Reviewerly/exHarmony huggingface dataset:
A Novel Approximation Methodology and Its Efficient VLSI Implementation for the Sigmoid Function

In this brief, a novel approximation method and its optimized hardware implementation are proposed for the sigmoid function used in Deep Neural Networks (DNNs). Based on piecewise approximation and truncated Taylor series expansion, the proposed method achieves very good approximation with low complexity while exploiting data representation with powers of two. In addition, by analyzing gradients of the sigmoid function, a small trick is introduced to improve the approximation precision. Furthermore, to reduce the hardware complexity and shorten the critical path, sampled values of the function are generated with simple logical-mapping. It is shown that the proposed approximation schemes can be implemented with purely combinational logic and the sigmoid function can be computed in one clock cycle. The experimental results demonstrate that the mean absolute errors are at the order of 1 × 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> . Compared with prior arts, the new design can obtain significant improvement in critical path with comparable performance.
Adaptive Cubic Spline Interpolation in CIELAB Color Space for Underwater Image Enhancement

Underwater images suffer from haze, blue/green tint, and color distortion, caused by attenuation and scattering of light. Existing methods for restoration depend on transmission maps to inverse the effects based on the underwater image formation model, and enhancement methods often result in unnatural colors. In this paper, we propose a method to adaptively estimate a color correction curve in the CIE L*a*b* color space for single image enhancement. Haze and blue-green tint in underwater images are removed through this process. We demonstrate improved canny edge detection on underwater images through enhancement. Results are evaluated and compared with state-of-art methods using standard underwater image datasets of coral reefs with marine populations and man-made concrete structures. Enhancement of underwater images finds its use in areas such as marine habitat mapping, improvement of 3D reconstruction of underwater scenes, and visual studies of marine life.
Smart Contract based Central Sector Scheme of Scholarship for College and University Students

Education plays a vital role in human life. To encourage higher education, the Indian government has taken many initiatives. These include the disbursement of scholarships to financially help the students belonging to low-income families. Central Sector Scheme of Scholarship for College and University Students (CSS) is one such scheme that financially helps meritorious students in pursuing graduation and post-graduation. This scheme was introduced in 2008. Scholarships from CSS have been helping students to fund their educational expenses. But there have been some difficulties faced by the students in availing this scholarship. These include lack of traceability of application form, loss of application form in transit through Indian Postal Service, lack of transparency between students and their respective Education Boards, and lack of bank account verification. In this paper, we propose a Smart Contract for CSS Scheme that overcomes the above-mentioned drawbacks. It has four entities: Students, Education Boards, Colleges, and Banks. The Blockchain-based Smart Contract proposed in this paper, has been developed to provide a user-friendly environment to a student and maintain a transparent and constant connection between students and their respective Education Boards. Many applications have been developed on the Blockchain platform but to the best of our knowledge, there does not exists any government scholarship scheme on the Blockchain platform in India.
Evolving fuzzy neural hydrocarbon networks: A model based on organic compounds

This paper presents a new evolving intelligent model capable of combining the techniques and concepts of artificial neural networks, fuzzy systems and artificial hydrocarbon networks, in which the latter aggregates concepts of organic chemistry to carry out the training of intelligent models. The proposed model has three layers where the first two form a fuzzy inference system and the third layer is responsible for the defuzzification process through concepts based on the bond between carbons and hydrogens. The fuzzification process of the model is based on the techniques of an autonomous data partitioning algorithm that can elicit the number and centers of the clouds that make up the fuzzy neurons in the first layer of the model. Thereby, an evolving algorithm is employed, which uses the data set as a stream in a single-pass incremental mode (allowing fast processing). This is achieved in an unsupervised manner, and thus, to eliminate possible overfitting problems in the subsequent supervised training process, a Bayesian pruning technique is used to identify the neurons that are finally most relevant to the actual supervised approximation and/or classification problem. To validate the proposed approach, binary pattern classification tests, multi-class classification problems and regression problems were performed. The results obtained and compared with other intelligent models in the literature prove that the approach becomes a model capable of extracting knowledge from data sets and using concepts of organic chemistry to perform learning tasks with high degree of reliability.
A Hybrid Multi-Step Probability Selection Particle Swarm Optimization with Dynamic Chaotic Inertial Weight and Acceleration Coefficients for Numerical Function Optimization

