Reviewerly/exHarmony huggingface dataset:
Mbl-2 gene polymorphisms in pediatric Burkitt lymphoma: an approach based on machine learning techniques

Introduction: Burkitt lymphoma belongs to the group of non-Hodgkin lymphomas. Although curable in 80% of less advanced stages, it presents in advanced stages in about 75% of cases in Brazil’s Northeast region, requiring urgent and intensive care in the early stages of treatment. Objectives: therefore, this study aimed to verify the participation of MBL-2 gene polymorphisms in the development of Burkitt lymphoma. Methods: In this article, computational approaches based on the Machine Learning technique were used, where we implemented the Random Forest and KMeans algorithms to classify patterns of individuals diagnosed with the disease and, therefore, differentiate them from healthy individuals. A group of 56 patients aged 0 to 18 years, with Burkitt lymphoma, from a reference hospital in the treatment of childhood cancer, was evaluated, together with a control group consisting of 150 samples, all of which were tested for exon 1 polymorphisms and the MBL2 gene -221 and -550 regions. Results: At first, an unsupervised classification was performed, which identified as two the number of groups that best represent the data present in our database, reaching 72.81% accuracy in the separation of patients and controls. Then, the supervised classification was performed, where the classifier obtained a 70.97% success rate, being possible to reach 75% accuracy in the best GridSearch configuration when performing a cross validation. Conclusion: It was not yet possible to conclude about the participation of the evaluated polymorphisms in the development of the BL, however the computational techniques used proved to be very promising for carrying out studies of this nature.
Proposals for policy development

Five proposals for policy development are made. First, a “Designed-By-Architecture” (DBA) taxonomical approach to the classification of cryptoassets is proposed that is tied to component concepts that enable cryptographic consensus technology (CCTech). Second, it is proposed that distinctions between the infrastructure of the code and the social (i.e. user) elements to which it is subject assist, together with the DBA approach, to provide a basis for expressing more granular regulatory expectations. The third and fourth proposals are higher level approaches to the principles-based functioning of regulation that will assist inform the first two proposals via, for example, the development of restated regulatory objectives such as an increased focus on attraction regulation, and the stimulation of private market solutions that align with public policy concerns. The fifth proposal suggests reform of the institutions that should drive the first four proposals.
ID-Reveal: Identity-Aware DeepFake Video Detection

A major challenge in DeepFake forgery detection is that state-of-the-art algorithms are mostly trained to detect a specific fake method. As a result, these approaches show poor generalization across different types of facial manipulations, e.g., from face swapping to facial reenactment. To this end, we introduce ID-Reveal, a new approach that learns temporal facial features, specific of how a person moves while talking, by means of metric learning coupled with an adversarial training strategy. The advantage is that we do not need any training data of fakes, but only train on real videos. Moreover, we utilize high-level semantic features, which enables robustness to widespread and disruptive forms of post-processing. We perform a thorough experimental analysis on several publicly available benchmarks. Compared to state of the art, our method improves generalization and is more robust to low-quality videos, that are usually spread over social networks. In particular, we obtain an average improvement of more than 15% in terms of accuracy for facial reenactment on high compressed videos.
Human-Centred Technology for Sustainable Development Goals: Challenges and Opportunities

A human-centred approach to technology design for addressing sustainable development goals demands rethinking the way technological solutions are developed and deployed, taking into account situated design solutions and their impact on the social, economic and environmental aspects. For technology designers, this provides an opportunity to bridge the gap between the real and the ideal worlds. This workshop aims at building an agenda that defines challenges and opportunities for the design of interactive technologies, which promote fairness and prosperity on the planet, and contribute towards one or more of the United Nations’ Sustainable Development Goals.
Expert System of Diagnosing Chikungunya Disease by Certainty Factor Method

One of the diseases distributed in the population is chikungunya fever transmitted by viruses from the genus Alphavirus, Togaviridae family, and transmitted by Aedes' bite aegypti and Ae mosquitoes. Virus albopictus involved. Chikungunya (CHIK) has three distinct symptoms: fever, joint pain (arthralgia), and skin infection (rash). Chikungunya (CHIK) is a self-limiting illness, and Chikungunya fever (CHIK) is not treated individually. Treatment efforts are symptomatic. Controlling and considering this disease is the right initiative. One consideration that can be achieved using expert system analysis is to use the form of certainty factor to derive the certainty value.
PENGARUH PROGRAM INDONESIA (PIP) TERHADAP MOTIVASI BELAJAR SISWA DI MADRASAH TSANAWIYAH MIFTAHUL ULUM CERMENAN NGORO JOMBANG

