Reviewerly/exHarmony huggingface dataset:
Plugin Networks for Inference under Partial Evidence

In this paper, we propose a novel method to incorporate partial evidence in the inference of deep convolutional neural networks. Contrary to the existing, top-performing methods, which either iteratively modify the input of the network or exploit external label taxonomy to take the partial evidence into account, we add separate network modules ("Plugin Networks") to the intermediate layers of a pretrained convolutional network. The goal of these modules is to incorporate additional signal, i.e. information about known labels, into the inference procedure, and adjust the predicted output accordingly. Since the attached plugins have a simple structure, consisting of only fully connected layers, we drastically reduced the computational cost of training and inference. Also, the proposed architecture allows propagating information about known labels directly to the intermediate layers to improve the final representation. Extensive evaluation of the proposed method confirms that our Plugin Networks outperform the state-of-the-art in a variety of tasks, including scene categorization, multilabel image annotation, and semantic segmentation.
Applications of Deep Learning in Arabic Sentiment Analysis: Research Perspective

With the development and emergence of the concept of the Web, as well as the advancement of social networks, forums, blogs and E-commerce a growing number of Arabs express their views and opinions using their native language on those platforms where we find a wealth of information that could be exploited. This information, whose size is expanding and which describes opinions, has become the subject of several research fields particularly: “Sentiment Analysis” or “Opinion Mining”. Sentiment Analysis considered a subfield of natural language processing (NLP), information retrieval and text mining. Currently, sentiment analysis is commonly used for English, while it is in its infancy in the Arabic language. The most important and critical step in feeling analysis is to select an appropriate technique for classifying feelings. In this paper, we will be reviewing the published work on the application of Deep Learning (DL) techniques to the problems of sentiment analysis. We focus on the Arabic language because of its importance, the large number of its speakers, the scarcity of Arabic resources on it and the challenges related to its work. We explain, categorize, summarize and compare the techniques offered in this area.
Comparing manual and automated feature location in conceptual models: A Controlled experiment

Maintenance activities cannot be completed without locating the set of software artifacts that realize a particular feature of a software system. Manual Feature Location (FL) is widely used in industry, but it becomes challenging (time-consuming and error prone) in large software repositories. To reduce manual efforts, automated FL techniques have been proposed. Research efforts in FL tend to make comparisons between automated FL techniques, ignoring manual FL techniques. Moreover, existing research puts the focus on code, neglecting other artifacts such as models. This paper aims to compare manual FL against automated FL in models to answer important questions about performance, productivity, and satisfaction of both treatments. We run an experiment for comparing manual and automated FL on a set of 18 subjects (5 experts and 13 non-experts) in the domain of our industrial partner, BSH, manufacturer of induction hobs for more than 15 years. We measure performance (recall, precision, and F-measure), productivity (ratio between F-measure and spent time), and satisfaction (perceived ease of use, perceived usefulness, and intention to use) of both treatments, and perform statistical tests to assess whether the obtained differences are significant. Regarding performance, manual FL significantly outperforms automated FL in precision and F-measure (up to 27.79% and 19.05%, respectively), whereas automated FL significantly outperforms manual FL in recall (up to 32.18%). Regarding productivity, manual FL obtains 3.43%/min, which improves automated FL significantly. Finally, there are no significant differences in satisfaction for both treatments. The findings of our work can be leveraged to advance research to improve the results of manual and automated FL techniques. For instance, automated FL in industry faces issues such as low discrimination capacity. In addition, the obtained satisfaction results have implications for the usage and possible combination of manual, automated, and guided FL techniques.
Advanced encryption of image based on S-box and chaos 2D (LSMCL)

Due to the excellent properties of the chaotic system and its bounded behavior from a nonlinear system, it appears as pseudo-random noise. It is therefore used to camouflage or amalgamate the information sent for secure transmission. This document discusses a new contribution of encryption of color images based on two chaotic logistic maps with 2D modulated sinusoidal coupling (LSMCL). This process goes through several operating cycles. First, we provide a procedure for generating a substitution box using a chaotic 2D map performing a total permutation of the pixels of the original image. In addition, we apply an XOR operator between the second map 2D and the resulting matrix this contributes to a remarkable change in the pixel values and to reinforce the scrambling of the image. We add two transformations grouping the three image channels. One transforms the rows and the other transforms the columns by a progressive shift. Finally, we provide some experimental results and theoretical simulations of our proposed algorithm which demonstrates its performance and efficiency with good protection against any illegal manipulation.
Cyber-Resiliency for Digital Enterprises: A Strategic Leadership Perspective

