Reviewerly/exHarmony huggingface dataset:
Integrity verification of Docker containers for a lightweight cloud environment

Virtualisation techniques are growing in popularity and importance, given their application to server consolidation and to cloud computing. Remote Attestation is a well-known technique to assess the software integrity of a node. It works well with physical platforms, but not so well with virtual machines hosted in a full virtualisation environment (such as the Xen hypervisor or Kernel-based Virtual Machine) and it is simply not available for a lightweight virtualisation environment (such as Docker). On the contrary, the latter is increasingly used, especially in lightweight cloud platforms, because of its flexibility and limited overhead as compared to virtual machines. This paper presents a solution for security monitoring of a lightweight cloud infrastructure, which exploits Remote Attestation to verify the software integrity of cloud applications during their whole life-cycle. Our solution leverages mainstream tools and architectures, like the Linux Integrity Measurement Architecture, the OpenAttestation platform and the Docker container engine, making it practical and readily available in a real-world scenario. Compared to a standard Docker deployment, our solution enables run-time verification of container applications at the cost of a limited overhead.
Appearance-Based Gaze Estimation Using Dilated-Convolutions

Appearance-based gaze estimation has attracted more and more attention because of its wide range of applications. The use of deep convolutional neural networks has improved the accuracy significantly. In order to improve the estimation accuracy further, we focus on extracting better features from eye images. Relatively large changes in gaze angles may result in relatively small changes in eye appearance. We argue that current architectures for gaze estimation may not be able to capture such small changes, as they apply multiple pooling layers or other downsampling layers so that the spatial resolution of the high-level layers is reduced significantly. To evaluate whether the use of features extracted at high resolution can benefit gaze estimation, we adopt dilated-convolutions to extract high-level features without reducing spatial resolution. In cross-subject experiments on the Columbia Gaze dataset for eye contact detection and the MPIIGaze dataset for 3D gaze vector regression, the resulting Dilated-Nets achieve significant (up to 20.8%) gains when compared to similar networks without dilated-convolutions. Our proposed Dilated-Net achieves state-of-the-art results on both the Columbia Gaze and the MPIIGaze datasets.
A Near Real-Time Automatic Speaker Recognition Architecture for Voice-Based User Interface

In this paper, we present a novel pipelined near real-time speaker recognition architecture that enhances the performance of speaker recognition by exploiting the advantages of hybrid feature extraction techniques that contain the features of Gabor Filter (GF), Convolution Neural Networks (CNN), and statistical parameters as a single matrix set. This architecture has been developed to enable secure access to a voice-based user interface (UI) by enabling speaker-based authentication and integration with an existing Natural Language Processing (NLP) system. Gaining secure access to existing NLP systems also served as motivation. Initially, we identify challenges related to real-time speaker recognition and highlight the recent research in the field. Further, we analyze the functional requirements of a speaker recognition system and introduce the mechanisms that can address these requirements through our novel architecture. Subsequently, the paper discusses the effect of different techniques such as CNN, GF, and statistical parameters in feature extraction. For the classification, standard classifiers such as Support Vector Machine (SVM), Random Forest (RF) and Deep Neural Network (DNN) are investigated. To verify the validity and effectiveness of the proposed architecture, we compared different parameters including accuracy, sensitivity, and specificity with the standard AlexNet architecture.
Automatic Detection of Hate Speech on Facebook Using Sentiment and Emotion Analysis

Hate speech has been an issue since the start of the Internet, but the advent of social media has brought it to unimaginable heights. To address such an important issue, in this paper, we explore a novel framework to effectively detect highly discussed topics that generate hate speech on Facebook. With the use of graph, sentiment, and emotion analysis techniques, we cluster and analyze posts on prominent Facebook pages. Consequently, the proposed framework is able to identify the pages that promote hate speech in the comment sections regarding sensitive topics automatically.
Social Group Identity and Perceptions of Online Hate*

Why do some people find online hate material more disturbing than others? We use a random sample of Americans between the ages 15 and 36 to address this question. Descriptive results indicate that a majority of respondents surveyed find online hate material very or extremely disturbing, while smaller shares find it moderately, slightly, or not at all disturbing. We utilize an ordinal logistic regression to explore factors associated with these varying perceptions of hate material. Results demonstrate that males and political conservatives find hate material less disturbing than females and political moderates or liberals. These results are expected, as online hate is largely dominated by right‐wing extremists who frequently target females and non‐conservatives. We also find that individuals who see hate material more frequently find it more disturbing, as do those who have been the target of hate or criminality online. Finally, individuals who are more accepting of violating social norms are less disturbed by online hate.
Real-value negative selection over-sampling for imbalanced data set learning

