Reviewerly/exHarmony huggingface dataset:
Experiments with Neural Net Object Detection System YOLO on Small Training Datasets for Intelligent Robotics

In this paper we’ve conducted multiple experiments with modern object detection system YOLO. Object detection systems are fundamental to many robotics tasks. Recognition algorithms involving object detection are often part of various intelligence systems for robots. Training object detection systems usually requires waste amounts of training data which can be expensive and time-consuming. In this paper we’ve conducted several experiments with YOLO on small training datasets investigating YOLO’s capacity to train on small number of examples. We measured accuracy metrics for object detector depending on the size of training dataset, compared training process of full and smaller versions of YOLO and their speed. Gathered information will be used for creating visual factographic intelligence system for robots. YOLO (You Only Look Once) is a special intelligent technology for computer vision techniques. Our results are useful for industry professionals and students from a broad range of disciplines related to robotics, intelligent technologies and other fields.
A new approach for developing a hybrid sun-tracking method of the intelligent photovoltaic blinds considering the weather condition using data mining technique

As a part of technology innovation in the building sector, an intelligent photovoltaic blind (i-PB) with direct and indirect sun-tracking methods were previously developed by this research team. Due to the shadows on the tightly aligned slats of the i-PB, however, there is a difference in the electricity according to the weather and sun-tracking method. Accordingly, this study aimed to develop a hybrid sun-tracking method of the i-PB, which can determine the sun-tracking method with highest electricity generation between the two sun-tracking methods according to the weather. To this end, this study proposed a new approach for developing a hybrid sun-tracking method by selecting the main climate factors and their threshold using data mining technique. As a result of the experimental study conducted in South Korea, a hybrid sun-tracking method in autumn was developed. To ensure the effectiveness of the new approach, a real-time sun-tracking system was developed and used for the experimental validation. As a result, the hybrid sun-tracking method showed the highest electricity generation (i.e., 97.3 Wh/m2) among the three sun-tracking methods, and 84.9% prediction accuracy. The proposed approach can provide a more comprehensive solution by maximizing the advantages of each sun-tracking method and minimizing its weaknesses.
A Unified Framework for Knowledge Intensive Gradient Boosting: Leveraging Human Experts for Noisy Sparse Domains

Incorporating richer human inputs including qualitative constraints such as monotonic and synergistic influences has long been adapted inside AI. Inspired by this, we consider the problem of using such influence statements in the successful gradient-boosting framework. We develop a unified framework for both classification and regression settings that can both effectively and efficiently incorporate such constraints to accelerate learning to a better model. Our results in a large number of standard domains and two particularly novel real-world domains demonstrate the superiority of using domain knowledge rather than treating the human as a mere labeler.
Multiobjective Mission Route Planning Problem: A Neural Network-Based Forecasting Model for Mission Planning

This paper presents a three-layered approach for the mission route planning problems involving a team of autonomous vehicles where they have to collectively navigate to a number of target locations in an environment with both static and dynamic obstacles. The first layer computes the maximum distance that need to be traveled to complete a mission by a team of vehicles. We have developed a nearest-neighbor-search based approach to assign closely located tasks to each vehicle in the team. We developed a stochastic optimization based path planning algorithm that can compute the collision-free (with both static and dynamic obstacles) trajectory for a vehicle to navigate from start to the target location. By combining task assignment with path planning algorithm, we can estimate the maximum traveled distance for a mission with a team of vehicles. The second layer determines the optimal number of vehicles required for a mission based on any user defined constraint by casting it as a multiobjective optimization problem with two competing objectives, i.e. time vs cost. The methods derived in layer one are utilized to evaluate the objective functions in layer two. Finally, we have proposed a data driven neural network-based prediction model that will forecast the mission completion time with a reasonable accuracy which will utilize the historical information of the previous missions. The forecasting model is intended to facilitate the effective planning of parallel and subsequent missions. We have demonstrated the effectiveness of our approach with numerical simulation results for every layer mentioned above.
Can we avoid digital structural violence in future learning systems?

