Reviewerly/exHarmony huggingface dataset:
Neighborhood Intervention Consistency: Measuring Confidence for Knowledge Graph Link Prediction

Link prediction based on knowledge graph embeddings (KGE) has recently drawn a considerable momentum. However, existing KGE models suffer from insufficient accuracy and hardly evaluate the confidence probability of each predicted triple. To fill this critical gap, we propose a novel confidence measurement method based on causal intervention, called Neighborhood Intervention Consistency (NIC). Unlike previous confidence measurement methods that focus on the optimal score in a prediction, NIC actively intervenes in the input entity vector to measure the robustness of the prediction result. The experimental results on ten popular KGE models show that our NIC method can effectively estimate the confidence score of each predicted triple. The top 10% triples with high NIC confidence can achieve 30% higher accuracy in the state-of-the-art KGE models.
Online Learning amid COVID-19 Pandemic: A Case Study of Vocabulary Learning Strategies

The outbreak of Coronavirus disease (COVID-19) has led countries to implement measures to prevent its spread. One of these measures included shutting down schools and universities. Consequently, the teaching, learning and assessment processes were entirely shifted from face-to-face to online. The current study aimed to investigate online vocabulary learning strategies (VLSs) amid COVID-19 pandemic used by Saudi EFL students. It attempted to find out the online strategies Saudi EFL astudents use to get the meaning of new vocabulary, the strategies they use to study new vocabulary and the strategies they follow to revise the learned vocabulary and keep them as part of their repertoire. The sample of the study was 119 male and female English and non-English majors. The study modified Kulikova’s (2015) questionnaire. This study was conducted at the University of Bisha, Saudi Arabia, during a fully online learning period in September and October 2020 at the time of COVID-19 outbreak. The data were collected via a self-administered questionnaire form. It was distributed via teachers of the English Department. The study found out that English majors used vocabulary learning strategies more than non-English majors. It also showed that students do not ask their teachers about the meaning of new vocabulary (77%), they also do not ask their classmates (92%), nor they ask their friends (85%), which could be attributed to online study and due to social distance during coronavirus pandemic (COVID-19).
Experimental Demonstration of XOR Separation by On-chip Training a Linear Silicon Photonic Circuit

We experimentally demonstrate XOR separation, a classical linearly inseparable problem, by on-chip training linear silicon photonic circuits that we proposed in OFC2020. This device implements nonlinear-projection-linear-separation principle and can perform classification tasks in machine learning.
An Entanglement-driven Fusion Neural Network for Video Sentiment Analysis

Video data is multimodal in its nature, where an utterance can involve linguistic, visual and acoustic information. Therefore, a key challenge for video sentiment analysis is how to combine different modalities for sentiment recognition effectively. The latest neural network approaches achieve state-of-the-art performance, but they neglect to a large degree of how humans understand and reason about sentiment states. By contrast, recent advances in quantum probabilistic neural models have achieved comparable performance to the state-of-the-art, yet with better transparency and increased level of interpretability. However, the existing quantum-inspired models treat quantum states as either a classical mixture or as a separable tensor product across modalities, without triggering their interactions in a way that they are correlated or non-separable (i.e., entangled). This means that the current models have not fully exploited the expressive power of quantum probabilities. To fill this gap, we propose a transparent quantum probabilistic neural model. The model induces different modalities to interact in such a way that they may not be separable, encoding crossmodal information in the form of non-classical correlations. Comprehensive evaluation on two benchmarking datasets for video sentiment analysis shows that the model achieves significant performance improvement. We also show that the degree of non-separability between modalities optimizes the post-hoc interpretability.
Towards the Quantum Internet: Satellite Control Plane Architectures and Protocol Design

The creation of the future quantum Internet requires the development of new systems, architectures, and communications protocols. As a matter of fact, the optical fiber technology is affected by extremely high losses; thus, the deployment of a quantum satellite network (QSN) composed of quantum satellite repeaters (QSRs) in low Earth orbit would make it possible to overcome these attenuation problems. For these reasons, we consider the design of an ad hoc quantum satellite backbone based on the Software-Defined Networking (SDN) paradigm with a modular two-tier Control Plane (CP). The first tier of the CP is embedded into a Master Control Station (MCS) on the ground, which coordinates the entire constellation and performs the management of the CP integrated into the constellation itself. This second tier is responsible for entanglement generation and management on the selected path. In addition to defining the SDN architecture in all its components, we present a possible protocol to generate entanglement on the end-to-end (E2E) path. Furthermore, we evaluate the performance of the developed protocol in terms of the latency required to establish entanglement between two ground stations connected via the quantum satellite backbone.
Analysis of 1KW Solar Rooftop System by Using PYSyst

