Reviewerly/exHarmony huggingface dataset:
Linear Time Runs Over General Ordered Alphabets.

A run in a string is a maximal periodic substring. For example, the string $\texttt{bananatree}$ contains the runs $\texttt{anana} = (\texttt{an})^{3/2}$ and $\texttt{ee} = \texttt{e}^2$. There are less than $n$ runs in any length-$n$ string, and computing all runs for a string over a linearly-sortable alphabet takes $\mathcal{O}(n)$ time (Bannai et al., SODA 2015). Kosolobov conjectured that there also exists a linear time runs algorithm for general ordered alphabets (Inf. Process. Lett. 2016). The conjecture was almost proven by Crochemore et al., who presented an $\mathcal{O}(n\alpha(n))$ time algorithm (where $\alpha(n)$ is the extremely slowly growing inverse Ackermann function). We show how to achieve $\mathcal{O}(n)$ time by exploiting combinatorial properties of the Lyndon array, thus proving Kosolobov's conjecture.
Promoting thinking in terms of causal structures: Impact on performance in solving complex problems

Author(s): Kessler, Franziska; Proske, Antje; Goldwater, Micah; Urbas, Leon; Greiff, Samuel; Narciss, Susanne | Abstract: Goldwater and Gentner (2015) showed that the sensitivity for causal structures can be promoted with an intervention combining explication of causal models and guided structural alignment of situations from disparate fields with the same underlying causal model. We extended this intervention with inference questions and combined it with a subsequent complex problem-solving (CPS) task, in order to investigate whether enhanced sensitivity for causal structures results in better performance in CPS. This study (N = 108) compares the CPS performance indicators knowledge acquisition and knowledge application among three experimental groups (intervention, intervention extended with inference questions, control group) and reveals the following results: 1) The effectiveness of the intervention in increasing the sensitivity for causal structures was replicated. 2) Sensitivity for causal structures and CPS performance indicators were significantly positively correlated. 3) There is no direct effect of the intervention on CPS performance, but an indirect-only effect via enhanced sensitivity.
Smart Traffic Light Management System Using Arducam and ESP8266

Traffic Congestion and traffic observing is one of the significant issues everywhere in the world. This work utilizes IoT and Adaptive Neuro-Fuzzy Inference System (ANFIS) to improve traffic conditions. An ANFIS traffic light regulator with contributions as holding up time and vehicle thickness is created utilizing MATLAB SIMULINK climate. A camera is utilized to catch the traffic scenes, and this image is moved to the cloud utilizing Arduino Platform. The image is then broke down in the worker utilizing ANFIS regulator, and reasonable control signals are shipped off the traffic signals.
Video Classification into Academic and Entertainment using Subtitles

Automated classification of text into predefined categories has always been considered as a vital method to manage and process a vast amount of documents in digital forms that are widespread and continuously increasing. This kind of web information is popularly known as the digital/electronic information. It is in the form of documents, conference material, publications, journals, editorials, web pages, videos, e mail etc. People largely access information from these online sources rather than being limited to archaic paper sources like books, magazines, newspapers etc. But the main problem is that this enormous information lacks organization which makes it difficult to manage. Text classification is recognized as one of the key techniques used for organizing such kind of digital data. This paper describes our work on building a Classification system for video subtitles. As an example, we report on our evaluation results for two TV genres ­Academic and Entertainment. Through this implementation, the user can classify the videos into academic and entertainment in any software or system, which will help the user to differentiate the videos properly into useful and distracting content. This enables applications like social networking and instant messaging apps to filter distracting content, thereby enhancing productivity of users.
Do High-Performance Work Practices Influence Organizational Innovation? Mediating Role of Human Capital

Due to globalization and competitive business environment, organizations need to update their information to remain innovative and competitive. Moreover, high-performance work practices (HPWPs) play a vital role that determines organizational success. The current study is performed to scrutinize the effect of high-performance work practices on the organizational innovation with the mediating effect of human capital. Survey questionnaire method was adopted, and data were collected from 202 employees by applying simple random sampling technique. Scales validation has been checked by confirmatory factor analysis (CFA) by using Smart PLS. Moreover, the structure equation modeling technique (SEM) has also been used to test the hypotheses. Findings revealed that there is a positive and significant relationship between high-performance work practices (participation, empowerment, work-life balance, recruitment & selection) and organizational innovation. Additionally, results also purported that human capital significantly mediates the relationship b/w high-performance work practices (participation, empowerment, work-life balance, recruitment & selection) and organizational innovation.
New cryptosystem using Vigenere and multiple bit-level permutations