This paper introduces the concept of digital structural violence and examines the negative role it could have in future learning systems. To address it, we propose a new interdisciplinary research agenda at the intersection of three current but disparate lines of work that: Use the concept of epistemic privilege to theorise the inclusion of marginalised learners in the design of learning systems, and utilise participatory action research and emancipatory methodologies to pragmatically ensure this happens;Support young learners and teachers to understand and build their own artificial intelligence algorithms;Develop sustainable interdisciplinary links with computer science to address digital structural violence at the algorithmic level and to make its societal implications and underlying processes more widely understood, especially by teachers.Taken together, these provide for a material form of resistance to digital structural violence and a theoretically and methodologically coherent future research agenda for building just learning systems.
Dynamics of a New Multi-Cavity Hyperchaotic Map and Its DSP Implementation

A new higher-dimensional hyperchaotic map, named Sine Chebyshev modulation map (SCMM), is proposed based on the closed-loop modulation coupling (CMC) model. The dynamics of the SCMM are evaluated by attractor diagrams, Lyapunov exponents and bifurcations. By designing a piecewise-linear function, the SCMM is expanded to the grid multi-cavity form. Dynamical behaviors of the two-dimensional grid multi-cavity SCMM are then analyzed. The results show that it has rich dynamical characteristics, including complicated phase space trajectories, hyperchaotic behaviors, large maximum Lyapunov exponent and typical bifurcations. The complexity of the new grid multi-cavity hyperchaotic map is large in the entire parameter space. Finally, digital circuits of 2D-SCMM and 2D grid multi-cavity SCMM are implemented based on the DSP technique. The feasibility of the circuit is verified, and it lays the foundation for applications in chaotic secure communication.
Deepkey: Keystroke Dynamics and CNN for Biometric Recognition on Mobile Devices

The use of mobile devices is not anymore limited to personal communication but, especially with the widespread of smart devices, it enables a variety of operations like accessing the Internet, storing personal data, making payments, and gaining access to restricted services or areas. Being used for such sensitive applications, the implementation of secure mechanisms to access such devices is of paramount importance. As a matter of fact, most modern smartphones implement access control mechanisms based on either face, iris, or fingerprint biometric traits for secure user recognition, and several researches are conducted on further alternatives offering potential advantages in terms of convenience or performance. In this paper we study the effectiveness of employing keystroke dynamics to discriminate between genuine and impostor users of mobile devices. Specifically, deep learning techniques, based on convolutional neural networks, are here exploited to derive discriminative characteristics from the typing patterns of subjects entering personal identification numbers (PINs). A large database comprising keystroke dynamics acquisitions from 150 subjects, each typing two different kinds of PINs, either short or long, is exploited to conduct the performed experimental tests.
Microresonators enhancing long-distance dynamical entanglement generation in chiral quantum networks

A chiral network scheme is modified by the addition of microcavities leading to the prediction of a boost in concurrence. The protocol is expected to be robust against experimental imperfections and can be implemented in state-of-the-art integrated photonic platforms.
Bug Bounty Marketplaces and Enabling Responsible Vulnerability Disclosure

Cybercrime caused by exploited vulnerabilities bears a huge burden on societies. Most of these vulnerabilities are detectable, and the damage is preventable if software vendors and firms that deploy such software adopt right practices. Bug Bounty Programs (BBPs) by vendors and intermediaries are one of the most important creations in recent years, that helps software vendors to create marketplaces and to detect and prevent such exploits. This article develops the theory of BBPs and present a typology of BBPs using established theories of incentive compatibility and mechanism design. The authors empirically analyze the market creation function of BBPs using granular data from two different types of BBPs on a popular intermediary platform. The research findings suggest that BBPs are valuable opportunities to source vulnerabilities in software; nevertheless, the rate of disclosure and hacker participation marginally increases with vendor's rewards and other incentives. Similarly, the results show that security researchers are motivated to contribute to BBPs that offer higher remuneration and not just those programs with a higher likelihood for bug discovery. Our findings will help researchers and practitioners in information security and allied domains to develop a theoretical and empirical perspective of BBPs, and their usefulness to curb incidents of cybercrime.
Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space

