Reviewerly/exHarmony huggingface dataset:
MSPNet: Multi-level Semantic Pyramid Network for Real-Time Object Detection

With increasing demand of running Convolutional Neural Networks (CNNs) on mobile devices, real-time object detection has made great progress in recent years. However, modern approaches usually compromise detection accuracy to achieve real-time inference speed. Some light weight top-down CNN detectors suffer from problems of spatial information loss and lack of multi-level semantic information. In this paper, we introduce an efficient CNN architecture, the Multi-level Semantic Pyramid Network (MSPNet), for real-time object detection on devices with limited resource and computational power. The proposed MSPNet consists of two main modules to enhance spatial details and multi-level semantic information. The multi-scale feature fusion module integrates different level features to tackle the problem of spatial information loss. Meanwhile, a light weight multi-level semantic enhancement module is developed which transforms multiple layer features to strengthen semantic information. The proposed light weight object detection framework has been evaluated on CIFAR-100, PASCAL VOC and MS COCO datasets. Experimental results demonstrate that our method achieves state-of-the-art results while maintains a compact structure for real-time object detection.
Performance Analysis of Binarization Strategies for Multi-class Imbalanced Data Classification

Multi-class imbalanced classification tasks are characterized by the skewed distribution of examples among the classes and, usually, strong overlapping between class regions in the feature space. Furthermore, frequently the goal of the final system is to obtain very high precision for each of the concepts. All of these factors contribute to the complexity of the task and increase the difficulty of building a quality data model by learning algorithms. One of the ways of addressing these challenges are so-called binarization strategies, which allow for decomposition of the multi-class problem into several binary tasks with lower complexity. Because of the different decomposition schemes used by each of those methods, some of them are considered to be better suited for handling imbalanced data than the others. In this study, we focus on the well-known binary approaches, namely One-Vs-All, One-Vs-One, and Error-Correcting Output Codes, and their effectiveness in multi-class imbalanced data classification, with respect to the base classifiers and various aggregation schemes for each of the strategies. We compare the performance of these approaches and try to boost the performance of seemingly weaker methods by sampling algorithms. The detailed comparative experimental study of the considered methods, supported by the statistical analysis, is presented. The results show the differences among various binarization strategies. We show how one can mitigate those differences using simple oversampling methods.
Coherent Control of Nonadiabatic Dynamics of Electron-Phonon Systems by Quantized Light Field

Coherent control of electron-phonon systems is studied by numerical calculations on a toy model. We found that the interplay of the electron-phonon nonadiabaticity and the Raman scattering determines the wavepacket motion particularly in the vicinity of the conical intersection of adiabatic potential energy surfaces. Entanglement between photons, phonons, or electrons is discussed by calculating the entanglement entropy. We showed that the quantum nature of irradiated light plays an important role in considering design and/or manipulation methods of the quantum-mechanical states of electron-phonon systems in coherent regime.
Inequality and the Economic Participation of Women in Sub-Saharan Africa: An Empirical Investigation

This study investigates the effect of inequality on female employment in 42 countries in sub-Saharan Africa for the period 2004-2014. Three inequality indicators are used, namely, the: Gini coefficient, Atkinson index and Palma ratio. Two indicators of gender inclusion are also employed, namely: female employment and female unemployment rates. The empirical analysis is based on the Generalised Method of Moments (GMM).The following main findings are established. First, inequality increases female unemployment in regressions based on the Palma ratio. Second, from the robustness checks, inequality reduces female employment within the frameworks of the Gini coefficient and Palma ratio.
Interspecific Interactions: Interaction Modes Between Sound and Movement in Collaborative Performance

