Reviewerly/exHarmony huggingface dataset:
Facial Expression Recognition Method Based on Shallow Small Convolution Kernel Capsule Network

Aiming at the problem of CNNs recognition and classification ability for the facial expression cannot accurately express the relative position relationship of facial organs and the problem of network training parameters, so a shallow and small convolution kernel capsule network structure optimization is designed for facial expression recognition. In this method, the capsule network is used to establish the spatial relationship of facial expression features. On the premise of ensuring the accuracy of the network, a shallow small convolution kernel is used to reduce the network parameters of the capsule network, which optimizes the quantity and quality of capsule of the capsule network to improve the calculation efficiency. The SCapsNet proposed in this paper is compared and verified with multiple benchmark models. The research results show that the SCapsNet has a higher accuracy rate in processing facial expression classification problems on the facial expression data sets CK+ and JAFFE.
Analysis of Cloud Forensics Techniques for Emerging Technologies

Cloud computing is the technology of the modern era having invaluable features that convince enterprises to adopt. It provides opportunities to the enterprises to compete with their opponents, to facilitate their customers, and grow up their day to day business by reducing cost and infrastructure management burden. Regardless of the enormous features offered by cloud computing, enterprises generally hesitate to adopt it due to the physical inaccessibility and deployment of their intellectual properties to a location that is unknown to them. On the other hand, the cyber-criminals sniffing around to find the loopholes to breach in, and compromise the confidentiality, integrity, and availability of the resources managed by Cloud Service Provider (CSP). The emergence of the Internet of Things (IoT) and Cyber-Physical Cloud Systems (CPCS) where the data is processed and stored in the cloud environment, demands the need to tighten up the security of the cloud itself. Identifying and tracing the source of a cyber-attack plays an integral role in the investigation process. Digital forensics in the cloud termed Cloud Forensics which boosts the confidence of the stack holders by reconstructing the past cloud events to give a hypothetical view of the crime scene and describe how the malicious activity had happened? The collection of evidence especially in the cloud environment is the most complex task due to its scattered architecture. Moreover, the collected evidence was used for the investigation to shield the cloud environment from a future security breach and bring the cybercriminal to the court of justice. In this paper, we have presented an analysis of cloud forensics techniques and their practical challenges/limitations to cope with cloud forensic investigators.
At-Scale Sparse Deep Neural Network Inference With Efficient GPU Implementation

This paper presents GPU performance optimization and scaling results for inference models of the Sparse Deep Neural Network Challenge 2020. Demands for network quality have increased rapidly, pushing the size and thus the memory requirements of many neural networks beyond the capacity of available accelerators. Sparse deep neural networks (SpDNN) have shown promise for reining in the memory footprint of large neural networks. However, there is room for improvement in implementing SpDNN operations on GPUs. This work presents optimized sparse matrix multiplication kernels fused with the ReLU function. The optimized kernels reuse input feature maps from the shared memory and sparse weights from registers. For multi-GPU parallelism, our SpDNN implementation duplicates weights and statically partition the feature maps across GPUs. Results for the challenge benchmarks show that the proposed kernel design and multi-GPU parallelization achieve up to 180 TeraEdges per second inference throughput. These results are up to 4.3x faster for a single GPU and an order of magnitude faster at full scale than those of the champion of the 2019 Sparse Deep Neural Network Graph Challenge for the same generation of NVIDIA V100 GPUs. Using the same implementation <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> Our code is open-source at https://github.com/merthidayetoglu/SpDNN_Challenge2020, we also show single-GPU throughput on NVIDIA A100 is 2.37x faster than V100.
Building an Oral and Written Learner Corpus of a School Programme: Methodological Issues

The importance of methodological transparency in Learner Corpus Research is paramount for correct understanding and interpretation of knowledge created using corpora. Increasing calls for open-access corpora amplify the need for transparency. This paper details a pilot study conducted to understand the importance of methodological decisions on the subsequent corpus by examining (a) the effects of language used to provide instructions on text length and L1 use and (b) the chosen elicitation tasks in terms of student interest and ability to differentiate proficiency across school grade levels. Furthermore, it details transcription and annotation challenges that had to be addressed. The aim of the chapter is to demonstrate the numerous decisions and their effects at all stages of corpus building, and the importance of working towards transparent reporting of methodologies and standardisation of transcription and annotation where possible.
Coupling of Random Systems

