Miracle-dz/UniFAR-FTUs huggingface dataset:
A variable neighbourhood search algorithm to generate piano fingerings for polyphonic sheet music

A good piano fingering is essential for pianists to play a piece fluently. Some examples of algorithms to generate a piano fingering automatically can be found in the literature. However, the existing methods can only generate fingerings for small, monophonic piano pieces for the right hand, minimising the difficulty. In this paper, we develop a variable neighbourhood search (VNS) algorithm that can generate good fingerings for large pieces of polyphonic piano music considering both hands in an acceptable amount of calculation time. The algorithm calculates the difficulty based on a user-specific finger distance matrix. The set of rules considered in the literature in order to define the quality of a fingering is expanded to deal with specific finger combinations in polyphonic music. The VNS is a local search metaheuristic that searches in different neighbourhoods and performs a perturbation when a local optimum is reached. The selection of the optimal parameters of the VNS and the perturbation strategy are determined using thorough computational experiments and statistical analysis.
TopicFlow : VisualizingTopic Alignment of Twitter Data over Time CMSC 734-Fall 2012

The analysis and visualization of social media [1] data is becoming an increasingly popular topic in the areas of Natural Language Processing and HCI. As of October 2011, Twitter users were producing approximately 250 million tweets per day [19]. This overwhelming amount of data is far beyond what any user could possibly read and understand without the help of analysis and visualization tools. In this paper, we present two main contributions. The first, was to expand on the “Aggregate Overall Metrics Feature” in NodeXL to allow aggregation of summary statistics (such as word and word pair frequencies) across multiple workbooks into a single workbook. The second task was to develop a tool to visualize this Twitter data. In analyzing the tweets, we hope to gain insights on what users have talked about during events that occur over a larger or smaller period of time. To this end, we run an automated topic detection algorithm on the Twitter data to learn more about changes in users’ attention over time. The goal of this tool is to support user interaction and enable an analyst to easily create an overview of changing topics of interest and their relative rates of mention as well as study a dataset of this kind in depth.
Metadata schema generation for data-driven smart buildings

A smart building is a combination of advanced information systems originating from different domains. Domains such as design and construction, maintenance, energy management, automation & control have complex yet important relationships, and ensuring their connectivity is crucial for building operations. Semantic web technologies can be used to model and link these domains and their relationships using domain ontologies. To that end, there are a number of smart building ontologies that are available in each domain. However, the process of generating a metadata schema by using those ontologies for a given building is not investigated adequately. Further, such tools that generate those metadata schemas are rare. Therefore, this study presents a semi-automatic metadata schema generator using an ontology database and a text search engine. The proposed approach is applied to a campus building. Building Automation System metadata was used in the metadata schema generator. Finally, this study shows how the generated metadata scheme can be used to efficiently query and visualize time-series data for developing data-driven smart building applications.
Proactive Eavesdropping via Cognitive Jamming in Fading Channels

To enhance the national security, there is a growing need for authorized parties to legitimately monitor suspicious communication links for preventing intended crimes and terror attacks. In this paper, we propose a new wireless information surveillance paradigm by investigating a scenario, where a legitimate monitor aims to intercept a suspicious wireless link over fading channels. The legitimate monitor can successfully eavesdrop (decode) the information of the suspicious link at each fading state only when its achievable data rate is no smaller than that at the suspicious receiver. We propose a new approach, namely, proactive eavesdropping via cognitive jamming, in which the legitimate monitor purposely jams the receiver in a full-duplex mode so as to change the suspicious communication (e.g., to a smaller data rate) for overhearing more efficiently. By assuming perfect self-interference cancelation (SIC) and global channel state information (CSI) at the legitimate monitor, we characterize the fundamental information-theoretic limits of proactive eavesdropping. We consider both delay-sensitive and delay-tolerant applications for the suspicious communication, under which the legitimate monitor maximizes the eavesdropping non-outage probability (for event-based monitoring) and the relative eavesdropping rate (for content analysis), respectively, by optimizing the jamming power allocation over different fading states subject to an average power constraint. Numerical results show that the proposed proactive eavesdropping via cognitive jamming approach greatly outperforms other benchmark schemes. Furthermore, by extending to a more practical scenario with residual SI and local CSI, we design an efficient online cognitive jamming scheme inspired by the optimal cognitive jamming with perfect SIC and global CSI.
Hypervisor App JVM OS VM App JVM OS VM App JVM OS VM IaaS OS JVM App JVM App JVM App PaaS with dedicated JVMs Platform-JVM App App App PaaS

