Reviewerly/exHarmony huggingface dataset:
Quantum simulation of nuclear Hamiltonian with a generalized transformation for Gray code encoding

We present the quantum simulation of the deuteron to calculate its binding energy using the variational quantum eigensolver, which is based on a hybrid quantum-classical approach. Apart from a commonly used Hamiltonian derived from pionless effective field theory, we consider the interaction which can be easily studied with conventional classical methods but the corresponding operator leading to nonzero off-tridiagonal matrix elements. To map the many-body basis states on the qubit states, three encodings are explored, namely, one-hot, Bravyi-Kitaev, and the Gray code. We perform a generalized transformation for many-body operators in Gray code encoding, and simulate the Hamiltonians with nonzero off-tridiagonal matrix elements. Furthermore, the analyses of the relative efficiency of all encodings and corresponding transformations are carried out using the noise model of a real IBM quantum device. We demonstrate that the Gray code is more efficient for a large basis, irrespective of the form of potential and the presence of hardware noise.
Secure VM placement analysis against co-location based attack in cloud

Cloud computing is most popular platform for IT industries provides numerous benefits to cloud computing users. Day by day increasing the number of users on cloud so security is a most important issue. Virtual machine co-residency and resource sharing rise chance to leak sensitive information, lack of restrictions for placement of virtual machine on same host machine, increase the chance for an attacker to conducting co-location based attack on neighbor virtual machine that running on same host machine. Without security scheme placement of VM in cloud environment may increase chance to thieve confidential information by the attackers. In this paper, we analyze the existing VM placement scheme against cross VM side channel attack. We categorized side channel attack (SCA) according to access of hardware and way to extract the confidential information. We have also done comparative study of VM placement scheme on the basis of previous history of user, number of VM placed and average running time of VM on cloud.
Discovering prerequisite relations from educational documents through word embeddings

Inferring prerequisite relations among educational documents, in terms of prior knowledge required to understand and complete assignments about certain topics, is a crucial task for instructional designers and teachers. Massive open online courses, electronic textbooks, public encyclopedias and repositories of learning objects and other forms of informative content create a huge availability of educational material, which can be exploited in online platforms for distance education, both for recommending specific resources and personalized learning paths. But public taxonomies of prerequisites, or learning object metadata useful to trace down prerequisites are not generally available. A description of a new approach for prerequisite discovering in educational documents is given. It is based on word embeddings, that is, statistical language models for the representation of text-based learning objects in low-dimensional latent spaces. It takes advantage of the latent representations to identify prerequisites in a binary classification setting. The accuracy of the approach is validated by means of an experimental benchmark covering multiple datasets of educational material.
Indian Sign Language Gesture Recognition Using Deep Convolutional Neural Network

Communication is extremely important in ones life and the most widely used type of communication is verbal communication. But there are people with hearing and speech impairment who cannot communicate verbally and the language which they use for communication is sign language. And in India, the Indian Sign Language (ISL) is used. These languages are visual language which uses a variety of visual signs or gestures. The majority of the people are not aware of the semantics of these gesture and this creates a communication gap between both the community. So there is a need for an automatic system. There has been a lot of research done in the field of American Sign language but unfortunately not in the case of ISL. This is due to lack of standard dataset and the variation in the language. The aim of this work is to recognize ISL gestures and convert it into text. Currently, an image recognition model was implemented using deep CNN (Inception V3 model) which accepts input image and it is passed through a series of layers and the output is generated. We have achieved an accuracy of 93%.
Learning Fair Graph Neural Networks with Limited and Private Sensitive Attribute Information

