Reviewerly/exHarmony huggingface dataset:
Dependable multi‐population improved brain storm optimization with differential evolution for optimal operational planning of energy plants

Abstract This paper proposes dependable multi‐population improved brain storm optimization with differential evolution for optimal operational planning of energy plants. The problem can be formulated as a mixed‐integer nonlinear programming problem and various evolutionary computation techniques such as particle swarm optimization (PSO), differential evolutionary PSO (DEEPSO), multi‐population DEEPSO (MP‐DEEPSO), and brain storm optimization have been applied so far. When optimal operational planning of numbers of energy plants is calculated simultaneously in a data center, a challenge is to generate optimal operational planning as rapidly as possible considering control intervals and numbers of treated plants. One of the solutions for the challenge is speeding up by parallel and distributed computing. It utilizes numbers of processes and countermeasures for various faults of the distributed processes should be considered. Moreover, successive calculation at every control interval is required for keeping customer services. Therefore, sustainable (dependable) calculation keeping appropriate solution quality is required even if some of the calculation results cannot be returned from distributed processes. It is verified that total energy cost by the proposed dependable multi‐population improved brain storm optimization with differential evolution strategy based method is lower than those by the compared methods, and higher quality of solutions can be kept even with high fault probabilities.
FullStop: A Camera-Assisted System for Characterizing Unsafe Bus Stopping

Road safety is a critical issue worldwide. We believe that mobile devices can play a positive role in this context by detecting dangerous conditions and providing feedback. This paper focuses on a specific problem in developing countries: the stopping behaviour of buses in the vicinity of bus stops. For instance, buses could arrive at a bus stop but continue rolling forward instead of coming to a complete halt, or could stop some distance away from the bus stop, possibly even in the middle of a busy road. Such behaviors put at risk the passengers boarding or alighting the bus, and also the people waiting at a bus stop. We present FullStop, a smartphone-based system that detects safety risks emanating from stopping behavior like the ones listed above. We show that the GPS and inertial sensors are unable to perform the fine-grained detection needed. Therefore, our approach in FullStop is based on the view obtained from looking out to the front of the vehicle using the camera of a smartphone that is mounted on the front windshield. Using optical flow vectors, with several refinements, FullStop running on a smartphone is able to effectively detect various unsafe bus stopping behaviours.
Download and Access Trade-offs in Lagrange Coded Computing

Lagrange Coded Computing (LCC) is a recently proposed technique for resilient, secure, and private computation of arbitrary polynomials in distributed environments. By mapping such computations to composition of polynomials, LCC allows the master node to complete the computation by accessing a minimal number of workers and downloading all of their content, thus providing resiliency to the remaining stragglers. However, in the most common case in which the number of stragglers is less than in the worst case scenario, much of the computational power of the system remains unexploited. To amend this issue, in this paper we expand LCC by studying a fundamental trade-off between download and access, and present two contributions. In the first contribution, it is shown that without any modification to the encoding process, the master can decode the computations by accessing a larger number of nodes, however downloading less information from each node in comparison with LCC (i.e., trading access for download). This scheme relies on decoding a particular polynomial in the ideal that is generated by the polynomials of interest, a technique we call Ideal Decoding. This new scheme also improves LCC in the sense that for systems with adversaries, the overall downloaded bandwidth is smaller than in LCC. In the second contribution we study a real-time model of this trade-off, in which the data from the workers is downloaded sequentially. By clustering nodes of similar delays and encoding the function with Universally Decodable Matrices, the master can decode once sufficient data is downloaded from every cluster, regardless of the internal delays within that cluster. This allows the master to utilize the partial work that is done by stragglers, rather than to ignore it, a feature that most past works in coded computing are lacking.
On Combinatorial Proofs for Logics of Relevance and Entailment

Hughes’ combinatorial proofs give canonical representations for classical logic proofs. In this paper we characterize classical combinatorial proofs which also represent valid proofs for relevant logic with and without the mingle axiom. Moreover, we extend our syntax in order to represent combinatorial proofs for the more restrictive framework of entailment logic.
Quantum coherence fraction

