Reviewerly/exHarmony huggingface dataset:
Load Balancing with the Help of Round Robin and Shortest Job First Scheduling Algorithm in Cloud Computing

Load balancing in cloud computing assigning the all load over each node of a mutual or shared system to make resource usage effective and to perform the response time to the job. This has used Round Robin (RR) and shortest job first (SJF) algorithm. Scheduling is the main part of cloud computing and jobs or workflow scheduling under the platform as a service. The work under infrastructure as a service work to virtual machine scheduling which machine is decided by scheduler should go on which job or virtual machine. Simply RR is a time quantum-based preemptive algorithm, and SJF is a burst time-based non-preemptive and preemptive algorithm. But RR work in two-phase like expected finish time adapted for each process. Each process with generally minimum completion times scheduled. This algorithm result as no longer waiting for the smallest task.
SudaBERT: A Pre-trained Encoder Representation For Sudanese Arabic Dialect

Bidirectional Encoder Representations from Transformers (BERT) has proven to be very efficient at Natural Language Understanding (NLU), as it allows to achieve state-of-the-art results in most NLU tasks. In this work we aim to utilize the power of BERT in Sudanese Arabic dialect, and produce a Sudanese word representation. We collected over 7 million sentences in Sudanese dialect and used them to resume training of the pre-trained Arabic-BERT, as it was trained on large Modern Standard Arabic (MSA) corpus. Our model -SudaBERT- has achieved better performance on Sudanese Sentiment Analysis, this clarifies that SudaBERT works better in understanding Sudanese Dialectic which is the domain we are interested in.
Loss-Aware Pattern Inference: A Correction on the Wrongly Claimed Limitations of Embedding Models

Knowledge graph embedding models (KGEs) are actively utilized in many of the AI-based tasks, especially link prediction. Despite achieving high performances, one of the crucial aspects of KGEs is their capability of inferring relational patterns, such as symmetry, antisymmetry, inversion, and composition. Among the many reasons, the inference capability of embedding models is highly affected by the used loss function. However, most of the existing models failed to consider this aspect in their inference capabilities. In this paper, we show that disregarding loss functions results in inaccurate or even wrong interpretation from the capability of the models. We provide deep theoretical investigations of the already exiting KGE models on the example of the TransE model. To the best of our knowledge, so far, this has not been comprehensively investigated. We show that by a proper selection of the loss function for training a KGE e.g., TransE, the main inference limitations are mitigated. The provided theories together with the experimental results confirm the importance of loss functions for training KGE models and improving their performance.
Categorical composable cryptography

Abstract We formalize the simulation paradigm of cryptography in terms of category theory and show that protocols secure against abstract attacks form a symmetric monoidal category, thus giving an abstract model of composable security definitions in cryptography. Our model is able to incorporate computational security, set-up assumptions and various attack models such as colluding or independently acting subsets of adversaries in a modular, flexible fashion. We conclude by using string diagrams to rederive the security of the one-time pad and no-go results concerning the limits of bipartite and tripartite cryptography, ruling out e.g., composable commitments and broadcasting.
Global Image Sentiment Transfer

Transferring the sentiment of an image is an unexplored research topic in computer vision. This work proposes a novel framework consisting of a reference image retrieval step and a global sentiment transfer step to transfer image sentiment according to a given sentiment tag. The proposed image retrieval algorithm is based on the SSIM index. The retrieved reference images by the proposed algorithm are more content-related than the algorithm based on the perceptual loss. Therefore, it can lead to a better image sentiment transfer result. In addition, we propose a global sentiment transfer step, which employs an optimization algorithm to iteratively transfer image sentiment based on the feature maps produced by the DenseNet121 architecture. The proposed sentiment transfer algorithm can transfer image sentiment while keeping the content of the input image intact. Both qualitative and quantitative evaluations demonstrate that the proposed sentiment transfer framework outperforms existing artistic and photo-realistic style transfer algorithms in producing satisfactory sentiment transfer results with fine and exact details.
Disentangled Representation Learning for Controllable Image Synthesis: An Information-Theoretic Perspective

In this paper, we look into the problem of disentangled representation learning and controllable image synthesis in a deep generative model. We develop an encoder-decoder architecture for a variant of the Variational Auto-Encoder (VAE) with two latent codes z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> and z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> . Our framework uses z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> to capture specified factors of variation while z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> captures the complementary factors of variation. To this end, we analyze the learning problem from the perspective of multivariate mutual information, derive optimizable lower bounds of the conditional mutual information in the image synthesis processes and incorporate them into the training objective. We validate our method empirically on the Color MNIST dataset and the CelebA dataset by showing controllable image syntheses. Our proposed paradigm is simple yet effective and is applicable to many situations, including those where there is not an explicit factorization of features available, or where the features are non-categorical.
CCIED: Cache-aided Collaborative Intelligence Between Edge Devices