The learning problem from imbalanced data set poses a major challenge in data mining community. Conventional machine learning algorithms show poor performance in dealing with the classification problems of imbalanced data set since they are originally designed to work with balanced class distribution. In this paper, we propose a new over-sampling technique, which uses the real-value negative selection (RNS) procedure to generate artificial minority data with no requirement of actual minority data available. The generated minority data with rare actual minority data if available are combined with the majority data as input to a bi-class classification approach for learning. In the experiments, we demonstrate the effectiveness of RNS in avoiding the problems often encountered by the existing over-sampling methods such as the generation of noisy instances and almost duplicated instances in the same clusters. Moreover, the extensive experimental results on the different imbalanced datasets from UCI repository and real-world imbalanced datasets show that when dealing with the classification of imbalanced datasets, the proposed hybrid approach can achieve better performance in terms of both G-Mean and F-Measure evaluation metrics as compared to the other existing imbalanced dataset classification techniques.
Human Body Parts Estimation and Detection for Physical Sports Movements

The main purpose of human body part detection is to estimate the size, orientation or position of the human body parts within the digital scene information. Estimation of various body parts of the human from an image is a critical step for several model-based systems and body-parts tracking. In this paper, body parts detection for pose estimation is implemented. During foreground silhouettes detection, the proposed method have used segmentation techniques to obtained salient region areas and skin tone detection. After successful silhouettes extraction, body parts estimation is applied by using body parts model. Five basic body key points was determined and in addition seven more body sub key points was estimated with the help of five basic body key points. The estimated key points of the body are then represented using circular marks on the original image. The experimental results over two challenging video datasets such as KTH-multiview football and UCF sports action datasets showed significant accuracies of 90.01% and 86.67. The proposed method performs better than the state-of the-art methods in terms of body-parts detection accuracy.
Systematic Literature Review of Sampling Process in Rapidly-Exploring Random Trees

Path planning is one of the most important process on applications such as navigating autonomous vehicles, computer graphics, game development, robotics, and protein folding. It ensures that a path is planned between an initial and final position on the collision-free region of a search space if one exists. One of the most wide algorithms used for this purpose is the rapidly-exploring random tree (RRT), in which each node of a tree data structure is generated from a search space by a random sampling process, which originally follows a uniform spatial distribution. However, some authors claim that the addition of a non-uniform/informed approach into the sampling process of the RRT could accelerate the planning time of the algorithm. Actually, many works on literature propose different strategies to include non-uniform/informed behavior on RRT-based algorithms. However, the large number of studies on path planning subject impose difficulties on the identification of new solutions on a review process. The aim of this paper is to structure a review process to deal with the massive volume of works on this subject, by presenting the planning, development, and results of a systematic literature review (SLR), to investigate non-uniform/informed sampling solutions applied to RRT-based algorithms on path planning literature. A review protocol with two scientific questions was developed to guide the investigation. As a result, 1136 studies were selected in the path planning literature, of which 53 were identified as claiming to contain a solution with non-uniform/informed sampling on RRT-based algorithms. As a specific work is considered a scientific contribution only when it has not yet been explored in scientific circles, the results of the SLR can be used as a tool to search for what has not yet been proposed, helping to identify opportunities to contribute with new sampling processes of RRT-based algorithms. To the best knowledge of the authors, this paper presents the first development of an SLR of a topic related to the RRT algorithm.
Sketching NLP

This paper investigates how to sketch NLP-powered user experiences. Sketching is a cornerstone of design innovation. When sketching, designers rapidly experiment with a number of abstract ideas using simple, tangible instruments such as drawings and paper prototypes. Sketching NLP-powered experiences, however, presents challenges, i.e. How to visualize abstract language interaction? How to ideate a broad range of technically feasible intelligent functionalities? As a first step towards understanding these challenges, we present a first-person account of our sketching process when designing intelligent writing assistance. We detail the challenges we encountered and emergent solutions, such as a new format of wireframe for sketching language interactions and a new wizard-of-oz-based NLP rapid prototyping method. Drawing on these findings, we discuss the importance of abstraction in sketching and other implications.
An Introduction to the Planning Domain Definition Language

Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given
Deep Learning for Recognizing Human Activities Using Motions of Skeletal Joints

With advances in consumer electronics, demands have increased for greater granularity in differentiating and analyzing human daily activities. Moreover, the potential of machine learning, and especially deep learning, has become apparent as research proceeds in applications, such as monitoring the elderly, and surveillance for detection of suspicious people and objects left in public places. Although some techniques have been developed for human action recognition (HAR) using wearable sensors, these devices can place unnecessary mental and physical discomfort on people, especially children and the elderly. Therefore, research has focused on image-based HAR, placing it on the front line of developments in consumer electronics. This paper proposes an intelligent HAR system which can automatically recognize the human daily activities from depth sensors using human skeleton information, combining the techniques of image processing and deep learning. Moreover, due to low computational cost and high accuracy outcomes, an approach using skeleton information has proven very promising, and can be utilized without any restrictions on environments or domain structures. Therefore, this paper discusses the development of an effective skeleton information-based HAR which can be used as an embedded system. The experiments are performed using two famous public datasets of human daily activities. According to the experimental results, the proposed system outperforms other state-of-the-art methods on both datasets.
Reshaping inputs for convolutional neural network: Some common and uncommon methods

Convolutional Neural Network has become very common in the field of computer vision in recent years. But it comes with a severe restriction regarding the size of the input image. Most convolutional neural networks are designed in a way so that they can only accept images of a fixed size. This creates several challenges during data acquisition and model deployment. The common practice to overcome this limitation is to reshape the input images so that they can be fed into the networks. Many standard pre-trained networks and datasets come with a provision of working with square images. In this work we analyze 25 different reshaping methods across 6 datasets corresponding to different domains trained on three famous architectures namely Inception-V3, which is an extension of GoogLeNet, the Residual Networks (Resent-18) and the 121-Layer deep DenseNet. While some of the reshaping methods like “interpolation” and “cropping” have been commonly used with convolutional neural networks, some uncommon techniques like “containing”, “tiling” and “mirroring” have also been demonstrated. In total, 450 neural networks were trained from scratch to provide various analyses regarding the convergence of the validation loss and the accuracy obtained on the test data. Statistical measures have been provided to demonstrate the dependence between parameter choices and datasets. Several key observations were noted such as the benefits of using randomized processes, poor performance of the commonly used “cropping” techniques and so on. The paper intends to provide empirical evidence to guide the reader to choose a proper technique of reshaping inputs for their convolutional neural networks. The official code is available in https://github.com/DVLP-CMATERJU/Reshaping-Inputs-for-CNN.
Survey on Revocation in Ciphertext-Policy Attribute-Based Encryption

Recently, using advanced cryptographic techniques to process, store, and share datasecurely in an untrusted cloud environment has drawn widespread attention from academicresearchers. In particular, Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is a promising,advanced type of encryption technique that resolves an open challenge to regulate fine-grainedaccess control of sensitive data according to attributes, particularly for Internet of Things (IoT)applications. However, although this technique provides several critical functions such as dataconfidentiality and expressiveness, it faces some hurdles including revocation issues and lack ofmanaging a wide range of attributes. These two issues have been highlighted by many existingstudies due to their complexity which is hard to address without high computational cost affectingthe resource-limited IoT devices. In this paper, unlike other survey papers, existing single andmultiauthority CP-ABE schemes are reviewed with the main focus on their ability to address therevocation issues, the techniques used to manage the revocation, and comparisons among themaccording to a number of secure cloud storage criteria. Therefore, this is the first review paperanalysing the major issues of CP-ABE in the IoT paradigm and explaining the existing approachesto addressing these issues.
Geographic Knowledge Graph (GeoKG): A Formalized Geographic Knowledge Representation

Formalized knowledge representation is the foundation of Big Data computing, mining and visualization. Current knowledge representations regard information as items linked to relevant objects or concepts by tree or graph structures. However, geographic knowledge differs from general knowledge, which is more focused on temporal, spatial, and changing knowledge. Thus, discrete knowledge items are difficult to represent geographic states, evolutions, and mechanisms, e.g., the processes of a storm “{9:30-60 mm-precipitation}-{12:00-80 mm-precipitation}-…”. The underlying problem is the constructors of the logic foundation (ALC description language) of current geographic knowledge representations, which cannot provide these descriptions. To address this issue, this study designed a formalized geographic knowledge representation called GeoKG and supplemented the constructors of the ALC description language. Then, an evolution case of administrative divisions of Nanjing was represented with the GeoKG. In order to evaluate the capabilities of our formalized model, two knowledge graphs were constructed by using the GeoKG and the YAGO by using the administrative division case. Then, a set of geographic questions were defined and translated into queries. The query results have shown that GeoKG results are more accurate and complete than the YAGO’s with the enhancing state information. Additionally, the user evaluation verified these improvements, which indicates it is a promising powerful model for geographic knowledge representation.
E-LSTM: An Efficient Hardware Architecture for Long Short-Term Memory