This paper makes three contributions. First, we present a simple theory of random systems. The main idea is to think of a probabilistic system as an equivalence class of distributions over deterministic systems. Second, we demonstrate how in this new theory, the optimal information-theoretic distinguishing advantage between two systems can be characterized merely in terms of the statistical distance of probability distributions, providing a more elementary understanding of the distance of systems. In particular, two systems that are $$\epsilon $$ -close in terms of the best distinguishing advantage can be understood as being equal with probability $$1-\epsilon $$ , a property that holds statically, without even considering a distinguisher, let alone its interaction with the systems. Finally, we exploit this new characterization of the distinguishing advantage to prove that any threshold combiner is an amplifier for indistinguishability in the information-theoretic setting, generalizing and simplifying results from Maurer, Pietrzak, and Renner (CRYPTO 2007).
Situating Robo-Advisory

Academic research into robo-advisory remains in its early stages. The debate has so far focused on (1) definitorial questions regarding the nature of robo-advisory, (2) an evaluation of competitive differentiation based on strengths and weaknesses, (3) strategic options for both legacy service providers and robo-advisors to compete, as well as (4) design and calibration of robo-advisors to provide maximum value to customers. Key insights generated so far support four conclusions. Robo-advisory is so far understood as a phenomenon whose competitive position will be defined by the automizability of processes. Therefore, robo-advisors are expected to focus on standardized low-complexity services targeting the lower- and mid-income ranges, while traditional advisory is expected to focus on high-complexity tasks provided to affluent customers. Traditional advisory is seen to have multiple strategic options to face attacks from challengers, primarily either taking a wait-and-do-nothing approach or engaging in strategic acquisitions to complement service offerings. Factors driving future success are expected to be the ease of interaction, work efficiency, information processing, and, most importantly, transparency, especially regarding pricing of advisory offerings.
Time-aware Service Recommendation Based on Dynamic Preference and QoS

The historical data of services usage shows us that both user preference and service quality are dynamic, and service quality has a certain influence on user preference. Due to the dynamic characteristics of both user preference and quality of service (QoS), how to recommend the best suitable services to users has become an urgent problem to be solved. But the most service recommendation approaches neglect the cyclical feature in dynamic preference model, and also neglect the impact of QoS on the user preference. We propose a time-aware recommendation method which considers the dynamic preference, the dynamic QoS and the impact of QoS on user preference comprehensively. Our experiments conducted on the real-world dataset WS-Dream, and the results show that our proposed approach outperforms several classical approaches and state-of-the-art approaches in terms of accuracy, recall, F1-value and Hamming distance.
Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field [...]
Automatic Semantic Analysis Framework of Dickinson’s Portfolio based on Character Recognition and Artificial Intelligence

Automatic semantic analysis framework of the Dickinson's portfolio based on the character recognition and artificial intelligence is designed in this paper. Because of the dynamic nature of the information source and the problem on updating and saving the potentially useful information, where the information is often ambiguous and also sometimes erroneous. Inspired by this, 2 novelties are proposed. (1) Multi search engine integrates multiple search engines and provides a unified search interface. It sends a query to multiple search engines at the same time and the system is optimized for efficiency. (2) After the character image is preprocessed, a series of the closed contour lines with length greater than the certain threshold are obtained. The AI model is used to analyze the information. The experiment results have shown a better performance.
An Immersive Mobile Application For Improved Learning and Virtual Tour Experience: A Nature Reserve Perspective

The purpose of this study was to develop an immersive virtual reality application for the University of Western Cape's nature reserve in South Africa. For this focus on a nature reserve project, the project team was requested to build a self-guided tour capable of achieving knowledge transfer, and which has aesthetic pleasure. The study was informed by the peculiar challenge of the nature reserve and existing literature to identify gaps that may occur in the body of knowledge. The scrum project methodology was used to manage the life cycle of the project. The application was successfully built within the given time frame and the client's feedback was overwhelmingly positive.
No-Reference Stereoscopic Image Quality Assessment Considering Multi-loss Constraints

In this paper, a three-channel convolutional neural network (CNN) constrained by multiple loss functions is designed for stereoscopic image quality assessment (SIQA). Given that both monocular and binocular information are crucial for SIQA, we take the patches of left images, right images and difference images as the inputs of the three channels respectively. Since using the ground truth as the labels of image patches cannot accurately characterize their quality, we propose to individually label each image patch to preserve the quality difference among different regions and views. Moreover, the multi-loss structure is adopted in the proposed method to consider both local features and global features simultaneously, which can constrain the feature learning from multiple perspectives. And the additional adaptive loss weights make the multi-loss network more flexible and universal. The experimental results show that the proposed method is superior to other existing SIQA methods with state-of-the-art performance.
An Accurate Registration Method Based on Global Mixed Structure Similarity (GMSIM) for Remote Sensing Images

