Reviewerly/exHarmony huggingface dataset:
Describing and Localizing Multiple Changes with Transformers

Change captioning tasks aim to detect changes in image pairs observed before and after a scene change and generate a natural language description of the changes. Existing change captioning studies have mainly focused on a single change.However, detecting and describing multiple changed parts in image pairs is essential for enhancing adaptability to complex scenarios. We solve the above issues from three aspects: (i) We propose a simulation-based multi-change captioning dataset; (ii) We benchmark existing state-of-the-art methods of single change captioning on multi-change captioning; (iii) We further propose Multi-Change Captioning transformers (MCCFormers) that identify change regions by densely correlating different regions in image pairs and dynamically determines the related change regions with words in sentences. The proposed method obtained the highest scores on four conventional change captioning evaluation metrics for multi-change captioning. Additionally, our proposed method can separate attention maps for each change and performs well with respect to change localization. Moreover, the proposed framework outperformed the previous state-of-the-art methods on an existing change captioning benchmark, CLEVR-Change, by a large margin (+6.1 on BLEU-4 and +9.7 on CIDEr scores), indicating its general ability in change captioning tasks.
Evaluating model-based planning and planner amortization for continuous control

There is a widespread intuition that model-based control methods should be able to surpass the data efficiency of model-free approaches. In this paper we attempt to evaluate this intuition on various challenging locomotion tasks. We take a hybrid approach, combining model predictive control (MPC) with a learned model and model-free policy learning; the learned policy serves as a proposal for MPC. We find that well-tuned model-free agents are strong baselines even for high DoF control problems but MPC with learned proposals and models (trained on the fly or transferred from related tasks) can significantly improve performance and data efficiency in hard multi-task/multi-goal settings. Finally, we show that it is possible to distil a model-based planner into a policy that amortizes the planning computation without any loss of performance. Videos of agents performing different tasks can be seen at https://sites.google.com/view/mbrl-amortization/home.
SATUAN POLISI PAMONG PRAJA SEBAGAI PELINDUNG MASYARAKAT

The purpose of this paper is to better understand and know what are the main tasks and functions of the Civil Service Police Unit, especially the police officers at the district level. As is known together, Satpol PP is an agency in the area that was formed to enforce Regional Regulations and Regulations of the Head of Region, to carry out public order and peace and to protect the community. The position of the district Satpol PP is under the Regent and administratively and operationally responsible to the Regent. In this paper the author will focus on the task of Satpol PP as protection of the community. Each region has different characteristics and experiencesdifferent disaster problems, ranging from floods, landslides, fires, droughts and so on. Disasters can be in the form of disasters caused by humans or because of nature and these events occur almost every year, even in some areas known as flood-subscribed areas. Writing this script is the result of a conceptual study, so the results of this writing are expected to be useful especially for readers so that they understand better what the Satpol is. Instruments for retrieving data are taken from several reference sources, electronic media and direct observations of the results of the author’s experience in the area.&#x0D; Keywords: Satpol pp, Community Protection and Disaster Mitigation
Crime Analysis using Machine Learning Algorithm

Counteraction is better that Cure. Forestalling a wrongdoing from happening is superior to examining what or how the wrongdoing had happened. When I pick out do expand this venture the fundamental hassle is growing the centralized server. Awful conduct scene want has relies mostly on the certain awful conduct record and various geospatial and part data. In existing machine they're proposed only getting the crime from the consumer most effective until now they didn’t have system for prediction the crime. Wrongdoing that happens nowadays are have following key qualities, for example, violations rehashing in an occasional style, wrongdoings happening because of some other action and event of violations pre shown by some other data .In our proposed system we overcome that answer and we enforce the Prediction System. We need to accumulate raw facts and method in addition. We use Random forest Algorithm
Self-Supervised Depth Completion for Active Stereo.

Active stereo systems are widely used in the robotics industry due to their low cost and high quality depth maps. These depth sensors, however, suffer from stereo artefacts and do not provide dense depth estimates. In this work, we present the first self-supervised depth completion method for active stereo systems that predicts accurate dense depth maps. Our system leverages a feature-based visual inertial SLAM system to produce motion estimates and accurate (but sparse) 3D landmarks. The 3D landmarks are used both as model input and as supervision during training. The motion estimates are used in our novel reconstruction loss that relies on a combination of passive and active stereo frames, resulting in significant improvements in textureless areas that are common in indoor environments. Due to the non-existence of publicly available active stereo datasets, we release a real dataset together with additional information for a publicly available synthetic dataset needed for active depth completion and prediction. Through rigorous evaluations we show that our method outperforms state of the art on both datasets. Additionally we show how our method obtains more complete, and therefore safer, 3D maps when used in a robotic platform
Deep convolutional neural network-based system for ﬁsh classiﬁcation