Generalised zero-shot learning (GZSL) is a classification problem where the learning stage relies on a set of seen visual classes and the inference stage aims to identify both the seen visual classes and a new set of unseen visual classes. Critically, both the learning and inference stages can leverage a semantic representation that is available for the seen and unseen classes. Most state-of-the-art GZSL approaches rely on a mapping between latent visual and semantic spaces without considering if a particular sample belongs to the set of seen or unseen classes. In this paper, we propose a novel GZSL method that learns a joint latent representation that combines both visual and semantic information. This mitigates the need for learning a mapping between the two spaces. Our method also introduces a domain classification that estimates whether a sample belongs to a seen or an unseen class. Our classifier then combines a class discriminator with this domain classifier with the goal of reducing the natural bias that GZSL approaches have toward the seen classes. Experiments show that our method achieves state-of-the-art results in terms of harmonic mean, the area under the seen and unseen curve and unseen classification accuracy on public GZSL benchmark data sets. Our code will be available upon acceptance of this paper.
A Very Gentle Introduction to Multiparty Session Types

Multiparty session types (MPST) are a formal specification and verification framework for message-passing protocols without central control: the desired interactions at the scale of the network itself are specified into a session (called global type). Global types are then projected onto local types (one for each participant), which describe the protocol from a local point of view. These local types are used to validate an application through type-checking, monitoring, and code generation. Theory of session types guarantees that local conformance of all participants induces global conformance of the network to the initial global type. This paper provides a very gentle introduction of the simplest version of multiparty session types for readers who are not familiar with session types nor process calculi.
Robust human gesture recognition by leveraging multi-scale feature fusion

In the state-of-the-art human–computer interaction (HCI) systems, gestures feature is more intuitive, natural and, easy-to-acquire than the other visual features. Due to the high descriptiveness of hand movements and the scarcity of labeled human gesture samples, gesture recognition models is prone to overfitting in practice. In order to handle this problem, to facilitate the model parameter learning during training, this paper introduces a disturbing IoU strategy to alleviate overfitting during the training stage from a ROI discriminative network. Moreover, during the deep gesture feature extraction, feature maps with the same size output by different layers are intelligently fused. This strategy can effectively preserve the key information in a small scale, reduce the information loss, and improve the generalization ability of the high-level gesture features. Comprehensive experimental results on the extended VIVA dataset and the NTU dataset have shown that the proposed model achieves a better mAP performance than its competitors
Multi-Scale Spatial Temporal Graph Convolutional LSTM Network for Skeleton-Based Human Action Recognition

Human skeleton sequences have enough spatial and temporal information for action recognition. However, the features in different spatial parts of skeleton play distinctive roles in the different temporal phases of the action. Effectively extracting spatial and temporal features becomes a challenging task. This paper proposes a novel Multi-Scale Spatial Temporal Graph Convolutional LSTM Network (M-GC-LSTM) approach, which employs multi-neighborhood graph convolution and multiple LSTMs with different time-windows to increase the spatial and temporal receptive fields of network simultaneously. In order to overcome the over-shoot problem in deep GCN networks, we propose a parallel multi-GCN module (M-GC) to accomplish the multi-neighborhood convolution process, which makes the GCN network wider instead of deeper. A LSTM module with attention gate is also proposed to promote the representation ability for long-term information. Multi-scale LSTMs are designed to form a M-LSTM module, which is used to improve the ability of extracting different scale temporal dynamics. Furthermore, a regularized cross-entropy loss is proposed to optimize the training process. The superiority of the proposed method is demonstrated by comparing with mainstream methods on two large scale datasets: NTU-RGBD and kinetics.
The Independence of Central Banks, Political Institutional Quality and Financial Sector Development in Africa

Central Bank Independence (CBI) as a mechanism for achieving lower inflation and effective regulation and supervision of the financial sector should promote financial sector development. Though there is not much difference in CBI legal provisions, it seems to be more effective in developed countries than in African countries. There are suggestions that this could be due to differences in political institutional quality. Using panel data from 1970 to 2012, we find that CBI does not promote financial development in Africa. The impact of CBI is dependent on the level of development of a country. CBI promotes financial development more in countries with strong political institutions. JEL codes: E02; E44; E58
Annotation Generation From IMU-Based Human Whole-Body Motions in Daily Life Behavior

This article describes a stochastic framework for integrating human whole-body motions with natural language. Human whole-body motions in daily life are measured by inertial measurement units (IMU) and subsequently encoded into motion primitives. Sentences are manually attached to the human motion primitives for their descriptions. Two aspects of semantics and syntactics are represented by stochastic modules. One stochastic module trains the linking of motion primitives to words, and the other module represents word order in the sentence structure. These two modules are helpful toward converting human whole-body motions into descriptions, where multiple words are generated from the human motions by the first module, and the second module searches for syntactically consistent sentences consisting of the generated words. The proposed framework is tested on a large dataset of human whole-body motions and their descriptive sentences. The linking of human motions to natural language enables robots to understand observations of human behavior as sentences.
CODES: Efficient Incremental Semi-Supervised Classification Over Drifting and Evolving Social Streams

