigading Makassar to find a solution to the problem of low student enthusiasm for face-to-face lecture methods online in a virtual room that previously seemed monotonous and less attractive.
On the structure of misère impartial games

We consider the abstract structure of the monoid ℳ of misère impartial game values. We present several new results, including a proof that the group of fractions ofℳis almost torsion-free, a method of calculating the number of distinct games born by day 7, and some new results on the structure of prime games. We also include proofs of a few older results due to Conway, such as the cancellation theorem, that are essential to the analysis, but whose proofs are not readily available in the literature.
Nonlocal latent low rank sparse representation for single image super resolution via self-similarity learning

&lt;p style='text-indent:20px;'&gt;In this paper, we propose a novel scheme for single image super resolution (SR) reconstruction. Firstly, we construct a new self-similarity framework by regarding the low resolution (LR) images as the low rank version of corresponding high resolution (HR) images. Subsequently, nuclear norm minimization (NNM) is employed to generate LR image pyramids from HR ones. The structure of our framework is beneficial to extract LR features, where we regard the quotient image, calculated between HR image and LR image at the same layer, as LR feature. This LR feature has the same dimension as LR image; however the dimension of commonly used gradient feature is 4 times than LR image. On the other hand, we employ nonlocal similar patch, within the same scale and across different scales, to generate HR and LR dictionaries. In the course of encoding, codes are calculated from both row and column of LR dictionary for each LR patch; at the same time, both low rank and sparse constraints on codes matrix give us a hand to remove coding noises. Finally, both quantitative and perceptual results demonstrate that our proposed method has a good SR performance.&lt;/p&gt;
Human Behavior Assessment using Ensemble Models

Behavioral analysis is a pertinent step in today’s automated age. It is important to judge a statement on a variety of parameters before reaching a valid conclusion. In today’s world of technology and automation, Natural language processing tools have benefited from growing access to data in order to analyze the context and scenario. A better understanding of human behaviors would empower a range of automated tools to provide users a customized experience. For precise analysis, behavior understanding is important. We have experimented with various machine learning techniques, and have obtained a maximum private score of 0.1033 with a public score of 0.1733. The methods are described as part of the ALTA 2020 shared task. In this work, we have enlisted our results and the challenges faced to solve the problem of the human behavior assessment.
Self-Regularity of Non-Negative Output Weights for Overparameterized Two-Layer Neural Networks

We consider the problem of finding a two-layer neural network with sigmoid, rectified linear unit (ReLU), or binary step activation functions that "fits" a training data set as accurately as possible as quantified by the training error; and study the following question: \emph{does a low training error guarantee that the norm of the output layer (outer norm) itself is small?} We answer affirmatively this question for the case of non-negative output weights. Using a simple covering number argument, we establish that under quite mild distributional assumptions on the input/label pairs; any such network achieving a small training error on polynomially many data necessarily has a well-controlled outer norm. Notably, our results (a) have a polynomial (in $d$) sample complexity, (b) are independent of the number of hidden units (which can potentially be very high), (c) are oblivious to the training algorithm; and (d) require quite mild assumptions on the data (in particular the input vector $X\in\mathbb{R}^d$ need not have independent coordinates). We then leverage our bounds to establish generalization guarantees for such networks through \emph{fat-shattering dimension}, a scale-sensitive measure of the complexity class that the network architectures we investigate belong to. Notably, our generalization bounds also have good sample complexity (polynomials in $d$ with a low degree), and are in fact near-linear for some important cases of interest.
Privacy Protection in Enterprise Social Networks Using a Hybrid De-Identification System

Enterprise social networks (ESN) have been widely used within organizations as a communication infrastructure that allows employees to collaborate with each other and share files and documents. The shared documents may contain a large amount of sensitive information that affect the privacy of persons such as phone numbers, which must be protected against any kind of disclosure or unauthorized access. In this study, authors propose a hybrid de-identification system that extract sensitive information from textual documents shared in ESNs. The system is based on both machine learning and rule-based classifiers. Gradient boosted trees (GBTs) algorithm is used as machine learning classifier. Experiments ran on a modified CoNLL 2003 dataset show that GBTs algorithm achieve a very high F1-score (95%). Additionally, the rule-based classifier is consisted of regular expression and gazetteers in order to complement the machine learning classifier. Thereafter, the sensitive information extracted by the two classifiers are merged and encrypted using Format Preserving Encryption method.
Research on Music Wireless Control Based on Motion Tracking Sensor and Internet of Things