In computer vision, image classiﬁcation is one of the potential image processing tasks. Nowadays, ﬁsh classiﬁcation is a wide considered issue within the areas of machine learning and image segmentation. Moreover, it has been extended to a variety of domains, such as marketing strategies. This paper presents an effective ﬁsh classiﬁcation method based on convolutional neural networks (CNNs). The experiments were conducted on the new dataset of Bangladesh’s indigenous ﬁsh species with three kinds of splitting: 80-20%, 75-25%, and 70-30%. We provide a comprehensive comparison of several popular optimizers of CNN. In total, we perform a comparative analysis of 5 different state-of-the-art gradient descent-based optimizers, namely Adaptive Delta (AdaDelta), Stochastic Gradient Descent (SGD), Adaptive Momentum (Adam), Adaptive Max Pooling (Adamax), Root Mean Square Propagation (Rmsprop), for CNN. Overall, the obtained experimental results show that Rmsprop, Adam, Adamax performed well compared to the other optimization techniques used, while AdaDelta and SGD performed the worst. Furthermore, the experimental results demonstrated that Adam optimizer attained the best results in performance measures for 70-30% and 80-20% splitting experiments, while the Rmsprop optimizer attained the best results in terms of performance measures of 70-25% splitting experiments. Finally, the proposed model is then compared with state-of-the-art deep CNNs models. Therefore, the proposed model attained the best accuracy of 98.46% in enhancing the CNN ability in classiﬁcation, among others.
Approximate Fault Tolerance for Edge Stream Processing

Existing distributed stream processing systems generally guarantee fault tolerance by switching to standby machines and reprocessing lost data. In edge computing environments, however, we have to duplicate each edge for this conventional approach. This duplication cost increases sharply with expansion in the system scale. To solve this problem, we propose an approach to support approximate fault tolerance without edge duplication. We focus on environmental monitoring applications and utilize the correlation between sensors. In this paper, we assume that each edge estimates missing data from the observed data and aggregates them approximately. We provide a method to estimate the outputs of failed edges taking care of the uncertainty of the processing results at each edge. Our method allows the server to continue processing without waiting for the recovery of failed edges. We also show that the validity of our method by experiments using synthetic data.
Anti-aliasing Deep Image Classifiers using Novel Depth Adaptive Blurring and Activation Function

Deep convolutional networks are vulnerable to image translation or shift, partly due to common down-sampling layers, e.g., max-pooling and strided convolution. These operations violate the Nyquist sampling rate and cause aliasing. The textbook solution is low-pass filtering (blurring) before down-sampling, which can benefit deep networks as well. Even so, non-linearity units, such as ReLU, often re-introduce the problem, suggesting that blurring alone may not suffice. In this work, first, we analyse deep features with Fourier transform and show that Depth Adaptive Blurring is more effective, as opposed to monotonic blurring. To this end, we outline how this can replace existing down-sampling methods. Second, we introduce a novel activation function -- with a built-in low pass filter, to keep the problem from reappearing. From experiments, we observe generalisation on other forms of transformations and corruptions as well, e.g., rotation, scale, and noise. We evaluate our method under three challenging settings: (1) a variety of image translations; (2) adversarial attacks -- both $\ell_{p}$ bounded and unbounded; and (3) data corruptions and perturbations. In each setting, our method achieves state-of-the-art results and improves clean accuracy on various benchmark datasets.
Improving fault tolerance of DNNs through weight remapping based on gaussian distribution

In this paper, we approach to improve the fault tolerance of Deep Neural Networks (DNNs) for safety-critical artificial intelligent applications. We propose to remap the range of 32-bit float to weights to reduce the influence of invalid weights caused by bit-flip faults. From preliminary experiments, we observe that weakening bit-flip faults which make positive weights larger can help to improve the reliability of DNNs. Then, we propose a gaussian distribution based mapping method to prevent weights from being influenced by bit-flip faults, in which a novel function is formulated to remap the relation between 32-bit float and the values of weights. Extensive experiments demonstrate that our approach can improve the accuracy of VGG16 from 13.5% to 80.5%, which is better than the other six tolerance approaches of DNNs.
Removing Adversarial Noise in Class Activation Feature Space

