Miracle-dz/UniFAR-FTUs huggingface dataset:
A Complete Review and Comparative Study on Analysis of Data Clusters in Mining

Clustering is a segment of data science into clusters of same items. Showing the data by less clusters essentially loses many fine points of interest, however accomplishes rearrangements. It shows data by the clusters. Data modeling places clustering in a verifiable pattern in data science, estimation, and numerical examination. As the machine learning is being considered so as to compare the clusters shrouded designs, the look of clusters is just like unsupervised learning, and the methodology says about the data idea. As the reasoning of the clusters is being considered which is taken as exception in the data mining, for eg, logical data analysis, data recovery and content mining, spatial database applications, Web examination, CRM, promoting, medicinal diagnostics, computational science, and numerous others. Clustering of data is being taken as the dynamic segment of many fields as estimation, design and artificial intelligence, which actually considers the clustering in the information science. Various type of the properties of the data points are considered for clustering the data points into the clusters. Some very meaningful algorithms are being used in the various clustering methodologies. The work in the paper provides the quick review of many clustering methodologies which works on
A hybrid fusion of wireless signals and RGB image for indoor positioning

In this article, we propose a new indoor positioning algorithm using smartphones, where wireless signals and images are deeply combined together to improve the positioning performance. Our approach is based on the use of local binary patterns’ feature, which has the advantages of rotation invariance and scale invariance. Moreover, the term “uniform” are fundamental properties of local image textures and their occurrence histogram is proven to be a very powerful texture feature. Besides, the received signal strength acts as a reliable cue on a person’s identity. We first obtain a coarse-grained estimation based on the visualization of wireless signals, which are presented by a vector, making use of fingerprinting methods. Then, we perform a matching process to determine correspondences between two-dimensional pixels and three-dimensional points based on images collected by the smartphone. After being evaluated by experiments, our proposed method demonstrates that the combination of the visual and the wireless data significantly improves the positioning accuracy and robustness. It can be widely applied to smartphones to better analyze human behavior and offer high-accuracy indoor location–based services.
Automatic extraction of specialized verbal units: A comparative study on Arabic, English and French

This paper presents a methodology for the automatic extraction of specialized Arabic, English and French verbs of the field of computing. Since nominal terms are predominant in terminology, our interest is to explore to what extent verbs can also be part of a terminological analysis. Hence, our objective is to verify how an existing extraction tool will perform when it comes to specialized verbs in a given specialized domain. Furthermore, we want to investigate any particularities that a language can represent regarding verbal terms from the automatic extraction perspective. Our choice to operate on three different languages reflects our desire to see whether the chosen tool can perform better on one language compared to the others. Moreover, given that Arabic is a morphologically rich and complex language, we consider investigating the results yielded by the extraction tool. The extractor used for our experiment is TermoStat ( Drouin 2003 ). So far, our results show that the extraction of verbs of computing represents certain differences in terms of quality and particularities of these units in this specialized domain between the languages under question.
LODmilla: a Linked Data Browser for All

Although the Linked Data paradigm is extremely popular, and there is immense amount of Linked Open Data available worldwide, the human exploration of these datasets is limited. In our work we try to evolve a generic platform called LODmilla for exploring and editing Linked Open Data. Our aim is to enable the extraction and sharing of data associations (or information) hidden in Linked Open Data. LODmilla is an open web application supporting graph views, graph searching and many other commodity features for surfing over Linked Data.
Assessment of various supervised learning algorithms using different performance metrics

Our work brings out comparison based on the performance of supervised machine learning algorithms on a binary classification task. The supervised machine learning algorithms which are taken into consideration in the following work are namely Support Vector Machine(SVM), Decision Tree(DT), K Nearest Neighbour (KNN), Naïve Bayes(NB) and Random Forest(RF). This paper mostly focuses on comparing the performance of above mentioned algorithms on one binary classification task by analysing the Metrics such as Accuracy, F-Measure, G-Measure, Precision, Misclassification Rate, False Positive Rate, True Positive Rate, Specificity, Prevalence.
Design Requirements to Educate and Facilitate Junior Design Professionals to Reflect more Effectively on Critical Situations and Conflicts at Work

Junior designers are not trained to cope with critical situations and conflict at work. Most design schools do not educate their design students to prepare them for (potential) conflict. As a result, junior designers often do not have conflict-handling skills to handle critical situations and conflicts. While some tools and methods exist to help them make responsible design choices, these often address value differences underlying (potential) conflict without taking the perspective of the designer, and thus without supporting young designers to start by reflecting on their own intrinsic values.The aim of this study is to find a way to help junior designers to reflect effectively on critical situations, thereby improving their conflict-handling skills. Data was collected through four steps in an action research. Researchers collaborated with an identity programme for junior design professionals. Insights from try-outs and small interventions were transferred into design requirements for an approach to educate and facilitate junior design professionals to reflect more effectively on critical situations.
Understanding the connectionist modeling of quasiregular mappings in reading aloud

