Miracle-dz/UniFAR-FTUs huggingface dataset:
Sistem Informasi Perpustakaan Online di Man Kota Solok

Research conducted at the MAN library of Solok city, found the problems of recording and making report book data and borrowing / returning books still using handwritten not stored into the database, as well as students difficulty in finding and knowing the required stock of books, so the required library information system online that can assist in recording, preparing reports and finding and knowing the required stock of books stored in the database. In this research is designed online library information system with PHP programming language and MySQL Database in question to help library MAN city of Solok in data processing book. The method used in this research is SDLC waterfall type and design tool with UML. The results of this study is an application that can provide convenience in recording, reporting and knowing availability of existing stock of books. Officers can make report book data or borrowing / returning more quickly and accurately. Availability of complete book data, making it easier for students to get book information.
Cooperative Network Formation between Swarm Robots

Swarm robot technology could be used in future daily applications. It does not require a lot of manpower once it is deployed into the field compared to doing large scale tasks manually done by humans. However, the downside of using swarm robots is that when the number of agents grow the communication between the swarms will also gradually get more complicated. Besides that, when moving large number of agents in a swarm towards completing a specific task together will also be a challenge due to the monitoring of each swarm agent’s location. Lastly, the swarms should be able to reestablish their location with the large swarms if it ever gets lost from the pack. In order to overcome these problems, engineers have been exploring various ways of making robots work together and communicate with each other with different methods and tools. For this project, it will focus more on the type of wireless communication used, ranging from short range Bluetooth to long range Wi-Fi. With the wireless communication established, the swarms should be able to perform multi-hop communication with each agents through different network topologies. Finally, the swarm robots requires a cooperative algorithm in order for it to adapt to various situation in the different environments. When each agent is deployed into outdoor environments, it will have to adapt to the surroundings while maintaining a certain predetermined flight formation and constant communication with each agent and the base station.
Imposing Category Trees Onto Word-Embeddings Using A Geometric Construction

We present a novel method to precisely impose tree-structured category information onto word-embeddings, resulting in ball embeddings in higher dimensional spaces ( N -balls for short). Inclusion relations among N -balls implicitly encode subordinate relations among categories. The similarity measurement in terms of the cosine function is enriched by category information. Using a geometric construction method instead of back-propagation, we create large N -ball embeddings that satisfy two conditions: (1) category trees are precisely imposed onto word embeddings at zero energy cost; (2) pre-trained word embeddings are well preserved. A new benchmark data set is created for validating the category of unknown words. Experiments show that N -ball embeddings, carrying category information, signiﬁcantly outperform word embeddings in the test of nearest neighborhoods, and demonstrate surprisingly good performance in validating categories of unknown words. Source codes and data-sets are free for public access https://github.com/GnodIsNait/nball4tree.git and
Determining the Relationship between Income Inequality, Economic Growth & Inflation

The objective of the study is to determine the relationship between economic growth, income distribution, and inflation and to test the existence of Kuznets Hypothesis in Pakistan. For this purpose, data on economic growth, income distribution and inflation have been collected from WDI for 22 years, i.e., from 1980 to 2002. Johnsonâ€™s Co-integration model has been used to analyze data. Empirical results proposed that income inequality has a long-term co-integrated movement with economic growth. In addition, economic growth hinders with the high income inequality. Furthermore, the empirical results show that there exists a log run relationship between inflation and income distribution. Another finding is that Kuznets hypothesis exists in Pakistan, despite the fact that inequality is affected positively by inflation. The government should control inflation to trim down income inequality. Government must revise tax policy, because indirect taxes cause high inflation due to which income distribution become unfair. Direct taxes should be employed in the in those people who have larger income.
COMPARATIVE ANALYSIS OF TWO BIOMETRIC ACCESS CONTROL SYSTEMS

This paper compared two Biometric Access Control Systems (BACS). The BACSs employed iris pattern for their authentication. Fast Fourier Transform-driven Access Control System (FACS) uses global iris features while Haar Wavelet Transform-driven Access Control System (HACS) uses local iris features for its template generation. Principal Component Analysis (PCA) was employed to select principal components of the extracted features (local and global). Fuzzy clustering was used for classification and Euclidean Distance (ED) as the distance metric. Experimental result showed that it took more time to train the HACS than FACS because of its intrinsic location in the iris images. It was discovered that global features driven Access Control System (FACS) with EER being 7.78 outperformed the local features driven Access Control System (HACS) with EER of 8.05. Though the two systems satisfied the benchmark of 80% for Recognition Accuracy (RA) of Biometric Systems, FACS exhibited RA of 89.87% while HACS achieved a RA of 83.83% when tested on iris images captured with CMITECH DMX-10 Portable USB 5.0 M pixel CCD Iris Camera at automatic convenient eye distances. Performance of global and local features on other biometric recognition systems can be tested and a means of combining the two features for hybridization can also be sought.
A Study to Evaluate Pattern of Rifampicin Resistance in Seropositive HIV Patients in Tertiary Care Center in Western Uttar Pradesh

INTRODUCTION: Tuberculosis in the PLHIV population is a major cause of morbidity and mortality and presents a substantial hazard of nosocomial disease transmission to other patients and health care workers. These risks are heightened when patients have multidrug-resistant TB. To address these challenges, there is a critical need in such a setting for rapid, effective screening for TB and the detection of drug resistance and early initiation of treatment. Delayed treatment is associated with higher morbidity and mortality. METHOD: This was a cross-sectional survey among HIV infected adult patients attending A.R. T Nodal centers, Medicine Opd and ward and chest, and T.B OPD and wards. All the patients with presumptive pulmonary TB were assessed for sputum for microscopy AFB and underwent Drug susceptibility test (CBAAT) for Rifampicin resistance. The primary aim of the Study is “TO EVALUATE PATTERN OF RIFAMPICIN RESISTANCE IN SEROPOSITIVE HIV PATIENTS IN TERTIARY CARE CENTER IN WESTERN UTTAR PRADESH” RESULT: The present study concluded that the prevalence of sputum positive pulmonary tuberculosis is very high of 39% among presumptive pulmonary tuberculosis patients. BY using CBNAAT we found that 27% of rifampicin resistance was prevalent among 78 patients. In the study population maximum sputum, positive pulmonary tuberculosis was in-between age group 31 to 50 years. Majority 81% of the patients were male. CONCLUSION: Sputum microscopy has very low sensitivity (35.9%) in our study for diagnosing tuberculosis in PLHIV. whereas CBNAAT has a sensitivity of 95 %,it detected double numbers of patients than AFB microscopy.
Many

This paper describes a push-the-button MT system combination toolkit. The combination is based on the creation of a lattice made on several confusion networks (CN) connected together. This lattice is then decoded with a token-pass decoder to provide the best and/or n-best outputs. Each CN is built using a modified version of the TERp tool. The toolkit is made of several scripts along a core program developed in Java. It is totally configurable and the parameters can be tuned quite easily.
"It's in the family circle": Communication promoting Indigenous family resilience.

Objective We use the Framework of Historical Oppression, Resilience, and Transcendence (FHORT) to investigate the framework's core concept of family resilience and related protective and promotive factors that contribute to greater resilience, namely communication. Background Scant research has examined communication in Indigenous families; yet general research suggests that family communication is a prominent aspect of family resilience. Methods In this exploratory sequential mixed-methods study with data from 563 Indigenous participants (n = 436 qualitative and n = 127 quantitative survey), thematic reconstructive analysis was used to qualitatively understand participants' experiences of family communication and quantitatively examine protective factors for family resilience. Results The following themes related to family communication as a component of family resilience emerged from qualitative analysis: "It's in the Family Circle": Discussing Problems as a Family with the subtheme: Honesty between Partners; (b) "Never Bring Adult Business into Kids' Lives": Keeping Adult Conversations Private; and (c) "Trust Us Enough to Come to Us": Open Communication between Parents and Children. Regression analysis indicated that higher community and social support, relationship quality, and life satisfaction were associated with greater family resilience. Conclusions Positive communication practices are a strong component of resilience, healthy Indigenous families. Promotive factors at the community (social and community support), relational (relationship quality), and individual (life satisfaction) levels positively contribute to Indigenous family resilience. Implications Clinical programs providing practical tools to foster healthy communication - both about difficult topics as well as positive topics - are promising avenues to foster resilience.
Affinity Propagation Based Document Clustering Using Suffix Tree

All documents and data are in digital form reason of easy maintaining, faster access and compact storage. To access relative document easily document clustering is used. Document clustering creates segments collection of textual documents into subgroups using similar contents. The purpose of document clustering is to meet human interests in information searching and understanding. An effective feature phrase of document is more informative feature for improving document clustering. This paper proposes phrase content based document similarities based on suffix tree. Content based similarity used term frequency (tf) and inverse document frequency (idf) weighting method to compute similarity of documents. Proposed affinity propagation clustering approach is very effective on clustering the documents of standard document OHSUMED dataset by comparison with existing document clustering methods.
An Approach to Identify and Evaluate Congestion in Data Envelopment Analysis

Congestion indicates an economic state where inputs are overly invested. Evidence of congestion occurs whenever reducing some inputs can increase outputs. In this paper, we present a new model to identify and evaluate congestion in Data Envelopment Analysis (DEA). We use output efficient DMUs to construct our proposed model to evaluate congestion. We also proposed a linear inequality and equality system to identify the occurrence of congestion. Finally, three numerical examples are presented to illustrate the use of our proposed method.
Automatic post-editing of phrase-based machine translation outputs

We present Depfix, a system for automatic post-editing of phrase-based English-to-Czech machine translation outputs, based on linguistic knowledge. First, we analyzed the types of errors that a typical machine translation system makes. Then, we created a set of rules and a statistical component that correct errors that are common or serious and can have a potential to be corrected by our approach. We use a range of natural language processing tools to provide us with analyses of the input sentences. Moreover, we reimplemented the dependency parser and adapted it in several ways to parsing of statistical machine translation outputs. We performed both automatic and manual evaluations which confirmed that our system improves the quality of the translations.
Joint Multi-Beam Training and Codebook Design for Indoor High-Throughput Transmissions Under Limited Training Steps

Along with the evolution of wireless technologies, users are expected to have high-throughput and low-latency indoor mobile applications, such as augmented reality and virtual reality. Therefore, the use of millimeter-wave for low-latency communications becomes attractive. Under low-latency criteria, a fast multi-link establishment is desired. In this paper, we present a novel concurrent multi-beam training jointly with a proposed multi-beam codebook. First, we propose a progressive multi-beam estimation (PMBE). This algorithm makes multiple coarse beams emerge within a few training steps. Second, we propose a Fast Fourier Transform (FFT)-based codebook which can be considered as a Discrete Fourier Transform based codebook built in a bit-reversal scrambler, thus forming a scattering multi-beam codebook. This FFT-based codebook design is optimally implemented on a sub-connected architecture, a hardware-efficient architecture. For fast multi-link establishment, we design our concurrent multi-beam training by combining the proposed PMBE jointly with the proposed FFT-based codebook, so as to further improve multiplexing gain. Since multiplexing gain can occur immediately, even under strictly-limited training steps, the high data rate can be achieved. Simulation results show that when reaching more than 95% capacity gain of exhaustive channel estimation, our work only spends 1% training steps of the exhaustive case.
Interactive Agent-Based Simulation for Experimentation: A Case Study with Cooperative Game Theory

Incorporating human behavior is a current challenge for agent-based modeling and simulation (ABMS). Human behavior includes many different aspects depending on the scenario considered. The scenario context of this paper is strategic coalition formation, which is traditionally modeled using cooperative game theory, but we use ABMS instead; as such, it needs to be validated. One approach to validation is to compare the recorded behavior of humans to what was observed in our simulation. We suggest that using an interactive simulation is a good approach to collecting the necessary human behavior data because the humans would be playing in precisely the same context as the computerized agents. However, such a validation approach may be suspectable to extraneous effects. In this paper, we conducted a correlation research experiment that included an investigation into whether game theory experience, an extraneous variable, affects human behavior in our interactive simulation; our results indicate that it did not make a significant difference. However, in only 42 percent of the trials did the human participants’ behavior result in an outcome predicted by the underlying theory used in our model, i.e., cooperative game theory. This paper also provides a detailed case study for creating an interactive simulation for experimentation.
Capacity Upper Bounds for Deletion-type Channels

We develop a systematic approach, based on convex programming and real analysis for obtaining upper bounds on the capacity of the binary deletion channel and, more generally, channels with i.i. d. insertions and deletions. Other than the classical deletion channel, we give special attention to the Poisson-repeat channel introduced by Mitzenmacher and Drinea (IEEE Transactions on Information Theory, 2006). Our framework can be applied to obtain capacity upper bounds for any repetition distribution (the deletion and Poisson-repeat channels corresponding to the special cases of Bernoulli and Poisson distributions). Our techniques essentially reduce the task of proving capacity upper bounds to maximizing a univariate, real-valued, and often concave function over a bounded interval. The corresponding univariate function is carefully designed according to the underlying distribution of repetitions, and the choices vary depending on the desired strength of the upper bounds as well as the desired simplicity of the function (e.g., being only efficiently computable versus having an explicit closed-form expression in terms of elementary, or common special, functions). Among our results, we show the following: (1) The capacity of the binary deletion channel with deletion probability d is at most (1 − d) φ for d ≥ 1/2 and, assuming that the capacity function is convex, is at most 1 − d log(4/φ) for d < 1/2, where φ = (1 + √5)/2 is the golden ratio. This is the first nontrivial capacity upper bound for any value of d outside the limiting case d → 0 that is fully explicit and proved without computer assistance. (2) We derive the first set of capacity upper bounds for the Poisson-repeat channel. Our results uncover further striking connections between this channel and the deletion channel and suggest, somewhat counter-intuitively, that the Poisson-repeat channel is actually analytically simpler than the deletion channel and may be of key importance to a complete understanding of the deletion channel. (3) We derive several novel upper bounds on the capacity of the deletion channel. All upper bounds are maximums of efficiently computable, and concave, univariate real functions over a bounded domain. In turn, we upper bound these functions in terms of explicit elementary and standard special functions, whose maximums can be found even more efficiently (and sometimes analytically, for example, for d = 1/2).
Network-based detection of malicious activities - a corporate network perspective

This dissertation is concerned with exploring how corporations can mitigate security threats from the Internet. The described research delves into two distinct topics. First, we improve on correlation and filtering of alerts from Intrusion Detection Systems to make it feasible in practice. Second, we explore how to detect malicious and abusive domain names, in order to block their usage and disable threats depending on the Domain Name System. Threats from the Internet are increasingly relevant as corporations continue to adopt processes that make use of the Internet, thereby increasing trust in the inherently insecure network of networks belonging to many different entities. This development adds to and multiplies the threats from malicious entities, as systems enabling business processes see increased Internetconnectivity and thereby exposure to e.g. cybercriminals. Correlation and filtering is explored, in particular how it can be performed without depending on costly feature engineering and tuning, thereby offering a feasible solution to the problem of too many correlated and false alerts from Intrusion Detection Systems. By introducing a general approach that precludes feature engineering and requires that alerts are ingested as text without assumptions on the format, we argue that our methods achieve significant lower deployment costs than existing methods, which makes practical applications feasible. Two presented implementations, one based on Recurrent Neural Networks and one based on Latent Semantic Analysis, are evaluated on public data, and found relevant to consider for practical use. Domain names, the Domain Names System, and abuse of both, are explored with a focus on the resulting threats. One finding in the analysis is that pre-registration detection is a promising approach for efficient prevention of many threats because it applies before the registration process for a domain is completed. Subject to accurate prediction of malicious intents, threats that rely on domain names can be mitigated efficiently by blocking registrations. A novel method for analysing domain name blacklists is proposed. The method applies over time and covers entire blacklists, as opposed to a sampled subset. It is demonstrated how lexical analysis on domain names can contribute to recognising malicious domain names. Finally,
Pre-Processing and Compression of ECG Records for Biotelemetry Purposes

Cardiovascular heart diseases are one of the leading causes of deaths in India and throughout the world. A majority of population suffers from these cardiovascular diseases due to inadequate access to healthcare facilities and non availability of healthcare personnel. Advanced technologies like Telemedicine, biotelemetry have been developed to eliminate the barriers of distance and topology. These technologies when used for detection of cardiovascular heart disease using ECG have limited operational bandwidth and face archival problems. This work is based on efficiently using the bandwidth and solving the archival problem by preprocessing the signal and ECG compression. ECG data compression algorithm will reduce the amount of data to be transmitted, stored and analyzed, without losing the clinical information. Direct compression techniques are evaluated to find an optimal compression strategy for ECG data compression. Keywords—ECG, ECG Preprocessing, ECG Data Compression, Telemedicine, Biotelemetry
Class Imbalance Learning for Software Defect Prediction

The objective of Software Defect Prediction is to find as many defective software modules as possible without hurting the overall performance of the constructed predictor. The imbalanced distribution of SDP data is a main cause of its learning difficulty, but has not received much attention.Class imbalance learning is an important research area in machine learning, where instances in some classes heavily outnumber the instances in other classes. This unbalanced class distribution causes performance degradation. In this paper, we study whether and how class imbalance learning can facilitate SDP. We investigated five class imbalance learning methods, covering three types (undersampling, threshold-moving, Boosting-based ensembles), in comparison with two top-ranked predictors (Naive Bayes, and Random Forest) in the SDP literature. Among those methods, AdaBoost.NC shows the best overall performance in terms of the measures including balance, G-mean, and Area Under the Curve (AUC). To further improve the performance of the algorithm, and facilitate its use in software defect prediction, we propose a dynamic version of AdaBoost.NC, which adjusts its parameter automatically during training. Without the need to pre-define any parameters, it is shown to be more effective and efficient than the original AdaBoost.NC. Keywords– Software Defect Prediction, Class Imbalance Learning, Ensemble Learning, Sampling.