Reviewerly/exHarmony huggingface dataset:
Optimasi Indikator Linear Weighted Moving Average Menggunakan Teknik Martingale sebagai Metode Peningkatan Profit Trading

Forex Trading is familiar to ordinary people as a way of investing, many of investor buying and selling transactions of foreign exchange or stocks using the MetaTrader software as a direct bridge to the forex or stock market. One method to find out the trends in the capital market is to use pre-installed indicator or directly listen to economic news. Linear Weighted Moving Average as one of the favorite indicators in forex or stock traders as it for individuals or bank managers, it is the most widely used for analyzing the direction of the capital market. The Linear Weighted Moving Average is not a holy grail whose are one hundred percent correct, it is because the volatile of forex and stock changes. The auto trading script (expert advisor) provided by Metatrader still has weaknesses due to incorrect predictions and resulting in reducing balance from investors' capital. The expectations from this research that martingale technique to optimize Linear Weighted Moving Average indicator in every forex transaction to increase profits in forex or stock trading.Keywords: Forex Trading, Martingale, Linear Weighted Moving Average
Unsupervised Out-of-Domain Detection via Pre-trained Transformers

Deployed real-world machine learning applications are often subject to uncontrolled and even potentially malicious inputs. Those out-of-domain inputs can lead to unpredictable outputs and sometimes catastrophic safety issues. Prior studies on out-of-domain detection require in-domain task labels and are limited to supervised classification scenarios. Our work tackles the problem of detecting out-of-domain samples with only unsupervised in-domain data. We utilize the latent representations of pre-trained transformers and propose a simple yet effective method to transform features across all layers to construct out-of-domain detectors efficiently. Two domain-specific fine-tuning approaches are further proposed to boost detection accuracy. Our empirical evaluations of related methods on two datasets validate that our method greatly improves out-of-domain detection ability in a more general scenario.
University Interactive Distance Education

The purpose of this paper is to discuss the evolution of Distance Education as a result of technological developments. Also, it is described the Integrated Program for University Teaching at PUC. This program was created in 1996 and its main goal is to face challenges and globalization associated to teaching, due to the development of telecommunications technology. To accomplish that goal, several activities and courses are being implemented.
Concurrent Composition of Differential Privacy

We initiate a study of the composition properties of interactive differentially private mechanisms. An interactive differentially private mechanism is an algorithm that allows an analyst to adaptively ask queries about a sensitive dataset, with the property that an adversarial analyst's view of the interaction is approximately the same regardless of whether or not any individual's data is in the dataset. Previous studies of composition of differential privacy have focused on non-interactive algorithms, but interactive mechanisms are needed to capture many of the intended applications of differential privacy and a number of the important differentially private primitives. We focus on concurrent composition, where an adversary can arbitrarily interleave its queries to several differentially private mechanisms, which may be feasible when differentially private query systems are deployed in practice. We prove that when the interactive mechanisms being composed are pure differentially private, their concurrent composition achieves privacy parameters (with respect to pure or approximate differential privacy) that match the (optimal) composition theorem for noninteractive differential privacy. We also prove a composition theorem for interactive mechanisms that satisfy approximate differential privacy. That bound is weaker than even the basic (suboptimal) composition theorem for noninteractive differential privacy, and we leave closing the gap as a direction for future research, along with understanding concurrent composition for other variants of differential privacy.
Educational Platforms in the Saudi Universities and their Role to Counter The Corona Pandemic between Reality and Hope (Prospective Vision).

The study aims to identify the role of electronic educational platforms in Saudi universities during the Corona pandemic through two questionnaires consisting of three main axes The questionnaire was distributed to some Saudi universities to know the actual reality of educational platforms and their role in facing the Corona pandemic from faculty and students' viewpoints The research also aims to compare some Saudi universities in identifying the role of educational platforms (blackboard) The study relies on the universities that ranked first (20) in terms of the number of users and relied on building a future vision for those platforms The research concluded that: There is a positive activation in the use of educational platforms in light of the Corona pandemic A positive role for Saudi universities in light of the pandemic King Abdulaziz University ranked first, King Faisal University ranked tenth, and Bisha University ranked twentieth in terms of the number of users of educational platforms;despite this, Bisha University obtained the highest statistically high rates in discussion boards, assessments and virtual classes, and a future vision for educational platforms is built [ABSTRACT FROM AUTHOR] Copyright of Journal of Education - Sohag University is the property of Journal of Education - Sohag University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission However, users may print, download, or email articles for individual use This abstract may be abridged No warranty is given about the accuracy of the copy Users should refer to the original published version of the material for the full abstract (Copyright applies to all Abstracts )
Analysis of the influence of graph characteristics on MAPFW algorithm performance

