Reviewerly/exHarmony huggingface dataset:
Monuments Recognition using Deep Learning VS Machine Learning

Egyptian monuments have too many features and information to investigate. Working on monuments in Egypt, which is one of the most remarkable industry, would surely have a huge impact on the Egyptian economy and development. Any culture, in general, is unique when it comes to the different features of monuments, writing, and music. Examining techniques to recognize these monuments is not easy to be done especially concerning the history and stories behind each monument. Monument Recognition is a challenging process in the field of image recognition and classification. Many obstacles have to be tackled since many factors can affect the recognition method. The purpose of this paper is to recognize monuments in images using traditional and non-traditional machine learning techniques. VGG16 and Resnet50 have been used as examples of non-traditional machine learning techniques(deep learning), while KNN is used as a traditional one for a comparison between both techniques. The benchmark dataset used is Indian monuments images, due to the lack of datasets for the Egyptian monuments. We started by training our model, then we moved for the cross-validation where we used the k-folds technique with 5 folds and 25 epochs per fold. The ResNet50 achieved the highest performance measures in classifying unseen data. Its accuracy is 88%, precision 88%, recall 87%, F1-score 87% and the area under the ROC curve is 0.96.
HAMIL: Hierarchical aggregation-based multi-instance learning for microscopy image classification

Multi-instance learning is common for computer vision tasks, especially in biomedical image processing. Traditional methods for multi-instance learning focus on designing feature aggregation methods and multi-instance classifiers, where the aggregation operation is performed either in the feature extraction or learning phase. As deep neural networks (DNNs) achieve great success in image processing via automatic feature learning, certain feature aggregation mechanisms need to be incorporated into common DNN architecture for multi-instance learning. Moreover, flexibility and reliability are crucial considerations to deal with varying quality and number of instances. In this study, we propose a hierarchical aggregation network for multi-instance learning, called HAMIL. The hierarchical aggregation protocol enables feature fusion in a defined order, and the simple convolutional aggregation units lead to an efficient and flexible architecture. We assess the model performance on two microscopy image classification tasks, namely protein subcellular localization using immunofluorescence images and gene annotation using spatial gene expression images. The experimental results show that HAMIL outperforms the state-of-the-art feature aggregation methods and the existing models for addressing these two tasks. The visualization analyses also demonstrate the ability of HAMIL to focus on high-quality instances.
GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning

Existing optical flow methods are erroneous in challenging scenes, such as fog, rain, and night because the basic optical flow assumptions such as brightness and gradient constancy are broken. To address this problem, we present an unsupervised learning approach that fuses gyroscope into optical flow learning. Specifically, we first convert gyroscope readings into motion fields named gyro field. Second, we design a self-guided fusion module to fuse the background motion extracted from the gyro field with the optical flow and guide the network to focus on motion details. To the best of our knowledge, this is the first deep learning-based framework that fuses gyroscope data and image content for optical flow learning. To validate our method, we propose a new dataset that covers regular and challenging scenes. Experiments show that our method outperforms the state-of-art methods in both regular and challenging scenes. Code and dataset are available at https://github.com/megvii-research/GyroFlow.
Organization and conduct of classes in educational institutions during distance learning

Abstract The article considers the use of cloud technologies during distance learning. The implementation of the mechanism of distance learning in general secondary education in two areas is described: distance form as a separate form of education and the use of distance learning technologies in the organization of education in various forms (day, evening, correspondence, etc.). The software for the organization of distance learning of pupils and students is listed. Examples of development of own electronic educational materials for teaching mathematics and computer science are given.
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results

Inference is the process of using facts we know to learn about facts we do not know. A theory of inference gives assumptions necessary to get from the former to the latter, along with a definition for and summary of the resulting uncertainty. Any one theory of inference is neither right nor wrong but merely an axiom that may or may not be useful. Each of the many diverse theories of inference can be valuable for certain applications. However, no existing theory of inference addresses the tendency to choose, from the range of plausible data analysis specifications consistent with prior evidence, those that inadvertently favor one’s own hypotheses. Because the biases from these choices are a growing concern across scientific fields, and in a sense the reason the scientific community was invented in the first place, we introduce a new theory of inference designed to address this critical problem. We introduce hacking intervals, which are the range of a summary statistic one may obtain given a class of possible endogenous manipulations of the data. Hacking intervals require no appeal to hypothetical data sets drawn from imaginary superpopulations. A scientific result with a small hacking interval is more robust to researcher manipulation than one with a larger interval and is often easier to interpret than a classical confidence interval. Some versions of hacking intervals turn out to be equivalent to classical confidence intervals, which means they may also provide a more intuitive and potentially more useful interpretation of classical confidence intervals. This paper was accepted by J. George Shanthikumar, big data analytics.
A Federated Framework for Fine-Grained Cloud Access Control for Intelligent Big Data Analytic by Service Providers

