Miracle-dz/UniFAR-FTUs huggingface dataset:
Scalad: an interactive type-level debugger

Dealing with statically-typed languages and their complex type systems is problematic for programmers of all levels. Resolving confusing type errors is a time consuming and not always successful process. In this tool demonstration we give an overview of Scalad, an interactive tool that can explain decisions made by the existing typechecker of a multi-paradigm programming language by visualizing the whole process in the form of a proof tree. The tool works for both type correct and incorrect programs, making it suitable for educational purposes as well as debugging. We provide examples on how the tool can be used to understand typing puzzles. The debugger comes with an expandable search mechanism that can precisely guide users in finding answers to the typechecking problems and improve exploration time.
Assertions for Dynamic Shape Analysis of List Data Structures

We introduce an assertion language for run-time checking of linked list data structure shapes. The assertion language is expressive enough to define characteristic predicates for data structures created with the use of pointers and dynamic memory allocation. Examples of such data structures include singly linked list, binary tree, doubly linked list, and cyclic list. These characteristic predicates may be used for automatic run-time detection of data constraint violations. Some results of experiments with a prototype assertion checker implementation for the PASCAL programming language are presented.
Conducting Polymers and Their Applications in Diabetes Management

Advances in conducting polymers (CPs) have promoted the development of diabetic monitoring and treatment, which is of great significance in human healthcare and modern medicine. CPs are special polymers with physical and electrochemical features resembling metals, inorganic semiconductors and non-conducting polymers. To improve and extend their properties, the fabrication of CPs and CP composites has attracted intensive attention in recent decades. Some CPs are biocompatible and suitable for biomedical use. Thus, the intriguing properties of CPs make wearable, noninvasive, continuous diabetes managing devices and other potential applications in diabetes possible in the near future. To highlight the recent advances of CPs and their derived materials (especially in conducting polymer hydrogels), here we discuss their fabrication and characterization, review the current state-of-the-art research in diabetes management based on these materials and describe current challenges as well as future potential research directions.
Scheduling High-Rate Unpredictable Traffic in IEEE 802.15.4 TSCH Networks

The upcoming Internet of Things (IoT) applications include real-time human activity monitoring with wearable sensors. Compared to the traditional environmental sensing with low-power wireless nodes, these new applications generate a constant stream of a much higher rate. Nevertheless, the wearable devices remain battery powered and therefore restricted to low-power wireless standards such as IEEE 802.15. 4 or Bluetooth Low Energy (BLE). Our work tackles the problem of building a reliable autonomous schedule for forwarding this kind of dynamic data in IEEE 802.15. 4 TSCH networks. Due to the a priori unpredictability of these data source locations, the quality of the wireless links, and the routing topology of the forwarding network, it is wasteful to reserve the number of slots required for the worst-case scenario, under conditions of high expected datarate, it is downright impossible. The solution we propose is a hybrid approach where dedicated TSCH cells and shared TSCH slots coexist in the same schedule. We show that under realistic assumptions of wireless link diversity, adding shared slots to a TSCH schedule increases the overall packet delivery rate and the fairness of the system.
Spatiotemporal Event Forecasting in Social Media

Event forecasting in Twitter is an important and challenging problem. Most existing approaches focus on forecasting temporal events (such as elections and sports) and do not consider spatial features and their underlying correlations. In this paper, we propose a generative model for spatiotemporal event forecasting in Twitter. Our model characterizes the underlying development of future events by jointly modeling the structural contexts and spatiotemporal burstiness. An effective inference algorithm is developed to train the model parameters. Utilizing the trained model, the alignment likelihood of tweet sequences is calculated by dynamic programming. Extensive experimental evaluations on two different domains demonstrated the effectiveness of our pro-
Towards a parallel algebraic multigrid solver using PGAS

The Algebraic Multigrid (AMG) method has over the years developed into an efficient tool for solving unstructured linear systems. The need to solve large industrial problems discretized on unstructured meshes, has been a key motivation for devising a parallel AMG method. Despite some success, the key part of the AMG algorithm; the coarsening step, is far from trivial to parallelize efficiently. We here introduce a novel parallelization of the inherently sequential Ruge-Stüben coarsening algorithm, that retains most of the good interpolation properties of the original method. Our parallelization is based on the Partitioned Global Address Space (PGAS) abstraction, which greatly simplifies the parallelization as compared to traditional message passing based implementations. The coarsening algorithm and solver is described in detail and a performance study on a Cray XC40 is presented.
Pattern-Based Semi-Automatic Analysis of Weaknesses in Semantic Business Process Models in the Banking Sector