Indonesia Pintar Program as an improvement of the Poor Students Program (BSM). Indonesia Pintar Program is a provision of cash education for children from the families who recipients of Prosperous family cards/Prosperous Family Cards (KKS), Social Protection Cards/Social Protection Cards (KPS), Family expectation Programs/Program Keluarga Harapan (PKH) or those that comply with the criteria who have been appointed from the government. The Indonesia Pintar Program is marked by the provision of a Smart Indonesia Card (KIP). Learning motivation will always be related to needs, the need for something to do, the need to please others, achieve the results, and need to overcome difficulties. With the cost of education both directly and indirectly, students have been borne by the government through the Indonesia Pintar Program so as to alleviate or reduce high school fees.The purpose of the study was to determine the effect of Indonesia Pintar Program on student learning motivation in Madrasah Tsanawiyah Miftahul Ulum Cermenan Ngoro Jombang.The object in this thesis research is students receiving assistance from Indonesia Pintar Program in Madrasah Tsanawiyah Miftahul Ulum Cermenan Ngoro Jombang, as many as 50 samples (respondents) by conducting interviews, observations and distributing questionnaires to the sample studied.Based on the results of data analysis, the final results as a conclusion in this thesis are: Indonesia Pintar Program are very significant influence on student learning motivation in Madrasah Tsanawiyah Miftahul Ulum Cermenan Ngoro Jombang.
Value-driven design of AI enabled experiences

From artificial intelligence to distributed ambient sensing, recent technological developments are reshaping the way we experience the tangible world. We're seeing manifestations of these emerging technologies applied in our everyday products as well, such as in virtual assistants, multimodal interfaces, and connected objects. As we are inundated with products with varied forms of intelligences, how should we design meaningful products that deliver value-experiences to users? We should turn our focus to identifying innate human experiential values, to envision AI-driven experiences that are meaningful and humane. This chapter will address these topics illustrated by case examples from work done at MIT Design Lab, with a focus on design methodologies adopted at the Lab to envision meaningful experiences between people and intelligent systems.
Data-Efficient Instance Segmentation with a Single GPU.

Not everyone is wealthy enough to have hundreds of GPUs or TPUs. Therefore, we've got to find a way out. In this paper, we introduce a data-efficient instance segmentation method we used in the 2021 VIPriors Instance Segmentation Challenge. Our solution is a modified version of Swin Transformer, based on the mmdetection which is a powerful toolbox. To solve the problem of lack of data, we utilize data augmentation including random flip and multiscale training to train our model. During inference, multiscale fusion is used to boost the performance. We only use a single GPU during the whole training and testing stages. In the end, our team named THU_IVG_2018 achieved the result of 0.366 for AP@0.50:0.95 on the test set, which is competitive with other top-ranking methods while only one GPU is used. Besides, our method achieved the AP@0.50:0.95 (medium) of 0.592, which ranks second among all contestants. In the end, our team ranked third among all the contestants, as announced by the organizers.
CANet: A Context-Aware Network for Shadow Removal

In this paper, we propose a novel two-stage context-aware network named CANet for shadow removal, in which the contextual information from non-shadow regions is transferred to shadow regions at the embedded feature spaces. At Stage-I, we propose a contextual patch matching (CPM) module to generate a set of potential matching pairs of shadow and non-shadow patches. Combined with the potential contextual relationships between shadow and non-shadow regions, our well-designed contextual feature transfer (CFT) mechanism can transfer contextual information from non-shadow to shadow regions at different scales. With the reconstructed feature maps, we remove shadows at L and A/B channels separately. At Stage-II, we use an encoder-decoder to refine current results and generate the final shadow removal results. We evaluate our proposed CANet on two benchmark datasets and some real-world shadow images with complex scenes. Extensive experimental results strongly demonstrate the efficacy of our proposed CANet and exhibit superior performance to state-of-the-arts.
Data retrieval based on the smart contract within the blockchain

Blockchain technology appears to be the ideal solution for storing data in a transparent and decentralized manner. It also allows open access to data and enhances its immutable nature. This technology has helped prove its usefulness in several industries so far, however, distributed ledger technology does not work as a pure database. Therefore, some problems occur in accessing data. Querying data in the blockchain leads to performance and bandwidth problems. This primarily occurs because the blockchain does not have a primary query language, unlike regular databases. The distributed nature of the blockchain is in this case an obstacle. In this paper, a safe and fast method will be proposed to retrieve consistent data from the blockchain-based on the smart contract that will be opened after completing the transaction procedures. All nodes will sign the proposed transaction (by adding a special hash to each node resulting from the transaction information and node data). Upon completion of Transaction procedures, A smart contract will be opened (in which a QR is placed) resulting from converting the signatures in the transaction to QR When the smart contract data is retrieved, the QR for each transaction will be used All node signatures and transaction data will be extracted. The data will be retrieved by the QR generated for each transaction after it is stored in all nodes servers participating in the system. A new method was proposed to generate a hash for each node present in the system. The proposed method was tested in terms of time and complexity, and the algorithm was statistically analyzed, and all the results proved successful.
Risk assessment for Big Data in cloud computing: trust as an impact

