Miracle-dz/UniFAR-FTUs huggingface dataset:
Data-Driven Creativity Enhancement Through Word Association

Writing creative stories from a blank page is a challenging task, particularly in the modern game industry where dozens of writers can contribute to the stories and settings of a constantly evolving artificial world. We introduce a system which recommends interesting, evocative, and thematically coherent words to help creators write thematically connected stories. We combine principles from human creativity enhancement and computational creativity to build a creative assistant based on word association research. We show that careful corpus selection, filtering based on emotional sentiment, and promoting remote associations through paragraph scale segmentation can produce recommendations that promote creative goals better than alternative word association algorithms according to our creative word indicators.
Compressive Spinal Codes

It is well known that spinal codes can essentially approach channel capacity for equiprobable source, and achieve seamless rate adaptation at the same time. But how to design an optimal spinal codes for non-equiprobable source is still an open problem. This paper proposes a novel Sparse Spinal (S-Spinal) codes design, which can essentially approach channel capacity for non-equiprobable and compressible source, and its seamless rate adaptation is also preserved. The main idea is to utilize source distribution information at the spinal decoder. The sparser the source is, the less symbols the receiver needs. The rateless feature of spinal codes can provide fine-grain compression ratio. The different decoder designs for Binary Symmetrical Channel (BSC) and Additive White Gaussian Noise (AWGN) channel are proposed, since their priori information is different. We also analyze and prove the S-Spinal codes with a practical pruning decoder can essentially achieve the channel capacity. The experimental results show that the proposed S-Spinal is very close to channel capacity of both BSC and AWGN channel for non-equiprobable source, and can achieve seamless rate adaption in practical system.
Implementasi Semantic Search Pada Open Library Menggunakan Metode Latent Semantic Analysis (studi Kasus: Open Library Universitas Telkom)

Abstrak Open Library Telkom University mengalami pertumbuhan yang pesat baik dari sisi jumlah maupun kekayaan kontennya. Sebagai konsekuensinya, dibutuhkan metode pencarian yang mampu memberikan hasil yang lebih akurat. Pencarian berbasis metadata sudah memberikan tambahan opsi, namun juga masih memiliki kelemahan tidak dapat menemukan dokumen yang memiliki kemiripan. Kelemahan ini bisa diatasi oleh pencarian semantik dengan memahami maksud dari pencari dan makna kontekstual istilah, seperti yang ditampilkan dalam data pencarian dengan mengkombinasiakan Latent Semantic Analysis (LSA) dan weighted tree similarity. Berdasarkan hasil pengujian, sistem yang dibagun mampu memberikan informasi relevan dengan rata-rata nilai precision dan recall yang baik. Nilai rata-rata precision 57.1181868% dan nilai rata-rata recall 85.0848178%. Hasil nilai rata-rata recall sudah baik karena hampir mendekati 100% artinya tingkat keberhasilan sistem dalam menemukan dokumen yang relevan. Sehingga dapat disimpulkan, metode LSA dan weigted tree mampu memberikan dokumen yang relevan kepada penggunanya serta ketepatan antara kueri masukkan dengan hasil pencarian dokumen. Kata Kunci: latent semantic analysis, weighted tree similarity Abstract The Telkom University Open Library promotes rapid growth in terms of both the amount and wealth of its content. As a consequence, a search method is needed that is able to provide more accurate results. Search based on metadata has provided additional options, but also has the disadvantage of not being able to find documents that have a similarity. This weakness can be overcome by semantic search with the intent and purpose of contextual terms, as referred to in search data by combining Latent Semantic Analysis (LSA) and weighted tree similarity. Based on the test results, the built system is able to provide relevant information with an average value of precision and good memory. The average precision value is 57.1181868% and the average recall value is 85.0848178%. The results of the average value of 100% withdrawal means the level of success in finding relevant documents. Detachable, the LSA method and the weigted tree provide relevant documents for its users as well as the accuracy between the queries provided with the document search results. Keywords: latent semantic analysis, weighted tree similarity
A Holonic-Based Self-Learning Mechanism for Energy-Predictive Planning in Machining Processes

