Reviewerly/exHarmony huggingface dataset:
Multiplicative Interactions and Where to Find Them

We explore the role of multiplicative interaction as a unifying framework to describe a range of classical and modern neural network architectural motifs, such as gating, attention layers, hypernetworks, and dynamic convolutions amongst others. Multiplicative interaction layers as primitive operations have a long-established presence in the literature, though this often not emphasized and thus under-appreciated. We begin by showing that such layers strictly enrich the representable function classes of neural networks. We conjecture that multiplicative interactions offer a particularly powerful inductive bias when fusing multiple streams of information or when conditional computation is required. We therefore argue that they should be considered in many situation where multiple compute or information paths need to be combined, in place of the simple and oft-used concatenation operation. Finally, we back up our claims and demonstrate the potential of multiplicative interactions by applying them in large-scale complex RL and sequence modelling tasks, where their use allows us to deliver state-of-the-art results, and thereby provides new evidence in support of multiplicative interactions playing a more prominent role when designing new neural network architectures.
2019 IEEE GRSS Data Fusion Contest: Large-Scale Semantic 3D Reconstruction [Technical Committees]

Presents information on the 2019 IEEE GRSS Data Fusion Contest.
Compositional Data Analysis in Tourism: Review and Future Directions

Compositional data analysis (CoDa) is the standard statistical methodology when data contain information about the relative importance of parts of a whole. Many research questions in tourism are either related to distribution of a whole (e.g., distribution, share, allocation, etc.) or relative importance (e.g., dominance, concentration, profile, etc.). Example research questions might be: How does time allocated to different types of activities relate to tourist satisfaction? or Which origins and destinations concentrate the most tourist flows per tourist segment? The first aim of this article is to present the manner in which CoDa solves statistical problems that arise when treating compositional data with classical statistical methods (e.g., spurious correlations, meaningless distances, assumption violation). The second aim is to review all CoDa applications in tourism and hospitality to date. The third is to present CoDa applications in related fields (e.g., finance, sociology, geography, economics, management, ecology, education), which can be translated into future research in tourism. In order to show how to apply the most common CoDa tools (exploratory analysis of compositions, and use of compositions as variables in a model) an example of restaurant menu styles is used.
Statistical Zaps and New Oblivious Transfer Protocols

We study the problem of achieving statistical privacy in interactive proof systems and oblivious transfer – two of the most well studied two-party protocols – when limited rounds of interaction are available. We obtain our first result by devising a public-coin approach to compress sigma protocols, without relying on trusted setup. To obtain our second result, we devise a general framework via a new notion of statistical hash commitments that may be of independent interest.
Belief and fairness: A secure two-party protocol toward the view of entropy for IoT devices

Internet of Things (IoT) devices strive to maximize their utilities and preserve security of IoT system within limited capacity. In effect, these kinds of devices can be resumed to be rational devices toward the view of game theory. Previous utilities inherit the definitions deriving from quintessential games (e.g. prisoner's dilemma game). However, they are criticized for either arbitrary or elaboration. In this paper, we circumvent the criticism by devising a new utility function based on beliefs and entropy, which are extracted from IoT devices (e.g. signals). The new utility function dwarfs others in terms of practicability. In addition, we present a general rational information exchange protocol and define the utilities based on entropy formally especially in the key round. Furthermore, we compare the utilities with existing ones with similar conclusions. That is, our formal utility definitions are equivalent to previous ones. To the best of our knowledge, it's the first time to relate utilities with entropy and meanwhile achieve desirable security properties.
C3DVQA: Full-Reference Video Quality Assessment with 3D Convolutional Neural Network

Traditional video quality assessment (VQA) methods evaluate localized picture quality and video score is predicted by temporally aggregating frame scores. However, video quality exhibits different characteristics from static image quality due to the existence of temporal masking effects. In this paper, we present a novel architecture, namely C3DVQA, that uses Convolutional Neural Network with 3D kernels (C3D) for full-reference VQA task. C3DVQA combines feature learning and score pooling into one spatiotemporal feature learning process. We use 2D convolutional layers to extract spatial features and 3D convolutional layers to learn spatiotemporal features. We empirically found that 3D convolutional layers are capable to capture temporal masking effects of videos. We evaluated the proposed method on the LIVE and CSIQ datasets. The experimental results demonstrate that the proposed method achieves the state-of-the-art performance.
Very Short-Term Solar Generation Forecasting Based on LSTM with Temporal Attention Mechanism

