Reviewerly/exHarmony huggingface dataset:
Internet Adoption and Financial Development in Sub‐Saharan Africa: Evidence from Nigeria and Kenya

Abstract Financial development is influenced by the dynamics of multiple factors which have remained insufficiently explored up to date. In view of this, an attempt is made in this paper to investigate the impact of internet adoption on financial development in sub‐Saharan Africa, using Nigeria and Kenya as case studies. The dynamic ordinary least squares and vector error correction mechanism methods were employed in the study which revealed that the internet, complemented by financial openness, exerted a significant positive impact on financial development in the period 2000–16. The null hypothesis which states that the internet does not encourage financial development is therefore rejected. It follows that the level of financial development in both countries, and indeed most countries in sub‐Saharan Africa, could be enhanced by adopting appropriate policies that encourage more inclusive use of the internet. The policy recommendations of this study therefore include (i) relaxing the stringent requirements for licensing internet operators in order to make more services available for financial transactions, (ii) integrating internet technology into the national infrastructure framework in order to sustain its application, (iii) fostering local skills and expertise that will be maintaining internet infrastructure and (iv) providing a legal framework that protects personal information and ensures responsible usage of internet.
Minimally entangled state preparation of localized wave functions on quantum computers

Initializing a single site of a lattice scalar field theory into an arbitrary state with support throughout the quantum register requires ${\cal O}(2^{n_Q})$ entangling gates on a quantum computer with $n_Q$ qubits per site. It is conceivable that, instead, initializing to functions that are good approximations to states may have utility in reducing the number of required entangling gates. In the case of a single site of a non-interacting scalar field theory, initializing to a symmetric exponential wavefunction requires $n_Q-1$ entangling gates, compared with the $2^{n_Q-1} + n_Q-3 + \delta_{n_Q,1}$ required for a symmetric Gaussian wavefunction. In this work, we explore the initialization of 1-site ($n_Q=4$), 2-site ($n_Q=3$) and 3-site ($n_Q=3$) non-interacting scalar field theories with symmetric exponential wavefunctions using IBM's quantum simulators and quantum devices (Poughkeepsie and Tokyo). With the digitizations attainable with $n_Q = 3,4$, these tensor-product wavefunctions are found to have large overlap with a Gaussian wavefunction, and provide a suitable low-noise initialization for improvement and \emph{Somma Inflation}. In performing these simulations, we have employed a workflow that interleaves calibrations to mitigate systematic errors in production. The calibrations allow tolerance cuts on gate performance including the fidelity of the symmetrizing Hadamard gate, both in vacuum ($|{\bf 0}\rangle^{\otimes n_Q}$) and in medium ($n_Q-1$ qubits initialized to an exponential function). The results obtained in this work are relevant to systems beyond scalar field theories, such as the deuteron radial wavefunction, 2- and 3-dimensional cartesian-space wavefunctions, and non-relativistic multi-nucleon systems built on a localized eigenbasis.
Muddling through cybersecurity: Insights from the U.S. healthcare industry

The U.S. healthcare sector is inadequately prepared to deal with the reality of cyber threats. The increasing use of smart medical equipment and mobile devices is making healthcare organizations more susceptible to ransomware and other types of malware. The size and complexity of operations, coupled with the presence of numerous legacy and incompatible systems, make it difficult to implement effective cybersecurity measures. The daunting nature of the problem often results in an if-it-ain’t-broke-don’t-fix-it stance among senior healthcare leaders. The preponderance of healthcare-related laws, compliance regulations, and security guidance frameworks serve to complicate the cybersecurity challenge further and too often results in senior leadership assuming a state of blissful ignorance. This study sheds light on the key factors contributing to the chaotic state of affairs and presents a roadmap to a more deliberate and proactive approach to cybersecurity risk management.
<i>SMART-C</i>: Developing a “Smart City” Assessment System Using Cognitive Mapping and the Choquet Integral

Rapid urbanization, high city population growth rates, technological development, environmental impacts, and human wellbeing in urban areas have over the years become increasingly challenging concerns. These trends are related to the complex, multiple factors to be considered when evaluating “smart” cities, that is, municipalities that seek to improve residents' quality of life by combining new technologies and environmentally sustainable practices. The above tendencies thus add to the myriad of indicators that need to be included in smart city assessments, whose intricacy hinders related decision-making processes. Based on the principles of multiple-criteria decision analysis, this paper seeks to integrate cognitive mapping and the Choquet integral to develop an assessment system that facilitates the evaluation of smart cities, which was named “SMART-C.” The research includes a panel of experts on smart cities who identifies the evaluation criteria and their respective interactions. The results of a real-life application of the assessment system developed in this paper are validated both by the panel members and a representative of the Lisboa E-Nova nonprofit association of the Lisbon City Council's Environment and Energy Department. These experts confirm that the proposed evaluation system facilitates making distinctions between cities according to how fully they develop “smartness.” The advantages and limitations of this assessment framework are also discussed.
Quantum singular value decomposer

