ppxscal/embedding_network huggingface dataset:
Practical Formal Verification of MPI and Thread Programs
Authors: Ganesh Gopalakrishnan, Robert M. Kirby
Venue: Proceedings of the 16th European PVM/MPI Users' Group Meeting on Recent Advances in Parallel Virtual Machine and Message Passing Interface (2009)

Large-scale simulation codes in science and engineering are written using the Message Passing Interface (MPI). Shared memory threads are widely used directly, or to implement higher level programming abstractions. Traditional debugging methods for MPI or thread programs are incapable of providing useful formal guarantees about coverage. They get bogged down in the sheer number of interleavings (schedules), often missing shallow bugs. In this tutorial we will introduce two practical formal verification tools: ISP (for MPI C programs) and Inspect (for Pthread C programs). Unlike other formal verification tools, ISP and Inspect run directly on user source codes (much like a debugger). They pursue only the relevant set of process interleavings, using our own customized Dynamic Partial Order Reduction algorithms. For a given test harness, DPOR allows these tools to guarantee the absence of deadlocks, instrumented MPI object leaks and communication races (using ISP), and shared memory races (using Inspect). ISP and Inspect have been used to verify large pieces of code: in excess of 10,000 lines of MPI/C for ISP in under 5 seconds, and about 5,000 lines of Pthread/C code in a few hours (and much faster with the use of a cluster or by exploiting special cases such as symmetry) for Inspect. We will also demonstrate the Microsoft Visual Studio and Eclipse Parallel Tools Platform integrations of ISP (these will be available on the LiveCD).
A Computational Approach to Detect CNVs Using High-throughput Sequencing
Authors: Myungjin Moon, Jaegyoon Ahn, Youngmi Yoon, Chihyun Park, Sanghyun Park, Jeehee Yoon
Venue: Proceedings of the 2009 Ninth IEEE International Conference on Bioinformatics and Bioengineering (2009)

Copy-Number Variations (CNVs) can be defined as gains or losses that are greater than 1kbs of genomic DNA among phenotypically normal individuals. CNVs detected by microarray based approach are limited to medium or large sized ones because of its low resolution. Here we propose a novel approach to detect CNVs by aligning the short reads obtained by high-throughput sequencer to the previously assembled human genome sequence, and analyzing the distribution of the aligned reads. Application of our algorithm demonstrates the feasibility of detecting CNVs of arbitrary length, which include short ones that microarray based algorithms cannot detect. Also, false positive and false negative rates of the results were relatively low compared to those of microarray based algorithms.
A Contrastive Study of Simulation Results between GWSC-VMR and Hybrid Runoff Model in Dianzi Basin
Authors: Guizuo Wang, Liliang Ren
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 02 (2009)

For the purpose of hydrological process simulation in cold and arid regions, the authors brought out the Vertically-mixed runoff model with Grid-based Water Storage Capacity(GWSC-VMR) on the base of the spatial distributed model of water storage capacity. In GWSC-VMR, factors such as topography, vegetation, soil and meteorology were taken into account. GWSC-VMR included canopy interception module, snow accumulation and melt module, evapotranspiration module and runoff yield and concentration module. To verify the simulation result of GWSC-VMR, both GWSC-VMR and hybrid runoff model were applied in Dianzi basin to simulate the daily runoff process in the period of 1973 to 1979. After simulations, the authors found that the annual average NASH-Sutcliffe coefficient of GWSC-VMR reached 0.80; as well as the annual average NASH-Sutcliffe coefficient of hybrid runoff model was only 0.67. Simulation results indicated that the annual NASH-Sutcliffe coefficient of GWSC-VMR is high and this hydrological model is adequate for arid and semiarid area daily runoff simulation. Furthermore, as a distributed hydrological model, GWSC-VMR could be applied in regional water resource management.
Explicit-state abstraction: a new method for generating heuristic functions
Authors: Malte Helmert, Patrik Haslum, Jörg Hoffmann
Venue: Proceedings of the 23rd national conference on Artificial intelligence - Volume 3 (2008)

Many AI problems can be recast as finding an optimal path in a discrete state space. An abstraction defines an admissible heuristic function as the distances in a smaller state space where arbitrary sets of states are "aggregated" into single states. A special case are pattern database (PDB) heuristics, which aggregate states if they agree on the state variables inside the pattern. Explicit-state abstraction is more flexible, explicitly aggregating selected pairs of states in a process that interleaves composition of abstractions with abstraction of the composites. The increased flexibility gains expressive power: sometimes, the real cost function can be represented concisely as an explicit-state abstraction, but not as a PDB. Explicit-state abstraction has been applied to planning and model checking, with highly promiSing empirical results.
Discovering semantically meaningful places from pervasive RF-beacons
Authors: Donnie H. Kim, Jeffrey Hightower, Ramesh Govindan, Deborah Estrin
Venue: Proceedings of the 11th international conference on Ubiquitous computing (2009)

