Reviewerly/exHarmony huggingface dataset:
Decoy-enhanced Saliency Maps

Saliency methods can make deep neural network predictions more interpretable by identifying a set of critical features in an input sample, such as pixels that contribute most strongly to a prediction made by an image classifier. Unfortunately, recent evidence suggests that many saliency methods poorly perform, especially in situations where gradients are saturated, inputs contain adversarial perturbations, or predictions rely upon inter-feature dependence. To address these issues, we propose a framework that improves the robustness of saliency methods by following a two-step procedure. First, we introduce a perturbation mechanism that subtly varies the input sample without changing its intermediate representations. Using this approach, we can gather a corpus of perturbed data samples while ensuring that the perturbed and original input samples follow the same distribution. Second, we compute saliency maps for the perturbed samples and propose a new method to aggregate saliency maps. With this design, we offset the gradient saturation influence upon interpretation. From a theoretical perspective, we show that the aggregated saliency map not only captures inter-feature dependence but, more importantly, is robust against previously described adversarial perturbation methods. Following our theoretical analysis, we present experimental results suggesting that, both qualitatively and quantitatively, our saliency method outperforms existing methods, in a variety of applications.
ANALISIS SPEKTRAL TREMBESI (Samanea saman Jacq Merr) MENGGUNAKAN CITRA SPOT 6 DI KAMPUS UNIVERSITAS TADULAKO TONDO PALU

Earthed are a wide variety of shapes as well as a very diverse color, this can be seen in maps as well as satellite imagery. SPOT 6 has a 60 km Coverage, a better resolution of 1.5 m, blue band addition to get a naturally original color image, allowing it to achieve better efficiently with a large coverage collection of over 3 million km2 per day. Maps can be created based on the way and method of each one of them is by looking at the value of the spectral pixel. The purpose of the study is to know the spectral value of the Trembesi tree (Samanea saman Jacq Merr) using the image of SPOT 6 that is on the Tadulako Tondo Palu University  Campus. The method used in this study is by performing image interpretations to be able to recognize the features/characteristics of objects from trambesy (Samanea saman Jacq Merr) by inflicting SPOT 6 imagery and Arcgis 10.0 software, as well as field surveys to obtain coordinate points). Based on the results achieved by SPOT 6 indicates that the spectral pixel value for the trembetic tree design category (Samanea saman Jacq Merr) has the lowest value of 124 while the highest value of 139, for the trembetic tree pole category (Samanea saman Jacq Merr) has the lowest spectral value of pixel 140 while the highest 159, for the spectral value of spectral pixel. The tree categoryral has the lowest value of 160 is at least value 197, and also the spectral pixel value of the composite yield of band321 that is the minimum (100) and maximum (200), average value (133,16), the and the value of stdv (18,51). Keywords : Interpretation, Spectral Pixel Value
Sampling Based Scene-Space Video Processing

Many compelling video processing effects can be achieved if per-pixel depth information and 3D camera calibrations are known. However, the success of such methods is highly dependent on the accuracy of this information. We present a novel, sampling-based framework for processing video that enables high-quality scene-space video effects in the presence of inevitable errors in depth and camera pose estimation. Instead of trying to improve the explicit 3D scene representation, the key idea of our method is to exploit the high redundancy of approximate scene information that arises due to most scene points being visible multiple times across many frames of video. Based on this observation, we propose a novel pixel gathering and filtering approach. The gathering step is general and collects pixel samples in scene-space, while the filtering step is application-specific and computes a desired output video from the gathered sample sets. Our approach is easily parallelizable and has been implemented on GPU, allowing us to take full advantage of large volumes of video data and facilitating practical runtimes on HD video using a standard desktop computer. Our generic scene-space formulation is able to comprehensively describe a multitude of video processing applications such as denoising, deblurring, super resolution, object removal, computational shutter functions, and other scene-space camera effects. We present results for various casually captured, hand-held, moving, compressed, monocular videos depicting challenging scenes recorded in uncontrolled environments.
Current Challenges in Cyber Deception

While the previous chapter focused on insights and promising directions based upon the current book, this chapter examines challenges in cyber deception that are found more broadly in the existing literature. As a point of reference, we refer to the research works mentioned in Chap. 4 . The literature discussed in Chap. 4 points towards four major challenges in cyber deception.
Attitude of university students towards virtual education during the COVID-19 pandemic