The connectionist approach to reading aloud has been a serious challenge to the traditional dual-route theory, but several issues concerning the theoretical distinctions of the connectionist approach from the dual-route theory remain unresolved. First, through what kind of internal structure a single-route connectionist model represents the two seemingly distinct kinds of ability to process regularities and exceptions without relying on dual-route structure, has yet to be fully answered. Second, even the question of whether the single-route connectionist model is indeed functionally a single mechanism is yet to be convincingly demonstrated. Third, whether a singleroute model can simulate surface dylexia, of which the dual-route theory has been the only traditional interpretation, has not been thoroughly investigated. By taking a model from Plaut et al. (1996) and examining it closely, the present study attempts to resolve these issues. Various forms of network analysis demonstrate that exception learning occurs when the single-route network makes only a slight change in the orthographic-hidden mappings while it preserves the basic topological structure of the hidden-phonological mappings. The results suggest that the representational system in hidden unit space is structured mainly via learning regularities regardless of learning exceptions. The relationship between the reading network’s exception learning and its nonword reading is also investigated. A systematic analysis that examines a network trained with either
A systematic literature review on necessity,challenges, applications and attacks of watermarking relational database

Systematic literature review (SLR) is a significant research methodology in software engineering and computer science. One precarious step in applying this methodology is to enterprise and perform appropriate and effective search approach. This is a time-consuming and error-prone step, which needs to be carefully planned and implemented. There is an apparent need for a systematic approach to designing and performing a suitable search strategy for optimally retrieving the target literature from digital libraries. To the best of our knowledge, five intensive review papers [1-5] have been found which are not SLR, instead they are a normal survey or literature review. In contrast to [1-9] this paper followed general guidelines for undertaking SLR in order to illustrate necessity, challenges, applications, and attacks of watermarking relational database. An advanced search has been performed in most relevant digital libraries to obtain potentially relevant articles published until the end of 2014. Forty-six primary studies (PSs) have been identified based on inclusion and exclusion criteria. The analytical study is mainly based on the PSs to achieve the objectives. The results illustrate the importance of digital watermark in protecting the relational database, the differences between watermarking relational database and multimedia objects and the demand to increase the level of attack resilience. In addition, the results indicate that watermarking relational database is an interested area for researchers.
Cross-document coreference between different types of collateral texts for films.

Recent systems merge information from texts describing video content for video annotation by employing cross-document coreference techniques, mostly realised between the same text genres or in texts including restricted sets of events. We introduce a new, interesting and challenging scenario - film and the variety of collateral text genres narrating its content, including unrestricted sets of events. In particular, cross-document coreference between plot summaries and audio description is challenging, as these two texts differ significantly. The resulting cross-referencing can potentially enrich video annotation. We address the questions of how plot summaries and audio description refer to events depicted in films, whether the same events are expressed by lexical regularities in both texts and how solutions to the cross-document coreference task can be extended to deal with different text genres and unconstrained sets of events. This thesis introduces a new research domain for information extraction and cross-document coreference, reports on a corpus based analysis of the language used in plot summaries and audio description focusing on how events are expressed, proposes and evaluates solutions to the cross-document coreference task for an unconstrained set of events in different text types and provides two data sets for information extraction related research. We make three claims. First, plot summaries and audio description use lexical regularities, such as frequent open class words occurring more frequently than in general language, to describe film content. Second, these two texts use similar terms in referring to entities, but different terms in referring to events, i.e. different frequent, verbs. Frequent plot summary events are referred to by a very few lexical regularities in audio description. Third, the task of cross-document coreference between plot summary and audio description can be automated achieving at least 50% Precision and 33% Recall, by matching nouns, functional roles and some verbs, and taking into account the event temporal aspect. The Recall may be improved mostly by resolving all references to entities, while the Precision may be increased when treating a restricted set of events.
Modeling Events and Affects in Social Media Stories

