ppxscal/embedding_network huggingface dataset:
The magic window: lessons from a year in the life of a co-present media space
Authors: Hyun Hoi James Kim, Carl Gutwin, Sriram Subramanian
Venue: Proceedings of the 2007 international ACM conference on Supporting group work (2007)

The windows and doorways that connect offices to public spaces are a site for people to gather awareness information and initiate interaction. However, these portals often reveal more information to the public area than the office occupant would like. As a result, people often keep doors and window blinds closed, which means that nobody can gather awareness information, even those with whom the occupant would be willing to share. One solution to this problem is a co-present media space - a computer-mediated video connection at the boundary between an office and a public area. These systems can provide both greater privacy control to the occupant and greater overall awareness information to observers. To see how co-present media spaces would work in real world settings, we built what we believe are the first ever co-present media spaces, and deployed them in two offices. From observations gathered over fifteen months, it is clear that the systems can do a better job of balancing the occupant's need for privacy and the observers' need for awareness better, than a standard window. However, we also identified a number of issues that affected the use and the success of the systems: the existence of alternate information sources, confusion with existing social norms, disparities between effort and need, and reduced interactional subtlety for observers in the public area. Our work contributes both a novel arrangement of a media space for co-present collaborators, and the first investigation into the design factors that affect the use and acceptance of these systems.
Interactions Between Compression and Prefetching in Chip Multiprocessors
Authors: Alaa R. Alameldeen, David A. Wood
Venue: Proceedings of the 2007 IEEE 13th International Symposium on High Performance Computer Architecture (2007)

In chip multiprocessors (CMPs), multiple cores compete for shared resources such as on-chip caches and off-chip pin bandwidth. Stride-based hardware prefetching increases demand for these resources, causing contention that can degrade performance (up to 35% for one of our benchmarks). In this paper, we first show that cache and link (off-chip interconnect) compression can increase the effective cache capacity (thereby reducing off-chip misses) and increase the effective off-chip bandwidth (reducing contention). On an 8-processor CMP with no prefetching, compression improves performance by up to 18% for commercial workloads. Second, we propose a simple adaptive prefetching mechanism that uses cache compression's extra tags to detect useless and harmful prefetches. Furthermore, in the central result of this paper, we show that compression and prefetching interact in a strong positive way, resulting in combined performance improvement of 10-51% for seven of our eight workloads.
Collaborative relevance assessment for task-based knowledge support
Authors: Duen-Ren Liu, I-Chin Wu
Venue: Decision Support Systems (2008)

The operations and management activities of enterprises are mainly task-based and knowledge intensive. Accordingly, an important issue in deploying knowledge management systems is the provision of task-relevant information (codified knowledge) to meet the information needs of knowledge workers during the execution of a task. Codified knowledge extracted from previously executed tasks can provide valuable knowledge about conducting the task-at-hand (current task), and is a valuable information source for constructing a task profile that models a worker's task needs, i.e., information needs for the current task. In this paper, we propose a novel task-relevance assessment approach that evaluates the relevance of previous tasks in order to construct a task profile for the current task. The approach helps knowledge workers assess the relevance of previous tasks through linguistic evaluation and the collaboration of knowledge workers. In addition, applying relevance assessment to a large number of tasks may create an excessive burden for workers. Thus, we propose a novel two-phase relevance assessment method to help workers conduct relevance assessment effectively. Furthermore, a modified relevance feedback technique, which is integrated with the task-relevance assessment method, is employed to derive the task profile for the task-at-hand. Consequently, task-based knowledge support can be enabled to provide knowledge workers with task-relevant information based on task profiles. Empirical experiments demonstrate that the proposed approach models workers' task-needs effectively and helps provide task-relevant knowledge.
An empirical, path-oriented approach to software analysis and testing
Authors: Branson W. Murrill
Venue: Journal of Systems and Software (2008)

Error flow analysis and testing techniques focus on the introduction of errors through code faults into data states of an executing program, and their subsequent cancellation or propagation to output. The goals and limitations of several error flow techniques are discussed, including mutation analysis, fault-based testing, PIE analysis, and dynamic impact analysis. The attributes desired of a good error flow technique are proposed, and a model called dynamic error flow analysis (DEFA) is described that embodies many of these attributes. A testing strategy is proposed that uses DEFA information to select an optimal set of test paths and to quantify the results of successful testing. An experiment is presented that illustrates this testing strategy. In this experiment, the proposed testing strategy outperforms mutation testing in catching arbitrary data state errors.
A Study of Secure Multi-party Ranking Problem
Authors: Wen Liu, Shou-Shan Luo, Ping Chen
Venue: Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02 (2007)

