Reviewerly/exHarmony huggingface dataset:
Improving Object Recognition in Crime Scenes via Local Interpretable Model-Agnostic Explanations

Object recognition is one of the fundamental and challenging problems in most visual monitoring applications and security systems. In assessing of crime scene, the detected objects from an image play a vital role in providing a narrative summary of a scene. It helps police officers in the investigation of the crime scenes and evidence collection in the criminal justice system from the viewpoints of law enforcement. In recent years, deep learning methods have emerged as a powerful strategy for the recognition and detection of objects in images. However, still, there are potential problems in the use of machine learning and Artificial Intelligence (AI) algorithms in policing. For instance, when using deep learning-based object detection approaches in crime scenes there are risks in that algorithms when making predictions. Because the complex structures of deep learning do not allow even the educated users to understand the mechanisms behind its decision-making processes. In this paper, we demonstrate how to enhance object recognition in crime scenes by utilising Local Interpretable Model-Agnostic Explanations (LIME). We use LIME to identify the object’s parts that have the highest impact on the class score and to show which critical features are employed in decision-making. To this end, the LIME heatmap is used that provides us valuable information that photography with which angles and perspectives can reduce the weak spots of deep learning-based object detection in the crime scenes. The platform has been tested with a wide variety of images of crime scenes. The results of our experiments provide support for our hypothesis.
Adjustable Model Compression Using Multiple Genetic Algorithm

Generative Adversarial Networks (GAN) is a popular machine learning method that possesses powerful image generation ability, which is useful for different multimedia applications (e.g., photographic filters, image editing). However, typical GAN models have a large memory footprint that limits their practical applications for resource-constrained devices (e.g., smartphones). To deploy GAN models on devices with various hardware constraints, we propose our method, AdjustableGAN, which can compress a pretrained GAN model to different compression ratios. Our method compresses GAN by performing filter-wise pruning that follows these objectives: (1) deactivate convolutional filters for minimal performance decrease, (2) reactivate convolutional filters for maximal performance increase. We implement multiple Genetic Algorithms (GA) to perform each of these objectives— Downsize GA for best filter deactivations, while Upsize GA searches for best filter reactivations. By selective utilization of Upsize/Downsize GA, we could explicitly control the compression ratio of the model. For finalization, we fine-tune the compressed output model using the training dataset of the original input model. Our experimental results show that our method can reliably compress generative networks with minimal accuracy drop compared to other state-of-the-art compression algorithms.
Deep-Learning-Based Survival Prediction of Patients in Coronary Care Units

A survival prediction model based on deep learning has higher accuracy than the CPH model in predicting the survival of CCU patients, and it also has a better discrimination ability. We collected information on patients with various diseases in coronary care units (CCUs) from the Medical Information Mart for Intensive Care III (MIMIC-III) database. The purpose of this study was to use this information to construct a neural-network model based on deep learning to predict the survival probabilities of patients with conditions that are common in CCUs.We collected information on patients in the United States with five common diseases in CCUs from 2001 to 2012. We randomly divided the patients into a training cohort and a testing cohort at a ratio of 7 : 3 and applied a survival prediction method based on deep learning to predict their survival probability. We compared our model with the Cox proportional-hazards regression (CPH) model and used the concordance indexes (C-indexes), receiver operating characteristic (ROC) curve, and calibration plots to evaluate the predictive performance of the model.The 3,388 CCU patients included in the study were randomly divided into 2,371 in the training cohort and 1,017 in the testing cohort. The stepwise regression results showed that the important factors affecting patient survival were the type of disease, age, race, anion gap, glucose, neutrophils, white blood cells, potassium, creatine kinase, and blood urea nitrogen (P < 0.05). We used the training cohort to construct a deep-learning model, for which the C-index was 0.833, or about 5% higher than that for the CPH model (0.786). The C-index of the deep-learning model for the test cohort was 0.822, which was also higher than that for the CPH model (0.782). The areas under the ROC curve for the 28-day, 90-day, and 1-year survival probabilities were 0.875, 0.865, and 0.874, respectively, in the deep-learning model, respectively, and 0.830, 0.843, and 0.806 in the CPH model. These values indicate that the survival analysis model based on deep learning is better than the traditional CPH model in predicting the survival of CCU patients.A survival prediction model based on deep learning has higher accuracy than the CPH model in predicting the survival of CCU patients, and it also has a better discrimination ability.
Accurate Parity Meter Based on Coherent State Measurement

