Reviewerly/exHarmony huggingface dataset:
Efficiently Identifying Task Groupings for Multi-Task Learning

Multi-task learning can leverage information learned by one task to benefit the training of other tasks. Despite this capacity, naively training all tasks together in one model often degrades performance, and exhaustively searching through combinations of task groupings can be prohibitively expensive. As a result, efficiently identifying the tasks that would benefit from co-training remains a challenging design question without a clear solution. In this paper, we suggest an approach to select which tasks should train together in multi-task learning models. Our method determines task groupings in a single training run by co-training all tasks together and quantifying the effect to which one task's gradient would affect another task's loss. On the large-scale Taskonomy computer vision dataset, we find this method can decrease test loss by 10.0\% compared to simply training all tasks together while operating 11.6 times faster than a state-of-the-art task grouping method.
Development of Smart Glove for Deaf-mute People

This paper illustrates a Smart glove used to bridge the between the speech impaired and normal people in their day-to-day life. Nearly 70 million people are deaf by birth and 230 million people are hearing impaired or speechless because of the conditions such as autism or stroke. To overcome this situation, a research has been started, which uses flex sensors and accelerometer to read the gesture, Arduino UNO to process the input and a web UI to display the text output.
Comparison and Generation of a Poem in Arabic Language Using the Lstm, Bilstm and Gru

In this paper, we will demonstrate how we can automatically generate a new poem in Arabic language from a dataset containing a number of poems written by renowned Arabic poets and writers based on classical Arabic language and from colloquial Arabic. A comparison was also made between the different LSTM (Long Short-Term Memory), BiLSTM (Bi-directional Long Short-Term Memory) and GRU (Gated recurrent unit) generation networks in order to obtain a satisfactory result, keeping in mind the main one. The difference between a text in Arabic language and another in English or French is the orientation of the text, which is from right to left. For this reason, we tried to use the Bi-LSTM to have the added value of this network, but the latter did not lead to correct results neither at the sentence level nor at the word level, contrary to the GRU to give good results in terms of performance.
КЛЮЧЕВЫЕ ЗАДАЧИ, ИНСТРУМЕНТЫ И ВЫЗОВЫ В РАЗВЕДКЕ КИБЕРУГРОЗ

Spam, viruses, spyware are the risks that they expose themselves to users, using the services of the Internet. Modern threats of the Internet is much more complicated than those that were before. They are more resistant to remedies. Threats of the 21st century possess such properties as dynamism and transformability. Often they are carried out using zero-day vulnerabilities - unresolved vulnerabilities or vulnerabilities for which a defense mechanism has not yet been developed. Such attacks often pass unnoticed by many protection tools, IPS, antivirus Software and firewalls. The market critically lacks not only the resources that can handle all incidents, but also the overall system, thanks to which it would be possible to respond to them in the early stages of cyberattacks - ideally before operation, as well as to accumulate distributed knowledge about threats, exchange received data, investigate causes of threats and respond immediately to them. For faster accumulation of information about possible threats, one should strive to share useful data from a wide range of sources. At the same time, it is important that this information is standardized, that is, the standards and protocols for the transmission and provision of data defined in advance. The capability to detect, analyze, and defend against such threats in near real-time conditions is not possible without the employment of threat intelligence. This paper reviews the main definitions, instruments in Threat Intelligence technology. Moreover, how this technology may help to reduce cyber security risk. Threat Intelligence can help to prevent many problems recurring in information systems.
Meta-Learning to Improve Pre-Training

Pre-training (PT) followed by fine-tuning (FT) is an effective method for training neural networks, and has led to significant performance improvements in many domains. PT can incorporate various design choices such as task and data reweighting strategies, augmentation policies, and noise models, all of which can significantly impact the quality of representations learned. The hyperparameters introduced by these strategies therefore must be tuned appropriately. However, setting the values of these hyperparameters is challenging. Most existing methods either struggle to scale to high dimensions, are too slow and memory-intensive, or cannot be directly applied to the two-stage PT and FT learning process. In this work, we propose an efficient, gradient-based algorithm to meta-learn PT hyperparameters. We formalize the PT hyperparameter optimization problem and propose a novel method to obtain PT hyperparameter gradients by combining implicit differentiation and backpropagation through unrolled optimization. We demonstrate that our method improves predictive performance on two real-world domains. First, we optimize high-dimensional task weighting hyperparameters for multitask pre-training on protein-protein interaction graphs and improve AUROC by up to 3.9%. Second, we optimize a data augmentation neural network for self-supervised PT with SimCLR on electrocardiography data and improve AUROC by up to 1.9%.
A robust multi-factor remote user authentication scheme for cloud-IoT services