Detecting visits to semantically meaningful places is important for many emerging mobile applications. We present PlaceSense, a place discovery algorithm suitable for mobile devices that exploits pervasive RF-beacons. By relying on separate mechanisms to detect entrance to and departure from a place and buffering overlapping data for subsequent visits, it is more robust than the state-of-the-art, especially in detecting short visits, places where people are mobile, or where inconsistent beacons are prevalent due to interference. We experimentally evaluate PlaceSense's effectiveness in discovering semantically meaningful places, and compare with other approaches that use coordinates or RF-beacon fingerprints. Our results demonstrate that PlaceSense correctly discovers 92% (compared to between 28% and 65% for previous work) of the visited places and accurately detects their entrance and departure times from both real-life and scripted data sets.
Toward a cross-framework parser annotation standard
Authors: Dan Flickinger
Venue: Coling 2008: Proceedings of the workshop on Cross-Framework and Cross-Domain Parser Evaluation (2008)

Efficient and precise comparison of parser results across frameworks will require a negotiated agreement on a target representation which embodies a good balance of three competing dimensions: consistency, clarity, and flexibility. The various annotations provided in the COLING-08 shared task for the ten 'required' Wall Street Journal sentences can serve as a useful basis for these negotations. While there is of course substantial overlap in the content of the various schemes for these sentences, no one of the schemes is ideal. This paper presents some desiderata for a negotiated target annotation scheme for which straightforward mappings can be constructed from each of the supplied annotation schemes.
Analysis of Moving Patterns of Moving Objects with the Proposed Framework
Authors: In-Hye Shin, Gyung-Leen Park, Abhijit Saha, Ho-Young Kwak, Hanil Kim
Venue: Proceedings of the International Conference on Computational Science and Its Applications: Part II (2009)

This paper proposes an analysis framework which enables us to analyze the moving patterns of moving objects. To show the effectiveness of the framework, we applied the framework to analyze moving patterns of taxis based on the real-life location history data accumulated from the Taxi Telematics system developed in Jeju, Korea. The analysis aims at obtaining value-added information necessary to provide empty taxis with location recommendation services for the efficient operations of taxis. The proposed framework provides the flow chart which would have a quick look around the overall analysis process and help quickly deal with the same or similar analysis, while saving the temporal and economic costs. Data mining tool used in the framework is Enterprise Miner (E-Miner) in SAS which is one of the most widely used statistics packages and can effectively address huge amounts of log data. Especially, we perform the refined analysis by means of doing repeatedly the well-known k-means clustering method under various spatial or temporal conditions. The paper proposes the refined data mining process 1) extracting the interested dataset about meaningful information driven from the previous cluster results, 2) performing again the detailed clustering with the extracted dataset, and 3) finally extracting the value-added information such as the good pick-up spots or 4) returning the feedback. As a result, the spatiotemporal pattern analysis within the each refined clustering method makes it possible to recommend that the empty taxis go to the nearby cluster location with a high pick-up frequency statistically, resulting in the reduction of empty taxi ratio.
Design and implementation of a protocol offload engine for TCP/IP and remote direct memory access based on hardware/software coprocessing
Authors: Hankook Jang, Sang-Hwa Chung, Dae-Hyun Yoo
Venue: Microprocessors Microsystems (2009)

This paper presents the design and implementation of a protocol offload engine that processes TCP/IP and remote direct memory access (RDMA) protocols by means of hardware/software coprocessing. In the offload engine, time-consuming operations such as TCP/IP header generation are implemented as hardware to improve performance. The software performs control operations and RDMA header generation. In the experiments and analyses, it is proved that the hardware can provide satisfactory performance to process all operations at speeds of over 1Gbps. Our engine can offload most protocol processing overheads - up to 95% to 100% - from the host CPU. Finally, although the embedded processors operate with a 300MHz clock that is seven times slower than the clock of the host CPU, our engine shows maximum bandwidths of 673Mbps for TCP/IP and 551Mbps for RDMA on a gigabit Ethernet network.
Did "Minority Report" Get It Wrong? Superiority of the Mouse over 3D Input Devices in a 3D Placement Task
Authors: François Bérard, Jessica Ip, Mitchel Benovoy, Dalia El-Shimy, Jeffrey R. Blum, Jeremy R. Cooperstock
Venue: Proceedings of the 12th IFIP TC 13 International Conference on Human-Computer Interaction: Part II (2009)

