Miracle-dz/UniFAR-FTUs huggingface dataset:
Origin of Dislocated Limestone Blocks on the Slope Side of Baranan ( Zirgoez ) Homocline : An Attempt to Outlook the Development of Western Part of Sharazoor Plain

The origin of huge dislocated limestone blocks on the southern part of Sharazoor plain and toe of Baranan Mountain (homocline) of are discussed. The homocline is elongate ridge, which is trending southeastnorthwest at southern boundary of western part of Sharazoor plain at 20 km south of Sulaimaniya city. Field study showed that the blocks are slid from upper part of the homocline scarp. The thin section study showed the blocks belong to Sinjar Formation. This slide is proved to include both plane sliding, on the limestone of Sinjar Formation, and rotational sliding on Kolosh formation. This slide is the largest one recorded in Iraq. The sliding and enlargement o f the plain is discussed and combined together through three stages. The main stage of enlargement of the plain is attributed to erosion by the stream and successive sliding of the cliff. The sliding is triggered by lateral and vertical erosion undercutting of the base of northeast side of the homocline by Tanjero stream during long time. These led to oversteepening of the paleoslope of scarp slope and consequently its failure and sliding. Both the sliding and the development of the Sharazoor plain are combined with northward and southward migration of the Tanjero stream during lateral and vertical erosion. The sliding failure is analyzed structurally and plotted on stereonet, which showed that the masses are dislocated mainly by plane sliding at upper part and by rotational siding at lower part on Kolosh Formation. The failure aided by low angle dipping of homocline and normal fault. This fault with two sets of conjugate hko joints might have act as release surfaces. The low dip angle of the homocline also promoted release of the blocks under the effect of the gravity.
Self-Explaining Neural Networks

Most recent work on interpretability for complex machine learning models has focused on estimating a posteriori explanations for particular predictions, but less attention has been devoted to designing the models themselves to be interpretable. We approach this problem by characterizing what makes archetypical “interpretable” models—such as linear classifiers—this way, and then deriving a set of principles that more complex models should satisfy to be interpretable. We progressively generalize various aspects of the simple prediction models to yield a rich class of complex models that are self-explaining: i.e., they provide human-understandable explanations for their predictions as an intrinsic part of their operation. The resulting model class in flexible, easy to train, and encompasses various existing interpretable models as particular cases. Experimental results in applications ranging from digit classification to cancer detection show that our framework offers a promising direction for reconciling model complexity and interpretability.
1 What is a rhetorical relation ?

Let us start with an illustration. Below is a fragment of Barack Obama’s speech at Nelson Mandela’s memorial service on December 10, 2013 in Johannesburg. Obviously, this is not an arbitrary sequence of utterances. The listener or reader immediately recognises that the utterances hold together forming one meaningful whole. The way the utterances are related can be characterised in rational terms. In (1-a) and (1-b), Obama draws a parallel between the situation in South Africa and the United States: both countries fought against racial subjugation. He continues the same parallel in (1-c) and (1-d), making it more specific: the fight took sacrifice but brought about improvements. We say that (1-a) and (1-b), as well as (1-c) and (1-d), respectively, are connected by a rhetorical relation of Parallel, whereas (1-c) + (1-d) as a whole is an Elaboration of (1-a) + (1-b) as a whole, i.e. the rhetorical relation between the two segments is Elaboration. This structure is represented schematically in Figure 1. Utterances and bigger discourse segments are enclosed in boxes; the arcs connecting the boxes represent the rhetorical relations (labelled with rhetorical relations’ names). 1
A Multi-Agent System for Auction-Based Resource Discovery in Semantic-Aware B 2 C Mobile Commerce

Next-generation mobile networks will create semantic-aware environments populated with diverse types of ubiquitous communication-enabled devices which will provide a remarkable selection of heterogeneous resources (information, multimedia, services and products). Consequently, such an environment will require efficient mechanisms which can match demands (requested resources) to supplies (available resources), anywhere and anytime. In this paper, an economic approach to solving this problem combined with AI concepts is proposed. A multi-agent system for enabling B2C mobile commerce within the semantic-aware environment of next-generation mobile networks is designed. The proposed multi-agent system provides an appropriate solution for both mobile consumers (who often posses several mobile terminals) and businesses that act as resource providers. The efficiency of our model is realized by utilizing mobile software agents and by applying a resource discovery process which uses two-level filtration of available resources before a final ranked set of eligible resources is recommended to consumers in response to their needs. The filtration processes not only consider the semantic information associated with available resources, but also analyze ratings regarding the actual performance of resource providers (with respect to both price and quality) and the prices paid by providers for advertising their resources.
1 1 F eb 2 01 6 Adaptive TXOP Assignment for QoS Support of Video Traffic in IEEE 802 . 11 e Networks

