Reviewerly/exHarmony huggingface dataset:
R-CNN Based 3D Object Detection for Autonomous Driving

3D object detection plays an important role in autonomous driving, which provides 3D location information of objects for subsequent decision-making modules. Existing 3D object detection algorithms can be divided into three ways: lidar-based, stereo image-based, and monocular image-based methods. Lidar-based methods depends on large and expensive lidar sensors to provide depth information which largely increases expense. Stereo image-based method mostly uses stereo images input with multistage networks which causes large computation cost thus limiting their using scenarios. Meanwhile some scholars proposed methods like Deep3DBox which only utilize monocular image input and can obtain competitive precision. However, their lack of depth property brings about unstable performance. To deal with that, we propose a novel method which uses both monocular image and cascade geometric constraints to obtain robust detection. The framework is divided into two stages. The first stage processes the monocular image input using key points-based detection network CenterNet with additional branch to regress the orientation, dimension, and center projection of bottom face. In the second stage, increasing IOU threshold can filter out unprecise 2D bounding boxes which cause performance degradation. After that cascade geometric constraints are utilized to obtain the final 3D box output. Our framework doesn’t depend on any external sources or subnetworks and can be trained end to end. We tested the proposed method on the KITTI-3D benchmark to test its ability and efficiency.
Higher-dimensional Hong-Ou-Mandel effect and state redistribution with linear-optical multiports

We expand the two-photon Hong-Ou-Mandel (HOM) effect onto a higher-dimensional set of spatial modes and introduce an effect that allows controllable redistribution of quantum states over these modes using directionally unbiased linear-optical four-ports without post-selection. The original HOM effect only allows photon pairs to exit in two directions in space. But when accompanied by beam splitters and phase shifters, the result is a directionally controllable two-photon HOM effect in four spatial modes, with direction controlled by changing the phases in the system. This controllable quantum amplitude manipulation also allows demonstration of a ``delayed'' HOM effect by exploiting phase shifters in a system of two connected multiport devices. By this means, both spatial and temporal control of the propagation of the two-photon superposition state through a network can be achieved.
Generating Sports News from Live Commentary: A Chinese Dataset for Sports Game Summarization.

Sports game summarization focuses on generating news articles from live commentaries. Unlike traditional summarization tasks, the source documents and the target summaries for sports game summarization tasks are written in quite different writing styles. In addition, live commentaries usually contain many named entities, which makes summarizing sports games precisely very challenging. To deeply study this task, we present SportsSum, a Chinese sports game summarization dataset which contains 5,428 soccer games of live commentaries and the corresponding news articles. Additionally, we propose a two-step summarization model consisting of a selector and a rewriter for SportsSum. To evaluate the correctness of generated sports summaries, we design two novel score metrics: name matching score and event matching score. Experimental results show that our model performs better than other summarization baselines on ROUGE scores as well as the two designed scores.
Cognitive Modeling of Destructive Malicious Impacts on Critical Information Infrastructure Objects

The security of a subject of critical information infrastructure (CII) is one of the key issues of its life support. The current approach (legal and regulatory) regulates solutions of this issue without taking into account the influence of the violator, which can have a destructive effect on the SCII. This, in our opinion, leads to significant errors in the analysis of the information security of the CII, therefore, reduces the effectiveness of the information protection means declared information security tools for CII objects. The purpose of this work is to develop a model of an information security (IS) intruder, presented in a formalized form using the "violator’s potential" parameter in the space of their implementation of destructive effects on the objects of the CII. The proposed model for assessing the capabilities of the offender to implement destructive influences on the CII subject as a set of objects is implemented in the developed cognitive map "Assessment of the IB of the CII Subject" for dynamic changes in the parameters of the "Malicious actions on the CII object" vertex.
Analyzing Cyber-Attack Intention for Digital Forensics Using Case-Based Reasoning

Cyber-attacks are increasing and varying dramatically day by day. It has become challenging to control cyber-attacks and to identify the perpetrators and their intentions. In general, the analysis of the intentions of cyber-attacks is one of the main challenges in digital forensics. In many cases of cyber-attacks, the analysis of the intent of the attacks determines the strategy and tools used in the attack, thus facilitating the process of identifying the perpetrator of the attack with greater accuracy. In this paper a model will be proposed to analyze the intentions of cyber-attacks. In this proposal, a set of steps will be conceived by linking them with a case-based reasoning methodology. This model will be examined by analyzing the intent of attacks for some cases and comparing the results with other methods of analyzing the intent of attacks. Hopefully the results will determine the intent of cyber-attacks more accurately.
COVID-19 and Artificial Intelligence: the pandemic pacifier

