Miracle-dz/UniFAR-FTUs huggingface dataset:
Abstractive Thai Opinion Summarization

With the advancement in the Internet technology, customers can easily share opinions on services and products in forms of reviews. There can be large amounts of reviews for popular products. Manually summarizing those reviews for important issues is a daunting task. Automatic opinion summarization is a solution to the problem. The task is more complicated for reviews written in Thai language. Thai words are written continuously without space, there is no symbol to signify the end of a sentence, and many reviews are written informally, thus accurate word identification and linguistic annotation cannot be relied upon. This research proposes a novel technique to generate abstractive summaries of customer reviews written in Thai language. The proposed technique, which consists of the local and the global models, is evaluated using actual reviews of fifty randomly selected products from a popular cosmetic website. The results show that the local model outperforms the other model and the two baseline methods both quantitatively and qualitatively.
Improving High Speed Railway Measured Channel Parameters Centered at 2.6 GHz with the De-noise Application Based on Wavelet Transform ⋆

In a wireless communication system for the high speed railway, system design engineers must have a thorough understanding of the radio-propagation characteristics, which is the essential aspect of wireless network designing and optimization. The channel impulse response can be generated by using slippage correlation measurement. Generally, the influence of the additive white Gaussian noise and the imperfect autocorrelation function is neglected, which will reduce the accuracy of the channel impulse response and the validity of the channel model. This paper proposes on a method which can eliminate the influence of the additive white Gaussian noise and the imperfect autocorrelation function through discrete wavelet transform. This method can provide a reliable guarantee to extract more accurate channel parameters, such as power delay profile, excess delay, and amplitude gain. And the results of simulation turn out that the de-noising method is effective.
The modified star graph and the petal diagram: two new visual aids for discrete alternative multicriteria decision making

Many studies have looked into the provisions of visual aids to multicriteria decision making. However, most of them have separated the display of alternative profiles and criteria weight information into two displays. This makes the analysis of the relationship between the criteria and alternatives and the effect of changing the criteria weights on the decision difficult. In this study, displays that can incorporate the display of both alternative profiles and criteria weight information for discrete alternative multicriteria decision-making problems are examined. The simple additive model is the multicriteria analysis method used. The result is two new visual aids for representing data of multicriteria decision-making problems, the modified star graph and the petal diagram. This paper discusses the two displays and compares their strengths and weaknesses. The results of a preliminary test conducted to evaluate the effectiveness of the displays are also included. © 1998 John Wiley & Sons, Ltd.
Research of AdaBoost Face Detection and OpenCV Implementation

We introduce the principle of AdaBoost face detection algorithm based on Haar-like feature in detail, and use OpenCV face detection module to realize AdaBoost algorithm on CMU face library. Experimental results show that the algorithm receives good effects. Meanwhile we analyze the relationship between the number of simple classifier and face detection result.
HUMAN AND MACHINE LOGIC

possibility of comparing the RPR of the analogue with that of the theory in formation, since I fail to understand what the RPR of such a theory would be. These basically negative comments should not be construed to mean that Swanson's notion of an RPR is necessarily ill taken. I think it a valuable tool in our attempt to understand the way in which metaphors work, and the degree to which they are successful. But its application to scientific discourse is vitiated, I think, by Swanson's failure to dwell at greater length on the process of theory formation, that is, by his attempt to apply to the language of science in general, and of physics in particular, a pre-conceived theoretical notion without first examining how physics may differ from ordinary language in this respect. To conclude. My point, essentially, in this: that Swanson has yet to show, by means of an appropriate example, how this theory of the metaphorical use of analogues applies to scientific discourse, and how the comparative study of RPRs can be performed on a theory that is in formation. G. L. FARRE Washington D.C.
Professional self-efficacy and role perception of school librarians and their impact on the development of students' information literacy: an evidence-based study

The current study evaluated the degree to which school librarians are involved in two different dimensions of their work that directly relate to developing information literacy (IL): (1) providing students with basic and advanced reference work services (RWS); and (2) assisting students at different stages of their research process assignments, as defined by the Big6 model. In addition, to examine factors that may affect the degree of involvement in these two dimensions, the professional self-efficacy of the librarians and their perceptions of their role within the school community were assessed. Data was collected from 71 Israeli school librarians through online questionnaires and analysed quantitatively. This analysis reveals that school librarians provide primarily basic RWS, which require a low degree of professional and technological skills and little collaboration with school teachers. Similarly, school librarians are mainly involved with two specific stages of the students’ research processes, namely, the seeking and evaluation of information, which again reflect a low degree of IL training. The degree of involvement of librarians in these dimensions of their work is significantly and positively correlated with (a) the perception of the librarians of their own self-learning ability and professional updating level; and (b) their perceptions of their role as co-teachers or leaders within the school community. These findings should increase the awareness of the IL community to the work of school librarians, to their role in shaping information-related processes in young students, and to possible means of achieving these goals.
Immune allied genetic algorithm for Bayesian network structure learning

