Miracle-dz/UniFAR-FTUs huggingface dataset:
Summarization by Logic Segmentation and Text Entailment

As the phenomenon of information overload grows day by day, systems that can automatically summarize documents become in- creasingly studied and used. This paper presents some original methods for text summarization of a single source document by extraction. The methods are based on some of our own text segmentation algorithms. We denote them logical segmentations because for all these methods the score of a sentence is the number of sentences of the text which are entailed by it. The flrst method of segmentation and summarization is called Logical TextTiling (LTT): for a sequence of sentences, the scores form a structure which indicates how the most important sentences al- ternate with ones less important and organizes the text according to its logical content. The second method, Pure Entailment uses deflnition of the relation of entailment between two texts. The third original method applies Dynamic Programming and centering theory to the sentences logically scored as above. The obtained ranked logical segments are used in the summarization. Our methods of segmentation and summarization are applied and evaluated against a manually realized segmentation and summarization of the same text by Donald Richie, "The Koan", (9). The text is reproduced at (14).
Using SVMs for Classification of Cross-Document Relationships

Cross-document Structure Theory (CST) has recently been proposed to facilitate tasks related to multi-document analysis. Classifying and identifying the CST relationships between sentences across topically related documents have since been proven as necessary. However, there have not been sufficient studies presented in literature to automatically identify these CST relationships. In this study, a supervised machine learning technique, i.e. Support Vector Machines (SVMs), was applied to identify four types of CST relationships, namely “Identity”, “Overlap”, “Subsumption”, and “Description” on the datasets obtained from CSTBank corpus. The performance of the SVMs classification was measured using Precision, Recall and F-measure. In addition, the results obtained using SVMs were also compared with those from the previous literature using boosting classification algorithm. It was found that SVMs yielded better results in classifying the four CST relationships.
Sound and Music Computing: Challenges and Strategies

Abstract Based on the current context of the Sound and Music Computing (SMC) field, the state of the art in research and the open issues that have been identified and described in other articles of this journal issue, in this article we make a step forward and try to identify the broad SMC challenges and we propose strategies with which to tackle them. On the research side we identify a clear need for designing better sound objects and environments and for promoting research to understand, model, and improve human interaction with sound and music. In the education domain we feel the need for better training our multidisciplinary researchers and to make sure that they can contribute to the multicultural society we live in. There is also a clear need for improving the transferring of the knowledge and technologies generated by our community. Finally we claim that the SMC field should be very much concerned with its social context and that a number of current social concerns should be addressed. We accompany each of these challenges with strategies that should help researchers, educators and policy makers take specific actions to advance in the proposed SMC roadmap.
Attribute-Based Encryption Scheme considering Wearable Context

According to the development of the technology for Wearable and Big Data, we need to consider Attribute-Based Encryption to protect the confidentiality of personal information. In the real world, especially for wearable context with the medical information communicating, the diversified contexts need to be considered to apply the Attribute-Based Encryption. However, it is hard to design the optimized dynamic access structures because it is static access structures and properties of Attribute-Based Encryption. In this paper, we propose the Attribute-Based Encryption using the algorithm of context-based service inference model to collect the attributes by data and to provide appropriate services by recognizing the situation.
Approach to Big Data Applications on Cloud System

Consistency management within cloud storage systems is of high importance. The Consistency–Performance tradeoff is, arguably, the main tradeoff. Many static consistency solutions fail in reaching an efficient equilibrium between consistency and performance for dynamic cloud workloads. In this context, more opportunistic, adaptive consistency models are needed in order to meet the requirements of Big Data Applications (BDAs). However, most of the existing adaptive policies either lack automation or fail to apprehend and include the specific consistency requirements of the application outside its access pattern. Hereafter, in this paper, we tackle this specific issue of consistency management for dynamic workloads in the cloud. Accordingly, we provide an adaptive model that tunes the consistency level at runtime in order to provide consistency when needed and performance when possible.
Moving object detection from a mobile robot using basis image matching

In this paper, we propose an image processing scheme for moving object detection from a mobile robot with a single camera. It especially aims at intruder detection for the security robot on either smooth or uneven ground surfaces. The proposed scheme uses the template matching with basis image reconstruction for the alignment between two consecutive images in the video sequence. The most representative template patches in one image are first automatically selected based on the gradient energies in the patches. The chosen templates then form a basis image matrix. A windowed subimage is constructed by the linear combination of the basis images, and the instances of the templates in the subsequent image are matched by evaluating their reconstruction error from the basis image matrix. For two well aligned images, a simple and fast temporal difference can thus be applied to identify moving objects from the background. The proposed template matching can tolerate ±10° in rotation and ±10% in scaling. By adding templates with larger rotational angles in the basis image matrixes, the proposed method can match images from severe camera vibrations. The proposed scheme achieves a fast processing rate of 32 frames per second for images of size 160×120 pixels.
A robotic wheelchair