The objective of this research was to analyze the attitude of the students of the National Amazonian University of Madre de Dios towards virtual education in times of the COVID-19 pandemic The research focus was quantitative, the non-experimental design and the descriptive transitional type of design The sample consisted of 154 students of both sexes from the professional education, career and was determined by means of a stratified probabilistic sampling The instrument used for data collection was the Scale of Attitudes towards virtual education, which has the required reliability and validity Based on the results, it was shown that the majority of students present an attitude of indifference towards virtual education and statistically significant differences were found between the sex and age of the students
Tackling the Unannotated: Scene Graph Generation with Bias-Reduced Models

Predicting a scene graph that captures visual entities and their interactions in an image has been considered a crucial step towards full scene comprehension. Recent scene graph generation (SGG) models have shown their capability of capturing the most frequent relations among visual entities. However, the state-of-the-art results are still far from satisfactory, e.g. models can obtain 31% in overall recall R@100, whereas the likewise important mean class-wise recall mR@100 is only around 8% on Visual Genome (VG). The discrepancy between R and mR results urges to shift the focus from pursuing a high R to a high mR with a still competitive R. We suspect that the observed discrepancy stems from both the annotation bias and sparse annotations in VG, in which many visual entity pairs are either not annotated at all or only with a single relation when multiple ones could be valid. To address this particular issue, we propose a novel SGG training scheme that capitalizes on self-learned knowledge. It involves two relation classifiers, one offering a less biased setting for the other to base on. The proposed scheme can be applied to most of the existing SGG models and is straightforward to implement. We observe significant relative improvements in mR (between +6.6% and +20.4%) and competitive or better R (between -2.4% and 0.3%) across all standard SGG tasks.
Machine Learning Mechanism for Adaptive Tourist Recommendation Using Bayesian Algorithm

Currently, recommender systems are available in many daily activities such as online shopping search, and social networks. Due to the increasing demand of the tourism industry through information technology, the recommender systems are integrated into the tourism website. This research aims at exploiting user data to recommend tourist attractions by arranging the attractions together with tourism-related information and making recommendations based on information relevant to the needs of each user. The proposed mechanism has an advantage as it can suggest information at the beginning of use without the need for usage history, rankings, and other special knowledge. Thus, new travelers can get recommendations when start using the recommender system. This research focuses on recommending tourist destinations in Thailand using machine learning methods based on Bayesian Personalized Ranking to predict tourist attraction rankings by comparing four methods: 1) Collaborative Filtering Only, 2) Demographic Filtering Only, 3) Collaborative Filtering and Demographic Filtering, and 4) Hybrid Method of Demographic Filtering and Demographic Filtering Combining with Tourist Attraction Category. The experimental results show that the hybrid method of collaborative filtering and demographic filtering combining with the ranking of tourist attractions recommends tourist attractions better than other methods. Therefore, this hybrid model can be used as a model to support the Recommender system of tourism.
METODE AUTOREGRESSIVE INTEGRATED MOVINGAVERAGE (ARIMA) DAN METODE ADAPTIVE NEURO FUZZY INFERENCE SYSTEM (ANFIS) DALAM ANALISIS CURAH HUJAN

Information of rainfall prediction is important for Indonesian peoples. Many statistical methods can be used in rainfall prediction, they are ARIMA (Autoregressive Integrated Moving Average) and ANFIS (Adaptive Neuro Fuzzy Inference System) methods. The purpose of this study was to compare between ANFIS method and ARIMA method to get rainfall prediction in some periods. The ARIMA method was time series data analysis often used in forecasting. While the ANFIS method was forecasting method based on rarely found time series events that are pure linear or non-linear. Based on this study, the ANFIS method has a good accuracy for time series data analysis compared with the ARIMA method. The ANFIS method has 6. 9811 for the result of correlation and 87.29% for the RMSE, while result of correlation for the ARIMA method is 14.037 with 24.92% for RMSE. The ARIMA method is not good for prediction of daily data cases and non-linear data, so that the result is not actual which has a constant and flat for data prediction. Keywords: ARIMA, ANFIS, time series data, linear, non-linear
Adaptive Dataset Sampling by Deep Policy Gradient