Numerous devices have been invented with three or more degrees of freedom (DoF) to compensate for the assumed limitations of the 2 DoF mouse in the execution of 3D tasks. Nevertheless, the mouse remains the dominant input device in desktop 3D applications, which leads us to pose the following question: is the dominance of the mouse due simply to its widespread availability and long-term user habituation, or is the mouse, in fact, more suitable than dedicated 3D input devices to an important subset of 3D tasks? In the two studies reported in this paper, we measured performance efficiency of a group of subjects in accomplishing a 3D placement task and also observed physiological indicators through biosignal measurements. Subjects used both a standard 2D mouse and three other 3 DoF input devices. Much to our surprise, the standard 2D mouse outperformed the 3D input devices in both studies.
Semi-supervised Document Clustering with Simultaneous Text Representation and Categorization
Authors: Yanhua Chen, Lijun Wang, Ming Dong
Venue: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I (2009)

In order to derive high quality information from text, the field of text mining has advanced swiftly from simple document clustering to co-clustering with words and categories. However, document co-clustering without any prior knowledge or background information is a challenging problem. In this paper, we propose a Semi-Supervised Non-negative Matrix Factorization (SS-NMF) framework for document co-clustering. Our method computes new word-document and document-category matrices by incorporating user provided constraints through simultaneous distance metric learning and modality selection. Using an iterative algorithm, we perform tri-factorization of the new matrices to infer the document, category and word clusters. Theoretically, we show the convergence and correctness of SS-NMF co-clustering and the advantages of SS-NMF co-clustering over existing approaches. Through extensive experiments conducted on publicly available data sets, we demonstrate the superior performance of SS-NMF for document co-clustering.
DDS Based High Fidelity Flight Simulator
Authors: Shupeng Zheng, Jingfeng He, Jun Jin, Junwei Han
Venue: Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 01 (2009)

The real-time distributed computing environment and reusable software architecture are important factors that affect the fidelity of flight simulation. We accomplished a flight simulator based on DDS (Data Distribution Service for Real-time Systems) middleware and structural software architecture and proved its high fidelity as a flight training device. According to the analysis of flight simulator’s functional blocks, we developed the real-time distributed computing environment which utilized DDS middleware through Ethernet and decreased the communication latency among functional blocks. Furthermore, we proposed the structural software architecture on the basis of layered and component-based design pattern to facilitate the higher simulation components’ reuse and replacement. The real-time communication procedures among simulation components are also described in this paper. The validation method and the contrasting simulation results are presented finally to show the feasible design based on DDS to carry out flight simulation with low communication latency and high quality.
Systematic verb stem generation for Arabic
Authors: Jim Yaghi, Sane M. Yagi
Venue: Proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages (2004)

Performing root-based searching, concordancing, and grammar checking in Arabic requires an efficient method for matching stems with roots and vice versa. Such mapping is complicated by the hundreds of manifestations of the same root. An algorithm based on the generation method used by native speakers is proposed here to provide a mapping from roots to stems. Verb roots are classified by the types of their radicals and the stems they generate. Roots are moulded with morphosemantic and morphosyntactic patterns to generate stems modified for tense, voice, and mode, and affixed for different subject number, gender, and person. The surface forms of applicable morphophonemic transformations are then derived using finite state machines. This paper defines what is meant by 'stem', describes a stem generation engine that the authors developed, and outlines how a generated stem database is compiled for all Arabic verbs.
Differential Fault Analysis on DES Middle Rounds
Authors: Matthieu Rivain
Venue: Proceedings of the 11th International Workshop on Cryptographic Hardware and Embedded Systems (2009)

Differential Fault Analysis (DFA) is a powerful cryptanalytic technique that disturbs cryptographic computations and exploits erroneous results to infer secret keys. Over the last decade, many works have described and improved DFA techniques against block ciphers thus showing an inherent need to protect their implementations. A simple and widely used solution is to perform the computation twice and to check that the same result is obtained. Since DFA against block ciphers usually targets the last few rounds, one does not need to protect the whole ciphering thus saving computation time. However the number of rounds to protect must be chosen very carefully in order to prevent security flaws. To determine this number, one must study DFA targeting middle rounds of the cipher. In this paper, we address this issue for the Data Encryption Standard (DES) algorithm. We describe an attack that breaks DES by introducing some faults at the end of round 9, 10, 11 or 12, more or less efficiently depending on the fault model and the round number.
Incorporating temporal and semantic information with eye gaze for automatic word acquisition in multimodal conversational systems
Authors: Shaolin Qu, Joyce Y. Chai
Venue: Proceedings of the Conference on Empirical Methods in Natural Language Processing (2008)