Bayesian network (BN) structure learning is a NP-hard problem. In this paper, we present an improved approach to enhance efficiency of BN structure learning. To avoid premature convergence in traditional single-group genetic algorithm (GA), we propose an immune allied genetic algorithm (IAGA) in which the multiple-population and allied strategy are introduced. Moreover, in the algorithm, we apply prior knowledge by injecting immune operator to individuals which can effectively prevent degeneration. To illustrate the effectiveness of the proposed technique, we present some experimental results.
Low-Energy Cluster Head Selection for Clustering Communication Protocols in Wireless Sensor Network

Abstract One of the most important issues in wireless sensor network is the energy-efficient clustering communication protocols. In the communication protocols, if a large number of cluster heads is selected, the energy consumption, which is generated by the communication between the cluster-heads and the base stations, will be increased largely. However, if we select a small number of cluster heads, the energy consumption, which is generated by the data aggregation and the communication between non-cluster heads and cluster heads, will be increased. To reduce the energy consumption of the cluster heads for clustering communication protocols in wireless sensor network, we propose a cluster-head selection algorithm to generate properly the number of cluster heads in this paper. The experimental results show that the lifetime of sensor network is more efficient to be prolonged by the proposed protocol than the existing methods.
With a Little Help from My Friends

Abstract This article highlights a project on information literacy that began in the fall semester of 2003 at the University of Redlands. The faculty in the School of Education needed an information literacy component for their teacher credential program. The Manager of Instructional Technology believed that a tutorial of this type would work well in the Blackboard Course Management System, and the Off-Campus Services Librarian, who is the library liaison for the School of Education, was a natural choice as the person to present the tutorial to the students. The beginning information literacy workshop was developed in the fall of 2003, and the advanced workshop was developed in the spring of 2004. This article provides an overview of the content of the tutorial and workshops, as well as some conclusions about the project in general.
A Methodology Based on Business Intelligence for the Development of Predictive Applications in Self-Adapting Environments

Based on different methodologies, we have developed a new methodology that synthesizes, accelerates and optimizes the application of Adaptive Business Intelligence (ABI) in medicine, genomics, pharmaceuticals and other related sciences. This methodology -in its final phase- culminates in a stage called "Predictive Software Development", where the model obtained may be used to generate a prediction oriented application that can be implemented in different programming languages. The work presents the methodology and also the steps followed in the development of a predictive application for self-adapting environments for a particular case study of hypothyroidism.
A Dual-Axis Electrostatically Driven MEMS Microgripper

This paper presents the design of a new monolithic two-axis electrostatically actuated MEMS microgripper with integrated capacitive position and force sensors working at the micro-scale level. Each of the two jaws of the microgripper possesses two degrees-of-freedom (DOF) and is capable of positioning in both x-and y-axes. Unlike existing works, where one gripper arm is actuated and other one is sensed, both arms of the proposed microgripper are actuated and sensed independently. A sensing scheme is constructed to provide the position and force signals in the noncontact and contact phases, respectively. By applying a 120V driving voltage, the jaw can provide 70 μm x-axis and 18 μm y-axis displacements with the force of 190 μN. By this design, the real-time position and grasping force information can be obtained in the dual sensing mode. Both analytical calculation and finite-element analysis (FEA) were performed to verify the performance of the proposed design. A scaled-up prototype is designed, fabricated and tested through the experiment to verify the structure design of the microgripper.
6D SLAM with GPGPU computation