As organizations increasingly view information as one of their most valuable assets, which supports the creation and distribution of their products and services, information security will be an integral part of the design and operation of organizational business processes. Yet, risks associated with cyber-attacks are on the rise. Organizations that are subjected to attacks can suffer significant reputational damage as well as loss of information and knowledge. As a consequence, effective leadership is cited as a critical factor for ensuring corporate level attention for information security. However, there is a lack of empirical understanding as to the roles strategic leaders play in shaping and supporting the cyber-security strategy. This article seeks to address this gap in the literature by focusing on how senior leaders support the cyber-security strategy. The authors conducted a series of exploratory interviews with leaders in the positions of Chief Information Officer, Chief Security Information Officer, and Chief Technology Officer. The findings revealed that leaders are engaged in both transitional, where the focus is on improving governance and integration and transformational support, which involves fostering a new cultural mindset for cyber-resiliency and the development of an ecosystem approach to security thinking.
A Structural Equation Modeling Approach to Understand the Relationship between Control, Cybersickness and Presence in Virtual Reality

The commercialization of Virtual Reality (VR) devices is making the technology increasingly accessible to users around the world. Despite the success that VR is starting to see with its growing popularity, it has yet to become widely adopted and achieve its ultimate goal- convincingly simulate real life like experiences. The inability to generate adequate levels of presence and to prevent the manifestation of cybersickness are the two prominent barriers that have hindered VR from achieving its ultimate goal. While traditional research has examined factors that influence (correlate with) the onset and severity cybersickness, there is still a gap in our knowledge about the consequences of having motion control on cybersickness in immersive virtual environments (IVE’s) achieved using tracked Head Mounted Displays (HMD’s). Furthermore, outside of a correlational capacity, it is still unclear as to what causes cybersickness to affect presence in immersive virtual environments. The success of immersive virtual reality as a technology will hence largely come down to our ability to understand the interrelationship between these variables and then address the challenges they pose. Towards this end, we investigated how the affordance of motion control affects cybersickness and presence in an HMD based VR driving simulation by conducting a between subjects study where we manipulated the affordance of control between three experimental conditions. We leverage structural equation modeling in an attempt to build a framework that explains the relationship between virtual motion control, workload, cybersickness, time spent in the simulation, perceived time and presence. Our structural model helps explain why motion control could be an important factor to consider in addressing VR’s challenges and realizing its ultimate aim to simulate reality.
Detecting Cyberbullying Activity Across Platforms

This article focuses on cyber security issues related to content. Recent surveys show that youth are being exposed to cyber-aggression at increasing rates, with over 43% of youth reporting in one recent survey that they have been bullied online. While research into this problem has been growing, the research community is hampered by a lack of authentic data for studying communication with and among youth. A large corpus with 800,000 instances of cell phone textual data from youth ages 10–14 has been developed to address this need. This article describes the dataset, as well as plans to enable access to the data while protecting the privacy of the study participants. The results from machine learning experiments for the detection of cyberbullying based on labeled data from several sources, including both SMS and social media messages, are also discussed. These algorithms are shown to be effective at detecting cyberbullying across platforms.
Dynamic imaging inversion with double deep learning networks for cameras

To achieve high-quality imaging in low-light conditions, a remote sensing camera usually adopts a dynamic time-delay-integration imaging approach, which requires an accurate matching relationship between the optical image field motion and the photo-induced charge transfer. High-frequency motion aberrations still exist due to the measurement frequency limitation of physical attitude and position measurement sensors. However, conventional inversion imaging methods, such as blind deconvolution, can only measure and remove low-frequency motion aberrations. Here, an efficient dynamic inversion imaging algorithm based on double deep learning networks is proposed, which is able to measure and remove high-frequency motion aberrations. To measure high-frequency motion aberrations, we constructed two supervised online deep learning networks, a high-frequency motion aberration inversion learning network (HMAILN) and an optical flow inversion learning network (OFILN). The OFILN can measure the accurate optical flow information, which forms the input training set of the HMAILN. The HMAILN completes the measurement of high-frequency motion aberrations. Finally, the measured high-frequency motion aberrations from the HMAILN were used to construct the motion point spread function for imaging compensation to remove high-frequency motion aberrations. The proposed method was experimentally confirmed, opening the door for the successful implementation of dynamic high-resolution imaging without high-frequency motion aberrations.
The impact of the use of the internet on the learning outcomes in physics for high school student