Collaboration between composers and choreographers is an approach possessing a long history and expansive list of creative outcomes. In recent years, such collaborative endeavours have further engaged with knowledge and practices from scientific disciplines, consequently giving rise to an emergent field of research focusing on novel modes of interaction between the expressive media of sound and movement as facilitated by new technologies and methodologies; from analogue proximity devices permitting dancers rudimentary control over recordings in the 1965 Variations V, to Marco Donnarumma utilizing biophysical sensors and machine learning algorithms as means of maintaining detailed command of digital signal processing (DSP) through specific body postures in the 2016 Corpus Nil. Whilst the contributions by this relatively new transdisciplinary field have produced practice and research outcomes of unquestionable value, its main focus concentrates on the expression of sound through movement, with lesser emphasis placed on perspectives of practitioners utilizing interactive sound as means of informing the arrangement of movement. This chapter aims to reflect those perspectives through the research conducted during my collaborations with dance practitioners, where we have together examined areas such as each performer’s role while operating interactive systems, employing different modes of system operation according to the desired determinacy of resulting material, and simplification of discipline-specific language when engaged in polydisciplinary collaboration. The contribution of this research concerns a novel taxonomy of interaction modes informed by the biological phenomenon of symbiosis. Defined as close and persistent relationships between organisms of different species, symbiosis manifests in three core types—mutualism, commensalism, and parasitism—with each type determined according to the change in fitness outcome experienced by each of the engaged organisms. In the context of my collaborative practice, the biological notion of fitness outcome is interpreted as the expressive range allocated to each performer and their respective media, while simultaneously relating to the level of determinacy from the score and choreography. The symbiotic modes of interaction are firstly described through examining three contemporary precedents, each showcasing distinct approaches in collaboration and use of gesture recognition technologies (GRT), followed by a practical demonstration of each mode by its activation during a practice outcome resulting from my collaborations with two dancers, including a detailed analysis of the developed interactive system and its principles of its operation.
Method for Detecting Anomaly Data of WAMS System Based on GA-iForest

Abstract Wide area measurement system problems such as anomalies, data missing, and real-time response deeply affect the operation and maintenance of the power grid. Considering the accuracy requirements of the system for anomaly detection, the detection accuracy and stability of traditional isolation forest are poor, so this paper proposes a new data anomaly detection method named GA-iForest. This method uses genetic algorithms to select isolated trees with high accuracy and obvious differences to optimize the structure of isolated forests. The new data anomaly detection method GA-iForest improves the accuracy and stability of detection, and mean while maintaining the high efficiency. The experiment uses standard simulation data sets and data from one province’s wide-area measurement system for experimental simulation. The result shows that the GA-iForest method has significantly improved accuracy and stability compared with the traditional isolation forest, LOF, and K-means methods.
From a ludic Loom of Ideas to the Spiral of Intercultural Co-creation

How to implement ludic co-creation from and for interculturality? The validation of a methodological model to design co-creation experiences and/or participatory design (PD) is presented in this paper, as part of a broader study that proposes interculturality as an object of study and practice of design. The process involved eight diverse sociocultural instances that led to the transfiguration of the model. The results showed that participants perceived the prefiguration process as a highly demanding challenge. This allowed us to formulate the following question as a design problem: How to generate pluricultural socio-technical microsystems aimed at prefiguring ludic co-creation experiences without this requiring a high level of design skills? This result being our greatest challenge and contribution to the PDC community, whom we invite to collaboratively assume this challenge derived from the empirical validation of the model shown here.
A Generalised Coordination Design Pattern for the EX-MAN Component Model

In the latest technologies for next generation using in Cyber-Physical Systems, 5G and IoT (Internet of Things) based solutions have a significant contribution. For the construction of such applications, component-based development approaches offer to produce systems by using pre-built tested and reliable components with shorter development time. At the architecture level, a software system can be viewed as a collection of two kinds of elements. One kind is responsible for computation and the other kind is responsible for communication. Using a component model, that separates the communication and computation into distinct layers, enables us to secure the communication part of the system. In this paper, we propose a design pattern which defines coordination/communication program units (referred to as exogenous connectors) for a repository of reusable connectors in the EX-MAN component model. There are many attempts of implementing exogenous connector in different tools in unspecified ways. Our proposed pattern for a generalised exogenous connector helps in specifying exogenous connectors with enough details that can be used for the implementation of these connectors. Our model enables in-depth analysis of different kinds of exogenous connectors with respect to its static/dynamic behaviour in a system. In this paper, we model and simulate the static/dynamic behaviour of sample exogenous connectors based on our proposed model. Using our specifications of exogenous connector, we have developed exogenous composition framework (ECF) for system development.
System Analysis of the Prospective Characteristics of the Helical Magnometric Detection Tool

This paper presents the results of the research of the helical transducer-based magnetometric detection tools. The authors have developed a mathematical model of signal formation, which enables calculation of useful signals at the output of the transducer for straight-line movement path of the object located at a random angle to the transducer's direct axis and the random original coordinate. A statistical check of the degree of agreement of the simulated signals and full-scale measurements has been carried out. A representative base of the estimated and demonstrated signals has been created. A research program apparatus complex (PAC) was developed for the analysis of signals and prospective characteristics of detection tools, which enables simulation of operation of the decision box and evaluation of the prospective detection characteristics. The authors have surveyed the threshold parameter-oriented operation algorithm of the decision box. The paper demonstrates that the helical magnetometric detection tool with a peak-reading detector and a threshold decision-making algorithm works well in terms of detection of the objects with a relatively large magnetic moment. It discusses several types of algorithms based on artificial neural networks: error back-propagation (BP) neural network, bidirectional associative memory, and real-time recurrent networks. The real-time recurrent network, which ensures compliance of the tactical and technical characteristics of the product with the requirements for a perimeter detection tool, demonstrated the best detection characteristics. Thus, the paperdescribes a systematic analysis of the designspecifics and prospective characteristics of an advanced perimeter magnetometric detection tool based on a helical transducer.
A Distributed Smart Camera System Based on an Edge Orchestration Architecture