As a meta-heuristic algoriTthm, particle swarm optimization (PSO) has the advantages of having a simple principle, few required parameters, easy realization and strong adaptability. However, it is easy to fall into a local optimum in the early stage of iteration. Aiming at this shortcoming, this paper presents a hybrid multi-step probability selection particle swarm optimization with sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients (MPSPSO-ST), which can strengthen the overall performance of PSO to a large extent. Firstly, we propose a hybrid multi-step probability selection update mechanism (MPSPSO), which skillfully uses a multi-step process and roulette wheel selection to improve the performance. In order to achieve a good balance between global search capability and local search capability to further enhance the performance of the method, we also design sine chaotic inertial weight and symmetric tangent chaotic acceleration coefficients inspired by chaos mechanism and trigonometric functions, which are integrated into the MPSPSO-ST algorithm. This strategy enables the diversity of the swarm to be preserved to discourage premature convergence. To evaluate the effectiveness of the MPSPSO-ST algorithm, we conducted extensive experiments with 20 classic benchmark functions. The experimental results show that the MPSPSO-ST algorithm has faster convergence speed, higher optimization accuracy and better robustness, which is competitive in solving numerical optimization problems and outperforms a lot of classical PSO variants and well-known optimization algorithms.
What do people “like” on Facebook? Content marketing strategies used by retail bank brands in Australia and Singapore

Previous research has yet to examine the effectiveness of social media content strategies in the financial services sector. This study advances knowledge of content strategies used by retail bank brands by examining how the type of message, the post format and the source of the content influence customer engagement independently and interactively. A total of 541 posts from the Facebook pages of ten banks in Australia and Singapore are analysed using multiple regressions. Results suggest that in the Singaporean sample, persuasive-only content and mixed content lead to more likes and emoji responses, whereas informative-only content is effective in generating comments and questions. Furthermore, videos and third-party sources strengthen the relationship between informative-only content and customer engagement. In the Australian sample, informative-only content and mixed content are more effective than persuasive content in engaging customers. The findings support an integrated view of content design and highlight the importance of adjusting the content strategy to suit the utilitarian nature of banking services and engage high-involvement customers.
Thermal-Aware Virtual Machine Allocation for Heterogeneous Cloud Data Centers

In recent years, a large and growing body of literature has addressed the energy-efficient resource management problem in data centers. Due to the fact that cooling costs still remain the major portion of the total data center energy cost, thermal-aware resource management techniques have been employed to make additional energy savings. In this paper, we formulate the problem of minimizing the total energy consumption of a heterogeneous data center (MITEC) as a non-linear integer optimization problem. We consider both computing and cooling energy consumption and provide a thermal-aware Virtual Machine (VM) allocation heuristic based on the genetic algorithm. Experimental results show that, using the proposed formulation, up to 30 % energy saving is achieved compared to thermal-aware greedy algorithms and power-aware VM allocation heuristics.
Unsupervised Large Graph Embedding Based on Balanced and Hierarchical K-means

There are many successful spectral based unsupervised dimensionality reduction methods, including Laplacian Eigenmap (LE), Locality Preserving Projection (LPP), Spectral Regression (SR), etc. We find that LPP and SR are equivalent if the symmetric similarity matrix is doubly stochastic, Positive Semi-Definite (PSD) and with rank <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> , where <inline-formula><tex-math notation="LaTeX">$p$</tex-math></inline-formula> is the reduced dimension. Since solving SR is believed faster than solving LPP based on some related literature, the discovery promotes us to seek to construct such specific similarity matrix to speed up LPP solving procedures. We then propose an unsupervised linear method called Unsupervised Large Graph Embedding (ULGE). ULGE starts with a similar idea as LPP but adopts an efficient approach to construct anchor-based similarity matrix and then performs spectral analysis on it. Moreover, since conventional anchor generation strategies suffer kinds of problems, we propose an efficient and effective anchor generation strategy, called Balanced <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -means based Hierarchical <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -means (BHKH). The computational complexity of ULGE can reduce to <inline-formula><tex-math notation="LaTeX">$O(ndm)$</tex-math></inline-formula> , which is a significant improvement compared to conventional methods need <inline-formula><tex-math notation="LaTeX">$O(n^2d)$</tex-math></inline-formula> at least, where <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$m$</tex-math></inline-formula> are the number of samples, dimensions, and anchors, respectively. Extensive experiments on several publicly available datasets demonstrate the efficiency and effectiveness of the proposed method.
Improving class noise detection and classification performance: A new two-filter CNDC model