The rapid growth of communication technologies with the internet as a backbone requires secure remote access. Cloud computing and internet of things (IoT) is a growing information and communication...
ELEMENTS OF INTERMEDIATE ANALYSIS IN LITERATURE LESSONS AT COLLEGES OF ART AND AESTHETIC PROFILE

The article reflects the results of the study of intermediality as the basis for the effective study of literature, which is advisable to introduce during classes in arts and aesthetics colleges. The author determines the content and meaning of the concept of «intermediality»; defines the peculiarities of teaching literature in high schools of art and aesthetic profile according to the cognitive features of students; substantiates the need to introduce intermediality in academic institutions of art and aesthetic profile, because the most significant means of formation of creative personality of students are different types of art, which predetermines the need to use the interaction of arts in the educational process; Specificity of introducing intermediality as the basis for the effective study of literature, because literature from this point of view is a universal form of art that combines signs of other arts, namely: expressiveness of music, plasticity of sculpture, visuality of painting; varieties of interrelations between literature and other types of arts are investigated; samples of intermediality in literature, samples of student intermedia created in literature classes are given; prospects of using intermediality as the basis for effective literature study in colleges of art and aesthetic profile are determined.
Change Management in a Global Distributed Environment: N/A

Context: in a distributed environment changing requirement results in project failure, changes come from different sites by multi-site stakeholder involvement.by considering change requests from a different site and managing stakeholder issue change requests can handle efficiently. Here the objective is to find and manage the change process and suggest a solution to the stakeholder issues. Methods: we have done a literature review to find limitations in the change request process model and issue that stakeholders faced in a distributed environment. Results: We have identified activities missing in the change request process and change management process. We have also highlighted the role and responsibility for the activities to carry out a process of change management effectively. Our research also highlights stakeholder issues related to change management in a distributed environment and suggest an appropriate solution to the faced challenges. Conclusion: Our framework process not only defines a good balance change process but also identifies change activity in each process, role and responsibility to carry out the process and artifact efficiently. But there is a need to find more stakeholder issues related to managing the change request and their appropriate solution also need to find process activities in detail so that there must be no misunderstanding related to task assignment
An Array of Processed Channel for Multiple Object Detection and Distance Estimation in a Video Using a Homographic Mono Camera System

Feature selection and optimization of anchors are important tasks for achieving a good level of object detection and frame rate for real-time implementation capability. The bounding boxes associated with the detected objects play a crucial role in determining the distance of objects in the current image frame because they have spatial pixel coordinates. The use of a fast and simple technique for determining distance using these coordinates becomes a necessity. The current work aims to address critical limitations associated with the above tasks. A novel way of replacing standard red-green-blue (RGB) images by a multiple channel type feature vector is called the “array of processed channels (AOPC).” A new image corpus is created using the AOPC and is used for training an optimized, custom deep learning You Look Only Once (YOLO)-based object detection framework. Another issue, which is addressed prior to the training phase, is the selection and optimization of anchor boxes based on the manually annotated ground truth data. Finally, the pixel coordinates associated with the bounding boxes of the detected objects are transformed into real-world coordinates. It helps to determine the actual distance of it from the ego vehicle. The proposed techniques have shown promising results compared with other research.
Multi-pedestrian Assignment and Detection Based on DE-Greedy Algorithm

This article is aimed at an actual project for venue security which means there are dynamic targets and detectors. To solve this problem, we conducted preliminary research and analysis. In order to improve the efficiency of UAV-detector for dynamic targets and minimize the execution time, this article proposes a model using DE (Differential Evolution) algorithm to simulate the moving of individuals and realize task-allocation. We also analysed different situations for evaluation function with high density and low density. Firstly, the initialization, mutation and cross-select of DE optimization algorithm are used to simulate moving individuals and selection is used to calculate cost of each decision. After that, the greedy algorithm is used to sort the execution order for which we choose different parameters in different situations. Usually the way of computer simulation will be adopted to simulate a variety of conditions of traction load and the static simulation is done to verify the evaluation and method.
PANDEMIC LEARNING ONTOLOGY: METHODOLOGICAL ASPECTS TO ASSESS SKILLS IN A PUBLIC HEALTH EMERGENCY