Accuracy solar generation forecasting could avoid serious challenges to large scale PV grid-connected systems. Thus, a very short-term solar generation forecasting method based on the LSTM with the temporal attention mechanism (TA-LSTM) is proposed in this paper. In our method, partial autocorrelation is first utilized to determine the length of time series, which is used as input of the LSTM forecasting model. Then, the TA-LSTM is trained by the data to learn the forecasting model. The LSTM is used here to learn the forecasting model because it can make full use of the information of the past time and has stronger adaptability in time series data analysis. To further improve forecasting accuracy, the temporal attention mechanism is integrated into the LSTM prediction model. The experiments are carried out to verify the performance of the proposed method. The experimental results show that the proposed method is feasible and effective.
Software Security Estimation Using the Hybrid Fuzzy ANP-TOPSIS Approach: Design Tactics Perspective

Increasing the number of threats against software vulnerabilities and rapidly growing data breaches have become a key concern for both the IT industry and stakeholders. Developing secure software systems when there is a high demand for software products from individuals as well as the organizations is in itself a big challenge for the designers and developers. Meanwhile, adopting traditional and informal learnings to address security issues of software products has made it easier for cyber-criminals to expose software vulnerabilities. Hence, it is imperative for the security practitioners to employ a symmetric mechanism so as to achieve the desired level of software security. In this context, a decision-making approach is the most symmetrical technique to assess the security of software in security tactics perspective. Since the security tactics directly address the quality attribute concerns, this symmetric approach will be highly effective in making the software systems more secure. In this study, the authors have selected three main attributes and fifteen sub-attributes at level 1 and level 2, respectively, with ten different software of an institute as alternatives. Furthermore, this study uses a fuzzy-based symmetrical decision-making approach to assess the security of software with respect to tactics. Fuzzy Analytic Network Process (F-ANP) is applied to evaluate the weights of criteria and fuzzy-Symmetrical technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to determine impact of alternatives. The proposed symmetrical assessment in this study will be beneficial for both the designers and developers to categorize and prioritize the security attributes and understand the importance of security tactics during software development life cycle.
Simplified binary cat swarm optimization

Inspired by the biological behavior of domestic cats, the Cat Swarm Optimization (CSO) is a metaheuristic which has been successfully applied to solve several optimization problems. For binary problems, the Boolean Binary Cat Swarm Optimization (BBCSO) presents consistent performance and differentiates itself from most of the other algorithms by not considering the agents as continuous vectors using transfer and discretization functions. In this paper, we present a simplified version of the BBCSO. This new version, named Simplified Binary CSO (SBCSO) which features a new position update rule for the tracing mode, demonstrates improved performance, and reduced computational cost when compared to previous CSO versions, including the BBCSO. Furthermore, the results of the experiments indicate that SBCSO can outperform other well-known algorithms such as the Improved Binary Fish School Search (IBFSS), the Binary Artificial Bee Colony (BABC), the Binary Genetic Algorithm (BGA), and the Modified Binary Particle Swarm Optimization (MBPSO) in several instances of the One Max, 0/1 Knapsack, Multiple 0/1 Knapsack, SubsetSum problem besides Feature Selection problems for eight datasets.
Personas and Analytics: A Comparative User Study of Efficiency and Effectiveness for a User Identification Task

Personas are a well-known technique in human computer interaction. However, there is a lack of rigorous empirical research evaluating personas relative to other methods. In this 34-participant experiment, we compare a persona system and an analytics system, both using identical user data, for efficiency and effectiveness for a user identification task. Results show that personas afford faster task completion than the analytics system, as well as outperforming analytics with significantly higher user identification accuracy. Qualitative analysis of think-aloud transcripts shows that personas have other benefits regarding learnability and consistency. However, the analytics system affords insights and capabilities that personas cannot due to inherent design differences. Findings support the use of personas to learn about users, empirically confirming some of the stated benefits in the literature, while also highlighting the limitations of personas that may necessitate the use of accompanying methods.
Malicious Text Identification: Deep Learning from Public Comments and Emails

Identifying internet spam has been a challenging problem for decades. Several solutions have succeeded to detect spam comments in social media or fraudulent emails. However, an adequate strategy for filtering messages is difficult to achieve, as these messages resemble real communications. From the Natural Language Processing (NLP) perspective, Deep Learning models are a good alternative for classifying text after being preprocessed. In particular, Long Short-Term Memory (LSTM) networks are one of the models that perform well for the binary and multi-label text classification problems. In this paper, an approach merging two different data sources, one intended for Spam in social media posts and the other for Fraud classification in emails, is presented. We designed a multi-label LSTM model and trained it on the joint datasets including text with common bigrams, extracted from each independent dataset. The experiment results show that our proposed model is capable of identifying malicious text regardless of the source. The LSTM model trained with the merged dataset outperforms the models trained independently on each dataset.
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles.