With the continuous development of society and rapid economic growth, intelligent music control technology has received more and more attention. At the same time, real-time motion tracking technology has also been developed more and more in the fields of virtual reality and human-machine control. This article is dedicated to developing a wireless music control system based on gesture tracking sensors. First, in the data collection part, an infrared sensor module based on the Internet of Things is used to automatically detect whether someone is approaching. When detecting that someone is approaching, the motion tracking sensor module captures and detects gestures and counts them through a counter. Then, the IoT data transmission module sends the acquired gesture information from the sending end to the receiving end. Finally, the particle swarm algorithm performs algorithmic intelligent processing and judgment on the transmitted data to realize wireless control of background music. After software and hardware debugging, a wireless music control model based on motion tracking was finally successfully established. The system has undergone a complete test, and the test results show that the system has strong stability. Users can easily control music equipment and achieve high accuracy of music control information.
Pengembangan Media Pembelajaran Berbasis Macromedia Flash 8 dalam Upaya Meningkatkan Efektifitas Belajar Ilmu Pengetahuan Sosial

The aims of this study were to produce and to find out the effectivity of using the Macromedia Flash Profesional 8-based learning media as well. The type of this study was a Research and Development model adopted from ADDIE. The steps of ADDIE model were including Analyze, Design, Develop, Implement, and Evaluate. The trial subjects consisted of material experts of social science, learning design experts, learning media experts, three students of individual trial, nine students of small group trial, and also thirty students of main field trial. The data about the quality of this product was collected by using questionairres. The data was collected and analyzed by qualitative descriptive analysis. The results of Macromedia Flash Profesional 8-based learning media on the subject material of local environmental maps showed that: the validation of the entire material experts showed that the media was extremely good (96.47%); the validation of the entire design experts showed that the media was extremely good (91.81%); the validation of learning media was extremely good (90.00%); the validation of individual trial was extremely good (88.59%); the validation of small group trial was extremely good (91.75%); and the validation of main field trial was extremely good (95.76%). Based on the result of hypothetical test proved that there was a significant difference between students’ learning taught by Macromedia Flash Profesional 8 conventional learning.
Sorkin parameter for type-I spontaneous parametric down-conversion biphotons and matter waves

We propose experimental measurements of the logarithmic negativity, which quantifies quantum correlations using Gouy phase measurements in an asymmetric double-slit interference experiment for twin photons. This is possible because both quantities have analogous dependence with the spatial confinement by the slits and enables one to manipulate the portion of entanglement by the Gouy phase. In order to obtain those measurements, we need to work in a regime where the position correlations between particles are strong, therefore we investigate such correlations for biphotons. Since we would like to handle entanglement quantifiers through the Gouy phase, we analyze the Gouy phase difference for two entangled photons in an asymmetric double-slit interference experiment.
Development of teaching material solar system using learning cycle 7E model completed with augmented reality

This study and development aimed to produce a valid and feasible teaching material using the learning cycle 7E model equipped with the augmented reality on the solar system material for VII class of Middle School/Islamic Middle School as teaching material in the COVID-19 pandemic era. The method used was the Lee and Owens’ model limited in three stages: assessment/analysis, design, and development. Validators and users determined the validity and feasibility of the teaching material. The developed teaching material is of A6 printed book consisting of BC that analyzes the solar system, Earth’s rotation and revolution, the moon’s rotation and revolution, and their effects for lives on Earth. The solar system material is presented in pictures, audios, videos, handouts, students’ worksheets, and AR animations. Based on the validity test conducted to material and media experts, the teaching material is valid and feasible, with a score percentage of more than 61%. The legibility test result on students obtained a score percentage of 85% with very feasible criteria.
Can we control the amount of useful nonclassicality in a photon added hypergeometric state?

