Reviewerly/exHarmony huggingface dataset:
Towards an Arabic Text Summaries Evaluation Based on AraBERT Model

The evaluation of text summaries remains a challenging task despite the large number of studies in this field for more than two decades. This paper describes an automatic method for assessing Arabic text summaries. In fact, the proposed method will predict the "Overall Responsiveness" manual score, which is a combination of the content and the linguistic quality of a summary. To predict this manual score, we aggregate, with a regression function, three types of features: lexical similarity features, semantic similarity features and linguistic features. Semantic features include multiple semantic similarity scores based on Bert model. While linguistic features are based on the calculation of entropy scores. To calculate the similarity between a candidate summary and a reference summary, we begin by doing an exact match between n-grams. For the unmatched n-grams, we present them as Bert vectors, and then we compute the similarity between Bert vectors. The proposed method yielded competitive results compared to metrics based on lexical similarity such as ROUGE.
Is Multitask Learning Always Better?

Multitask learning has been a common technique for improving representations learned by artificial neural networks for decades. However, the actual effects and trade-offs are not much explored, especially in the context of document analysis. We demonstrate a simple and realistic scenario on real-world datasets that produces noticeably inferior results in a multitask learning setting than in a single-task setting. We hypothesize that slight data-manifold and task semantic shifts are sufficient to lead to adversarial competition of tasks inside networks and demonstrate this experimentally in two different multitask learning formulations.
Counter Mode for Long Messages and a Long Nonce

This paper proposes "Compound-CTR" mode—a simple variation of Counter mode (CTR) with an n bits block cipher. Its goal is to increase the allowed length of a single message and the total number of messages that can be encrypted under a single key. Compound-CTR encrypts a message and a (randomly chosen) nonce with length greater or equal n bits. It uses a master key to derive a nonce-based encryption key and subsequently uses it for encrypting the message in CTR mode. We show how Compound-CTR mode achieves its goal and explain why it can be used as a valid variation of CTR mode that could be of interest in some practical scenarios. Compared to CTR mode, the overhead of Compound-CTR is only the per-message key derivation and one extra key expansion (for the block cipher). We show here key derivation options that require only a few extra block cipher calls.
SipMaskv2: Enhanced Fast Image and Video Instance Segmentation

We propose a fast single-stage method for both image and video instance segmentation, called SipMask, that preserves the instance spatial information by performing multiple sub-region mask predictions. The main module in our method is a light-weight spatial preservation (SP) module that generates a separate set of spatial coefficients for the sub-regions within a bounding-box, enabling a better delineation of spatially adjacent instances. To better correlate mask prediction with object detection, we further propose a mask alignment weighting loss and a feature alignment scheme. In addition, we identify two issues that impede the performance of single-stage instance segmentation and introduce two modules, including a sample selection scheme and an instance refinement module, to address these two issues. Experiments are performed on both image instance segmentation dataset MS COCO and video instance segmentation dataset YouTube-VIS. On MS COCO test-dev set, our method achieves a state-of-the-art performance. In terms of real-time capabilities, it outperforms YOLACT by a gain of 3.0% (mask AP) under the similar settings, while operating at a comparable speed. On YouTube-VIS validation set, our method also achieves promising results. The source code is available at https://github.com/JialeCao001/SipMask.
Understanding New Age of Intelligent Video Surveillance and Deeper Analysis on Deep Learning Techniques for Object Tracking

Surveillance is an imminent part of a smart city model. The persistent possibility of terrorist attacks at public and secured locations raises the need for powerful monitoring systems with subsystems for embedded object tracking. Object tracking is one of machine vision's basic challenges and has been actively researched for decades. Object tracking is a process to locate a moving object over time across a series of video frames. Object tracking powered with the Internet of Things (IoT) technology provides a broad range of applications such as smart camera surveillance, traffic video surveillance, event prediction and identification, motion detection, human-computer interaction, and perception of human behavior. Real-time visual tracking requires high-response time sensors, tracker speed performance, and large storage requirements. Researchers have ascertained and acknowledged that there is a significant change in the efficacy of drone-based surveillance systems towards object tracking with the inception of the deep learning technologies. Several tracking approaches and models have been proposed by researchers in the area of object tracking and have experienced major improvements with advancement in methods, but object tracking is still considered to be a hard problem to solve. This chapter explains state-of-the-art object tracking algorithms and presents views on current and future trends in object tracking and deep learning surveillance. It also provides an analytical discussion on multi-object tracking experiments based on various datasets available for surveillance and the corresponding results obtained from the research conducted in the near past. FairMOT, GNNMatch, MPNTrack, Lif T, GSDT, and Tracktor++ are among the methods investigated. For the MOT16 and MOT17 datasets, FairMOT generated accuracy of 74.9 and 73.7, respectively, whereas GSDT provided accuracy of 60.7 and 67.1 for the 2DMOT15 and MOT20 datasets. FairMOT is an efficient tracker among the models tested, while MPNTrack is significantly more stable and retains tracklet IDs intact across frames in a series. This concludes FairMOT being an efficient tracker and MPNTrack a stable one. It also discusses a case study on the application of IoT in multi-object tracking and future prospects in surveillance.
Deep Metric Color Embeddings for Splicing Localization in Severely Degraded Images