Recently, deep learning technology has shined in the fields of computer vision, natural language processing and speech recognition, and related products have sprung up like mushrooms. Due to the storage and calculation of deep neural network (DNN) models are relatively large and mobile edge devices are often resource-constrained, how to efficiently deploy DNN models on resource-constrained edge devices has attracted great attention from academia and industry. There's strength in numbers, so we propose CCIED, a framework which lets edge devices cooperate with each other to complete DNN inference tasks. Due to task inputs in the mobile edge computing scenarios usually have great similarities, the outputs of the middle layer of the neural network and the corresponding labels are cached. When a similar input already exists in the cache, the device does not need to perform the remaining calculations, but directly returns the cached results. One of the challenges of collaborative inference is that the communication overhead associated with transferring intermediate data can be significant. We therefore perform weight pruning only on the layer that obtains the intermediate results, which can greatly reduce the redundant parameters of the intermediate results, thereby reducing the time for transferring data between devices, and basically does not reduce the complexity of the model. Experimental results show that CCIED can efficiently deploy the DNN model on edge devices with almost no loss of precision, and can significantly reduce the total latency during cache hits.
Research on the trajectory planning method of custom pattern robot based on image processing

At present, robot spraying is mainly used in single color spraying, but there is still a lack of effective automatic spraying method for complex customized patterns of multiple colors. This paper presents a robot automatic spraying method for customized digital camouflage patterns. According to the relevant technical manuals, the digital camouflage patterns are designed to meet the needs, and the image processing techniques such as corner detection are used to extract the coordinate information of each color block of the digital camouflage patterns. Considering the spray deposition model of the spray gun and combining with the robot path planning technology, the spray trajectory of the spray gun is planned in each extracted digital camouflage block. The automatic spraying of customized digital camouflage patterns can be realized by importing the trajectory information of the gun into the spraying robot.
Providing online STEM workshops in times of isolation

The global spread of COVID-19 has resulted in learning and teaching being confronted with immense challenges and changes since spring 2020. Measures to contain the COVID-19 pandemic had and continue to have a particularly strong impact on non-formal and informal learning, which are important features of out-of-class on-line STEM workshops combining science, technology, engineering and mathematics. We developed and carried out online STEM workshops for approximately 250 students during times of isolation in the spring and summer of 2020. To identify potential success factors and stumbling blocks for designing and implementing online STEM workshops, we conducted a qualitative interview-based study with a selection of eight experts in the summer and autumn 2020, i.e. after the STEM workshops. The experts were Austrian and German teachers who planned and implemented STEM workshops for students age 10 to 18. Our collected data was examined using techniques of grounded theory approaches. Using techniques of qualitative interview studies and grounded theory approaches, we found that removing learning barriers and creating new types of learning spaces, online socio-constructivist learning, and teachers' TPACK for STEM Workshops are important factors when considering and designing STEM workshops for online learning environments. These same factors will also be central when planning and implementing online STEM workshops in post-COVID-19 times. To facilitate student participation and to increase social interaction are critical elements for practitioners working on online STEM workshops-both during and after COVID-19. Highly trained teachers with in-depth technical, pedagogical, and content skills are essential to facilitate participation and interactions among students, teachers, and learning contents.
VisTAS: blockchain-based visible and trusted remote authentication system

The information security domain focuses on security needs at all levels in a computing environment in either the Internet of Things, Cloud Computing, Cloud of Things, or any other implementation. Data, devices, services, or applications and communication are required to be protected and provided by information security shields at all levels and in all working states. Remote authentication is required to perform different administrative operations in an information system, and Administrators have full access to the system and may pose insider threats. Superusers and administrators are the most trusted persons in an organisation. “Trust but verify” is an approach to have an eye on the superusers and administrators. Distributed ledger technology (Blockchain-based data storage) is an immutable data storage scheme and provides a built-in facility to share statistics among peers. Distributed ledgers are proposed to provide visible security and non-repudiation, which securely records administrators’ authentications requests. The presence of security, privacy, and accountability measures establish trust among its stakeholders. Securing information in an electronic data processing system is challenging, i.e., providing services and access control for the resources to only legitimate users. Authentication plays a vital role in systems’ security; therefore, authentication and identity management are the key subjects to provide information security services. The leading cause of information security breaches is the failure of identity management/authentication systems and insider threats. In this regard, visible security measures have more deterrence than other schemes. In this paper, an authentication scheme, “VisTAS,” has been introduced, which provides visible security and trusted authentication services to the tenants and keeps the records in the blockchain.
Research on Personalized Recommendation System Based on Big Data Mining Technology