The purpose of this work is to show the experience of methodological aspects which were used to assess competencies with regard to conceiving, designing, implementing and operating (CDIO), in a curriculum for competencies in the face of state of emergency in public health due to coronavirus.The situation and advances are displayed as a case study, in training civil engineering professionals in computer science at the Catholic University of Temuco (CUT) in Chile.The questions intented to be resolved are: Is it possible to improve the capacities of students from the Araucanía region (Chile) in a social-economic-cultural system, through teaching and learning based on education which allows them to discover, expand and exploit the improvement of the training of professional human capital with competencies in an emergency situation in public health due to coronavirus?.The process of validation of competencies responds to conceive, design, implement and operate (CDIO) being adapted.The way in which the transition in the levels of competencies that the progressing student advances occurs is achieved in different teaching and learning activities through online training.The implementation of competency validation is produced in different contexts in the phases: conceive, design, implement and operate, which have had to be faced by teachers, instructors, assistants for the achievement of students.
The Effect of Cooperative Learning Methods Picture and Picture to Reading Interests

The problem of this research is the low reading interest of elementary school students. This study aims to determine the effectiveness of cooperative learning method of picture and picture type for reading interest of 4th grade students at SDN Margasari II, East Karawang District. This research is a type of quantitative research. The sample used in this study were 64 students in 4th grade students at SDN Margasari II. Data collection technique through the Reading Interest questionnaire. Data analysis technique to test the hypothesis is done by calculating the Independent-Smple T test. The results of hypothesis testing, there is an influence on the method of cooperative learning type picture and picture on the reading interest of 4th grade students. This significance was obtained by 0.025 because of the significance value of 0,025 < 0,05 or with a price of 2,292 ttable of 1,69 and n = 64 possible that Ho is rejected. From the results of this study it can be concluded that the cooperative learning method of picture and picture type affects the reading interest of 4th grade students
Uncertainty Measures in Neural Belief Tracking and the Effects on Dialogue Policy Performance

The ability to identify and resolve uncertainty is crucial for the robustness of a dialogue system. Indeed, this has been confirmed empirically on systems that utilise Bayesian approaches to dialogue belief tracking. However, such systems consider only confidence estimates and have difficulty scaling to more complex settings. Neural dialogue systems, on the other hand, rarely take uncertainties into account. They are therefore overconfident in their decisions and less robust. Moreover, the performance of the tracking task is often evaluated in isolation, without consideration of its effect on the downstream policy optimisation. We propose the use of different uncertainty measures in neural belief tracking. The effects of these measures on the downstream task of policy optimisation are evaluated by adding selected measures of uncertainty to the feature space of the policy and training policies through interaction with a user simulator. Both human and simulated user results show that incorporating these measures leads to improvements both of the performance and of the robustness of the downstream dialogue policy. This highlights the importance of developing neural dialogue belief trackers that take uncertainty into account.
Sphere sets from point clouds for efficient collision detection in robot motion planning

Increasingly, industrial robots are being programmed using advanced offline programming tools that can generate collision-free optimised motions. One of the critical requirements for offline programming is an accurately modelled environment. Generating this model can be labour intensive as measurements are manually taken and entered. 3D scanning tools can expedite this process by generating a point cloud that roboticists can use to measure the dimensions of obstacles digitally. Point clouds generated by 3D scanning tools are not well suited to robot motion planning due to their complexity. However, algorithms that generate a simplified model from a point cloud exist and improve collision detection performance. This paper presents an algorithm for converting a point cloud model into an efficient collision model for robot programming. Our algorithm handles noisy data sets and point clouds missing data caused by shadowing. The generated model is a collection of spheres, which performs well in collision detection during motion planning.
On the feasibility of crawling-based attacks against recommender systems1