Mini-batch SGD is a predominant optimization method in deep learning. Several works aim to improve naive random dataset sampling, which appears typically in deep learning literature, with additional prior to allow faster and better performing optimization. This includes, but not limited to, importance sampling and curriculum learning. In this work, we propose an alternative way: we think of sampling as a trainable agent and let this external model learn to sample mini-batches of training set items based on the current status and recent history of the learned model. The resulting adaptive dataset sampler, named RLSampler, is a policy network implemented with simple recurrent neural networks trained by a policy gradient algorithm. We demonstrate RLSampler on image classification benchmarks with several different learner architectures and show consistent performance gain over the originally reported scores. Moreover, either a pre-sampled sequence of indices or a pre-trained RLSampler turns out to be more effective than naive random sampling regardless of the network initialization and model architectures. Our analysis reveals the possible existence of a model-agnostic sample sequence that best represents the dataset under mini-batch SGD optimization framework.
COMPETENCIES IN PEDAGOGICAL ACTIVITY

The scientific article discusses the problems that negatively affect the education of teachers as: an extra managerial approach, not being organized on the condition of self- awareness and reasoning for students, paying attention to the quality of tasks, but to the number and ways of solving them. There were conclusions that the teacher needed to be trained on the basis of the following competencies: creativity, organization, ability, activity, punctuality, hardworking and sociability.
New Challenges of Daegu Education to Learn for the Future and Grow Together - International Baccalaureate (IB) Program -

The Daegu Metropolitan Office of Education (DMOE) is introducing and implementing the International Baccalaureate (IB) program in public education in order to foster future global talents with creativity and critical thinking skills in preparation for the 4th Industrial Revolution society, to develop the efforts with teachers to improve class instruction, integrating a consistent ‘Curriculum-Teaching & Learning-Assessment-Records’ system into classes, and to bridge the regional educational gap by providing priority support for schools in areas with difficult educational conditions. In particular, in order to implement the Diploma Programme without language barriers, DMOE and the IB has singed a Memorandum of Cooperation (MOC) and the Dual Language (English & Korean) Diploma Programme (DLDP) is being introduced. Currently, there are 9 IB candidate schools and 6 IB interested schools, a total of 15 schools, under the support of DMOE, and the first IB World School in the language of Korean in public education field is expected to come out early next year. DMOE is spurring the implementation of the IB programmes as an educational model to develop students’ future essential competencies preparing for the future society, through various efforts such as strengthening teacher training and drawing support from the community on the policy.
Creating a more immersive and "vR-like-360-degree video experience- development of an immersive and interactive alcohol resistance training tool

The presented design case gives an example of how to create more engaging 360-degree video applications. In contrast to VR, users of 360-video are often passive observers; they cannot move around in the virtual environment nor affect it. This limits the user's chance of feeling immersed and present in the environment, resulting in less engaging experiences and poorer conditions for experience-based learning. This article examines immersion in 360-video through the design case VR FestLab (ed. In English PartyLab), which is a party simulation for alcohol prevention, consisting of 125 360-degree movie sequences. The application is used in a virtual reality headset, where users create their own uniqueexperience through a choose your own adventure experience. The application is an example of a new approach towardsmore engaging 360-degree video content. In the light of this, we suggest six elements that should be considered, whendesigning an engaging, motivating and immersive experience with 360-degree video. The six elements are 1) user influence, 2) noticed user existence, 3) 3D environment, 4) spatial sound, 5) movement in scene and 6) technical aspects.Further tests are needed to fully determine the extent to which these six factors enhance immersion and presence.
PROBLEMS OF QUALITY OF DISTANCE LEARNING ONLINE

From this paper work we see distance learning and its relationship to emerging computer technologies have together offered many promises to the field of education. In practice however, the combination often falls short of what it attempts to accomplish. Some of the shortcomings are due to problems with the technology; others have more to do with administration, instructional methods, or students.
Multiple Character Modification for Huffman Algorithm

Data compression is a method to reduce a file size. The reduction will also speed up the data transmission between devices. Huffman algorithm is one of the lossless data compressions methods, which calculates characters' occurrence as the reference to convert the characters to the related bit strings. The more frequent the occurrence, the shorter the converted bit string obtained. In a text file, a sequence of characters (or letter) may occur frequently. These sequences consist of twin characters or sequences of vowels and consonants (eg. 'aa', 'gg', 'ny', and 'kan'), as a part of a word. If a sequence is replaced with a new symbol outside the alphabet, the vocabulary will increase. However, if the symbols frequently appear in the text, they will be coded to a shorted bit string that we believe will increase the Huffman text ratio compression. We did experiments by converting some sequences of characters in a text file to new symbols. To find the recommended sequences, we only converted the sequences that their probability was higher than an observed threshold. Therefore, we observed several thresholds to find which sequences that potentially converted to symbols. Our test data consisted of three text files: one raw text file and two files of doc extension. To verify whether conversion of some sequence characters improved the Huffman ratio compression, the Huffman compression was implemented on the original test data and on the converted test data. The experimental results showed that the threshold of 1% is the best. Evaluation on the original test data resulted the Huffman ratio compression of 44.88% and 88.84% for the raw text file and for the doc extension file respectively. This experiment proofs that the conversion of some sequence characters to symbols benefits to the Huffman Algorithm by obtaining 45.93% and 89.09% for the raw text file and for the doc extension file respectively.
iPTR: Learning a representation for interactive program translation retrieval

