Miracle-dz/UniFAR-FTUs huggingface dataset:
Report on the 3rd Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2018)

The 3rd joint BIRNDL workshop was held at the 41st ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2018) in Ann Arbor, USA. BIRNDL 2018 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated three paper sessions and the 4th edition of the CL-SciSumm Shared Task.
A review of coupling mechanism designs for modular reconfigurable robots

SUMMARY With the increasing demands for versatile robotic platforms capable of performing a variety of tasks in diverse and uncertain environments, the needs for adaptable robotic structures have been on the rise. These requirements have led to the development of modular reconfigurable robotic systems that are composed of a numerous self-sufficient modules. Each module is capable of establishing rigid connections between multiple modules to form new structures that enable new functionalities. This allows the system to adapt to unknown tasks and environments. In such structures, coupling between modules is of crucial importance to the overall functionality of the system. Over the last two decades, researchers in the field of modular reconfigurable robotics have developed novel coupling mechanisms intended to establish rigid and robust connections, while enhancing system autonomy and reconfigurability. In this paper, we review research contributions related to robotic coupling mechanism designs, with the aim of outlining current progress and identifying key challenges and opportunities that lay ahead. By presenting notable design approaches to coupling mechanisms and the most relevant efforts at addressing the challenges of sensorization, misalignment tolerance, and autonomous reconfiguration, we hope to provide a useful starting point for further research into the field of modular reconfigurable robotics and other applications of robotic coupling.
An Intelligent Approach for Handling Complexity by Migrating from Conventional Databases to Big Data

Handling complexity in the data of information systems has emerged into a serious challenge in recent times. The typical relational databases have limited ability to manage the discrete and heterogenous nature of modern data. Additionally, the complexity of data in relational databases is so high that the efficient retrieval of information has become a bottleneck in traditional information systems. On the side, Big Data has emerged into a decent solution for heterogenous and complex data (structured, semi-structured and unstructured data) by providing architectural support to handle complex data and by providing a tool-kit for efficient analysis of complex data. For the organizations that are sticking to relational databases and are facing the challenge of handling complex data, they need to migrate their data to a Big Data solution to get benefits such as horizontal scalability, real-time interaction, handling high volume data, etc. However, such migration from relational databases to Big Data is in itself a challenge due to the complexity of data. In this paper, we introduce a novel approach that handles complexity of automatic transformation of existing relational database (MySQL) into a Big data solution (Oracle NoSQL). The used approach supports a bi-fold transformation (schema-to-schema and data-to-data) to minimize the complexity of data and to allow improved analysis of data. A software prototype for this transformation is also developed as a proof of concept. The results of the experiments show the correctness of our transformations that outperform the other similar approaches.
A Proof Rule for Euclid Procedures

Abstract : The proof rules of Euclid, like the axiomatization of Pascal, presents a single definition of the various features of the language being defined. Little effort is made to explain the proof rules or to compare them to alternatives. In this paper we take one of our more complex proof rules, the rule for procedure definition and call, and attempt both to explain its workings and to compare it to two alternative rules. The rules we have chosen for comparison are Hoare's adaptation rule, from which our rule is derived, and the Pascal procedure-call rule. (Author)
Blaze-Tasks

Compared to threads, tasks are a more fine-grained alternative. The task parallel programming model offers benefits in terms of better performance portability and better load-balancing for problems that exhibit nonuniform workloads. A common scenario of task parallel programming is that a task is recursively decomposed into smaller sub-tasks. Depending on the problem domain, the number of created sub-tasks may be nonuniform, thereby creating potential for significant load imbalances in the system. Dynamic load-balancing mechanisms will distribute the tasks across available threads. The final result of a computation may be modeled as a reduction over the results of all sub-tasks. This article describes a simple, yet effective prototype framework, Blaze-Tasks, for task scheduling and task reductions on shared memory architectures. The framework has been designed with lock-free techniques and generic programming principles in mind. Blaze-Tasks is implemented entirely in C++17 and is thus portable. To load-balance the computation, Blaze-Tasks uses task stealing. To manage contention on a task pool, the number of lock-free attempts to steal a task depends on the distance between thief and pool owner and the estimated number of tasks in a victim’s pool. This article evaluates the Blaze framework on Intel and IBM dual-socket systems using nine benchmarks and compares its performance with other task parallel frameworks. While Cilk outperforms Blaze on Intel on most benchmarks, the evaluation shows that Blaze is competitive with OpenMP and other library-based implementations. On IBM, the experiments show that Blaze outperforms other approaches on most benchmarks.
Bounds on the Cost of Stabilizing a Cooperative Game