The main goal was to improve a state of the art 6D SLAM algorithm with a new GPGPU-based implementation of da- ta registration module. Data registration is based on ICP (Iterative Closest Point) algorithm that is fully implemented in the GPU with NVIDIA FERMI architecture. In our research we focus on mobile robot inspection intervention systems applicable in hazardous envi- ronments. The goal is to deliver a complete system capable of being used in real life. In this paper we demonstrate our achievements in the field of on line robot localization and mapping. We demon- strated an experiment in real large environment. We compared two strategies of data alignment - simple ICP and ICP using so called meta scan. Abstract: The main goal was to improve a state of the art 6D SLAM algorithm with a new GPGPU-based implementation of da- ta registration module. Data registration is based on ICP (Iterative Closest Point) algorithm that is fully implemented in the GPU with NVIDIA FERMI architecture. In our research we focus on mobile robot inspection intervention systems applicable in hazardous envi- ronments. The goal is to deliver a complete system capable of being used in real life. In this paper we demonstrate our achievements in the field of on line robot localization and mapping. We demon- strated an experiment in real large environment. We compared two strategies of data alignment - simple ICP and ICP using so called meta scan.
Detecting misinformation and knowledge conflicts in relational data

Information fusion is required for many mission-critical intelligence analysis tasks. Using knowledge extracted from various sources, including entities, relations, and events, intelligence analysts respond to commander’s information requests, integrate facts into summaries about current situations, augment existing knowledge with inferred information, make predictions about the future, and develop action plans. However, information fusion solutions often fail because of conflicting and redundant knowledge contained in multiple sources. Most knowledge conflicts in the past were due to translation errors and reporter bias, and thus could be managed. Current and future intelligence analysis, especially in denied areas, must deal with open source data processing, where there is much greater presence of intentional misinformation. In this paper, we describe a model for detecting conflicts in multi-source textual knowledge. Our model is based on constructing semantic graphs representing patterns of multi-source knowledge conflicts and anomalies, and detecting these conflicts by matching pattern graphs against the data graph constructed using soft co-reference between entities and events in multiple sources. The conflict detection process maintains the uncertainty throughout all phases, providing full traceability and enabling incremental updates of the detection results as new knowledge or modification to previously analyzed information are obtained. Detected conflicts are presented to analysts for further investigation. In the experimental study with SYNCOIN dataset, our algorithms achieved perfect conflict detection in ideal situation (no missing data) while producing 82% recall and 90% precision in realistic noise situation (15% of missing attributes).
Circuit techniques for a VLSI memory

This paper describes circuit techniques necessary for dynamic RAMs with high-packing density to implement submicron device technology. An on-chip error checking and correcting technique using bidirectional parity checking is proposed to reduce the soft error rate. In a sense-refresh amplifier, capacitor-coupled presenting is introduced to compensate for threshold imbalance. An on-chip supply voltage conversion is described as a solution for a hot carrier-injection problem. A 256K CMOS dynamic RAM has been designed and fabricated as a test vehicle for these techniques.
One-Hop Path Routing Based on Heuristic-K Method Using ACO

Overlay networks have emerged as a promising paradigm for providing customizable and reliable services at the application layer, such as fault-resilient routing, multicast, and content delivery. Among the overlay network architecture, overlay routing is an important aspect of the overlay network design. In this paper, we develop a one-hop source routing, called heuristic-K algorithm using Ant Colony Optimization, to allow individual nodes to optimize route selection based on specific metrics like delay, loss rate, or throughput. Moreover, due to the selfish operating manner of overlay routing, we also take the traffic engineering element into consideration in the design process of our proposal. The experimental results demonstrate the effectiveness of the routing algorithm.
N-(2-Nitrophenylsulfonyl)glycine methyl ester

The structure of the title compound, C9H10N2O6S, has been determined as part of an ongoing investigation into the preparation of N-allyl-substituted amino acids suitable for alkene cross-metathesis reactions in the generation of combinatorial libraries. The conformational structure is determined by intra- and intermolecular N—H⋯O and C—H⋯O hydrogen-bonding interactions.
Dynamic Decomposition Strategies in the Conceptual Modelling of Design Objects

The representation of design objects in a conceptual schema is essential for any computational process model of design With the developments in artificial intelligence and in knowledge-based design have come many conceptual modelling schemas for representing design objects However, when generalized domain models are constructed using any of these schemas, these models make assumptions regarding a particular view of the design object In an environment where many disciplines are involved in the design of an object, each discipline will have its own view of the object All these views must be respected While all of the schemas address the issues of generalization-specialization and aggregation-decomposition relationships, these are not the only important issues that need be addressed for a competent and efficient conceptual modelling schema. This paper addresses some of these other important issues, namely multiple and dynamic views of decomposition, the recognition and referencing of features, and the problems of common parts and parts of elements
Polymer-Supported (Salen)Mn Catalysts for Asymmetric Epoxidation: A Comparison between Soluble and Insoluble Matrices

