Miracle-dz/UniFAR-FTUs huggingface dataset:
Unique Topic Query Processing On Cloud

With the rapid growth of text data, text analysis plays an increasingly important role for various applications. Tranditional database management systems provide basic data operations for various users. But most of them only provide data access and basic calculation for relational datasets, we can not perform text analysis operations on them directly. To support users analyzing unstructured text, this paper designs a set of SQL-like information extraction and unique topic query language for users to extract information and find out unique topics on unstructured data. This paper proposes a unique topic query processing system on cloud based on relational information extraction with Spark as cloud infrastructure.
Simultaneous Translation using Optimized Segmentation

Previous simultaneous translation approaches either use a separate segmentation step followed by a machine translation decoder or rely on the decoder to segment and translate without training the segmenter to minimize delay or increase translation quality. We integrate a segmentation model and an incremental decoding algorithm to create an automatic simultaneous translation framework. Oda et al. (2014) propose a method to provide annotated data for sentence segmentation. This work uses this data to train a segmentation model that is integrated with a novel simultaneous translation decoding algorithm. We show that this approach is more accurate than previously proposed segmentation models when integrated with a translation decoder. Our results on the speech translation of TED talks from English to German show that our system can achieve translation quality close to the ofﬂine translation system while at the same time minimizing the delay in producing the translations incrementally. Our approach also outperforms other comparable simultaneous translation systems in terms of translation quality and latency.
DSSDN: Demand‐supply based load balancing in Software‐Defined Wide‐Area Networks

One of the unexplored research areas in Software Defined Networks (SDN) is load balancing of control messages (e.g. packet_in) among distributed controllers in Wide Area Networks. In SDN, on every unsuccessful match in the flow table for the incoming traffic flows, the switch sends packet_in to the controller for further action against the traffic flow. The packet_in messages are one of the major contributors of the control request (load) received by the controller. When it exceeds a certain threshold limit, the response time for the control request increases nonlinearly due to the over CPU utilization and congestion. When the controller gets overloaded, typically the OpenFlow‐enabled Devices (OFDevices) are migrated from the current controller to another under loaded controller domain. This migration might cause large degradation of end users' QoS metrics. To resolve this issue, we introduce basic demand and supply curve based DSSDN, a new load balancing method that utilizes the load factors of Software Defined Wide Area Networks controllers. This method selects the OFDevice which causes maximum load on the controller and traversing minimum users traffic through it. The Karush‐Kuhn‐Tucker conditions are employed during the optimal controller selection by the OFDevices to improve the response time effectively. During implementation, virtual threads running on the controller representing the OFDevices are used to take the optimal decision instead of actual OFDevices. The experimental results show that during migration, the DSSDN stabilizes the load hikes, improves QoS, and increase the end users' utility without much disruptions in the network state.
Plan Stitch: Harnessing the Best of Many Plans

Query performance regression due to the query optimizer selecting a bad query execution plan is a major pain point in production workloads. Commercial DBMSs today can automatically detect and correct such query plan regressions by storing previously-executed plans and reverting to a previous plan which is still valid and has the least execution cost. Such reversion-based plan correction has relatively low risk of plan regression since the decision is based on observed execution costs. However, this approach ignores potentially valuable information of efficient subplans collected from other previously-executed plans. In this paper, we propose a novel technique, Plan Stitch, that automatically and opportunistically combines efficient subplans of previously-executed plans into a valid new plan, which can be cheaper than any individual previously-executed plan. We implement Plan Stitch on top of Microsoft SQL Server. Our experiments on TPC-DS benchmark and three real-world customer workloads show that plans obtained via Plan Stitch can reduce execution cost significantly, with a reduction of up to two orders of magnitude in execution cost when compared to reverting to the cheapest previously-executed plan.
Genetic Algorithm for Solving Multiple Traveling Salesmen Problem using a New Crossover and Population Generation

In this paper, we proposed a new crossover operator and a population initialization method for solving multiple traveling salesmen (MTSP) problem in genetic algorithm (GA) framework. The group theory based technique of intital population generation ensures the uniqueness of members in population, hence no redundancy in the search space and also remove the random initialization effect. The new crossover is based on the intuitive idea that the city in sub optimal / optimal tours occurs at same position. In this crossover the Hamming distance is preserved and there is very less chance to generate child same as members in the population, so diversity of the population is not much affected. For efficient representation of search space we exploited the multi-chromosome representation technique to encode the search space of MTSP. We evaluate and compare the proposed technique with the methods using crossover TCX, ORX +A, CYX +A and PMX +A reported in [35] for two standard objective functions of the MTSP, namely, minimising total travel cost of m tours of the m salesman and minimising the longest tour cast travel by any one salesman. Experimental results show that the GA with proposed population initialization and crossover gives better result compared to all four methods for second objective, however, in very few cases slightly degraded result for first objective.
Best of both worlds: Stochastic & adversarial best-arm identification