Reinforcement learning (RL) methods have been shown to be capable of learning intelligent behavior in rich domains. However, this has largely been done in simulated domains without adequate focus on the process of building the simulator. In this paper, we consider a setting where we have access to an ensemble of pre-trained and possibly inaccurate simulators (models). We approximate the real environment using a state-dependent linear combination of the ensemble, where the coefficients are determined by the given state features and some unknown parameters. Our proposed algorithm provably learns a near-optimal policy with a sample complexity polynomial in the number of unknown parameters, and incurs no dependence on the size of the state (or action) space. As an extension, we also consider the more challenging problem of model selection, where the state features are unknown and can be chosen from a large candidate set. We provide exponential lower bounds that illustrate the fundamental hardness of this problem, and develop a provably efficient algorithm under additional natural assumptions.
Bringing Context Inside Process Research with Digital Trace Data

Context is usually conceptualized as “external” to a theory or model and treated as something to be controlled or eliminated in empirical research. We depart from this tradition and conceptualize context as permeating processual phenomena. This move is possible because digital trace data are now increasingly available, providing rich and fine-grained data about processes mediated or enabled by digital technologies. This paper introduces a novel method for including fine-grained contextual information from digital trace data within the description of process (e.g., who, what, when, where, why). Adding contextual information can result in a very large number of fine-grained categories of events, which are usually considered undesirable. However, we argue that a large number of categories can make process data more informative for theorizing and that including contextual detail enriches the understanding of processes as they unfold. We demonstrate this by analyzing audit trail data of electronic medical records using ThreadNet, an open source software application developed for the qualitative visualization and analysis of process data. The distinctive contribution of our approach is the novel way in which we contextualize events and action in process data. Providing new, usable ways to incorporate context can help researchers ask new questions about the dynamics of processual phenomena.
Hardware-Assisted Intellectual Property Protection of Deep Learning Models

The protection of intellectual property (IP) rights of well-trained deep learning (DL) models has become a matter of major concern, especially with the growing trend of deployment of Machine Learning as a Service (MLaaS). In this work, we demonstrate the utilization of a hardware root-of-trust to safeguard the IPs of such DL models which potential attackers have access to. We propose an obfuscation framework called Hardware Protected Neural Network (HPNN) in which a deep neural network is trained as a function of a secret key and then, the obfuscated DL model is hosted on a public model sharing platform. This framework ensures that only an authorized end-user who possesses a trustworthy hardware device (with the secret key embedded on-chip) is able to run intended DL applications using the published model. Extensive experimental evaluations show that any unauthorized usage of such obfuscated DL models result in significant accuracy drops ranging from 73.22 to 80.17% across different neural network architectures and benchmark datasets. In addition, we also demonstrate the robustness of proposed HPNN framework against a model fine-tuning type of attack.
Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks

Super-resolution and denoising are ill-posed yet fundamental image restoration tasks. In blind settings, the degradation kernel or the noise level are unknown. This makes restoration even more challenging, notably for learning-based methods, as they tend to overfit to the degradation seen during training. We present an analysis, in the frequency domain, of degradation-kernel overfitting in super-resolution and introduce a conditional learning perspective that extends to both super-resolution and denoising. Building on our formulation, we propose a stochastic frequency masking of images used in training to regularize the networks and address the overfitting problem. Our technique improves state-of-the-art methods on blind super-resolution with different synthetic kernels, real super-resolution, blind Gaussian denoising, and real-image denoising.
Integrated and adaptive AR maintenance and shop-floor rescheduling

By virtue of the Digitalization of Manufacturing Systems, the importance of interconnecting multiple digital systems is becoming a necessity. By extension, the need for connecting the shop-floor technician to the rest of the production system is highlighted more than ever. Internet and Communication technologies (ICT) may connect the technician with data collected from the machine sensors, knowledge repositories and integrated production software. Mobile devices offer highly usable interfaces that can be exploited to involve the operator in this process, digitalize their inputs and also visualize machine information on top of the physical system through Augmented Reality (AR). Towards that direction, an application that allows the operator to monitor machine status and call event-driven AR remote machine maintenance and rescheduling based on maintenance time estimation is developed. The framework is tested and validated in a laboratory-base machine shop.
Campus Violence Detection Based on Artificial Intelligent Interpretation of Surveillance Video Sequences