Author(s): Rahimtoroghi, Elahe | Advisor(s): Walker, Marilyn A | Abstract: Stories play an important role in human perception of the world and therefore the computational analysis of narrative structure is a key area in natural language processing. The focus of this thesis is to develop and evaluate computational models for two main elements of the narrative structure: Events and Desires. Our work first aims to test a theory that proposes a linear structure of narratives and identifies different parts of a story based on their function. Unlike most of the previous work that use the news articles or other simpler and more conventional genres, we use a corpus of personal stories from social media that have a wider range of topical content and variations of discourse relations. We present an unsupervised method for modeling narrative events, focusing on specific event relations based on the Penn Discourse Treebank’s definition of contingency. We use a weakly supervised approach to extract the key events from stories and create a topic-sorted corpus of personal narratives using a bootstrapping method. We additionally propose new evaluation methods for testing the contingent event pairs. Our results show that most of the relations we learn from blog stories are not found in the existing event collections.In our final contribution, we develop supervised methods for modeling the protagonist’s goals and their outcome in personal narratives, as a sub-problem of modeling affects. Our studies show that both prior and post context are useful for modeling desire fulfillment. In addition, we show that exploiting narrative structure is helpful, both directly in terms of the utility of discourse relation features and indirectly by using a sequential model. We further examine our analysis of the human desires by identifying and studying the expressions of unfulfilled goals.
The Application of Legal-Statistical Method in Analysing Manufacturing Property’s Institutional Development

Purpose: The study defines the specifics and opportunities, that arise due to the application of statistical method in judicial research of manufacturing property. Design/Methodology/Approach: The authors put the ground of the study on the aggregation of a vast spectrum of statistical data and indicators of patent activity of leading countries in the sphere of technological innovation. Findings: The authors outlined the main tendencies of legal regulation of scientific and artistic results of intellectual property development and define the specifics and opportunities that arise due to the application of statistical method in judicial research of manufacturing property. Lack of coordination of approaches to protect manufacturing property on the international level, obstructs the development of patent activity amongst the members of the Paris Convention and significantly reduces the effectiveness of the current patent system. Practical implications: Authors’ development could be utilized in future developments of Russian institute of manufacturing property and its legal framework. Originality/Value: The contribution of the article is the comprehensive analysis of legal background of intellectual property institute and its development prospects.
Implementasi Penggunaan Aplikasi AutoCAD dalam Meningkatkan Kompetensi Dasar Menggambar teknik bagi Masyarakat

Dengan berkembangnya teknologi, maka gambarpun ikut mengalami kemajuan dalam penggunaannya. Dengan kecanggihan dan kemajuan teknologi dapat melahirkan suatu cara menggambar dengan media komputer. Teknologi dapat membantu mempercepat menyelesaikan suatu pekerjaan, tidak terkecuali aplikasi CAD ( Computer Aided Design ). Program yang sering digunakan untuk pengajaran CAD adalah AutoCAD yang merupakan sebuah aplikasi (software) yang digunakan untuk menggambar, mendesain gambar, menguji material dimana program tersebut mempunyai kemudahan dan keunggulan untuk membuat gambar secara tepat dan akurat. Berdasarkan temuan dilapangan yang didasari tinjauan langsung, didapatkan informasi bahwa materi AutoCAD yang diperoleh dari jenjang sebelumnya masih banyak yang jauh dari kondisi ideal. Materi AutoCAD yang didapatkan masih banyak tentang teori sedikit praktek, perangkat hardware yang terbatas sampai pada ketersediaan sumber daya manusia yang kurang adalah permasalahan umum yang terjadi. Adapun tujuan yang ingin dicapai adalah (1) Meningkatkan mutu peserta pelatihan melalui workshop pemanfaatan aplikasi AutoCAD (2) Meningkatkan ketrampilan peserta pelatihan dengan membuat suatu proyek desain gambar bangun berbasis aplikasi AutoCAD . Manfaat kegiatan pelatihan adalah: (1) Peserta pelatihan dapat meningkatkan pemahaman tentang program AutoCAD ; (2) Peserta pelatihan dapat menyalurkan pengetahuan dan ketrampilan yang didapatkan nantinya kepada yang membutuhkan dan terutama akan sangat membantu para calon civil engineer dalam menyelesaikan pekerjaan yang berkaitan dengan desain rancang bangun; (3) Peserta pelatihan dapat mendesain gambar menggunakan program AutoCAD .
Countermeasures for timing-based side-channel attacks against shared, modern computing hardware

There are several vulnerabilities in computing systems hardware that can be exploited by attackers to carry out devastating Microarchitectural Timing-Based Side-Channel Attacks against these systems and as a result compromise the security of the users of such systems. By exploiting Microarchitectural resources, adversaries can potentially launch different variants of Timing Attacks, for instance, to leak sensitive information through timing. In view of these security threats against computing hardware, in a recent study, titled “Are Timing-Based Side-Channel Attacks Feasible in Shared, Modern Computing Hardware? ”, currently undergoing the review process, we presented and analysed several such attacks. This extended study proceeds to build upon our recent study in question. To this end, we analyse the existing countermeasures against Timing Attacks and propose new strategies in dealing with such attacks.
Multi-dimension feature fusion for action recognition