In this paper, we study a secure multi-party ranking problem: there are n parties, P_1, P_2, ..., P_n. Each party P_i has a secret input m_i \in {1, 2, ...,N}. He wants to get the order of mi in the ascending ranking sequence of the n parties' inputs, while not leaking the value of mi. This problem extends the famous Yao's Millionaires' problem form two paries to n parties and is a multi-party Yao's Millionaires' problem. We propose a protocol for solving the secure multi-party ranking problem, using the encoding scheme and the El Gamal cryptosystem. We also prove the correctness and the security of our solution in the semi-honest model. The solution of secure multi-party ranking problem can make new-style electronic transactions, such as private biding and auction, secret online transaction and so on, become better security.
Fault Diagnosis of Power Equipment Based On Dissolved Gas Analysis And LS Fusion Combining Neural Network
Authors: Ganyun Lv, Xiaodong Wang
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 01 (2007)

In this paper, a new method for power equipment fault diagnosis is presented based on a least square (LS) fusion combining neural network and dissolved gas analysis (DGA). Contents of five characteristic gases obtained by DGA are preprocessed through a special dada dealing process, and 6 features for fault diagnosis are extracted. Then five child back-propagation (BP) artificial neural networks (ANNs) with different structure are applied to diagnosis the fault respectively. The diagnosing results of the child ANNs are fused by the LS weighted fusion algorithm. The fault is identified based on the fused results at last. Compared with single neural network, the LS fusion combining network can identify fault type safely when the fault is deceptive, however, a single neural network may fail in this case. Furthermore, the combining neural network is more reliable than single neural network. The test results of power transformer fault diagnosis proved the conclusions.
Evaluating the Model Accuracy in Automated Design Space Exploration
Authors: Kalle Holma, Mikko Setala, Erno Salminen, Timo D. Hamalainen
Venue: Proceedings of the 10th Euromicro Conference on Digital System Design Architectures, Methods and Tools (2007)

Design space exploration is used to shorten the design time of System-on-Chips (SoCs). The models used in the exploration need to be both accurate and fast to simulate. This paper introduces a multi-level communication cost to improve the accuracy of the abstracted system models. During the simulation, one of three different communication costs is applied for each inter- task communication event based on the mapping of the communicating tasks. The accuracy of three system abstraction models including the presented communication cost is evaluated using a Motion-JPEG (MJPEG) application described in Unified Modeling Language (UML). According to the results, the average error in frames per second (FPS) is 3.8% for the trace model, 4.3% for the modulo model, and 12.8% for the probabilistic model compared to FPGA execution. The results show that with the multi-level communication cost the accuracy is increased significantly, and accurate results can be achieved with arbitrary mappings.
Application of Artificial Neural Network Supported by BP and Particle Swarm Optimization Algorithm for Evaluating the Criticality Class of Spare Parts
Authors: Lin Wang, Yurong Zeng, Chao Gui, Hong Wang
Venue: Proceedings of the Third International Conference on Natural Computation - Volume 02 (2007)

This paper presents artificial neural networks (ANNs) for the criticality class evaluating of spare parts in a power plant. Two learning methods are utilized in the ANNs, namely back propagation (BP) and BP-particle swarm optimization (BP-PSO). The reliability of the models is tested by comparing their classification ability with a hold-out sample and an external data set. The results show that both ANN models have high predictive accuracy. The results also indicate that the BP-PSO algorithm has better recognition rate than the BP algorithm. The proposed ANNs are successful in decreasing inventories holding costs significantly by modifying the unreasonable target service level setting which is confirmed by the corresponding criticality class of a spare part.
On generalized separating hash families
Authors: D. R. Stinson, R. Wei, K. Chen
Venue: Journal of Combinatorial Theory Series A (2008)

Separating hash families are useful combinatorial structures which are studied in a general form in this paper. Necessary and sufficient conditions for the existence of certain types of generalized hash functions are considered.
Area Overhead and Test Time Co-Optimization through NoC Bandwidth Sharing
Authors: Fawnizu Azmadi Hussin, Tomokazu Yoneda, Hideo Fujiwara
Venue: Proceedings of the 16th Asian Test Symposium (2007)

In this paper, a new approach to NoC test scheduling based on bandwidth-sharing is presented. The test scheduling is performed under the objective of co-optimizing the wrapper area overhead and the resulting test application time using two complementary NoC wrappers. Experimental results showed that the area overhead can be optimized (to an extent) without compromising the test application time. Compared to other NoC scheduling approaches based on dedicated paths, our bandwidth sharing approach can reduce the test application time by up to 75.4%.
On the profile of frequency dependent series resistance and surface states in Au/Bi4Ti3O12/SiO2/n-Si(MFIS) structures
Authors: F. Parlaktürk, Ş. Altındal, A. Tataroğlu, M. Parlak, A. Agasiev
Venue: Microelectronic Engineering (2008)

