Reviewerly/exHarmony huggingface dataset:
A2X: An Agent and Environment Interaction Benchmark for Multimodal Human Trajectory Prediction

In recent years, human trajectory prediction (HTP) has garnered attention in computer vision literature. Although this task has much in common with the longstanding task of crowd simulation, there is little from crowd simulation that has been borrowed, especially in terms of evaluation protocols. The key difference between the two tasks is that HTP is concerned with forecasting multiple steps at a time and capturing the multimodality of real human trajectories. A majority of HTP models are trained on the same few datasets, which feature small, transient interactions between real people and little to no interaction between people and the environment. Unsurprisingly, when tested on crowd egress scenarios, these models produce erroneous trajectories that accelerate too quickly and collide too frequently, but the metrics used in HTP literature cannot convey these particular issues. To address these challenges, we propose (1) the A2X dataset, which has simulated crowd egress and complex navigation scenarios that compensate for the lack of agent-to-environment interaction in existing real datasets, and (2) evaluation metrics that convey model performance with more reliability and nuance. A subset of these metrics are novel multiverse metrics, which are better-suited for multimodal models than existing metrics. The dataset is available at: https://mubbasir.github.io/HTP-benchmark/.
GiNA: A Blockchain-based Gaming scheme towards Ethereum 2.0

With the advent of the Internet, the gaming industry has grown tremendously in business, which also raises concerns for cheating and unfair gameplay. In this paper, we propose a novel approach (GiNA) using Blockchain technology to address a few problems with online Peer-to-Peer (P2P) games. GiNA uses two different data packet transfer schemes to ensure the security and authenticity of the data packet sent and received by game clients. More sensitive data uses a Smart contract-based ON-CHAIN data packet transfer solution and less sensitive data uses an OFF-CHAIN data packet transfer solution with end-to-end encryption for data security. A marketplace where peers can buy and sell purchasable assets with the help of Gicoins. Gicoins is a stable token with compliance with the ERC 20 token of Etheruem Blockchain. Later, a low cost and low bandwidth utilization data storage solution is proposed for storing data in a decentralized and distributed manner. Results show that the performance of the proposed approach GiNA is better in comparison to the traditional approaches with parameters such as latency, scalability, packet loss percentage, Blockchain (BC) performance, and data storage comparison.
Federated Traffic Synthesizing and Classification Using Generative Adversarial Networks

With the fast growing demand on new services and applications as well as the increasing awareness of data protection, traditional centralized traffic classification approaches are facing unprecedented challenges. This paper introduces a novel framework, Federated Generative Adversarial Networks and Automatic Classification (FGAN-AC), which integrates decentralized data synthesizing with traffic classification. FGAN-AC is able to synthesize and classify multiple types of service data traffic from decentralized local datasets without requiring a large volume of manually labeled dataset or causing any data leakage. Two types of data synthesizing approaches have been proposed and compared: computation-efficient FGAN (FGAN-I) and communication-efficient FGAN (FGAN-II). The former only implements a single CNN model for processing each local dataset and the later only requires coordination of intermediate model training parameters. An automatic data classification and model updating framework has been proposed to automatically identify unknown traffic from the synthesized data samples and create new pseudo-labels for model training. Numerical results show that our proposed framework has the ability to synthesize highly mixed service data traffic and can significantly improve the traffic classification performance compared to existing solutions.
Witness Authenticating NIZKs and Applications

We initiate the study of witness authenticating NIZK proof systems (waNIZKs), in which one can use a witness w of a statement x to identify whether a valid proof for x is indeed generated using w. Such a new identification functionality enables more diverse applications, and it also puts new requirements on soundness that: (1) no adversary can generate a valid proof that will not be identified by any witness; (2) or forge a proof using her valid witness to frame others. To work around the obvious obstacle towards conventional zero-knowledgeness, we define entropic zero-knowledgeness that requires the proof to leak no partial information, if the witness has sufficient computational entropy. We give a formal treatment of this new primitive. The modeling turns out to be quite involved and multiple subtle points arise and particular cares are required. We present general constructions from standard assumptions. We also demonstrate three applications in non-malleable (perfectly one-way) hash, group signatures with verifier-local revocations and plaintext-checkable public-key encryption. Our waNIZK provides a new tool to advance the state of the art in all these applications.
Tuning Database-Friendly Random Projection Matrices for Improved Distance Preservation on Specific Data