The present work proposes a holonic-based mechanism for self-learning factories based on a hybrid learning approach. The self-learning factory is a manufacturing system that gains predictive capability by machine self-learning, and thus automatically anticipates the performance results during the process planning phase through learning from past experience. The system mechanism, including a modeling method, architecture, and operational procedure, is structured to agentize machines and manufacturing objects under the paradigm of Holonic Manufacturing Systems. This mechanism allows machines and manufacturing objects to acquire their data and model interconnection and to perform model-driven autonomous and collaborative behaviors. The hybrid learning approach is designed to obtain predictive modeling ability in both data-existent and even data-absent environments via accommodating machine learning (which extracts knowledge from data) and transfer learning (which extracts knowledge from existing knowledge). The present work also implements a prototype system to demonstrate automatic predictive modeling and autonomous process planning for energy reduction in milling processes. The prototype generates energy-predictive models via hybrid learning and seeks the minimum energy-using machine tool through the contract net protocol combined with energy prediction. As a result, the prototype could achieve a reduction of 9.70% with respect to energy consumption as compared with the maximum energy-using machine tool.
A Review on Machine Learning Algorithms

: Machine learning is a field of computer science which gives computers an ability to learn without being explicitly programmed. Machine learning is used in a variety of computational tasks where designing and programming explicit algorithms with good performance is not easy. Machine Learning (ML) is a vast interdisciplinary field which builds upon concepts from computer science, statistics, cognitive science, engineering, optimization theory and many other disciplines of mathematics and science. Machine learning algorithms consist of identifying and validating models to optimize a performance criterion using historical, present, and future data. A large number of techniques has been developed so far to tell the diversity of machine learning. The proposed paper briefly explains the various types of machine learning algorithms. The main advantage of using machine learning is that, once an algorithm learns what to do with data, it can do its work automatically.
Vision-based Autonomous Landing of UAV on Moving Platform using a New Marker

The research of Unmanned Aerial Vehicle (UAV) flight autonomous landing on a moving platform based on computer vision has important significance, especially for the rescue and spray UAV in the future. A vision-based relative position and attitude estimation algorithm with a novel color marker and its recognition method was proposed for the UAV autonomous landing system. The new marker is designed with a pattern which make it is as visible as possible in the whole landing process. The proposed relative position and attitude estimation algorithm is based on the four corner points on the marker which are detected by the combined approach of vanishing point of parallel lines and Levenberg-Marquardt (L-M) optimization method. Indoor and outdoor experiments were carried out. The results indicate that the method of marker detection has real-time and robust performance and the position and attitude estimation accuracy attain cm-level, which shows the feasibility of landings on moving platform automatically.
Optimización de algoritmos de agrupamiento aplicados a metagenómica usando big data

In the field of metagenomics, the use of ballistic metagenomics as the extraction process of amino acids chains of previously unidentified microorganisms, using environmental samples from diverse sources as its base. These amino acid chains, due to the extraction process, are separated in sub-chains of variable sizes, which will be used afterwards for comparison and identification with a database to not only determine which of the already recognized species are present in the samples, but also what portion of these amino acid sequences have not been previously categorized. Seeking for this method for identification to produce better results, clustering algorithms will be used as enablers in the identification process for the different species. These algorithms group amino acid chains with a certain similarity rate, producing sub-chain clusters, so these can then be compared in group and be analyzed faster. Platforms like Apache Spark and TensorFlow were used with the objective of reducing the execution times, as they include native implementations of these clustering algorithms in their libraries. With these libraries as a base, an implementation of Iterative K-means was created and then used as a comparison point. In the results it is apparent that Iterative K-means improves the cluster purity compared to the single-iteration alternative, using a metagenomics base with 4mer as features, and using the cosine distance. Due to the latter, and the fact that the K-means implementation in Apache Spark doesn’t include the cosine distance, TensorFlow was the primary platform used for the gathering of the results. The use of TensorFlow improved the execution times in general, with a more significative difference in the case of the Iterative K-means, with the disadvantage that it requires much more processing power.
A Theoretical Approach to Study the Perception of Women as Credible Leaders in Male-Dominated Organizations

This study is intended to conduct a theoretical analysis of the perception of women as credible leaders in male-dominated organizations and its association with advancement. The factors associated with the failure of women to advance to leadership positions were examined and whether credibility contributes to this failure to advance. Inspired by the words of Roderick Hart , "What one studies inevitably affects how one studies it," I have approached this study with an analytical style to dissect pertinent theoretical perspectives (Hart, 1994, p. 75). This study involved 120 participants from the Store Support Center of Consumer Value Store (CVS) located in Woonsocket, Rhode Island. The participants completed a survey based on the McCroskey Source Credibility Scale to assess the relationship between source credibility and advancement. Two surveys were distributed : Survey One requested employees to indicate how they felt his/her manager or supervisor felt about them as an employee. Survey Two was distributed to a total of fourteen managers requesting their perceptions of their male or female employees . In addition , pertinent information was gleaned from participants who responded to a research question about barriers they encounter regarding advancement to a leadership or management position. In particular , participants were asked to indicate if credibility is considered a barrier to advancement in a male-dominated organization. The results indicated that , first, no significant difference exists between men and women's perceived credibility in a male-dominated organization. Secondly, no significant data support the claim that women are not advancing at the same rate as men in a male-dominated organization. Important to this study are the obstacles to advancement identified by the participants , among which include family responsibilities, lack of mentoring programs and limited work-related experience .
PHARMACOGNOSTICAL EVALUATION OF NAGAKESARA USED IN DIFFERENT PARTS OF INDIA