Graph neural networks (GNNs) have shown great power in modeling graph structured data. However, similar to other machine learning models, GNNs may make biased predictions w.r.t protected sensitive attributes, e.g., skin color and gender. This is because machine learning algorithms including GNNs are trained to reflect the distribution of the training data which often contains historical bias towards sensitive attributes. In addition, we empirically show that the discrimination in GNNs can be magnified by graph structures and the message-passing mechanism of GNNs. As a result, the applications of GNNs in high-stake domains such as crime rate prediction would be largely limited. Though extensive studies of fair classification have been conducted on independently and identically distributed (i.i.d) data, methods to address the problem of discrimination on non-i.i.d data are rather limited. Generally, learning fair models require abundant sensitive attributes to regularize the model. However, for many graphs such as social networks, users are reluctant to share sensitive attributes. Thus, only limited sensitive attributes are available for fair GNN training in practice. Moreover, directly collecting and applying the sensitive attributes in fair model training may cause privacy issues, because the sensitive information can be leaked in data breach or attacks on the trained model. Therefore, we study a novel and important problem of learning fair GNNs with limited number of private sensitive attributes, i.e., sensitive attributes that are processed with a privacy-preserving mechanism. In an attempt to address these problems, FairGNN is proposed to eliminate the bias of GNNs whilst maintaining high node classification accuracy by leveraging graph structures and limited sensitive information. To further preserve the privacy, private sensitive attributes with privacy guarantee are obtained by injecting noise based on local differential privacy. And We further extend FairGNN to NT-FairGNN to handle the limited and private sensitive attributes to simultaneously achieve fairness and preserve privacy. Theoretical analysis and extensive experiments on real-world datasets demonstrate the effectiveness of FairGNN and NT-FairGNN in achieving fair and high-accurate classification.
Research on privacy protection of multi source data based on improved gbdt federated ensemble method with different metrics

Federated learning is a hot topic in the field of multi-source data. It has the advantage that the data cannot be localized, and it has the problems that the parameters of the local model are difficult to integrate and the accuracy of the model is low. In this paper, an improved gradient boosting decision tree (GBDT) federated ensemble learning method is proposed, which takes the average gradient of similar samples and its own gradient as a new gradient to improve the accuracy of the local model. Different ensemble learning methods are used to integrate the parameters of the local model, thus improving the accuracy of the updated global model. The experimental results show that the accuracy of the global model trained by the method on magic-gamma-telescope datasets and eeg-eye-state datasets is compared with that of GBDT federated ensemble method and traditional multi-source data processing technology without finding similar samples. The accuracy of the model is improved by 0.6%, 0.45% and 0.27% and 0.08% respectively, which makes it possible to improve the security of data and model while improving the accuracy of the model.
Detecting Online Recruitment Fraud Using Machine Learning

Online Recruitment fraud (ORF) is becoming an important issue in the cyber-crime region. Companies find it easier to hire people with the help of the internet rather than the old traditional way. But it has greatly attracted scammers. In this paper, we have proposed a solution on how to detect ORF. We have presented our results based on the previous model and the methodologies, to create the ORF detection model where we have used our own dataset. We have created our dataset based on the Bangladesh job field and by using a publicly accessible dataset as a reference. Furthermore, Logistic Regression, AdaBoost, Decision Tree Classifier, Random Forest Classifier, Voting Classifier, LightGBM, Gradient Boosting are the algorithms that have been used. We have found the accuracy of different prediction models, where LightGBM (95.17%) and Gradient Boosting (95.17%) give the highest accuracy. Through this paper, we tried to create a precise way for detecting fraudulent hiring posts.
A hyper-heuristic methodology for coordinating swarms of robots in target search

Target search aims to discover elements of various complexity in a physical environment, by minimizing the overall discovery time. Different swarm intelligence algorithms have been proposed in the literature, inspired by biological species. Despite the success of bio-inspired techniques (bio-heuristics), there are relevant algorithm selection and parameterization costs associated with every new type of mission and with new instances of known missions. In this paper, evolutionary optimization is proposed for achieving significant improvements of the mission performance. Although adaptive, the logic of bio-heuristics is nevertheless constrained by models of biological species. To generate more adaptable logics, a novel design approach based on hyper-heuristics is proposed, in which the differential evolution optimizes the aggregation and tuning of modular heuristics for a given application domain. A modeling and optimization testbed has been developed and publicly released. Experimental results on real-world scenarios show that the hyper-heuristics based on stigmergy and flocking significantly outperform the adaptive bio-heuristics.
Guruswami-Sudan Decoding of Elliptic Codes Through Module Basis Reduction