The largest part of the image encryption algorithms operates on the pixel as a central element by implementing diffusion confusion and eventually a permutation. On the other side, a permutation applied at the bit level changes not only the pixel value, but also its location within the image. In this work, we will propose a new technology for medical and color image encryption, which starts with an application of an improved Vigenere method acting at the pixel level to overcome any differential attack, followed by an implementation of several chaotic permutations acting at the bit level. Simulations performed on a large number of images of different sizes and formats ensure that our method is not subject to any known attacks.
Use of static surrogates in hyperparameter optimization

Optimizing the hyperparameters and architecture of a neural network is a long yet necessary phase in the development of any new application. This consuming process can benefit from the elaboration of strategies designed to quickly discard low quality configurations and focus on more promising candidates. This work aims at enhancing HyperNOMAD, a library that adapts a direct search derivative-free optimization algorithm to tune both the architecture and the training of a neural network simultaneously, by targeting two keys steps of its execution and exploiting cheap approximations in the form of static surrogates to trigger the early stopping of the evaluation of a configuration and the ranking of pools of candidates. These additions to HyperNOMAD are shown to improve on its resources consumption without harming the quality of the proposed solutions.
POSTER: Resistance Analysis of Two AES-Like Against the Boomerang Attack

Introduced by Carlos Cid et al. in EUROCRYPT 2018, boomerang uniformity is an essential tool to handle and analyze vectorial functions (S-boxes) to resist boomerang attacks. This mathematical tool quantifies the resistance against differential cryptanalysis, which is highly dependent on the non-linearity features of the S-box. This poster gives a brief analysis examining three AES S-Box’s resistance to the boomerang attack. We used the boomerang Uniformity as a theoretical tool, and we tested the implementation of the boomerang attack to three S-Box over 500 keys. All three S-boxes share the same boomerang uniformity, and the probability of success of the attack is similar.
Upgrading and Reforming for CCTV System of Offshore Drilling Platform

In order to realize real-time video monitoring of key parts and high-risk operation areas of Kan Tan IV semi-submersible offshore drilling platform, and reduce the intensity of daily patrol work and improve work efficiency, the networking method of video surveillance system is discussed. The analog video surveillance system is connected to digital network video surveillance system and the video surveillance signal is transmitted to land surveillance terminal, which facilitates computer to access video surveillance resources on LAN, and increases the storage function of the original analog video surveillance system in a more reasonable way. It can provide reference for the design and upgrading of similar video surveillance systems.
The human-AI relationship in decision-making: AI explanation to support people on justifying their decisions.

The explanation dimension of Artificial Intelligence (AI) based system has been a hot topic for the past years. Different communities have raised concerns about the increasing presence of AI in people's everyday tasks and how it can affect people's lives. There is a lot of research addressing the interpretability and transparency concepts of explainable AI (XAI), which are usually related to algorithms and Machine Learning (ML) models. But in decision-making scenarios, people need more awareness of how AI works and its outcomes to build a relationship with that system. Decision-makers usually need to justify their decision to others in different domains. If that decision is somehow based on or influenced by an AI-system outcome, the explanation about how the AI reached that result is key to building trust between AI and humans in decision-making scenarios. In this position paper, we discuss the role of XAI in decision-making scenarios, our vision of Decision-Making with AI-system in the loop, and explore one case from the literature about how XAI can impact people justifying their decisions, considering the importance of building the human-AI relationship for those scenarios.
The Jordanian Legal Approach on Educational Data Mining: Privacy of Individuals V. Data Mining

Educational institutions collect various data from several sources, but this sort of data is rendered worthless if it is unprocessed effectively by the institution. Typically processing this data is beneficial to decision-makers in educational institutions, but the enormous amount of data present and stored in educational institutions is difficult to be processed traditionally, which has promoted the effective employment of machine intelligence when applying data mining algorithms. Processing raw data stored in an educational institution's databases raises legal concerns, most noticeably the legality of such a procedure and whether data processing would violate individuals' privacy since the Jordanian legislature has not directly regulated the issue of educational data mining and processing and its legal consequences. Accordingly, this research aimed to examine the Jordanian legal approach towards the use educational data mining algorithms and the legality of processing raw data in light of the current legislations in force and present proposals and recommendations in this regard.
Insights for Business Centric Earthquake Early Warning and Operational Forecasting Systems

