Reviewerly/exHarmony huggingface dataset:
Classical versus quantum models in machine learning: insights from a finance application

Although several models have been proposed towards assisting machine learning (ML) tasks with quantum computers, a direct comparison of the expressive power and efficiency of classical versus quantum models for datasets originating from real-world applications is one of the key milestones towards a quantum ready era. Here, we take a first step towards addressing this challenge by performing a comparison of the widely used classical ML models known as restricted Boltzmann machines (RBMs), against a recently proposed quantum model, now known as quantum circuit Born machines (QCBMs). Both models address the same hard tasks in unsupervised generative modeling, with QCBMs exploiting the probabilistic nature of quantum mechanics and a candidate for near-term quantum computers, as experimentally demonstrated in three different quantum hardware architectures to date. To address the question of the performance of the quantum model on real-world classical data sets, we construct scenarios from a probabilistic version out of the well-known portfolio optimization problem in finance, by using time-series pricing data from asset subsets of the S&P500 stock market index. It is remarkable to find that, under the same number of resources in terms of parameters for both classical and quantum models, the quantum models seem to have superior performance on typical instances when compared with the canonical training of the RBMs. Our simulations are grounded on a hardware efficient realization of the QCBMs on ion-trap quantum computers, by using their native gate sets, and therefore readily implementable in near-term quantum devices.
Intrusion Detection in Intelligent Transportation System and its Applications using Blockchain Technology

Privacy and Trust are critical issues in automation systems/ transportation systems. Today’s Vehicle is need of everyone for moving one place to another. Together this, data security plays an important role in automation systems as critical user’s (vehicle’s user) data is moved to another user though internet with the help of wireless devices and routes which includes optical fiber, radio channels, etc. In fact, each and every device is connected to the internet and is linked to each other, thus forming the Internet of Things (IoT). As the network is moving towards wireless applications, many threats to vehicles (autonomous vehicles) are becoming a critical problem for vehicles users and service providers. A majority of these attacks can be spotted and detected with the hep of a number of intrusion detection techniques which were elucidated in the earlier decade. These techniques are highly efficient in the identification of any form of individual breaches into the system by catching hold of invalid data access. A few of the systems which need safety are an integral part of Wireless Networks which consist of WLANs (Wireless Local Area Networks), WPANs (Wireless Personal Area Networks), etc. WPAN family further constitutes of three networks which are WSNs (Wireless Sensor Networks), mobile phones and RFID (Radio Frequency Identification like On Board Units (OBUs)). Since digitization is taking place in each and every sector, i.e., defence, healthcare, education, automation industries etc., and so the threat to data also exist. In this article, we protect IoT based environment based smart/ Intelligent Transportation Systems (ITS) using a novel concept “Blockchain Technology”. With proposing novel solution called ‘’PChain using Blockchain Technology (BT), we received many benefit in ITS’s applications. We discuss several open issues and challenges for the respective technology in near future (or next decade).
Remerging Feigenbaum Trees, Coexisting Behaviors and Bursting Oscillations in a Novel 3D Generalized Hopfield Neural Network

This paper, a novel 3D generalized Hopfield neural network is proposed and investigated in order to generate and highlight some unknown behaviors related to such type of neural network. This generalized model is constructed by exploiting the effect of an external stimulus on the dynamics of a simplest 3D autonomous Hopfield neural network reported to date. The stability of the model around its ac-equilibrium point is studied. We note that the model has three types of stability depending on the value of the external stimulus. The stable node-focus, the unstable saddle focus, and the stable node characterize the stability of the equilibrium point of the model. Traditional nonlinear analyses tools are used to highlight and support several complex phenomena such as remerging Feigenbaum trees, the coexistence of up to two, four and six disconnected stable states when the amplitude of the external stimulus is set to zero. Furthermore, for some values of the frequency and amplitudes of the external stimulus, bursting oscillations occur. This latter behavior is characterized by the fact that oscillations switch between quiescent states and spiking states, repetitively. The transformed phase portraits are used to support the bursting mode of oscillations found. Finally, PSpice simulations enable to support the results of the theoretical studies.
Combining forecasts of day-ahead solar power