Class noise is an important issue in classification with a lot of potential consequences. It can decrease the overall accuracy and increase the complexity of the induced model. This study investigates ensemble filtering, removing and relabeling noisy instances issues and proposes a new two-filter model for Class Noise Detection and Classification (CNDC). The proposed two-filter CNDC model comprises two major parts, which are noise detection and noise classification. The noise detection part involves ensemble and distance filtering to overcome ensemble issues. In latter part, a Removing-Relabeling (REM-REL) technique is proposed to enhance overall performance of noise classification. To evaluate the performance of the proposed model, several experiments were conducted on six real data sets. The proposed REM-REL technique was found to be successful to classify noisy instances. The final results showed that the proposed model led to a significant performance improvement compared with ensemble filtering.
λPSI: exact inference for higher-order probabilistic programs

We present λPSI, the first probabilistic programming language and system that supports higher-order exact inference for probabilistic programs with first-class functions, nested inference and discrete, continuous and mixed random variables. λPSI’s solver is based on symbolic reasoning and computes the exact distribution represented by a program.
Application of Lah transform for security and privacy of data through information hiding in telecommunication

Abstract Due to rapid growth of digital communication attributed to advancement of Internet technologies, at every time instant huge amount of multimedia information is created and transmitted on the Internet. However, ownership identification, copyright violation, and identity theft are proving as the serious concerns because of the malicious attacks or hacking of open information. Attackers can alter or delete the information, and claim the ownership or prevent the receiver to get the data. Hence, researchers try to address such challenges. One such effort is concerned with multimedia data hiding through Steganography. In this article, a polynomial sequence generator called Lah Transform (LhT) is applied for developing a Steganography method. LhT generates an integer polynomial sequence in coefficient form by evaluating the pixel values using addition and multiplication. Initially, the cover image is partitioned into nonoverlapping b ‐pixel groups (where, b = 3 or 4) which in succession are converted into transform domain using LhT in row‐major order. Secret bits are embedded into the LhT coefficients in varying proportions to achieve variable payload. Coefficient adjustment followed by the embedding process ensures minimum quality distortion. Inverse LhT is applied to regenerate b ‐pixel groups in the spatial domain. Results assure that incongruity between the cover pixels and stego‐pixels rises as the value of b increases. Hence, to achieve a high payload, smaller values of b are chosen. The proposed method yields higher peak signal to noise ratio values and payload compared to some recent methods found in the literature. Code of our method is available here .
COCO (Creating Common Object in Context) Dataset for Chemistry Apparatus

In order to create machine learning, we need to build a model. The model is created from a process called training. The goal of training is to develop an accurate model that answers some questions and in order to train a model, we need to collect a dataset. The quality and quantity of the data gathered will determine how good the predictive model can be. Helping the model to understand datasets like humans do is one of the important processes of machine learning. Datasets need to be constructed and transformed correctly. In this research, we compare the difference between creating a COCO dataset manually and creating a synthetic COCO dataset. Creating datasets for chemistry apparatus is not as difficult as creating a human object. The apparatus has a specific shape and form, thus the dataset had to have a limited number. As a result, we create both a dataset both manually and synthetically. The synthetic dataset helps to gain more datasets by combining some objects with different backgrounds.
A Novel Case Base Reasoning and Frequent Pattern Based Decision Support System for Mitigating Software Risk Factors

Software risk management is crucial for the success of software project development.The existing literature has models for risk management, but is too complex to be used in practice.The information in the existing studies is scattered over different articles which makes it difficult to find relevant knowledge to establish relationship between risk factors and mitigations.This paper presents a novel model which identifies the relationship between risk factors and mitigations automatically by using intelligent Decision Support System (DSS).The proposed model has four steps.Firstly, the input of the system has been designed where risk factors and mitigations have been inputted into it.Secondly, rule based machine learning approach has been used for mining of associations between risks and mitigations.Thirdly, Case Based Reasoning (CBR) approach has been used to determine the previous cases as rules.Finally, automated rules have been generated to develop an intelligent DSS to mitigate the software risks.The proposed technique copes with the highly cited existing limitations of risk handling like, lack of generic DSS and intelligent relationship between software risks and mitigations.Automated rules have been discovered with a novel idea of CBR and frequent pattern.The proposed model is capable of mitigating upcoming risks in future.Star schema has been implemented to support our proposed DSS.Moreover, from highly cited literature 40 studies were identified from which 26 risk factors, 57 mitigations, 14 questions and 26 automated rules have been extracted.According to the validation of IT industry experts, the average of the effectiveness of DSS is 51-55%.The novelty of the proposed research is that it uses two state of the art methods (Rule Based Machine Learning and CBR) to identify software risk mitigations.The results of the proposed model show that the chances of risks in software development have been reduced significantly.
Examining the correctness of anonymity for practical quantum networks

