Reviewerly/exHarmony huggingface dataset:
Reverse engineering learned optimizers reveals known and novel mechanisms

Learned optimizers are algorithms that can themselves be trained to solve optimization problems. In contrast to baseline optimizers (such as momentum or Adam) that use simple update rules derived from intuitive principles, learned optimizers use flexible, high-dimensional, nonlinear parameterizations. Although this can lead to optimizers with better performance in certain settings, their inner workings remain a mystery. How is it that a learned optimizer is able to outperform a well tuned baseline? Has it learned a sophisticated method for combining existing optimization techniques, or is it implementing completely new behavior? In this work, we address these questions by visualizing and understanding learned optimizers. We study learned optimizers trained from scratch on three disparate tasks, and discovered that they have learned interpretable mechanisms, including: momentum, gradient clipping, schedules, and a new form of learning rate adaptation. Moreover, we show how the dynamics of trained learned optimizers enables these behaviors. Our results elucidate the previously murky understanding of what learned optimizers learn, and establishes tools for interpreting future learned optimizers.
Perception of female users towards facilities management on high ropes activity at Skytrex Adventure Shah Alam

Adventure based activities are exciting, challenging and both physically andmentally demanding. Some activities may be stressful and possibly hazardous.However, with proper understanding on the risk and safety measures, it willenhance quality experience. This study was conducted at Skytrex AdventureShah Alam, Selangor. The purpose of this study were to identify users’demographic profiles in enhancing quality experience on high ropes activityand to determine users’ perception towards facilities management safety onhigh ropes activity. The instrument used in the study was a structuredquestionnaire. This study considered several factors in doing high ropesactivity such as safety, behaviour and characteristics, staffs and organization,equipment and infrastructure, activity and surrounding and weather. From thefindings, the results showed that the percentage of females are higher thanmales in participating high ropes activity. Additionally, the study also found thatfemales are more self-disciplined and they consider equipment inspection asan important safety aspect in order to gain quality experience. Besides, thisstudy also found that young females made their own decision to participate inhigh ropes activity. The result also shows that females participated in highropes activity to seek new experience and increase their self-confidence. Toconclude, all the respondents gave positive perception about the safety offacilities management that will affect their quality experience.
Multisectoral Analysis On The Awareness And Acceptability Of CSU Vision and Mission And College Of Teacher Education Goals and Program objectives

The vision, mission, goals, and objectives (VMGO) of any institution serve as the cornerstone of an educational institution in realizing its thrusts, mandates as well as aspirations. With the objective to assess the level of awareness and level of acceptability of the vision and mission of Cagayan State University along with the College of Teacher Education goals and program objectives, this descriptive-inferential study was conducted. A total of 750 respondents, composed of students, parents, alumni, faculty and administration, and linkages and community served as study respondents.  Data were gathered through a questionnaire and analyzed using mean and analysis of variance. It is evident from the results of the study that the different stakeholders are very much aware of the VMGO as a result of the various information and dissemination strategies utilized by the university. Likewise, the data also showed that the diverse sectors manifested a high level of acceptance of the VMGO due to their full participation in its formulation and the relevance of the objectives to the challenging demands of society. Furthermore, the findings indicated that the expressed level of awareness and level of acceptance of the different stakeholders on the VMGO did not differ.  Key Words: Awareness, Acceptability, College of Teacher Education, CSU, Goals, Mission, Program Objectives, Vision,
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models

Automatic construction of relevant Knowledge Bases (KBs) from text, and generation of semantically meaningful text from KBs are both long-standing goals in Machine Learning. In this paper, we present ReGen, a bidirectional generation of text and graph leveraging Reinforcement Learning to improve performance. Graph linearization enables us to re-frame both tasks as a sequence to sequence generation problem regardless of the generative direction, which in turn allows the use of Reinforcement Learning for sequence training where the model itself is employed as its own critic leading to Self-Critical Sequence Training (SCST). We present an extensive investigation demonstrating that the use of RL via SCST benefits graph and text generation on WebNLG+ 2020 and TekGen datasets. Our system provides state-of-the-art results on WebNLG+ 2020 by significantly improving upon published results from the WebNLG 2020+ Challenge for both text-to-graph and graph-to-text generation tasks. More details at https://github.com/IBM/regen.
Approximating the Permanent with Deep Rejection Sampling