We study bandit best-arm identification with arbitrary and potentially adversarial rewards. A simple random uniform learner obtains the optimal rate of error in the adversarial scenario. However, this type of strategy is suboptimal when the rewards are sampled stochastically. Therefore, we ask: Can we design a learner that performs optimally in both the stochastic and adversarial problems while not being aware of the nature of the rewards? First, we show that designing such a learner is impossible in general. In particular, to be robust to adversarial rewards, we can only guarantee optimal rates of error on a subset of the stochastic problems. We give a lower bound that characterizes the optimal rate in stochastic problems if the strategy is constrained to be robust to adversarial rewards. Finally, we design a simple parameter-free algorithm and show that its probability of error matches (up to log factors) the lower bound in stochastic problems, and it is also robust to adversarial ones.
Field‐Aligned Isotropic Surface Remeshing

We present a novel isotropic surface remeshing algorithm that automatically aligns the mesh edges with an underlying directional field. The alignment is achieved by minimizing an energy function that combines both centroidal Voronoi tessellation (CVT) and the penalty enforced by a six‐way rotational symmetry field. The CVT term ensures uniform distribution of the vertices and high remeshing quality, and the field constraint enforces the directional alignment of the edges. Experimental results show that the proposed approach has the advantages of isotropic and field‐aligned remeshing. Our algorithm is superior to the representative state‐of‐the‐art approaches in various aspects.
Webis at TREC 2017: Open Search and Core Tracks

We give a brief overview of the Webis group’s participation in the TREC 2017 Open Search and Core tracks. Our submission to the Open Search track is similar to our last year’s approach, we axiomatically re-rank a BM25-ordered result list to come up with a final ranking. The axiomatic re-ranking idea is also applied in our Core track contribution but with an emphasis on argumentativeness as a not-yet-covered aspect in retrieval
On the Capacity of Symmetric M-User Gaussian Interference Channels With Feedback

A general time-varying feedback coding scheme is proposed for <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>-user fully connected symmetric Gaussian interference channels. Based on the analysis of the general coding scheme, we prove a theorem which gives a criterion for designing good time-varying feedback codes for Gaussian interference channels. The proposed scheme improves the Suh-Tse and Kramer inner bounds of the channel capacity for the cases of weak and not very strong interference when <inline-formula> <tex-math notation="LaTeX">$M=2$ </tex-math></inline-formula>. This capacity improvement is more significant when the signal-to-noise ratio (SNR) is not very high. In addition, our coding scheme can be proved mathematically and numerically to outperform the Kramer code for <inline-formula> <tex-math notation="LaTeX">$M\geq 2$ </tex-math></inline-formula> when the SNR is equal to the interference-to-noise ratio (INR). Besides, the generalized degrees-of-freedom (GDoF) of our proposed coding scheme can be proved to be optimal in the all network situations (very weak, weak, strong, very strong) for any <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>. The numerical results show that our coding scheme can attain better performance than the Suh-Tse coding scheme for <inline-formula> <tex-math notation="LaTeX">$M=2$ </tex-math></inline-formula> or the Mohajer-Tandon-Poor lattice coding scheme for <inline-formula> <tex-math notation="LaTeX">$M > 2$ </tex-math></inline-formula>. Furthermore, the simplicity of the encoding/decoding algorithms is another strong point of our proposed coding scheme compared with the Suh-Tse coding scheme when <inline-formula> <tex-math notation="LaTeX">$M=2$ </tex-math></inline-formula> and the Mohajer-Tandon-Poor lattice coding scheme when <inline-formula> <tex-math notation="LaTeX">$M > 2$ </tex-math></inline-formula>. More importantly, our results show that an optimal coding scheme for the symmetric Gaussian interference channels with feedback can be achieved by only using marginal posterior distributions under a better cooperation strategy between transmitters.
Recognizing Complex Entity Mentions: A Review and Future Directions

Standard named entity recognizers can effectively recognize entity mentions that consist of contiguous tokens and do not overlap with each other. However, in practice, there are many domains, such as the biomedical domain, in which there are nested, overlapping, and discontinuous entity mentions. These complex mentions cannot be directly recognized by conventional sequence tagging models because they may break the assumptions based on which sequence tagging techniques are built. We review the existing methods which are revised to tackle complex entity mentions and categorize them as tokenlevel and sentence-level approaches. We then identify the research gap, and discuss some directions that we are exploring.
Example-based Body Model Optimization and Skinning