A key issue in cooperative game theory is coalitional stability, usually captured by the notion of the core---the set of outcomes that are resistant to group deviations. However, some coalitional games have empty cores, and any outcome in such a game is unstable. We investigate the possibility of stabilizing a coalitional game by using subsidies. We consider scenarios where an external party that is interested in having the players work together offers a supplemental payment to the grand coalition, or, more generally, a particular coalition structure. This payment is conditional on players not deviating from this coalition structure, and may be divided among the players in any way they wish. We define the cost of stability as the minimum external payment that stabilizes the game. We provide tight bounds on the cost of stability, both for games where the coalitional values are nonnegative (profit-sharing games) and for games where the coalitional values are nonpositive (cost-sharing games), under natural assumptions on the characteristic function, such as superadditivity, anonymity, or both. We also investigate the relationship between the cost of stability and several variants of the least core. Finally, we study the computational complexity of problems related to the cost of stability, with a focus on weighted voting games.
Synthesis of 3, 4-Biaryl-2, 5-Dichlorothiophene through Suzuki Cross-Coupling and Theoretical Exploration of Their Potential Applications as Nonlinear Optical Materials

We report herein the efficient one-pot synthesis of 3,4-biaryl-2,5-dichlorothiophene derivatives (2a–2i) via a palladium-catalyzed Suzuki cross-coupling reaction. A series of thiophene derivatives were synthesized, starting from 3,4-dibromo-2,5-dichlorothiophene (1) and various arylboronic acids using Pd(PPh3)4 and K3PO4 with moderate to good yields. For further insights about the structure and property relationship, density functional theory (DFT) calculations were performed. A relaxed potential energy surface (PES) scan was performed to locate the minimum energy structure. A frontier molecular orbitals analysis was performed to explain the reactivity of all synthesized derivatives. As the synthesized derivatives had extended conjugations, therefore the first hyperpolarizability (βo) was calculated to investigate their potential as non-linear optical (NLO) materials and significant βo values were found for the 2b and 2g derivatives.
A Proportionate Normalized Maximum Correntropy Criterion Algorithm with Correntropy Induced Metric Constraint for Identifying Sparse Systems

A proportionate-type normalized maximum correntropy criterion (PNMCC) with a correntropy induced metric (CIM) zero attraction terms is presented, whose performance is also discussed for identifying sparse systems. The proposed sparse algorithms utilize the advantage of proportionate schemed adaptive filter, maximum correntropy criterion (MCC) algorithm, and zero attraction theory. The CIM scheme is incorporated into the basic MCC to further utilize the sparsity of inherent sparse systems, resulting in the name of the CIM-PNMCC algorithm. The derivation of the CIM-PNMCC is given. The proposed algorithms are used for evaluating the sparse systems in a non-Gaussian environment and the simulation results show that the expanded normalized maximum correntropy criterion (NMCC) adaptive filter algorithms achieve better performance than those of the squared proportionate algorithms such as proportionate normalized least mean square (PNLMS) algorithm. The proposed algorithm can be used for estimating finite impulse response (FIR) systems with symmetric impulse response to prevent the phase distortion in communication system.
Spectral–Spatial Rotation Forest for Hyperspectral Image Classification

Rotation Forest (RoF) is a recent powerful decision tree (DT) ensemble classifier of hyperspectral images. RoF exploits random feature selection and data transformation techniques to improve both the diversity and accuracy of DT classifiers. Conventional RoF only considers data transformation on spectral information. To overcome this limitation, we propose a spectral and spatial RoF (SSRoF), to further improve the performance. In SSRoF, pixels are first smoothed by the multiscale (MS) spatial weight mean filtering. Then, spectral–spatial data transformation, which is based on a joint spectral and spatial rotation matrix, is introduced into the RoF. Finally, classification results obtained from each scale are integrated by a majority voting rule. Experimental results on two datasets indicate the competitive performance of the proposed method when compared to other state-of-the-art methods.
A Domain-independent Framework for building Conversational Recommender Systems

Conversational Recommender Systems (CoRSs) implement a paradigm where users can interact with the system for defining their preferences and discovering items that best fit their needs. A CoRS can be straightforwardly implemented as a chatbot. Chatbots are becoming more and more popular for several applications like customer care, health care, medical diagnoses. In the most complex form, the implementation of a chatbot is a challenging task since it requires knowledge about natural language processing, human-computer interaction
Processor-Tracing Guided Region Formation in Dynamic Binary Translation

