Reviewerly/exHarmony huggingface dataset:
Multi‐resource allocation in cloud data centers: A trade‐off on fairness and efficiency

Summary Fair allocation has been studied intensively in both economics and computer science. Many existing mechanisms that consider fairness of resource allocation focus on a single resource. With the advance of cloud computing that centralizes multiple types of resources under one shared platform, multi‐resource allocation has come into the spotlight. In fact, fair/efficient multi‐resource allocation has become a fundamental problem in any shared computer system. The widely used solution is to partition resources into bundles that contain fixed amounts of different resources, so that multiple resources are abstracted as a single resource. However, this abstraction cannot satisfy different demands from heterogeneous users, especially on ensuring fairness among users competing for resources with different capacity limits. A promising approach to this problem is dominant resource fairness (DRF), which tries to equalize each user's dominant share (share of a user's most highly demanded resource, that is, the largest fraction of any resource that the user has required for a task), but this method may still suffer from significant loss of efficiency (i.e., some resources are underused). This article develops a new allocation mechanism based on DRF aiming to balance fairness and efficiency. We consider fairness not only in terms of a user's dominant resource, but also in another resource dimension which is secondarily desired by this user. We call this allocation mechanism 2‐dominant resource fairness (2‐DF). Then, we design a non‐trivial on‐line algorithm to find a 2‐DF allocation and extend this concept to k ‐dominant resource fairness ( k ‐DF).
Human Pose Estimation based Speed Detection System for Running on Treadmill

Nowadays, with the improvement of life quality, people gradually realize the importance of mass entertainment and physical fitness. A growing number of people watch sports events and plan to run together in their leisure time, but the existing researches on human running speed detection always ignored the characteristics of the human body. For complex detection, auxiliary equipment needs to be worn during detection, and intelligent detection cannot be achieved. Therefore, we have designed a system that intelligently detects the running speed of people, provides a platform for runners to monitor and share their running status in real-time, and is an auxiliary means for sports events. In this paper, we design a human pose based system that extracts key point information from images to detect the speed with high precision. Additionally, based on this system, we realize intelligent detection while solving the detection of the relatively static position of the human body. Data were collected while the participant was walking/running at different speeds on a treadmill. In the experiment, the key point accuracy of the human pose estimation system based on the Simple Baselines combined with the MPII data set is 89.2%. The model can meet the accuracy requirements of speed detection. In the actual operation process, the system completed the detection task. In the experiment, the average relative error of speed sequences is 4.89%.
Period Classification of 3D Cuneiform Tablets with Geometric Neural Networks

Clay tablets are the oldest handwritten documents using the cuneiform script named after the wedge shaped imprints of a rectangular stylus left in their surface. Therefore the most suitable documentation technique is 3D acquisition using e.g. structured light. The tablets are heterogeneously shaped and may contain damage in varying degrees. Convolutional neural networks enabled large advances in the analysis of historical script written with ink on paper. However, cuneiform tablets remain inaccessible to common raster-image based recognition methods for handwriting, because the characters are actually represented by 3D shapes. With the release of the Heidelberg Cuneiform Benchmark Dataset (HeiCuBeDa) a plethora of machine learning opportunities for the underlaying representation, 3D surface meshes, was made accessible. Additionally HeiCuBeDa contains transliterations and meta-data such as language-type for approx. 1/3 of the 1.977 tablets. In this work, we combine and adapt the convolution operation of SplineNet with the pooling from PointNet++ to predict the time-period of a tablet directly on basis of its mesh representation. The classification tasks were performed on 336 tablets for training and 158 tablets for testing. Our proposed approach reaches a classification accuracy of 84 % on 4 time-period classes.
Multiple Kernel <i>k</i>-Means With Low-Rank Neighborhood Kernel

Multiple kernel clustering algorithms achieve promising performances by exploring the complementary information from kernel matrices corresponding to each data view.Most of the existing methods aim to construct a consensus kernel for the afterward clustering.However, they ignore that the desired kernel is supposed to reveal the cluster structure among samples and thus to be low rank.As a consequence, the corresponding clustering performance could decrease.To address this issue, we propose a low-rank kernel learning approach for multiple kernel clustering.Specifically, instead of regularizing the consensus kernel with low-rank constraints, we use a re-parameterize scheme for the kernel matrix.Meanwhile, the consensus kernel is located in the neighborhood area of the linear combination of base kernels.An alternate optimization strategy is designed to solve the resulting optimization problem.We evaluate the proposed method on 13 benchmark datasets with 9 state-of-the-art algorithms.As is demonstrated by experimental results, our proposed algorithm achieves superior clustering scores against the compared algorithms on the reported popular multiple kernel datasets.
Some Experiments on the Influence of Problem Hardness in Morphological Development Based Learning of Neural Controllers