As an analogy of the fully entangled fraction in the framework of entanglement theory, we have introduced the notion of quantum coherence fraction ${C}_{\mathcal{F}}$, which quantifies the closeness between a given state and the set of maximally coherent states. By providing an alternative formulation of the robustness of coherence ${C}_{\mathcal{R}}$, we have elucidated the relationship between the quantum coherence fraction and the normalized version of ${C}_{\mathcal{R}}$, i.e., ${\overline{C}}_{\mathcal{R}}$, where the role of genuinely incoherent operations is highlighted. Numerical simulation shows that, though as expected ${C}_{\mathcal{F}}$ is upper bounded by ${\overline{C}}_{\mathcal{R}}$, ${C}_{\mathcal{F}}$ constitutes a good approximation to ${\overline{C}}_{\mathcal{R}}$ especially in low-dimensional Hilbert spaces. Even more intriguingly, we can analytically prove that ${C}_{\mathcal{F}}$ is exactly equivalent to ${\overline{C}}_{\mathcal{R}}$ for qubit and qutrit states. Moreover, some intuitive properties and implications of ${C}_{\mathcal{F}}$ are also indicated.
A Novel Approach to Extend KM Models with Object Knowledge Model (OKM) and Kafka for Big Data and Semantic Web with Greater Semantics

In knowledge management (KM), everything is identified by an object and the world is a collection of objects. In the context of NLP, identifying grammar is a tedious task. Every sentence is being identified as a triplet: subject, predicate and object. The circumstantial detail of different tenses representing past and future of the grammar are very difficult to analyze. To identify such diversity of data - including streaming data, we propose a model, called Object Knowledge Model (OKM). Current scenario of RDF(S) defines metadata at every stage which is static in nature and lacks flexibility. Mainly OKM intends to provide a common framework for expressing machine-processable information with greater semantics than RDF—being modeled to identify the metadata in streams built on Kafka. This paper primarily discusses OKM grammar and how metadata can be analyzed implementing OKM. With known Stanford NLP technique plus others, the proposed OKM grammar is more flexible and demonstrates to be superior. The grammar checks genuinity of framing sentences (in various languages) as each language has its roots and connotations intrigued, and we claim that this proposed model is a better fitter for NLP understanding and projecting the merits of using them in KM models.
RACKNet

Despite their impressive success when these hyper-parameters are suitably fine-tuned, the design of good network architectures remains an art-form rather than a science: while various search techniques, such as grid-search, have been proposed to find effective hyper-parameter configurations, often these parameters are hand-crafted (or the bounds of the search space are provided by a user). In this paper, we argue, and experimentally show, that we can minimize the need for hand-crafting, by relying on the dataset itself. In particular, we show that the dimensions, distributions, and complexities of localized features extracted from the data can inform the structure of the neural networks and help better allocate limited resources (such as kernels) to the various layers of the network. To achieve this, we first present several hypotheses that link the properties of the localized image features to the CNN and RCNN architectures and then, relying on these hypotheses, present a RACKNet framework which aims to learn multiple hyper-parameters by extracting information encoded in the input datasets. Experimental evaluations of RACKNet against major benchmark datasets, such as MNIST, SVHN, CIFAR10, COIL20 and ImageNet, show that RACKNet provides significant improvements in the network design and robustness to change in the network.
Are DevOps and Automation Our Next Silver Bullet?

Fred brooks, in his well-known classic <i>The Mythical Man-Month</i>, already told the software engineering industry in 1975 that there are no silver bullets in gaining an order-of-magnitude improvement in software productivity.<sup>1</sup> He also observed that "most of the big past gains in software productivity have come from removing artificial barriers that have made the accidental tasks inordinately hard, such as severe hardware constraints, awkward programming languages, lack of machine time." The hope and goal of software development processes in orchestrating the essential and accidental software engineering and development tasks is precisely to remove artificial barriers to delivering better, faster, cheaper software to the users. Our next silver bullet seems to have emerged as automating repeatable, manual process tasks. While, on one hand, we debate how to scale agile, on the other, we run to DevOps, continuous integration, and continuous delivery tools to achieve the so-called orders of magnitude of productivity improvement.
Exploring Low-Occlusion Qwerty Soft Keyboard Using Spatial Landmarks