We present a randomized approximation scheme for the permanent of a matrix with nonnegative entries. Our scheme extends a recursive rejection sampling method of Huber and Law (SODA 2008) by replacing the upper bound for the permanent with a linear combination of the subproblem bounds at a moderately large depth of the recursion tree. This method, we call deep rejection sampling, is empirically shown to outperform the basic, depth-zero variant, as well as a related method by Kuck et al. (NeurIPS 2019). We analyze the expected running time of the scheme on random $(0, 1)$-matrices where each entry is independently $1$ with probability $p$. Our bound is superior to a previous one for $p$ less than $1/5$, matching another bound that was known to hold when every row and column has density exactly $p$.
Does Pretraining for Summarization Require Knowledge Transfer

Pretraining techniques leveraging enormous datasets have driven recent advances in text summarization. While folk explanations suggest that knowledge transfer accounts for pretraining’s benefits, little is known about why it works or what makes a pretraining task or dataset suitable. In this paper, we challenge the knowledge transfer story, showing that pretraining on documents consisting of character n-grams selected at random, we can nearly match the performance of models pretrained on real corpora. This work holds the promise of eliminating upstream corpora, which may alleviate some concerns over offensive language, bias, and copyright issues. To see whether the small residual benefit of using real data could be accounted for by the structure of the pretraining task, we design several tasks motivated by a qualitative study of summarization corpora. However, these tasks confer no appreciable benefit, leaving open the possibility of a small role for knowledge transfer.
واقع استخدام معلمي العلوم الشرعية للبرامج التعليمية القائمة على التعلم المتنقل (الجوال) من وجهة نظرهم The Reality of Using Mobile Learning-Based Instructional Programs by Sharia Sciences Teachers from their Viewpoint

هدفت هذه الدراسة إلى التعرف على واقع استخدام معلمي العلوم الشرعية للبرامج التعليمية القائمة على التعلم المتنقل (الجوال) من وجهة نظرهم، من خلال التعرف على مستوى معرفة معلمي العلوم الشرعية بالبرامج التعليمية القائمة على التعلم المتنقل (الجوال)، وتحديد درجة استخدام المهارات التدريسية بالبرامج التعليمية القائمة على التعلم المتنقل (الجوال) لدى معلمي العلوم الشرعية بالتعليم العام، والكشف عن وجهة نظرهم نحو استخدام البرامج التعليمية القائمة على التعلم المتنقل (الجوال). وقام الباحث باستخدام المنهج الوصفي حيث تم بناء استبانة مكونة من (40) فقرة. وتكون مجتمع الدراسة من جميع معلمي العلوم الشرعية في مدارس التعليم العام بمنطقة عسير في الفصل الدراسي الثاني من العام الدراسي 1439/1440. وتوصلت الدراسة الى أن درجة استخدام معلمي العلوم الشرعية للبرامج التعليمية القائمة على التعلم المتنقل (الجوال) من وجهة نظرهم كانت متوسطة، وأن مستوى معرفة معلمي العلوم الشرعية للبرامج التعليمية القائمة على التعلم المتنقل (الجوال) من وجهة نظرهم كانت بدرجة متوسطة، وجاء درجة وجهة نظرهم نحو استخدام البرامج التعليمية القائمة على التعلم المتنقل (الجوال) عالية. وفي ضوء ما تم التوصل إليه من نتائج قدم الباحث تصورا مقترحا لتنمية مهارات معلم العلوم الشرعية في استخدام تطبيقات التعلم المتنقل (الجوال)، كما تم وضع جملة من التوصيات والمقترحات.  This study aimed at identifying the reality of using mobile learning-based instructional programs by Sharia sciences teachers from their own viewpoints. The study also attempted to investigate the levels of knowledge of using mobile learning-based programs and the levels of including teaching skills in these programs in public education, as well as their point of view toward using mobile learning in general. The study used a descriptive approach and developed a questionnaire of 40 paragraphs as its study instrument. The population of the study consisted of all Sharia Sciences teachers in public schools in Aseer region during the second semester of the 2018/2019 academic year. The results revealed that the degree of using mobile learning-based instructional programs was at an average. The level of familiarity of Sharia teachers with mobile learning-based instructional programs from their own point of view was average and the levels of their point of view toward using such programs were high. In the light of the findings of this study, the researcher presented a proposal for developing the skills of Sharia sciences teachers in the use of mobile learning applications and a set of recommendations.
CLIP-It! Language-Guided Video Summarization