Classification over data streams is a crucial task of explosive social stream mining and computing.Efficient learning techniques provide high-quality services in the aspect of content distribution and event browsing.Due to the concept drift and concept evolution in data streams, the classification performance degrades drastically over time.Many existing methods utilize supervised and unsupervised learning strategies.However, supervised strategies require labeled emerging records to update the classifiers, which is unfeasible to work in the practical social stream applications.Although unsupervised strategies are popularly applied to detect concept evolution, it takes tremendous run-time computation cost to run online clustering.To this end, in this paper, we address these major challenges of social stream classification by proposing an efficient incremental semi-supervised classification method named CODES (Classification Over Drifting and Evolving Stream).The proposed CODES method consists of an efficient incremental semi-supervised learning module and a dynamic novelty threshold update module.Thus, in the drifting and evolving social streams, CODES is able to provide: 1) semi-supervised learning ability to eliminate dependency on the labels of emerging records; 2) fast incremental learning with real-time update ability to tackle concept drift; 3) efficient novel class detection ability to tackle concept evolution.Extensive experiments are conducted on several real-world datasets.The results indicate a higher performance than several state-of-the-art methods.CODES achieves efficient learning performance over drifting and evolving social streams, which improves practical significance in the real-world social stream applications.
An improved fruit fly algorithm-unscented Kalman filter-echo state network method for time series prediction of the network traffic data with noises

With the complexity of the network system rapidly increasing, network traffic prediction has great significance for the safety pre-warning of the network load, network management and control, and improvement of the quality of the network service. In this paper, the time series analysis is used for the network traffic prediction, and a prediction method combined with an optimized unscented Kalman filter (UKF) by an improved fruit fly algorithm (IFOA) and echo state network (ESN) is proposed, which is named by IFOA-UKF-ESN. The researches mainly solve the problem that the prediction accuracy might be greatly affected by the actual network traffic data with unknown and time-varying noises. UKF is used to train the best state vector (formed by spectral radius, scale of the reservoir, scale of the input units and connectivity rate) of ESN; and the proposed IFOA algorithm is proposed to optimize the weights of the predicted state value and the covariance in UKF, which makes UKF have adaptive ability for unknown and time-varying noise. Three actual network traffic data sets with different Gaussian white noise distributions are constructed for experiments, and the experimental results show that the proposed prediction method makes an average improvement by reducing at least 20.60%, 43.23% and 41.85% of RMSE, at least 23.66%, 52.38% and 47.50% of MAE, and at least 23.58%, 52.10% and 47.28% of MAPE, which verify the effectiveness of the proposed method.
Blind image quality assessment using natural scene statistics of stationary wavelet transform

Image quality assessment (IQA) by human observers, who are the ultimate users, is of utmost importance. Since IQA performed by human observers could be time consuming therefore, computational models are required that can assess the quality of images using objective metrics. Blind image quality assessment (BIQA) methods could predicts the perceived quality of distorted images without requiring information regarding the pristine version of the image. The natural scene statistics (NSS) based BIQA methods predict the quality score by extracting features either in spatial or transform domain. We propose a new BIQA technique that extracts features both in spatial and transform domain. The NSS of stationary wavelet transform (SWT), morphological gradient, and discrete Laplacian are known to be similar to human visual system, and hence can be used for BIQA. Morphological gradient has the advantage that it is easy to generalize for any type of feature space and discrete Laplacian has the property to retain key structural information inherent in the image. SWT provides edge and high frequency information while preserving the scale of the image. We remove the redundancies between the extracted features from spatial and transform domain using an adaptive joint normalization framework. Our proposed BIQA technique is tested on five publicly available databases (i.e., TID2008, TID2013, LIVE, CSIQ, and LIVE from the wild image quality challenge). Our experimental results have shown that our proposed BIQA technique achieves higher accuracy in terms of image quality prediction in comparison to the state-of-the-art BIQA and full reference IQA techniques.