Semantic Similarity for English and Arabic Texts: A Review

Semantic similarity is the task of measuring relations between sentences or words to determine the degree of similarity or resemblance. Several applications of natural language processing require semantic similarity measurement to achieve good results; these applications include plagiarism detection, text entailment, text summarisation, paraphrasing identification, and information extraction. Many researchers have proposed new methods to measure the semantic similarity of Arabic and English texts. In this research, these methods are reviewed and compared. Results show that the precision of the corpus-based approach exceeds 0.70. The precision of the descriptive feature-based technique is between 0.670 and 0.86, with a Pearson correlation coefficient of over 0.70. Meanwhile, the word embedding technique has a correlation of 0.67, and its accuracy is in the range 0.76–0.80. The best results are achieved by the feature-based approach.

Tumor Subtyping: Making Sense of Heterogeneity with a Goal Toward Treatment

BACKGROUND: Bladder cancers have high total mutation burdens resulting in genomic diversity and intra- and inter-tumor heterogeneity that may impact the diversity of gene expression, biologic aggressiveness, and potentially response to therapy. To compare bladder cancers among patients, an organizational structure is necessary that describes the tumor at the histologic and molecular level. These “molecular subtypes”, or “expression subtypes” of bladder cancer were originally described in 2010 and continue to evolve secondary to next generation sequencing (NGS) and an increasing public repository of well-annotated cohorts. OBJECTIVE: To review the history and methodology of expression-based subtyping of non-muscle invasive (NMIBC) and muscle invasive bladder cancer (MIBC). METHODS: A literature review was performed of primary papers from PubMed that described subtyping methods and their descriptive feature including search terms of “subtype”, and “bladder cancer”. RESULTS: 21 papers were identified for review. Tumor subtyping developed from N = 2 to N = 6 subtyping schemes with most subtypes comprised of at least luminal and basal tumors. Most NMIBCs are luminal cancers and luminal MIBCs may be associated with less aggressive features, while one study of basal tumors identified a better clinical outcome with systemic chemotherapy. Tumors with a 53-like signature may have intrinsic resistance to chemotherapy. The heterogeneity of tumors, which is likely derived from stromal components and immune cell infiltration, affect subtype calls. CONCLUSION: Subtyping, while still evolving, is ready for testing in clinical trials. Improved patient selection with tumor subtyping may help with tumor classification and potentially match patient or tumor to therapy.

Celestial objects periphrastic naming and synonym correlation

We consider the ability of language to express identical content by periphrastic means that reflects in the periphrase and synonym ratio. We prove that in the narrow sense periphrastic names cannot be considered as a simple synonymous replacement of the word-nominee, since the meaning of the periphrase is not identical with the meaning of the word-nominee. Periphrasis is more voluminous, wider in its content due to the presence of certain structural components or the presence of associative features. These features focus attention of the linguistic personality and allow allocating a specific descriptive feature in the periphrasis image. In a broad sense periphrases and synonyms are considered as stylistic synonyms. Meanwhile in language there are non-stylistic periphrases for celestial object. Opportunity of duplicate use of the words “moon” and “month” shows semantic opposition. As exemplified in the names of celestial bodies there is tendency of periphrasis and synonym, but not the rules. According to the narrow and broad understanding of periphrasis and synonyms, which relies on the representative function of language, it is necessary to take into account the cognitive factors that determine the relationship between the nominating word (periphrasisable concept) and the periphrasing concept.

A Comprehensive Feature Comparison Study of Open-Source Container Orchestration Frameworks

(1) Background: Container orchestration frameworks provide support for management ofcomplex distributed applications. Different frameworks have emerged only recently, and they havebeen in constant evolution as new features are being introduced. This reality makes it difficult forpractitioners and researchers to maintain a clear view of the technology space. (2) Methods: wepresent a descriptive feature comparison study of the three most prominent orchestrationframeworks: Docker Swarm, Kubernetes, and Mesos, which can be combined with Marathon,Aurora or DC/OS. This study aims at (i) identifying the common and unique features of allframeworks, (ii) comparing these frameworks qualitatively ánd quantitatively with respect togenericity in terms of supported features, and (iii) investigating the maturity and stability of theframeworks as well as the pioneering nature of each framework by studying the historical evolutionof the frameworks on GitHub. (3) Results: (i) we have identified 124 common features and 54 uniquefeatures that we divided into a taxonomy of 9 functional aspects and 27 functional sub-aspects. (ii)Kubernetes supports the highest number of accumulated common and unique features for all 9functional aspects; however, no evidence has been found for significant differences in genericitywith Docker Swarm and DC/OS. (iii) Very little feature deprecations have been found and 15 out of27 sub-aspects have been identified as mature and stable. These are pioneered in descending orderby Kubernetes, Mesos, and Marathon. (4) Conclusion: there is a broad and mature foundation thatunderpins all container orchestration frameworks. Likely areas for further evolution and innovationinclude system support for improved cluster security and container security, performance isolationof GPU, disk and network resources, and network plugin architectures.