The Multi-Agent Path Finding (MAPF) problem is a problem in which a route must be found for multiple agents such that they do not collide. The Multi-Agent PathFinding with Waypoints problem extends this problem by adding waypoints that the agents must visit before travelling to their end location. This paper compares five algorithms for MAPF that have been extended to incorporate waypoints. It also analyzes which influence map characteristics like corridors, chokepoints, overlapping waypoints and the average degree have on the performance of these algorithms. It concludes that EMLA and WM* perform best overall with some variations per characteristic.
Activity Segmentation by Using Window Variance Comparison Method

In the activity recognition, it is very important to extract the data when the activity occurs from the whole data. In order to solve this problem, we propose a window variance comparison algorithm. Calculate the window variance of the preprocessed data and compare it with the window variance before or after, to determine the occurrence and continuation of the activity. Experimental results show that this algorithm can accurately and effectively achieve activity segmentation.
Batch Reinforcement Learning With a Nonparametric Off-Policy Policy Gradient

Off-policy reinforcement learning (RL) holds the promise of better data efficiency as it allows sample reuse and potentially enables safe interaction with the environment. Current off-policy policy gradient methods either suffer from high bias or high variance, delivering often unreliable estimates. The price of inefficiency becomes evident in real-world scenarios such as interaction-driven robot learning, where the success of RL has been rather limited, and a very high sample cost hinders straightforward application. In this paper, we propose a nonparametric Bellman equation, which can be solved in closed form. The solution is differentiable w.r.t the policy parameters and gives access to an estimation of the policy gradient. In this way, we avoid the high variance of importance sampling approaches, and the high bias of semi-gradient methods. We empirically analyze the quality of our gradient estimate against state-of-the-art methods, and show that it outperforms the baselines in terms of sample efficiency on classical control tasks.
SynthRef: Generation of Synthetic Referring Expressions for Object Segmentation

Recent advances in deep learning have brought significant progress in visual grounding tasks such as language-guided video object segmentation. However, collecting large datasets for these tasks is expensive in terms of annotation time, which represents a bottleneck. To this end, we propose a novel method, namely SynthRef, for generating synthetic referring expressions for target objects in an image (or video frame), and we also present and disseminate the first large-scale dataset with synthetic referring expressions for video object segmentation. Our experiments demonstrate that by training with our synthetic referring expressions one can improve the ability of a model to generalize across different datasets, without any additional annotation cost. Moreover, our formulation allows its application to any object detection or segmentation dataset.
Pendampingan Pelatihan Menyulam Pada Remaja Panti Asuhan As-Salam Kota Pekanbaru

Embroidery crafts are one of the riches of Indonesian traditional culture. Embroidery is a decoration made on cloth with a sewing needle and thread. This embroidery training was attended by the As-Salam Orphanage young women. Community service activities in the field of embroidery skills training on the introduction of basic stitching to decorate fabrics to improve skills in the city of Pekanbaru. This is closely related to skills education that can be used in everyday life and entrepreneurship, namely decorating cloth. The method of implementation is through outreach and training approaches. The results of the activities felt by the training participants were that they could get to know the basics of embroidery skills and in the end they could have the skills to increase activity and productivity and it was hoped that it would increase family income. By providing the As-Salam Orphanage with knowledge and skills regarding basic stitching in embroidery, it can be concluded that in this Community Service activity, it is necessary to carry out repeated exercises to produce better work.
InfoGuides: Business Research Toolkit: Elective: Academic Integrity

Modeled on the University of Alabama at Birgmingham, Sterne Library's award winning Business Research Toolkit Program, GMU's version is now self-paced and asynchronous. The hands-on program introduces participants to business resources and research.
A Novel Algorithm for Denoising Image with Deep Neural Network and Spatial Filters

When the image is transmitted noise is added during transmission and reception process due to various factor Like thermal noise in antennas or faulty devices, intentionally and interference of other signals. Gaussian noise is prominent among the noises and can be removed. Different methods are available for Denoising. Nowadays Deep neural network along with different algorithms are used which gives satisfactory result. One of the algorithms is proposed in this paper which uses iterative median, low pass-high boost technique along with deep neural network to do the Denoising of a noisy image.
Factors of organizing physical experiments based on non-traditional technologies

