Reviewerly/exHarmony huggingface dataset:
Control-oriented modelling of proof-of-work blockchains

Blockchain (BC) technology is a rather new conception of a mixed hardware and software platform to achieve distributed consensus among peers. Its diffusion is related to cryptocurrency, the most widespread of which is Bitcoin. The protocol on which BCs operate sees the interaction between users, interested in performing their transactions, and miners, who certify the trust behind the transactions by putting some form of effort that allows acknowledging their trustfulness, obtaining Bitcoins in reward for their work. In the so-called proof-of-work implementation of the BC, such effort is the computational power needed to find a specific string of bits called nonce. The resulting game-theoretic setting has subtle dynamics, and its functioning could be strongly improved using closed-loop control. This work is an attempt to define a control-oriented description of the agent-based BC dynamics and offer a redesign of the difficulty control system that regulates the amount of work needed to add a new trusted block to the BC. This control loop directly relates to the energy consumption of the overall system, which is one of the major drivers that will determine the future sustainability of the BC paradigm.
Remote Sensing Image Registration Algorithm Based on Multi-scale Corner and Haar Transformation

Abstract This paper proposed a new image registration algorithm based on multi-scale corner and Haar transformation, which its core idea is the combination of Haar operator and the Laplace image pyramid are used in down sampling the original images, and detect corner at the resulting images, Angle of the corner and most value point of scale response (scale precision positioning) will get, registrating images will obtained after rotation and translation. Theoretical analysis and simulation results shows the algorithm has higher registration accuracy and small amount of computation.
A Dimension Reduction Method Based on Adaptive RBF Neural Network in Supervised Machine Learning

For the purpose of high dimensional data reduction in supervised machine learning, an adaptive RBF (Radial Basis Function) neural network algorithm was proposed. According to the relationship between the total error of this training and last training, the learning rate and momentum factor in the algorithm were adjusted dynamically and adaptively. So, the learning direction of RBF neural network was enhanced and the learning speed was improved. This algorithm was used to reduce the dimension of high dimensional data. The data set auto-mpg in UCI machine learning database was taken as an example to carry out dimension reduction experiments. The experimental results show that the learning performance of RBF neural network was improved and its general error was reduced after reducing the dimensionality of high dimensional data set by this algorithm. This reduction method is of great significance to solve the "dimensionality disaster" and "cost disaster" in machine learning.
Online adaptation of uncertain models using neural network priors and partially observable planning

One of the key challenges in realizing a robot that is capable of completing a variety of manipulation tasks in the real world is the need to utilize sufficiently compact and rich world models. If the assumed prediction model does not match real observations, planning systems are unable to perform properly. We propose a system that corrects the models based on information collected from the robot's sensors. We encode prior experiences in a neural network to generate possible parameters of the models for a physics engine from real observations. An online POMDP solver is used to plan actions to complete the task while progressively validating and improving the models. We perform experiments in simulations and on a real robot. The results show that this approach appropriately clarifies observed environments, can handle dynamics with discontinuities, and with increasing domain complexity achieves a better success rate than baseline methods.
AMNet

DeepLabv3+ is one of the most accurate algorithms in semantic segmentation. CBAM is an attention mechanism proposed to improve the performance of obect detection model which can be used in a convolutional neural network. Given an intermediate feature map, CBAM sequentially infers attention maps along two separate dimensions, channel and spatial, then the attention maps are multiplied to the input feature map for adaptive feature refinement. In the image segmentation tasks, in order to achieve the goal of enhancing feature representation and improving segmentation accuracy without extra overheads. In this paper, we proposed AMNet which is an end-to-end semantic segmentation network based on DeepLabv3+ which is embeded with CBAM. Further, CBAM activates when the input image passes through CNN.Channel attention module in CBAM focues on 'what' is meaningful given an input image and spatial attention module focus on 'where'. Our network acheives 77.66% mIoU on the PASCAL VOC2012, which is a 2.73% better mIoU than DeepLabv3+ with 6 batchsize using only one single Nvidia 2080 GPU. Beyond that, for getting a faster segmentation model, we also embed the attention mechanism into ENet, one of the fastest lightweight networks. After our evaluation on the Cityscapes dataset, we got a better performance in the case of fast training speed. The feasibility that attention mechanism can be integrated into semantic segmentaion network is proved.
Spatial-Transformed Regional Quality Estimation Network for Large-Variance Person Re-Identification