Abstract Research in this area has been carried out a lot, but the research this time has its own uniqueness, where the use of IT in high school learning in Aceh province varies greatly. There are schools that prohibit students from accessing the internet on the grounds that they will have a negative impact on student behavior, but there are also schools that encourage students to use the internet as an additional source of reading. However, their learning outcomes have no significant difference. Based on some of these studies, it needs to be studied more deeply about the impact of internet usage on the learning outcomes of high school students, especially on Physics subjects. Research uses a quantitative approach, survey methods and data collection through documentation techniques and questionnaires. The target population of all high school students is in the city of Banda Aceh, but with a variety of considerations a sample of 31 students was taken randomly. Data processing is done by using product moment correlation test and the value is 0.2646 with a positive low category or there is no significant relationship between the use of the internet as a learning resource and student learning outcomes in physics subjects. Inference from the results of research, teachers are encouraged to motivate students to use IT in learning while still controlling their use.
Learning a Multi-scale Deep Residual Network of Dilated-Convolution for Image Denoising

Due to the favorable denoising performance of the discriminative learning model, a large number of discriminative learning models have been designed to remove noise. However, most discriminative learning models can only deal with the noise that already exists in the training data. In this paper, a multi-scale trainable deep residual convolutional neural network (DCMSNet) based on dilated convolution is proposed. DCMSNet consists of a chain of dilated convolution layers, convolution layers, normalized multi-scale convolution blocks (BNMCBlock), multi-scale convolution blocks (MCBlock) and multi-scale dropout convolution blocks (MCDBlock). The use of dilated convolution can avoid over-parameterization problems caused by too deep networks. In order to capture more feature information during feature extraction and image reconstruction, we designed novel BNMCBlock and MCBlock. In order to reduce the degree of coupling between image features and improve the generalization ability of the network, we also designed a MCDBlock. Meanwhile, residual learning, batch normalization and dropout are utilized to speed up the training process and boost the denoising performance. Unlike existing denoising models, DCMSNet is able to remove different degrees of noise. Compared with state-of-the-art image denoising methods, DCMSNet has achieved relatively competitive results.
Arabic Dialect Identification in Social Media

Although the Arabic language is spoken in twenty-two countries by more than 250 million speakers, it is still considered by Natural Language Processing NLP practitioners as a low resource language. Formal sources of Arabic texts are typically written in Modern Standard (or Written) Arabic (MSA), which is a form that is used in formal writing and taught in schools to Arabic speakers. However, informal communication among Arabic speakers is through informal local diglossic dialects. A diglossic language is one where the speakers of the same language have varying dialects. In Arabic, there are multiple dialects in different regions of the Arab world: Gulf, Levantine and North Africa. Users commonly communicate in social media using their local dialect rather than the formal MSA. This introduces a core NLP problem for Arabic, which is dialect identification. It is essential to identify the specific dialect prior to performing tasks such as parsing, tokenizing and other downstream tasks such as semantic inferences. Processing massive amounts of data written in these local dialects requires this identification step to improve accuracies, especially for automatic text comprehension tasks. Although Arabic dialects share a majority of common words, it is not uncommon for the same word to have different meanings across dialects. In addition to improving NLP task accuracies, Arabic Dialect Identification ADI enables a finer-grained demographic identification for mining texts related to consumer reports, health forums, entertainment and tourism reviews, and many others which ultimately lead to improved services for each demographic. The problem of ADI has been addressed by several studies such as (Al-Walaie & Khan, 2017), and (Harrat et al., 2019). Some works focus mainly on curating data sets for the problem such as the Sham dataset proposed by (Abu Kwaik et al., 2018). In this work we focus on both tasks: we curate an Arabic dialect dataset for two variants of Arabic (Saudi Arabian and Egyptian) and we train supervised machine learning models to address the identification task.
Arsenic residues from historic gold extraction, Snowy River, Westland, New Zealand

The Snowy River processing site was used to extract gold for the large historic Blackwater mine, Westland, between 1906 and 1938, and then abandoned. The site is now one of the best-preserved historic gold-processing localities, and access has been enhanced for visitors. The site has some high arsenic (As) concentrations on the surface, with some spots exceeding 40 wt% As. A variety of As-bearing minerals occurs along the former processing stream, from primary sulphides (arsenopyrite, pyrite) at the upstream end to arsenolite (As2O3) and scorodite (FeAsO4.2H2O) formed by the roasting of ore materials at the downstream end. Weathering over the past century of sulphide residues and some of the arsenolite has yielded more scorodite and some Ca arsenates. Minor authigenic As sulphide precipitates have formed on rotting wood. Arsenolite is principally distributed near to the roaster site, and this is the most soluble and potentially toxic mineral on site. Despite the locally abundant arsenolite, only minor As is mobilised into nearby waters.
An Ensemble of 2D Convolutional Neural Network for 3D Brain Tumor Segmentation