&amp;lt;p&amp;gt;A large part of Europe is at risk of earthquake disaster events. In the last decade, earthquakes in Europe led to direct economic losses of approximately &amp;amp;#8364;29 billion as well as close to 19,000 fatalities. As a result of public awareness campaigns, business organisations are increasingly cognisant of earthquake risks. However, to date there is limited support available to help them systematically manage these risks. Our research supports decision makers based at European business organizations in their efforts to prepare for and respond to earthquakes. We look at how earthquake early warning (EEW) systems and earthquake operational forecasting (OEF) systems can best support business disaster risk management (DRM). We focus hereby on EEW and OEF based decision support systems for preparedness and rapid response, and also their integration with business continuity planning. Our article is based on an extensive literature review of the state of the art in OEF and EEW systems. We have validated and built on these insights through participatory action research (PAR) with potential business users of EEW and OEF systems in Europe. There is great variability in the ways in which different European businesses currently manage earthquake risk. Our research has given us insights into business users&amp;amp;#8217; needs and expectations of EEW and OEF systems. We have harmonized and integrated these insights towards the development of a common earthquake decision support protocol for business organisations. This protocol covers business considerations that extend beyond the prevention of fatalities and direct economic loss to long-term organisational resilience. Combining insights from facilities management, organization science and engineering, we present various considerations for the development of business centric EEW and OEF decision support systems. We outline barriers to the development and uptake of such systems and describe what opportunities they present for different stakeholders.&amp;lt;/p&amp;gt;
A Research on Accuracy Improvement of Diabetes Recognition Factors Based on XGBoost

Recently, the number of people who visit the hospital due to diabetes is increasing. According to the Korean Diabetes Association, it is statistically indicated that one in seven adults aged 30 years or older in Korea suffers from diabetes, and it is expected to be more if the pre-diabetes, fasting blood sugar disorders, are combined. In the last study, the validity of Triglyceride and Cholesterol associated with diabetes was confirmed and analyzed using Random Forest. Random Forest has a disadvantage that as the amount of data increases, it uses more memory and slows down the speed. Therefore, in this paper, we compared and analyzed Random Forest and XGBoost, focusing on improvement of learning speed and prevention of memory waste, which are mainly dealt with in machine learning. Using XGBoost, the problem of slowing down and wasting memory was solved, and the accuracy of the diabetes recognition factor was further increased.
Educational Software for Dyslexic Children : A Systematic Literature Review

Educational software nowadays gives something beneficial to the users, especially to the student. Many features in the educational software can help students to learn more and are exciting to do the task. This paper presents how game features are used for dyslexic children in mobile game applications. The purpose of this literature review is to explore the methodology used in research related to educational software for dyslexic children. This methodology used in this paper is adapted from Kencheman and Keele and Petterson.The results show that most of the literature used quantitative methodology in their research. In addition, the studies were more focused on children rather than adult respondents. The literature suggests several software design guidelines for dyslexic children. Most of the educational software discussed in the literature are games, mobile applications and couseware. Findings supported that the use of the application to educate and develop children skills in learning is vital for dyslexic children.
The Curious Case of Adversarially Robust Models: More Data Can Help, Double Descend, or Hurt Generalization

Adversarial training has shown its ability in producing models that are robust to perturbations on the input data, but usually at the expense of a decrease in the standard accuracy. To mitigate this issue, it is commonly believed that more training data will eventually help such adversarially robust models generalize better on the benign/unperturbed test data. In this paper, however, we challenge this conventional belief and show that more training data can hurt the generalization of adversarially robust models in classification problems. We first investigate the Gaussian mixture classification with a linear loss and identify three regimes based on the strength of the adversary. In the weak adversary regime, more data improves the generalization of adversarially robust models. In the medium adversary regime, with more training data, the generalization loss exhibits a double descent curve, which implies the existence of an intermediate stage where more training data hurts the generalization. In the strong adversary regime, more data almost immediately causes the generalization error to increase. Then we analyze a two-dimensional classification problem with a 0-1 loss. We prove that more data always hurts generalization of adversarially trained models with large perturbations. Empirical studies confirm our theoretical results.
Design and Implementation of Low Cost Dual Axis Solar Tracking System using Microcontroller