A generic video summary is an abridged version of a video that conveys the whole story and features the most important scenes. Yet the importance of scenes in a video is often subjective, and users should have the option of customizing the summary by using natural language to specify what is important to them. Further, existing models for fully automatic generic summarization have not exploited available language models, which can serve as an effective prior for saliency. This work introduces CLIP-It, a single framework for addressing both generic and query-focused video summarization, typically approached separately in the literature. We propose a language-guided multimodal transformer that learns to score frames in a video based on their importance relative to one another and their correlation with a user-defined query (for query-focused summarization) or an automatically generated dense video caption (for generic video summarization). Our model can be extended to the unsupervised setting by training without ground-truth supervision. We outperform baselines and prior work by a significant margin on both standard video summarization datasets (TVSum and SumMe) and a query-focused video summarization dataset (QFVS). Particularly, we achieve large improvements in the transfer setting, attesting to our method's strong generalization capabilities.
Implementasi Ant Colony Optimization (ACO) pada Sistem Irigasi Lahan Tadah Hujan

The purpose of this research is to produce an optimal water distribution system for irrigation of rainfed land. The problem with conventional irrigation systems is that the water distribution process cannot be controlled and monitored automatically and in real time. The impact on water distribution becomes ineffective. The implementation of Ant Colony Optimization (ACO) is used in research as a method to determine the location or node based on the pheromone pattern of the soil dryness level at the sprinkler nodes to be distributed by the water flow, taking into account the criteria level on the soil as a trend of probability values ??and determining the nodes according to the needs in the flow water. The results obtained from this study indicate that the data displayed is the level of dryness of each node, the volume of water in the reservoir, and the flow of water flowing. The ACO test shows the sequence of nodes that will be passed after the optimization process of water distribution in a rainfed irrigation system using the ACO method gets an error value calculated by the MAPE method of 43% so that it gets an accuracy value of 57%.
Enhancement in cloud data security using searchable cipher text policy ABE encryption

Cloud Storage is an important aspect of cloud services. It provides a secure access to store and retrieve the data for validated and authenticated users. However, with the advancement with data storage in cloud, there are few challenges and security concern in cloud computing.  To enhances the data security in the existing cloud architecture, cipher text policy ABE access control schema is analysed in this research. In the proposed research homomorphic encryption is integrated with the cipher text attribute-based encryption. The proposed searchable cipher text policy ABE encryption schema provides an additional security and search feature in the ciphertext data in cloud architecture. The proposed research provides a security analysis and experimental result analysis for the proposed architecture. The experimental results shows that the proposed searchable cipher text policy ABE encryption enhances the security and it also improve the time complexity with implementation of ciphertext retrieval.
Geoscience and petroleum / by Mac Beggs