Many people who are mobility impaired are incapable, for a variety of reasons, of using an ordinary wheelchair. These people must rely on either a power wheelchair, which they control, or another person to push and guide them while they are in an ordinary or power wheelchair. Power wheelchairs can be difficult to operate. If a person has additional disabilities, either in perception or fine motor control of their hands, a power chair can be difficult or impossible for them to use safely. Having one person push and guide a person who is mobility impaired is very expensive, and if the disabled person is otherwise independent, very inefficient and frustrating. This paper describes a low-cost robotic addition to a power wheelchair that assists the rider of the chair in avoiding obstacles, going to pre-designated places, and maneuvering through doorways and other narrow or crowded areas. This system can be interfaced to a variety of input devices, and can give the operator as much or as little moment by moment control of the chair as they wish.
Autonomous Robot Localization Using WiFi Fingerprinting

We are using widely available 802.11 wireless networks to determine the location of autonomous robots. Before a robot can accomplish a simple task such as moving to a specific coordinate, it must accurately know its current location with-in a certain degree of accuracy. Humans often take their eye sight and spatial awareness for granted. For a robot, the computational difficulty of solving the same problem becomes apparent. Our implementation creates a database of wireless signal strengths of a given area and uses the current signal strength reading within the area to find a weighted signal space distance. The “closest” point in the database should also correlate with the current position of the robot. Given the robots correct location we can successfully navigate around any area with sufficient Wi-Fi coverage.
Simple online algorithm for detecting cow's ECG beat-to-beat interval using a microcontroller

This paper describes an online algorithm for detecting cow’s beat-to-beat interval on a small embedded microcontroller. The target device is an ECG implant which only provides limited calculation power and insufficient storage memory for long term complete ECG data logging. No common computationally efficient method for detecting the human R-wave was found successful for cattle ECG data with the used measurement configuration. Our algorithm detects a cow’s S-wave, which is the most distinguishable part of the QRS-complex. The offset and amplitude adaptive algorithm utilizes only arithmetic operations and logic conditions. Normal 0 21 false false false FI X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Normaali taulukko"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;}
Forecasting, Structural Time Series Models and the Kalman Filter

In this book, Andrew Harvey sets out to provide a unified and comprehensive theory of structural time series models. Unlike the traditional ARIMA models, structural time series models consist explicitly of unobserved components, such as trends and seasonals, which have a direct interpretation. As a result the model selection methodology associated with structural models is much closer to econometric methodology. The link with econometrics is made even closer by the natural way in which the models can be extended to include explanatory variables and to cope with multivariate time series. From the technical point of view, state space models and the Kalman filter play a key role in the statistical treatment of structural time series models. The book includes a detailed treatment of the Kalman filter. This technique was originally developed in control engineering, but is becoming increasingly important in fields such as economics and operations research. This book is concerned primarily with modelling economic and social time series, and with addressing the special problems which the treatment of such series poses. The properties of the models and the methodological techniques used to select them are illustrated with various applications. These range from the modellling of trends and cycles in US macroeconomic time series to to an evaluation of the effects of seat belt legislation in the UK.
How Does Exploration Impact IR Performance in Large Document Collections

A significant problem for IR researchers is how to efficiently handle large collections of electronic documents. Manual review is time consuming and expensive. Automated methods can be imprecise and fail to yield relevant documents in the retrieval set. This problem is receiving significant attention in two major domains: the legal community, with the increase in search of electronic documents in litigation (eDiscovery), and in the medical community, with the increase of mandates for ehealth systems such as electronic patient records (EMR and EHR) and health informatics. This paper examines how the construct of exploration may be implemented as a methodology to improve user performance when searching and sorting through large electronic document collections by facilitating context and content understanding through multiple iterations. The study reported in this paper examines the research questions of: How does exploration impact IR performance, and how can exploration be implemented to achieve improvement in IR results? The study examines the correlation between an individual user’s exploration of iterated sample selections from a large corpus of electronic documents and the individual’s IR performance. Our findings support that: (1) IR performance can be manipulated by using an exploration method, (2) Time spent exploring a collection is correlated with performance in both recall and precision, and (3) Number of documents viewed in a collection is correlated with performance in precision.
Algorithm Based on Double Counter Bloom Filter for Large Flows Identification: Algorithm Based on Double Counter Bloom Filter for Large Flows Identification

