Miracle-dz/UniFAR-FTUs huggingface dataset:
Expanding Common Sense Knowledge Database using Data Mining on Reference Corpus [an abstract of dissertation and a summary of dissertation review]

In the information society of today it becomes increasingly difficult to have a creative and enjoyable conversation without a broad background knowledge. People like to discuss concepts and ideas, and this requires a mixture of commonsense and general knowledge spanning through a broad spectrum of topics. If we want machines to understand such conversations and interact with people using natural language, it is necessary to equip them with the ability to not only analyze linguistic features such as syntax, but also with means to process semantics, that is the knowledge of the underlying concepts represented by the surface text. The conceptual information is gathered in large-scale general knowledge bases such as Cyc, YAGO and ConceptNet. In this thesis I focus on ConceptNet, a knowledge representation project that provides a large semantic graph describing general human knowledge. I have chosen ConceptNet as it captures a wide range of common sense concepts and relations, and its simple semantic network structure makes it easy to use and manipulate. ConceptNet’s basic data unit is an assertion, that is two concepts connected with a relation. ConceptNet was designed to contain knowledge collected by the Open Mind Common Sense project’s website, as well as knowledge from similar websites and online word games which automatically collect general knowledge in several languages. This open-source knowledge base is used for many applications such as topic-gisting, affect-sensing, dialog systems, daily activities recognition, social media analysis and handwriting recognition. Manual expansion of the knowledge base would be a long and labor-intensive process. For example, nadya.jp, an online project that aims to gather knowledge by using a game with a purpose, since its launch in 2010 has been able to introduce a little over 43,500 entries to ConceptNet. It is therefore evident that we need to develop automatic methods to gather new data. Creating such method and confirming its effectiveness is the goal of my research. This thesis presents a method for extracting IsA assertions (hyponymy relations), AtLocation
Inference of Shared Risk Link Groups

The Shared Risk Link Group (SRLG) concept introduced in [IPO-Frame] is considered as one of the most important criteria concerning the constrained-based path computation of optical channel routes. By applying the SRLG criteria to the constrained-based path computation, one can select a route taking into account physical resource and logical structure disjointness that implies a lower probability of simultaneous lightpath failure. This contribution describes the various physical and logical resource types considered in the SRLG concept. The proposed model focuses on the inference of SRLG information between the network physical layers as well as logical structures such as geographical locations. The main applications of the proposed model are the related Constraint-based Shortest Path First (CSPF) algorithm for optical channel path computation and the reduction of the SRLG information flooded through traffic engineering extensions of the IGP routing protocols (such as OSPF and IS-IS). Notice: This contribution has been created to assist the Optical Internetworking Forum (OIF). This document is offered to the OIF solely as a basis for discussion and is not a binding proposal on the companies listed as resources above. Each company in the source list, and the OIF, reserves the rights to at any time to add, amend, or withdraw statements contained herein.
Impact of Climate Change on Irrigation Need and Groundwater Resources in Pinios Basin

Initially an area-differentiated modelling of groundwater recharge in River Pinios Basin (Greece) was carried out for the reference period 1971–2000 based on the mGROWA model. Subsequently, the model was applied to assess the impacts of climate change on groundwater recharge and irrigation need. For this purpose one bias-corrected RCM–GCM combination from the EURO-CORDEX ensemble of climate models for two emission scenarios (RCP 4.5 and RCP 8.5) have been used as input data for the projected periods 2011–2040 and 2040–2070 and 2700–2100. Results of the mGROWA model runs for the projected periods and the two emission scenarios indicate a different evolution of groundwater recharge and a general increase in irrigation need, however with different degrees of intensity.
Kernel methods for relation extraction

We present an application of kernel methods to extracting relations from unstructured natural language sources. We introduce kernels defined over shallow parse representations of text, and design efficient algorithms for computing the kernels. We use the devised kernels in conjunction with Support Vector Machine and Voted Perceptron learning algorithms for the task of extracting person-affiliation and organization-location relations from text. We experimentally evaluate the proposed methods and compare them with feature-based learning algorithms, with promising results.
THE CORRELATION BETWEEN STUDENTS’ SELF- ESTEEM AND THEIR WRITING ABILITY AT THE SECOND GRADE OF SMA N 1 PRINGSEWU

This study aimed at investigating the correlation between students’ self-esteem and their writing ability. The design was ex-post facto design. The subject of this research were 28 students of class XI IPS 4 of SMAN 1 Pringsewu. Self-esteem questionnaire and writing test were used to collect the data. The scores of the two tests were analyzed by using Pearson Product Moment Correlation. The result showed that there was a statistically significant correlation between students’ self-esteem and their writing ability with the significant level 0.01. It indicates that students’ self-esteem influences EFL students’ learning process and outcomes.
Hand Gesture Recognition Using Open CV