Deep neural networks (DNNs) are vulnerable to adversarial noise. Preprocessing based defenses could largely remove adversarial noise by processing inputs. However, they are typically affected by the error amplification effect, especially in the front of continuously evolving attacks. To solve this problem, in this paper, we propose to remove adversarial noise by implementing a self-supervised adversarial training mechanism in a class activation feature space. To be specific, we first maximize the disruptions to class activation features of natural examples to craft adversarial examples. Then, we train a denoising model to minimize the distances between the adversarial examples and the natural examples in the class activation feature space. Empirical evaluations demonstrate that our method could significantly enhance adversarial robustness in comparison to previous state-of-the-art approaches, especially against unseen adversarial attacks and adaptive attacks.
Land Evaluation of East Qattara Depression, Egypt using Remote Sensing and GIS

Availability of sufficient and accurate spatial data concerning land resources is a pillar for sustainable agricultural development.The current work was aimed at using remote sensing (RS) and geostatistical analysis tools within geographic information system (GIS) to map land capability and crop suitability for an area in the Qattara Depressing, Western Desert of Egypt, located east of the Qattara Depression between latitude 30° 10' 4" to 30° 20' 57" N and longitude 28° 32' 26" to 28° 52' 10" E, covering 630 km 2 (63000 ha).Thirty-seven soil profiles were dug to 150 cm.Soil samples were collected from different horizons and analyzed for their main properties.Applied System for Land Evaluation (ASLE) software was used to assess land capability and land suitability.Landforms include sand sheets, sand dunes, depressions, sabkha and water bodies.The 74.69% of the soils are"poor" (C4), 0.47% are"good" (C2), 11.68% are"fair" (C3), 11.12% are"very poor" (C5), and 0.47% are"non-agricultural" (C6).Soil texture, salinity and alkalinity are the main limiting factors.Classes for suitability (considering 22 crops) are highly suitable (S1), suitable (S2), moderately suitable (S3), marginally suitable (S4), currently not-suitable (N1), and potentially not-suitable (N2) for 22 crops.The most recommended crops are date palm and tomatoes.The studied soils require precise management practices to be promising for agricultural expansion.
Cosine similarity-based algorithm for social networking recommendation

Social media have become a discussion platform for individuals and groups. Hence, users belonging to different groups can communicate together. Positive and negative messages as well as media are circulated between those users. Users can form special groups with people who they already know in real life or meet through social networking after being suggested by the system. In this article, we propose a framework for recommending communities to users based on their preferences; for example, a community for people who are interested in certain sports, art, hobbies, diseases, age, case, and so on. The framework is based on a feature extraction algorithm that utilizes user profiling and combines the cosine similarity measure with term frequency to recommend groups or communities. Once the data is received from the user, the system tracks their behavior, the relationships are identified, and then the system recommends one or more communities based on their preferences . the Finally, experimental studies are conducted using a prototype developed to test the proposed framework, and results show the importance of our framework in recommending people to communities.
Usage of interactive Infographic in achieving the readability of Sudanese electronic newspapers

This study aims to identify the extent of the use of interactive infographics in achieving readability in Sudanese electronic newspapers, by selecting a sample from it in the period between April 2020 to April 2021. This study is one of the descriptive studies with its quantitative approach, using content analysis and interview tools. The most important findings of the study are that Sudanese electronic newspapers rarely use interactive infographics as part of their media content, although interactive infographics contribute in achieving readability despite its scarcity of use, and that the majority of the interview sample believes that interactive infographics facilitate the presentation of information to the reader and more than half of them see The interactive infographic makes the reader enjoy reading media content. The most important recommendations of this study are the necessity of conducting more scientific studies about the infographic, their uses in electronic newspapers, and the importance of providing all means that facilitate the use of infographics of all kinds of the media content of Sudanese electronic newspapers.
Resource Shocks, Corruption, and Local Business in Africa

The negative correlation between growth outcomes and natural resource abundance is well-known. Researchers have proposed a number of channels - such as corruption, education, and conflict - through which resource rents may negatively affect growth in a society. We utilize a more nuanced approach and focus on the heterogeneous effect of resource rents on firms across different firm sizes. Specifically, we combine firm-level World Enterprise Survey Data with resource data across Africa from Berman et al. (2017). We find small firms suffer across a range of categories - including corruption - in response to positive exogenous resource shocks. Large firms, however, tend to benefit from resource shocks and experience less corruption and crime. These results are robust to a number of alternative specifications as well as a placebo analysis where resource shocks are randomly assigned to areas where the mineral in question does not exist. Our findings contribute to the understanding of how resource rents exacerbate the existing difficulties faced by small firms in the developing world.
Investigations on Automorphism Groups of Quantum Stabilizer Codes