Campus violence is a common social phenomenon all over the world, and is the most harmful type of school bullying events. As artificial intelligence and remote sensing techniques develop, there are several possible methods to detect campus violence, e.g., movement sensor-based methods and video sequence-based methods. Sensors and surveillance cameras are used to detect campus violence. In this paper, the authors use image features and acoustic features for campus violence detection. Campus violence data are gathered by role-playing, and 4096-dimension feature vectors are extracted from every 16 frames of video images. The C3D (Convolutional 3D) neural network is used for feature extraction and classification, and an average recognition accuracy of 92.00% is achieved. Mel-frequency cepstral coefficients (MFCCs) are extracted as acoustic features, and three speech emotion databases are involved. The C3D neural network is used for classification, and the average recognition accuracies are 88.33%, 95.00%, and 91.67%, respectively. To solve the problem of evidence conflict, the authors propose an improved Dempster–Shafer (D–S) algorithm. Compared with existing D–S theory, the improved algorithm increases the recognition accuracy by 10.79%, and the recognition accuracy can ultimately reach 97.00%.
Advanced Color Edge Detection Using Clifford Algebra in Satellite Images

Edge detection is widely used for image processing to improve the detection and classification of objects, segmentation, and extraction of other features.Satellite images are rich in information about objects with different color intensity and have a large amount of noise, so it is difficult to achieve recognition, classification, and feature extraction of small objects through traditional edge detection algorithms.The colors in satellite images suffer from a large amount of overlap due to areas or weather conditions that generate a lot of noise.Edge detection provides detailed information about objects in an image by reducing unnecessary feature information.Edge detection in color images is more challenging than edge detection in gray-level images.This paper proposes a method for the edge detection of color images using Clifford algebra and its sub-algebra, quaternions.Quaternion-based Fourier transform is used to process red, green and blue (RGB) images separately in the vector field.A 3×3 quaternion mask is developed to filter out frequencies of the image in multiple directions and only provides details about the edges.The algorithm works on three channels individually; the output is then processed through quaternion Fourier transform (QFT) and inverse QFT with a 3×3 mask to filter high frequencies.The proposed algorithm is compared with traditional edge detection algorithms using a satellite image dataset that has different types of objects and detailed information.Results are validated through entropy, structure similarity, and noise error to prove that our proposed algorithm provides satisfactory performance on different remote sensed images.
Efficient Automated Processing of the Unstructured Documents Using Artificial Intelligence: A Systematic Literature Review and Future Directions

The unstructured data impacts 95% of the organizations and costs them millions of dollars annually. If managed well, it can significantly improve business productivity. The traditional information extraction techniques are limited in their functionality, but AI-based techniques can provide a better solution. A thorough investigation of AI-based techniques for automatic information extraction from unstructured documents is missing in the literature. The purpose of this Systematic Literature Review (SLR) is to recognize, and analyze research on the techniques used for automatic information extraction from unstructured documents and to provide directions for future research. The SLR guidelines proposed by Kitchenham and Charters were adhered to conduct a literature search on various databases between 2010 and 2020. We found that: 1. The existing information extraction techniques are template-based or rule-based, 2. The existing methods lack the capability to tackle complex document layouts in real-time situations such as invoices and purchase orders, 3. The datasets available publicly are task-specific and of low quality. Hence, there is a need to develop a new dataset that reflects real-world problems. Our SLR discovered that AI-based approaches have a strong potential to extract useful information from unstructured documents automatically. However, they face certain challenges in processing multiple layouts of the unstructured documents. Our SLR brings out conceptualization of a framework for construction of high-quality unstructured documents dataset with strong data validation techniques for automated information extraction. Our SLR also reveals a need for a close association between the businesses and researchers to handle various challenges of the unstructured data analysis.
How large is the quantum enhancement of two-photon absorption by time-frequency entanglement of photon pairs?

We present a theoretical proof that the “quantum enhancement” of two-photon absorption, thought to be a means to improve molecular spectroscopy and imaging, is tightly bounded by the physics of photonic entanglement and nonlinear response.
Algorithmic Error Mitigation Scheme for Current Quantum Processors

We present a hardware agnostic error mitigation algorithm for near term quantum processors inspired by the classical Lanczos method. This technique can reduce the impact of different sources of noise at the sole cost of an increase in the number of measurements to be performed on the target quantum circuit, without additional experimental overhead. We demonstrate through numerical simulations and experiments on IBM Quantum hardware that the proposed scheme significantly increases the accuracy of cost functions evaluations within the framework of variational quantum algorithms, thus leading to improved ground state calculations for quantum chemistry and physics problems beyond state-of-the-art results.