As banks have realized the need to look on their business in a process-oriented way, they have been engaged in numerous business process optimization or even reengineering (BPR) projects in the last decade to make their organizations more efficient. However, the success of BPR projects in banks varies significantly and it remains a challenge to systematically discover weaknesses in business process landscapes. In particular, automatic semantic analysis of business processes for different types of weaknesses (i.e. media breaks, redundant consistency checks, or missing or inconsistent information) is yet in its infancy. Value from business process modeling can, however, only be unveiled when time-consuming, mostly manual business process analysis is performed and results in business process optimization. In this paper we develop a methodology for semi-automatic analysis and detection of weaknesses in semantically analyzable business process models. We argue that this contributes to systematically identify possible weaknesses in process models more efficiently and more effectively than manual analysis.
Systematic review of measurement tools to assess surgeons' intraoperative cognitive workload

Surgeons in the operating theatre deal constantly with high‐demand tasks that require simultaneous processing of a large amount of information. In certain situations, high cognitive load occurs, which may impact negatively on a surgeon's performance. This systematic review aims to provide a comprehensive understanding of the different methods used to assess surgeons' cognitive load, and a critique of the reliability and validity of current assessment metrics.
Gesture Elicitation Study on How to Opt-in & Opt-out from Interactions with Public Displays

Public interactive displays with gesture-recognizing cameras enable new forms of interactions. However, often such systems do not yet allow passers-by a choice to engage voluntarily or disengage from an interaction. To address this issue, this paper explores how people could use different kinds of gestures or voice commands to explicitly opt-in or opt-out of interactions with public installations. We report the results of a gesture elicitation study with 16 participants, generating gestures within five gesture-types for both a commercial and entertainment scenario. We present a categorization and themes of the 430 proposed gestures, and agreement scores showing higher consensus for torso gestures and for opting-out with face/head. Furthermore, patterns indicate that participants often chose non-verbal representations of opposing pairs such as 'close and open' when proposing gestures. Quantitative results showed overall preference for hand and arm gestures, and generally a higher acceptance for gestural interaction in the entertainment setting.
Experimental verification of sawtooth control by energetic particles in ion cyclotron resonance heated JET tokamak plasmas

Experimental evidence from the JET tokamak is presented supporting the predictions of a recent theory (Graves et al 2009 Phys. Rev. Lett. 102 065005) on sawtooth instability control by toroidally propagating ion cyclotron resonance waves. Novel experimental conditions minimized a possible alternate effect of magnetic shear modification by ion cyclotron current drive, and enabled the dependence of the new energetic ion mechanism to be tested over key variables. The results have favourable implications on sawtooth control by ion cyclotron resonance waves in a fusion reactor.
A biochemical approach to identifying microRNA targets

Identifying the downstream targets of microRNAs (miRNAs) is essential to understanding cellular regulatory networks. We devised a direct biochemical method for miRNA target discovery that combined RNA-induced silencing complex (RISC) purification with microarray analysis of bound mRNAs. Because targets of miR-124a have been analyzed, we chose it as our model. We honed our approach both by examining the determinants of stable binding between RISC and synthetic target RNAs in vitro and by determining the dependency of both repression and RISC coimmunoprecipitation on miR-124a seed sites in two of its well characterized targets in vivo. Examining the complete spectrum of miR-124 targets in 293 cells yielded both a set that were down-regulated at the mRNA level, as previously observed, and a set whose mRNA levels were unaffected by miR-124a. Reporter assays validated both classes, extending the spectrum of mRNA targets that can be experimentally linked to the miRNA pathway.
Parametric Velocity Synthetic Aperture Radar:Signal Modeling and Optimal Methods

Velocity synthetic aperture radar (VSAR) is equipped with a linear array to receive the echoes from a radar illuminating area via multiple channels, each of which can reconstruct a reflectivity image for the same stationary scene. Based on analysis of pixel vector sampled among multi-images, VSAR may effectively suppress the strong ground clutter and improve moving target detection and location. In this paper, different Doppler-distributed properties are derived for the moving target and clutter, respectively. Then, we propose a novel parametric statistical model for VSAR by dividing the pixel vector into three components, namely, target, clutter, and noise. Furthermore, a method of adaptive implementation of optimal processing (AIOP-VSAR) is presented for moving target detection. It is shown that the optimum detection performance may be obtained via AIOP-VSAR, particularly for the slowly moving target in an inhomogeneous clutter environment. Also, the Cramer-Rao bounds (CRBs) are derived for the estimation of unknown model parameters, as well as the azimuth locations of moving targets, and the maximum-likelihood methods are proposed to reach these CRBs. Based on the proposed target detection and parameter estimation methods, we present a complete parametric flowchart for VSAR. It is demonstrated that the proposed flowchart may effectively mitigate the "azimuth location ambiguity" of VSAR and has the super-resolution ability to resolve "velocity layover" for multiple targets. Finally, some detailed numerical experiments and scene simulations are provided to show the effectiveness of the proposed methods.
Scaling up Genome Similarity Search Services through Content Distribution