This paper proposes a novel data-owner-driven privacy-aware cloud data acquisition framework for intelligent big data analytics for service providers and users. To realize this idea, we propose three main components. The first one is a new global identity provider concept to support fine-grained access control for a federated outsourcing cloud, namely called P-FIPS (Privacy-enhanced Federated Identity Provider System), in which data owners perform identity access control with the operator of the federated outsourcing cloud so that the service providers can selectively use their encrypted data on the cloud for various purpose such as intelligent big data analytics. In P-FIPS, data owners manage the access privilege of service providers over their encrypted data on the cloud by (a) labeling the scope of use (e.g., user connection, user disconnection, user tracking) on each encrypted data on the cloud, and (b) by selectively providing the information regarding the data owners to the service provider. The label also includes the attributes related to the data owner’s identity, and this allows service providers to locate the target data with the assist of cryptographic computation according to the scope of the use at the cloud outsourcing server. The second one is a new ambiguous data acquisition mechanism integrated with P-FIPS from a cloud to a service provider. The last one is the Decentralized Audit and Ordering (DAO) Chain mechanism which provides the correctness of obtained data to the service provider as well as ensures the owners that their data is being used for the approved purpose only. Most importantly, we show that our framework is much more efficient than the existing alternative in the scheme.
Advanced Healthcare System using Artificial Intelligence

Matters like prescription and maintenance of health records hugely impact the efficiency of health care. Advanced technologies could be used to change the current scenario. This paper proposes a resourceful, web interface which enables access of medical records to the patients and a neural network model which predicts medication for ailments. The web interface acts as a medium between doctor and patient, allowing them to access the required information. The proposed method uses custom trained speech to text model and applies Natural Language Processing (NLP) on the acquired text, to provide the patient with a prescription. The proposed method further develops a neural network model which predicts the medication to be used by the patient, based on the symptoms. The proposed system achieved a Word Error Rate (WER) of 21.5% for the custom trained Speech to Text (STT) model. The AI bot used for medication prediction has achieved an accuracy of 88%.
Progressive and Aligned Pose Attention Transfer for Person Image Generation

This paper proposes a new generative adversarial network for pose transfer, i.e., transferring the pose of a given person to a target pose. We design a progressive generator which comprises a sequence of transfer blocks. Each block performs an intermediate transfer step by modeling the relationship between the condition and the target poses with attention mechanism. Two types of blocks are introduced, namely pose-attentional transfer block (PATB) and aligned pose-attentional transfer block (APATB). Compared with previous works, our model generates more photorealistic person images that retain better appearance consistency and shape consistency compared with input images. We verify the efficacy of the model on the Market-1501 and DeepFashion datasets, using quantitative and qualitative measures. Furthermore, we show that our method can be used for data augmentation for the person re-identification task, alleviating the issue of data insufficiency. Code and pretrained models are available at: https://github.com/tengteng95/Pose-Transfer.git.
Algorithmic fog of war: When lack of transparency violates the law of armed conflict

Under international law, weapon capabilities and their use are regulated by legal requirements set by International Humanitarian Law (IHL). Currently, there are strong military incentives to equip capabilities with increasingly advanced artificial intelligence (AI), which include opaque (less transparent) models. As opaque models sacrifice transparency for performance, it is necessary to examine whether their use remains in conformity with IHL obligations. First, we demonstrate that the incentives for automation drive AI toward complex task areas and dynamic and unstructured environments, which in turn necessitates resort to more opaque solutions. We subsequently discuss the ramifications of opaque models for foreseeability and explainability. Then, we analyse their impact on IHL requirements from a development, pre-deployment and post-deployment perspective. We find that while IHL does not regulate opaque AI directly, the lack of foreseeability and explainability frustrates the fulfilment of key IHL requirements to the extent that the use of fully opaque AI could violate international law. States are urged to implement interpretability during development and seriously consider the challenging complication of determining the appropriate balance between transparency and performance in their capabilities.
Online Rebuilding Regression Random Forests