Abstract Parity meter is the core step of many quantum information works. In this paper, a robust and accurate protocol to construct a parity meter of two artificial atoms coupled to a cavity is proposed. Specifically, when the atomic system is in states with different parities, the cavity will evolve into different states. Then, the parity information of the atomic system can be distinguished by measuring the state of the cavity via the homodyne measurement. In addition, the influence of decoherence (including the cavity decay, the atomic spontaneous emission, and the dephasing) and systematic errors are numerically discussed. The results show that the protocol is robust against these negative factors. Therefore, this protocol may provide helpful perspectives for accurate parity meters.
Teacher–student training and triplet loss to reduce the effect of drastic face occlusion

We study a series of recognition tasks in two realistic scenarios requiring the analysis of faces under strong occlusion. On the one hand, we aim to recognize facial expressions of people wearing virtual reality headsets. On the other hand, we aim to estimate the age and identify the gender of people wearing surgical masks. For all these tasks, the common ground is that half of the face is occluded. In this challenging setting, we show that convolutional neural networks trained on fully visible faces exhibit very low performance levels. While fine-tuning the deep learning models on occluded faces is extremely useful, we show that additional performance gains can be obtained by distilling knowledge from models trained on fully visible faces. To this end, we study two knowledge distillation methods, one based on teacher–student training and one based on triplet loss. Our main contribution consists in a novel approach for knowledge distillation based on triplet loss, which generalizes across models and tasks. Furthermore, we consider combining distilled models learned through conventional teacher–student training or through our novel teacher–student training based on triplet loss. We provide empirical evidence showing that, in most cases, both individual and combined knowledge distillation methods bring statistically significant performance improvements. We conduct experiments with three different neural models (VGG-f, VGG-face and ResNet-50) on various tasks (facial expression recognition, gender recognition, age estimation), showing consistent improvements regardless of the model or task.
<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e3162" altimg="si21.svg"><mml:mrow><mml:mi>K</mml:mi><mml:mi>e</mml:mi><mml:mi>y</mml:mi><mml:mi>C</mml:mi><mml:mi>l</mml:mi><mml:mi>a</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi></mml:mrow></mml:math>: Efficient keyword matching for network traffic classification

Network traffic classification is required for a range of network management activities like meeting the Quality of Service demands of applications and security monitoring. Deep Packet Inspection (DPI) based methods achieve better classification accuracy compared to other techniques. However, DPI is computationally demanding and requires searching patterns in the payload. Methods found in the literature suffer from performance issues as they perform multiple scans of payload. In this paper, we describe KeyClass, which is a DPI based traffic classifier and can classify network flows with single scan of payload using keyword based signatures. KeyClass achieves performance gains (speed of classification) with a combination of two things. It quickly identifies potential application(s) by scanning few initial bytes of payload and optimize the number of character comparisons while searching remaining keywords of potential application(s). In order to identify potential applications, it uses a finite state machine constructed with first keyword of every application using classic Aho–Corasick multi-pattern matching algorithm. KeyClass has an application specific signature which is generated with the remaining set of keywords of an application. By skipping portions of payload from inspection, coupled with an efficient string matching algorithm, it practically achieves sub-linear search complexity. We evaluate the classification and execution performance of KeyClass with experiments using two large datasets containing 173619 and 885405 network flows and report that it has a good average classification accuracy of ≈ 98%. In our evaluation, KeyClass is found to be 3.79 times faster than state of the art methods.
Fixed complexity tiny reservoir heterogeneous network for on-line ECG learning of anomalies

The electrocardiogram (ECG), being one of the most extensively used signals to monitor cardiovascular diseases (CVDs), captures the heart’s arrhythmias. Patients with such pathology are often monitored for extended periods of time, requiring data storage, and a very time-consuming off-line search of anomalies. This is especially inefficient when indicative patterns in the biological signals are infrequent, requiring more analysis time of medical doctors, and entailing a difficult visual search task for the diagnosis.In this paper, we propose an automated deep learning pipeline based on reservoir computing (RC), followed by principal component analysis (PCA) and one-class support vector machine (OC-SVM), that can be used to perform on-line learning and real-time anomaly detection of pathological conditions and therefore to raise warnings. The learning step requires fixed computational complexity and memory that are affordable on a low-power microcontroller unit (MCU). During the learning phase, it uses a very limited amount of input data, e.g., 60 KB, which makes this work suitable for fast personalization when device restart is required. The detection accuracy has been evaluated on the publicly available MIT-BIH arrhythmia dataset, also modified to mimic temporal sliding of input tensors on the ECG streaming data. Best F1 score and accuracy are 91.5%, 95.4% with variance over the processed data of 0.001.
Automatic Drone Identification Through Rhythm-based Features for the Internet of Drones