Distributed cameras have been used widely for real-time image recognition. There are two main approaches in distributed camera systems: (1) The cameras are equipped with a powerful high-end processor for local image processing, (2) Low-cost cameras with resource-constrained processors are used for capturing the images and transferring them to a cloud server for classification purposes. The first approach is costly and not scalable. The second approach is too slow for real-time object detection due to the transfer delays to a remote server. These problems exacerbate in multi-view image recognition, where a central platform is required for collective image processing of multiple images from the same scene. For this purpose, typically, a cloud server is used, which does not meet real-time recognition, network bandwidth, scalability and power consumption constraints of such systems. This paper proposes hierarchical neural network structures that can be realized in an edge orchestration architecture at different levels, i.e., cameras, edge devices, and cloud servers to enable deep learning capabilities for real-time multi-view image recognition. This would enable us to detect objects in the proximity of multiple cameras and transfer data for deeper layer processing to the cloud server and training of all connected edge devices and cameras.
Evaluating Robustness for Intensity Based Image Registration Measures Using Mutual Information and Normalized Mutual Information

Numerous Applications envisage the need of image registration. The term image registration is the process of alignment of multimodal, multitemporal or multiview images onto a single co-ordinate system by applying certain transformations e.g. rotation, translation, scaling etc. This paper uses Information Measures such as Mutual Information (MI), and Normalized Mutual Information (NMI) to obtain the aligned image and then evaluate their robustness. Various Parameters such as Pearsons correlation coefficient (PCC), Peek Signal to Noise Ratio (PSNR), have been used to quantify the image registration results obtained through MI and NMI. The methodology for registration has been validated using above parameters and elapsed time is also evaluated for rigid registration of natural Images.
Adaptive and Efficient Transfer for Online Remote Visualization of Critical Weather Applications

Critical weather applications such as cyclone tracking require online visualization simultaneously performed with the simulations so that the scientists can provide real-time guidance to decision makers. However, resource constraints such as slow networks can hinder online remote visualization. In this work, we have developed an adaptive framework for efficient online remote visualization of critical weather applications. We present three algorithms, namely, most-recent, auto-clustering and adaptive, for reducing lag between the simulation and visualization times. Using experiments with different network configurations, we find that the adaptive algorithm strikes a good balance in providing reduced lags and visualizing most representative frames, with up to 72% smaller lag than auto-clustering, and 37% more representative than most-recent for slow networks.
An Energy-Efficient Bagged Binary Neural Network Accelerator

Recently, binary neural networks (BNNs) have been extensively studied since they can address the challenge of large memory footprint and power consumption caused by floating-point convolutional neural networks (CNNs) while maintaining tolerable accuracy. There have been many efforts designing BNN hardware accelerators and showed very well results. But even that will require huge improvements in reduction of memory cost and energy efficiency. In this paper, we first propose the bagged binary neural network accelerator (BBNA), which is a fully pipelined BNN accelerator with bagging ensemble unit for aggregating several BNN pipelines to achieve better model accuracy. In other words, the proposed architecture provides an opportunity for embedded devices to obtain acceptable accuracy with smaller ensemble BNNs. As a result, compared to other works, our design achieves 1.9x better energy efficiency with better performance, and the ensemble method saves more than 79% and 94% memory footprint, respectively, with nearly accuracy on MNIST dataset.
A Service Selection Method Based on QoS in IOT

Over the past few years, the Internet of Things has been noticeably developed. The access devices number of the Internet of Things has exploded. In this scenario, numerous Internet of Things services exhibiting similar functions appear in the platform. Accordingly, when selecting the services of the Internet of Things, the non-functional requirements are started to be considered more (e.g., service quality). Though a considerable number of methods for Quality of Services-based services selection are available, they exhibit certain defects and do not form a standard. Some emphases are only placed on the wishes of the user, while others stress is given only to the relevance of service quality. A feasible method is required to consider the mentioned two aspects. In this study, the service selection algorithm is investigated based on the minimal deviation, a method adopted to combine weights based on subjective and objective weights of Quality of Services. Such method is capable of evenly arranging the advantages of subjective and objective weights.
Counting invariant subspaces and decompositions of additive polynomials