In a recent paper [A. Unnikrishnan et al., Phys. Rev. Lett. 122, 240501 (2019)], a quantum anonymous communication protocol was proposed in the presence of malicious agents and an untrusted source. Here, we point out that malicious agents can change the generated anonymous entanglement between the sender and the receiver without being detected, which means that the correctness of the generated anonymous entanglement should be reexamined. The way to check the above correctness is discussed.
Developing and Testing Robust Autonomy: The University of Sydney Campus Data Set

Vision and lidar are complementary sensors that are incorporated into many applications of intelligent transportation systems. These sensors have been used to great effect in research related to perception, navigation, and deep learning applications. Despite this success, the validation of algorithm robustness has recently been recognized as a major challenge for the massive deployment of these new technologies.
Jitter-Quantizing-Based TRNG Robust Against PVT Variations

Security is critical to the growing popularity of the Internet of Things(IoT), and true random number generator (TRNG) plays an increasingly important role in information security systems. Conventional TRNGs use natural physical stochastic processes including thermal noise, chaos-based circuit, and so on to generate random numbers. These analog circuits based TRNG structures often consume excessive hardware resources. Meanwhile, it is difficult to incorporate them into digital system. In this paper, a novel all-digital true random number generator in SRAM-based FPGAs is proposed by using Vernier technique that precisely quantize random edge jitter. The proposed TRNG design is implemented on Xilinx Virtex-6 XC6VLX240T-1FF1156 FPGA and shows a high quality of randomness which has passed the NIST test suite with relatively high p-values, achieves a high throughput of 127 Mbps with occupying 32 slices. Experimental results show a good tolerance to bias phenomenon induced by process, voltage, and temperature variations.
Histological Image Classification using Deep Features and Transfer Learning

A major challenge in the automatic classification of histopathological images is the limited amount of data available. Supervised learning techniques cannot be applied without some adjustment. We compare two common techniques to deal with limited domain data: using deep features and fine-tuning convolutional neural networks (CNN). We examine the following state-of-art CNN models: SqueezeNet-v1.1, MobileNet-v2, ResNet-18, and DenseNet-201. We demonstrate that using feature vectors that are extracted from one of the four CNN models with a classical support vector machine (SVM) for training and testing can lead to higher accuracy on publicly available datasets: Warwick-QU, Epistroma, BreaKHis, multi-class Kather, than previously published results. Similar results can be obtained with fully fine-tuning the aforementioned CNN models. We also study the effectiveness of block-wise fine-tuning of two models (i.e., SqueezeNet-v1.1 and ResNet-18) and show that it is not necessary to fully fine-tune leading to savings in time and space.
Resource estimate for quantum many-body ground-state preparation on a quantum computer

We estimate the resources required to prepare the ground state of a quantum many-body system on a quantum computer of intermediate size. This estimate is made possible using a combination of quantum many-body methods and analytic upper bounds. Our routine can also be used to optimize certain design parameters for specific problem instances. Lastly, we propose and benchmark an improved quantum state preparation procedure. We find that it reduces the circuit $T$-depth by a factor as large as ${10}^{6}$ for intermediate-size lattices.
Semisupervised PCA Convolutional Network for Vehicle Type Classification

In the vehicle type classification area, the necessity to improve classification performance across traffic surveillance cameras has garnered attention in research especially on high level feature extraction and classification. The backpropagation (BP) training approach of traditional deep Convolutional Neural Network (CNN) approach is time-consuming without using a Graphics Processing Unit (GPU). In this paper, we propose a semisupervised strategy for the end-to-end Principal Component Analysis Convolutional Network (PCN) in the area of vehicle type classification. Even without using a GPU, the proposed model eliminates the time-consuming training procedure of convolutional filter bank. In particular, the convolutional filters of the network are generated using unsupervised learning by Principal Component Analysis (PCA) which has tremendously reduced training cost and also reinforced the robustness of extracted features against various distortions. In order to further improve the training procedure while still preserving the discriminative characteristic of the system, only the fully-connected layer is fine-tuned in the supervised classification stage. The PCN is tested using a public BIT-Vehicle dataset which comprises 9850 surveillance-nature vehicle frontal-view images. The PCN can be easily implemented and readily compatible with many effective classifiers. Two classifiers, namely softmax classifier and Support Vector Machine (SVM) are employed in this network and their classification performances are then compared. Both classifiers take less than 100 seconds in the training process and are able to produce an average accuracy of above 88.35%, even under various inferior imaging conditions.