The discussion on how to effectively program embedded systems has often in the past revolved around issues like the ideal instruction set architecture (ISA) or the best operating system. Much of this has been motivated by the inherently resource-constrained nature of embedded devices that mandates efficiency as the primary design principle. In this paper, we advocate a change in the way we see and treat embedded systems. Not only have embedded systems become much more powerful and resources more affordable, we also see a trend towards making embedded devices more consumable, programmable, and customizable by end users. In fact, we see a strong similarity with recent developments in cloud computing. We outline several challenges and opportunities in turning a language runtime system like the Java Virtual Machine into a cloud platform. We focus in particular on support for running multiple tenants concurrently within the platform. Multi-tenant support is essential for efficient resource utilization in cloud environments but can also improve application performance and overall user experience in embedded environments. We believe that today’s modern language runtimes, with extensions to support multitenancy, can form the basis for a single continuous platform for emerging embedded applications backed by cloud-based service infrastructures.
Composition of Nondeterministic Services for LTLf Task Specification

In this paper, we study the composition of services so as to obtain runs satisfying a task specification in Linear Temporal Logic on finite traces (ltl 𝑓 ). We study the problem in the case services are nondeterministic and the ltl 𝑓 specification can be exactly met. To do so, we combine techniques from ltl 𝑓 synthesis, service composition à la Roman Model and reactive synthesis.
On the effect of populations in evolutionary multi-objective optimization

Multi-objective evolutionary algorithms (MOEAs) have become increasingly popular as multi-objective problem solving techniques. An important open problem is to understand the role of populations in MOEAs. We present a simple bi-objective problem which emphasizes when populations are needed. Rigorous runtime analysis point out an exponential runtime gap between the population-based algorithm Simple Evolutionary Multi-objective Optimizer (SEMO) and several single individual-based algorithms on this problem. This means that among the algorithms considered, only the population-based MOEA is successful and all other algorithms fail.
Successive interference cancellation in a low‐earth orbit satellite system

Successive interference cancellation is a promising technique for increasing the capacity of uplink CDMA channels. In this paper, the technique is extended to a low‐earth orbit satellite channel. Unlike terrestrial cellular channels, satellite channels typically have a limited multipath profile, but significant closed‐loop power control error due to the round‐trip delay incurred in communicating with an orbiting satellite. In this paper, a recently developed power control algorithm with considerable robustness to channel estimation and power control error is applied to an architecture capable of multiple‐access interference cancellation and multipath combining. Even in the presence of large channel estimation and power control error, it will be shown that performance is increased significantly over previous research on interference cancellation for multipath fading channels, provided that the number of multipath components is small, as is the case for satellite communication systems. In addition, the proposed scheme has a low complexity relative to competing interference cancellation schemes. Copyright © 2003 John Wiley & Sons, Ltd.
A heuristic genetic algorithm for product portfolio planning

For any manufacturing company, product portfolio planning constitutes one of the most important decisions regarding how to offer the "right" products to the target market. Essentially, such decisions exhibit a typical combinatorial optimization problem, which deems to be very complex and hard to solve using conventional optimization techniques. Enumeration is inhibitive if the problem size is extremely large. Genetic algorithms (GAs) have been proven to excel in solving combinatorial optimization problems. This paper develops a heuristic GA to tackle the product portfolio planning problem.
A Globally Convergent Augmented Lagrangian Pattern Search Algorithm for Optimization with General Constraints and Simple Bounds

We give a pattern search method for nonlinearly constrained optimization that is an adaption of a bound constrained augmented Lagrangian method first proposed by Conn, Gould, and Toint [SIAM J. Numer. Anal. , 28 (1991), pp. 545--572]. In the pattern search adaptation, we solve the bound constrained subproblem approximately using a pattern search method. The stopping criterion proposed by Conn, Gould, and Toint for the solution of the subproblem requires explicit knowledge of derivatives. Such information is presumed absent in pattern search methods; however, we show how we can replace this with a stopping criterion based on the pattern size in a way that preserves the convergence properties of the original algorithm. In this way we proceed by successive, inexact, bound constrained minimization without knowing exactly how inexact the minimization is. As far as we know, this is the first provably convergent direct search method for general nonlinear programming.
PTAS for Ordered Instances of Resource Allocation Problems