We propose an ensemble of 2D convolutional neural networks to predict the 3D brain tumor segmentation mask using the multi-contrast brain images. A pretrained Resnet50 and Nasnet-mobile architecture were used as an encoder, which was appended with a decoder network to create an encoder-decoder neural network architecture. The encoder-decoder network was trained end to end using T1, T1 contrast-enhanced, T2 and T2-Flair images to classify each pixel in the 2D input image to either no tumor, necrosis/non-enhancing tumor (NCR/NET), enhancing tumor (ET) or edema (ED). Separate Resent50 and Nasnet-mobile architectures were trained for axial, sagittal and coronal slices. Predictions from 5 inferences including Resnet at all three orientations and Nasnet-mobile at two orientations were averaged to predict the final probabilities and subsequently the tumor mask. The mean dice scores calculated from 166 were 0.8865, 0.7372 and 0.7743 for whole tumor, tumor core and enhancing tumor respectively.
Tightly secure inner product functional encryption: Multi-input and function-hiding constructions

Tightly secure cryptographic schemes have been extensively studied in the fields of chosen-ciphertext secure public-key encryption (CCA-secure PKE), identity-based encryption (IBE), signatures and more. We extend tightly secure cryptography to inner product functional encryption (IPFE) and present the first tightly secure schemes related to IPFE. We first construct a new IPFE schemes that are tightly secure in the multi-user and multi-challenge setting. In other words, the security of our schemes do not degrade even if an adversary obtains many ciphertexts generated by many users. Our schemes are constructible on a pairing-free group and secure under the matrix decisional Diffie-Hellman (MDDH) assumption, which is the generalization of the decisional Diffie-Hellman (DDH) assumption. Applying the known conversions by Lin (CRYPTO 2017) and Abdalla et al. (CRYPTO 2018) to our schemes, we can obtain the first tightly secure function-hiding IPFE schemes and multi-input IPFE (MIPFE) schemes respectively. Our second main contribution is the proposal of a new generic conversion from function-hiding IPFE to function-hiding MIPFE, which was left as an open problem by Abdalla et al. (CRYPTO 2018). We can obtain the first tightly secure function-hiding MIPFE schemes by applying our conversion to the tightly secure function-hiding IPFE schemes described above.
Multi-Agent System Based on Machine Learning for Early Diagnosis of Diabetes

Diabetes is increasing all over the world. In Morocco, more than 2 million people aged 18 and over are diabetic. In order to diagnose and treat diabetes, early detection is needed. Thus, the aim of this qualitative study was to overcome this difficulty by giving more autonomy and initiative to the different software modules specialized in the medical diagnosis. To enable robust, reliable medical diagnostic support, Multi-Agent System can be the tool for distributed diagnostic. This article will attempt to create a new multi-agent system that evaluates the performance of three well-known machine learning algorithms: the artificial neural network (ANN), support vector machines (SVM), and logistic regression or logit model (LR), based on the diabetes Database. Then the system aggregates the classifications of these algorithms with a controller agent to increase the accuracy of the classification using a majority vote. In addition, this article discusses the current gap and the challenges of adopting machine learning algorithms in multi-agent systems.
Research on abnormal object detection in specific region based on Mask R-CNN

As an information carrier with rich semantics, image plays an increasingly important role in real-time monitoring of logistics management. Abnormal objects are typically closely related to the specific region. Detecting abnormal objects in the specific region is conducive to improving the accuracy of detection and analysis, thereby improving the level of logistics management. Motivated by these observations, we design the method called abnormal object detection in a specific region based on Mask R-convolutional neural network: Abnormal Object Detection in Specific Region. In this method, the initial instance segmentation model is obtained by the traditional Mask R-convolutional neural network method, then the region overlap of the specific region is calculated and the overlapping ratio of each instance is determined, and these two parts of information are fused to predict the exceptional object. Finally, the abnormal object is restored and detected in the original image. Experimental results demonstrate that our proposed Abnormal Object Detection in Specific Region can effectively identify abnormal objects in a specific region and significantly outperforms the state-of-the-art methods.
Controllable Skin Lesion Synthesis Using Texture Patches, Bézier Curves and Conditional GANs

Data synthesis is an important tool for improving data availability in cases where data is hard to capture or annotate. In the context of skin lesions data, data synthesis has been used for data augmentation in automated classification methods or for supporting training of dermoscopic images visual inspection. In this paper, we propose a simple yet effective approach for diverse skin lesion image synthesis using conditional generative adversarial networks. Our pipeline takes as input a random Bézier curve representing the lesion mask, and two texture patches: one for skin, and one for lesion; and synthesizes a new dermoscopic image. Our method generates images where lesions and skin reproduce the corresponding provided texture patches, and the lesion conforms to the provided Bézier mask. Our results report realistic controllable synthesis and improved performance for skin lesion segmentation task considering different semantic segmentation networks in a public challenge in comparison to classic data augmentation.