Solar power forecasting is important for the reliable and economic operation of power systems with high penetration of solar energy. The solar power forecasts for the day-ahead time horizon are more erroneous than the hour-ahead time horizon. Numerical weather prediction (NWP) variables such as irradiance, cloud cover, precipitation etc. are used as input to day-ahead forecasting models. The uncertainty in NWP varies with weather conditions. Different forecasting algorithms based on a single method are available in the literature. Combination of individual forecasting algorithms increases the accuracy of the forecasts. However, the combined-forecast has yet not been analysed much in the area of day-ahead solar power forecasting. This paper thus explores different combined-forecast methods such as mean, median, linear regression and non-linear regressions using supervised machine learning algorithms. The number of models required for day-ahead solar power forecasts is studied. One for all hour (same) or separate models for each hour of the day are possible. The effects of retraining frequency on the performance of the forecasting models, which is important for the computational burden of the system, are also studied. Forecasting algorithms are applied to three solar plants in Australia.
Too Good to Be True: Firm Social Performance and the Risk of Data Breach

Data breaches are now a daily occurrence. What corporate leaders may not realize is that certain actions they are taking in the social responsibility space may, in fact, be placing a proverbial target on their backs. Indeed, there is evidence that the hacking community is not homogeneous, and at least some hackers from both internal and external sources appear to be motivated by what they dislike as opposed to solely financial gain. Recent hacks against the World Health Organization, as a result of its actions (or supposed inactions) related to the COVID-19 pandemic, are a case in point. In this paper, we put forth the idea that espoused positive social performance in areas that are peripheral to core business operations (e.g., philanthropy, recycling programs) can be a detriment to information security, particularly when firms have simultaneous high levels of social concerns (e.g., poor employee relations, product safety concerns, involvement in an environmental controversy). Our results support this outcome. It appears that some perpetrators can “sniff out” firm social actions that attempt to give the appearance of social responsibility and possibly mask poor social performance, and consequently, these firms are victimized by a malicious data breach more often.
A novel high dynamic range 3D measurement method based on adaptive fringe projection technique

Fringe projection techniques have been widely used in three-dimensional (3D) small-field measurement; however, measuring the 3D shapes of small objects with high ranges of reflectivity across their surfaces remains challenging because image saturation leads to measurement error. In this study, a novel adaptive fringe projection technique was proposed to address this issue. First, a 255 uniform gray-level pattern was projected onto the surface to mark the saturated region in the captured image. Second, another uniform gray-level pattern with lower intensity was used to calculate the reflectivity across the surface and an appropriate projection gray level in the camera coordinate system was calculated at the pixel level. Third, a set of orthogonal sinusoidal fringe patterns were projected onto the object to establish the spatial relationship between the camera and projection systems, and adaptive fringe patterns were generated in the projection coordinate system. Subsequently, the generated adaptive fringe patterns were used to measure the object. The experimental results demonstrate that the proposed method could retrieve the 3D shapes of small shiny objects with high accuracy and relatively few images.
Traveling salesman problem: a perspective review of recent research and new results with bio-inspired metaheuristics

Abstract The traveling salesman problem (TSP) is one of the most studied problems in computational intelligence and operations research. Since its first formulation, a myriad of works has been published proposing different alternatives for its solution. Additionally, a plethora of advanced formulations have also been proposed by the related practitioners, trying to enhance the applicability of the basic TSP. This chapter is firstly devoted to providing an informed overview on the TSP. For this reason, we first review the recent history of this research area, placing emphasis on milestone studies contributed in recent years. Next, we aim at making a step forward in the field proposing an experimentation hybridizing three different reputed bio-inspired computational metaheuristics (namely, particle swarm optimization, the firefly algorithm, and the bat algorithm) and the novelty search mechanism. For assessing the quality of the implemented methods, 15 different datasets taken from the well-known TSPLIB have been used. We end this chapter by sharing our envisioned status of the field, for which we identify opportunities and challenges which should stimulate research efforts in years to come.
MRCDRL: Multi-robot coordination with deep reinforcement learning

This paper proposes a multi-robot cooperative algorithm based on deep reinforcement learning (MRCDRL). We use end-to-end methods to train directly from each robot-centered, relative perspective-generated image, and each robot’s reward as the input. During training, it is not necessary to specify the target position and movement path of each robot. MRCDRL learns the actions of each robot by training the neural network. MRCDRL uses the neural network structure that was modified from the Duel neural network structure. In the Duel network structure, there are two streams that each represents the state value function and the state-dependent action advantage function, and the results of the two streams are merged. The proposed method can solve the resource competition problem on the one hand and can solve the static and dynamic obstacle avoidance problems between multi-robot in real time on the other hand. Our new MRCDRL algorithm has higher accuracy and robustness than DQN and DDQN and can be effectively applied to multi-robot collaboration.
NetTaxo: Automated Topic Taxonomy Construction from Text-Rich Network