COVID-19 remains a threat to the entire world. In an attempt to curb its spread and facilitate its treatment, the technological tool that is Artificial Intelligence (AI) is being researched as a potential alternative to conventional methods. Industrial Revolution 4.0 marks the dawn to the combination of digital, physical and biological systems, by application of digital skills such as Blockchain, Internet of things, Artificial Intelligence and Big data. AI tools in SARS-COV-2 pandemic are highly competitive to human performance, such as rapid screening and diagnosis of the disease, surveilling the efficacy of the treatment, keeping record and depicting active cases and mortality, inventions of medications and vaccines, relieving the workload of healthcare workers and extinguishing the spread of the disease. Contact tracing platforms like Aarogya Setu App, implemented by the Government of India, Australian Government's COVID Safe app, Trace Together- a Bluetooth-based contact tracing app developed in Singapore; based on syndromic mapping/surveillance technology. Artificial intelligence will become a mainstay in both the diagnosis and treatment of COVID-19 as well as similar pandemics in future. The application and system development will be challenging; the accuracy and rapidity of its use far outweigh this drawback. The current global technological leaders have proven that the retro modification of current data systems and applications have been indispensable in the war on COVID-19, thus permanently securing their development and application in future.
Detection of Dementia-Related Abnormal Behaviour Using Recursive Auto-Encoders

Age-related health issues have been increasing with the rise of life expectancy all over the world. One of these problems is cognitive impairment, which causes elderly people to have problems performing their daily activities. Detection of cognitive impairment at an early stage would enable medical doctors to deepen diagnosis and follow-up on patient status. Recent studies show that daily activities can be used to assess the cognitive status of elderly people. Additionally, the intrinsic structure of activities and the relationships between their sub-activities are important clues for capturing the cognitive abilities of seniors. Existing methods perceive each activity as a stand-alone unit while ignoring their inner structural relationships. This study investigates such relationships by modelling activities hierarchically from their sub-activities, with the overall goal of detecting abnormal activities linked to cognitive impairment. For this purpose, recursive auto-encoders (RAE) and their linear vs. greedy and supervised vs. semi-supervised variants are adopted to model the activities. Then, abnormal activities are systematically detected using RAE’s reconstruction error. Moreover, to apply RAEs for this problem, we introduce a new sensor representation called raw sensor measurement (RSM) that captures the intrinsic structure of activities, such as the frequency and the order of sensor activations. As real-world data are not accessible, we generated data by simulating abnormal behaviour, which reflects on cognitive impairment. Extensive experiments show that RAEs can be used as a decision-supporting tool, especially when the training set is not labelled to detect early indicators of dementia.
On the Legal Issues of Face Processing Technologies

The article analyzes the problems and prospects of using recognition technologies for human faces. The authors note that their development over recent years brings together the problems of the right to a personal image and the right to privacy, enshrined in the constitutions of most democratic countries. This is due to the fact that these technologies make it difficult, and, in some cases, impossible (or inappropriate) to use traditional legal mechanisms to protect these rights. In this regard, the authors propose to extend the concept of personal integrity to the “digital forms of existence” of an individual reflected in personal images, videos, virtual accounts, etc. The authors propose to put some approaches formulated in the article as the basis of the legal regulation of the use of facial processing technologies. In particular, there should be a legislative ban on the development and use of programs and systems that search and process photo and video images that are not publicly available, and legal liability measures should be established for its violation. On the contrary, a person’s posting of such information in the public domain should be interpreted as his consent to their search and comparison. Otherwise, issues should be resolved with the processing of photo and video images, as a result of which they are subjected to various kinds of distortions. Although the prohibition on creating such fakes is unreasonable, their publication and distribution may be restricted by law.
FedGroup: Accurate Federated Learning via Decomposed Similarity-Based Clustering