Sistem Informasi Pemilihan Perumahan Terbaik

Peningkatan kepadatan penduduk saat ini yang tinggi mendorong akan kebutuhan tempat tinggal yang semakin besar dimanfaatkan oleh developer perumahan dalam mempromosikan perumahannya. Banyaknya pilihan perumahan yang ditawarkan menyebabkan konsumen perlu memilih rumah yang sesuai dengan kriterianya. Dengan sedikitnya informasi yang didapat konsumen sering mengalami penyesalan karena rumah yang sudah dipilih memiliki harga yang lebih mahal dibanding rumah yang berada didekatnya. Aplikasi ini dibangun menggunakan metode case-based reasoning (CBR) dan algoritma Euclidean distance. Metode ini mendapatkan solusi melalui kasus-kasus yang sebelumnya ada dan melakukan perhitungan kemiripan antar kasus. Kriteria-kriteria yang diinginkan pengguna seperti harga, luas tanah, luas bangunan, jumlah kamar tidur, jumlah kamar mandi dan jumlah lantai menjadi adjustment feature. Descriptive feature meliputi lokasi kecamatan, fasilitas, akses jalan, kondisi jalan dan freedesign. Keluaran yang dihasilkan berupa rangking informasi rumah yang diurutkan berdasarkan nilai kecocokan yang paling tinggi dan ditambah dengan tampilan rumah dan keterangan rumah. Hasil yang didapatkan dari penelitian ini adalah aplikasi sistem pendukung keputusan pemilihan perumahanyang dapat memberikan informasi yang lebih lengkap secara efektif dalam memilih rumah di Kotamadya Malang yang sesuai dengan kriteria yang diinputkan.

Global Distribution Adjustment and Nonlinear Feature Transformation for Automatic Colorization

Automatic colorization is generally classified into two groups: propagation-based methods and reference-based methods. In reference-based automatic colorization methods, color image(s) are used as reference(s) to reconstruct original color of a gray target image. The most important task here is to find the best matching pairs for all pixels between reference and target images in order to transfer color information from reference to target pixels. A lot of attractive local feature-based image matching methods have already been developed for the last two decades. Unfortunately, as far as we know, there are no optimal matching methods for automatic colorization because the requirements for pixel matching in automatic colorization are wholly different from those for traditional image matching. To design an efficient matching algorithm for automatic colorization, clustering pixel with low computational cost and generating descriptive feature vector are the most important challenges to be solved. In this paper, we present a novel method to address these two problems. In particular, our work concentrates on solving the second problem (designing a descriptive feature vector); namely, we will discuss how to learn a descriptive texture feature using scaled sparse texture feature combining with a nonlinear transformation to construct an optimal feature descriptor. Our experimental results show our proposed method outperforms the state-of-the-art methods in terms of robustness for color reconstruction for automatic colorization applications.

Penerapan Strategi Pembelajaran Point-Counterpoint Bervariasi untuk Meningkatkan Daya Kritis dan Hasil Belajar pada Mata Pelajaran PKn Topik Usaha Pembelaan Negara Bagi Siswa Kelas IX E SMP Negeri 1 Mojosongo

The objective of this research “The application of Point- Counterpoint Variety” is to increase the students’ creativity capable and the outcome of Pkn subject with “Pembelaan Negara” topic for the students of IX E SMPN 1 Mojosongo the first semester academic year 2013/ 2014. This research using qualitative method with descriptive feature, describe the data and interprise the data. The kind of this research is action research (PTK) that was done by the researcher directly. The setting of this reseach is IXE class SMP N 1 Mojosongo the first semester academic year 2013/2014 which has low capable from one of the sevent classses paralel. The technic of collecting the data are test and non test. For collecting the data using the observation and the items of test. To know the efectiveness the process of learning using the strategy “Point-CounterPoint Variety”, the researcher and the collaborator have done the observation in the process of learning. While the validity of the data using content validity and triangulasi. The analysis of the data using the analysis descriptive comparative and qualitative. Indicators which be hope in this research are : 1) Increase the students’ critical capacity from 13,33% (before treatment) become 26,00 % in the 1st cycle, and 35,00 % in 2nd cycle; 2) Increase the students’ average 71,90 (before tretment) in the 1St cycle to 80,00 in the 1nd cycle and 85,00 in the 2 nd cycle. After the process of collected and analised the data, the result of the research is significant. This result show that the strategy of learning Point-counterpoint variety can improve :1) The students’s critical capacity 13,33% ( before treatment) to 28,57% in the 1st cycle, and 41,90% in the 2nd cycle. 2) The students’ average 71,90 (before treatment) to 86,67 in the 1st cycle, and 92,14 in the 2rd cycle. Increase the critical capacity and the outcome for “Pendidikan Kewarganegaraan” with topic “Usaha Pembelaan Negara” for the students IXE class SMP Negeri 1 Mojosongo the first semester academic year 2013/2014.

Visual and Non-Visual Analysis in Archaeology

As we have discussed in previous chapters, an artificial neural network is an information-processing system that maps a descriptive feature vector into a class assignment vector. In so doing, a neural network is nothing more than a complex and intrinsically nonlinear statistical classifier. It extracts the statistical central tendency of a series of exemplars (the learning set) and thus comes to encode information not just about the specific exemplars, but about the stereotypical featureset displayed in the training data (Churchland, 1989; Clark, 1989, 1993; Franklin, 1995). That means, it will discover which sets of features are most commonly present in the exemplars, or commonly occurring groupings of features. In this way, semantic features statistically frequent in a set of learning exemplars come to be both highly marked and mutually associated. “Highly marked” means that the connection weights about such common features tend to be quite strong. “Mutually associated” means that co-occurring features are encoded in such a way that the activation of one of them will promote the activation of the other.