The frequency dependent capacitance-voltage (C-V) and conductance-voltage (G/@w-V) characteristics of the metal-ferroelectric-insulator-semiconductor (Au/Bi"4Ti"3O"1"2/SiO"2/n-Si) structures (MFIS) were investigated by considering series resistance (R"s) and surface state effects in the frequency range of 1kHz-5MHz. The experimental C-V-f and G/@w-V-f characteristics of MFIS structures show fairly large frequency dispersion especially at low frequencies due to R"s and N"s"s. In addition, the high frequency capacitance (C"m) and conductance (G"m/@w) values measured under both reverse and forward bias were corrected for the effect of series resistance to obtain the real capacitance of MFIS structures. The R"s-V plots exhibit anomalous peaks between inversion and depletion regions at each frequency and peak positions shift towards positive bias with increasing frequency. The C^-^2-V plot gives a straight line in wide voltage region, indicating that interface states and inversion layer charge cannot follow the ac signal in the depletion region, but especially in the strong inversion and accumulation region. Also, it has been shown that the surface state density decreases exponentially with increasing frequency. The C-V-f and G/w-V-f characteristics confirm that the interface state density (N"s"s) and series resistance (R"s) of the MFIS structures are important parameters that strongly influence the electrical properties of MFIS structures.
Experience with Automated Testing Analysis
Authors: M. A. Holthouse
Venue: Computer (1979)

Automated testing analyzers are popular software test tools. Such an analyzer, using a branch testing strategy, provides a cost-effective way of increasing confidence in software behavior.
The Procedure Geometry Detail Enhancement Algorithm of Chunklod
Authors: Yan-chun Shen, You-hong Zhu, Zhuan-ping Wen, Li Cao
Venue: Proceedings of the Fourth International Conference on Image and Graphics (2007)

This paper represents the Procedure geometry detail enhancement algorithm of Chunklod. The enhancement algorithm adopts massive model as processing unit. Because the Chunklod algorithm lacks process that viewpoint is near to the ground. The enhancement algorithm improves the efficiency of rendering by combining the local triangle surface simplified algorithm and the error evaluation & control algorithm, it avoids large-scale computing caused by over-LOD. Finally ,the paper shows that the visual effect of terrain information is expressed well when the algorithm is working virtually--taking the geographical information system for example .The results indicates that the improved level of detail enhancement of ChunkLod is a practical terrain algorithm with total levels of detail technology.
Search Algorithms for Unstructured Peer-to-Peer Networks
Authors: Reza Dorrigiv, Alejandro Lopez-Ortiz, Pawel Pralat
Venue: Proceedings of the 32nd IEEE Conference on Local Computer Networks (2007)

We study the performance of several search algorithms on unstructured peer-to-peer networks, both using classic search algorithms such as flooding and random walk, as well as a new hybrid algorithm proposed in this paper. This hybrid algorithm first uses flooding to find sufficient number of nodes and then starts random walks from these nodes. We compare the performance of the search algorithms on several graphs corresponding to common topologies proposed for peer-to- peer networks. In particular, we consider binomial random graphs, regular random graphs, power-law graphs, and clustered topologies. Our experiments show that for binomial random graphs and regular random graphs all algorithms have similar performance. For power-law graphs, flooding is effective for small number of messages, but for large number of messages our hybrid algorithm outperforms it. Flooding is ineffective for clustered topologies in which random walk is the best algorithm. For these topologies, our hybrid algorithm provides a compromise between flooding and random walk. We also compare the proposed hybrid algorithm with the k-walker algorithm on power-law and clustered topologies. Our experiments show that while they have close performance on clustered topologies, the hybrid algorithm has much better performance on power-law graphs. We theoretically prove that flooding is effective for regular random graphs which is consistent with our experimental results.
Multi-class Protein Classification Using Adaptive Codes
Authors: Iain Melvin, Eugene Ie, Jason Weston, William Stafford Noble, Christina Leslie
Venue: The Journal of Machine Learning Research (2007)

Predicting a protein's structural class from its amino acid sequence is a fundamental problem in computational biology. Recent machine learning work in this domain has focused on developing new input space representations for protein sequences, that is, string kernels, some of which give state-of-the-art performance for the binary prediction task of discriminating between one class and all the others. However, the underlying protein classification problem is in fact a huge multi-class problem, with over 1000 protein folds and even more structural subcategories organized into a hierarchy. To handle this challenging many-class problem while taking advantage of progress on the binary problem, we introduce an adaptive code approach in the output space of one-vs-the-rest prediction scores. Specifically, we use a ranking perceptron algorithm to learn a weighting of binary classifiers that improves multi-class prediction with respect to a fixed set of output codes. We use a cross-validation set-up to generate output vectors for training, and we define codes that capture information about the protein structural hierarchy. Our code weighting approach significantly improves on the standard one-vs-all method for two difficult multi-class protein classification problems: remote homology detection and fold recognition. Our algorithm also outperforms a previous code learning approach due to Crammer and Singer, trained here using a perceptron, when the dimension of the code vectors is high and the number of classes is large. Finally, we compare against PSI-BLAST, one of the most widely used methods in protein sequence analysis, and find that our method strongly outperforms it on every structure classification problem that we consider. Supplementary data and source code are available at <tt><a href="http://www.cs.columbia.edu/compbio/adaptive">http://www.cs.columbia.edu/compbio/adaptive</a></tt>.
The development of computer science: a sociocultural perspective
Authors: Matti Tedre
Venue: Proceedings of the 6th Baltic Sea conference on Computing education research: Koli Calling 2006 (2006)