Considers the symbiosis between the geoscience profession in New Zealand and the petroleum industry, including its customers (which includes geoscientists), both historically and into the future. Highlights the opportunities for geoscientists presented by oil and gas exploration. Mentions the revival of the Geoscience Society of New Zealand (GSNZ) Oil and Gas Special Interest Group (SIG) in this regard.
Current approach in education to computer-controlled machines

Recently, there have been more and more articles and discussions about the need to educate the next generation in the field of computer-controlled machines. In this context, the machines may include not only industrial manipulators, production units, CNC machines etc., but it may also involve household appliances such as washing machines, intelligent electrical installation and so on.Many authors from all over the world deal with this issue. The situation has developed so far that even the governments of many countries have realized the need of teaching young generation in primary and secondary school and not to leave the issue up to technical high-schools. In the case of the Czech Republic, the background study Man and Technology (Clověk a technika, Dostal, 2018) deals with the issue. The author looks for solutions and ways how to prepare a generation of technically educated people and thus ensure enough potential applicants to study at a technical school. Unlike the rest of the world, the Czech Republic has not decided yet on the exact extent to which the issue will be taught. At
IMPACT OF AGRICULTURAL PROJECTS ON ECONOMIC GROWTH IN EGYPT

Purpose of the study: The purpose of the study was to examine the impact of agricultural projects on economic growth in EgyptStatement of the problem: The Egyptian economy has commonly depended dramatically on agriculture as a source of growth and support for the non-agricultural sectors of the economy. Agriculture is a vital element of the Egyptian economy because it provides livelihoods for about 55% of the population, mainly in rural areas. However, agricultural production has dropped drastically in the past three years, which formed the study's motive.Research Methodology: The study was literature-based. The inferences were made based on the verdicts from the preceding studies.Research Findings: The study discovered that irrigation and agricultural productivity are key drivers of Egypt's economic growth. The agricultural outputs supply the industries with the raw materials. Moreover, it was found the sector employs more than 57% of the total population in the country. It was established that agriculture is key to the economy, contributing 37 percent of the Gross Domestic Product (GDP) and another 41 percent of GDP indirectly through linkages with other sectors.  Moreover, it was established that agriculture is also a market for industrial goods such as machinery, equipment and fertilizers used in the farming process.Conclusion: The study concluded that irrigation and agricultural productivity is one of critical drivers of economic growth in Egypt. The study also concluded that the major source of water for irrigation is from River Nile. The agriculture sector creates employment and contributes significantly to the country's GDPRecommendations: The study recommended that the government should ensure there is a continuous supply of water from the River Nile for irrigation. Moreover, it was recommended that there be other water sources if the supply of water from the River Nile decreases. The suggested sources of water include digging plenty of boreholes in the country. The government and other stakeholders should put strategies that will enhance the performance of such training on the most profitable products. The government should also offer subsidies to the farmers.Keywords: Irrigation, Agricultural productivity, Economic growth, Egypt
Relative stability toward diffeomorphisms indicates performance in deep nets

Understanding why deep nets can classify data in large dimensions remains a challenge. It has been proposed that they do so by becoming stable to diffeomorphisms, yet existing empirical measurements support that it is often not the case. We revisit this question by defining a maximum-entropy distribution on diffeomorphisms, that allows to study typical diffeomorphisms of a given norm. We confirm that stability toward diffeomorphisms does not strongly correlate to performance on benchmark data sets of images. By contrast, we find that the stability toward diffeomorphisms relative to that of generic transformations $R_f$ correlates remarkably with the test error $\epsilon_t$. It is of order unity at initialization but decreases by several decades during training for state-of-the-art architectures. For CIFAR10 and 15 known architectures, we find $\epsilon_t\approx 0.2\sqrt{R_f}$, suggesting that obtaining a small $R_f$ is important to achieve good performance. We study how $R_f$ depends on the size of the training set and compare it to a simple model of invariant learning.
PERBANDINGAN ALGORITMA NAÏVE BAYES DAN C4.5 DALAM MENENTUKAN TINGKAT PENJUALAN MOTOR HONDA

