Miracle-dz/UniFAR-FTUs huggingface dataset:
Building sparse models for traffic flow prediction: an empirical comparison between statistical heuristics and geometric heuristics for Bayesian network approaches

ABSTRACT As the sensors become pervasive in transportation systems, traffic prediction becomes more challenging due to the significant increase of data. In this paper, we study how to design BNs for traffic flow prediction with an emphasis on node selection strategy. Three node selection strategies are evaluated based on PeMS (Performance Measurement System) dataset: the Graphical Lasso approach, geographically neighboring approach, and direct correlation approach. Test results show that feeding all the available data into the prediction model might not be a wise choice. The three node selection strategies mentioned above are generally equivalent in prediction performance. We also find that direct correlation approach may lead a sparser model and meanwhile keeps good prediction performance on the PeMS dataset. The obtained conclusions not only help simplify the design of traffic flow prediction models in practice, but also shed light on how to effectively use the massive data.
Robust Solutions for the WDM Routing and Provisioning Problem With Uncertain Demands and Single Link Failure Protection

In a previous investigation, we developed a robust optimization model for the wavelength division multiplexing routing and provisioning problem. The robust model incorporates a fixed budget and uses a piece-wise linear regret function to model over and under provisioning. In this investigation, the robust optimization model has been extended to provide either dedicated protection or shared protection against single link failures. Both models are mixed integer linear programs with a large number of continuous variables and only two integer variables per link. Each has been implemented using A Mathematical Programming Language (AMPL) and computational solvability has been demonstrated on a pair of test cases. Cost reductions up to 35% were observed via the use of shared protection versus dedicated protection. Acknowledgement This investigation was partially supported by grants from Nortel Networks and the Office of Naval Research under Award Number N00014-96-1-0315.
Sensor Glove for an Intuitive Human-Machine Interface for Exoskeletons as Manual Load Handling Assistance

Wearable assistive robots, also called exoskeletons, require an intuitive human-machine interface to reproduce human flexibility and to ensure user acceptance. In this paper, a concept for such an intuitive interface for assistance in manual handling of loads in industry is presented. The concept is based on the measurement of the interaction force between the human and the robot, on an impedance-based control approach and, in contrast to existing solutions, on an intelligent sensor glove. Suitable sensor types and sensor locations on the hand are selected. To show the feasibility of the concept a prototype of a sensor glove equipped with six piezoresistive force is developed and the force signals are recorded for two different grasp types. The resulting characteristic signal patterns could be recognized using a feedforward neural networks. Further, an outlook on future research is given.
Teachers' document annotating: models for a digital memory tool

In this paper, we propose a model of semantic annotations suitable for teachers. It is aimed at implementing a tool that provides teachers with a digital memory based on annotations, as a basis. The model adapts to teachers' activities with regard to both their pedagogical and domain expertise. We analyse the specifics of an annotation language that would enable teachers to express their own expertise. We first discuss requirements for teachers' annotations and then propose a conceptual model, complying with these requirements. By using ontologies to represent teachers' annotations semantics we have obtained a specialised teacher annotation model, which we have used to create an annotation tool called 'MemoNote'. We present implementation details of the MemoNote tool as well as the results of an evaluation study of the proposed model conducted with chemistry teachers.
Malware Collection and Analysis via Hardware Virtualization

Malware is one of the biggest security threat today and deploying effective defensive solutions requires the collection and rapid analysis of a continuously increasing number of samples. The collection and analysis is greatly complicated by the proliferation of metamorphic malware as the efficacy of signature-based static analysis systems is greatly reduced. While honeypots and dynamic malware analysis has been effectively deployed to combat the problem, significant challenges remain. The rapidly increasing number of malware samples poses a particular challenge as it greatly inflates the cost of the hardware required to process the influx. As modern malware also deploys anti-debugging and obfuscation techniques, the time it takes to formulate effective solutions is further exacerbated. There is a clear need for effective scalability in automated malware collection and analysis. At the same time, modern malware can both detect the monitoring environment and hide in unmonitored corners of the system. It has also been observed that malware modifies its run-time behavior to lead the analysis system astray when it detects a monitoring environment. Consequently, it is critical to create a stealthy environment to hide the presence of the data collection from the external attacker. Such systems Tamas Kristof Lengyel University of Connecticut, 2015 also need to isolate critical system components from the executing malware sample while keeping the concurrent collection and analysis sessions separate. Furthermore, the fidelity of the collected data is essential for effective dynamic analysis. As rootkits now employ a variety of techniques to hide their presence on a system, the broader the scope of data collection, the more likely the analysis will reveal useful features. Over the last decade hardware virtualization has been proposed to develop such tools with promising results. In this dissertation we present a systematic evaluation of hardware virtualization as an underlying technology to construct effective malware collection and analysis systems. The evaluation is realized via the combination of four distinct objectives such systems need to fulfill: scalability, stealth, fidelity and isolation. Malware Collection and Analysis via Hardware Virtualization Tamas Kristof Lengyel B.Sc., University of Connecticut, 2008 M.Sc., University of Connecticut, 2015 A Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy at the University of Connecticut 2015
Enhancing the Area Efficiency of FPGAs With Hard Circuits Using Shadow Clusters