Computer science is a broad discipline, and computer scientists often disagree about the content, form, and practices of the discipline. The processes through which computer scientists create, maintain, and modify knowledge in computer science---processes which often are eclectic and anarchistic---are well researched, but knowledge of those processes is generally not considered to be a part of computer science. On the contrary, I argue that understanding of how computer science works is an important part of the knowledge of an educated computer scientist. In this paper I discuss some characteristics of computer science that are central to understanding how computer science works.
An Empirical Study on MBASE and LeanMBASE
Authors: Supannika Koolmanojwong, Barry Boehm
Venue: Proceedings of the First International Symposium on Empirical Software Engineering and Measurement (2007)

From 1998-2005, the successful Model-Based (Systems) Architecting and Software Engineering (MBASE) had been used as a set of guidelines for the keystone two-semester real-client team project graduate software engineering course sequence. However, to fit with small-sized and limited schedule projects, MBASE was trimmed to reduce the huge amount of efforts in documentation. Consequently, LeanMBASE, which is a light-weight software process framework that helps teams identify the high-value activities and helps balance the workload of a development, is being used in the software engineering course. This paper reports the comparison and improvement of the projects that use MBASE and LeanMBASE in terms of content, performance, and customer satisfaction.
Credit Card Forensics
Authors: Hal Berghel
Venue: Communications of the ACM (2007)

Decoding the magnetic attraction of criminals to swiping.
E-Mailing from Armenia
Authors: David Alan Grier
Venue: Computer (2007)

The development of an information technology industry in the remains of the old Soviet Union brought the discipline of a market economy to the region.
Content-based Table Tennis Games Highlight Detection Utilizing Audiovisual Clues
Authors: Bin Zhang, Wei Chen, Weibei Dou, Yu-jin Zhang, Liming Chen
Venue: Proceedings of the Fourth International Conference on Image and Graphics (2007)

Both audio and video are considered as important information carriers of multimedia content. In this paper, we propose an algorithm utilizing audiovisual clues for a scenario of sports game highlight detection, where the highlight detection for table tennis games are studied. Since audio and video contain different aspects of information that is helpful to locate highlights, we build two algorithms detecting highlight candidates based on audio and video, respectively, where hidden Markov model (HMM) audio keyword modeling and unsupervised shot clustering are applied. Decision fusion is invoked to combine audio and video highlight candidates and generate final highlights. Promising experimental results up to 90 % average precision are achieved.
ARM MPCore; The streamlined and scalable ARM11 processor core
Authors: Kazuyuki Hirata, John Goodacre
Venue: Proceedings of the 2007 Asia and South Pacific Design Automation Conference (2007)

The required processing performance of embedded processor core is getting higher and higher without increasing power consumption dramatically. In same time, large SoC design has more risk of re-spin and long design time due to the complexity and difficulty of verification. ARM offers multi core solution to overcome such a situation over various applications.
Bond Graph Modeling of Rail Wheelset on Curved Track
Authors: Nilotpal Banerjee, Ranjit Karmakar
Venue: Simulation (2007)

This paper presents a bond graph model of a free rail wheelset rolling on fleXible curved track, considering siX degrees of freedom without linearity approXimations. Modeling of engineering systems through bond graphs is based on eXchange of power amongst the basic elements of a system and several energy domains can be represented in a unified manner. In this paper, the modeling approach is based on creating subsystem bond graph models or capsules representing various analytical aspects of wheelset dynamics. The model is finally converted to a wheelset capsule, which can be further used for the development of truck and carbody models with suspension elements. The model can accommodate any creep force formulation for the rail&#x2014;wheel contact forces. In this work, both Kalker's linear theory and heuristic modification based on non-linear formulation by Vermeulen and Johnson are used separately in a comparative study. Both these formulations allow the creep forces to be modeled by a non-linear bond graph R elements acted upon by the corresponding slip velocities between the wheel and track. Kinematic analysis of the wheelset is carried out to determine velocities of the contact points. The bond graph is created using software SYMBOLS 2000 and simulated for a set of parameter values.