Nowadays, online services, like e-commerce or streaming services, provide a personalized user experience through recommender systems. Recommender systems are built upon a vast amount of data about users/items acquired by the services. Such knowledge represents an invaluable resource. However, commonly, part of this knowledge is public and can be easily accessed via the Internet. Unfortunately, that same knowledge can be leveraged by competitors or malicious users. The literature offers a large number of works concerning attacks on recommender systems, but most of them assume that the attacker can easily access the full rating matrix. In practice, this is never the case. The only way to access the rating matrix is by gathering the ratings (e.g., reviews) by crawling the service’s website. Crawling a website has a cost in terms of time and resources. What is more, the targeted website can employ defensive measures to detect automatic scraping. In this paper, we assess the impact of a series of attacks on recommender systems. Our analysis aims to set up the most realistic scenarios considering both the possibilities and the potential attacker’s limitations. In particular, we assess the impact of different crawling approaches when attacking a recommendation service. From the collected information, we mount various profile injection attacks. We measure the value of the collected knowledge through the identification of the most similar user/item. Our empirical results show that while crawling can indeed bring knowledge to the attacker (up to 65% of neighborhood reconstruction on a mid-size dataset and up to 90% on a small-size dataset), this will not be enough to mount a successful shilling attack in practice.
Design and Development of Smart Health Risk Prediction System Using Iot and Fuzzy Logic

Nowadays Health-care Environment has developed science and knowledge based on Wireless-Sensing node Technology oriented. Patients are facing a problematic situation of non-predictable death’s due to the specific reason of heart problems and attacks which is because of nonexistence of good medical maintenance to patients at the needed time. This project is for specially monitoring the patients and informing doctors and the patients loved ones. So we are proposing an innovative project to avoid such sudden death rates by using Patient Health Monitoring system that uses sensor technology and uses internet to communicate to the loved ones in case of problems. This system uses Temperature and heartbeat sensors for tracking patients’ health. Both the sensors are connected to the Node MCU. To track the patient health micro-controller is in turn interfaced to a website and wi-fi connection to send the data to the web-server (wireless sensing node). In case of any abrupt changes in patient heart-rate or body temperature alert is sent about the patient using IoT. This system also shows patients temperature and heartbeat tracked live data with timestamps over the Internetwork. Thus, Patient health monitoring system based on IoT uses internet to effectively monitor patient health and helps the user monitoring their loved ones and saves lives.
Performance Analysis of a Hypervisor and the Container-based Migration Technique for Cloud Virtualization

Containers emerged as a lightweight alternative to a virtual machine (VM) and offer better microservice architecture support. The major limitation of a VM is that it requires a large image size due to the fully installed guest OS, binaries, and library files. This disadvantage hinders its applicability to real-time applications. On the other hand, containers are comprised of only the required files, thus making them lighter in size compared to a VM. With the development of container technology, the issue of how to facilitate migration support needs to be addressed. Therefore, in this study the author proposes an experimental setup to facilitate the migration of the running containers and evaluates their performance compared to the existing hypervisor-based VM migration scheme. To provide a solution for this, the author used LXD as a container technology and a checkpoint/restore mechanism to facilitate the migration of running containers. Experimental results demonstrate that, compared to the existing VM migration scheme, the proposed container-based migration technique shows improvement with a reduction in: down time by 72%, migration time by 55%, and the number of pages transferred by 97%.
Gone Fishing: Neural Active Learning with Fisher Embeddings

There is an increasing need for effective active learning algorithms that are compatible with deep neural networks. While there are many classic, well-studied sample selection methods, the non-convexity and varying internal representation of neural models make it unclear how to extend these approaches. This article introduces BAIT, a practical, tractable, and high-performing active learning algorithm for neural networks that addresses these concerns. BAIT draws inspiration from the theoretical analysis of maximum likelihood estimators (MLE) for parametric models. It selects batches of samples by optimizing a bound on the MLE error in terms of the Fisher information, which we show can be implemented efficiently at scale by exploiting linear-algebraic structure especially amenable to execution on modern hardware. Our experiments show that BAIT outperforms the previous state of the art on both classification and regression problems, and is flexible enough to be used with a variety of model architectures.
The Students’ Perspective on The Use of Iqra’ Method In Pronunciation and Phonetics Course

This study aims to identify whether the students can read IPA symbol before attending Pronunciation and Phonetics course. This study adopted a mixed method approach in which the questionnaire consisting of close ended and open-ended questions was used to collect the data. The subjects in this study were 117 first semester students of University of Muhammadiyah Surakarta in the academic year in 2019. The analysis consisted of diagram analysis and data analysis with following the stages; by understanding the data; selecting themes; clarifying data and drawing conclusions. The responses from the open-ended questionnaire showed the following results: Iqra’ method was perceived as an effective method to improve pronunciation and favored by students; there were suggestions proposed by students to improve Iqra’ method and there were few students still having difficulties in understanding English pronunciation.