— In the present fast-moving and tech-savvy world, virtual environments and virtual elements which can fraternize with real-world objects have become common. Robots or machines that are palpable can be handled and supervised through mere gestures using advanced technology. Most gesture recognition algorithms focus on bodily gestures originating from the face or hands (although research is being done in gait recognition as well). Not only are the face and hands easier body parts to detect (imagine trying to create an object detector to detect the presence of a torso, for example), but also they can easily convey a huge amount of information with only simple gestures. The human face can convey various emotions such as happiness, pain, anguish, and elation, while the hands can quickly convey more complex concepts using sign language. The overall goal of hand gesture recognition is to bridge the gap between our human body language and computer interpretation, allowing us to interact with our computers more richly. Perhaps in the future, we will no longer need keyboards and mice as input! However, we are far from this level of gesture recognition, and much more research needs to be performed. As the name suggests, hand gesture recognition involves the movement of hands and fingers. The computer then applies mathematical models to interpret these gestures and interact with the human. In this project motion detection and background, subtractors are applied as a precursor to gesture recognition. 'Malima et al.' method is used to build a computer vision system that adds two numbers together based on hand gestures.
Multi-receiver Certificate-Based Encryption and Application to Public Key Broadcast Encryption

In this paper we firstly introduce the notion of multi- receiver certificate-based encryption that avoids the inherent key escrow problem while preserving the implicit certification of multi-receiver identity-based encryption. We also construct a highly efficient multi-receiver certificate-based encryption scheme which eliminates pairing computation to encrypt a message for multiple receivers. Moreover, the proposed scheme only needs one pairing computation for decrypting the ciphertext. We compare our scheme with the most efficient multi-receiver identity-based encryption scheme[2] in terms of the computational point of view, and show that our scheme provides better efficiency than [2]. Finally, we discuss how to properly transform our scheme into a new public key broadcast encryption scheme based on subset-cover framework, which enjoys the advantages of certificate-based encryption.
Institute for Empirical Pedagogy and Pedagogical Psychology Fostering Collaborative Knowledge Construction in Desktop Videoconferencing. Effects of Content Schemes and Cooperation Scripts in Peer Teaching Settings Fostering Collaborative Knowledge Construction in Desktop Video-conferencing. Effects 

Video-conferencing is expected to become increasingly important for tele-learning environments. In contrast to asynchronous, text-based computer-mediated communication , video-conferencing facilitates cooperation tasks that require highly frequent and continuous coordination. Typical kinds of such cooperation tasks are found in peer teaching settings. Despite the growing application of video-conferencing, only little is known about possibilities of enhancing collaboration in video-conferencing settings. This study investigates the effects of different types of support for cooperation on the learning outcomes of peer dyads in a video-conferencing scenario. The main research question is how cooperation scripts and content schemes enhance the students' cognitive activities and foster the outcomes of cooperative learning. Two factors were varied experimentally: The content scheme (with/without) and the cooperation script (with/without). 86 university students of educational psychology participated in the study. Each student of a dyad received a text dealing with a psychological theory in the field of the nature-nurture-debate. The students' tasks were (1) to teach their partners the relevant contents of their text and (2) to reflect ideas that went beyond the scope of the text. Results indicate that in particular the cooperation script enhances learning outcomes of collaborative knowledge construction.
Effects of precipitation and temperature on net primary productivity and precipitation use efficiency across China’s grasslands

Net primary production (NPP) is a crucial feature of ecosystem function and structure. Furthermore, precipitation use efficiency (PUE) is a critical indicator for exploring NPP in grassland ecosystem responses to variations in precipitation and temperature. In this study, we examined the spatial patterns of NPP and PUE in China’s grasslands from 2000 to 2010 and explored the effects of environmental factors on NPP and PUE at different scales. The results showed first that the spatial distribution of NPP and PUE decreased from the northeast to southwest. NPP increased in most places across China’s grasslands; however, there was no obvious change in PUE during 2000–2010. Second, in most regions across China’s grasslands, positive and negative correlations existed between precipitation and NPP and PUE, respectively. The spatial distribution of the relationship between temperature and NPP and PUE was consistent with the relationship between precipitation with NPP and PUE. Finally, for the gradients of the various environmental factors in different regional grassland ecosystems, on the Tibetan Plateau, there were unimodal correlations between precipitation with NPP and PUE and aridity with NPP and PUE, but a positive linear correlation existed for temperature with NPP and PUE. However, in the Inner Mongolian Plateau, there were positive correlations between precipitation and NPP, temperature and NPP, and aridity and both NPP and PUE and negative correlations between precipitations and PUE and temperature and PUE. Our findings improve understanding of grassland ecosystem responses to global climate change and provide a basis for the protection of grassland ecosystems in arid and semi-arid regions.
INVESTIGATING RATIONALITY, IN CONCEPT AND IN USE, IN A MODEL FOR IT GOVERNANCE