The act of a photovoltaic system depends on the geographical location and type of PV modules implemented. PV systems are beneficial in areas having a moral amount of incident solar radiation. Pune gets an annual average solar radiation of about 5.4 kWh/m2 a day. In this paper 1KW, the photovoltaic system is designed and simulated using PVSyst software for Pune, Maharashtra, India using measured data of the location. The total amount of energy generated by the system and various losses occurring in the system is analyzed and presented Performance ratio of the system over the whole year is estimated as which shows that generation of electricity from the PV system is a variable option for Pune to supplement the increasing energy needs.
Research on correction and recognition of QR code on cylinder

In the actual application, the QR code was affected by the collection conditions, environment and surface of substrate, which would cause a series of defects such as noise pollution, local highlight and geometric distortion. These defects would lead to the reduction of recognition rate. The research of preprocessing, area detection, extraction and correction processing for these defects was based on the principle of image processing. Methods were proposed to optimize the QR code and improve the recognition rate. According to the basic principles of digital image processing, the processing technology of QR code was analyzed. The algorithm of threshold segmentation, the molecular block Otsu method was used to deal with the uneven illumination. Based on the special structural features of the position detection pattern, the QR code was detected and extracted from the image. The method of anti-perspective transformation was used to correct the QR code with geometric distortion. The QR code printed on the surface was fitted to surface area, and the corrected graphic was obtained. Then grayscale was interpolated into the corresponding coordinates to get the corrected QR code. The jagged and virtual dots in the image were eliminated by the morphological close operation. The results showed that the Otsu method that combined with the principle of threshold segmentation was applied to process the QR code with uneven lighting and low brightness.
Mitigating Virtualization Failures Through Migration to a Co-Located Hypervisor

Many organizations are moving their systems to the cloud, where providers consolidate multiple clients using virtualization, which creates challenges to business-critical applications. Research has shown that hypervisors fail, often causing common-mode failures that may abruptly disrupt dozens of virtual machines simultaneously. We hypothesize and empirically show that a significant percentage of virtual machines affected by a hypervisor failure are capable of continuing execution on a new hypervisor. Supported by this observation, we design a technique for recovering from hypervisor failures through efficient virtual machine migration to a co-located hypervisor, which allows virtual machines to continue executing with minimal downtime and which can be transparently applied to existing applications. We evaluate a proof-of-concept implementation using fault injection of hardware and software faults and show that it can recover, on average, 41-46% of all virtual machines, as well as having a mean virtual machine downtime of 3 seconds.
Nonlinear Impairment Compensation Using Neural Networks

Neural networks are attractive for nonlinear impairment compensation applications in communication systems. In this paper, several approaches to reduce computational complexity of the neural network-based algorithms are presented.
Poverty and Inequality in Tunisia: Recent Trends

Tunisia's reforms and agile shift to a more democratic political system since a major political revolution in 2011 has not prevented continued and rising citizen discontent. While this paper does not directly analyze this vexing problem, it assesses welfare indicators and labor markets nationally, regionally, and across different population groups—such as women and youth—over the last two decades. The paper shows that while Tunisia has significantly reduced poverty between 2000 and 2019, the profile of the poor has not changed much: poverty remains concentrated in rural and western regions, mainly among households with younger men without education and headed by someone working in low-productivity sectors such as agriculture and construction. Moreover, the share of the vulnerable Tunisian population at risk of falling into poverty is quite large, especially after the COVID-19 pandemic, even though poverty had been declining over the past two decades. Non-monetary dimensions of well-being, such as access to basic services, are also unevenly distributed across regions and population groups. COVID-19 has further aggravated these disparities and is reversing Tunisia's poverty reduction gains. The paper sheds light into the issues that require policy attention on poverty.
Learning Aggregation Functions