Federated Learning (FL) enables the multiple participating devices to collaboratively contribute to a global neural network model while keeping the training data locally. Unlike the centralized training setting, the non-IID and imbalanced (statistical heterogeneity) training data of FL is distributed in the federated network, which will increase the divergences between the local models and global model, further degrading performance. In this paper, we propose a novel clustered federated learning (CFL) framework FedGroup based on a similarity-based client clustering strategy, in which we 1) group the training of clients based on the similarities between the clients' optimize directions for high training performance; 2) reduce the complexity of client clustering algorithm by decomposing the high-dimension low-sample size (HDLSS) direction vectors. 3) implement a newcomer device cold start mechanism based on the auxiliary global model for framework scalability and practicality. FedGroup can achieve improvements by dividing joint optimization into groups of sub-optimization, and can be combined with FedProx, the state-of-the-art federated optimization algorithm. We evaluate FedGroup and FedGrouProx (combined with FedProx) on several open datasets. The experimental results show that our proposed frameworks significantly improving absolute test accuracy by +14.7% on FEMNIST compared to FedAvg, +5.4% on Sentiment140 compared to FedProx.
Representation Balancing Offline Model-based Reinforcement Learning

One of the main challenges in offline and off-policy reinforcement learning is to cope with the distribution shift that arises from the mismatch between the target policy and the data collection policy. In this paper, we focus on a model-based approach, particularly on learning the representation for a robust model of the environment under the distribution shift, which has been first studied by Representation Balancing MDP (RepBM). Although this prior work has shown promising results, there are a number of shortcomings that still hinder its applicability to practical tasks. In particular, we address the curse of horizon exhibited by RepBM, rejecting most of the pre-collected data in long-term tasks. We present a new objective for model learning motivated by recent advances in the estimation of stationary distribution corrections. This effectively overcomes the aforementioned limitation of RepBM, as well as naturally extending to continuous action spaces and stochastic policies. We also present an offline model-based policy optimization using this new objective, yielding the state-of-the-art performance in a representative set of benchmark offline RL tasks.
On Handwriting Pressure Normalization for Interoperability of Different Acquisition Stylus

In this paper, we present a pressure characterization and normalization procedure for online handwritten acquisition. Normalization process has been tested in biometric recognition experiments (identification and verification) using online signature database MCYT, which consists of the signatures from 330 users. The goal is to analyze the real mismatch scenarios where users are enrolled with one stylus and then, later on, they produce some testing samples using a different stylus model with different pressure response. Experimental results show: 1) a saturation behavior in pressure signal 2) different dynamic ranges in the different stylus studied 3) improved biometric recognition accuracy by means of pressure signal normalization as well as a performance degradation in mismatched conditions 4) interoperability between different stylus can be obtained by means of pressure normalization. Normalization produces an improvement in signature identification rates higher than 7% (absolute value) when compared with mismatched scenarios.
Olympic Games Event Recognition via Transfer Learning with Photobombing Guided Data Augmentation

Automatic event recognition in sports photos is both an interesting and valuable research topic in the field of computer vision and deep learning. With the rapid increase and the explosive spread of data, which is being captured momentarily, the need for fast and precise access to the right information has become a challenging task with considerable importance for multiple practical applications, i.e., sports image and video search, sport data analysis, healthcare monitoring applications, monitoring and surveillance systems for indoor and outdoor activities, and video captioning. In this paper, we evaluate different deep learning models in recognizing and interpreting the sport events in the Olympic Games. To this end, we collect a dataset dubbed Olympic Games Event Image Dataset (OGED) including 10 different sport events scheduled for the Olympic Games Tokyo 2020. Then, the transfer learning is applied on three popular deep convolutional neural network architectures, namely, AlexNet, VGG-16 and ResNet-50 along with various data augmentation methods. Extensive experiments show that ResNet-50 with the proposed photobombing guided data augmentation achieves 90% in terms of accuracy.
Ameliorated Frenet Trajectory Optimization Method Based on Artificial Emotion and Equilibrium Optimizer

An ameliorated Frenet trajectory optimization (AFTO) method based on artificial emotion (AE) and an equilibrium optimizer (EO) is proposed for the local trajectory planning of an unmanned ground vehicle (UGV). An artificial emotional potential field (AEPF) model is established to simulate AE. To realize a humanoid driving mode with emotional intelligence, AE is introduced into the Frenet trajectory optimization (FTO) method to determine the optimal trajectory. Based on the optimal discrete goal state of the FTO method, a first-sampling-then-optimization (FSTO) framework combining the FTO method with the EO is designed to obtain the optimal trajectory in a continuous goal state space. With different AEPF levels corresponding to different types of obstacles, simulation results show that the AEPF effectively adjusts the trajectory into different levels of safe distance between the UGV and obstacles corresponding to the humanoid driving mode. From the results of 30 independent experiments based on the AEPF, the FSTO framework in the AFTO method is effective for optimizing the trajectory of the FTO method at a lower cost. Moreover, the effectiveness of the proposed method for different types of roads is verified on a straight road and a curved road with obstacles in simulation. The improvement based on emotional intelligence and trajectory optimization in the AFTO method provides a humanoid driving mode for the UGV in the continuous goal state space.
Innovative Saliency based Deep Driving Scene Understanding System for Automatic Safety Assessment in Next-Generation Cars