The Internet of Drones (IoD) refers to a robust mobile network with well-defined airways where drones perform interoperable services, enhancing the deployment of Smart Cities. Automatic Drone Identification (ADI) is a protection mechanism to detect and avoid malicious drones, where different techniques have been used, such as acoustic signals. In this field, the sound generated by propellers and motors has particular characteristics, being a potential aspect to explore; however, this investigation is still missing. This study examines the use of rhythm-based descriptors as input features to ADI, based on the hypothesis that the acoustic signal generated by different drones has different rhythmic properties. Aiming to explore and validate our approach, we formulate an ADI methodology using rhythm-based features. We use a freely available drone audio dataset, comparing our results with a baseline study. As a result, our classification model improves 3.47% the baseline binary classification and 2.97% the multiclass classification, reaching accuracy rates of 0.9985 and 0.9591, respectively. Although the improvements are narrow, they point out that acoustic features have a great potential to enhance ADI mainly in dense drone-based environments, where drone identification is an essential task.
Sentiment Analysis: Developing an Efficient Model Based on Machine Learning and Deep Learning Approaches

Sentiment analysis is a subfield of text mining. It is the process of categorizing opinions expressed in a piece of text. a simple form of such analysis would be to predict whether the opinion about something is positive or negative (polarity). The present paper proposes an efficient sentiment analysis model based on machine learning ML and deep learning DL approaches. A DNN (Deep Neural Network) model is used to extract the relevant features from customer reviews, perform a training task on almost of samples of the dataset, validate the model on a small subset called the test set and consequently compute the accuracy of sentiment classification. For the programming stage, we benefited from the large opportunities offered by Python language, as well as Tensorflow and Keras libraries.
ATMCC: Design of the Integration Architecture of Cloud Computing and Blockchain for Air Traffic Management

The air traffic management (ATM) system is an integrated intelligent system that integrates the ground computer network, the airborne network and the space satellite (communication and navigation) network. The development of ATM systems cannot be achieved without the application of new technologies, and this paper proposes an integration architecture of cloud computing and blockchain for ATM systems. The research content of this paper is introduced by an overview of the research on cloud computing and blockchain technology in ATM systems. The overall design architecture and the main functional module architecture of the solution are given. In the process of describing the architecture design, the advantages of the new technology architecture over the traditional architecture of existing ATM systems are pointed out. In addition, this paper analyzes and demonstrates the security of the solution in terms of process security and system security. The solution will help improve the security, reliability and scalability of ATM services, and provide reference for the deployment of the third-generation ATM networks and the design and research of the fourth-generation ATM networks.
Defending local poisoning attacks in multi-party learning via immune system

Multi-party learning provides effective solutions for building a jointly-trained model with scattered data while meeting user privacy, data security and government regulations. Nevertheless, since the knowledge sharing process among multiple participants is conducted by uploading model parameters instead of individual private data, local poisoning attacks in the multi-party setting could be more covert and destructive. In this paper, we propose a novel immune system deployed in the multi-party learning scenario (MPIS) to defend against local poisoning attacks. We investigate the commonality between the biological immunity and the defense against poisoning attacks, and analogize the secure defense framework as an immune system. The approach achieves antigen recognition, immune response and immunological memory with an adversarial pipeline, which is not limited by the number of compromised clients or the duration of their involvement, and it is capable of adaptively determine the aggregation weight of different local models. Extensive experimental results on image and text datasets with different neural networks demonstrate the superiority of the MPIS framework in both model performance and robustness against poisoning attacks.
SSAPPIDENTIFY: A robust system identifies application over Shadowsocks’s traffic

As an effective tool for penetrating firewalls and bypassing supervision and censorship, Shadowsocks is widely used, and there are also illegal network activists. If there is a traffic identification system that can identify applications over Shadowsocks, it can greatly facilitate the supervision, traceability, and evidence collection of cybercrime activities. In this paper, we propose an application over Shadowsocks’s traffic identification system, which adds the sliding window JS divergence feature on the basis of the traditional statistics and distribution based on traffic packet length and timestamp. This feature can effectively reduce the impact on smart phone differences without reducing the accuracy of the same individual device application traffic recognition, while maintaining the characteristics of the application as much as possible, thereby greatly improve our traffic recognition the robustness of the system. Experimental results show that our system can achieve an accuracy of 94.5% on the same smart phone. On the data sets collected by different smart phones, our work has achieved an accuracy of 80.4%, which has certain practicality.
A Robust Video Watermarking Scheme with Squirrel Search Algorithm