One common task in image forensics is to detect spliced images, where multiple source images are composed to one output image. Most of the currently best performing splicing detectors leverage high-frequency artifacts. However, after an image underwent strong compression, most of the high frequency artifacts are not available anymore. In this work, we explore an alternative approach to splicing detection, which is potentially better suited for images in-the-wild, subject to strong compression and downsampling. Our proposal is to model the color formation of an image. The color formation largely depends on variations at the scale of scene objects, and is hence much less dependent on high-frequency artifacts. We learn a deep metric space that is on one hand sensitive to illumination color and camera white-point estimation, but on the other hand insensitive to variations in object color. Large distances in the embedding space indicate that two image regions either stem from different scenes or different cameras. In our evaluation, we show that the proposed embedding space outperforms the state of the art on images that have been subject to strong compression and downsampling. We confirm in two further experiments the dual nature of the metric space, namely to both characterize the acquisition camera and the scene illuminant color. As such, this work resides at the intersection of physics-based and statistical forensics with benefits from both sides.
Background-Aware Robust Context Learning for Weakly-Supervised Temporal Action Localization

Weakly supervised temporal action localization (WTAL) aims to localize temporal intervals of actions in an untrimmed video using only video-level action labels. Although the learning of the background is an important issue in WTAL, most previous studies have not utilized an effective background. In this study, we propose a novel method for robustly separating contexts, e.g., action-like background, from the foreground to more accurately localize the action intervals. First, we detect background segments based on their probabilities to minimize the impact of background estimation errors. Second, we define the entropy boundary of the foreground and the positive distance between the boundary and background entropy. The background probability and entropy boundary allow the segment-level classifier to robustly learn the background. Third, we improve the performance of the overall actionness model based on a consensus of the RGB and flow features. The results of extensive experiments demonstrate that the proposed method learns the context separately from the action, consequently achieving new state-of-the-art results on the THUMOS-14 and ActivityNet-1.2 benchmarks. We also confirm that using feature adaptation helps overcome the limitation of a pretrained feature extractor on datasets that contain many backgrounds, such as THUMOS-14.
Region-Based Random Color Highlighting in Artistic Style Transfer Using CNN

Artistic Image Generation is a significant field of Image Processing and Computer Vision with a wide variety of use cases. With advancements in the high-end GPU-based computing, this field has gotten the limelight. AI-based artworks are being sold as Non-Fungible Token on various marketplaces. AI-based artworks are becoming increasingly popular among digital artists. With the Artistic style transfer technique, one image could be generated in the flavor of another image, like turning some portrait into Vincent van Gogh's "The Starry Night" flavor. The artistic image generation process uses Convolutional Neural Network (CNN) as a feature extractor. Extracted features are later used to define a total loss, which is composed of content loss and style loss. Once the total loss is minimized, a target is generated with content from the content image and style from the style image. In the loss minimization process, the color information in the target image will be synthesized from a style image. All recent techniques for style transfer have the same characteristics, which make results look more or less similar to each other in terms of color information. This study proposes a novel method for creating an artistic image in which transferred color information is not limited to a style image. The proposed study introduces the use of a new image called "region-based random color image," which is generated using a content image. This study also proposes a new loss term, "region-based random color loss," which contributes to a total loss to transfer color information from region-based random color image to target image.
An Updated IoU Loss Function for Bounding Box Regression