The automated construction of topic taxonomies can benefit numerous applications, including web search, recommendation, and knowledge discovery. One of the major advantages of automatic taxonomy construction is the ability to capture corpus-specific information and adapt to different scenarios. To better reflect the characteristics of a corpus, we take the meta-data of documents into consideration and view the corpus as a text-rich network. In this paper, we propose NetTaxo, a novel automatic topic taxonomy construction framework, which goes beyond the existing paradigm and allows text data to collaborate with network structure. Specifically, we learn term embeddings from both text and network as contexts. Network motifs are adopted to capture appropriate network contexts. We conduct an instance-level selection for motifs, which further refines term embedding according to the granularity and semantics of each taxonomy node. Clustering is then applied to obtain sub-topics under a taxonomy node. Extensive experiments on two real-world datasets demonstrate the superiority of our method over the state-of-the-art, and further verify the effectiveness and importance of instance-level motif selection.
SCLNet: Spatial context learning network for congested crowd counting

Accurate congested crowd counting is a challenging task, especially in complex crowd scenes. Many existing counting models easily fail in such cases. To solve this problem, we propose a spatial context learning network (SCLNet) for congested crowd counting. SCLNet consists of three parts: feature encoder (FE), spatial context learning decoder (SCLD), and density regression module (DRM). FE firstly processes each input image for feature extracting. Then, the extracted features are fed to SCLD for acquiring spatial context information from different depths of the network. Specially, SCLD consists of three dilated attention modules (DAM). Each DAM applies channel attention mechanism (CA) and spatial attention mechanism (SA) to process extracted features for obtaining spatial context information from the channel and spatial dimensions, respectively. Finally, the features with spatial context information are processed by DRM for crowd density estimation. Experiments are conducted on the ShanghaiTech, UCF_CC_50, UCF-QNRF datasets, and the performance of our method is competitive to the other state-of-the-art methods. Besides, we also evaluate SCLNet on the crowd localization task with the UCF-QNRF dataset, and the results demonstrate the effectiveness of our model.
Deep historical long short-term memory network for action recognition

Human action recognition technology has received increasing interest recently. The technology is very useful in sports video analysis. Most of the action recognition methods in sports mainly focus on recognizing which sport is being performed. However, recognizing of the specific action in videos is important for the analysis of some sports video such as tennis matches. Hence, in this paper, we proposed a deep historical long short-term memory network for video-based tennis action recognition and general action recognition. First, the spatial representations are extracted from each frame using a pre-trained convolutional neural network (CNN). To describe the temporal information, a stacked multi-layer long short-term memory network (LSTM) was used. The historical information of the past frames is important for modeling the action. So we propose a historical information layer that is added to the top of the multi-layered LSTM network. A historical feature of each video is generated for classification by hybridizing the hidden state of LSTM at time t and the historical updated feature at time t-1 with an updating scheme and utilized for classification. Experiments on the benchmark datasets demonstrate that our method that using only simple raw RGB video can outperform the state-of-the-art baselines for both general action recognition and tennis action recognition.
Conceptual framework of STEM based on Japanese subject principles

Abstract Background School education should improve science, technology, engineering, and mathematics (STEM) ability not only in science and mathematics but also in technology and engineering. However, practice and research are being conducted without clear definitions and methods for STEM education. Moreover, the positioning and characteristics of the technology included in each STEM field are unclear. Therefore, it is necessary to propose an appropriate framework for practice of STEM from the viewpoint of technology education. In response to this need, this commentary proposes a conceptual framework for the appropriate practice of STEM education. Results First, we referred to the perspectives and thinking styles of the fields of science, technology, and mathematics in the Japanese curriculum to specify the approach of the education system to each subject included in STEM. Next, to determine the concept of engineering in STEM education, we referred to the definition of engineering presented in Japan, the USA, and the UK. We positioned engineering, which means creating structures, processes, systems, etc., as a practical STEM activity and attempted to relate it to the unique perspectives and thinking styles of mathematics, science, and technology. Conclusion We proposed a conceptual framework for the appropriate practice of STEM education based on the principles of subjects in the Japanese curriculum. The conceptual framework suggests that a means to improve the practice of STEM education is to retain the principles of science, technology, and mathematics in the activity of engineering. It can be inferred that the key point for practicing STEM education is to examine and design the appropriate order and combination of the learning process and activities based on the proposed conceptual framework. Although this framework is theoretical, it can be useful in determining an adequate practice of STEM education and clarifying the relationship between STEM education and technology education.
Geometry-Aware Generation of Adversarial Point Clouds