Abstract Random Projection is one of the most popular and successful dimensionality reduction algorithms for large volumes of data. However, given its stochastic nature, different initializations of the projection matrix can lead to very different levels of performance. This paper presents a guided random search algorithm to mitigate this problem. The proposed method uses a small number of training data samples to iteratively adjust a projection matrix, improving its performance on similarly distributed data. Experimental results show that projection matrices generated with the proposed method result in a better preservation of distances between data samples. Conveniently, this is achieved while preserving the database-friendliness of the projection matrix, as it remains sparse and comprised exclusively of integers after being tuned with our algorithm. Moreover, running the proposed algorithm on a consumer-grade CPU requires only a few seconds.
Feedback particle filter for collective inference

&lt;p style='text-indent:20px;'&gt;The purpose of this paper is to describe the feedback particle filter algorithm for problems where there are a large number (&lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ M $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;) of non-interacting agents (targets) with a large number (&lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ M $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;) of non-agent specific observations (measurements) that originate from these agents. In its basic form, the problem is characterized by data association uncertainty whereby the association between the observations and agents must be deduced in addition to the agent state. In this paper, the large-&lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ M $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; limit is interpreted as a problem of collective inference. This viewpoint is used to derive the equation for the empirical distribution of the hidden agent states. A feedback particle filter (FPF) algorithm for this problem is presented and illustrated via numerical simulations. Results are presented for the Euclidean and the finite state-space cases, both in continuous-time settings. The classical FPF algorithm is shown to be the special case (with &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ M = 1 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;) of these more general results. The simulations help show that the algorithm well approximates the empirical distribution of the hidden states for large &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ M $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;.&lt;/p&gt;
An Energy-Efficient Inference Method in Convolutional Neural Networks Based on Dynamic Adjustment of the Pruning Level

In this article, we present a low-energy inference method for convolutional neural networks in image classification applications. The lower energy consumption is achieved by using a highly pruned (lower-energy) network if the resulting network can provide a correct output. More specifically, the proposed inference method makes use of two pruned neural networks (NNs), namely mildly and aggressively pruned networks, which are both designed offline. In the system, a third NN makes use of the input data for the online selection of the appropriate pruned network. The third network, for its feature extraction, employs the same convolutional layers as those of the aggressively pruned NN, thereby reducing the overhead of the online management. There is some accuracy loss induced by the proposed method where, for a given level of accuracy, the energy gain of the proposed method is considerably larger than the case of employing any one pruning level. The proposed method is independent of both the pruning method and the network architecture. The efficacy of the proposed inference method is assessed on Eyeriss hardware accelerator platform for some of the state-of-the-art NN architectures. Our studies show that this method may provide, on average, 70% energy reduction compared to the original NN at the cost of about 3% accuracy loss on the CIFAR-10 dataset.
Outcomes-Driven Clinical Phenotyping in Cardiogenic Shock using a Mixture of Experts

Cardiogenic shock (CS) is a deadly and complicated illness. Despite extensive research into its treatment, mortality remains high and has not decreased over time. Patients suffering from CS are highly heterogeneous. Developing an understanding of phenotypes among these patients is crucial for understanding this disease and appropriate treatments for individual patients. In this work, we develop a deep mixture of experts approach to jointly find phenotypes among patients with CS while simultaneously estimating their risk of in-hospital mortality. This model is applied to a cohort of 28,304 patients with CS, predicting in-hospital mortality with an AUROC of 0.85 ± 0.01 and discovering five phenotypes among the population. This approach allows for grouping patients in clinical clusters with different rates of device utilization and different risk of mortality. This approach jointly finds phenotypes within a clinical population and in modeling risk among that population.
Trust-based Cognitive Decision Making by Social Things - A Case Study of Cancer Treatment

Social things face great uncertainty and complexity in their decision-making. This is true whether the social thing is as large as the electric grid of a country or as small as drug carrier targeted nanomachines employed for cancer treatment. The problem is further complicated when it is tasked with serving humans, with the intricate and ill-defined meaning of service. Therefore, there is a need for cognitive decision-making in which human factors of time, attitude, attention, trust, and bias are considered in the recommendation, prediction, analysis, estimation, and automated decision making. Here, we introduce a trust-based decision-making architecture for swarms of bioinspired nanomachines. Particularly, the factor of trust is used as an index for swarm joining and disjoining. Each nanomachine's decision is considered as a new attitude that is weakened or reinforced by a trust factor. The trust factor is derived using a Fuzzy Cognitive Map which is composed of integrity, competence, consistency, loyalty, and openness. Nanomachines with high trust factors form a dense group and change to a “trustee” swarm. The trustee converges to the cancer site. The result shows the proposed method in targeted drug delivery outperforms the competing strategies with lower hypoxic and endothelial cell density as the marker of cancer.
Research Trend Analysis of Artificial Intelligence