We consider the problem of fair allocation of indivisible goods where we are given a set I of m indivisible resources (items) and a set P of n customers (players) competing for the resources. Each resource j in I has a same value vj > 0 for a subset of customers interested in j and it has no value for other customers. The goal is to find a feasible allocation of the resources to the interested customers such that in the Max-Min scenario (also known as Santa Claus problem) the minimum utility (sum of the resources) received by each of the customers is as high as possible and in the Min-Max case (also known as R||C_max problem), the maximum utility is as low as possible. In this paper we are interested in instances of the problem that admit a PTAS. These instances are not only of theoretical interest but also have practical applications. For the Max-Min allocation problem, we start with instances of the problem that can be viewed as a convex bipartite graph; there exists an ordering of the resources such that each customer is interested (has positive evaluation) in a set of consecutive resources and we demonstrate a PTAS. For the Min-Max allocation problem, we obtain a PTAS for instances in which there is an ordering of the customers (machines) and each resource (job) is adjacent to a consecutive set of customers (machines). Next we show that our method for the Max-Min scenario, can be extended to a broader class of bipartite graphs where the resources can be viewed as a tree and each customer is interested in a sub-tree of a bounded number of leaves of this tree (e.g. a sub-path).
Optimal charging of electric vehicles in low-voltage distribution systems

Summary form only given. Advances in the development of electric vehicles, along with policy incentives, will see a wider uptake of this technology in the transport sector in future years. However, the widespread adoption of electric vehicles could lead to adverse effects on the power system, especially for existing distribution networks. These effects would include excessive voltage drops and overloading of network components, which occur mainly during periods of simultaneous charging of large numbers of electric vehicles. This paper demonstrates how controlling the rate at which electric vehicles charge can lead to better utilization of existing networks. A technique based on linear programming is employed, which determines the optimal charging rate for each electric vehicle in order to maximize the total power that can be delivered to the vehicles while operating within network limits. The technique is tested on a section of residential distribution network. Results show that, by controlling the charging rate of individual vehicles, high penetrations can be accommodated on existing residential networks with little or no need for upgrading network infrastructure.
Communication in Asymmetric Group Competition Over Public Goods

This paper examines whether and how cheap talk communication can facilitate within-group coordination when two unequal sized groups compete for a prize that is shared equally among members of the winning group, regardless of their (costly) contributions to the group’s success. We find that allowing group members to communicate before making contribution decisions improves coordination. To measure how much miscoordination remains, we employ a control treatment where miscoordination is eliminated by asking group members to reach a unanimous contribution decision. Average group contributions are not significantly different in this control treatment. Cheap talk communication thus completely solves miscoordination within groups and makes group members act as a single agent. Furthermore, it is the larger group that benefits from communication at the expense of the smaller group. Finally, content analysis of group communication reveals that after the reduction of within-group strategic uncertainty, groups reach self-enforcing agreements on how much to contribute, designate specific contributors according to a rotation scheme, and quickly discover the logic of the mixed-strategy equilibrium.
A Cluster-Based Approach for identifying East Asian Economies : A foundation for monetary integration

This paper uses the agglomerative hierarchical cluster analysis as a grouping procedure to classify relatively similar regional economies into clusters. The hierarchical cluster technique permits a set of macroeconomic indicators to be jointly assessed, as a basis for identifying similar groups of economies better poised for monetary integration. In particular, economies of the European and Asia-Pacific regions were assessed respectively. The cluster results supported a natural ‘core’ and ‘periphery’ group of European countries prior to the formation of the European monetary union (EMU). When the EMU convergence criteria were applied to the AsiaPacific economies, it is found that they were not sufficient to sort the heterogeneous regional economies into relatively similar clusters. Due to the diversity of the Asia-Pacific region, a practical approach towards regional monetary integration would be to begin with smaller clusters first. The rationale is that the economic costs of monetary integration reduce with the degree of similarity or convergence of its potential union members.
Heuristic Minimization Modulo Theory of Modal Decision Trees Class-Formulas

One way towards the design of trustable, explainable, and interpretable artificial intelligence models is to focus on symbolic machine learning models, such as decision trees. While decision trees are already intelligible in principle, the logical rules they enclose may still be redundant, in particular with respect to some underlying theory. Moreover, propositional decision trees have been recently generalized to the case of modal logic; modal decision trees turn out to be more expressive than propositional ones, so their corresponding modal rules are proportionally harder to understand and minimize. In this paper we approach the problem of minimizing logical rules extracted from (modal) decision trees modulo some external theory.
Achieving Full Secure Degrees-of-Freedom for the MISO Wiretap Channel With an Unknown Eavesdropper