Abstract Non-Gaussianity inducing operations are studied in the recent past from different perspectives. Here, we study the role of photon addition, a non-Gaussianity inducing operation, in the enhancement of nonclassicality in a finite dimensional quantum state, namely hypergeometric state with the help of some quantifiers and measures of nonclassicality. We observed that measures to characterize the quality of single photon source and anticlassicality lead to the similar conclusion, i.e. to obtain the desired quantum features one has to choose all the state parameters such that average photon numbers remains low. Wigner logarithmic negativity of the photon added hypergeometric state and concurrence of the two-mode entangled state generated at the output of a beamsplitter from this state show that nonclassicality can be enhanced by increasing the state parameter and photon number addition but decreasing the dimension of the state. In principle, decreasing the dimension of the state is analogous to holeburning and is thus expected to increase nonclassicality. Further, the variation of Wigner function not only qualitatively illustrates the same features as observed quantitatively through concurrence potential and Wigner logarithimic negativity, but illustrate non-Gaussianity of the quantum state as well.
DECISION SUPPORT FOR ENVIRONMENTAL IMPACT ASSESSMENT FOR MALAYSIAN BAUXITE MINING INDUSTRY USING ANALYTIC NETWORK PROCESS

The mining industry plays an important role in the economic development of Malaysia. However, uncontrolled mining activities have caused serious environmental impacts. Recently, bauxite mining in Kuantan, in the state of Pahang, stained fifteen kilometers of Pahang’s coastline with red arsenic particles and heavy metal pollution washed from open-pit bauxite mines into the nearby sea. This has caused potentially catastrophic damages to the ecosystem off the coast of Pahang. This triggered the Government of Malaysia to issue a temporary ban on bauxite mining while the state government engaged in expensive clean-up. Mining activities require an Environmental Impact Assessment (EIA). Environmental decisions are complex and multidisciplinary including knowledge bases which incorporate natural, physical, and social sciences, politics, and ethics. This research proposes a decision support framework that uses the Analytic Network Process (ANP) to help decision makers in EIA pertaining to the bauxite mining industry.
Prototype for consultation of COVID information through a voice assistant

With the advent of voice assistants, many of the activities that people do daily are more comfortable.Sending simple voice commands to attendees to perform predefined actions provides accessibility for people with disabilities who have difficulty manipulating technologies or applications.The data collection for this work focuses on the Covid-19 pandemic.The data sources are the National Service for Risk and Emergency Management and the Ministry of Public Health of Ecuador.A database is designed based on the collected data.A web application prototype uses the database and applies Speech to Text, Text to Speech, and Watson Assistant to provide a query service.The recognition of intentions and entities by Watson Assistant is used to respond simulating a conversation and with the recognized information structure the required query.This prototype aims to rapidly provide information on the Covid-19 pandemic through dynamically created queries using natural language input.
A Single Image Enhancement Technique Using Dark Channel Prior

In this paper, we propose a novel single image enhancement technique for defogging by using dark channel prior. The traditional dark channel prior methods for defogging have problems of high time complexity, edge effect, and failure of dark channel prior. To overcome the problems of high time complexity and edge effect, firstly, a four-point weighting algorithm is proposed to estimate the atmospheric light value accurately, and the dark channel prior is used to estimate the rough transmittance. Then, the gray-scale image of the input image is used to refine the transmittance. After that, an atmospheric scattering model is designed to restore the fog-free image. To solve the problem that the dark channel prior can not process the high brightness area, a combination of edge detection and maximum inter-class variance is used to segment the sky area and non-sky area. Finally, the improved defogging method is used for processing the non-sky area, and the enhancement algorithm via sequential decomposition is used for handling the sky area. Extensive experiments show that the improved algorithm can not only reduce the time complexity, but also effectively improve the edge effect. At the same time, it can also solve the problem of failure of dark channel prior.