We present a variational quantum circuit that produces the singular value decomposition of a bipartite pure state. The proposed circuit, which we name quantum singular value decomposer or QSVD, is made of two unitaries respectively acting on each part of the system. The key idea of the algorithm is to train this circuit so that the final state displays exact output coincidence from both subsystems for every measurement in the computational basis. Such circuit preserves entanglement between the parties and acts as a diagonalizer that delivers the eigenvalues of the Schmidt decomposition. Our algorithm only requires measurements in one single setting, in striking contrast to the ${3}^{n}$ settings required by state tomography. Furthermore, the adjoints of the unitaries making the circuit are used to create the eigenvectors of the decomposition up to a global phase. Some further applications of QSVD are readily obtained. The proposed QSVD circuit allows us to construct a SWAP between the two parties of the system without the need of any quantum gate communicating them. We also show that a circuit made with QSVD and CNOTs acts as an encoder of information of the original state onto one of its parties. This idea can be reversed and used to create random states with a precise entanglement structure.
Hardware Trojan Design on Neural Networks

Recent innovations and breakthroughs in deep neural networks have advanced or evolved many industries as well as human daily life. To facilitate the deployment of these models to edge devices, custom hardware for deep neural networks have been designed to bridge the gap between performance and efficiency. However, due to the absence of underlying theory and an intractable nature, deep learning is susceptible to adversarial attacks. Furthermore, hardware solutions are also vulnerable to various threats through the globalized supply chains. Therefore, it is of great importance to study the implications of adversarial deep learning from the hardware perspective. This paper presents a novel methodology for injecting hardware Trojans in neural network implementations, particularly on a unique functional block to neural networks, i.e., rectified linear unit (ReLU). Experimental results show that a carefully designed hardware Trojan can always achieve the desired misclassification on the selected input trigger key. Additionally, in an experimental setting where a perturbation is only required on one neuron, 100% of the test data are unaltered while requiring only a 0.0022% overhead in hardware, which validates that the proposed designs are effective yet stealthy.
Object Detection Based on VGG with ResNet Network

We introduce an improved very deep convolutional network for accurate and significant object detection. It extracts high-level features that help to achieve tremendous performance to classify the image and detecting objects. The very deep convolutional neural network classifies data very profoundly. The VGG network constructed based on the very deep convolutional neural network. To constructing VGG network a stack of small convolutional filters used. The VGG network achieved high accuracy to large-scale image classification, but it has some training and localization problem. To overcome this, we proposed an improved method that able to solve the problem. We implement ResNet network in VGG that minimize VGG networks training error and able to detect more small objects.
Efficient convolutional hierarchical autoencoder for human motion prediction

Human motion prediction is a challenging problem due to the complicated human body constraints and high-dimensional dynamics. Recent deep learning approaches adopt RNN, CNN or fully connected networks to learn the motion features which do not fully exploit the hierarchical structure of human anatomy. To address this problem, we propose a convolutional hierarchical autoencoder model for motion prediction with a novel encoder which incorporates 1D convolutional layers and hierarchical topology. The new network is more efficient compared to the existing deep learning models with respect to size and speed. We train the generic model on Human3.6M and CMU benchmark and conduct extensive experiments. The qualitative and quantitative results show that our model outperforms the state-of-the-art methods in both short-term prediction and long-term prediction.
More confusion and frustration, better learning: The impact of erroneous examples

Prior research suggests students can sometimes learn more effectively by explaining and correcting example problems that have been solved incorrectly, compared to problem-solving practice or studying correct solutions. It remains unclear, however, what role students' affect might play in the process of learning from erroneous examples. Specifically, it may be that students experience greater confusion and frustration while studying erroneous examples, but that their confusion and frustration lead to greater learning. We analyzed student log data from previously published research comparing erroneous example instruction of decimal number mathematics to problem-solving instruction in a computer-based intelligent tutoring system. We created and applied affect detectors for a combination of confusion and frustration (“confrustion”) and compared the role of confrustion across conditions. As predicted, students in the erroneous example condition experienced greater confrustion while working through the instructional materials. However, contrary to predictions, confrustion was negatively correlated with posttest and delayed posttest performance across conditions, though less so for the erroneous example condition. Given that students in the erroneous example condition performed better on the delayed posttest than students in the problem-solving condition, it appears they learned more despite also experiencing greater confrustion rather than because of it. Results suggest that learning from erroneous examples may be an inherently more confusing and frustrating process than traditional problem solving. More generally, this research demonstrates that logging student actions at a step-by-step problem-solving level and analyzing those logs to infer affect can be a powerful way to investigate learning.
Feature Selection: Filter Methods Performance Challenges