Machine learning models have been shown to be vulnerable to adversarial examples. While most of the existing methods for adversarial attack and defense work on the 2D image domain, a few recent attempts have been made to extend them to 3D point cloud data. However, adversarial results obtained by these methods typically contain point outliers, which are both noticeable and easy to defend against using the simple techniques of outlier removal. Motivated by the different mechanisms by which humans perceive 2D images and 3D shapes, in this paper we propose the new design of geometry-aware objectives, whose solutions favor (the discrete versions of) the desired surface properties of smoothness and fairness. To generate adversarial point clouds, we use a targeted attack misclassification loss that supports continuous pursuit of increasingly malicious signals. Regularizing the targeted attack loss with our proposed geometry-aware objectives results in our proposed method, Geometry-Aware Adversarial Attack ( GeoA3). The results of GeoA3 tend to be more harmful, arguably harder to defend against, and of the key adversarial characterization of being imperceptible to humans. While the main focus of this paper is to learn to generate adversarial point clouds, we also present a simple but effective algorithm termed Geo+A3-IterNormPro, with Iterative Normal Projection (IterNorPro) that solves a new objective function Geo+A3, towards surface-level adversarial attacks via generation of adversarial point clouds. We quantitatively evaluate our methods on both synthetic and physical objects in terms of attack success rate and geometric regularity. For a qualitative evaluation, we conduct subjective studies by collecting human preferences from Amazon Mechanical Turk. Comparative results in comprehensive experiments confirm the advantages of our proposed methods. Our source codes are publicly available at https://github.com/Yuxin-Wen/GeoA3.
3D Hand Pose Estimation Using Synthetic Data and Weakly Labeled RGB Images

Compared with depth-based 3D hand pose estimation, it is more challenging to infer 3D hand pose from monocular RGB images, due to the substantial depth ambiguity and the difficulty of obtaining fully-annotated training data. Different from the existing learning-based monocular RGB-input approaches that require accurate 3D annotations for training, we propose to leverage the depth images that can be easily obtained from commodity RGB-D cameras during training, while during testing we take only RGB inputs for 3D joint predictions. In this way, we alleviate the burden of the costly 3D annotations in real-world dataset. Particularly, we propose a weakly-supervised method, adaptating from fully-annotated synthetic dataset to weakly-labeled real-world single RGB dataset with the aid of a depth regularizer, which serves as weak supervision for 3D pose prediction. To further exploit the physical structure of 3D hand pose, we present a novel CVAE-based statistical framework to embed the pose-specific subspace from RGB images, which can then be used to infer the 3D hand joint locations. Extensive experiments on benchmark datasets validate that our proposed approach outperforms baselines and state-of-the-art methods, which proves the effectiveness of the proposed depth regularizer and the CVAE-based framework.
A Two-Parameter Modified Logistic Map and Its Application to Random Bit Generation

This work proposes a modified logistic map based on the system previously proposed by Han in 2019. The constructed map exhibits interesting chaos related phenomena like antimonotonicity, crisis, and coexisting attractors. In addition, the Lyapunov exponent of the map can achieve higher values, so the behavior of the proposed map is overall more complex compared to the original. The map is then successfully applied to the problem of random bit generation using techniques like the comparison between maps, X O R , and bit reversal. The proposed algorithm passes all the NIST tests, shows good correlation characteristics, and has a high key space.
Fuzzy color histogram equalization with weighted distribution for image enhancement

Low light often degrades the quality of an image, which in turn affects the computer vision algorithm’s performance. The development of image enhancement algorithms improves the visual quality and valuable details of an image. Nevertheless, the existing algorithms inevitably introduce unwanted artifacts and color distortion while preserving the tones and information. Implementing a new fuzzy color histogram equalization with a weighted distribution algorithm will address these drawbacks effectively and yields a better-quality image. In the proposed method, fuzzy dissimilarity histogram is constructed from the neighbourhood characteristics of an intensity to improve the contrast and naturalness of an image. Then, incorporate gamma correction for further enhancement in dark regions. Finally, modify the saturation to the permittable maximum saturation range to avoid the fading effect. The extended experimental results on different scenes demonstrate that the proposed algorithm can enhance the quality and details of an image efficiently. Objective measures show the competitive performance of the proposed algorithm compared with the other existing methods.