The Qwerty soft keyboard is widely used on mobile devices. However, keyboards often consume a large portion of the touchscreen space, occluding the application view on the smartphone and requiring a separate input interface on the smartwatch. Such space consumption can affect the user experience of accessing information and the overall performance of text input. In order to free up the screen real estate, this article explores the concept of Sparse Keyboard and proposes two new ways of presenting the Qwerty soft keyboard. The idea is to use users’ spatial memory and the reference effect of spatial landmarks on the graphical interface. Our final design K3-SGK displays only three keys while L5-EYOCN displays only five line segments instead of the entire Qwerty layout. To achieve this, we employ a user-centered computational design method: first study the reference effect of a single landmark key (line segment) from empirical data, then make assumptions to generalize the effect to multiple landmarks, and finally optimize the best designs. To make the text entry function more complete, we also design and implement gestural interactions for editing operations and non-alphabetical characters’ input. User evaluation shows that participants can quickly learn how to type with K3-SGK and L5-EYOCN . After five 15-phrase typing sessions, participants achieve 88.1%--92.8% of the full Qwerty keyboard in terms of words per minute on the smartphone and 98.4%--99.1% on the smartwatch. The differences on character and word error rate between our keyboard designs and the full Qwerty keyboard are not significant. The results of out-of-vocabulary words input are also promising. In addition, participants can quickly recall the typing skills and maintain the input performance even after a few days. User feedbacks in real application contexts show that with the low occlusion keyboard, users can acquire more information and perform less scrolling on the smartphone and achieve a higher input efficiency on the smartwatch with a more fluent input experience.
Explorations into the Use of Word Embedding in Math Search and Math Semantics

Word embedding, which represents individual words with semantically rich numerical vectors, has made it possible to successfully apply deep learning to NLP tasks such as semantic role modeling, question answering, and machine translation. As math text consists of natural text as well as math expressions that similarly exhibit linear correlation and contextual characteristics, word embedding can be applied to math documents as well. On the other hand, math terms also show characteristics (e.g., abstractions) that are different from textual words. Accordingly, it is worthwhile to explore the use and effectiveness of word embedding in math language processing and MKM. In this paper, we present exploratory investigations of math embedding by testing it on some basic tasks such as (1) math-term similarity, (2) analogy, (3) basic numerical concept-modeling using a novel approach based on computing the (weighted) centroid of the keywords that characterize a concept, and (4) math search, especially query expansion using the weighted centroid of the query keywords and then expanding the query with new keywords that are most similar to the centroid. Due to lack of benchmarks, our investigations were done using carefully selected illustrations on the DLMF. We draw from our investigations some general observations and lessons that form a trajectory for future statistically significant testing on large benchmarks. Our preliminary results and observations show that math embedding holds much promise but also point to the need for more robust embedding.
Blockchain Enabled Distributed Data Management - A Vision

Blockchain has gained much attention in recent academic and research works not only in crypto-currency but also in many other fields such as supply chain, health, storage etc. The application of blockchain in data management domain, however, is mostly geared towards the aspect of security and immutability. In this paper we propose integrating blockchain with distributed data management and study some open challenges and assumptions in doing so. We claim that, from data management perspective, blockchain's ability to handle unequal participants is more important than security and immutability. Finally, we propose possible ideas to integrate blockchain into distributed data transaction and management workflows to design a globally consistent data store ensuring availability guarantees along with support for unifying heterogeneous data backends.
Reactive Auto-Completion of Modeling Activities

Assisting and automating software engineering tasks is a state-of-the-art way to support stakeholders of development projects. A common assistance function of IDEs is the auto-completion of source code. Assistance functions, such as auto-completion, are almost entirely missing in modeling tools though auto-completion in general gains continuously more importance in software development. We analyze a user’s performed editing operations in order to anticipate modeling activities and to recommend appropriate auto-completions for them. Editing operations are captured as events and modeling activities are defined as complex event patterns, facilitating the matching by complex-event-processing. The approach provides adapted auto-completions reactively upon each editing operation of the user. We implemented the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RapMOD</i> prototype as add-in for the modeling tool Sparx Enterprise Architect™ . A controlled user experiment with 37 participants performing modeling tasks demonstrated the approach’s potential to reduce modeling effort significantly. Users having auto-completions available for a modeling scenario performed the task 27 percent faster, needed to perform 56 percent less actions, and perceived the task 29 percent less difficult.
Cloud computing resource scheduling based on improved differential evolution ant colony algorithm

Due to the uneven distribution of cloud computing resources and the long processing time of resource scheduling, a cloud computing resource scheduling strategy based on improved differential evolution ant colony algorithm is proposed. By changing the size of the mutation operator F in the differential evolution algorithm, the differential evolution algorithm is controlled not to fall into the local search state and the convergence premature phenomenon. Then the improved differential evolution algorithm is combined with the ant colony algorithm to preserve the ant colony. The algorithm local search optimal characteristics, and has the characteristics of improved global evolution of differential evolution algorithm. The combination of the two can well optimize the problem of unbalanced load and long processing time in the cloud computing resource scheduling process.
Evaluation of Tools for Analyzing Smart Contracts in Distributed Ledger Technologies