As the increase of genome database size, there are increasing number of methods for detecting sequence similarity and increasing demands for genome sequence search and alignment services. It is a challenge to scale up the computer systems for serving these demands in a timely manner. This paper tackles this problem from a novel perspective, which treats the sequence search requests as content requests to both genome databases and similarity detection methods; therefore, scaling up the computer systems that serve these contents is a process of constructing content distribution network. The paper gives a decentralized method to construct content distribution network for a variety of genome sequence similarity detection services. It also gives a scheduling algorithm for efficiently using content nodes. Our simulation results show that scalability and high content node utilization can be achieved in such a system while the costs of achieving these are controllable.
Tableaux for logics of time and knowledge with interactions relating to synchrony

The paper describes tableaux based proof methods for temporal logics of knowledge allowing non-trivial interaction axioms between the modal and temporal components, namely those of synchrony and no learning and synchrony and perfect recall. The interaction axioms allow the description of how knowledge evolves over time and makes reasoning in such logics theoretically more complex. Such logics can be used to specify systems that involve the knowledge of processes or agents and which change over time, for example agent based systems, knowledge games or security protocols. Soundness, completeness and termination results for the tableaux are proved.
Time-Aware Web Service Recommendations Using Implicit Feedback

With the rapid development of SOA (Service Oriented Architecture), an increasing number of Web services have been published on the Internet. How to recommend suitable Web services to users becomes a challenging problem. Existing Web services recommendation approaches based on collaborative filtering mainly focus on QoS (Quality of Service) prediction. Recommending services based on users' ratings on services are seldom reported since such explicit feedback data is difficult to collect. In this paper, we report a dataset of implicit feedback on real-world Web services, which consist of more than 280,000 user-service interaction records, 65,000 service users and 15,000 Web services or mashups. In addition, time is becoming an increasingly important factor in recommenders since time effects influence users' preferences to a large extent. Based on the collected dataset, we propose a time-aware service recommendation approach. Temporal information is sufficiently considered in our approach, where three time effects are analyzed and modeled including user bias shifting, Web service bias shifting, and user preference shifting. Experimental results show that the proposed approach outperforms seven existing collaborative filtering approaches on the prediction accuracy.
Predictive Guidance and Control Framework for (Semi-)Autonomous Vehicles in Public Traffic

In this paper, a predictive trajectory guidance and control framework is proposed that enables the safe operation of autonomous and semiautonomous vehicles considering the constraints of operating in dynamic public traffic. The core module of the framework is a nonlinear model predictive guidance module that uses a computationally expedient curvilinear frame for the description of the road and of the motion of the vehicle and other objects. The module enforces constraints generated from information about obstacles/other vehicles/objects, public traffic rules for speed limits and lane boundaries, and the limits of the vehicle’s dynamics. The module can be configured in two basic modes. The first is a tracking mode, where the control inputs computed by the model predictive guidance module act as references for traditional lower level control systems. The second is a planning mode, where the traffic-optimal state trajectories computed by the model predictive control are reinterpreted for planning the optimal path and speed, which in turn can be tracked by an elaborate speed and path tracking controller. The performance of most aspects of the proposed scheme is illustrated by considering various simulations of the control framework applied to a high-fidelity vehicle dynamics model of the (semi-)autonomous vehicle in typical public driving events, such as intersections, passing, emergency braking, and collision avoidance. The feasibility of the proposed control framework for real-time application is highlighted with the discussions of the computational execution times observed for these various scenarios.
A Dichotomy Theorem for the Inverse Satisfiability Problem

The inverse satisfiability problem over a set of Boolean relations Gamma (Inv-SAT(Gamma)) is the computational decision problem of, given a set of models R, deciding whether there exists a SAT(Gamma) instance with R as its set of models. This problem is co-NP-complete in general and a dichotomy theorem for finite Γ containing the constant Boolean relations was obtained by Kavvadias and Sideri. In this paper we remove the latter condition and prove that Inv-SAT(Gamma) is always either tractable or co-NP-complete for all finite sets of relations Gamma, thus solving a problem open since 1998. Very little of the techniques used by Kavvadias and Sideri are applicable and we have to turn to more recently developed algebraic approaches based on partial polymorphisms. We also consider the case when Γ is infinite, where the situation differs markedly from the case of SAT. More precisely, we show that there exists infinite Gamma such that Inv-SAT(Gamma) is tractable even though there exists finite Delta is subset of Gamma such that Inv-SAT(Delta) is co-NP-complete.
Code-aided antenna selection for spectrally shaped DFT-precoded OFDM spatial modulation