The stabilizer formalism for quantum error-correcting codes has been, without doubt, the most successful at producing examples of quantum codes with strong error-correcting properties. In this paper, we discuss strong automorphism groups of stabilizer codes, beginning with the analogous notion from the theory of classical codes. Two weakenings of this concept, the weak automorphism group and Clifford-twisted automorphism group, are also discussed, along with many examples highlighting the possible relationships between the types of "automorphism groups". In particular, we construct an example of a $[[10,0,4]]$ stabilizer code showing how the Clifford-twisted automorphism groups might be connected to the Mathieu groups. Finally, nonexistence results are proved regarding stabilizer codes with highly transitive strong and weak automorphism groups, suggesting a potential inverse relationship between the error-correcting properties of a quantum code and the transitivity of those automorphism groups.
Using ‘Medicalenglish’ to Improve EFL Students’ Reading Motivation: a Case Study in Nursing Department

&lt;p&gt;The importance of reading comprehension is immense. It contributes to the success of language learning and teaching. However, there are some factors such as less interesting and difficult text sources and other non-contextual factors that demotivate the students in reading. To deal with that issue, the implementation of a Learning Management System (LMS) called MedicalEnglish is introduced to the students in order to amplify the students’ motivation in learning English. This study is intended to investigate the motivation of 22 Diploma students of the Nursing department in Semarang in reading using MedicalEnglish. In this qualitative case study, questionnaire and interview were selected as the instrument. The data collected from the two instruments were then descriptively elaborated. In conclusion, there is a significant improvement in students’ motivation in learning reading after the implementation of MedicalEnglish. The result of this study is expected to provide additional reliable sources regarding the implementation of LMS in sustaining students’ reading motivation.&lt;/p&gt;
Pembuatan Film Animasi 3D Si Molek

Animated films are films that are made by utilizing images (drawings) and other inanimate objects, such as dolls, tables and chairs that can be brought to life with animation techniques. The idea of ​​animation is the process of recording and playing back a series of static images to get the illusion of motion. Animation technology itself has spread to the creation of regional stories / folk tales which were originally only told orally / in writing. Folklore is a story that is basically conveyed by one person to another through oral narrative. Folklore, if it is inherited or implanted in students from an early age, will equip motor and psimotor development, especially in building the character of students from an early age with superior personalities. The cultivation of ethics is intended to shape a person's character which leads to positive things. Almost every region in Indonesia has its own folklore. One of the folk tales from Riau province is the folklore of Si Molek. The story of Si Molek can only be found through printed media such as picture books or told orally. The folktale of Si Molek is a folk tale originating from Riau. Based on the survey that was conducted, the results of the questionnaire conducted on (150) students showed that 83.3 percent did not know the folklore of Si Molek, and 89.3 percent did not know the characters in the Riau Si Molek folklore. and there are 96.7 percent like animationKeywords: Animated Movies, Folklore, Technology
PELAKSANAAN GERAKAN LITERASI SEKOLAH BERBASIS KEARIFAN LOKAL UNTUK MENGEMBANGKAN KARAKTER PESERTA DIDIK

The School Literacy Movement (GLS) is an overall effort to make schools as a learning organization whose make all residents at school are literate throughout their lives through public involvement. Various parties who can drive this activity include school residents, stakeholders and the community. It aims to jointly own, carry out, and make this movement an important part of life. The aims of this research are to determine whether the existence of school literacy movement based on local wisdom can develop the character of students, how to link the school literacy movement based on local wisdom in order to develop the character of students in and how teachers’ perceptions of and other stakeholders regarding the school literacy movement. This research is using qualitative research. The subjects in this research are the students of SDN 2 Tuk and several other informant. The research subjects taking by using purposive sampling technique. This literacy activities based on local can develop the character of students. In this activity, researcher and teachers class have provided literacy-based learning by giving assignments that related to the value of local wisdom, as writing how to make emping and writing the history of the village of Tuk.&#x0D; &#x0D; Keywords: School-Literacy-Movement (GLS), local-wisdom, character.
Примена техника за постизање праведних модела машинског учења

Machine learning models are becoming more and more present in everyday life and various industries. People make decisions based on models who learn from available data. The data describe human behavior that has often had a discriminatory character towards certain groups in society. Today, we are witnessing attempts to eliminate discrimination wherever possible, but as machine learning models learn from historical data, it happens that models learn to discriminate against those groups. Therefore, it becomes very important to make a fair model. However, it is certainly not enough for the model to be just fair. The main goal remains to make an accurate model. The challenge lies in finding a compromise between the accuracy and fairness of the model. This paper will focus on answering the question of what is the cost of increasing the fairness of a machine learning model. The paper will provide an overview of the currently most important techniques for achieving fair machine learning models, evaluation methods as well as their costs to explain the trade-off between the accuracy and fairness of models.