In this paper, we present an example-based framework for the generation of a realistic kinematic 3D human body model that optimizes shape, pose and skinning parameters. For enhanced realism, the skinning is realized as a combination of Linear Blend Skinning (LBS) and Dual quaternion Linear Blending (DLB) which nicely compensates the deficiencies of using only one of these approaches (e.g. candy wrapper, bulging artifacts) and supports interpolation of more than two joint transformations. The optimization framework enforces two objectives: resembling both shape and pose as closely as possible by iteratively minimizing the objective function with respect to (a) the vertices, (b) the skinning weights and (c) the joint parameters. Smoothness is ensured by using a weighted Laplacian besides a typical data term in the objective function, which introduces the only parameter to be specified. With experimental results on publicly available datasets we demonstrate the effectiveness of the resulting shape model, exposing convincing naturalism. By using examples for the optimization of all parameters, our framework is easy to use and does not require sophisticated parameter tuning or user intervention.
Spatial Correlation Variability in Multiuser Systems

Spatial correlation across an antenna array is known to be detrimental to the terminal signal-to- interference-plus-noise-ratio (SINR) and system spectral efficiency. For a downlink multiuser multiple-input multiple-output system (MU-MIMO), we show that the widely used, yet overly simplified, correlation models which generate fixed correlation patterns for all terminals tend to underestimate the system performance. This is in contrast to more sophisticated, yet physically motivated, remote scattering models that generate variations in the correlation structure across multiple terminals. The remote scattering models are parameterized with measured data from a recent 2.53 GHz urban macrocellular channel measurement campaign in Cologne, Germany. Assuming spatially correlated Ricean fading, with maximum-ratio transmission precoding, tight closed-form approximations to the expected (average) SINR, and ergodic sum spectral efficiency are derived. The expressions provide clear insights into the impact of variable correlation patterns on the above performance metrics. Our results demonstrate the sensitivity of the MU-MIMO performance to different correlation models, and provide a cautionary tale of its impact.
Local Convergence Properties of SAGA/Prox-SVRG and Acceleration

Over the past ten years, driven by large scale optimisation problems arising from machine learning, the development of stochastic optimisation methods have witnessed a tremendous growth. However, despite their popularity, the theoretical understandings of these methods are quite limited in contrast to the deterministic optimisation methods. In this paper, we present a local convergence analysis for a typical type of stochastic optimisation methods: proximal variance reduced stochastic gradient methods, and mainly focus on the SAGA [12] and Prox-SVRG [43] algorithms. Under the assumption that the non-smooth component of the optimisation problem is partly smooth relative to a smooth manifold, we present a unified framework for the local convergence analysis of the SAGA/Prox-SVRG algorithms: (i) the sequences generated by the SAGA/Prox-SVRG are able to identify the smooth manifold in a finite number of iterations; (ii) then the sequence enters a local linear convergence regime. Beyond local convergence analysis, we also discuss various possibilities for accelerating these algorithms, including adapting to better local parameters, and applying higher-order deterministic/stochastic optimisation methods which can achieve super-linear convergence. Concrete examples arising from machine learning are considered to verify the obtained results.
Hybrid Beamforming with One-Bit Quantized Phase Shifters in mmWave MIMO Systems

Economical and energy-efficient analog/digital hybrid beamforming has been widely considered as a promising approach for millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems. While most hybrid beamforming techniques consider a fully-connected structure with a large number of phase shifters (PSs), the partially-connected structure has drawn more attention recently since it requires much less PSs and can further improve energy-efficiency. However, the impractical assumption of infinite or high resolution of PSs in existing solutions frustrates the real-world deployment of hybrid beamforming designs, and low- resolution PSs are typically adopted to reduce the hardware complexity and power consumption. In an effort to achieve maximum hardware efficiency, this paper focuses on the partially-connected architecture with one-bit (binary) PSs and considers the problem of joint hybrid precoder and combiner design for such mmWave MIMO systems. We propose to successively design the analog beamformers associated with each pair of sub- array, aiming at conditionally maximizing the spectral efficiency. A novel binary analog precoder and combiner optimization algorithm is proposed under a rank-1 approximation of the interference-included equivalent channel with polynomial complexity in the number of antennas. Then, the digital precoder and combiner are computed based on the obtained effective baseband channel to further enhance the spectral efficiency. Simulation results demonstrate the advantages of proposed hardware-efficiency hybrid precoder and combiner design.
ElasticBF: Fine-grained and Elastic Bloom Filter Towards Efficient Read for LSM-tree-based KV Stores