In this study we investigate the deployment of a model for IT governance named pm in a large healthcare organisation. We are especially interested in if and how the basic goals, the inherent rationality, of the pm model-in-concept change in translation during the model deployment process. The concept of model rationality is suggested as a beneficial approach for analysing differences between pm3-in-concept and pm-in-use. We have identified three main contributions with the suggested approach that are of benefit for both research and practice: 1) We can define and express the rationality in the pm3 model for IT and governance in a clear manner, 2) we have used the pm3 rationality as an evaluation tool that has proven to be useful in practice, and 3) we have also identified the main problems in deploying pm in the investigated healthcare organisation.
Proceedings from the 2016 NSF–Sloan Workshop on Practical Privacy

On October 14, 2016, we hosted a workshop that brought together economists, survey statisticians, and computer scientists with expertise in the field of privacy preserving methods: Census Bureau staff working on implementing cutting-edge methods in the Bureau’s flagship public-use products mingled with academic researchers from a variety of universities. The four products discussed as part of the workshop were 1. the American Community Survey (ACS); 2. Longitudinal Employer-Household Data (LEHD), in particular the LEHD Origin-Destination Employment Statistics (LODES); the 3. 2020 Decennial Census; and the 4. 2017 Economic Census. The goal of the workshop was to 1. Discuss the specific challenges that have arisen in ongoing efforts to apply formal privacy models to Census data products by drawing together expertise of academic and governmental researchers 2. Produce short written memos that summarize concrete suggestions for practical applications to specific Census Bureau priority areas.
On the Word Boundaries of Emergent Languages Based on Harris’s Articulation Scheme

The purpose of this paper is to investigate whether Harris’s articulation scheme 1 (HAS) also holds in emergent languages. HAS is thought to be a universal property 2 in natural languages that articulatory boundaries can be obtained from statistical 3 information of phonems alone, without referring to word meanings. Emergent 4 languages are artificial communication protocols that arise between agents in a 5 simulated environment and have been attracting attention in recent years. It is 6 considerd important to study the structure of emergent languages and the simi-7 larity to natural languages. In this paper, we employ HAS as an unsupervised 8 word segmentation method and verify whether emergent languages arising from 9 signaling games have meaningful segments. Our experiments showed that the 10 emergent languages arising from signaling games satisfy some preconditions for 11 HAS. However, it was also suggested that the HAS-based segmentation boundaries 12 are not necessarily semantically valid.
Purdue e-Pubs Purdue e-Pubs

Path sensitivity improves the quality of static analysis by avoiding approximative merging of dataﬂow facts collected along distinct program paths. Because full path sensitivity has prohibitive cost, it is worthwhile to consider hybrid approaches that provide path sensitivity on selected subsets of paths. In this paper, we consider such a technique based on an edge string , a compact abstraction of a set of static program paths. The edge string es = [ e 1 , e 2 , . , e k ] , where each e i is an edge label found in a program’s control-ﬂow graph, is used to disambiguate dataﬂow facts that manifest only on paths in which es occurs as a subsequence. The length of es dictates the tradeoff between precision and analysis cost. Loosely speaking, edge strings are a path-sensitive analog to the notion of call-strings exploited by context-sensitive analyses . We present a formalization of edge strings and discuss optimizations that incorporate additional relevance measures, based on the structure of the control-ﬂow graph, to avoid exploring edge-string paths if no added precision accrues. We also provide a detailed implementation study in the context of the functional SSA intermediate representation used by MLton, a whole-program optimizing compiler for Standard ML. Our results indicate that small edge strings provide the necessary precision to identify infeasible paths for functional programs that leverage complex control and dataﬂow.
TR 04-006 A GA-based Approach for scheduling Decomposable Data Grid Applications

Data Grid technology promises geographically distributed scientists to access and share physically distributed resources such as compute resource, networks, storage, and most importantly data collections for large-scale data intensive problems. The massive size and distributed nature of these datasets poses challenges to data intensive applications. Scheduling Data Grid applications must consider communication and computation simultaneously to achieve high performance. In many Data Grid applications, Data can be decomposed into multiple independent sub datasets and distributed for parallel execution and analysis. In this paper, we exploit this property and propose a novel Genetic Algorithm based approach that automatically decomposes data onto communication and computation resources. The proposed GA-based scheduler takes advantage of the parallelism of decomposable Data Grid applications to achieve the desired performance level. We evaluate the proposed approach comparing with other algorithms. Simulation results show that the proposed GA-based approach can be a competitive choice for scheduling large Data Grid applications in terms of both scheduling overhead and the quality of solutions as compared to other algorithms.
Capturing User Intent for Information Retrieval