Trends analysis of bibliographic libraries in the context of text mining has gained popularity during the recent era. Many fields have been explored to find the trends of research during different periods. The purpose of this study is to provide the essential key phrases in the field of Artificial Intelligence over a selected time spans to show the evolution of researchers' interest in this specific area of computer science over a time period. More precisely, for analyzing the trends of the esteemed field, Artificial Intelligence, we scrapped the metadata of research articles published by IEEE between 2010 and 2019 through IEEE Xplore API and created a corpus. The preprocessing is performed, and after that, a phrase mining technique is applied to this data to extract quality key phrases for research trend analysis of different time spans. The results are visualized for readers' better understanding. Also, the similarity between our extracted phrases and author given keywords was calculated by us.
Co-Designing a Controversy-Based Educational Programme in a Science Centre

In this chapter, we investigate the use of collaborative design techniques and principles as a way to develop the practice of addressing controversial and wicked problems in science centres. One researcher and four science centre educators co-designed a controversy-based educational programme for upper secondary schools in a Norwegian science centre. The process provided insight into the complexity of designing controversy-based educational programmes, as the concept, content and pedagogical activities all call for new ways of thinking about educational design. In addition, co-design calls for new ways of thinking about research in that we experienced conflicts with distributing responsibility and roles, as well as with expectations of the outcome of the co-design among the educators and the researcher. Nevertheless, we argue that co-design was a suitable approach for creating a common language and awareness about the intentions and aims of addressing controversial issues.
From Data and Models to Decision Support Systems: Lessons and Advice for the Future

Model-based Decision Support Systems (DSSs) employ various types of models, such as statistical, optimization, simulation, or rule-based. Models are used to assess and analyze the given decision situation, and on this basis advise the decision-maker. Generally, the DSS development process involves three steps: (1) model development, (2) implementing the model(s) in a DSS, and (3) using the DSS. In this chapter, we focus on two model development approaches: Data Mining and Expert Modeling. We advocate for combing the two in order to get better models and better DSSs in general. We illustrate some points and potential pitfalls using an example of the PD_manager DSS, which is aimed at supporting medication change decisions in the management of Parkinson’s disease. Based on the experience from PD_manager and some other DSS development projects, we propose the so-called 5C requirements for better DSS models: correctness, completeness, consistency, comprehensibility, and convenience. Finally, we summarize the lessons learned and give advice to DSS developers and researchers.
A Non-uniform Sampling Approach for Fast and Efficient Path Planning

In this paper, we develop a non-uniform sampling approach for fast and efficient path planning of autonomous vehicles. The approach uses a novel non-uniform partitioning scheme that divides the area into obstacle-free convex cells. The partitioning results in large cells in obstacle-free areas and small cells in obstacle-dense areas. Subsequently, the boundaries of these cells are used for sampling; thus significantly reducing the burden of uniform sampling. When compared with a standard uniform sampler, this smart sampler significantly 1) reduces the size of the sampling space while providing completeness and optimality guarantee, 2) provides sparse sampling in obstacle-free regions and dense sampling in obstacle-rich regions to facilitate faster exploration, and 3) eliminates the need for expensive collision-checking with obstacles due to the convexity of the cells. This sampling framework is incorporated into the RRT* path planner. The results show that RRT* with the non-uniform sampler gives a significantly better convergence rate and smaller memory footprint as compared to RRT* with a uniform sampler.
Automatic Optimization of YOLOv3 Based on Particle Swarm Algorithm

YOLO (look once at a time) is a target detection method of deep learning, which has achieved great success in image classification and localization and has been widely used. YOLOv3 is the third version of YOLO. Compared with previous versions of software, YOLOv3 employs a better basic network (ResNet) and classifier in the network structure. It also uses multi-scale functions for object detection. For a neural network, the setting of hyperparameters is one of the important factors that determine the performance of the model. The hyperparameter of YOLOs is manually designed by experienced researchers. This process requires researchers to constantly turn parameters to enhance network performance and the experimental process is clueless. This chapter presents a method of particle swarm optimization combined with YOLOv3 for automatic parameter tuning. According to the optimization idea of particle swarm algorithm, the optimal hyperparameter is taken as the search target of particle swarm algorithm. Based on a series of comparative experiments, we can prove on the VOC dataset that the YOLOv3 network optimized by PSO achieves higher accuracy than the YOLOv3 network model before optimization.
VISFF: An Approach for Video Summarization Based on Feature Fusion