There is a dramatic logic density gap between field-programmable gate arrays (FPGAs) and application-specific integrated circuits, and this gap is the main reason FPGAs are not cost-effective in high-volume applications. Modern FPGAs narrow this gap by including “hard” circuits such as memories and multipliers, which are very efficient when they are used. However, if these hard circuits are not used, they go wasted (including the very expensive programmable routing that surrounds the logic), and have a negative impact on logic density. In this paper, we present an architectural concept, called shadow clusters, which seeks to mitigate this loss. A shadow cluster is a standard FPGA logic “cluster” (typically consisting of a group of lookup tables and flip-flops) that is placed “behind” every hard circuit, and can programmably, through simple, small multiplexers, replace the hard circuit in the event it is not needed. A shadow cluster is effective because the largest area cost, by far, in an FPGA is for the programmable routing that connects the logic. The shadow cluster area cost is small, and yet it enables more consistent employment of the programmable routing across applications with varying demand for hard circuits. We introduce new terminology to describe the economics of hard circuits on FGPAs, and provide a scientific way to measure the area effectiveness. We measure the area efficiency of FPGAs with and without shadow clusters, and show that a modern commercial architecture (with a fixed ratio of multipliers to soft logic) would gain 4.7% in area efficiency by employing shadow clusters. Indeed, every architecture we studied under “reasonable” conditions never showed a loss of area efficiency. Furthermore, we show that most area-efficient architecture that employs the shadow cluster concept is 12.5% better than the most area-efficient architecture without shadow clusters.
A New Feature for Night-time Pedestrian Detection ⋆

The Histograms of Oriented Gradients (HOG) feature is widely used for pedestrian detection because of its high performance in accuracy, but the information it describes still has room for improvement. In order to increase the accuracy of pedestrian detection, an Improved Histograms of Oriented Gradients (IHOG) feature is proposed at the basis of HOG feature. After computation of HOG feature, an improved feature is added to the HOG feature. It applies an 8 8-pixel block with 4-pixel strides to obtain nine images of 15 31 pixels, each image representing one direction range (BIN), in which all blocks’ vectors within the same BIN will be mapped respectively in pixels. By using tri-linear interpolation in these nine images and using linear SVM to train all these feature vectors, an I-HOG feature is obtained. I-HOG contains a larger range of information than HOG and is tested on night-time datasets. The experiment result shows that, by using I-HOG, the detection rate could increase by about 4.26% than when using HOG.
Personalized Information Recommendation based on Polysemy Tag Optimization

Personalized information recommendation based on social tagging is quite a hot issue nowadays, but quality of tagging is criticized at the same time, especially the polysemy and synonym tag problems. In order to solve problems triggered by polysemy tags in recommendation algorithm, Clique Percolation Method (CPM) is adopted in this paper to identify polysemy tags, and neighbor tag set is established based on tag clusters. Then, cosine similarities between user profile and tag sets corresponding to each sense of the target tag are calculated, and tag set with highest similarity value is selected to optimize the algorithm. Finally, performance of the algorithm is verified based on Bibsonomy dataset with recall and precision ratio.
Escalonamento de Aplicações que Processam Grandes Quantidades de Dados em Grids Computacionais

Data-intensive applications executing over a computational grid demand large data transfers. These are costly operations. Therefore, taking them into account is mandatory to achieve efficient scheduling of data-intensive applications on grids. Further, within an heterogeneous environment such as a grid, good schedules are typically attained by heuristics that use dynamic information about the grid and the applications (network and CPU loads, completion time of tasks, etc). However, these information are often difficult to be obtained accurately. Although there are schedulers that attain good performance without requiring that kind of information, they were not designed to take data transfer delays into account. This work presents Storage Affinity, a novel scheduling heuristic for Bag-of-Tasks and data-intensive applications running on grid environments. Storage Affinity exploits a data reuse pattern, common on many data-intensive applications, allowing it to take data transfer delays into account and reduce the makespan of the application. Further, it uses a replication strategy that yields efficient schedules without relying upon dynamic information that is difficult to obtain. Our results show that Storage Affinity may attain performance that is in average better than that of state-of-the-art knowledge-dependent schedulers, even in the unlikely case when the latter are fed with perfect information. This is achieved at the expense of consuming more CPU cycles (in average, more than not using replication).
A DoS attack mitigation strategy for software defined networks using Frenetic