Learning on sets is increasingly gaining attention in the machine learning community, due to its widespread applicability. Typically, representations over sets are computed by using fixed aggregation functions such as sum or maximum. However, recent results showed that universal function representation by sum- (or max-) decomposition requires either highly discontinuous (and thus poorly learnable) mappings, or a latent dimension equal to the maximum number of elements in the set. To mitigate this problem, we introduce LAF (Learning Aggregation Function), a learnable aggregator for sets of arbitrary cardinality. LAF can approximate several extensively used aggregators (such as average, sum, maximum) as well as more complex functions (e.g. variance and skewness). We report experiments on semi-synthetic and real data showing that LAF outperforms state-of-the-art sum- (max-) decomposition architectures such as DeepSets and library-based architectures like Principal Neighborhood Aggregation, and can be effectively combined with attention-based architectures.
Investigating Protected Health Information Leakage from Android Medical Applications

As smartphones and smartphone applications are widely used in a healthcare context (e.g., remote healthcare), these devices and applications may need to comply with the Health Insurance Portability and Accountability Act (HIPAA) of 1996. In other words, adequate safeguards to protect the user’s sensitive information (e.g., personally identifiable information and/or medical history) are required to be enforced on such devices and applications. In this study, we forensically focus on the potential of recovering residual data from Android medical applications, with the objective of providing an initial risk assessment of such applications. Our findings (e.g., documentation of the artifacts) also contribute to a better understanding of the types and location of evidential artifacts that can, potentially, be recovered from these applications in a digital forensic investigation.
Linked Data Triples Enhance Document Relevance Classification

Standardized approaches to relevance classification in information retrieval use generative statistical models to identify the presence or absence of certain topics that might make a document relevant to the searcher. These approaches have been used to better predict relevance on the basis of what the document is “about”, rather than a simple-minded analysis of the bag of words contained within the document. In more recent times, this idea has been extended by using pre-trained deep learning models and text representations, such as GloVe or BERT. These use an external corpus as a knowledge-base that conditions the model to help predict what a document is about. This paper adopts a hybrid approach that leverages the structure of knowledge embedded in a corpus. In particular, the paper reports on experiments where linked data triples (subject-predicate-object), constructed from natural language elements are derived from deep learning. These are evaluated as additional latent semantic features for a relevant document classifier in a customized news-feed website. The research is a synthesis of current thinking in deep learning models in NLP and information retrieval and the predicate structure used in semantic web research. Our experiments indicate that linked data triples increased the F-score of the baseline GloVe representations by 6% and show significant improvement over state-of-the art models, like BERT. The findings are tested and empirically validated on an experimental dataset and on two standardized pre-classified news sources, namely the Reuters and 20 News groups datasets.
M-ary Quadrature Ampplitude Modulation Classification Using Skewness and Kurtosis

The aim of modulation classification is to identify the modulation scheme of unknown modulated signals. In this paper, we present a new modulation classification approach which has two main steps. First, the valuable information of the signals is extracted by utilizing skewness and kurtosis as a features extraction. The extracted features are then introduced to a hybrid system which combines a neural network and a fuzzy inference system to identify the modulated signal. The proposed M-QAM signals classification system was investigated that identifies 16QAM, 32QAM, 64QAM, 128QAM, 256QAM, in presence of white Gaussian noise channel at -2, 0, 2, 4, 6, 8, 10 dB signal to noise ratio. MATLAB programs were designed to fulfill all the tasks. The simulation results show the high performance of the classification, and exhibits 97% correct classification ratio.
RETRACTED: A Brief Analysis of the Reform of British and American Literature Education Mode Based on Artificial Intelligence

Abstract With the development of society and progress, the computer technology has been widely used to the life of people, now fairy many teaching using multimedia technology, British and American literature course makes students’ compulsory course for English majors, for students to British and American literature, oh sister have a vital role to improve your English, multimedia technology has been widely applied to the British and American education mode, education technology in British and American literature play a crucial role in the education reform, the application of multimedia teaching, make the British and American literature course more vivid, more effective.
A Sustainable Method for Publishing Interoperable Open Data on the Web

Smart cities need (sensor) data for better decision-making. However, while there are vast amounts of data available about and from cities, an intermediary is needed that connects and interprets (sensor) data on a Web-scale. Today, governments in Europe are struggling to publish open data in a sustainable, predictable and cost-effective way. Our research question considers what methods for publishing Linked Open Data time series, in particular air quality data, are suitable in a sustainable and cost-effective way. Furthermore, we demonstrate the cross-domain applicability of our data publishing approach through a different use case on railway infrastructure—Linked Open Data. Based on scenarios co-created with various governmental stakeholders, we researched methods to promote data interoperability, scalability and flexibility. The results show that applying a Linked Data Fragments-based approach on public endpoints for air quality and railway infrastructure data, lowers the cost of publishing and increases availability due to better Web caching strategies.
RETRACTED: The Reform of Digital Media Art Professional Curriculum System Based on Internet Technology