In object detection, bounding box regression (BBR) is a crucial portion that optimizes size of an object and determines the object localization performance. To improve convergence and performance in object detection, many researchers have modified and proposed Intersection over Union (IoU) loss functions. In existing researches, the loss functions have some main drawbacks. Firstly, the IoU-based loss functions are inefficient enough to perform the detection of object in BBR. Secondly, the loss functions ignore the imbalance issues in BBR that the large number of anchor boxes which have small overlaps with the target boxes contribute most to the optimization of BBR. Thirdly, loss functions own redundant parameters which make for extending process. Therefore, to solve these problems, we propose a new method by using an Updated-IoU loss function. Three geometric factors are considered in the proposed function including: (i) the overlap area; (ii) the distances; (iii) the side length. Especially, the Updated-IoU loss function is extremely concentrates on the overlap areas, and predicted object localization to obtain the higher position accuracy performance. In this way, the proposal allows for optimizing and relocating an anchor box in order that the box closes to the ground truth in the training process. The proposal is performed on MS COCO and VOC Pascal dataset. The results are compared to existing IoU models and show that the proposal improves detection ability of object for bounding box regression.
Developing and Testing a New Reinforcement Learning Toolkit with Unreal Engine

In this work we tried to overcome the main limitations that can be found in current state-of-the-art development and benchmarking RL platforms, namely the lack of a user interface, a closed-box approach to scenarios, the lack of realistic environments and the difficulty in extending the obtained results in real applications, by introducing a new development framework for reinforcement learning built over the graphics engine Unreal Engine 4. Unreal Reinforcement Learning Toolkit (URLT) was developed with the idea of being modular, flexible, and easy to use even for non-expert users. To do this, we have developed flexible and modular APIs, through which it's possible to setup the major learning techniques. Using these APIs, users can define all the elements of a RL problem, such as agents, algorithms, tasks, and scenarios, and to combine them with each other to always have new solutions. By taking advantage of the editor's UI, users can select and execute existing scenarios and change the parameters of agents and tasks without to recompile the code. Users also have the possibility to create new scenarios from scratch using an intuitive level editor. Furthermore, task design is made accessible to non-expert users using a node-oriented visual programming system called Blueprint. To validate the tool, we produced a starter pack containing a suite of state-of-the-art RL algorithms, some example scenarios, a small library of props and a couple of trainable agents. Moreover, we ran an evaluation test with users in which the latter were required to try URLT and competing software (OpenAI Gym), then to evaluate both using a questionnaire. The obtained results showed a general preference for URLT in all key parameters of the test.
Digital Competence of Future Teachers as a Topic of Russian Scientific Discourse

The purpose of the article is to study the state and trends in the development of pedagogical problems, related to the use of approaches and technologies for the formation of information culture of future teachers in higher educational institutions of the Russian Federation. It is shown that the analysis of the formation and the development of the problems of the education informatization in the period from 2000 to the present should be carried out on the basis of the system of introduction of the State Educational Standards of Higher Professional Education of 1995 that has developed in the Russian Federation. Taking into account the sequence of adoption and qualitative originality of the requirements of state and federal educational standards of different generations, as well as the requirements of professional standards that determine the professional functions of teachers of various typological groups, the following will be considered in detail: dissertation research; publications published in special pedagogical periodicals; reports on the implementation of research projects within the programs of academic institutions of the Russian Academy of Education in the framework of fundamental and applied complex research. Based on the criterion “requirements for mastering digital culture by a future teacher”, two stages of research development on the problem of forming the information competence of future teachers (2000–2016, and 2017–present) are identified and characterized. Conclusions are drawn about the trends in the development of the theory and practice of the formation of information competence of future teachers, which are currently neglected in the norms and requirements of the UNESCO Recommendations on Information Competence of Teachers and ISTE standards for teachers. The necessity is noted for developing diagnostic tools that allow objective assessment of the level and quality of the formation of digital competencies among future teachers.
Process-Aware Dialogue System With Clinical Guideline Knowledge

Task-oriented dialogue systems aim to engage in interactive dialogue with people to ultimately complete specific tasks. Typical application domains include ticket booking, online shopping, and healthcare providing. Medical dialogue systems can interact with patients, provide initial clinical advice, and improve the efficiency and quality of healthcare services. However, current medical dialogue systems lack the ability to utilize domain knowledge. This paper extracts regular domain knowledge as well as medical process knowledge from clinical guidelines to improve the performance of dialogue systems. Regular knowledge is used to generate accurate responses for a given input, and process knowledge is used to steer the conversation. The authors divide the task of multi-turn conversation generation into four sub-tasks and propose a 4-layer knowledge-based process-aware dialogue model that incorporates the domain knowledge to generate responses. Results indicate that the approach can lead medical conversations actively while providing accurate responses.
Artificial Intelligence, Big Data Analytics, and Smart Cities