In this paper, we study the achievable secure degrees-of-freedom (sdof) for the multiple-input single-output (MISO) wiretap channel with an unknown eavesdropper. It is assumed that the eavesdropper’s (Eve’s) channel state information (CSI) is unknown to the transmitter (Alice) and legitimate receiver (Bob). Recent studies have shown that the achievable sdof in the sense of strong secrecy is zero when Eve’s number of antennas is equal to or more than Bob’s number of antennas, which is the scenario considered in this paper. To this end, we propose a novel precoding technique and a coding strategy that together achieve full sdof in the sense of strong secrecy without knowing Eve’s CSI and without using artificial noise. The proposed precoding method uses the CSI of the Alice-Bob channel in a nonlinear fashion, which makes the transmitted symbols undecodable at Eve. The proposed coding scheme is based on the channel resolvability concept and ensures strong secrecy. Achieving full sdof with an unknown Eve’s CSI is significant, because it is contrary to what is believed about the achievable sdof for the MISO wiretap channel in the sense of strong secrecy. We also show that the proposed scheme achieves near Alice-Bob’s channel capacity in the sense of strong secrecy with a probability approaching one at finite signal-to-noise ratio.
Testing Pointing Device Performance and User Assessment with the ISO 9241

Electrochemical generator with a zinc electrode comprising a positive electrode, a collector core constituted by a metal grid covered by a material containing zinc, an electrolyte comprising a base aqueous solution of an alkaline polyacid salt and more particularly a phosphate in a concentration such that the pH be maintained at a substantially constant value, the said material being thermo-hardening and having a composition, by weight, of 10 to 100 parts of the said polyacid alkaline salt, 50 to 200 parts of a compound containing zinc, and 2 to 20 parts of at least one oxide of a metal chosen among the elements of columns IIA and IIB of the periodic classification as well as lithium, the said electrolyte comprising, moreover, at least one alkaline halide.
An Algorithmic Approach for Checking ClosureProperties of !-Regular

In concurrency theory, there are several examples where the interleaved model of concurrency can distinguish between execution sequences which are not signiicantly diierent. One such example is sequences that diier from each other by stuttering, i. e. , the number of times a state can adjacently repeat. Another example is executions that diier only by the ordering of independently executed events. Considering these sequences as diierent is semantically rather meaningless. Nevertheless , speciication languages that are based on interleaving semantics, such as linear temporal logic (LTL), can distinguish between them. This situation has led to several attempts to deene languages that cannot distinguish between such equivalent sequences. In this paper, we take a diierent approach to this problem: we develop algorithms for deciding if a property cannot distinguish between equivalent sequences, i. e. , is closed under the equivalence relation. We focus on properties represented by regular languages, ! -regular languages, or propositional LTL formu-lae and show that for such properties there is a wide class of equivalence relations for which determining closure is decidable, in fact in PSPACE. Hence, checking the closure of a speciication is no more diicult than checking satissability of a temporal formula. Among the closure properties we are able to handle, one nds trace closedness, stutter closedness and projective closedness, for all of which we are also able to prove a PSPACE lower bound. Being able to check that a property is closed under an equivalence relation has an immediate application in state-space exploration based veriication. Indeed, the knowledge that the speciica-tion does not distinguish between equivalent execution sequences allows constructing a reduced state space where it is suucient that at least one sequence per equivalence class is represented.
Systematic review of melatonin in cerebral ischemia-reperfusion injury: critical role and therapeutic opportunities

Cerebral ischemia-reperfusion (I/R) injury is the predominant causes for the poor prognosis of ischemic stroke patients after reperfusion therapy. Currently, potent therapeutic interventions for cerebral I/R injury are still very limited. Melatonin, an endogenous hormone, was found to be valid in preventing I/R injury in a variety of organs. However, a systematic review covering all neuroprotective effects of melatonin in cerebral I/R injury has not been reported yet. Thus, we perform a comprehensive overview of the influence of melatonin on cerebral I/R injury by collecting all available literature exploring the latent effect of melatonin on cerebral I/R injury as well as ischemic stroke. In this systematic review, we outline the extensive scientific studies and summarize the beneficial functions of melatonin, including reducing infarct volume, decreasing brain edema, improving neurological functions and attenuating blood-brain barrier breakdown, as well as its key protective mechanisms on almost every aspect of cerebral I/R injury, including inhibiting oxidative stress, neuroinflammation, apoptosis, excessive autophagy, glutamate excitotoxicity and mitochondrial dysfunction. Subsequently, we also review the predictive and therapeutic implications of melatonin on ischemic stroke reported in clinical studies. We hope that our systematic review can provide the most comprehensive introduction of current advancements on melatonin in cerebral I/R injury and new insights into personalized diagnosis and treatment of ischemic stroke.