In this paper, we investigate coded antenna selection for spectrally shaped Spatial Modulation (SM). Spectrally shaped SM aims at reducing the complex envelope fluctuations. This is done at the expense of a bandwidth expansion given by a roll-off factor β when root raised cosine (RRC) spectral filtering is considered. Nevertheless, this extra redundancy can be exploited by enabling the use of redundancy on the bit antenna selection stream. Indeed, we can associate a high performance error correcting code with a corresponding rate R = γ+β used to encode antenna selection bits and thus, we can achieve better performance at the receiver. Simulation results show that the proposed scheme exhibit a lower peak-to-average power ratio (PAPR) compared with the conventional SM scheme while achieving enhanced performance and diversity when an efficient coding scheme is used to improve antenna selection.
Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance Loss

Hierarchical classification is supervised multi-class classification problem over the set of class labels organized according to a hierarchy. In this report, we study the work by Ramaswamy et. al. on hierarchical classification over symmetric tree distance loss. We extend the consistency of hierarchical classification algorithm over asymmetric tree distance loss. We design a $\mathcal{O}(nk\log{}n)$ algorithm to find Bayes optimal classification for a k-ary tree as a hierarchy. We show that under reasonable assumptions over asymmetric loss function, the Bayes optimal classification over this asymmetric loss can be found in $\mathcal{O}(k\log{}n)$. We exploit this insight and attempt to extend the Ova-Cascade algorithm \citet{ramaswamy2015convex} for hierarchical classification over the asymmetric loss.
Co-Cr Material Selection in Prosthetic Restoration: Laser Sintering Technology

Selective laser sintering (SLS) is a manufacturing technique that uses a high power laser to fuse small particles of metal powders into a mass representing a desired 3-dimensional object. The topography of the dental restoration is designed by numerical monitoring after having scanned devised prostheses. In our application the alloy consists of 64-67% Co, 28-30% Cr and 5-6% Mo, and has at equilibrium of a γ monophasic structure. Evaluation and characterisation was made by: Microstructure analysis; Corrosion resistance evaluation; Polarization test; Crevice corrosion test; Release of cations. Dimensional observations of Co-Cr restorations show that adjustment leads to satisfactory clinical results with a precision of 25 μm. The metallographic observations show a slight porosity in the horizontal plan. Punctual analysis shows a high regularity of the local chemical composition. Potentiodynamic polarization curves confirm the presence of the porosity in the structure of the restoration. The technique of manufacturing by selective laser sintering allows obtaining prosthetic elements of high dimensional precision which present mechanical properties in agreement with the clinical requirements. However, the residual porosity inherent to the sintering process may present a risk for fracture and crevice corrosion.
A scheduling and control system for electric vehicle charging at parking lot

This paper proposes a new electric vehicle (EV) charging scheduling and control system for a parking lot (PL), which would minimize the PL's electricity cost of recharging all the EVs. This system is to determine an optimal charging schedule for each incoming EV by allocating the electric quantities to the parking time slots of each EV considering the varied electricity price during the day. The schedule would satisfy the EV's charging rate limit and the PL's transformer limit. This paper proposes a heuristics & proportion-based assignment (HPBA) method to generate the initial population, and adapts the particle swarm optimization (PSO) algorithm to solve the optimization problem. The performance of the proposed system is compared with random search (RS), first-in-first-serve (FIFS) and earliest-deadline-first (EDF) mechanisms, and the results show that the new scheduling system would achieve the goal on minimizing the electricity cost and satisfying the charging demands and constraints.
Impact of Clock Drifts on Communication-Free Secondary Control Schemes for Inverter-Based Islanded Microgrids

In inverter-based microgrids, individual inverters operate usually with their own digital processor. The clocks used to generate the time signals of these processors differ from each other due to clock drifts. This paper analyzes the impact that the drifts of the processors clocks produce on the operation of inverter-based islanded microgrids. Several communication-free secondary control schemes, which avoid well-known problems caused by digital communication networks, are considered. Active power sharing and frequency regulation are the metrics used to evaluate the performance of the control schemes. The study reveals advantages, drawbacks, and practical limitations of the control schemes caused by the clocks drifts. Therefore, it facilitates the selection of the most suitable control scheme for the practical deployment of microgrids. The theoretical results are validated by experimental tests in a laboratory microgrid equipped with three inverters and three digital signal processors driven by autonomous internal clocks.