Abstract With the rapid development of science and technology today, people’s lives and learning are inseparable from the support of big data, especially in the digital everyday industry. With the rapid development of the digital media industry, relevant technical talents in this industry are also facing huge challenges. The main reason is the shortage of talents and the high-quality requirements for employees [1, 2]. Therefore, there are many problems in the cultivation of mathematics media professionals. For example, due to the lack of established training goals and unreasonable curriculum settings, it is difficult for teachers to teach better [3]. Therefore, based on the internet technology, this article first analyzes the setting of the digital media curriculum system, then analyzes the reasons for the curriculum reform, and finally discusses the planning and implementation of the curriculum reform, hoping that big data will be helpful to the teaching of digital media majors.
Fuzzy logic research work in Mexico motivated by Lotfi Zadeh

We provide in this article a short review of the research work that has been done in Mexico on developing new methods and theory for designing intelligent systems utilizing type-2 fuzzy systems in combination with soft computing techniques. Soft Computing (SC) is an area formed by intelligent paradigms, like fuzzy systems, neural networks, and bio-inspired and swarm algorithms, which may be utilized to build high performance hybrid systems. The combination of type-2 fuzzy systems with SC enables the constructing of efficient intelligent systems for solving complex problems in a wide diversity of areas, such as control, pattern recognition, medical diagnosis and others. We also recall some of the main moments and memories of encounters and meetings with the father of fuzzy logic (Prof. L. Zadeh), which were very positive and motivated us to continue his work and legacy.
Thoughts on the Mixed Teaching Mode of Ideological and Political Courses in Colleges and Universities Based on the MOOC Platform

In the information age, with the rise of MOOC network education platform, the traditional education mode has changed. Universities work actively in education innovation, especially in the mixed education of concept and politics classroom. In order to develop education, reasonably use network education resources, and strengthen the introduction of MOOC platform resources. Taking into account the rapid development of the current information age, the report studies the mixed education mode of university concept and political courses with the rapid development of MOOC platform, and puts forward innovative education methods. The research methods adopted in this paper are: first, literature research method is used to query the leaflets and news, analyze and research consulting materials, and then use substantive education and comparative methods to continuously deepen the integration and development of information technology and the role of ideas and political education. The educational model of school's concept and political theory course is studied in multi-stage and multi perspective. The experimental results show that MOOC platform has great significance in the implementation of Ideological and political courses in universities. This has changed the traditional ideological and political curriculum mode of universities, improved the overall level of Ideological and political education in universities, and promoted the ideological and political education in universities. It plays a positive role in cultivating students' good social views, values and life views.
Phase unwrapping method combining dual-frequency heterodyne with dual complementary phase encoding

Phase encoding and phase-shift profilometry are two commonly used 3D measurement techniques. However, the acquired phases in the techniques are subject to jump errors due to phase ambiguity and phase errors caused by multiple heterodyne. The phase-shifting profilometry also makes the selection of fringe period difficult. To overcome this problem and achieve high-precision measurement, a phase unwrapping method that combines dual-frequency heterodyne with double complementary phase encoding is proposed. First, two wrapped phases are obtained by two groups of sinusoidal fringes; the heterodyne phase is obtained after heterodyne processing, and the high-frequency phase is expanded by heterodyne phase. Second, the fringe levels are obtained using the complementary phase encoding fringes that are shifted by half an order, and then the absolute phase is obtained by selecting different phase coding levels according to different regions for the first phase unwrapping; Finally, the phase noise is removed by exploiting the difference between the phase slopes of adjacent pixels. Experimental results show that a system with the proposed method achieves an RMS error of 0.015 mm. In addition, the period of dual-frequency heterodyne synthesis does not need to cover the whole field of view, which breaks the limitation of frequency selection of the traditional dual-frequency heterodyne method and triple frequency heterodyne method, enabling high-precision measurement with higher frequency fringes. This method overcomes the limitations of the phase principal value error when using higher frequency fringes for high-precision measurement, improves the measurement effect of reflective objects, and effectively avoids the error caused by phase jump.