Region formation is an important step in dynamic binary translation to select hot code regions for translation and optimization. The quality of the formed regions determines the extent of optimizations and thus determines the final execution performance. Moreover, the overall performance is very sensitive to the formation overhead, because region formation can have a non-trivial cost. For addressing the dual issues of region quality and region formation overhead, this article presents a lightweight region formation method guided by processor tracing, e.g., Intel PT. We leverage the branch history information stored in the processor to reconstruct the program execution profile and effectively form high-quality regions with low cost. Furthermore, we present the designs of lightweight hardware performance monitoring sampling and the branch instruction decode cache to minimize region formation overhead. Using ARM64 to x86-64 translations, the experiment results show that our method achieves a performance speedup of up to 1.53× (1.16× on average) for SPEC CPU2006 benchmarks with reference inputs, compared to the well-known software-based trace formation method, Next Executing Tail (NET). The performance results of x86-64 to ARM64 translations also show a speedup of up to 1.25× over NET for CINT2006 benchmarks with reference inputs. The comparison with a relaxed NETPlus region formation method further demonstrates that our method achieves the best performance and lowest compilation overhead.
Intra Prediction of Depth Picture with Plane Modeling

In this paper, an intra prediction method is proposed for coding of depth pictures using plane modelling. Each pixel in the depth picture is related to the distance from a camera to an object surface, and pixels corresponding to a flat surface of an object form a relationship with the 2D plane surface. The plane surface can be represented by a simple equation in the 3D camera coordinate system in such a way that the coordinate system of depth pixels can be transformed to the camera coordinate system. This paper finds the parameters which define the plane surface closest to given depth pixels. The plane model is then used to predict the depth pixels on the plane surface. A depth prediction method is also devised for efficient intra prediction of depth pictures, using variable-size blocks. For prediction with variable-size blocks, the plane surface that occupies a large part of the picture can be predicted using a large block size. The simulation results of the proposed method show that the mean squared error is reduced by up to 96.6% for a block size of 4 × 4 pixels and reduced by up to 98% for a block size of 16 × 16, compared with the intra prediction modes of H.264/AVC and H.265/HEVC.
Improving Thread-level Parallelism in GPUs Through Expanding Register File to Scratchpad Memory

Modern Graphic Processing Units (GPUs) have become pervasive computing devices in datacenters due to their high performance with massive thread level parallelism (TLP). GPUs are equipped with large register files (RF) to support fast context switch between massive threads and scratchpad memory (SPM) to support inter-thread communication within the cooperative thread array (CTA). However, the TLP of GPUs is usually limited by the inefficient resource management of register file and scratchpad memory. This inefficiency also leads to register file and scratchpad memory underutilization. To overcome the above inefficiency, we propose a new resource management approach EXPARS for GPUs. EXPARS provides a larger register file logically by expanding the register file to scratchpad memory. When the available register file becomes limited, our approach leverages the underutilized scratchpad memory to support additional register allocation. Therefore, more CTAs can be dispatched to SMs, which improves the GPU utilization. Our experiments on representative benchmark suites show that the number of CTAs dispatched to each SM increases by 1.28× on average. In addition, our approach improves the GPU resource utilization significantly, with the register file utilization improved by 11.64% and the scratchpad memory utilization improved by 48.20% on average. With better TLP, our approach achieves 20.01% performance improvement on average with negligible energy overhead.
Symmetric Evaluation of Multimodal Human-Robot Interaction with Gaze and Standard Control

Control of robot arms is often required in engineering and can be performed by using different methods. This study examined and symmetrically compared the use of a controller, eye gaze tracker and a combination thereof in a multimodal setup for control of a robot arm. Tasks of different complexities were defined and twenty participants completed an experiment using these interaction modalities to solve the tasks. More specifically, there were three tasks: the first was to navigate a chess piece from a square to another pre-specified square; the second was the same as the first task, but required more moves to complete; and the third task was to move multiple pieces to reach a solution to a pre-defined arrangement of the pieces. Further, while gaze control has the potential to be more intuitive than a hand controller, it suffers from limitations with regard to spatial accuracy and target selection. The multimodal setup aimed to mitigate the weaknesses of the eye gaze tracker, creating a superior system without simply relying on the controller. The experiment shows that the multimodal setup improves performance over the eye gaze tracker alone ( p < 0.05 ) and was competitive with the controller only setup, although did not outperform it ( p > 0.05 ).
Incentive-Compatible Mechanisms for Norm Monitoring in Open Multi-Agent Systems (Extended Abstract)