Although remote sensing image registration has been studied for several years, achieving accurate image registration remains a challenging task due to the complicated conditions surrounding remote sensing images. To improve the accuracy and robustness of image registration, we proposed a registration method based on the global mixed structure similarity (GMSIM) measure. This measure mixes the structure similarity in both the frequency domain and the intensity domain because phase-based structure similarity in the frequency domain is sensitive to intensity contrast and spatial translation, and gray-based structure similarity in the intensity domain is efficient to structure change. Feature-based registration methods are used to generate the initial registration parameters. After that, we calculate the final registration parameters by maximizing GMSIM. Quantum-behaved particle swarm optimization (QPSO) is utilized to solve the optimal results of GMSIM due to its high efficiency. The proposed method has been evaluated on several remote sensing images differing in scale, gray, and scene and compared with three state-of-the-art registration methods. Experimental results demonstrate the high accuracy of the proposed scheme.
Bayesian profiling multiple imputation for missing hemoglobin values in electronic health records

Electronic health records (EHRs) are increasingly used for clinical and comparative effectiveness research, but suffer from missing data. Motivated by health services research on diabetes care, we seek to increase the quality of EHRs by focusing on missing values of longitudinal glycosylated hemoglobin (A1c), a key risk factor for diabetes complications and adverse events. Under the framework of multiple imputation (MI), we propose an individualized Bayesian latent profiling approach to capture A1c measurement trajectories subject to missingness. The proposed method is applied to EHRs of adult patients with diabetes in a large academic Midwestern health system between 2003 and 2013 and had Medicare A and B coverage. We combine MI inferences to evaluate the association of A1c levels with the incidence of acute adverse health events and examine patient heterogeneity across identified patient profiles. We investigate different missingness mechanisms and perform imputation diagnostics. Our approach is computationally efficient and fits flexible models that provide useful clinical insights.
Show, Attend to Everything, and Tell: Image Captioning with More Thorough Image Understanding

Image captioning is one of the most important cross-modal tasks in machine learning. Attention-based encoder-decoder frameworks have been utilized for this task, abundantly. For visual understanding of an image, via the encoder, most of these networks use the last convolutional layer of a network designed for some computer vision tasks. There are several downsides to that. First, these models are specialized to detect certain objects from the image. Thus, when we get deeper into the network, the network focuses on these objects, becoming almost blind to the rest of the image. These blindspots of the encoder sometimes are where the next word in the caption lies. Moreover, many words in the caption are not included in the target classes of these tasks, such as "snow". having this observation in mind, in order to reduce the blind spots of the last convolutional layer of the encoder, we propose a novel method to reuse other convolutional layers of the encoder. Doing so provides us diverse features of the image while not neglecting almost any part of the image and hence, we "attend to everything" in the image. Using the flickr30k [1] dataset, we evaluate our method and demonstrate comparable results with the state-of-the-art, even with simple attention mechanisms.
Lie Another Day: Demonstrating Bias in a Multi-round Cyber Deception Game of Questionable Veracity

Prior work has explored the use of defensive cyber deception to manipulate the information available to attackers and to proactively lie on behalf of both real and decoy systems. Such approaches can provide advantages to defenders by delaying attacker forward progress and thereby decreasing or eliminating attacker payoffs. In this work, we expand previous work by incorporating new parameters relating to attacker costs and choices. The extended model includes attacker costs for probing a system to learn its declared type (“real” or “fake”) and allows an attacker to proactively choose to leave the game early by walking away. While these additional parameters represent extensions to our prior model, they are key to understanding attacker behavior when confronted with deceptive cyber defenses. We first present the extended model and an analysis of the expected rewards for rational players. We then present the behavior of an adaptive attacker in a Markov Decision Process (MDP) simulation. Lastly, we relate our analytic and empirical findings to cognitive bias effects and speculate on how the manipulation of game parameters may be used in future work to both estimate and trigger bias effects during defender-attacker interactions.
Anticipating Identification of Technical Debt Items in Model-Driven Software Projects