This paper proposes the Guruswami-Sudan (GS) list decoding algorithm for one-point elliptic codes, in which the interpolation is realized by the module basis reduction (BR). Elliptic codes are a kind of algebraic-geometric (AG) codes with a genus of one. Over the same finite field, they have a greater codeword length than Reed-Solomon (RS) codes, capable of correcting more errors. The GS decoding consists of interpolation and root-finding, while the former that determines the interpolation polynomial <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {Q}(\text {x}, \text {y}, \text {z})$ </tex-math></inline-formula> dominates the decoding complexity. By defining the Lagrange interpolation function over an elliptic function field, a basis of the interpolation module can be constructed. The desired Gröbner basis that contains <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {Q}(\text {x}, \text {y}, \text {z})$ </tex-math></inline-formula> can be determined by reducing the constructed basis. This is namely the BR interpolation and it requires less finite field arithmetic operations than the conventional Kötter’s interpolation, facilitating the GS decoding. Re-encoding transform (ReT) is further introduced to facilitate the BR interpolation. This work also shows that both the BR interpolation and its ReT variant will have a lower complexity as the code rate <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}/{n}$ </tex-math></inline-formula> increases, where n and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${k}$ </tex-math></inline-formula> are the length and dimension of the code, respectively. Our numerical results demonstrate the complexity advantage of the BR interpolation over Kötter’s interpolation, and the performance advantage of elliptic codes over RS codes.
An Efficient Inspection System Based on Broad Learning: Nondestructively Estimating Cement Compressive Strength With Internal Factors

Cement has been widely used in civil engineering, whose quality directly affects the safety of buildings. Cement compressive strength, as an important quality indicator, its accurate estimation is of great significance in quality inspections and the design of high-performance products. However, existing measurement technology remains traditional and destructive. Except for high time-consuming and the waste of various resources, it requires significant improvement since the unprofessional operations will give rise to large errors. In this article, an efficient system is proposed to estimate the cement compressive strength based on the broad learning and internal factors, in which the index system describes the internal factors affecting the compressive strength, and the broad learning system distills the potential correlation between the compressive strength and those factors. It can nondestructively estimate the strength directly with the internal factors, e.g., clinker composition and physical properties. In addition, to verify its practicability and to assist the formula optimization in the application, the robustness test and factorial analysis are designed. The experimental results prove that this model can accurately estimate the strength with excellent generalization ability, which saves labor power and material, avoids large errors caused by unprofessional operations, and aids high-performance cement production. Especially, its ability to rapidly build an accurate estimation model is beneficial for the production of various cement in industry.
Coded sparse matrix computation schemes that leverage partial stragglers

Coded matrix computation utilizes concepts from erasure coding to mitigate the effect of slow worker nodes (stragglers) in the distributed setting. While this is useful, there are issues with applying, e.g., MDS codes in a straightforward manner for this problem. Several practical scenarios involve sparse matrices. MDS codes typically require dense linear combinations of submatrices of the original matrices which destroy their inherent sparsity; this leads to significantly higher worker computation times. Moreover, treating slow nodes as erasures ignores the potentially useful partial computations performed by them. In this work we present schemes that allow us to leverage partial computation by stragglers while imposing constraints on the level of coding that is required in generating the encoded submatrices. This significantly reduces the worker computation time as compared to previous approaches and results in improved numerical stability in the decoding process. Exhaustive numerical experiments support our findings.
Whole-slide imaging in cytopathology: state of the art and future directions

Whole slide imaging (WSI) has been increasingly adopted for digital evaluation of surgical pathology specimens. Unlike histological slides, cytological preparations frequently display a heterogeneous distribution of cells throughout slides in different focal planes sometimes admixed with obscuring material, therefore requiring multiple scanning planes which significantly lengthens image acquisition and evaluation times. Although examination of digital images can be more advantageous than conventional glass slides, the challenges of focusing, scanning and screening cytological specimens and the associated increase in scan times and data storage needs have limited the routine application of WSI in cytopathology practice. Emerging digital systems designed to overcome image acquisition obstacles coupled with artificial intelligence algorithms augmenting screening of digital cytology slides offer innovative solutions to address these limitations. The aim of this review is to critically address the potential benefits and pitfalls of employing WSI in cytopathology practice and to introduce promising state-of-the-art solutions on the horizon.
SeizeMaliciousURL: A novel learning approach to detect malicious URLs