The phrase the sun is the source of all energy implies that solar energy is an essential element for the earth. Sun-powered vitality is fast becoming a substantial approach for renewable energy source assets. The sun is a plentiful source of vitality, and this sun-powered vitality may be effectively dealt with by employing sunlight-based photovoltaic cells and photovoltaic impact to convert sun-powered energy into electrical vitality. The solar tracking system maximizes the power generation of solar system by following the sun through panels throughout the day, optimizing the angle at which panels receive solar radiation. Compared to stable solar panels, a solar tracking system using solar panel linear actuators or gear motors can increase the efficiency of solar panels by 25% to 40%. The transformation efficiency of any sun-based application increases when the modules are consistently adjusted to the optimal edge as the sun crosses the sky. A dual-axis tracker allows panels to move on two axes, both north-south and east-west parallel. This paper presents the design and implementation of a dual-axis solar panel based on the Arduino microcontroller.
Development of PVC Flute and Acoustic Testing for Commercial Production

Flute is a musical instrument generally made of bamboo. Bamboo flute has less life and high price. Thus more durable material can be used which cannot be affected by environment. The PVC is most popular material which possesses these qualities and also cost effective. However, it should produce same pitch quality. This work focused on an idea to develop a high quality PVC pipe flute. A simple drill is used to make desired holes on a particular distant position. Their acoustic properties are checked and verified by a chromatic tuner application and FFT analyzer DS 3000 with competitive Hindustani bamboo flutes. All seven musical notes are tested on an acoustic level meter. Results have been satisfactory matched and found capable enough that it can provide more suitable solution for traditional bamboo for making the flute. This paper also describes the advantages of PVC flute over bamboo flute with the procedure of making PVC flute.
Incorporating Causal Graphical Prior Knowledge into Predictive Modeling  via Simple Data Augmentation

Causal graphs (CGs) are compact representations of the knowledge of the data generating processes behind the data distributions. When a CG is available, e.g., from the domain knowledge, we can infer the conditional independence (CI) relations that should hold in the data distribution. However, it is not straightforward how to incorporate this knowledge into predictive modeling. In this work, we propose a model-agnostic data augmentation method that allows us to exploit the prior knowledge of the CI encoded in a CG for supervised machine learning. We theoretically justify the proposed method by providing an excess risk bound indicating that the proposed method suppresses overfitting by reducing the apparent complexity of the predictor hypothesis class. Using real-world data with CGs provided by domain experts, we experimentally show that the proposed method is effective in improving the prediction accuracy, especially in the small-data regime.
Known unknowns: Learning novel concepts using reasoning-by-elimination

People can learn new visual concepts without any samples, from information given by language or by deductive reasoning. For instance, people can use \textit{elimination} to infer the meaning of novel labels from their context. While recognizing novel concepts was intensively studied in zero-shot learning with semantic descriptions, training models to learn by elimination is much less studied. Here we describe the first approach to train an agent to reason-by-elimination, by providing instructions that contain both familiar concepts and unfamiliar ones ( ``pick the red box and the green wambim''). In our framework, the agent combines a perception module with a reasoning module that includes internal memory. It uses reinforcement learning to construct a reasoning policy that, by considering all available items in a room, can make a correct inference even for never-seen objects or concepts. Furthermore, it can then perform one-shot learning and use newly learned concepts for inferring additional novel concepts. We evaluate this approach in a new set of environments, showing that agents successfully learn to reason by elimination, and can also learn novel concepts and use them for further reasoning. This approach paves the way to handle open-world environments by extending the abundant supervised learning approaches with reasoning frameworks that can handle novel concepts.
EFEKTIFITAS PENERAPAN FLIPPED CLASSROOM DAN JUMPING TASK DI SMAN 15 PADANG

This study aims to determine the effectiveness of the implementation of the Flipeed Classroom and the Jumping Task is reviewed from the aspect of learning outcomes. This type of research is classified in experimental research, This research aims to assess the effect of an educational treatment or action on a student's behavior about the or whether or not the effect of that action when we compare it to other actions.The subjects of this study were students of class IX IPS at SMA 15 Padang. The data collection techniques used were observation and simple random sampling technique. The results showed that the average results of final test study (Postest), amounted to 83.47 for experiment classes and 77.74 for control classes. Based on the test t use SPSS software for pretest with α = 0.05 if the sig value is 199 > 0.05 then there is no significant difference. For (Postest) otherwise if signification 0.00 < α = 0.05 then there is a significant difference. So this learning model can be applied in improving students' learning outcomes
Reconhecimento automático de atividades físicas comportamentais utilizando um acelerômetro.

This paper presents a process used for automatic physical activity recognition with accelerometer data located on the user's wrist. The device has its use intended for people with diabetes as an alternative for keeping up with their daily needs. Activity recognition provides important information for creating a recommendation system intended for people with diabetes health. A prototype of this wearable device was developed to validate the recognition system. Finally, is presented a discussion about the obtained results with the prototype related to the public datasets reproducibility.