Program translation contributes to many real world scenarios, such as porting codebases written in an obsolete or deprecated language to a modern one or re-implementing existing projects in one's preferred programming language. Existing data-driven approaches either require large amounts of training data or neglect significant characteristics of programs. In this paper, we present iPTR for interactive code translation retrieval from Big Code. iPTR uses a novel code representation technique that encodes structural characteristics of a program and a predictive transformation technique to transform the representation into the target programming language. The transformed representation is used for code retrieval from Big Code. With our succinct representation, the user can easily update and correct the returned results to improve the retrieval process. Our experiments show that iPTR outperforms supervised baselines in terms of program accuracy.
LibGuides: An Introduction to DIY Zines: How to Make a Zine

This guide will teach users about DIY zines, where to find them, and how to make your own!
Impact of Graph Structures for QAOA on MaxCut

The quantum approximate optimization algorithm (QAOA) is a promising method of solving combinatorial optimization problems using quantum computing. QAOA on the MaxCut problem has been studied extensively on specific families of graphs, however, little is known about the algorithm on arbitrary graphs. We evaluate the performance of QAOA at depths at most three on the MaxCut problem for all connected non-isomorphic graphs with at most eight vertices and analyze how graph structure affects QAOA performance. Some of the strongest predictors of QAOA success are the existence of odd-cycles and the amount of symmetry in the graph. The data generated from these studies are shared in a publicly-accessible database to serve as a benchmark for QAOA calculations and experiments. Knowing the relationship between structure and performance can allow us to identify classes of combinatorial problems that are likely to exhibit a quantum advantage.
Managing information security for mobile devices in small and medium-sized enterprises : Information management, Information security management, mobile device

The rapid proliferation of mobile devices makes mobile security a weak point in many organisations’ security management. Though there are a number of frameworks and methods available for improving ...
Observable degree analysis for the bearing only tracking system with bounded unknown input

In this paper, the problem of quantitatively measuring the observability of the bearing only tracking system with bounded unknown input is investigated. According to the linearization target tracking model, the connection between the initial state vector and measurement sequence is established, then the observable degrees for the bearing only tracking system are defined and some properties of the observable degrees are proved. First, the observable degrees can be applied to measure the estimation error lower bounds. Second, the observable degrees are inherent properties of the target tracking system and independent of the estimation method. In addition, some numerical simulations on the bearing only tracking system are implemented to demonstrate the effectiveness of the observable degrees.
Performance Enhancement of Multi-User Key Extraction Scheme (MKES) Based on Imperfect Signal Reciprocity

The key extraction scheme using the randomness of the received signal strength is the general technique to secure messages in a wireless environment. However, the imperfect received signal strength reciprocity needs to be overcome to reduce the bit mismatch so it will increase the probability of getting the same key. In this paper, we propose a key extraction scheme between several users, namely a multi-user key extraction scheme that adds a polynomial regression method. This scheme is proven to reduce imperfect signal reciprocity due to noise interference and the limited ability of wireless devices. In addition, we also use the multi-bit extraction method to enhance the speed of key extraction. The results of the tests showed that our proposed multi-user key extraction scheme proved to be able to improve the performance of the key extraction scheme by reducing the bit mismatch up to 30% and improving the key extraction speed up to 35% compared to the existing key extraction scheme.
Evaluation a Process using Fuzzy Principles and Tools of Matlab

Expert systems are currently used in several areas. Their advantage is that they can simulate the decisionmaking process of an expert in a particular application area. In conjunction with the principles of fuzzy logic, which can be converted numerical value into verbal expressing are expanding their area of application. The paper deals with design of simple expert system to evaluate the achieved quality of the production process using tools of Matlab. There are data which are gained through process and which are written to a file. They are subsequently evaluated using fuzzy toolbox of Matlab system. The rules designed for individual decision-making processes reflect the experience of experts in the field. These tools allow easy application of fuzzy rule by Fuzzy Toolbox and the connection with Simulink and its tools allow display the results.