The video-based person re-identification aims to search a query video in a large video gallery. The major restrictions on the performance of this task are image misalignment and partial noises caused by detection error, occlusion, blur, and illumination. The misalignment between the different frames in a video caused by excessive background or part missing may play the devil with pedestrian matching. In addition, the influence of partial noises on pedestrian matching performance is also unfavorable. Since different spatial regions of a single frame have various qualities, and the quality of the same region also varies across frames in a tracklet. A good way to address the problem is to effectively aggregate complementary information from all frames in a sequence, using better regions from other frames to compensate for the influence of an image region with poor quality. To achieve this, we propose a novel Spatial-transformed Regional Quality Estimation Network (SRQEN), where a well-designed spatial-transformed unit is used to automatically learn the alignment from an identification procedure and another ingenious training mechanism enables the effective learning to extract the complementary region-based information between different frames. Visual examples indicate that pedestrians are better aligned with SRQEN and the proposed method can learn complementary information. Extensive experiments show that compared with other feature extraction methods, we achieved comparable results of 93.5%, 79.8%, and 74.85% on the PRID 2011, iLIDS-VID, and MARS, respectively.
Multi-Input-Multi-Output Interface Video Retrieval Method

In this paper, we propose a Multi-Input-Multi-Output (MIMO) interface mixed video retrieval system that is a generalization of the conventional Single-Input-Multi-Output (SIMO) interface content-based image retrieval (CBIR) system. Multi-input query retrieval can achieve high performance even if a user doesn't have relevant query input. Our method can retrieve mixed images such as still image as well as video. To realize such functions, we have added two processing parts, called input and output selections in retrieval algorithm. To show the effectiveness of our method, experimental content-based video retrieval (CBVR) was executed. Precision rate was calculated to evaluate the performance of the retrieval. High performance retrieval was achieved compared with the SIMO approach.
A Collaborative Feature Learning Method for Accurate and Robust Tracking

Model update scheme is a challenging issue to develop a robust object tracking system. This paper puts forward to a collaborative feature learning for accurate tracking with extended particle filter framework. Meanwhile, different model update schemes are employed to select the precise target model. With the collaborative feature learning, the template library with salient features can maintain the rich target appearances. Even when appearance of the target significantly changes, it can still find appropriate appearance model from the template library. Experimental results on several image sequences demonstrate the robustness and accuracy of our approach in a complex scenario.
Immutables in C++: Language Foundation for Functional Programming

The C++ programming language is a multiparadigm language, with a rich set of procedural, object-oriented, generative and, since C++11, functional language elements. The language is also well-known for its capability to map certain semantic features into the language syntax; therefore, the compiler can reason about them at compile time. Supporting functional programming with immutables is one of such aspects: the programmer can mark immutable components and the compiler checks potential violation scenarios and also optimizes the code according to the constant expectations. The paper targets the non-C++ programmer audience less familiar with the technical details of C++ immutables and functional elements, as well as those C++ programmers who are interested in the development of the newest standard. We will survey the functional programming features of modern C++. The various types of constants and immutable memory storage will be discussed as well as the rules of const correctness to enable the static type system to catch const violations. Const and static const members of classes represent support for immutables in object-oriented programming. Specific programming tools, like mutable and const_cast enable the programmer changing constness for exceptional cases. Constexpr and relaxed constexpr (since C++14) objects and functions as well as lambda expressions have recently been added to C++ to extend the language support for functional programming. We also discuss the fundamentals of C++ template metaprogramming, a pure functional paradigm operating at compile time working with immutable objects. Understanding the immutable elements and the rich set of functional language features with their interactions can help programmers to implement safe, efficient and expressive C++ programs in functional style.
Adapting ClusTree for more challenging data stream environments

Data stream mining seeks to extract useful information from quickly-arriving, infinitely-sized and evolving data streams. Although these challenges have been addressed throughout the literature, none of them can be considered “solved.” We contribute to closing this gap for the task of data stream c lustering by proposing two modifications to the well-known ClusTree data stream clustering algorithm: pruning unused branches and detecting concept drift. Our experimental results show the difficulty in tackling these aspects of data stream mining and the sensitivity of stream mining algorithms to parameter values. We conclude that further research is required to better equip stream learners for the data stream clustering task.
Design and Test of a Headset Activation Platform Controlled by Eye Gaze Tracking