Learning is the heart of intelligence. The focus in machine learning is to automate methods that achieve objectives, improve predictions or encourage informed behavior. Feature selection is a vital step in data analysis that often reduces dataset dimensionality by eliminating irrelevant and/or redundant attributes to simplify the learning process or improve outcomes' quality. This research critically analyses different filter methods based on ranking procedures (Information Gain (IG), Chi-square (CHI), V-score, Fisher Score, mRMR, Va and ReliefF) and identifies possible challenges that arise. We particularly concentrate on how threshold determination can affect results of different filter methods based on ranked scores. We show that this issue is vital, especially in the era of big data in which users deal with attributes in the magnitudes of tens of thousands with only a limited number of instances.
HolyLight: A Nanophotonic Accelerator for Deep Learning in Data Centers

Convolutional Neural Networks (CNNs) are widely adopted in object recognition, speech processing and machine translation, due to their extremely high inference accuracy. However, it is challenging to compute massive computationally expensive convolutions of deep CNNs on traditional CPUs and GPUs. Emerging Nanophotonic technology has been employed for on-chip data communication, because of its CMOS compatibility, high bandwidth and low power consumption. In this paper, we propose a nanophotonic accelerator, HolyLight, to boost the CNN inference throughput in datacenters. Instead of an all-photonic design, HolyLight performs convolutions by photonic integrated circuits, and process the other operations in CNNs by CMOS circuits for high inference accuracy. We first build HolyLight-M by microdisk-based matrix-vector multipliers. We find analog-to-digital converters (ADCs) seriously limit its inference throughput per Watt. We further use microdisk-based adders and shifters to architect HolyLight-A without ADCs. Compared to the state-of-the-art ReRAM-based accelerator, HolyLight-A improves the CNN inference throughput per Watt by 13× with trivial accuracy degradation.
Design of Secure Image Fusion Technique Using Cloud for Privacy-Preserving and Copyright Protection

Nowadays, the use of digital content or digital media is increasing day by day. Therefore, there is a need to protect the digital document from both unauthorized users and authorized users. The digital document should be protected from authorized users who try to redistribute it illegally. Digital watermarking techniques along with cryptography are insufficient to ensure an adequate level of security of digital media. The security of the transferring digital data in the modern world is also a big challenge because there is a high risk of security breaches. In this article, a secure technique of image fusion using hybrid domains (spatial and frequency) for privacy preserving and copyright protection is proposed. The proposed method provides a secure technique for the digital content in cloud environment. Two cloud services are used to develop this work, which eliminates the role of a trusted third party (TTP). First is the design of an infrastructure as a service (IaaS) to store different images with encryption processes to speed up the image fusion process and save storage space. Second, a Platform as a Service (PaaS) is used to enable the digital content to improve computation power and to increase the bandwidth. The prime objective of the proposed scheme is to transfer the digital media between a service provider and customer in a secure way using a hybrid domain along with cloud storage. Imperceptibility and robustness measures are used to calculate the performance of the proposed approach.
Collaborative SLA and reputation-based trust management in cloud federations

Industry and academia shift from the single cloud provider paradigm to cloud federations and alternative models, which orchestrate heterogeneous resources, such as Mobile Edge Computing and Fog Computing. In such complex environments, incorrect selection of deployment platform can lead to underwhelming application performance. This dictates the necessity of Service Level Agreements (SLA) and trust management services in order to enforce performance guarantees and enable customers to simultaneously evaluate their application’s performance and give performance indicators for future provider selection. In this paper, we propose a collaborative SLA and Reputation-based Trust Management (RTM) solution for federated cloud environment. The SLA service defines clearly the performance metrics and measures the actual performance of the deployed cloud applications. Based on the SLA, the RTM service of the collaborative solution leverages several technical and user’s experience metrics to compute the reliability of the cloud providers and the credibility of the customers. A proof of concept of the collaborative solution in a realistic federated environment is provided and validated. The corresponding experimental results demonstrate that it objectively computes the cloud providers’ reputation values under various scenarios.
Information asymmetry in initial coin offerings (ICOs): Investigating the effects of multiple channel signals