Model-driven development (MDD) and Technical Debt (TD) are software engineering approaches that look for promoting the quality of systems under development. Most research on TD focuses on application code as primary TD sources. In an MDD project, however, dealing with technical debt only on the source code may not be an adequate strategy because MDD projects should focus their software building efforts on models. Besides, in MDD projects, code generation is often done at a later stage than creating models, then dealing with TD only in source code can lead to unnecessary interest payments due to unmanaged debts, such as model and source codes artifacts desynchronization. Recent works concluded that MDD project codes are not technical debt free, making it necessary to investigate the possibility and benefits of applying TD identification techniques in earlier stages of the development process, such as in modeling phases. The use of TD concept in an MDD context is also known as Model-Driven Technical Debt (MDTD). This paper intends to analyze whether it is possible to use source code technical debt detection strategies to identify TD on code-generating models in the context of model-driven development projects. A catalog of nine different model technical debt items for platform-independent code-generating models was specified. An evaluation was performed to observe the effectiveness of the proposed catalog compared to existing source code identification techniques found in the literature. Through three different open source software projects, more than 78 thousand lines of code were investigated. Results revealed that, although the catalog items present different precision rates, it is possible to identify these model-driven technical debts before source code is generated. We hope that sharing this catalog version provides future contributions and improvements.
APPLICATION OF OPINION CLASSIFICATION FOR FASTFOOD RESTAURANT POPULARITY USING INSTAGRAM

Abstract The purpose of this study is to identify several opinions regarding Instagram account of fast food restaurant in Indonesia. By using a system that can provide the information about the popularity ranking of fast food restaurants, researcher conduct an analysis using Naïve Bayes Classifier. To identify the popularity ranking, researcher collected restaurants data from Instagram, preprocessing text, using Naïve Bayes classification, and implementing the programing. The design of this system be able to determine the popularity ranking of fast food restaurants using collected data (number of likes and comments on Instagram’s post). All of the predictions obtained exceeds of 90%, indicating a fairly accurate prediction of the popularity ranking, where all results almost touch the maximum score. Data collected from the restaurants’ Instagram account, indicating positive results and ratings, based on number of likes and comments per post. The result of this research shows that, Burger King restaurants are the most popular, second is Kentucky Fried Chicken, third is McDonalds, fourth is Hokben, and fifth are Wingstop..
Recovering Architectural Variability from Source Code

Context: Systematic variability management helps efficiently manage commonalities and differences in software systems (e.g., in software product lines and families). This enables the reuse of development artifacts in organizations and increases the quality of product variants. In software product lines, the product line architecture (PLA) is the core architecture for all product line variants. In practice, software architectures are often not documented in detail. Architecture recovery techniques can recover a system's architecture from development artifacts (e.g., source code). To recover the architecture of product lines, we need recovery techniques that are able to identify variability from different sources. Goal: We present SAVaR, an approach to recover architectural variability from the source code of product variants of a product line. SAVaR aims to help developers to (a) create architectural documentation for a product line, and (b) understand and improve the implementation of variability. SAVaR identifies the smallest subset of architectural information that is common across products of a product line. To limit the explosion of variability (and hence the complexity of architecture documentation) in the product line architecture, SAVaR allows architects to exclude architecture elements that appear in only a few product variants. Method: We performed an exploratory study with SAVaR to recover the architectures in ten academic product line projects. We verified how the elimination of exclusive optional modules improves the results of SAVaR. Results: The results showed that SAVaR is able to present improvements for the recovered PLAs and it helped to identify that some projects maintained the variability under control.
Improve performance of extreme learning machine in classification of patchouli varieties with imbalanced class

Patchouli has various varieties with almost the same physical characteristics. This often makes it difficult to recognize varieties with a high PA (Patchouli Alcohol) content. In this study an improvisation was introduced in the identification of patchouli varieties using leaf images using Extreme Learning Machine (ELM). However, problems occur in ELM if the data used is not balanced where the training process can not able to recognize data in the minority class well. Therefore, this study conducted a process to balance the composition of the data using the Synthetic Minority Over-sampling Technique (SMOTE) method. The test results of 93 data on the imbalanced composition with a comparison of 70% of training data and 30% of test data obtained an average accuracy of 93.57%. After implementing SMOTE in the Tetraploid, Patchoulina and Sidikalang classes where the amount of data in each class becomes 58 data, an average accuracy of 96.00% is achieved. This shows the existence of an increase in the process of identification with ELM when new data generation with SMOTE is carried out to balance the composition of the data.