An algorithm based on double counter bloom filter for long flows identification (CCBF) is proposed in this paper. Double counter bloom filter structure is used to distinguish the process of the long flow filtration from the long flow existence. The false positive rate of the algorithm is analyzed. The relationship of the memory requirement and the error rate is analyzed through simulation. It is shown that with the same restriction of the memory resource, the average error of this algorithm is less than the existing similar algorithms. The analysis of the time performance shows this algorithm is capable of dealing with traffic up to 1 500kpps.The results reflect that this algorithm can be used to monitor the long flows on backbone network.
Synthesis of 1-Glycosyl Derivatives of Benzocamalexin.

The linear synthesis of 1-(β-D-glucopyranosyl)-, 1-(β-D-galactopyranosyl)-, 1-(β-D-mannopyranosyl)- and 1-(β-D-ribofuranosyl)benzocamalexin was elaborated from indoline as a starting compound and corresponding pentaacetylhexoses or 1- O -acetyl-2,3,5-tri- O -benzoyl-D-ribose as suitable glycosyl donors.
Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA

Landslide detection from extensive remote-sensing imagery is an important preliminary work for landslide mapping, landslide inventories, and landslide hazard assessment. Aimed at development of an automatic procedure for landslide detection, a new method for automatic landslide detection from remote-sensing imagery is presented in this study. We achieved this objective using a scene classification method based on the bag-of-visual-words (BoVW) representation in combination with the unsupervised probabilistic latent semantic analysis (pLSA) model and the k-nearest neighbour (k-NN) classifier. Given a remote-sensing image, we divided it into equal-sized square sub-images and then described each sub-image as a BoVW representation. The pLSA model was applied to sub-images by using the BoVW representation to discover the object classes depicted in the sub-images, and then a k-NN classifier was used to classify the sub-images into landslide areas and non-landslide areas based on object distribution. We investigated the performance and applicability of the method using remote-sensing imagery from the Ili area. The results show that the method is robust and can produce good performance without the acquisition of three-dimensional (3D) topography. We anticipate that these results will be helpful in landslide inventory mapping and landslide hazard assessment in landslide-stricken areas.
A novel local dimming algorithm for RGB-LED backlight

A large size RGB LED backlight unit and its local dimming algorithm are presented in this paper. The LED backlight unit and the LCD panel are controlled synchronously to enhance image contrast and improve power efficacy. Based on the image content, the backlight intensity is locally dimmed, and pixel values are compensated according to the backlight brightness distribution. In addition, a linear function algorithm typical for a backlight system is applied to remove the grid artifacts between LED blocks. Our newly developed 32" RGB LED backlight LCD-TV prototype shows that a static display contrast of over 20000:1 can be achieved. Compared with CCFL backlight LCD-TV, an average of about 30% of the power can be saved depending on the image content.
Using Genetic Algorithms in Integer Programming for Decision Support

Genetic Algorithms is a new developed quantitative method used in management decision support; it’s an Artificial intelligence technique that simulates scientific explanations in genetics and natural evolution for getting an optimal solution population. Thus this study aims to recognize Genetic Algorithms including: definition, properties, basic elements and steps. Then we try to employ it in practical study. A local Mill shapes a suitable case to use Genetic Algorithms in Integer Programming as one of its application forms. After using Evolver software, and making a comparison with Branch and Bound method, we have got results around this case. At last we have concluded the efficiency of Genetic Algorithms in Integer Programming, and its superiority concerning characteristics, it also deserves to be used in more complicated problems and when usual methods fail. DOI: 10.5901/ajis.2014. v3n6p11
Social Creativity: Making All Voices Heard

The power of the unaided individual mind is highly overrated. Much human creativity is social, arising from activities that take place in a social context in which interaction with other people and the artifacts that embody collective knowledge are essential contributors. Social creativity is not a luxury but a necessity to address the problems faced by societies in the 21 st century. Our research has focused specifically on complex design problems requiring the contributions of many stakeholders. These stakeholders come from many different backgrounds, requiring cultural and epistemological pluralism to make all voices heard. We have developed socio-technical environments supporting these objectives in the specific contexts of urban planning, collaborative learning, and collaborative software design.
Tangible Augmented Reality for Human Computer Interaction