An initial coin offering (ICO) provides a new entrepreneurial finance approach for highly innovative blockchain ventures. However, the information asymmetry and adverse selection is severe in the ICO market. In this study, we focus on signals released in multiple channels in different ICO stages to investigate the relations between signal processing and information asymmetry. Referring to the multiple-channel communication theory, we characterize signal sources by their information schema, source credibility, and communication direction. Empirical results indicate that in the crowd sale stage, high credible and easy-interpretable signals have significant influences on token sales. In the listing stage, low credible and easy-interpretable signals have significant effects on token trading. High credible and hard-interpretable signals, which deliver project fundamental information, lose their functions in both stages, causing information asymmetry in ICOs. We also find that investor comments on social media, which is a multiple-way communication channel, play the role of information surveillance for ventures' voluntary disclosures. Those findings provide insights for signal processing and asset pricing in the ICO market to reduce information asymmetry and have significant practical implications.
Eliminating overfitting of probabilistic topic models on short and noisy text: The role of dropout

Probabilistic topic models are powerful tools for discovering hidden structures/semantics in discrete data, e.g., texts, images, links. However, on short and noisy texts, directly applying topic models may not work well or face severe overfitting. In this article, we investigate the benefits of dropout for preventing topic models from overfitting. We integrate dropout into several stochastic methods for learning latent Dirichlet allocation (LDA). From extensive experiments on four large-scale datasets, our findings are: (1) dropout helps to prevent overfitting and significantly enhance predictiveness and generalization of LDA on short texts; (2) for long documents, dropout may provide little benefit; (3) dropout can be easily integrated into any learning methods to avoid overfitting for short and noisy text. Furthermore, dropout can be straightforwardly employed in a wide range of topic models. In evidence, we apply dropout to BTM (Biterm topic model), one of the state-of-the-art models for short texts. Our experiments illustrate that BTM with dropout not only remains its good results in term of predictiveness, but also improves the learning time significantly.
Modified deep residual network architecture deployed on serverless framework of IoT platform based on human activity recognition application

In the last few years, human activity recognition (HAR) is a subject undergoing intense study in various contexts such as pattern recognition and human-device interaction. HAR applications come to an aid of Telecare system which is paving the way for doctors and nurses to measure the health status of their patients. Due to the ubiquitous influence of smartphones in an individual's life, we take embedded smartphone sensors into account as our case study. The proposed method, Modified Deep Residual Network, outperforms the accuracy of Human activity recognition compared with state-of-the-art machine learning techniques which are using Raw signals as their input. we defined new pooling layer called smooth-pooling to leverage the model performance. The accuracy of proposed architecture is evaluated on three common dataset that comprises accelerometer and gyroscope raw data. The results demonstrated the proposed method outperforms accuracy of classification while requiring just raw data with lower parameters compared to other works. Furthermore, The proposed HAR method is deployed in our IoT cloud platform which enables users to create scenarios based on what they are doing at home. Using Function as a Service (FaaS) architecture in this platform solves the scalability issues by running each function in a separate container. The IoT platform prepares an infrastructure for developers who want to integrate their application into the platform and use its functionality along with other IoT platform options.
SEBDB: Semantics Empowered BlockChain DataBase

Blockchain has been adopted in many applications to construct trust among multiple participants, such as supply chain management, digital assets transfer, philanthropy, etc. Blockchain platforms are often used as decentralized databases. However, existing blockchain platforms are far less convenient to use than traditional databases. They are lack of the capability of modelling complex tasks conveniently and efficiently, especially when both on-chain and off-chain data are involved at the same time. In this paper, we propose and implement a novel blockchain database, called SEBDB, which leverages the existing databases' functionality which are optimized for decades. Comparing to existing works, SEBDB is the first platform which considers both useability and scalability. Specifically, first, weaddrelationaldata semantics into blockchain platform, where each transaction is a tuple with multiple attributes in a pre-defined table. Second, we use SQL-like language as the general interface, instead of code-level APIs, to support convenient application development, in which intrinsic operations are re-defined and re-implemented to suit for blockchain platform. Third, as RDBMS has achieved great success in the past decades, our system, though not relying on RDBMS, treats it as an important component. Finally, we define a mini-benchmark to evaluate the performance of the blockchain database. Extensive experiments demonstrate the effectiveness and efficiency of our proposed system.