This article presents the factors of organizing physical experiments based on non-traditional technologies and algorithms for virtual organization and execution of individual experiments in the field of physics of electromagnetic oscillations using electronic resources. Assessing students ‘knowledge in non-traditional teaching technology is an important task. Since this technology is mainly focused on independent learning, it is advisable to take into account not only the results of the experiment but also the creative activity and independent working skills of students in the process of assessing students’ knowledge.
Formation Engineering and Pedagogy of E-Learning in Light of Corona Pandemic

As a result of the global epidemiological reality, E-learning has become a necessity for advanced educational systems because universities are the backbone of scientific, economic, political and social development for all nations alike because they are a major source of minds, it is based on two main pillars, namely the teacher who is the foundation building block and the basic structure of the other pillar, which is the student who relies on his teacher in terms of his cognitive and scientific development in addition to psychological, educational and social guidance. As for the basic role that every student relies on, it is qualifying him to obtain the certificate, all of which is in the end in the interest of society, so a relationship is formed between the student and the teacher and the focus of which is the pedagogical interaction based on respect, care and exhortation to convey knowledge and abilities, whether by the student or the teacher. The current paper aims to formulate a vision for E-learning according to the perspective of training engineering planning (components, programs, financial capacity, implementation, follow-up, results and questionnaire) with pedagogical support based on the consistency of roles between the student and the teacher from the scientific and psychological point of view, and the psychological component is a fundamental factor in the success of Educational process. Key words: E-learning, Corona pandemic, Formation engineering, Pedagogy.
On the Robustness of Average Losses for Partial-Label Learning

Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL). In PLL, identification-based strategy (IBS) purifies each PL on the fly to select the (most likely) TL for training; average-based strategy (ABS) treats all candidate labels equally for training and let trained models be able to predict TL. Although PLL research has focused on IBS for better performance, ABS is also worthy of study since modern IBS behaves like ABS in the beginning of training to prepare for PL purification and TL selection. In this paper, we analyze why ABS was unsatisfactory and propose how to improve it. Theoretically, we formalize five problem settings of PLL and prove that average PL losses (APLLs) with bounded multi-class losses are always robust, while APLLs with unbounded losses may be non-robust, which is the first robustness analysis for PLL. Experimentally, we have two promising findings: ABS using bounded losses can match/exceed state-of-the-art performance of IBS using unbounded losses; after using robust APLLs to warm start, IBS can further improve upon itself. Our work draws attention to ABS research, which can in turn boost IBS and push forward the whole PLL.
Processing Queries over Multidimensional Encrypted Data

In new computing architectures, more and more data is outsourced to remote storage providers. When data outsourced, sensitive information becomes vulnerable to theft.  Data owners want to make sure their data is safe from breaches by third parties, insider threats, and untrusted service providers. When queries are run over encrypted data, a certain level of secrecy is maintained. However, how to efficiently query encrypted multidimensional for data stored in an untrusted server environment residue a research challenge. In this paper, we propose an effective technique based on encryption by different keys in multidimensional mode to execute computations on data encrypted for multi principals. The proposed technique run queries directly without decrypting data as sensitive data are protected and insured.
ON THE PATH TOWARDS OPEN INNOVATION: ASSESSING ROLE OF KNOWLEDGE ORIENTED LEADERSHIP WITH KNOWLEDGE CAPABILITIES

This paper aims to examine the outcomes of the knowledge-oriented leadership on the organization’s open innovation through the mediating effect of the Knowledge Process Capability and Knowledge Infrastructure Capability and moderating role of Intellectual Capital. Data collection was done from the manufacturing sector through convenient sampling. The sample size was 105 respondents. The questionnaire was used and SEM is applied for the data analysis. The study concluded that there is a direct optimistic relation between KOL and OOI. Mediating role of KPC and KIC between KOL and OOI was found significant. Although the moderating results shows that by adding the intellectual capital as the moderator it increases the Organization’s KPC and KIC and in return organizations will become capable for increasing its open innovation’s targets. This study’s finding can be helpful for employers in adopting better open innovation’s trends with providing significant support for future investigation and research in related field.
Development of learning media for earth physics based on augmented reality as an interactive learning media

Abstract Augmented Reality (AR) technology has been applied in various fields, including education. One of the courses that can apply AR technology is the earth physics course. The use of AR as a supporting tool can help visualize things that cannot be seen directly in their form to improve learning that is directly difficult to imagine. AR supports studying complex subjects, in particular, geology and geophysics as part of earth physics. AR development using the Rapid-Application Development (RAD) technique. The results of this study are mobile-based learning media to visualize geology and geophysics subjects using AR.
Gesture Classification using Machine Learning with Advanced Boosting Methods