Quality of Service (QoS) is provided in IEEE 802.11e protocol by means of HCF Controlled Channel Access (HCCA) scheduler which is efficient for supporting Constant Bit Rate (CBR) applications. Numerous researches have been carried out to enhance the HCCA scheduler attempting to accommodate the needs of Variable Bit Rate (VBR) video traffics which probabl y demonstrates a non-deterministic profile during the time. This paper presents an adaptive TXOP assignment mechanism for supporting the transmission of the prerecorded video traffics over IEEE 802.11e wireless networks. The proposed mechanism use s a feedback about the size of the subsequent video frames of th e uplink traffic to assist the Hybrid Coordinator (HC) accurat ely assign TXOP according to the fast changes in the VBR profile. The simulation results show that our mechanism reduces the delay experienced by VBR traffic streams comparable to HCCA scheduler due to the accurate assignment of the TXOP which preserve the channel time for data transmission.
Recognizing warblers : a probabilistic model for event detection in Twitter

Event detection in Twitter poses a set of new challenges since social networks were not specifically designed for this task. The event identification capabilities of existing probabilistic models are far from state-of-the-art. In this paper we identify three key factors which, when combined, boost the accuracy of such models. Firstly, we notice that the large amount of meaningless social data requires modeling non-event observations. Secondly, we note that the tweeting activity varies in space and time. Thirdly, we observe that the shortness of tweets hampers the application of traditional topic models. Consequently, we propose WARBLE, a new probabilistic model and variational learning algorithm for retrospective event detection that explicitly considers all three factors. The preliminary results show that the proposed model outperforms other state-ofthe-art techniques in detecting various types of events while relying on a principled probabilistic framework to reason under uncertainty.
How to improve the Employability of Higher Education Graduates

Association for the Development of Education in Africa Association pour le développement de l’éducation en Afrique Associação para o Desenvolvimento da Educação em África This policy brief is produced by the Association for the Development of Education in Africa (ADEA) for the Summit on Higher Education on Revitalizing Higher Education for Africa’s future (Dakar, Senegal, March 10-12, 2015) ExEcutivE summary
Relationship between hsC-reactive protein and some biochemical variables in Cardiovascular and Type-2-Diabetic Mellitis patients

C-reactive protein (CRP) is a protein found in the blood, the levels of which rise in response to inflammation. its' study as a risk marker for Cardiovascular and Type -2Diabetes Mellitus patients . This study was done in the period from 1st July 2011 to 1 st November 2012 in Al-Hussein hospital in Karballa government . The study groups were divided into four groups ,one is control ,the others were patients ( Type -2Diabetic , Type 2Diabetic with Cardiovascular and Cardiovascular disease only . The results were showed, there were lowering in Fasting serum adiponectin hormone concentration for (three study groups ) Type -2Diabetic , Type 2Diabetic with Cardiovascular, and Cardiovascular diseases groups with respect to than normal subjects . Significant different ( p< 0.001 ) as shown in tables 1 ,2 ,3 . When use hs-CRP measurement as a marker to limitation the kind of pathogenic case and from L.S. D statistical system were observed there were highly significant different between (DMII with CVD and CVD )groups compared to control group and medium significant different with DMII group . There weren’t significant different between (DMII with CVD and CVD) groups ,while there were significant different between DMII group and control group less than two previous groups (DMII with CVD and CVD). Therefore measurement of hs-CRP were considered as a predictor to incident by Cardiovascular diseases more than Type -2Diabetes .
Artificial Intelligent support for Medical Diagnosis

Multiple Sclerosis (MS) is a chronic disease of unknown cause that affects the nervous system. The process of determining this disease is very complicated because the final diagnosis depends on many parameters and, additionally, the dependencies between parameters are unknown. In these kind of problems, computer aided diagnosis could help medical experts to recognize all important aspects of a problem. The research at our department is focused on the special area of artificial intelligence – Case Based Reasoning (CBR). CBR is an approach where every new problem is solved by adapting the solutions of the previously successfully solved problems. This approach is equivalent to human process of solving problems by reminding the similar situations from the past. This paper is mainly focused on the memory organization and the retrieval algorithm used for this domain and application type.
Learning to Generate Backup Paths in Cooperative Transportation of Human-Robot Teams

Many interactions between humans and robots are cooperative, but humans can easily make mistakes that cause failure. A human-robot system is fail-safe if the robots can steer the system to a safe state after a human error occur. However, the success of this emergency maneuver depends on how quickly the system generates a backup path (i.e., a motion plan) for the robots. In this paper, we describe a new neural network model that can be used to speed up the generation of backup paths for robots in emergency situations. To test our idea, we consider a cooperative transportation task in which a human and a robot move a rectangular object from one location to another. We collected training data from a simulation in which human deviates from his intended path. Then we train our machine learning model using the backup motion plans generated by RRT in these emergency situations. The model can be utilized by a modified RRT algorithm to speed up the generation of backup paths for new emergency situations. Our experimental results shows that our approach can speed up the generation of backup paths by an order of magnitude.
Learning Algorithms for Dynamic Resource Allocation in Virtualised Networks