One major bottleneck in conversational systems is their incapability in interpreting unexpected user language inputs such as out-of-vocabulary words. To overcome this problem, conversational systems must be able to learn new words automatically during human machine conversation. Motivated by psycholinguistic findings on eye gaze and human language processing, we are developing techniques to incorporate human eye gaze for automatic word acquisition in multimodal conversational systems. This paper investigates the use of temporal alignment between speech and eye gaze and the use of domain knowledge in word acquisition. Our experiment results indicate that eye gaze provides a potential channel for automatically acquiring new words. The use of extra temporal and domain knowledge can significantly improve acquisition performance.
Optimal efficient learning equilibrium: imperfect monitoring in symmetric games
Authors: Ronen I. Brafman, Moshe Tennenholtz
Venue: Proceedings of the 20th national conference on Artificial intelligence - Volume 2 (2005)

Efficient Learning Equilibrium (ELE) is a natural solution concept for multi-agent encounters with incomplete information. It requires the learning algorithms themselves to be in equilibrium for any game selected from a set of (initially unknown) games. In an optimal ELE, the learning algorithms would efficiently obtain the surplus the agents would obtain in an optimal Nash equilibrium of the initially unknown game which is played. The crucial part is that in an ELE deviations from the learning algorithms would become nonbeneficial after polynomial time, although the game played is initially unknown. While appealing conceptually, the main challenge for establishing learning algorithms based on this concept is to isolate general classes of games where an ELE exists. Unfortunately, it has been shown that while an ELE exists for the setting in which each agent can observe all other agents' actions and payoffs, an ELE does not exist in general when the other agents' payoffs cannot be observed. In this paper we provide the first positive results on this problem, constructively proving the existence of an optimal ELE for the class of symmetric games where an agent can not observe other agents' payoffs.
Voice Pathology Classification by Using Features from High-Speed Videos
Authors: Daniel Voigt, Michael Döllinger, Anxiong Yang, Ulrich Eysholdt, Jörg Lohscheller
Venue: Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine (2009)

For the diagnosis of pathological voices it is of particular importance to examine the dynamic properties of the underlying vocal fold (VF) movements occurring at a fundamental frequency of 100---300 Hz. To this end, a patient's laryngeal oscillation patterns are captured with state-of-the-art endoscopic high-speed (HS) camera systems capable of recording 4000 frames/second. To date the clinical analysis of these HS videos is commonly performed in a subjective manner via slow-motion playback. Hence, the resulting diagnoses are inherently error-prone, exhibiting high inter-rater variability. In this paper an objective method for overcoming this drawback is presented which employs a quantitative description and classification approach based on a novel image analysis strategy called Phonovibrography. By extracting the relevant VF movement information from HS videos the spatio-temporal patterns of laryngeal activity are captured using a set of specialized features. As reference for performance, conventional voice analysis features are also computed. The derived features are analyzed with different machine learning (ML) algorithms regarding clinically meaningful classification tasks. The applicability of the approach is demonstrated using a clinical data set comprising individuals with normophonic and paralytic voices. The results indicate that the presented approach holds a lot of promise for providing reliable diagnosis support in the future.
Groundwater Environment Evolution and Evaluating Indexes
Authors: Xing Li-ting, Liu Li
Venue: Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 01 (2009)

Water environment problems received extensive attention in recent years, take Jinan karst spring region for example, groundwater evolution characteristics were analyzed by observing changes of spring level, spring runoff, water quality and safety yield, authors thought the main reason of drying-up was inadequate understanding of the safety yield index. According to the characteristics of Jinan spring region, on the basis of comprehensive analysis environment-hydrogeologic problems which generated by groundwater exploitation and utilization, ascertain that evaluation index system of groundwater environment contains 7 primary evaluation indexes and 11 constraint indexes. Groundwater evaluation indexes relate to water quality, water quantity, water level, water temperature, economy, society, etc, and they were the most ultimately exhibiting of groundwater environment. They have nature property, society property and dynamic changeability, which values depend on groundwater system per se. Groundwater environment evaluation indexes provide decision-making basis for groundwater resource management and water environment protection.
Neural Network Gait Classification for On-Body Inertial Sensors
Authors: Mark A. Hanson, Harry C. Powell Jr., Adam T. Barth, John Lach, Maïté Brandt-Pearce
Venue: Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks (2009)

Clinicians have determined that continuous ambulatory monitoring provides significant preventative and diagnostic benefit, especially to the aged population. In this paper we describe gait classification techniques based on data obtained using a new body area sensor network platform named TEMPO 3.The platform and its supporting infrastructure enable six-degrees-of-freedom inertial sensing, signal processing, and wireless transmission. The proposed signal processing includes data normalization to improve robustness, feature extraction optimized for classification, and wavelet pre-processing. The effectiveness of the platform is validated by implementing a binary classifier between shuffle and normal gait.Artificial neural networks and classifiers based on the Cerebellar Model Articulation Controller were tested and yielded classification accuracies (68%-98%) comparable to previous efforts that required more restrictive or intrusive apparatus. These results suggest a viable path to resource-constrained, on-body gait classification.