In recent years, the amount of video data has been increasing explosively, and the requirements for video summarization technology have also increased. Video summarization is a summary of the video. By browsing the video summarization, users can quickly understand the content of the video. The traditional video summarization algorithms extract the global features of the video frames to form video summarization. However, these algorithms have obvious disadvantages. Therefore, we propose a method to generate video summarization by fusing the global and local features of video frames, and clustering video frames by DBSCAN algorithm. By comparing with the video summarization manually selected by multiple users, we achieve better results on OVP and YouTube datasets than previous algorithms.
Investigation of light wave propagation in atmospheric disturbance toward quantum illumination

Quantum illumination is a technology that aims to improve the detection sensitivity of a target by utilizing quan- tum entangled state of light. We are studying quantum illumination using two-mode squeezed light. In quantum illumination, one light wave in a quantum entangled state is emitted toward a target during atmospheric disturbance. Therefore, it is extremely important to investigate the propagation characteristics of light waves under various atmospheric conditions toward the realization of quantum illumination. As an example of atmospheric disturbances, we have investigated the effect of uniform fog on light wave propagation in previous studies. It has been found that the effect of uniform fog on the propagation of laser light is mainly energy attenuation. Here we measure the propagation characteristics of single-mode squeezed light in a fog. We succeeded in observing the squeezed light that passed through the fog, and confirmed that the noise level of the squeezed light was degraded by the influence of the fog. It was found that the degradation of the noise level can be explained by considering that the influence of fog is mainly the attenuation of energy.
A comprehensive study of the Design and Security of the IoT layer Attacks

IoT is the network of thousands of individuals, resources and expertise interconnecting and sharing. IoT implementations are strongly validated by increased usability, reliability and automation. Authentication and recovery are important in order to set up an automated IoT environment for the protection and privacy of attacks. This research work presents a survey on the design and security of the IoT layer attack. In this thesis, security and privacy concerns will be detailed. This paper also performs a comprehensive overview on the security threats and sources of threat in IoT applications addressing current IoT security principles and IoT protection and privacy potential proposals.
Balanced recommendation algorithm based on new association rules and decision-making process

With the rapid growth of information and the development of the intelligent catering industry, the application of association rules in recipe recommendation has become more and more extensive. However, due to the limitations of data mining, it is necessary to improve the recommendation system based on the attributes of the dishes. The purpose of this research is to develop a dish recommendation system based on new association rules, according to the various types of relationships between dishes and their combinations, and to design a recommendation algorithm with the goal of balanced collocation. This article proposes a balanced recommendation algorithm BRA based on the newly-designed association rules and decision-making process. The algorithm combines the historical frequency and attribute characteristics of dishes to update orders and recommend dishes at each decision stage. This article evaluate algorithms through a new indicator called “overflow rate”. The experiments show that the high-quality orders generated by BRA is 90%, and the pass rate is 100%; while the high-quality generated by the scheme based on association rules is only 10%, and the pass rate is about 83.3%.
An optical computing chip executing complex-valued neural network and its on-chip training

The optical implementation of neural networks is proposed to have advantages over electronic implementations with lower power consumption and higher computation speed. However, most optical neural networks (ONNs) utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many advantages of optical computing such as efficient complex-valued operations. Complex-valued neural networks are advantageous to their real-valued counterparts by offering rich representation space, fast convergence, and strong generalizations. We propose and demonstrate an ONN that implements truly complex-valued neural networks, achieving high accuracy and strong learning capability in many benchmark tasks.<sup>1</sup> On the other hand, efficiently training ONNs remains a formidable challenge, due to the difficulty in obtaining gradient information from a physical device. We propose an efficient on-chip training protocol for ONNs and demonstrate it by several practical tasks.<sup>2</sup> The protocol is gradient-free and physical agnostic, and is applicable for various types of chip structures, especially those that cannot be analytically decomposed and characterized. The protocol is robust to experimental perturbations like imperfect phase detection and photodetection noise. Our results present a promising avenue towards deep complex networks with smaller chip size, stronger performance, and flexible reconfiguration to realistic applications (e.g., facial recognition, natural language processing, and autonomous vehicles).