近年,GUI(Graphical User Interface)がパーソナルコン ピュータの標準的インタフェースとして広く使用されて いる. 適切にデザインされたGUIは,ある程度経験を積 んだユーザには非常にわかりやすい. しかし,初心者に 対しては,まだ問題が残されている. 例えば,アイコン は,現実世界の対象物のメタファとして適切にデザイン されることで,ユーザに正しい操作を連想させることが 可能であるが,文化や年齢などの差異にも影響されない 万能なデザインを考案することは難しい. ユーザは,学 習によって,そのアイコンの意味を理解しなければなら ない. また,GUIアプリケーションに対するメンタルモ デルの構築には,日常的な道具に対するそれとは全く異 なり,外形から推定することが不可能である. この点も 初心者の学習の障害になっていると思われる. さらに, GUIアプリケーションによる作業は,ユーザの作業空間 をディスプレイ内に閉じこめる効果があり,実世界での 作業との間に大きなギャップを作る. 本来ならコン ピュータを使用せずとも容易にできる作業を,わざわざ 拡張現実感技術を用いたタンジブルインタフェース
Using Fault Injection to Analyze the Scope of Error Propagation in Linux

Operating systems (OSes) are crucial for achieving high availability of computer systems. Even if applications running on an operating system are highly available, a bug inside the kernel may result in a failure of the entire software stack. The objective of this study is to gain some insight into the development of the Linux kernel that is more resilient against software faults. In particular, this paper investigates the scope of error propagation. The propagation scope is process-local if the error is confined in the process context that activated it. The scope is kernel-global if the error propagates to other processes’ contexts or global data structures. The investigation of the scope of error propagation gives us some insight into 1) defensive coding style, 2) reboot-less rejuvenation, and 3) general recovery mechanisms of the Linux kernel. For example, if most errors are process-local, we can rejuvenate the kernel without reboots because the kernel can be recovered simply by killing faulty processes. To investigate the scope of error propagation, we conduct an experimental campaign of fault injection on Linux 2.6. 18, using a kernel-level fault injector widely used in the OS community. Our findings are (1) our target kernel (Linux 2.6.18) is coded defensively. This defensive coding style contributes to lower rates of error manifestation and kernel-global errors, (2) the scope of error propagation is mostly process-local in Linux, and (3) global propagation occurs with low probability. Even if an error corrupts a global data structure, other processes merely access to them.
An algorithm which learns multiple covers via integer linear programming. Part I: the CLILP2 algorithm

Presents an inductive machine learning algorithm called CLILP2 that learns multiple covers for a concept from positive and negative examples. Although inductive learning is an error‐prone process, multiple meaning interpretation of the examples is utilized by CLILP2 to compensate for the narrowness of induction. The algorithm is tested on data sets representing three different domains. Analyses the complexity of the algorithm and compares the results with those obtained by others. Employs measures of specificity, sensitivity, and predictive accuracy which are not usually used in presenting machine learning results, and shows that they evaluate better the “correctness” of the learned concepts. The study is published in two parts: I – the CLILP2 algorithm; II – experimental results and conclusions.
Involving the user Through Library 2.0

Libraries, as we know them today, can be defined by the term Library 1.0. This defines the way resources are kept on shelves or at a computer behind a login. These resources can be taken from a shelf, checked out by the library staff, taken home for a certain length of time and absorbed, and then returned to the library for someone else to avail of. Library 1.0 is a one-directional service that takes people to the information they require. Library 2.0 – or L2 as it is now more commonly addressed as – aims to take the information to the people by bringing the library service to the Internet and getting the users more involved by encouraging feedback participation. This paper presents an overview of Library 2.0 and introduces web 2.0 concepts.
Learning object detectors from online image search

Being able to detect distinguishable objects is a key component in many high level computer vision applications. Traditional methods for building such detectors require a large amount of carefully collected and cleaned data. For example to build a face detector, a large number of face images need to be collected and faces in each image need to be cropped and aligned as the data for training. This process is tedious and error-pruning. Recently more and more people are sharing their photos on the internet, if we could leverage these data for building a detector, it will save tremendous amount of effort in collecting training data. Popular internet search engines and community photo websites like Google image search, Picassa, Flickr make it possible to harvesting online images for image understanding tasks. In this paper, we develop a method leveraging images obtained from online image search to build an object detector. The proposed method can automatically identify the most distinguishable features across the downloaded images. Using these learned features, a detector can be built to detect the object in a new image. Experiments show promising results of our approach.