We consider the problem of detecting norm violations in open multi-agent systems (MAS). In this extended abstract, we outline the approach of [Alechina et al., 2018], and show how, using ideas from scrip systems, we can design mechanisms where the agents comprising the MAS are incentivised to monitor the actions of other agents for norm violations.
From Feature to Paradigm: Deep Learning in Machine Translation (Extended Abstract)

In the last years, deep learning algorithms have highly revolutionized several areas including speech, image and natural language processing. The specific field of Machine Translation (MT) has not remained invariant. Integration of deep learning in MT varies from re-modeling existing features into standard statistical systems to the development of a new architecture. Among the different neural networks, research works use feed-forward neural networks, recurrent neural networks and the encoder-decoder schema. These architectures are able to tackle challenges as having low-resources or morphology variations. This extended abstract focuses on describing the foundational works on the neural MT approach; mentioning its strengths and weaknesses; and including an analysis of the corresponding challenges and future work. The full manuscript [Costa-jussà, 2018] describes, in addition, how these neural networks have been integrated to enhance different aspects and models from statistical MT, including language modeling, word alignment, translation, reordering, and rescoring; and on describing the new neural MT approach together with recent approaches on using subword, characters and training with multilingual languages, among others.
Knowledge-aware Recommender System for Software Development

Open source software (OSS) forges contain rich data sources that are useful for the development process. We promote techniques and tools for providing open source developers with innovative features aiming at obtaining improvements in terms of development effort, cost savings, developer productivity. Our work is a coherent paradigm that facilitates multiple recommendations to assist software developers in different phases of the development process. In the scope of this paper, we introduce a graph-based representation to encode in a homogeneous manner different aspects of the OSS ecosystem. Furthermore, we develop a knowledge-aware recommender system for providing developers with suitable API function calls. An initial evaluation on real datasets shows that the system is able to produce relevant API calls.
CryoDrill: Near-Data Processing in Deep and Cold Storage Hierarchies

MOTIVATION Nowadays, modern high-performance, high-resolution observational instruments and complex models of the earth system and of physical, chemical, and biological processes generate multiple hundreds of petabytes of scientific data per year contributing significantly to the infamous data deluge. Important application domains, which generate large amounts of scientific data, include earth observation, high-energy physics, radio astronomy, and weather forecasting, among others. Increasingly, digital data archives store such scientific data in private cloud infrastructures for further investigation and longterm preservation, and disseminate them through data platforms via order-based catalogs. To reduce the total cost of ownership, such data platforms employ a hierarchical storage management system with large, disk-based caches and robotic tape libraries [2]. Often, data is stored in coarse-grained, value-added data products, whose granularity is determined by observational instead of data access characteristics. Fine-granular data tiling of such data products is often only applied late in the data analysis workflow. Prefetching all the data from a slower storage layer in advance is typically not possible due to the ad-hoc nature of scientific analysis tasks and the sheer size of the required data working set to achieve satisfactory results for long-term trend analysis and prediction. With the proliferation of scientific data for general analysis by the academic community, wasteful data transfers across storage layers and unacceptable high data access latencies will likely become a major research barrier in the near future.
Linear Types can Change the World!

The linear logic of J.-Y. Girard suggests a new type system for functional languages, one which supports operations that \change the world". Values belonging to a linear type must be used exactly once: like the world, they cannot be duplicated or destroyed. Such values require no reference counting or garbage collection , and safely admit destructive array update. Linear types extend Schmidt's notion of single threading; provide an alternative to Hudak and Bloss' update analysis; and ooer a practical complement to Lafont and Holmstrr om's elegant linear languages. An old canard against functional languages is that they cannot change the world: they do not \naturally" cope with changes of state, such as altering a location in memory, changing a pixel on a display, or sensing when a key is pressed. As a prototypical example of this, consider the world as an array. An array (of type Arr) is a mapping from indices (of type Ix) to values (of type Val). For instance, the world might be a mapping of variable names to values, or le names to contents. At any time, we can do one of two things to the world: nd the value associated with an index, or update an index to be associated with a new value. Of course it is possible to model this functionally; we just use the two operations A program that interacts with the world might have the form main : Args ! Arr ! Arr; where the rst parameter is the list of arguments that make up the command line, the second parameter is the old world, and the result is the new world. An example of a program is main les a = update \stdout" (concat lookup i a j i les]) a: