Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges Affecting The Implemetation Of Technical Curriculum In Public Tvet Institutions In Kenya: A Case Of Nandi And Uasin Gishu Counties

Education is a medium through which the social values and norms of the learners are transmitted through the formal schooling system. Formal education is a vital instrument for attaining technological progress and economic growth judging by the experience of industrialized nations. Investing in education is a potent means that could be explored to fast-track technological progress, economic growth and boosting citizens’ capacities. The main purpose of the study was to investigate the challenges that affected the implementation of the technical curriculum of TVET programs in TVET institutions. This was to improve on the quality of the programs offered in the TVET institutions thus making the graduates marketable and in tandem with the industrial needs. This research adopted a descriptive research design. The study targeted all technical teachers and graduating students of public technical training institutions in Uasin-Gishu and Nandi County. The study sampled 136 technical teachers and 276 technical students from two technical institutions in UasinGishu and Nandi Counties. Questionnaires were content and face validated and reliability was determined using Cronbach’salpha. Data from the questionnaires were analyzed using descriptive statistics. The study established that student’s poor reading culture, student’s peer group, learners’ irregular attendance to class, lecturers’ competence among other factors, had a high influence on the implementation of the technical curriculum. The study recommends that the lectures should be competence in their area of specialty and that the students should have regular attendance to classes. It was recommended that a further research needs to be carried out to establish how the stakeholders who are the major beneficiaries can participate effectively in the implementation of TVET programs.
Comparing the Variability Using Louis’ Method and Resampling Methods

There is a problem when a relatively simple analysis is changed into a complex one just because some of the information is missing. Louis showed how to estimate the standard deviation of maximum likelihood estimate (MLE) for a parameter θ using the missing information. In the meantime, the resampling method is one of the best methods to calculate the standard deviation of sample estimates. In this article, we define and compare the standard deviation of a parameter θ using complete data, incomplete data, and the EM algorithm. As an illustration, we analyze a data from Rao and compare all methods for estimating variability. The paper is organized as follows, in section 2, we give a brief overview of the MLE and EM algorithm. Then, in section 3 we provide a more detailed, structured proof of the Louis’ method. In the final section 4, we illustrate with an example from Rao’s paper in which 197 animals are multinomially distributed into four categories. We describe a simulation study that was carried out to compare the results on standard deviation estimate using Louis’ method and resampling methods. In the 5th and final section of the paper, we discuss the performance of all methods including Louis’ method and bootstrap methods in terms of variability and then make our conclusions. These bootstrap methods are very useful to some data which are also discussed in this section. The Maximum Likelihood Estimation and EM Algorithm In this article, we denote the vector x represents the complete data and it is denoted as c, while the vector y represents the actual observed data or incomplete data or partial data and it is denoted as p. We let X be an n dimensional random vector with a probability density function (pdf) f(x;θ) on a sample space , where x=(x1,x2,.,xn) T and θ=(θ1,θ2,.,θd) T is the vector containing the unknown d parameters in the postulated form for the pdf of X, and the corresponding likelihood function, say Lc(x,θ) Similarly, y=(y1,y2,.,ym) T denotes an observed random sample of size m on random vector Y with pdf g(y; θ) and the corresponding likelihood function, say Lp(y,θ). The superscript T denotes the transpose of a matrix. The objective is to find the MLE of θ say θ̂ . An estimate θ̂ can be obtained as a solution of the log-likelihood equation: log ( , ) = 0. c L x θ θ ∂ ∂ (1) Citation: Gunaratnam B, Rai SN (2019) Comparing the Variability Using Louis’ Method and Resampling Methods. J Biom Biostat 10: 424.
Estimating the Efficiency Improvement of the Resource Allocation in the Yahoo! Keyword Auction

The keyword auction is a successful pricing mechanism which helps search engine companies sell navigation service to advertisers. Correctly understanding the performance differences among different types of keyword auctions will not only affect the multibillion dollar revenue of search engine companies, but it will also help develop more superior keyword auctions in the future. For the two popular keyword auctions— the Generalized First Price (GFP) auction and the Generalized Second Price (GSP) auction—current consensus in both the industry and academia is that the GSP auction is more efficient than the GFP auction. Specifically, a bidder with a higher value will be more likely to win a higher and better slot for her advertisement. However, there is no empirical examination on this claim. I investigate this issue by exploiting a natural experiment of the Yahoo! keyword auction system upgrade in 2002. I construct an efficiency index and show that the GSP auction mechanism is at least 4% more efficient.
Automated Discovery of Cross-Plane Event-Based Vulnerabilities in Software-Defined Networking

—Software-deﬁned networking (SDN) achieves a programmable control plane through the use of logically centralized, event-driven controllers and through network applications (apps) that extend the controllers’ functionality. As control plane decisions are often based on the data plane, it is possible for carefully crafted malicious data plane inputs to direct the control plane towards unwanted states that bypass network security restrictions ( i.e., cross-plane attacks ). Unfortunately, because of the complex interplay among controllers, apps, and data plane inputs, at present it is difﬁcult to systematically identify and analyze these cross-plane vulnerabilities. We present E VENT S COPE , a vulnerability detection tool that automatically analyzes SDN control plane event usage, discovers candidate vulnerabilities based on missing event-handling routines, and validates vulnerabilities based on data plane effects. To accurately detect missing event handlers without ground truth or developer aid, we cluster apps according to similar event usage and mark inconsistencies as candidates. We create an event ﬂow graph to observe a global view of events and control ﬂows within the control plane and use it to validate vulnerabilities that affect the data plane. We applied E VENT S COPE to the ONOS SDN controller and uncovered 14 new vulnerabilities.
Theory and Evidence from Germany

This paper indicates that the extent of collective bargaining coverage in an industry may depend on the differences in firms’ productivity levels within the industry. Less pronounced differences in productivity levels make it easier to design collective wage contracts that are accepted by a wider range of firms within an industry. Higher dispersion in productivity levels gives rise to the use of firm-level wage agreements reached by unions, while coverage by industry-wide contracts is likely to decrease. We measure correlations between productivity variation and collective bargaining coverage in various industries using German linked-employer-employee data from 2000-2008 and find that the share of industry-wide collective bargaining agreements may indeed be negatively correlated with the dispersion of plant productivity within an industry, while the opposite might hold true for firm-level contracts. JEL-Classification: D22, J24, J51, L16
Study of incidence of auto-immune thyroiditis in western Maharashtra

Autoimmune thyroiditis (AIT) is the most common thyroid disorder in current scenario. The disease results from an as yet poorly characterized defect or defects in immunoregulation and a cascade of events progressing from lymphocyte infiltration of the thyroid, to T-celland cytokine-mediated thyroid follicular cell injury, and apoptotic cell death. Approximately 70% of disease risk has been attributed to genetic background with environmental factors being important in triggering disease in susceptible individuals. The contribution of individual genes is small and probably polymorphisms in multiple genes play a role. Some immunosusceptibility genes affect immune recognition or response in general, while others are thyroid-specific. Environmental agents may act through an epigenetic mechanism. In a tertiary care multispecialty hospital in Ahmednagar district a prospective study was undertaken in 264 patients with thyroid swelling and related complaints who attended medicine outpatient department after surgical opinion. It needs early diagnosis as well as early management , to prevent further complications and for decreasing morbidity and mortality in these patients. From this study, we can conclude that FNAC is a very reliable and cheap tool for the diagnosis of the thyroid nodule.
How do Dancers Want to Use Interactive Technology?: Appropriation and Layers of Meaning Beyond Traditional Movement Mapping

There has been an increased interest in HCI research regarding the possibilities of interactive technology applied to the field of dance performance, particularly contemporary dance. This has produced numerous strategies to capture data from the moving bodies of the dancers and to map that data into different types of display formats. In this paper, we look at the role of interactive technology in dance performance from a broader perspective, aiming at understanding the needs of dancers and their relation with the audience. To this end, we ran a focus group with ten dancers with expertise in technology. We analysed the focus group using thematic analysis. We discuss the implications for design of our results by framing the role of technology in dance performance, proposing design guidelines related to the communication to the audience, use of technology, and mapping. Moreover, we propose different levels of ambiguity and appropriation related to the creators of the performance and the audience.
Multiple Set Matching with Bloom Matrix and Bloom Vector

Bloom Filter is a space-efficient probabilistic data structure for checking the membership of elements in a set. Given multiple sets, a standard Bloom Filter is not sufficient when looking for the items to which an element or a set of input elements belong. An example case is searching for documents with keywords in a large text corpus, which is essentially a multiple set matching problem where the input is single or multiple keywords, and the result is a set of possible candidate documents. This article solves the multiple set matching problem by proposing two efficient Bloom Multifilters called Bloom Matrix and Bloom Vector, which generalize the standard Bloom Filter. Both structures are space-efficient and answer queries with a set of identifiers for multiple set matching problems. The space efficiency can be optimized according to the distribution of labels among multiple sets: Uniform and Zipf. Bloom Vector efficiently exploits the Zipf distribution of data for further space reduction. Indeed, both structures are much more space-efficient compared with the state-of-the-art, Bloofi. The results also highlight that a Lookup operation on Bloom Matrix is significantly faster than on Bloom Vector and Bloofi.
UBAL: a Universal Bidirectional Activation-based Learning Rule for Neural Networks

Artificial neural networks, in particular the deep end-to-end architectures trained by error backpropagation (BP), are currently the topmost used learning systems. However, learning in such systems is only loosely inspired by the actual neural mechanisms. Algorithms based on local activation differences were designed as a biologically plausible alternative to BP. We propose Universal Bidirectional Activation-based Learning, a novel neural model derived from contrastive Hebbian learning. Similarly to what is assumed about learning in the brain, our model defines a single learning rule that can perform multiple ways of learning via special hyperparameters. Unlike others, our model consists of mutually dependent, yet separate weight matrices for different directions of activation propagation. We show that UBAL can learn different tasks (such as pattern retrieval, denoising, or classification) with different setups of the learning hyperparameters. We also demonstrate the performance of our algorithm on a machine learning benchmark (MNIST). The experimental results presented in this paper confirm that UBAL is comparable with a basic version BP-trained multilayer network and the related biologically-motivated models.
Research on Power Terminal Security Monitoring Method Based on Business Logic Consistency

In order to reduce the failure rate of smart grid and effectively improve the security protection capability of power terminals, this paper proposes a power terminal security monitoring method based on business logic consistency. The method can effectively correlate "timing label", "equipment status characteristics" and "business process", and can achieve security monitoring and abnormality detection for common attacks of power terminals from the business level. Based on this method, a monitoring platform for metering service is further built, and simulation experiments are conducted on the platform. The results show that the proposed method and platform can effectively monitor the operation status of power terminals that participating in the metering service. Besides, the method improves the monitoring efficiency and fault identification rate of safety hazards. The research in this paper can be extended to various power business in the power industry. It provides a theoretical basis and implementation method for preventing penetration and safety monitoring of power terminals from the business level.
Segmification: Solving road segmentation and scene classification tasks for self-driving cars using one neural network

In this work we show our developed model for a self-driving car that solves a road segmentation task and at the same time classifies whether the vehicle is moving in or off the lane. We demonstrate how scene classifier can be efficiently embedded into a segmentation neural network model by the means of branching additional layers with small number of trainable parameters. The model was trained and tested on our own dataset with good accuracy performance. Experiment with external dataset proves efficiency of the proposed approach and shows reasonable results in both segmenation and classification tasks.
Captured by an Algorithm

Captured by an Algorithm is a commemorative plate series that looks at romance novels through the lens of the Amazon Kindle Popular Highlight algorithm. A passage in a Kindle e-book becomes a Popular Highlight after a certain number of people independently highlight the same passage. Popular Highlights show up as underlined along with the number of people who highlighted that passage. Each commemorative plate features a Kindle Popular Highlight from a romance novel and a landscape generated by running Photoshop's Photomerge algorithm on scanned romance novel covers. Over seventy thousand individual acts of highlighting were used to determine the content for this work telling the story of the intense loneliness, grief, vulnerability, and discontent felt by the readers. This work reveals a glimpse of a positive, anonymous social network emerging unintentionally through this minor Kindle feature. This ongoing project draws attention to this existing example of collective social support to change society's vision for the future of social technologies.
Cyberbullying Detection Using Deep Learning and Word Embeddings: An Empirical Study

Cyberbullying detection has become a pressing need in Internet usage governance due to its harmful consequences. Different approaches have been proposed to tackle this problem, including deep learning. In this paper, an empirical study is conducted to evaluate the effectiveness and efficiency of deep learning algorithms, coupled with word embeddings in detecting cyberbullying texts. Three deep learning algorithms were experimented, namely GRU, LSTM and BLSTM. Data pre-processing steps, including oversampling were performed on the selected social media datasets. For feature representations, four different word embeddings models were explored, including word2vec, GloVe, Reddit and ELMO models. Elmo cares of word context by capturing information from the word surroundings which eliminates some of the shortcomings of pre-trained word embeddings models. For more accurate results, 10-fold cross-validation technique was implemented. The experimental results show that BLSTM performs best with ELMO in detecting cyberbullying texts. The efficiency of each model is also measured by calculating the average time taken for training each model. GRU outperforms in terms of time efficiency. Based on the analysis done on false negative cases, three observations were made, which highlight the limitations of word embeddings models on top of GRU algorithm in cyberbullying detection.
Generalizing Long Short-Term Memory Network for Deep Learning from Generic Data