Natural beings undergo a morphological development process of their bodies while they are learning and adapting to the environments they face from infancy to adulthood. In fact, this is the period where the most important learning processes, those that will support learning as adults, will take place. However, in artificial systems, this interaction between morphological development and learning, and its possible advantages, have seldom been considered. In this line, this paper seeks to provide some insights into how morphological development can be harnessed in order to facilitate learning in embodied systems facing tasks or domains that are hard to learn. In particular, here we will concentrate on whether morphological development can really provide any advantage when learning complex tasks and whether its relevance towards learning increases as tasks become harder. To this end, we present the results of some initial experiments on the application of morphological development to learning to walk in three cases, that of a quadruped, a hexapod and that of an octopod. These results seem to confirm that as task learning difficulty increases the application of morphological development to learning becomes more advantageous.
Analysis of lackadaisical quantum walks

The lackadaisical quantum walk is a quantum analogue of the lazy random walk obtained by adding a self-loop to each vertex in the graph. We analytically prove that lackadaisical quantum walks can find a unique marked vertex on any regular locally arc-transitive graph with constant success probability quadratically faster than the hitting time. This result proves several speculations and numerical findings in previous work, including the conjectures that the lackadaisical quantum walk finds a unique marked vertex with constant success probability on the torus, cycle, Johnson graphs, and other classes of vertex-transitive graphs. Our proof establishes and uses a relationship between lackadaisical quantum walks and quantum interpolated walks for any regular locally arc-transitive graph.
Governance and financial development: Evidence from a global sample of 120 countries

Abstract This study empirically investigates the impact of governance quality on financial development. The study employs a Dynamic Common Correlated Mean group technique using data from a global sample of 120 countries, among which 32 high‐ and 88 low‐income countries, for the period 2002–2017 to examine this linkage. Political stability and absence of violence, rule of law, control of corruption, regulatory quality, government effectiveness, and voice and accountability are used as proxies for governance quality, whereas financial institutions depth and credit to private sector are proxies of financial development. The following findings have been established. Regulatory quality and political stability and absence of violence are positively associated with financial development, whereas voice and accountability, and government effectiveness are negatively associated with it. Moreover, the degree of the impact of these governance indicators on financial development is relatively greater in high‐income countries as compared to low‐income countries. On the basis of these results, governments in high‐ and low‐income countries must implement the following policies in order to improve the development of their financial systems: Firstly, they should promote a better regulation quality by implementing sound policies and regulations that permit and improve private sector development. Secondly, they should maintain a stable political environment by allowing people to enjoy, but not excessively, their rights and freedoms under the constitution. Policy makers should lastly moderate their commitment towards certain public policies, such as imposition of high tax rate to the private sector.
Driving Reinforcement Learning with Models

In this paper we propose a new approach to complement reinforcement learning (RL) with model-based control (in particular, Model Predictive Control - MPC). We introduce an algorithm, the MPC augmented RL (MPRL) that combines RL and MPC in a novel way so that they can augment each other’s strengths. We demonstrate the effectiveness of the MPRL by letting it play against the Atari game Pong. For this task, the results highlight how MPRL is able to outperform both RL and MPC when these are used individually.
Performance Evaluation of Silicon-Based Irradiance Sensors Versus Thermopile Pyranometer

There are several sensors available in the market to measure the plane-of-array irradiance for photovoltaic applications. The prices of these sensors vary according to the design, calibration procedure, and conducted characterization. In this article, two types of silicon-based sensors with and without temperature correction capabilities are compared with a high-accuracy thermopile pyranometer to check their performance. The obtained results showed that silicon-based sensors deviate from the output of the pyranometers. The tested silicon-based pyranometers overestimate the irradiance with the median bias deviations of around 1.43% (with the average measured irradiance of 256 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). For temperature-corrected silicon pyranometer, the bias deviation is 0.07% with the deviation range of -6.5%-10% (with the average measured irradiance of 257 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). A working-class reference cell was also tested, resulting in a bias deviation of -1.74% and the deviation range of -13%-7% (with the average measured irradiance of 304 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). The effect of air mass on the performance of cost-effective sensors was additionally analyzed. Within the measurement time window, the result also showed that for the silicon-based sensors under tests, the effects of the environmental conditions have the following qualitative order of influence: angle of incidence > red-shift > temperature. The performance of silicon-based sensors also showed seasonal dependence, being more accurate during summertime and wintertime, respectively, for the silicon pyranometer and the working-class reference cell. Finally, using the statistical evaluation, simple linear correction functions are introduced for silicon-based sensors.
Research Ethics, Open Science and CRIS