Despite the fact that the extent of interest in distributed ledger technologies has slightly decreased after the peak of Bitcoin popularity this area continues to evolve. One of the popular areas is the development of smart contracts which introduces a new paradigm of writing programs. This inflicts additional difficulties associated primarily with the high costs of error. This paper reviews the typical vulnerabilities that are widespread during development in the Solidity language. It also presents an analysis of existing tools to help identify software bugs. It is shown that there is no universal technique at the moment and if the risks are high, one should not solely check the code with available instruments but also conduct a manual audit with help of an expert.
Adopting Collaborative Games into Agile Software Development

Although the emergence of agile methods has triggered a growing awareness that social factors have a crucial impact on the success of software projects, neither the Scrum Guide nor the Agile Manifesto prescribe techniques that aid the human side of software development. To address this challenge, we enriched the Scrum process with a set of collaborative games. Collaborative games refer to techniques inspired by game play, but designed for the purpose of solving practical problems. Our approach was evaluated in two companies. The feedbacks received from Scrum teams indicate that the implementation of collaborative games leads to a variety of measurable societal outcomes. In particular, the adopted games improved participants’ communication, involvement, and creativity, make participants more willing to attend Scrum meetings, and produce better results than the standard approach. This paper is an extended version of our previous work [60].
Bounds and Optimal $q$ -Ary Codes Derived From the $\mathbb{Z}_qR$ -Cyclic Codes

Motivated by the recent studies on Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> -additive cyclic codes, Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> [u]-cyclic codes and Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2(s)</sub> -additive codes have been introduced by Aydogdu et al.. In this paper, we study Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> R-linear cyclic codes where R = Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> + uZ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> , q is a prime number and u <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = 0. There are two major ingredients in this paper. The first is to investigate the algebraic structure of cyclic codes and their duals over ring Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> R, the spanning sets, the types and sizes of Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> R-cyclic codes and their duals as well. Based on this, the second ingredient is to present an infinite family of MDSS codes and obtain some illustrative examples of q-ary cyclic codes with optimal parameters derived from the Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> R-cyclic codes.
Structured grammatical evolution for glucose prediction in diabetic patients

Structured grammatical evolution is a recent grammar-based genetic programming variant that tackles the main drawbacks of Grammatical Evolution, by relying on a one-to-one mapping between each gene and a non-terminal symbol of the grammar. It was applied, with success, in previous works with a set of classical benchmarks problems. However, assessing performance on hard real-world problems is still missing. In this paper, we fill in this gap, by analyzing the performance of SGE when generating predictive models for the glucose levels of diabetic patients. Our algorithm uses features that take into account the past glucose values, insulin injections, and the amount of carbohydrate ingested by a patient. The results show that SGE can evolve models that can predict the glucose more accurately when compared with previous grammar-based approaches used for the same problem. Additionally, we also show that the models tend to be more robust, since the behavior in the training and test data is very similar, with a small variance.
Matrix Structure Driven Interior Point Method for Quadrotor Real-Time Trajectory Planning

Sequential convex programming (SCP) has been recently employed in various trajectory planning problems, including entry flight, planetary landing, and aircraft formation. In SCP, convex programming subproblems are sequentially solved to obtain the optimum of original nonconvex problems. For SCP-based quadrotor trajectory planning, this paper proposes a matrix-structure-driven interior point method (MSD-IPM) to improve the efficiency of solving search directions in convex programming. In MSD-IPM, primal-dual systems for solving search directions are derived from the Karush-Kuhn-Tucher (KKT) conditions of quadrotor trajectory planning subproblems. Then, the successive elimination technique is used to solve the inverse of large-scale coefficient matrices of primal-dual systems by more efficient operations on small-scale matrices. In successive elimination, the positive definiteness of several small-scale matrices is used to enhance the numerical stability of computing search directions, and the specific diagonal structures of small-scale matrices are exploited to efficiently compute the search directions. The complexity analysis shows that the efficiency of the proposed method is about one order of magnitude higher than that of the standard IPM. The comparative studies on simulation experiments demonstrate that the MSD-IPM generally outperforms several well-known optimizers (e.g., MOSEK, SDPT3, and SeDuMi) in terms of efficiency and robustness. Finally, the indoor trajectory tracking experiments indicate that the proposed method can generate smooth trajectories for real-world applications.