Long Short-Term Memory (LSTM) network, a popular deep-learning model, is particularly useful for data with temporal correlation, such as texts, sequences, or time series data, thanks to its well-sought after recurrent network structures designed to capture temporal correlation. In this article, we propose to generalize LSTM to generic machine-learning tasks where data used for training do not have explicit temporal or sequential correlation. Our theme is to explore feature correlation in the original data and convert each instance into a synthetic sentence format by using a two-gram probabilistic language model. More specifically, for each instance represented in the original feature space, our conversion first seeks to horizontally align original features into a sequentially correlated feature vector, resembling to the letter coherence within a word. In addition, a vertical alignment is also carried out to create multiple time points and simulate word sequential order in a sentence (i.e., word correlation). The two dimensional horizontal-and-vertical alignments not only ensure feature correlations are maximally utilized, but also preserve the original feature values in the new representation. As a result, LSTM model can be utilized to achieve good classification accuracy, even if the underlying data do not have temporal or sequential dependency. Experiments on 20 generic datasets show that applying LSTM to generic data can improve the classification accuracy, compared to conventional machine-learning methods. This research opens a new opportunity for LSTM deep learning to be broadly applied to generic machine-learning tasks.
An Experimental Study of Automatic Detection and Measurement of Counterfeit in Brand Search Results

Brand search results are poisoned by fake ecommerce websites that infringe on the trademark rights of legitimate holders. In this article, we study how to tackle and measure this problem automatically. We present a pipeline with two machine learning stages that can detect the ecommerce websites present in the list of brand search results and distinguish between legitimate and fake ecommerce websites. For each classification task, we identify and extract suitable learning features and study their relative importance. Through a prototype system termed RI.SI. CO., we show that this approach is feasible, fast, and more accurate than both existing systems for trustworthiness assessment and non-expert humans. We next introduce two complementary metrics for evaluating the counterfeit incidence in brand search results: namely, a chart-based and a single-value measure. They allow us to analyze and compare counterfeit at various levels, including single brands within a specific sector as well as whole sectors. Experimenting with two luxury goods sectors, we report a number of interesting findings about how the main search parameters (e.g., search engine, query type, number of search results seen) affect counterfeiting and how this activity changes with time. On the whole, our research offers new insights and some very practical and useful means of analyzing and measuring counterfeit in brand search results, thus increasing awareness of and knowledge about this phenomenon and enabling targeted anti-counterfeiting actions.
Bladder Tumor Grading and Staging Prediction of Magnetic Resonance Imaging Based on Transfer Learning

Bladder cancer ranks fourth in male malignant tumors and eighth in mortality. Early detection of bladder tumors is important for preventing bladder cancer, reducing mortality, and improving patients' quality of life. In this paper, we used the deep learning model such as Resnet_v2_50 and Inceptionv3 to predict Bladder tumor grading and staging. When the model was trained and tested, the experiment data set coming from the Chinese University Computer Design Competition - Big Data and Artificial Intelligence Challenge, was used. The test set which accounted for 10% of the data set was randomly disrupted. First, the method of transfer learning was used to train the corresponding network where the convolutional layer parameters were fixed and the fully connected layer was retrained. Then for this data set, we compared the impact of three improved strategies on its classification accuracy. The three improved strategies were that: 1. Doing data augmentation by randomly changing brightness and contrast of images and then added the changed images to the training set. 2. Features extracted by Resnet_v2_50 and Inception_v3 were stitched before they were sent into the fully connected layer. 3. Combining the previous two methods. The experiments results showed that for the grading prediction, the inception_v3 model with data augmentation had the highest classification accuracy of 95.5%; for the staging prediction, stitching models based on the inception_v3 and Resnet_v2_50 without data augmentation had the highest classification accuracy of 92.8%. It implied the effectiveness of these two improved strategies (feature stitching and data augmentation) for different classification tasks. However, it may not work well if the two methods are used at the same time.
Data Mining in Health Care Sector: Literature Notes

A standout amongst the most essential strides of the knowledge discovery in database KDD is data mining. Data mining is defined as a basic advance during the time spent learning discovery in databases in which understanding strategies are utilized in order to pattern discovery. Due to the huge amount of data available within the healthcare systems, data mining is important for the healthcare sector in the clinical and diagnosis diseases. However, data mining and healthcare organizations have developed some of dependable early discovery frameworks and different healthcare related frameworks from the clinical treatment and analysis information. The main motivation of this paper is to give a survey of data extraction in health care. In addition, the benefits and obstacles of the use of data extraction strategies in health care and therapeutic information have been thought.