Typical human actions last several seconds and exhibit characteristic spatio-temporal structure. The challenge for action recognition is to capture and fuse the multi-dimension information in video data. In order to take into account these characteristics simultaneously, we present a novel method that fuses multiple dimensional features, such as chromatic images, depth and optical flow fields. We built our model based on the multi-stream deep convolutional networks with the help of temporal segment networks and extract discriminative spatial and temporal features by fusing ConvNets towers multi-dimension, in which different feature weights are assigned in order to take full advantage of this multi-dimension information. Our architecture is trained and evaluated on the currently largest and most challenging benchmark NTU RGB-D dataset. The experiments demonstrate that the performance of our method outperforms the state-of-the-art methods.
NPP: A New Privacy-Aware Public Auditing Scheme for Cloud Data Sharing with Group Users

Today, cloud storage becomes one of the critical services, because users can easily modify and share data with others in cloud. However, the integrity of shared cloud data is vulnerable to inevitable hardware faults, software failures or human errors. To ensure the integrity of the shared data, some schemes have been designed to allow public verifiers (i.e., third party auditors) to efficiently audit data integrity without retrieving the entire users’ data from cloud. Unfortunately, public auditing on the integrity of shared data may reveal data owners’ sensitive information to the third party auditor. In this paper, we propose a new privacy-aware public auditing mechanism for shared cloud data by constructing a homomorphic verifiable group signature. Unlike the existing solutions, our scheme requires at least t group managers to recover a trace key cooperatively, which eliminates the abuse of single-authority power and provides non-frameability. Moreover, our scheme ensures that group users can trace data changes through designated binary tree; and can recover the latest correct data block when the current data block is damaged. In addition, the formal security analysis and experimental results indicate that our scheme is provably secure and efficient.
An Investigation of Hermaphroditism in R sp SB 347

Rhabditis sp. SB347 is a triecious species with three sexes: males, females, and self-fertile hermaphrodites. This study aims to characterize the patterns and cytological characteristics of reproduction in R. sp. SB347 hermaphrodites. The first chapter investigates the sex ratios and brood structure of self-fertilizing R. sp. SB347 hermaphrodites. This study finds that hermaphrodites produce 10.5% males in total broods and often have several bursts (>10.5%) of male production within individual broods. This analysis of complete broods is the first of its kind in R. sp. SB347 and one of the few reports of population studies in a triecious nematode species with skewed sex ratios. The second chapter explores patterns of chromosome segregation during oocyte meiosis as a potential mechanism for enabling self-fertilizing XX hermaphrodites to produce XO male progeny. This study reveals that during hermaphrodite oogenesis, aberrant X-chromosome segregation patterns may contribute to X-chromosome loss specifically during meiosis I. During metaphase I, I observed 6 paired autosomes and two X chromosomes in hermaphrodite oocytes. The X chromosomes were variably paired in a bivalent (paired homologs) or unpaired as univalents (unpaired homologs). My data suggests that the unpaired X chromosomes lag during anaphase I and are either segregated reductively to yield a haplo-X ovum or are both extruded into the first polar body to yield a nullo-X ovum. Fertilization with haplo-X sperm would yield an XX feminine or XO male, respectively. Importantly, no dead embryos were observed during brood studies, suggesting that meiotic segregation defects are X-chromosome specific. These findings suggest that Xchromosome loss serves as a mechanism for XO male production in R. sp. SB347 self-broods. In the final chapter, we investigate spermatogenesis in R. sp. SB347 hermaphrodites using Caenorhabditis elegans as a comparative model. As in the well-studied model nematode C. elegans, most hermaphroditic nematodes are assumed to make a one-time switch from producing spermatocytes to producing oocytes. However our analysis of older R. sp. SB347 hermaphrodites revealed not only the expected presence of spermatozoa, but also the continued production of spermatocytes and spermatids. Furthermore, we documented the novel presence within a nematode species of mitotically-dividing spermatogonia that give rise to spermatocytes. Spermatogonia have not been previously reported among nematodes, and thus this study contributes to the understanding of hermaphroditism in a triecious species.
Research on the Algorithm for Solving the Indoor Vision Positioning Model of Mobile Robot

In the analysis of the features and properties of camera based on the data, constructs the projection model and error model positioning calculation model, expanded beam method traditional adjustment model is only for image observations are optimized, constructed model with two or three dimensional positioning information integration RGB-D camera. The design experiments verified the accuracy of detection and matching, and combined with the depth measurement characteristics, we constructed two weight matrixes of observation information, thus improving the mathematical model of the whole location calculation. Keywords—RGB-D camera; indoor location; mathematical model