LSM-tree based KV stores suffer from severe read amplification, especially for large KV stores. Even worse, many applications may issue a large amount of lookup operations to search for nonexistent keys, which wastes a lot of extra I/Os. Even though Bloom filters can be used to speedup the read performance, existing designs usually adopt a uniform setting for all Bloom filters and fail to support dynamic adjustment, thus results in a high false positive rate or large memory consumption. To address this issue, we propose ElasticBF, which constructs more small filters for each SSTable and dynamically load into memory as needed based on access frequency, so it realizes a fine-grained and elastic adjustment in running time with the same memory usage. Experiment shows that ElasticBF can achieve 1.94×-2.24× read throughput compared to LevelDB under different workloads, and preserves the same write performance. More importantly, ElasticBF is orthogonal to existing works optimizing the structure of KV stores, so it can be used as an accelerator to further speedup their read performance.
Transformation of the BPMN Design Model into a Colored Petri Net Using the Partitioning Approach

Formal verification is a process to ensure that the business process model and notation (BPMN) design model is free of deadlock, livelock, and other undesirable properties that can cause a system crash. Formal verification is a complicated procedure involving model abstraction and model checking tools used for property checking. There are several existing transformation techniques that yield an abstract model, but the data perspective is not considered. These techniques are also inappropriate for the large-scale BPMN design model. An automated transformation can reduce the flaws, time consumption, and complexity of the large-scale BPMN design model. This paper proposes a transformation technique and provides a framework for transforming the BPMN design models into colored Petri nets (CPNs). Our techniques cover both the control-flow and data-flow perspectives. The partitioning technique of the BPMN design model is applied to reduce the complexity and leads to a CPN model that can support the hierarchical and compositional verification techniques that are suitable for the large-scale BPMN design models. The proposed transformation has been implemented as a transformation framework and is able to consistently transform the BPMN design models into CPN models.
Akoman: Hardware-Level Malware Detection Using Discrete Wavelet Transform

Malware, short for malicious software, is a general collective term for any program that gains access to a system without the knowledge of the owner and fulfills the malicious intent of an attacker. Over the past few years, various techniques have been proposed that focus on the run-time behavior of programs in order to dynamically detect malware. Most of the techniques rely on the analysis of system call traces provided by the underlying operating system. An alternative and promising approach is to perform malware detection at the hardware level. In this paper, we pursue this line of research by presenting Akoman, a novel technique that uses hardware events in current modern processors to build behavioral models of malware. Akoman follows a two-stage heuristic matching strategy to rapidly determine whether or not a running program belongs to a known malware family. It first applies the singular value decomposition to select candidate malware families that the running program is most likely to belong to. Then, it applies the Haar-based discrete wavelet transform to determine whether the running program is benign or it is matched to one of the selected candidate malware families. Our proof-of-concept evaluations performed on a real dataset of benign programs and malware samples suggest Akoman achieves negligible overhead with acceptable detection performance.
Visual Notifier: A Timeline-Based Visualization for Notifications from Several Environments

In this paper we describe the visual notifier which is a timeline-based visualization that provides an overview about incoming notifications while it also supports an easier way to manage such notifications from several environments. Moreover, it is also possible to link them by certain subject types, author names or groups, or textual content. Such a visualization is in particular useful if there are too many notifications occurring in a short time period to answer and react on immediately, or to keep track of. The interactive visualization is implemented in Java and is easily extendable by additional functionality and interaction techniques. We illustrate theusefulness bytesting itforseveralemailaddresses andmessages from Facebook, Twitter, Ebay, Linkedin, and Researchgate.
Acquisition and Validation of Spectral Ground Truth Data for Predictive Rendering of Rough Surfaces

Physically based rendering uses principles of physics to model the interaction of light with matter. Even though it is possible to achieve photorealistic renderings, it often fails to be predictive. There are two major issues: first, there is no analytic material model that considers all appearance critical characteristics; second, light is in many cases described by only 3 RGB‐samples. This leads to the problem that there are different models for different material types and that wavelength dependent phenomena are only approximated. In order to be able to analyze the influence of both problems on the appearance of real world materials, an accurate comparison between rendering and reality is necessary. Therefore, in this work, we acquired a set of precisely and spectrally resolved ground truth data. It consists of the precise description of a new developed reference scene including isotropic BRDFs of 24 color patches, as well as the reference measurements of all patches under 13 different angles inside the reference scene. Our reference data covers rough materials with many different spectral distributions and various illumination situations, from direct light to indirect light dominated situations.