For years, eye tracking technology has been making huge improvements and has become very popular in the robotics industry. This paper covers a new concept resulting in the creation of a fully functioning prototype utilising eye tracking technology that can aid with such things as improved visibility or better communication for people in the workplace or at home. The concept involves a portable headset that can track the user's gaze direction and accurately point an illumination source at the point of eye fixation. Use cases for such a device could include assisting people working in low light conditions by directing a beam of light in the gaze direction or for people living with disabilities that possess substandard verbal communication skills, by acting as an eye-controlled laser pointer. The prototype uses digital servo motors, a 3D printed pan-tilt platform, a microcontroller, an illumination source and off-the-shelf eye-tracking glasses. The result is a responsive and accurate device that could potentially be transformative for many people around the globe.
Designing Federated Architectures for Multimodal Interface Design and Human Computer Interaction in Virtual Environments

Advances in 3D immersive environments offer great potential for collaboration in circumstances that rely heavily on 3D visual models for collaboration, coordination, and data visualization (e.g., construction, medical, architecture). As existing platforms quickly evolve, become more widespread, and research aims to validate the benefits of AR/VR over more traditional platforms, user-centered approaches become more important, especially when supporting multiple users in 3D immersive environments. This article discusses our experience designing an adaptable, non-domain-specific, federated architecture for multimodal systems and virtual environments based on Human Factors and Human Computer Interaction methodologies.
Applications and Advancements of Nature-Inspired Optimization Algorithms in Data Clustering: A Detailed Analysis

In the last decade, nature-inspired optimization algorithm has been a keen interest among the researchers of optimization community. Most of nature-inspired algorithms are developed through the simulating behavior of natural agents in nature. In comparison with evolutionary- and swarm-based algorithms, these are most effective techniques for all real-life applications. Although both swarm- and evolutionary-based algorithms are one of the subsets of nature-inspired optimization algorithm but the efficiency and effectiveness of such algorithm make them more attractive to use in various data mining problems. Among the other tasks of data mining, it has been always a challenging task to solve clustering problem, which is unsupervised in nature. In this paper, a brief study has been conducted on the applications of nature-inspired optimization algorithms in clustering techniques. Also, few challenging issues along with the advancements of various nature-inspired optimization algorithms are realized in the field of clustering.
Hidden Markov Model-Based Sense-Through-Foliage Target Detection Approach

In this paper, we propose sense-through-foliage targetdetection approach based on Hidden Markov Models (HMMs). Separate Hidden Markov Models are trained for signals containing target signature and no target (clutter), respectively. Less correlated features are selected as input of Hidden Markov Models for training and testing. Foliage data is collected from three different UWB radar locations, and experimental results show that position 1 data gives the best detection result. All three locations have above 0.8 AUC from the ROC curves.
Safety and robustness for deep learning with provable guarantees (keynote)

Computing systems are becoming ever more complex, with decisions increasingly often based on deep learning components. A wide variety of applications are being developed, many of them safety-critical, such as self-driving cars and medical diagnosis. Since deep learning is unstable with respect to adversarial perturbations, there is a need for rigorous software development methodologies that encompass machine learning components. This lecture will describe progress with developing automated verification and testing techniques for deep neural networks to ensure safety and robustness of their decisions with respect to input perturbations. The techniques exploit Lipschitz continuity of the networks and aim to approximate, for a given set of inputs, the reachable set of network outputs in terms of lower and upper bounds, in anytime manner, with provable guarantees. We develop novel algorithms based on feature-guided search, games, global optimisation and Bayesian methods, and evaluate them on state-of-the-art networks. The lecture will conclude with an overview of the challenges in this field.
Acoustical analysis of Vietnamese flat and bossed gongs before and after tuning

One Vietnamese gong set of nine flat and six bossed gongs was acoustically studied. Main partial (primary pitch) for the flat gong was estimated by human hearing sense, suggesting that the primary pitch was the second partial for flat gongs and the third partial for bossed gongs. Tuning controlled the balance of these primary pitches among the other partials and made two octaves (about 1200 and 1250 cents) within one gong set more distinct in both flat and bossed gongs. All the gongs were related to each other by a perfect fifth and/or the above two octaves.
Position-independent Physical Activity Monitoring: Development and Comparison with Market Devices