This paper describes the synthesis of both soluble and insoluble polymer-supported chiral (salen)Mn complexes and their use in asymmetric epoxidation reactions. These studies were undertaken to establish whether high enantioselectivities could be achieved with a polymer-bound catalyst and if the complexes could be recovered and reused for multiple cycles. Poly(ethylene glycol) monomethyl ether (MeO-PEG) and non-cross-linked polystyrene (NCPS) were used as soluble supports while JandaJel and Merrifield resins served as insoluble supports. Each polymer was linked to the salen catalyst through a glutarate spacer. The soluble catalysts were recovered by precipitation with a suitable solvent while the insoluble catalysts were simply filtered from the reaction mixture. Three olefins were utilized as epoxidation substrates: styrene, cis-β-methylstyrene, and dihydronaphthalene. Best results were obtained with cis-β-methylstyrene as the enantioselectivity obtained with each polymer-bound catalyst (86−90%) was equ.
Varying alpha: New constraints from seasonal variations

We analyze the constraints obtained from new atomic clock data on the possible time variation of the fine structure ``constant'' and the electron-proton mass ratio, and show how they are strengthened when the seasonal variation of the Sun's gravitational field at the Earth's surface is taken into account. We compare these bounds with those obtainable from tests of the weak equivalence principle and high redshift observations of quasar absorption spectra.
Polar organic marker compounds in atmospheric aerosols during the LBA-SMOCC 2002 biomass burning experiment in Rondônia, Brazil: sources and source processes, time series, diel variations and size distributions

Abstract. Measurements of polar organic marker compounds were performed on aerosols that were collected at a pasture site in the Amazon basin (Rondonia, Brazil) using a high-volume dichotomous sampler (HVDS) and a Micro-Orifice Uniform Deposit Impactor (MOUDI) within the framework of the 2002 LBA-SMOCC (Large-Scale Biosphere Atmosphere Experiment in Amazonia – Smoke Aerosols, Clouds, Rainfall, and Climate: Aerosols From Biomass Burning Perturb Global and Regional Climate) campaign. The campaign spanned the late dry season (biomass burning), a transition period, and the onset of the wet season (clean conditions). In the present study a more detailed discussion is presented compared to previous reports on the behavior of selected polar marker compounds, including levoglucosan, malic acid, isoprene secondary organic aerosol (SOA) tracers and tracers for fungal spores. The tracer data are discussed taking into account new insights that recently became available into their stability and/or aerosol formation processes. During all three periods, levoglucosan was the most dominant identified organic species in the PM2.5 size fraction of the HVDS samples. In the dry period levoglucosan reached concentrations of up to 7.5 μg m−3 and exhibited diel variations with a nighttime prevalence. It was closely associated with the PM mass in the size-segregated samples and was mainly present in the fine mode, except during the wet period where it peaked in the coarse mode. Isoprene SOA tracers showed an average concentration of 250 ng m−3 during the dry period versus 157 ng m−3 during the transition period and 52 ng m−3 during the wet period. Malic acid and the 2-methyltetrols exhibited a different size distribution pattern, which is consistent with different aerosol formation processes (i.e., gas-to-particle partitioning in the case of malic acid and heterogeneous formation from gas-phase precursors in the case of the 2-methyltetrols). The 2-methyltetrols were mainly associated with the fine mode during all periods, while malic acid was prevalent in the fine mode only during the dry and transition periods, and dominant in the coarse mode during the wet period. The sum of the fungal spore tracers arabitol, mannitol, and erythritol in the PM2.5 fraction of the HVDS samples during the dry, transition, and wet periods was, on average, 54 ng m−3, 34 ng m−3, and 27 ng m−3, respectively, and revealed minor day/night variation. The mass size distributions of arabitol and mannitol during all periods showed similar patterns and an association with the coarse mode, consistent with their primary origin. The results show that even under the heavy smoke conditions of the dry period a natural background with contributions from bioaerosols and isoprene SOA can be revealed. The enhancement in isoprene SOA in the dry season is mainly attributed to an increased acidity of the aerosols, increased NOx concentrations and a decreased wet deposition.
Research on the Algorithm of Date Mining Based on Cluster Analysis Algorithm of K-means Based on the Pheromone

Cluster analysis becoming increasingly essential in data mining field, and is mainly used to discover the valuable data distribution and data mode in the potential datum. Based on the pheromone studies on basic clustering model, the theory of information entropy and two classical clustering analysis algorithms, an algorithm of K-means based on the pheromone is presented firstly. The algorithm works with the transformation probability to realize the clustering. It has reduced the number of the parameters and improved the speed of clustering. Keywords-ant colony; data mining; K-means algorithm