The purpose of this paper is to analyze how current research information systems (CRIS) take into account ethical issues, especially in the environment of open science. The analysis is based on a review of the literature on research information management, CRIS, open science and research ethics. The paper provides a framework for the assessment of CRIS on two levels: are CRIS (= their data model, format, functionalities, etc.) compliant with ethical requirements from the research community, funding bodies, government, etc., i.e., can they appropriately process data on research ethics (protocols, misconduct, etc.), and which are the ethical issues of the development, implementation and usage of CRIS? What is the impact of new ethical requirements from the open science movement, such as integrity or transparency? Can CRIS be considered as ethical infrastructures or “infraethics”? Concluding this analysis, the paper proposes an empirical approach for further investigation of this topic. The originality of the paper is that there are very few studies so far that assess the implications of research ethics and open science on the CRIS.
Subspace clustering using ensembles of <i>K</i>-subspaces

Abstract Subspace clustering is the unsupervised grouping of points lying near a union of low-dimensional linear subspaces. Algorithms based directly on geometric properties of such data tend to either provide poor empirical performance, lack theoretical guarantees or depend heavily on their initialization. We present a novel geometric approach to the subspace clustering problem that leverages ensembles of the $K$-subspace (KSS) algorithm via the evidence accumulation clustering framework. Our algorithm, referred to as ensemble $K$-subspaces (EKSSs), forms a co-association matrix whose $(i,j)$th entry is the number of times points $i$ and $j$ are clustered together by several runs of KSS with random initializations. We prove general recovery guarantees for any algorithm that forms an affinity matrix with entries close to a monotonic transformation of pairwise absolute inner products. We then show that a specific instance of EKSS results in an affinity matrix with entries of this form, and hence our proposed algorithm can provably recover subspaces under similar conditions to state-of-the-art algorithms. The finding is, to the best of our knowledge, the first recovery guarantee for evidence accumulation clustering and for KSS variants. We show on synthetic data that our method performs well in the traditionally challenging settings of subspaces with large intersection, subspaces with small principal angles and noisy data. Finally, we evaluate our algorithm on six common benchmark datasets and show that unlike existing methods, EKSS achieves excellent empirical performance when there are both a small and large number of points per subspace.
A Human-Robot Cooperative Vehicle for Tea Plucking

A human-robot cooperative vehicle has been developed for tea plucking to solve the problem of the lack of labor in the tea industry. A cooperative framework, instead of a fully autonomous solution, was proposed to leverage human expertise in tea harvesting and power assistance to handle the tea cutting apparatus. The method of side-by-side walking was used to implement the collaboration between the human and robot when carrying a tea cutting apparatus to harvest tea leaves along tea rows. For the experiments, the semiautonomous vehicle was implemented to be lightweight, of compact size, and driven by tracked wheels. The experimental results showed that the human could easily guide the tea plucking machine to harvest tea because the robot shares most of the weight of the machine. The proposed cooperative working robot could be particularly effective in improving the working conditions of farmers during small scale tea harvesting on tea plantations.
Detection of Tripartite Genuine Entanglement by Two Bipartite Entangled States

Abstract There are practical motivations to construct genuine tripartite entangled states based on the collective use of two bipartite entangled states. Here, the case that the states are two‐qubit Werner states is considered. The intervals of parameters of two‐qubit Werner states are revealed such that the tripartite state is genuinely entangled. Furthermore, we also investigate the lower bound of genuine multipartite entanglement concurrence for tripartite qudit states. Several examples are given to show the effectiveness of the lower bound.
Precise inference of expressive units of measurement types

Ensuring computations are unit-wise consistent is an important task in software development. Numeric computations are usually performed with primitive types instead of abstract data types, which results in very weak static guarantees about correct usage and conversion of units. This paper presents PUnits, a pluggable type system for expressive units of measurement types and a precise, whole-program inference approach for these types. PUnits can be used in three modes: (1) modularly check the correctness of a program, (2) ensure a possible unit typing exists, and (3) annotate a program with units. Annotation mode allows human inspection and is essential since having a valid typing does not guarantee that the inferred specification expresses design intent. PUnits is the first units type system with this capability. Compared to prior work, PUnits strikes a novel balance between expressiveness, inference complexity, and annotation effort. We implement PUnits for Java and evaluate it by specifying the correct usage of frequently used JDK methods. We analyze 234k lines of code from eight open-source scientific computing projects with PUnits. We compare PUnits against an encapsulation-based units API (the javax.measure package) and discovered unit errors that the API failed to find. PUnits infers 90 scientific units for five of the projects and generates well-specified applications. The experiments show that PUnits is an effective, sound, and scalable alternative to using encapsulation-based units APIs, enabling Java developers to reap the performance benefits of using primitive types instead of abstract data types for unit-wise consistent scientific computations.
A novel terrain adaptive omni-directional unmanned ground vehicle for underground space emergency: Design, modeling and tests

With the striking development of underground space of smart cities, the detection and emergency treatment of underground space has become more and more important. At a dangerous search and rescue scene, an unmanned ground vehicle can perform the tasks instead of a rescue worker such as incident localization, hazardous material identification, toxic gas collection, and security surveillance. In this paper, we present a novel terrain adaptive omni-directional unmanned ground vehicle (TAOUGV) for search and rescue missions in underground space. The proposed system consists of three main parts: obstacle detection, terrain adaptive mechanical module and control module. In the first part, a binocular camera system and an ultrasonic sensor are used to detect obstacles as the TAOUGV is moving. Using a dilation and erosion algorithm, we can obtain all obstacle information including distance, width and height in front of the TAOUGV on condition of complicated environment. In the second step, the terrain adaptive mechanical mechanism with the minimum number of wheels can navigate passively through rough terrain, climb obstacles actively through a lifting unit, and utilize omni-directional motion for avoiding obstacles. Finally, the control module controls the central controller to implement mode selection such as passive crossing mode, active climbing mode, omni-directional motions, and sensor control for hazardous material and toxic gas collection. The simulation and testing results demonstrate the feasibility of the proposed TAOUGV for use in an underground space emergency detection.
Vietnamese Text Classification with TextRank and Jaccard Similarity Coefficient

Text classification is considered one of the most fundamental and essential problems that deal with automatically classifying textual resources into pre-defined categories.Numerous algorithms, datasets, and evaluation measurements have been proposed to address the task.Within the era of information redundancy, it is challenging and time-consuming to engineering a sizable amount of data in multi-languages manually.However, it is time-consuming to consider all words in a text, but rather several key tokens.In this work, the authors proposed an effective method to classify Vietnamese texts leveraging the TextRank algorithm and Jaccard similarity coefficient.TextRank ranks words and sentences according to their contribution value and extracts the most representative keywords.First, we collected textual sources from a wide range of Vietnamese news websites.We then applied data preprocessing, extracted keywords by TextRank algorithm, measured similarity score by Jaccard distance and predicted categories.The authors have conducted numerous experiments, and the proposed method has achieved an accuracy of 90.07% on real-world datasets.We have proved that it is entirely applicable in practice.
Learning Recurrent Memory Activation Networks for Visual Tracking

Facilitated by deep neural networks, numerous tracking methods have made significant advances. Existing deep trackers mainly utilize independent frames to model the target appearance, while paying less attention to its temporal coherence. In this paper, we propose a recurrent memory activation network (RMAN) to exploit the untapped temporal coherence of the target appearance for visual tracking. We build the RMAN on top of the long short-term memory network (LSTM) with an additional memory activation layer. Specifically, we first use the LSTM to model the temporal changes of the target appearance. Then we selectively activate the memory blocks via the activation layer to produce a temporally coherent representation. The recurrent memory activation layer enriches the target representations from independent frames and reduces the background interference through temporal consistency. The proposed RMAN is fully differentiable and can be optimized end-to-end. To facilitate network training, we propose a temporal coherence loss together with the original binary classification loss. Extensive experimental results on standard benchmarks demonstrate that our method performs favorably against the state-of-the-art approaches.