Malicious websites are increasing in abundance, which brings serious web security threats to users. As a result, individuals lose their assets, values, information, etc. to unauthorized parties and become victims while visiting such websites. The research community put efforts into developing effective and efficient models for detecting malicious URLs to make available notifications about websites that users access. Perceiving this, numerous methods make use of various types of machine learning (ML) approaches. However, until now, no technique has perfectly detected malicious URLs, as they are susceptible to false positive and false negative decisions in classifying URLs in malicious and non-malicious groups. To improve this issue, this article proposes a new approach based on a machine learning technique. The proposed approach uses multiple classifiers (i.e., ensemble learning) to predict the class probabilities of URLs and then applies a threshold to filter the decisions of multiple classifiers. Next, the decisions are combined concerning its corresponding class probabilities and find the class label with the highest class probability as the final decision on unlabeled URLs. The results show that the proposed method offers better performance in terms of malicious URL detection than other methods.
WhatsApp as part of an EFL programme: participation and interaction

Abstract This study examines the results of an intervention conducted with adult EFL learners in Barcelona, Spain, to foster engagement with the target language outside class time. As part of the programme, the messaging service WhatsApp was used to carry out a range of voluntary communicative tasks, in learners’ own time. Familiarity with this tool made it easy to use, and learner feedback was positive. Analysis of the resulting chat transcript indicated that (1) off-task participation increased as learners appropriated the space as a vehicle for their own communication, (2) off-task interactions fell predominantly into two categories: administrative and social, and (3) despite being voluntary and unevaluated, with minimum teacher intervention, English was overwhelmingly the language of choice within these interactions. We hope this study can serve as an example of the possibilities offered by the WhatsApp medium in an EFL context.
Cyclic Sparsely Connected Architectures for Compact Deep Convolutional Neural Networks

In deep convolutional neural networks (DCNNs), model size and computation complexity are two important factors governing throughput and energy efficiency when deployed to hardware for inference. Recent works on compact DCNNs as well as pruning methods are effective, yet with drawbacks. For instance, more than half the size of all MobileNet models lies in their last two layers, mainly because compact separable convolution (CONV) layers are not applicable to their last fully connected (FC) layers. Also, in pruning methods, the compression is gained at the expense of irregularity in the DCNN architecture, which necessitates additional indexing memory to address nonzero weights, thereby increasing memory footprint, decompression delays, and energy consumption. In this article, we propose cyclic sparsely connected (CSC) architectures, with memory/computation complexity of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(N\log {}N)$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> is the number of nodes/channels given a DCNN layer that, contrary to compact depthwise separable layers, can be used as an overlay for both FC and CONV layers of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {O}(N^{2})$ </tex-math></inline-formula> . Also, contrary to pruning methods, CSC architectures are structurally sparse and require no indexing due to their cyclic nature. We show that both standard convolution and depthwise convolution layers are special cases of the CSC layers, whose mathematical function, along with FC layers, can be unified into one single formulation and whose hardware implementation can be carried out under one arithmetic logic component. We examine the efficacy of the CSC architectures for compression of LeNet, AlexNet, and MobileNet DCNNs with precision ranging from 2 to 32 bits. More specifically, we surge upon the compact 8-bit quantized 0.5 MobileNet V1 and show that by compressing its last two layers with CSC architectures, the model is compressed by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 1.5\times $ </tex-math></inline-formula> with a size of only 873 kB and little accuracy loss. Finally, we design a configurable hardware that implements all types of DCNN layers including FC, CONV, depthwise, CSC-FC, and CSC-CONV indistinguishably within a unified pipeline. We implement the hardware on a tiny Xilinx field-programmable gate array (FPGA) for total on-chip processing of the compressed MobileNet that, compared to the related work, has the highest Inference/J while utilizing the smallest FPGA.