Visual saliency is the human attention mechanism that encodes such visio-sensing information to extract features from the observation scene. In the last few years, visual saliency estimation has received significant research interests in the automotive field. While driving the vehicle, the car driver focuses on specific objects rather than others by deterministic brain-driven saliency mechanisms inherent perceptual activity. In this study, we propose an intelligent system that combines a driver’s drowsiness detector with a saliency-based scene understanding pipeline. Specifically, we implemented ad-hoc 3D pre-trained Semantic Segmentation Deep Network to process the frames captured by automotive-grade camera device placed outside the car. We used an embedded platform based on the STA1295 core (ARM A7 Dual-Cores) with a hardware accelerator for hosting the proposed pipeline. Besides, we monitor the car driver’s drowsiness by using an innovative bio-sensor installed on the steering wheel, to collect the PhotoPlethysmoGraphy (PPG) signal. Ad-hoc 1D Temporal Deep Convolutional Network has been designed to classify the collected PPG time-series in order to assess the driver’s attention level. Finally, we compare the detected car driver’s attention level with corresponding saliency-based scene classification in order to assess the overall safety level. Experimental results confirm the effectiveness of the proposed pipeline.
Cigarette end detection based on EfficientDet

Abstract In the process of building a harmonious society in China, smoking in public places is difficult to curb. However, with the continuous development of computer vision, object detection algorithm plays a key role in the above video detection scene. However, if the detection process is not real-time, it can not be applied to smoking scenes in public places, giving people timely judgment. After analyzing a variety of target detection algorithms, this paper considers that EfficientDet can achieve high efficiency under a wide range of resource constraints, and can easily carry out multi-scale feature fusion, so that it has high speed and accuracy in the process of target detection. In this paper, Efficientnet is used as the backbone feature extraction network, and a set of fixed scaling coEfficients are used to scale the depth, width and resolution of the network for preliminary feature extraction, so as to obtain three effective feature layers. Then, in order to improve the prediction level and enhance the feature extraction, the three effective feature layers are transferred to bifpn with cross-scale connection optimization The prediction results can be used as the evaluation results of the model. The evaluation results show that EfficientDet has high mAP.
Analysis of phase noise effects in a coupled Mach–Zehnder interferometer for a much stabilized free-space optical link

Recently, new physics for unconditional security in a classical key distribution (USCKD) has been proposed and demonstrated in a frame of a double Mach-Zehnder interferometer (MZI) as a proof of principle, where the unconditional security is rooted in MZI channel superposition. Due to environmental phase noise caused by temperature variations, atmospheric turbulences, and mechanical vibrations, free-space optical links have been severely challenged for both classical and quantum communications. Here, the double MZI scheme of USCKD is analyzed for greatly subdued environment-caused phase noise via double unitary transformation, resulting in potential applications of free-space optical links, where the free-space optical link has been a major research area from fundamental physics of atomic clock and quantum key distribution to potential applications of geodesy, navigation, and MIMO technologies in mobile communications systems.
Prediction Error-Driven Memory Consolidation for Continual Learning: On the Case of Adaptive Greenhouse Models

This work presents an adaptive architecture that performs online learning and faces catastrophic forgetting issues by means of episodic memories and prediction-error driven memory consolidation. In line with evidences from the cognitive science and neuroscience, memories are retained depending on their congruency with the prior knowledge stored in the system. This is estimated in terms of prediction error resulting from a generative model. Moreover, this AI system is transferred onto an innovative application in the horticulture industry: the learning and transfer of greenhouse models. This work presents a model trained on data recorded from research facilities and transferred to a production greenhouse.
Temporal Parallelization of Inference in Hidden Markov Models

This paper presents algorithms for parallelization of inference in hidden Markov models (HMMs). In particular, we propose parallel backward-forward type of filtering and smoothing algorithm as well as parallel Viterbi-type maximum-a-posteriori (MAP) algorithm. We define associative elements and operators to pose these inference problems as parallel-prefix-sum computations in sum-product and max-product algorithms and parallelize them using parallel-scan algorithms. The advantage of the proposed algorithms is that they are computationally efficient in HMM inference problems with long time horizons. We empirically compare the performance of the proposed methods to classical methods on a highly parallel graphical processing unit (GPU).