Network virtualisation is being proposed as a key technology to support dynamic service composition and diverse protocol suites in the future Internet. It involves a sharing of physical resources by multiple virtual networks. Every virtual network can then use its share of the resources to provide its users with a custom set of services and protocol stacks. Sharing resources involves mapping of virtual nodes and links onto physical nodes and links respectively, and thereafter managing the allocated resources to ensure efficient resource utilisation. In this paper, we employ machine learning to achieve a dynamic, decentralised and autonomous allocation of physical network resources to the virtual networks. The objective is to achieve better efficiency in the utilisation of substrate network resources while ensuring that the quality of service requirements of the virtual networks are not violated. Keywords—Machine learning, reinforcement learning, network virtualisation, dynamic resource allocation, future Internet.
Ontology-Driven Application Architectures with KOMMA

The Knowledge Modeling and Management Architecture is an application framework for Java software systems based on Semantic Web technologies. KOMMA supports all application layers by providing solutions for persistence with object triple mapping, management of ontologies using named graphs as well as domain-speci c visualization and editing of RDF data. This paper gives a short introduction to KOMMA's architecture and illustrates its usage with examples.
Research on Fault Diagnosis of a Certain Launch Vehicle Based On RBF Neural Network

In view of the multi-fault frequently occurring and cannot be efficiently diagnosed in dynamic system of a certain launching vehicle chassis, this paper analyzes the produce mechanism and sets up the experiment combining with the practical use. In this experiment, vibration sensors are installed in the appropriate location to collect vibration signals in different state and wavelet packet energy spectrum feature information is extracted. The diagnosis of multi-fault of dynamic system is attained by using the ability for RBF neural network to learn, train and test the feature information. Diagnosis results show that the wavelet packet energy spectrum can effectively describe the multi-fault information of the chassis of the launching vehicle and RBF network diagnosis technology is effective and high-accuracy.
Adaptive planner for facilitating the management of tasks in MOOCs

Massive Open Online Courses (MOOCs) provide open access to a wide variety of learning contents in many different knowledge areas, so that eventually anyone in the world can join these courses and learn, no matter the level of education. However, the majority of participants that currently take advantage of MOOCs already have Higher Education qualifications. Therefore, there is an open challenge in promoting the embracement of MOOCs by those who have not achieved a college degree, in a society that demands more and more up-todate competences. MyLearningMentor is an early-stage application aimed at facilitating the acquisition of work habits and study skills when learning through MOOCs, so that even people without college degrees can easily address the challenge of successfully completing MOOCs. This paper presents a planning algorithm to suggest students’ study time efficiently and dynamically, considering their profiles, study preferences and the MOOCs they signed up. This algorithm is implemented by the adaptive planner, the main component in MyLearningMentor. Keywords—MOOCs, planning, mentor, study skills, work
Selecting Negative Training Documents for Better Learning

— In general, there are two types of feedback documents: positive feedback documents and negative feedback documents. These types share some knowledge because they retrieved using the same query. It is clear that all feedback documents contain some noise knowledge that affects the quality of the extracted features. The amount of noise is different from document to another. Therefore, the number of feedback documents affects the amount of extracting noise features. Then, using all feedback documents can increase the number of extracted noise features. However, we believe that negative feedback documents contain more noise than positive feedback documents. In this paper, we introduce a methodology to select some negative feedback documents to extract high-quality features and to reduce the amount of noises features.
Exploring the effects of threatening stimuli on visual search performance using different experimental designs

5 1. General Introduction . 7 1.1 Visual search . 10 1.2 The role of emotions in attentional processes . 14 1.3 Definitions of fear and phobias . 16 1.3. 1 Fear . 16 1.3. 2 The three models of fear acquisition . 18 1.4 Fear and threat detection . 22 1.4. 1 Stage of information processing . 22 1.4. 2 The fear module of threat detection . 23 1.4. 3 Modern fearful stimuli . 25 1.4. 4 Role of the context . 26 2. STUDY 1 – The Classical Visual Search Task . 28 2.
ANALYSIS OF MRI IMAGES USING DATA MINING FOR DETECTION OF BRAIN TUMOR