In this paper, a detailed study on gesture classifica- tion using a dataset from Kaggle and optimizing the dataset is presented. The machine learning algorithms, which are SGD, kNN, SVM, MLP, Gaussian Naive Bayes classifier, Random Forest, LightGBM, XGBoost, and CatBoost classifiers, to conduct the research and, are used. The results are compared with each other to conclude which models perform the best in gesture classification. Except for the Gaussian Naive Bayes classifier, all methods resulted in high accuracy.
Deterministic modelling of seismic waves in the Urgent Computing context: progress towards a short-term assessment of seismic hazard

&amp;lt;p&amp;gt;Seismic wave propagation is currently computationally prohibitive at high frequencies relevant for earthquake engineering or for civil protection purposes (up to 10 Hz). Developments of computational high-performance computing (HPC) infrastructures, however, will render routine executions of high-frequency simulations possible, enabling new approaches to assess seismic hazard - such as Seismic Urgent Computing (UC) in the immediate aftermath of an earthquake. The high spatial resolution of near-real time synthetic wavefields could complement existing live data records where dense seismic networks are present or provide an alternative to live data in regions with low coverage. However, time to solution for local near-field simulations accounting for frequencies above 1 Hz, as well as availability of substantial computational resources pose significant challenges that are incompatible with the requirements of decision makers. Moreover, the simulations require fine tuning of the parameters, as uncertainties in the underlying velocity model and in earthquake source information translate into uncertainties in final results. Estimating such uncertainties on ground motion proxies is non-trivial from a scientific standpoint, especially for the higher frequencies that remain an uncharted territory. In this talk we wish to address some of these key challenges and present our progress in the design and development of a prototype of a Seismic UC service. In the long run, we hope to demonstrate that deterministic modelling of ground motions can indeed in the future contribute to the short-term assessment of seismic hazard. &amp;amp;#160;&amp;lt;/p&amp;gt;
On the development of a flexible borescope fringe projection system

The 3D measurement of the interior of hollow technical bodies is still a challenge for state-of-the-art measurement systems. This is especially true for complex industrial free-form objects inside confined spaces. Fringe projection enables accurate and fast 3D measurements of object surfaces. To fulfill the task of geometric inspection of confined spaces, we have developed an endoscopic and flexible fringe projection system. The fringe patterns are generated with a RGB LED projector. Fibre-coupling of the structured light is achieved by using a microscope lens and bundle of coherent image fibres. A compact sensor head can be achieved by using a micro-objective for projecting the fringes and a Chip-on-Tip camera to capture the images. A micro lens with a diameter of 1.7 mm was used as the projector lens and a 2 megapixel 1/6” chip was used as the image sensor. By synchronizing the projector with the camera, the system is capable of capturing up to 10 grayscale patterns per second. The measurement volumes result to approximately 20 x 13 x 4 mm3. Typically, the measurement time is in the range of 1 - 3 s, depending on the number of projected images. Measurements on a 50 µm step standard confirmed that a measurement uncertainty of less than 29.4 ± 3.2 µm is achieved with this system. Mounted on a carrier system, the presented fringe projection system offers the possibility to enter confined areas and perform high-precision 3D measurements.
Hybrid Method Based on NARX models and Machine Learning for Pattern Recognition

This work presents a novel technique that integrates the methodologies of machine learning and system identification to solve multiclass problems. Such an approach allows to extract and select sets of representative features with reduced dimensionality, as well as predicts categorical outputs. The efficiency of the method was tested by running case studies investigated in machine learning, obtaining better absolute results when compared with classical classification algorithms.
Unbiased Methods for Multi-Goal Reinforcement Learning

In multi-goal reinforcement learning (RL) settings, the reward for each goal is sparse, and located in a small neighborhood of the goal. In large dimension, the probability of reaching a reward vanishes and the agent receives little learning signal. Methods such as Hindsight Experience Replay (HER) tackle this issue by also learning from realized but unplanned-for goals. But HER is known to introduce bias, and can converge to low-return policies by overestimating chancy outcomes. First, we vindicate HER by proving that it is actually unbiased in deterministic environments, such as many optimal control settings. Next, for stochastic environments in continuous spaces, we tackle sparse rewards by directly taking the infinitely sparse reward limit. We fully formalize the problem of multi-goal RL with infinitely sparse Dirac rewards at each goal. We introduce unbiased deep Q-learning and actor-critic algorithms that can handle such infinitely sparse rewards, and test them in toy environments.