Continuous data streams mining is particularly challenging for machine learning. Many efforts have been devoted to propose online learning algorithms that can train iteratively from new coming data and provide evolutionary predictions. Compared to off-line approaches, these algorithms have shown better predictive performance and certain adaptation to high volume continuous data stream. However, a wide range of practical applications calls for regression models that can make adequate use of the large volume of pre-collected training data, meanwhile, handle continuous data stream with multi-type concept drifts, such as abrupt, gradual, incremental, recurring concept drifts. Random Forests(RFs) are an effective ensemble learning model for regression tasks. However, the fixed structure of RFs by off-line training has restricted its applicability for real-world tasks with dynamic data streams. To address these issues, we propose an online rebuilding strategy for the pre-trained Random Forests model, which is called Online Rebuilding Regression Random Forests(ORB-RRF). Specifically, a leaf-pruning technique and online reconstruction of subtrees based on the change of feature space on certain nodes are designed to adjust the local structure of regression tree to adapt to dynamic inputs. Numerical experiments with ORB-RRF show remarkable improvements in the adaptability in data stream and the predictive accuracy in several benchmark real datasets and synthetic datasets, compared to several state-of-art methods. Moreover, we show the convergence and stability of the proposed method.
T-EGAT: A Temporal Edge Enhanced Graph Attention Network for Tax Evasion Detection

Tax evasion refers to the illegal act of taxpayers using deception and concealment to avoid paying taxes. How to detect tax evasion effectively is always an important topic for the government and academic researchers. Recent research has proposed using machine learning technologies to detect tax evasion and has achieved good results in some specific conditions. However, recent methods have three shortcomings. First, recent methods mainly use the basic features extracted based on expert experience. Second, recent methods do not make full use of the edge features of the transaction network. Third, recent methods cannot adapt to a dynamic transaction network. To overcome these challenges, we propose a novel tax evasion detection method, the temporal edge enhanced graph attention network (T-EGAT), which combines the edge enhanced graph attention network (EGAT) and the recurrent weighted average unit (RWA). Specifically, the EGAT is used to learn complex topological structures for capturing spatial dependence and the RWA is used to learn the dynamic changes of transaction data for capturing temporal dependence. Experimental tests using real-world tax data demonstrate that our method achieves better performance at detecting tax evaders than existing methods.
Estimating community feedback effect on topic choice in social media with predictive modeling

Social media users post content on various topics. A defining feature of social media is that other users can provide feedback -- called community feedback -- to their content in the form of comments, replies, and retweets. We hypothesize that the amount of received feedback influences the choice of topics on which a social media user posts. However, it is challenging to test this hypothesis as user heterogeneity and external confounders complicate measuring the feedback effect. Here, we investigate this hypothesis with a predictive approach based on an interpretable model of an author's decision to continue the topic of their previous post. We explore the confounding factors, including author's topic preferences and unobserved external factors such as news and social events, by optimizing the predictive accuracy. This approach enables us to identify which users are susceptible to community feedback. Overall, we find that 33\% and 14\% of active users in Reddit and Twitter, respectively, are influenced by community feedback. The model suggests that this feedback alters the probability of topic continuation up to 14\%, depending on the user and the amount of feedback.
Playing optical tweezers with deep reinforcement learning: in virtual, physical and augmented environments

Abstract Reinforcement learning was carried out in a simulated environment to learn continuous velocity control over multiple motor axes. This was then applied to a real-world optical tweezers experiment with the objective of moving a laser-trapped microsphere to a target location whilst avoiding collisions with other free-moving microspheres. The concept of training a neural network in a virtual environment has significant potential in the application of machine learning for experimental optimization and control, as the neural network can discover optimal methods for problem solving without the risk of damage to equipment, and at a speed not limited by movement in the physical environment. As the neural network treats both virtual and physical environments equivalently, we show that the network can also be applied to an augmented environment, where a virtual environment is combined with the physical environment. This technique may have the potential to unlock capabilities associated with mixed and augmented reality, such as enforcing safety limits for machine motion or as a method of inputting observations from additional sensors.
Online learning in higher education during the Covid-19 pandemic: A case study in the Department of Electrical Engineering, Universitas Negeri Semarang