Modern urban life is seeing an increasing rate of adoption of artificial intelligence and smart solutions; however, citizens are still struggling to keep up the pace, and the rate at which they acquire skills and knowledge around artificial intelligence and data analysis in smart cities is lagging behind. This paper is an attempt to determine which digital skills are necessary when dealing with smart cities. This article is structured as follows: we first refer to the two basic and fundamental branches of artificial intelligence and continue with applications that exist in these branches regarding smart environments. The research contribution of this article is important since it is one of the few in the international literature dealing with all branches of AI and big data (e.g., machine learning and rule-based applications) in smart cities. The conclusion of the present work is that there is an urgent need to create an education system in the new concepts of AI and big data analysis not only for scientists but also for citizens.
TreeNet Based Fast Task Decomposition for Resource-Constrained Edge Intelligence

Edge intelligence is an emerging technology that integrates edge computing and deep learning to bring AI to the network&#x2019;s edge. It has gained wide attention for its lower network latency and better privacy preservation abilities. However, the inference of deep neural networks is computationally demanding and results in poor real-time performance, making it challenging for resource-constrained edge devices. In this paper, we propose a hierarchical deep learning model based on TreeNet to reduce the computational cost for edge devices. Based on the similarity of the classification categories, we decompose a given task into disjoint sub-tasks to reduce the complexity of the required model. Then a lightweight binary classifier is proposed for evaluating the sub-task inference result. If the inference result of a sub-task is unreliable, our system will forward the input samples to the cloud server for further processing. We also proposed a new strategy for finding and sharing common features across sub-tasks to improve training speed and accuracy. The experimental results on several popular datasets demonstrate the effectiveness of our approach in speeding up inferences while processing most of the input data with a low error rate.
Scalable Management of Heterogeneous Cloud Resources Based on Evolution Strategies Algorithm

The emergence and rapid development of Cloud computing have intensively changed the Information Technology paradigm. Cloud computing found its wide application in science and engineering. Cloud-based systems provide new solutions for scientific experiments and access to data distributed across data centers. Optimization of Cloud resource management is becoming a significant area of research. This research focuses on scheduling tasks and processing scientific data in a heterogeneous Cloud environment where most jobs require a large number of resources and computing power. Our approach proposes a strategy for optimizing task scheduling across different virtual machines and data centers based on the metaheuristic Evolution Strategies algorithm. The Evolution Strategies algorithm was tested, which has not been used in this domain. As an essential property of the system, scalability has played an important role in selecting the algorithm. We created a model and added a Longest Job First broker policy. Compared to the standard Genetic Algorithm, our approach has shown improvements in measured metrics. After testing under different loads, the proposed strategy gave promising results and achieved a better makespan, larger average resource utilization, better throughput, less average execution time, a smaller degree of imbalance, and scalability.
Migration Model for Distributed Server Allocation

This paper proposes a migration model for distributed server allocation. In distributed server allocation, each user is assigned to a server to minimize the communication delay. In the conventional model, a user cannot migrate to another server to avoid instability. We develop a model where each user can migrate to another server while receiving services. We formulate the proposed model as an integer linear programming problem. We prove that the considered problem is NP-complete. We introduce a heuristic algorithm. Numerical result shows that the proposed model reduces the average communication delay by 59% compared to the conventional model at most.
A Survey of Event Relation Extraction

Human beings recognize and understand the real world in units of events. In recent years, events have been used as the basic unit to process unstructured text in the field of natural language processing, but there is often a connection between events and events. Therefore, recognizing the relationship between events and events in unstructured text has become an important task in the field of natural language processing and has attracted more and more researchers’ attention. This paper first introduces the evolution of the method of event temporal relation and causal relation in the extraction research, comparing the advantages and disadvantages and method performance; Then, the event relation extraction model based on deep learning can be divided into strong supervision method and weak supervision method, and the extraction methods of event relation are analyzed, compared and summarized respectively, among them, the method of strong supervision based on deep learning can be further divided into pipeline method and joint learning method, and the method of weak supervision based on deep learning can be divided into semi-supervised learning method, remote learning supervised method and unsupervised learning method. Finally, this paper summarizes the methods of event relation extraction and points out the future research direction.