Advancement in multimedia technology has resulted in protection against distortion, modification, and piracy. For implementing such protection, we have an existing technique called watermarking but obtaining desired distortion level with sufficient robustness is a challenging task for watermarking in multimedia applications. In the paper, we proposed a smart technique for video watermarking associating meta-heuristic algorithms along with an embedding method to gain an optimized efficiency. The main aim of the optimization algorithm is to obtain solutions with maximum robustness, and which should not exceed the set threshold of quality. To represent the accuracy of the proposed scheme, we employ a popular video watermarking technique (DCT domain) having frame selection and embedding method for watermarking. A squirrel search algorithm is chosen as a meta-heuristic algorithm that utilizes the stated fitness function. The results indicate that quality constraint is fulfilled, and the proposed technique gives improved robustness against different attacks with several quality thresholds. The proposed technique could be practically implemented in several multimedia applications such as the films industry, medical imagery, OOT platforms, etc.
Accurate Strain Field Measurement During Strip Rolling by Exploiting Recurring Material Motion with Time-Integrated Digital Image Correlation

Abstract Background 95% Of all metals and alloys are processed using strip rolling, explaining the great number of existing strip rolling optimization models. Yet, an accurate in-situ full-field experimental measurement method of the deformation, velocity and strain fields of the strip in the deformation zone is lacking. Objective Here, a novel time-Integrated Digital Image Correlation (t-IDIC) framework is proposed and validated that fully exploits the notion of continuous, recurring material motion during strip rolling. Methods High strain accuracy and robustness against unavoidable light reflections and missing speckles is achieved by simultaneously correlating many (e.g. 200) image pairs in a single optimization step, i.e. each image pair is correlated with the same average global displacement field but is multiplied by a unique velocity corrector to account for differences in material velocity between image pairs. Results Demonstration on two different strip rolling experiments revealed previously inaccessible subtle changes in the deformation and strain fields due to minor variations in pre-deformation, elastic recovery, and geometrical irregularities. The influence of the work roll force and entry/exit strip tension has been investigated for strip rolling with an industrial pilot mill, which revealed unexpected non-horizontal material feed. This asymmetry was reduced by increasing the entry strip tension and rolling force, resulting in a more symmetric strain distribution, while increased distance between the neutral and entry point was found for a larger rolling force. Conclusions The proposed t-IDIC method allows for robust and accurate characterization of the strip’s full-field behavior of the deformation zone during rolling, revealing novel insights in the material behavior.
COVID-19 surveillance in Israeli press: Spatiality, mobility, and control

In March 2020, Israel passed emergency regulations authorizing its internal security agency to track citizens' mobile phone geolocations in order to tackle the spread of COVID-19. This unprecedented surveillance enterprise attracted extensive media attention and sparked a vigorous public debate regarding technology and democratic values such as privacy, mobility, and control. This article examines press coverage of Israel's surveillance of its citizens during the COVID-19 pandemic by four leading news sites to identify and map the frames that informed their reports. Based on a thematic analysis, our findings point to supportive and critical constructions of mobile phone location-tracking and organize them within two scapes: personal; and international. These attest to the collective imagining of intimacies and public life, respectively. We draw on the case study to articulate mobile phones as devices that reduce movement into manageable mapped information and individuals into controllable data. Mobile phone location-tracking during the COVID-19 pandemic is understood as turning mobility into order and control.
Knowledge graph embedding by reflection transformation

Due to the incompleteness of knowledge graph, knowledge graph embedding(KGE) has become a key technique for automatically predict missing facts in knowledge graph. KGE aims to learn low dimensional representations for both relations and entities. Despite existing KGE models achieved state-of-the-art(SOTA) performances, modeling and inferring relation connectivity(such as symmetry/antisymmetry, inversion, and composition), as well as complex relations prediction(such as M-to-1, 1-to-M and M-to-M) still have great challenges. In this paper, we propose a new KGE model called ReflectE, which regards each relation as a normal vector of a relation-specific reflection hyperplane. Specifically, ReflectE regards the tail entity(or head entity) in a triple as the reflection of the head entity(or tail entity) on a relation-specific hyperplane. Therefore it can model symmetric and inverse relations by reflection transformation naturally. Furthermore, ReflectE models complex relations by learning a relation-specific dynamic reflection hyperplane. In order to evaluate the effectiveness of our proposed model ReflectE, we choose previous SOTA models as baselines and conduct link prediction task on three popular datasets. Experimental results show that, compared with conventional distance-based KGE models, ReflectE achieves SOTA results for link prediction.