The last years have seen great growth in wearable devices for activity monitoring. Fueled by miniaturization and market demand, many devices can accurately estimate physical activity. However, and despite the variety of solutions, existing wearables are tied to classifying activity from a single body position. Once users move their wearable, accuracy is affected. This paper describes the development and validation of a position-independent activity monitoring solution, the GoLiveClip. The performance of GoLiveClip is comparable to position-dependent devices, such as Fitbit Flex 2, Mi Band 2, Garmin Vivoactive HR, and Jawbone Up 24, for activity recognition, step detection, and distance estimation. These results motivate the development of position-independent activity monitoring solutions.
Technology executives in the sixth R&amp;D generation context: new ways to organisational innovativeness via ‘expeditionary’ leadership

This article is focused on the technology management issues and covers the following core topics: leadership in the technology-related organizations, technology executives' - and especially, Chief Technology Officers'- functionality, open versus close innovations and innovation support. In this paper, the authors attempt to analyze the changing conditions of the current technology management processes associated with the accelerating dynamics of organizational transformations, strengthening boundaries of cross-disciplinary technologies application and generally increased the speed of the advanced technologies development. Such drastic changes obviously affect the way and manner of the modern management processes implementation. Nowadays each company - to a more or less extent - is a technology-driven organization. Digitization, cyber-physical systems and artificial intelligence are the key drivers for the changing business circumstances. Consequently, management and technology today are so intertwined that without a holistic and conceptual understanding of the core principles of this interdisciplinary tandem it is just not possible to be at the crest of the `business success wave'. Overall, the socio-economical effect of the increasingly complex technologies obviously requires new forms of management and leadership so as new - appropriate and workable in the actual conditions - management tools. In terms of the technology executives' professional functionality, it is important also to pay attention to the `back effect' of the aforementioned technological impact. On the one hand, technology executives are increasingly appointed as members of the top management team (TMT). But on the other hand, it means that they should be understood by specialists from different non-directly related to technologies areas (i.e. financiers, strategists, business process managers, etc.). In other words, in order to bring the technological perspective in the general management process and further have a sustainable managerial impact on the internal and external stakeholders, technology executives should also be leaders with abilities to recognize how applied technologies are perceived from other perspectives of organizational functioning. In this context, modern technology executives should perform the role of `boundary spanners' by means of establishing informational and managerial links among different groups and persons at the intra- and extra-organizational levels. Thus, integrating different facets of technology perception from the core stakeholders and directly or indirectly maintaining coordination of their activities, technology executives could increasingly act as agents of organizational change and innovations promotion.
A novel proximity algorithm for an unconstrained convex segmentation model

A new proximity algorithm for an active contour model is proposed in this paper. In order to derive a mathematical form of the energy, the level set method is employed. In the new energy, a penalty term is introduced to make sure that the level set function can be restricted in the interval [-1, 1]. By introducing this term, the energy still keeps convex and is easy to construct its minimization algorithms. Based on the proximity operator and the corresponding theories, we deduce a proximity algorithm to minimize the energy. Experimental results demonstrate that the proposed model is powerful in its segmentation ability and accuracy. And comparisons with other popular algorithms show that the proposed algorithm is more efficient.
Meaning of the Term “Tai-iku” in the &lt;i&gt;Principles of Physical Education&lt;/i&gt; by Heizaburo Takashima*

This paper clarifies the meaning of the term tai-iku (physical education) in the Principles of Physical Education by Heizaburo Takashima (1865-1946), who advocated physical education (PE) in the latter part of Japan's Meiji era. Takashima wrote many books on PE, which included examples of the term tai-iku. PE-related subjects were discussed from various angles, but the meaning of tai-iku was not self-evident. In Japanese, Tai-iku has many meanings throughout history. Therefore, previous references to tai-iku must be clarified through an empirical approach when reading books on PE written in past times, as there is a risk that Takashima's term tai-iku may unconsciously conform to our concept of PE. This paper represents the first attempt to define the term. We concluded that first, Takashima's concept of tai-iku should be defined in terms of his Principles of Physical Education (1904), and second, the definition was methodological, by which intellectual and moral education can be perfected, while retaining the previous meaning in the context of the human body. In Takashima's book, tai-iku has these two meanings. Other issues include whether alternative meanings exist in Takashima's books, or whether these (Principles of Physical Education etc.) can be read consistently and coherently.