Abstract The Covid-19 pandemic that hit the world has posed significant challenges for the world of higher education globally. The main challenge that is very urgent and inevitable for lecturing at the university is face-to-face in class but must be taught online. Online teaching and learning implies a certain pedagogical content knowledge, especially related to designing and organizing for a better learning experience and creating a different learning environment, and the role of information technology. This article, concludes that five things play a role in online learning, namely first, a reliable information technology facility to support access to online learning; Second, the need for teachers to make variations and innovations to deliver teaching material so that it attracts students; Third, it needs an in-depth study in practicum lectures; Lecturers can do a practicum with simulations. Fourth, there is a need for material to be delivered based on light, clear, concise and interactive principles. Fifth, there is an increase in student participation in online learning.
A Survey on Amazon Alexa Attack Surfaces

Since being launched in 2014, Alexa, Amazon's versatile cloud-based voice service, is now active in over 100 million households worldwide [1]. Alexa's user-friendly, personalized vocal experience offers customers a more natural way of interacting with cutting-edge technology by allowing the ability to directly dictate commands to the assistant. Now in the present year, the Alexa service is more accessible than ever, available on hundreds of millions of devices from not only Amazon but third-party device manufacturers. Unfortunately, that success has also been the source of concern and controversy. The success of Alexa is based on its effortless usability, but in turn, that has led to a lack of sufficient security. This paper surveys various attacks against Amazon Alexa ecosystem including attacks against the frontend voice capturing and the cloud backend voice command recognition and processing. Overall, we have identified six attack surfaces covering the lifecycle of Alexa voice interaction that spans several stages including voice data collection, transmission, processing and storage. We also discuss the potential mitigation solutions for each attack surface to better improve Alexa or other voice assistants in terms of security and privacy.
Object detection for drones on Raspberry Pi potentials and challenges

Abstract The paper presents preliminary research results about implementing an object detection program on a Single Board Computer. These results are used later to develop applications for drones. The object identification program is developed in Python using the TensorFlow library. The authors have succeeded in implementing and testing this object identification module using the artificial neural network model SSDMobileNet V2 on the Raspberry Pi 3B+. The results in this paper demonstrate the potential of this module for further development in the future. Based on the simulation and real-world results, the authors showed that a good outcome is achievable with limited resources for the AI module. Along with a high-precision object detection feature, this module can also estimate the distance and velocity of the “human” object with good accuracy. Besides, the paper also proposes several solutions to increase the performance and most importantly, the real-time feature of the developed module.
VLGrammar: Grounded Grammar Induction of Vision and Language

Cognitive grammar suggests that the acquisition of language grammar is grounded within visual structures. While grammar is an essential representation of natural language, it also exists ubiquitously in vision to represent the hierarchical part-whole structure. In this work, we study grounded grammar induction of vision and language in a joint learning framework. Specifically, we present VLGrammar, a method that uses compound probabilistic context-free grammars (compound PCFGs) to induce the language grammar and the image grammar simultaneously. We propose a novel contrastive learning framework to guide the joint learning of both modules. To provide a benchmark for the grounded grammar induction task, we collect a large-scale dataset, PARTIT, which contains human-written sentences that describe part-level semantics for 3D objects. Experiments on the PARTIT dataset show that VLGrammar outperforms all baselines in image grammar induction and language grammar induction. The learned VLGrammar naturally benefits related downstream tasks. Specifically, it improves the image unsupervised clustering accuracy by 30%, and performs well in image retrieval and text retrieval. Notably, the induced grammar shows superior generalizability by easily generalizing to unseen categories. Code and pre-trained models are released at https://github.com/evelinehong/VLGrammar.
Ensuring employees' information security policy compliance by carrot and stick: the moderating roles of organizational commitment and gender

Purpose Employees’ information security policy (ISP) compliance exerts a significant strain on information security management. Drawing upon the compliance theory and control theory, this study attempts to examine the moderating roles of organizational commitment and gender in the relationships between reward/punishment expectancy and employees' ISP compliance. Design/methodology/approach Using survey data collected from 310 employees in Chinese organizations that have formally adopted information security policies, the authors applied the partial least square method to test hypotheses. Findings Punishment expectancy positively affects ISP compliance, but reward expectancy has no significant impact on ISP compliance. Compared with committed employees, both reward expectancy and punishment expectancy have stronger impacts on low-commitment employees' ISP compliance. As for gender differences, punishment expectancy exerts a stronger effect on females' ISP compliance than it does on males. Originality/value By investigating the moderating roles of organizational commitment and gender, this paper offers a deeper understanding of reward and punishment in the context of ISP compliance. The findings reveal that efforts in building organizational commitment will reduce the reliance on reward and punishment, and further controls rather than the carrot and stick should be applied to ensure male employees' ISP compliance.