Brain tumor is a formation of abnormal cells within the brain that can disrupt the function of the brain. In India, every year 40,000-50,000 persons are diagnosed with brain tumor. Traditionally, radiologist manually detect and calculate the size of the tumor from CT Scan images during regular screening. Out of which, approximately, 10% to 30% of tumors are missed by them. In this paper, a computer aided system for brain tumor detection and it's area calculation using K - means clustering algorithm has been proposed using an interface.
Fingerprint Template Protection using Compact Minutiae Patterns

• Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person. • Authors control the copyright of their thesis. You will recognise the author's right to be identified as the author of this thesis, and due acknowledgement will be made to the author where appropriate. • You will obtain the author's permission before publishing any material from their thesis.
J an 2 01 9 Sparsification of Binary CSPs ∗

A cut ε-sparsifier of a weighted graph G is a re-weighted subgraph of G of (quasi)linear size that preserves the size of all cuts up to a multiplicative factor of ε. Since their introduction by Benczúr and Karger [STOC’96], cut sparsifiers have proved extremely influential and found various applications. Going beyond cut sparsifiers, Filtser and Krauthgamer [SIDMA’17] gave a precise classification of which binary Boolean CSPs are sparsifiable. In this paper, we extend their result to binary CSPs on arbitrary finite domains.
Testing the Balanced Growth Hypothesis : Evidencefrom Bangladesh

In this paper we investigate, using the Johansen’s cointegration procedures, the relevance of balanced growth hypothesis in Bangladesh, a least developed country. We find that major macroeconomic variables, viz. output, consumption and investment share a common stochastic trend in the sample period. The parametric restrictions consistent with strict balanced growth in the closed economy model is weakly supported within the Johansen framework. However, the same framework rejects balanced growth in the open economy model of Bangladesh. Direct stationarity tests outside the Johansen framework suggest non-stationarity of some great ratios indicating the absence of strict balanced growth in both closed and open economy model of Bangladesh.
A Survey on Various Routing Protocols In Wireless Sensor Network

: Wireless sensor networks have advanced so much in the recent times that they have led to the development of many protocols, whose utmost concern is the energy efficiency of the sensor nodes. A Wireless sensor network (WSN) is a collection of various low cost sensor nodes which communicate through radio interface. They sense, compute, and transmit information to the base stations. But they have limited energy, capability, and storage. This paper is based on a survey of the various types of WSNs, their applications and the various routing protocols being used.
Privacy and security in the big data paradigm

ABSTRACT Privacy and security in the big data age have drawn significant attention in the academia and industry. This article examines privacy and security in the big data paradigm through proposing a model for privacy and security in the big data age and a classification of big data-driven privacy and security. It extends the big data body of knowledge, highlights important research topics, and identifies critical gaps through statistical analysis of big data and its impacts on privacy and security based on literature data published from 1916 to 2016. It also presents the state-of-the-art privacy and security based on the analysis of SCOPUS data from 2012 to 2016. The result shows that privacy and security face new challenges and require new policies, technologies, and tools for protecting privacy in the big data paradigm. The proposed approach might facilitate the research and development of privacy and security, and big data-driven privacy and security in terms of technology, governance, and policy development.
INTEGRATED SUBJECT-LINGUISTIC APPROACH AS THE BASIS OF SUBJECT TEACHING MODELLING USING A FOREIGN LANGUAGE AT HIGH SCHOOL

The author developed the model of subject teaching using a foreign language at high school on the basis of an integrated subject-linguistic approach, aimed at the development of a subject foreign language competence of a student. An integrated subject-linguistic approach in learning (CLIL Contentand Language Integrated Learning) is recommended by European Commission for the use at high schools, because, first of all, it provides students with the opportunity to study a discipline and a foreign language simultaneously; secondly, the study of a foreign language does not require additional hours in a curriculum, which contributes to the intensification of professional training at a high school. Theoretical foundations of CLIL are based on L.S. Vygotsky theory of thought and speech relationship, BICS/CALP J. Cummins theory and the taxonomy of Bloom skills, covering the cognitive area of learning activities. The model was implemented on the basis of the discipline "The fundamentals of data mathematical processing", lasting 72 hours (2 credits). To test the model effectiveness on the basis of the Kazan Federal University during 2011-2014 a pedagogical experiment was performed. The paper presents and analyzes its results.
The C Secured Secret Sharing over Single Path in VoIP with

Voice over Internet Protocol (VoIP) is a new fancy and up growing technology. A major change in telecommunication industry is VoIP. The transmission of Real time voice data is not as easy as ordinary text data. The real time voice transmission faces lot of difficulties. It suffers from packet loss, delay, quality and security. These factors will affects and degrade the performance and quality of a VoIP addresses the security and packet delivery ratio modified secret sharing algorithm over a single path with reduced packet loss. The simulation results show that higher security is achieved in terms of route request, route reply error, delay, and packet delivery ratio.