A DoS attack mitigation strategy for Software Defined Networks using Frenetic Erick Mauricio Carvajal Barboza, M.S.E The University of Texas at Austin, 2017 Supervisor: Jacob Abraham Despite efforts to improve its robustness, the Internet still has important vulnerabilities that can be easily exploited. Denial-of-Service (DoS) attacks are a good example; this kind of attack has the potential to bring down large portions of the Internet by overwhelming the network with a huge amount of useless traffic. One of the possibilities to increase network robustness is a software defined networking (SDN) approach. This type of networking has a logically centralized controller that contains information about the state of the entire network, allowing it to make better decisions than when the network state is distributed between all the switches. In this type of network, is easier for the network manager to detect and mitigate an attack. This thesis presents a DoS mitigation technique that detects and stops such attacks on hosts providing services on a software defined network. Protection of the network infrastructure itself is not targeted by our approach.
Razor flop based on chip delay measurement

Moving towards deep submicron technologies, consideration for design margin will increase. In these technologies, process variation will increase the requirements of design margins. Lower supply voltage and higher clock frequency will further alleviate the issue of design margin. As technology progresses, STA (static timing analysis) is becoming a very important issue. With the increase in frequencies, there is a clear trend towards increasing setup and hold time violations. Thus, there is an increasing requirement of accurate and quick measurement techniques, to take care of all such requirements. Over reliance on tools for STA analysis can lead to inflexible designs. Off chip measurement techniques, such as spectrum analyzer, sampling oscilloscope and time interval analyzer measure delay but as the complexity of the system and chip integration increases, these off chip instruments become unviable. This dissertation discusses available techniques for on chip delay measurement. Several metrics, such as resolution, area and linearity have been compared. Furthermore, discussions on setup time violations are provided. Violation in setup time occurrence has been ingeniously used to predict the access time for data in NVM. (Non Volatile Memory).
An efficient multi join query optimization for relational database management system using swarm intelligence approaches

Currently, it is fairly obvious that the Multi Join Query Optimization (MJQO) is becoming the centre of attention in the context of Database Management System (DBMS). The functions consist of combination of data from multiple tables, reducing the number of needed queries, optimizing the Query Execution Plan (QEP), and moving processing abounded database servers to enhance both data integrity and performance. MJQO is an optimization task, which serves to locate the optimal QEP of a RDBMS in query processing. A major problem associated with RDBMS is the fact that they are still unable to fully meet the demands of big data. The majority of MJQO techniques encompass solution space at an extremely reduced pace. Many queries attempted to gather information from multiple sites or correlations, while every relation are compelled to answer these query via their limited resources. This lead to the access of data from many locations that are limited in their memory retention capabilities, which inevitably increase the size of the database, the number of the join, and Query Execution Time (QET). In order to eschew trapping and slow coverage difficulties in the quest to discover the optimal QEP and slow query execution time, this work proposes a total of three optimization algorithm that are based on Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Two-Phase Artificial Bee Colony (TPAPC) to solve the optimization problem in RDBMS Framework. The TPABC algorithm can be utilized to solve MJQO problems via simulation and increasing exploration and exploitation whilst balancing them for optimal results from giving queries. A directed acyclic graph, based on materialized query graph, aids in the optimization of algorithms and solving MJQO by removing non-promising QEP, which decreases the QEP combination space. Finally, experimental results demonstrate that the performance of TPABC, when compared to PSO, ACO, and native technique in the context of computational time, is very promising, which is indicative of the fact that the TPABC algorithm is capable of solving MJQO problems in shorter amounts of time and at lower costs compared to other approaches.
Dependency Syntax in the CoNLL Shared Task 2008

This document gives a brief overview of the conversion of the Penn Treebank (Marcus et al., 1993, 1994) to the dependency structures used in the CoNLL-2008 SharedTask. Our dependency framework has the following properties: • single-head: every word has exactly one parent, except the root, which has no parent. • single-root: only one word in the sentence is root. • traceless: the dependency structures use no empty categories. Special arc labels are used to encode gapping. • nonprojective: some long-distance syntactic phenomena are represented in the dependency structure by means of non-local links. The conversion procedure relies on earlier work on constituent-to-dependency conversion (Magerman, 1994; Collins, 1999; Yamada and Matsumoto, 2003; Johansson and Nugues, 2007). In addition, we imported dependencies inside NPs and hyphenated words from a version of the Penn Treebank mapped into GLARF, the Grammatical and Logical Argument Representation Framework (Meyers et al., 2001). To assign dependency labels, we used the following general principles: • If there is a Treebank label other than CLR, HLN, NOM, TPC, or TTL: use this label. • If the link is inside an NP or a hyphenated word: use the label from GLARF. • Else infer a label using a set of rules. The complete set of labels is listed in Section 4.
Inferring Human Activities from Observation via Semantic Reasoning