Reduced IT cost, scalability, business continuity, collaboration efficiency and flexibility of work practice are some of the benefits that attract companies to move their business operation into the cloud. Cloud computing serves companies as IaaS, PaaS, and SaaS. Even though cloud has many benefits, it also has its own risks, the risk of being exposed unnecessarily as the cloud is owned and managed by a third party. Therefore, the risks need to be monitored and accessed by companies as they are the cloud customers who choose to use the cloud computing environment in handling their big data. Thus, the objective of this paper is to assess risk using trust measure as the impact.
Digital Document Management Application Web and Android Based

The development of technology has an impact on the way documents are managed. Documents that are stored manually in paper form become digital documents. Digitalization aims to save documents as an effort to maintain accessibility so that documents can be stored in a longer lasting form. Digital documents employed in the application are documents contained in the Informatics Engineering study program, Muhammadiyah University of Bengkulu by means of scanning. The digital document management application developed using the web and Android is to minimize technical problems when the application is implemented because the data recorded both on the web and on Android are integrated with each other. Testing of the application used the black box method and questionnaire. The test results indicate that the application is free from syntax errors and is functionally successful and has a very good aspect of usability value with a value of 4.36
Image Encryption Based on A Novel Memristive Chaotic System with Super Wide Range and Multiple Image Morphing

In this paper, we propose a 4D memristor-based chaotic system with super wide range, and explore its application in image encryption. Firstly, by analyzing the dynamical behavior of the memristor-based chaotic system, it is found that the system has a special attractor and that the maximum Lyapunov exponent of the system is increasing gradually in . Further, we apply the wide range and stable chaotic states of the proposed memristive chaotic system to cryptography to present an image encryption algorithm. All the ciphers used in the permutation-diffusion are drawn out by associating the plain image with the chaotic sequences. We treat the color image as a 3D matrix for multiple image morphing operations, so that the pixels of the RGB channels of the color image are highly mixed. Moreover, the Latin-cube based scrambling method is improved to enhance the generalizability of the cryptographic algorithm. The results of various simulation experiments indicated that the algorithm has excellent performance in terms of security.
Attainment Regions in Feature-Parameter Space for High-Level Debugging in Autonomous Robots.

Understanding a controller's performance in different scenarios is crucial for robots that are going to be deployed in safety-critical tasks. If we do not have a model of the dynamics of the world, which is often the case in complex domains, we may need to approximate a performance function of the robot based on its interaction with the environment. Such a performance function gives us insights into the behaviour of the robot, allowing us to fine-tune the controller with manual interventions. In high-dimensionality systems, where the actionstate space is large, fine-tuning a controller is non-trivial. To overcome this problem, we propose a performance function whose domain is defined by external features and parameters of the controller. Attainment regions are defined over such a domain defined by feature-parameter pairs, and serve the purpose of enabling prediction of successful execution of the task. The use of the feature-parameter space -in contrast to the action-state space- allows us to adapt, explain and finetune the controller over a simpler (i.e., lower dimensional space). When the robot successfully executes the task, we use the attainment regions to gain insights into the limits of the controller, and its robustness. When the robot fails to execute the task, we use the regions to debug the controller and find adaptive and counterfactual changes to the solutions. Another advantage of this approach is that we can generalise through the use of Gaussian processes regression of the performance function in the high-dimensional space. To test our approach, we demonstrate learning an approximation to the performance function in simulation, with a mobile robot traversing different terrain conditions. Then, with a sample-efficient method, we propagate the attainment regions to a physical robot in a similar environment.
Semi-supervised Yolo Network for Induced Pluripotent Stem Cells Detection

Induced pluripotent stem cells (iPSCs) have broad prospects in clinical and industrial applications, and the automatic identification of iPSCs is essential to optimize the iPSCs manufacturing. However, the challenge of obtaining large amounts of labeled images has limited the use of image analysis in the field of iPSCs. In this paper, we propose Semi-Yolo, a simple yet effective semi-supervised learning (SSL) network that combines mean-teacher SSL paradigm and the Yolo object detection network for the iPSCs identification. The designed burn-in stage in Semi-Yolo makes full use of available labeled images, providing good initialization for both student and teacher model. Then in the teacher-student mutual learning stage, the gradually progressing teacher model of Semi-Yolo generates highly confident pseudo labels for unlabeled images, which provides extra training data to update the student model in a mutually-beneficial manner. Together with a Mosaic data augmentation technique to increase the diversity of our data, Semi-Yolo achieves favorable detection performance on the collected iPSCs detection dataset. Extensive experimental results on our dataset demonstrates the effectiveness of Semi-Yolo with great improvement compared to supervised baseline, which shows better detection precision and faster detection speed than the state-of-the-art SSL object detection algorithm.
METHODOLOGICAL WORKSHOP «CRITERIA FOR ASSESSING THE QUALITY OF PUBLICATIONS IN SELECTION OF SCIENTIFIC INFORMATION»