Gathering information and making decisions based on retrieved information are the important tasks that every intelligence analyst is doing. User modeling techniques have been exploited to help analysts to search for information effectively. To justify the effects of any user modeling technique on helping analysts retrieve quality documents relevant to their tasks, we need to have a comprehensive evaluation method, which assesses the improvement of retrieval performance and user performance. In this paper, we describe our evaluation of a cognitive user model for information retrieval with regards to retrieval performance. Our user model captures user intent dynamically by analyzing behavioral information from retrieved relevant documents for improving the retrieval performance and user's performance. In this evaluation, we assess the user model's short-term effects on a single query, and the user model's long-term effects on the whole search session. We compare our approach with the best traditional approach for relevance feedback in information retrieval, the Ide dec-hi, which is the approach of modifying queries using term frequency from relevant/non-relevant documents. We use the oldest collection of information retrieval on aerodynamics called CRANFIELD. The results of this evaluation show that by exploring user intent, we achieve competitive performance in the feedback run compared to Ide dec-hi. At the same time, our user model approach offers the advantages of retrieving more quality documents at the initial run compared to the term frequency inverted document frequency (TFIDF) approach. Our results have shown that our user modeling approach can be used to improve efficiency, learnability and interactivity of an information retrieval system.
Impact of the North Pacific subtropical sea surface temperature front on El Niño‐Southern Oscillation

The North Pacific subtropical front (NPSTF) of sea surface temperature (SST) is an important subtropical feature in the North Pacific. In this study, we reveal that the inter‐annual variability of the spring NPSTF has a robust correlation with the El Niño‐Southern Oscillation (ENSO) in the following winter over the period of 1961–2016. When there is a strong spring NPSTF, anomalous cyclonic and anticyclonic circulations in the low troposphere occur to its north and south, respectively. The northeasterly wind anomaly associated with the anticyclonic circulation increases the climatological northeasterly wind over the subtropical Northeast Pacific, resulting in SST decrease via the wind–evaporation–SST feedback. The SST and wind perturbations propagate southwards from the subtropical to the eastern central equatorial Pacific in summer and grow via the Bjerknes feedback from summer to winter, eventually leading to a La Niña event in winter.
An On-Line PSO-Based Fuzzy Logic Tuning Approach: Microgrid Frequency Control Case Study

Modern power systems require increased intelligenc e and flexibility in the control and optimization to ensure the capability of maintaining a generation-load balance, following serious disturbances. This issue is becoming more si gn ficant today due to the increasing number of Microgrids (MGs). The MGs mostly use renewable energies in electrical pow er production that are varying naturally. These changes and usual uncertainties in power systems cause the classic co ntrollers to be unable to provide a proper performance over a wide range of operating conditions. In response to this challenge , the present paper addresses a new online intelligent approach b y using a combination of the fuzzy logic and the particle swa rm optimization (PSO) techniques for optimal tuning of the most popular existing proportional-integral (PI) based frequency controllers in the ac MG systems. The control design methodology is examined on an ac MG case study. The performance of the proposed intelligent control syn thesis is compared with the pure fuzzy PI and the Ziegler–Nichols PI control design methods.
A Technique for Presenting a Deceptive Dynamic Network Topology

Abstract : Adversaries scan Department of Defense networks looking for vulnerabilities that allow surveillance or the embedding of destructive malware weapons. In cyberspace, adversaries either actively probe or passively observe defended computer networks in attempts to determine, among other attributes, the topology of the network. We develop a novel strategic deceptive methodology, based on principles of military deception, for deceiving a malicious traceroute probe in defense of a physical data communications network. We construct a proof-of-concept network to show that a remote adversary who uses traceroute to map the defended network's topology can be presented with a false route of the defender's choosing. Akin to military deception operations in the field and at sea, a network that employs a deception scheme implemented on an intelligent border router can present a deceptive topology to an adversary. Our experiments show that a defender using our technique can successfully deceive a traceroute probe, the first in a sequence of steps to mount a credible deception scheme against an adversary.
On the channel usability of Wireless Mesh Networks: when stability plays with you

In this work, we are striving for a perfect balance on utilisation between reliable channels and unreliable channels, where the network throughput can be maximised. We propose 'p-stable', a distributed Multi-phase Maximum Weighted Matching algorithm. We also apply channel bundles to effectively improve stability in the network, and prove the problem NP-hard. The time complexity is O(Δ³ + (log. *n)²), and overhead complexity is O(Δ × (Δ + n) + n α + log n), with Δ denoting maximum number of node degree in an n nodes network. Simulation results show that p-stable design effectively improves network stability, especially upon the existence of large number of unreliable channels.