The functional (de)composition of polynomials is a topic in pure and computer algebra with many applications. The structure of decompositions of (suitably normalized) polynomials f=g∘h in F[x] over a field F is well understood in many cases, but less well when the degree of f is divisible by the positive characteristic p of F. This work investigates the decompositions of r-additive polynomials, where every exponent and also the field size is a power of r, which itself is a power of p. The decompositions of an r-additive polynomial f are intimately linked to the Frobenius-invariant subspaces of its root space V in the algebraic closure of F. We present an efficient algorithm to compute the rational Jordan form of the Frobenius automorphism on V. A formula of Fripertinger (2011) then counts the number of Frobenius-invariant subspaces of a given dimension and we derive the number of decompositions with prescribed degrees.
MUTE: Inter-class Ambiguity Driven Multi-hot Target Encoding for Deep Neural Network Design

Target encoding is an effective technique to boost performance of classical and deep neural networks based classification models. However, the existing target encoding approaches require significant increase in the learning capacity, thus demand higher computation power and more training data. In this paper, we present a novel and efficient target encoding method, Inter-class Ambiguity Driven Multi-hot Target Encoding (MUTE), to improve both generalizability and robustness of a classification model by understanding the inter-class characteristics of a target dataset. By evaluating ambiguity between the target classes in a dataset, MUTE strategically optimizes the Hamming distances among target encoding. Such optimized target encoding offers higher classification strength for neural network models with negligible computation overhead and without increasing the model size. When MUTE is applied to the popular image classification networks and datasets, our experimental results show that MUTE offers better generalization and defense against the noises and adversarial attacks over the existing solutions.
A Chatbot based on Deep Neural Network and Public Cloud Services with TJBot Interface

This paper describes an adaptation of the IBM TJBot as a chatbot interface. TJBot is an open source project designed to use artificial intelligence services in user friendly way. Primary it has been developed to be used with IBM Watson services. The adaptation has been done in three steps. In the first step deep neural network (DNN) based chat has been designed. Subsequently, three DNN where designed to perform experiments. They differed in training sets. The second step presents a join of DNN based chat with IBM Watson services (Speech-To-Text, Text-To-Speach). Originally, IBM Watson provides these services for limited number of languages. Slovak language is not included, too. Google cloud services fill this gap quite in good manner. This led to replacement of the IBM Watson services by the Google services. Finally, the chatbot is able to communicate in multiple languages, Slovak language including. Any non-English conversation has to be translated to English language and vice-versa by the Google translate service. This modified chatbot was tested by chat with randomly selected users.
Hardware Implementation of a Cryptographically Secure Pseudo-Random Number Generators Based on Koblitz Elliptic Curves

In this brief, a cryptographically secure pseudo-random number generator based on the NIST Koblitz elliptic curve K-163 is implemented. A 3-stage pipelined multiplier is adopted to speed up point additions. In addition, Frobenius map and point additions are performed in parallel to reduce the clock cycles required for scalar multiplication. By expanding the multiplier with a multiplexer, exponentiation and multiplication can be executed simultaneously, thus greatly reducing the clock cycles needed for inversion. Implementation results on Xilinx Virtex-4 show that the frequency of the multiplier is up to 248 MHz, therefore it takes only 2.21 us for scalar multiplication over K-163. The cryptographically secure pseudo-random number generator can produce 452 Kbit random number every second.
A study of personalized training strategies for vocational education based on big data systems

Abstract The investigation and analysis of personalized training of vocational education based on big data system has the characteristics of simplicity, operation and persuasiveness. This article comprehensively elaborates on the characteristics and meaning of personalized education, combines the big data analysis method and selects five colleges and universities in Guangzhou to conduct a questionnaire survey, to find out the problems in the personalized training of vocational education, and to propose measures.
Unbiased hybrid generation network for zero-shot learning

While promising progress has been achieved in the zero-shot learning (ZSL) task. The existing approaches still suffer from the strong bias problem between the unseen and seen classes. This Letter presents a unified feature generating framework equipped with a boundary decision loss to tackle this issue in ZSL. Specifically, the hybrid semantic and visual classification strategy is proposed, which can effectively align the bidirectional visual-semantic interactions. Furthermore, this Letter introduces a decision loss that optimises the decision boundary of seen and unseen classes to further alleviate the confusion of generated features. Extensive experiments on three popular datasets animals with attributes, Caltech-UCSD-Birds 200-2011, and SUN show that the proposed approach outperforms previous state-of-the-art works under both traditional ZSL and challenging generalised ZSL settings.