The article describes the content of a research workshop “Criteria for assessing the quality of publications in the selection of scientific information”, focused on the teaching staff, graduate students and undergraduates. This workshop was developed during the course and internship program “Research component in adult education”. The purpose of the workshop is to familiarize students with the system of criteria for evaluating a scientific publication to select the most relevant ones in determining the current state of a certain scientific field and compiling a literature review.
Meaning-making and Embodied Cognition in Sound Design Research

This chapter examines the historically prevalent models of cognition that have shaped research methods and techniques in some of the fields associated with sound design. It discusses the efficacy of disembodied models of cognition, which, in favor of reductive explanations, overlook how embodied and perceptual experience can shape and constrain cognitive function. The chapter is particularly concerned with sonic meaning-making - the process by which a listener interprets or assigns meaning to a sonic symbol - as this process is crucial to sound design research. It chronicles developments in psychoacoustics, computing, cognitive science and music, and suggests that sound design research should adopt embodied models of cognition. Such models have more recently come to the fore in these fields, and offer more convincing accounts of meaning-making by addressing how our physical, perceptual and sensorimotor dimensions shape and constrain cognition.
Multi-Agent Systems: Model-checking in Logics of Protocols

Dissertation under the supervision of Dr.Sujata Ghosh, Associate Professor, Computer Science Unit,Indian Statistical Institute, Chennai
Self-Supervised Product Quantization for Deep Unsupervised Image Retrieval

Supervised deep learning-based hash and vector quantization are enabling fast and large-scale image retrieval systems. By fully exploiting label annotations, they are achieving outstanding retrieval performances compared to the conventional methods. However, it is painstaking to assign labels precisely for a vast amount of training data, and also, the annotation process is error-prone. To tackle these issues, we propose the first deep unsupervised image retrieval method dubbed Self-supervised Product Quantization (SPQ) network, which is label-free and trained in a self-supervised manner. We design a Cross Quantized Contrastive learning strategy that jointly learns codewords and deep visual descriptors by comparing individually transformed images (views). Our method analyzes the image contents to extract descriptive features, allowing us to understand image representations for accurate retrieval. By conducting extensive experiments on benchmarks, we demonstrate that the proposed method yields state-of-the-art results even without supervised pretraining.
DEVELOPING TENTH GRADE STUDENTS OF ISLAMIC SENIOR HIGH SCHOOL LISTENING COMPREHENSION : A CASE STUDY

AbstractListening is one important aspect in the process of learning English listening. In connection with the explanation above, the writer is interested in conducting research Developing a Tenth Grade Students Listening to Listening MA (Case Study) MA Al Rosyid Kendal – Dander Bojonegoro. The purpose of the this study was to know students listening comprehension development and To find out students responses in learning listening. By using qualitative research. The research findings consist of all activities in the study. The activity is interviewing students and observing activities in learning. The researcher presents the researchers' findings in one part is the interview. The implication of this research give assigning. Giving assignments to students to do both in class and at home can improve students listening comprehension, because students fell they have the responsibility to do it.Keywords : Listening ,comprehension ,listening comprehension
Neural network-based anomaly detections for nonlinear dynamical systems

We present two methods of neural network-based anomaly detections for nonlinear dynamical systems. The first method is based on a combination of long short-term memory and Mahalanobis’ distance of an estimation error. The proposed method is applied to detect the pitchfork bifurcation and the anomaly event of nonperiodic function with external force. We find that the proposed method can successfully identify the anomaly event while the detection ability highly relies on the choice of training data. We also consider the convolutional neural network-based autoencoder (CNN-AE) based anomaly detection. The present CNN-AE is trained using a two-dimensional cylinder wake without anomaly data processing, e.g., noise and spatial data lack. Since the CNN-AE trained with clean data cannot handle the unlearned anomaly (noisy or damaged) data well, it can be utilized as an anomaly detector. The reconstruction ability of the proposed method apparently differs among the trained regular data and the untrained anomaly data. We can expect that the proposed anomaly detection methods can be applied to a detection of transition phenomena of fluid flows.