This thesis presents a framework that includes a new learning technique based on semantic representations that considers the re-usability and the inclusion of new skills in a robust manner. The obtained semantics extracts the essence of the observed activities in terms of human motions and object properties. The introduced framework has been assessed on a humanoid robot using different perceptual modalities, under different constraints and in several scenarios, demonstrating that our framework does not depend on the analyzed task.
An investigation of the relative strength index

Using daily data for the Swiss franc/US dollar exchange rate, this paper studies the trading profitability of the technical indicator Relative Strength Index (RSI). The authors find that for the past decade or so, using the standard configuration of RSI < = 30 and RSI > = 70 as buy or sell threshold, RSI offers no trading profit, but a small loss instead. However, when the buy/sell threshold parameters are altered, to deviate from the combination most commonly used, using RSI as the trading signal still yields profits. The authors also provide an explanation of this phenomenon. One implication of our findings is that consistent profit opportunities should no longer exist in what is already commonly and widely known, but taking a path less travelled could still lead to profit opportunities not yet discovered and utilized.
Beyond the simulator : instructions for high-performance tasks

High-performance tasks are difficult because of their complexity and the fact that they are time critical. Because they often have to be executed in a dangerous or hostile environment they require an extensive amount of training before they can be executed safely. From an operational as well as a didactical point of view, simulation offers many advantages to train these tasks. The didactical possibilities of simulation, however, don't seem to be exploited on a large scale in training currently. The present research focuses on tutoring to provide insight in the utility of augmented cues. Central question is if deviating from reality (by enhancing the salience of critical cues or adding new cues during tutoring) can increase effectiveness and efficiency of the training process.
Investigating The Effectiveness Of Redundant Text And Animation In Multimedia Learning Environments

In multimedia learning environments, research suggests that simultaneous presentation of redundant text (i.e. identical narration and on-screen text) may inhibit learning when presented with animation at the same time. However, related studies are limited to testing with cause-andeffects content information (e.g., Moreno & Mayer, 1999, 2002). This study examined the effects of redundant text on learners’ memory achievement and problem solving ability. The study replicated and extended prior research by using descriptive, rather than cause-and-effect content information. The primary research questions were (a) does redundant text improve learning performance if learners are presented with instructional material that addresses subject matter other than cause-and-effect relationship? and (b) does sequential presentation of animation followed by redundant text help learning? To answer the research questions, five hypotheses were tested with a sample of 224 Taiwanese students enrolled in a college level Management Information System (MIS) courses at a management college in southern Taiwan. Statistically significant differences were found in memory achievement and problem solving test scores between simultaneous and sequential groups; while no statistically significant differences were found in memory achievement and problem solving test scores between verbal redundant and non-redundant groups. These results were supported by interviewees expressing difficulty in connecting animation and verbal explanation in the two sequential presentation groups. The interview responses also helped to explain why insignificant results were obtained
Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing

We are delighted to introduce the proceedings of the 11th Conference on Empirical Methods in Natural Language Processing, organized under the auspices of SIGDAT, the ACL Special Interest Group for linguistic data and corpus-based approaches to NLP. This was a wonderfully fruitful year for EMNLP; we received 234 submissions, drawn from every area of language processing. Of these we were able to accept 73 papers (an acceptance rate of 31%), making for an unusually broad and exciting program. 43 of the papers were presented as talks, and 30 as posters.
Performance Evaluation and Study of Routing Protocols: MANET vs VANET

In the recent years the number of vehicles on road increases at high speed. A lot of road accidents and traffic congestion emerges. So to control it vehicular ad-hoc network came in picture. In the last few years many protocols were proposed to route the packets efficiently and correctly. But no one was capable of routing the packets in case of cross links. So, a protocol named GEOCROSS was proposed. The introduction of MANETs, VANETs, their routing protocols, protocol structure of VANET, how broadcasting takes place in VANET, how information is disseminated in VANET and VANET communication routing protocols like vehicular collision warning communication (VCWC), vehicle heading based routing protocol (VHRP) and GEOCROSS routing protocol will discussed in this paper.