At this time, the mobility of the community activities will be very high. The mobility of the high society will have an impact to the transportation needs are increasing. Coupled with the opportunities of the community towards taxi online, will have an impact on the number of sales of motorcycles in Indonesia. In this research we will classify the sales data of the motor with rapidminer software by applying two algorithm: Algorithm C45, and Naive Bayes. The second algorithm is a method of classification based on statistics and probability. Two of the above algorithms compared the rate of speed in the best-selling motorcycle classification of data, to get the decision tree the best selling motorcycle was purchased by the society, and should be in more production. Research using Algorithm C45 motor with the lowest prices is the best selling, than Algorithm Naïve Bayes where the best selling honda motor is a type of Scooter, medium of CC, and low price.
A Comparative Study on Shortest Path Visualization using Artificial Intelligence

In the modern computation system, that rely on various aspects to obtain the optimal results in easy manner appears more deterministic. There are several algorithms available that can distinguish a probable shortest path between two points, which helps students actively study algorithms with visualization. Therefore, in this study, we developed the GUI based shortest path finding tool consists of Dijkstra and A* algorithm. Further, in this study, the obtained shortest path results were graphically visualised and tabular output are stored in the database. The implementation of the algorithm and visualization was developed using Java AWT API and SWING package of Java. The Dijkstra and A* algorithm comparative analysis showed that the checks value and path length in terms of the A* algorithm is comparatively less than Dijkstra. Thus, it is affirmative that A * approach produces faster results and appeared as more efficient in terms of destination path finding than Dijkstra algorithm. Path finding is a fundamental feature of many significant applications and can be applied in static, interactive, and real-time situations. Such information shed light on the efficient application for computer gaming, robots, logistics, and crowd simulation.
Corruption Robust Active Learning.

We conduct theoretical studies on streaming-based active learning for binary classification under unknown adversarial label corruptions. In this setting, every time before the learner observes a sample, the adversary decides whether to corrupt the label or not. First, we show that, in a benign corruption setting (which includes the misspecification setting as a special case), with a slight enlargement on the hypothesis elimination threshold, the classical RobustCAL framework can (surprisingly) achieve nearly the same label complexity guarantee as in the non-corrupted setting. However, this algorithm can fail in the general corruption setting. To resolve this drawback, we propose a new algorithm which is provably correct without any assumptions on the presence of corruptions. Furthermore, this algorithm enjoys the minimax label complexity in the non-corrupted setting (which is achieved by RobustCAL) and only requires $\tilde{\mathcal{O}}(C_{\mathrm{total}})$ additional labels in the corrupted setting to achieve $\mathcal{O}(\varepsilon + \frac{C_{\mathrm{total}}}{n})$, where $\varepsilon$ is the target accuracy, $C_{\mathrm{total}}$ is the total number of corruptions and $n$ is the total number of unlabeled samples.
Hybrid Denoising and Contrast Enhancement Algorithm for Satellite Image Preprocessing

Image denoising and contrast enhancement is a vital issue in image processing. An assortment of image de noising methods have been created to manage added substance commotion in late many years, including variety based methods, partial differential equation (PDE) - based methods, filter-based methods, sparse representation (SR)- based methods, wavelet-based methods, profound neural organization (DNN)- based methods and so on Notwithstanding, clamor is as yet a troublesome issue to manage it by different methods. Accordingly in this paper we proposed another preprocessing technique called Hybrid Denoising and Contrast Enhancement (HDCE) calculation is proposed. Here Contrast Limited Adaptive Histogram Equalization (CLALE) is utilized to work on the nearby contrast of an image after that wavelet change and anisotropic diffusion model utilized for image denoising. This calculation naturally upgrades the contrast of the image and further develops the power variety remedy of the image by eliminating the speckle and hence denoising the satellite image all the while.