Long Short-Term Memory (LSTM) and its variants have been widely adopted in many sequential learning tasks, such as speech recognition and machine translation. Significant accuracy improvements can be achieved using complex LSTM model with a large memory requirement and high computational complexity, which is time-consuming and energy demanding. The low-latency and energy-efficiency requirements of the real-world applications make model compression and hardware acceleration for LSTM an urgent need. In this paper, several hardware-efficient network compression schemes are introduced first, including structured top- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> pruning, clipped gating, and multiplication-free quantization, to reduce the model size and the number of matrix operations by 32 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> and 21.6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> , respectively, with negligible accuracy loss. Furthermore, efficient hardware architectures for accelerating the compressed LSTM are proposed, which support the inference of multi-layer and multiple time steps. The computation process is judiciously reorganized and the memory access pattern is well optimized, which alleviate the limited memory bandwidth bottleneck and enable higher throughput. Moreover, the parallel processing strategy is carefully designed to make full use of the sparsity introduced by pruning and clipped gating with high hardware utilization efficiency. Implemented on Intel Arria10 S <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 660 FPGA running at 200MHz, the proposed design is able to achieve 1.4–2.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> energy efficiency and requires significantly less hardware resources compared with the state-of-the-art LSTM implementations.
"If you want, I can store the encrypted password"

In 2017 and 2018, Naiakshina et al. (CCS'17, SOUPS'18) studied in a lab setting whether computer science students need to be told to write code that stores passwords securely. The authors' results showed that, without explicit prompting, none of the students implemented secure password storage. When asked about this oversight, a common answer was that they would have implemented secure storage - if they were creating code for a company. To shed light on this possible confusion, we conducted a mixed-methods field study with developers. We hired freelance developers online and gave them a similar password storage task followed by a questionnaire to gain additional insights into their work. From our research, we offer two contributions. First of all, we reveal that, similar to the students, freelancers do not store passwords securely unless prompted, they have misconceptions about secure password storage, and they use outdated methods. Secondly, we discuss the methodological implications of using freelancers and students in developer studies.
Differential Evolution for Multimodal Optimization With Species by Nearest-Better Clustering

Multimodal optimization problems (MMOPs) are common in real-world applications and involve identifying multiple optimal solutions for decision makers to choose from. The core requirement for dealing with such problems is to balance the ability of exploration in the global space and exploitation in the multiple optimal areas. In this paper, based on the differential evolution (DE), we propose a novel algorithm focusing on the formulation, balance, and keypoint of species for MMOPs, called FBK-DE. First, nearest-better clustering (NBC) is used to divide the population into multiple species with minimum size limitations. Second, to avoid placing too many individuals into one species, a species balance strategy is proposed to adjust the size of each species. Third, two keypoint-based mutation operators named DE/keypoint/1 and DE/keypoint/2 are proposed to evolve each species together with traditional mutation operators. The experimental results of FBK-DE on 20 benchmark functions are compared with 15 state-of-the-art multimodal optimization algorithms. The comparisons show that the proposed FBK-DE performs competitively with these algorithms.
Investigating Slowness as a Frame to Design Longer-Term Experiences with Personal Data

We describe the design and deployment of Olly, a domestic music player that enables people to re-experience digital music they listened to in the past. Olly uses its owner's Last.FM listening history metadata archive to occasionally select a song from their past, but offers no user control over what is selected or when. We deployed Olly in 3 homes for 15 months to explore how its slow pace might support experiences of reflection and reminiscence. Findings revealed that Olly became highly integrated in participants lives with sustained engagement over time. They drew on Olly to reflect on past life experiences and reactions indicated an increase in perceived value of their Last.FM archive. Olly also provoked reflections on the temporalities of personal data and technology. Findings are interpreted to present opportunities for future HCI research and practice.