Nagakesara though have no mentioning in Vedic literature is a widely mentioned drug in the Ayurvedic classics both in Brihattrayees and Laghutrayees . In Nighantukaala Nagakesara included in almost all Nighantus which mentions its prime importance and wide utility in therapeutics especially in disorders of GIT, skin and bleeding disorders. It has more than 20 synonyms. Modern literature mentions its considerable role in bleeding disorders and explanation regarding its morphological characters, habitat, chemical composition, characters and action. There is a need to study the position of Nagakesara in the crude drug market. It was found that there is a lot of confusion regarding the acceptance of genuine drug under the name Nagakesara . As there is scarcity and unavailability of Nagkeshara , also due to lack of awareness, instead of Nagkeshara , the Pratinidhidravya like Surpunnag, Punnag, Tamalpatra are used commonly. And gradually the use of real Nagkesara is diminished and the Ayurvedic Vaidya, teachers and students also understood the Pratinidhidravyas of it as real Nagkeshara.
FRACTURE SURFACES ANALYSIS OF MINE SUPPORT ROOT

The work deals with fractal response study of root of mine supports profile from carbon steel micro-alloyed with vanadium (0.12 %), niobium (0.04%) and nitrogen (0.012 %) designated for mine supports. The profile was evaluated both after hot rolling and after subsequent straightening that showed different impact energy values, even when the feature of fracture surfaces did not differ significantly. It was reason for fractal response study which proofed important differences between both treated materials. For solution Mandelbrot-Richardson concept was used. Surface roughness parameters (Rs) and fractal dimensions (D) for root demonstrated higher values after hot rolling. In this case microstructure evaluation also showed lower acicular ferrite portion supporting balance between strengthening, toughness and elimination of harmful segregation banding.
Encryption based Privacy Preservation on Big Data using Dynamic Data Encryption Strategy

: Big data is termed as huge volume of both structured and unstructured dataset. When dealing with these huge dataset several challenges are encountered by the users such as analysis, capture, storage, search, transfer, sharing, and visualization. To handle these complex data sets numerous technologies have been developed. With the developing technologies and all its connected devices, it is predicted that vast amount of data is produced in the last couple of years. There are many algorithms available for clustering the big data. This section focus on the density based clustering algorithms and its related works. Technology is enhancing each and every day especially in the field of Information Technology and data is very momentous elements. The large volume of data generated through devices is a major obstacle to handle in real time. The accomplishment of data ciphering is a crucial problem during the data progression and dissemination. In order to achieve pursuance, many application disregards data encryption. This article represents a concern about data privacy and suggests a novel data encryption approach known as Dynamic Data Encryption Strategy (DDES).
Global Cooperative Image Prefetching and GSA in Mobile Ad Hoc Networks

: Prefetching the data is a popular technique that improves data accessibility in wired or wireless networks. In mobile ad hoc networks(MANET), the possibility to improve the access latency and cache hit ratio may reduce due to the mobility of nodes and only limited cache space available in mobile hosts (MHs). To eliminate this problem in wireless networks, Global Cooperative Caching(GCC) with Image Prefetching (IP) algorithm is proposed that pre-fetches the image based on similarity measurement between data items and integrated with Gateway Selection Algorithm (GSA) for increasing throughput and reducing delay in the networks. Design implementation and analysis are done using Network Simulator 2(NS2) and MATLAB tool used to calculate the performance parameters of the proposed algorithm and also the parameters are compared with GCCIP to prove that proposed GCCIP-GSA algorithm have greatly improved the system performance in terms of packet loss, delay, throughput and resource utilization compared to existing schemes. Throughput analysis shows that GSA GCCIP results 13.63% higher throughput and 31.25% reduction in latency than conventional GCCIP. The Mean Square Error(MSE) and Peak Signal to Noise Ratio (PSNR) has been evaluated for different kind of images such as cameraman, lena, and baboon. In Proposed GCCIP-GSA, 28.8%, 23.75%, and 18.66% of MSE has been reduced compared to GCCIP for three different images respectively. At that same time, 60%, 55.6%, and 50% of PNSR has been improved in proposed method than conventional method.
Visualization for the ABS Modelling Language

Discovering defects early in the system development phase can have a big impact on the overall cost of developing a system. With Cloud Computing being the emerging trend in deploying large-scale systems, there can be significant economic and environmental benefits in utilizing more efficient resource usage. ABS is an executable modelling language designed for distributed systems. The users of ABS are utilizing the language to design and model systems early in the development phase. Running ABS simulations can imitate how a given system would behave in deployment. In doing so, service providers could obtain greater control over their resource usage. ABS has at present time no official support for visualization of simulation data to its users. Computer generated data is often nonintuitive to the human eye. By visualizing data we provide users with an additional tool to interpret the information at hand. In this thesis we propose a proof of concept application where ABS users can get their simulation data visualized. Furthermore, this thesis will expand on traditional information visualization principles, and how these can be incorporated into the application.
Multi-Connectivity as an Enabler for Reliable Low Latency Communications—An Overview

Emerging applications such as wireless industrial control or vehicular communications pose strict requirements in terms of latency and reliability on wireless communication systems. Since contemporary communication standards are not able to fulfill all of these requirements, reliable low-latency communication is an important research topic. Using multiple communication paths at once, i.e., multi-connectivity (MC), is a promising approach to get closer to reaching this goal of reliable low-latency communication. In this survey we provide a definition for MC, identify main scheduling categories, compare different network architectures and consider different layers for implementing MC. We provide an overview of MC methods already established in IEEE and 3GPP standardization and discuss recent MC approaches on a layer-by-layer basis. Moreover, we reflect on the existing literature and identify open issues. We suggest further research directions such as the OSI Layer where separation and aggregation of the different paths for MC is performed, the characteristics of the paths to be used for MC and dynamic scheduling techniques for MC settings.
Investigate the Reflective Evaluations of Prospective Teacher on STEM Education, Based on Their Experiences with Planning and Implementing STEM Activities

This study intends to investigate the reflective evaluations of prospective teacher on STEM education, based on their experiences with planning and implementing STEM activities. The research’s study group, designed qualitatively, is composed of 18 candidate teachers who graduated from the department of mathematics, 16 candidate teachers who graduated from the department of physics, 9 candidate teachers who graduated from the department of physics engineering and one teacher candidate from the department of textile engineering. STEM education programs carried out within the scope of the research lasted for 12 weeks, with four hours per week. Prior to the training programs, it was determined whether the teacher candidates had previous knowledge of STEM. After six weeks of training, study groups were formed which were composed of teacher candidates from different branches. Each team performed, with students, what they thought was an STEM activity in the classrooms. A structured interview was undertaken with the teams following completion of the activities. The content analysis method was used to analyze the data garnered from the interviews. The defining criteria for the STEM activities chosen within the scope of the study were their applicability, their compliance with STEM disciplines, their teaching potential and the features of the learning outcome (product). Teachers mentioned a few problems they encountered during the activities. It was found that the participants considered incorporating STEM activities into their teaching routine but that some had concerns about the preparation stage and the implementation process. In this context, it was established that the most remarkable concern regarding the preparation stage was the supply of materials and the need for preliminary preparation for the activities; while among the most frequently voiced concerns concerning the implementation stage were the time-consuming nature of the activities, efficient implementation of the STEM activities by the students, lack of cooperation between students, lack of active participation on the part of students and a lack of materials.
Valuation of Impaired Property.pmd

Authors’ Note: There are a variety of disciplines that address various aspects of the real estate valuation problem, including land planning, accounting, and business consulting. This chapter focuses on the appraisal model, since it is most widely accepted and understood in the U.S., particularly by the courts. However, while noting that there are other ways of viewing the problem of estimating damages to real estate that are outside the scope of the appraisal process, this chapter will focus on appraisal as the paradigm of choice.
A Holistic Heterogeneity-Aware Data Placement Scheme for Hybrid Parallel I/O Systems

We present H2DP, a holistic heterogeneity-aware data placement scheme for hybrid parallel I/O systems, which consist of HDD servers and SSD servers. Most of the existing approaches focus on server performance or application I/O pattern heterogeneity in data placement. H2DP considers three axes of heterogeneity: server performance, server space, and application I/O pattern. More specifically, H2DP determines the optimized stripe sizes on servers based on server performance, keeps only critical data on all hybrid servers and the rest data on HDD servers, and dynamically migrates data among different types of servers at run-time. This holistic heterogeneity-awareness enables H2DP to achieve high performance by alleviating server load imbalance, efficiently utilizing SSD space, and accommodating application pattern variation. We have implemented a prototype of H2DP under MPICH2 atop OrangeFS. Extensive experimental results demonstrate that H2DP significantly improve I/O system performance compared to existing data placement schemes.