The rapid popularization of the Internet has promoted the transformation of the Internet industry, and a large number of new businesses have emerged. It has ushered in new development opportunities and challenges in all walks of life. Information technology is deeply integrated with human production and life, global data is showing explosive growth, and the problem of information explosion is getting worse. With the increasing volume of data, people's ability to understand and process data is limited. How to help individuals effectively manage these data and help them obtain the information they need is extremely important. Researchers try to use information filtering technology to solve the problem of information overload. Big data mining technology represented by personalized recommendation has become an important tool. This article discusses related theories such as big data and big data mining technology, and uses it as a guide basis, combined with related recommendation algorithms, to build a personalized recommendation system based on big data mining technology. The article analyzes the application cases of personalized information recommendation systems in some industries, discusses the processing methods and basic processes of massive data mining, analyzes the basic framework of personalized recommendation systems, and provides references for data mining and application researchers and related workers.
A Telepresence System using Toy Robots the Users can Assemble and Manipulate with Finger Plays and Hand Shadow

The authors report a telepresence system using toy robots. The robots can be assembled and set up for telepresence by the user easily. The robots are manipulated with hand gestures of finger play and hand shadow without instructtion, since the users already know these gestures by their experience. Through the experiment, the system was found to be easy to use, and the users felt familiar with the robot and didn't feel nervous.
Small Object Detection by Generative and Discriminative Learning

With the development of deep convolutional neural networks (CNNs), the object detection accuracy has been greatly improved. But the performance of small object detection is still far from satisfactory, mainly because small objects are so tiny that the information contained in the feature map is limited. Existing methods focus on improving classification accuracy but still suffer from the limitation of bounding box prediction. To solve this issue, we propose a detection framework by generative and discriminative learning. First, a reconstruction generator network is designed to reconstruct the mapping from low frequency to high frequency for anchor box prediction. Then, a detector module extracts the regions of interest (ROIs) from generated results and implements a RoI-Head to predict object category and refine bounding box. In order to guide the reconstructed image related to the corresponding one, a discriminator module is adopted to tell from the generated result and the original image. Extensive evaluations on the challenging MS-COCO dataset demonstrate that our model outperforms most state-of-the-art models in detecting small objects, especially the reconstruction module improves the average precision for small object (APs) by 7.7%.
Deep Learning Semantic Segmentation for High-Resolution Medical Volumes

Automated semantic segmentation in the domain of medical imaging can enable a faster, more reliable, and more affordable clinical workflow. Fully convolutional networks (FCNs) have been heavily used in this area due to the level of success that they have achieved. In this work, we first leverage recent architectural innovations to make an initial segmentation: (i) spatial and channel-wise squeeze and excitation mechanism; (ii) a 3D U-Net++ network and deep supervision. Second, we use classical methods for refining the initial segmentation: (i) spatial normalization and (ii) local 3D refinement network applied to patches. Finally, we put our methods together in a novel segmentation pipeline. We train and evaluate our models and pipelines on a dataset of a 120 abdominal magnetic resonance - volumetric - images (MRIs). The goal is to segment five different organs of interest (ORI): liver, kidneys, stomach, duodenum, and large bowel. Our experiments show that we can generate high resolution segmentation of comparable quality to the state-of-the-art methods on low resolution without adding significant computational cost.
Constriction Coefficient-Based Particle Swarm Optimization and Gravitational Search Algorithm for Image Segmentation

Image segmentation is one of the pivotal steps in image processing. Actually, it deals with the partitioning of the image into different classes based on pixel intensities. In this work, a new image segmentation method has been introduced based on the constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA). The random samples of the image histogram act as searcher agents of the CPSOGSA. Besides, the optimal number of thresholds is determined using Kapur’s entropy method. The effectiveness and applicability of CPSOGSA have been accomplished by applying it to four standard images from the USC-SIPI image database including airplane, cameraman, clock, and truck. Various performance metrics have been employed to investigate the simulation outcomes including optimal thresholds, standard deviation, mean, run-time analysis, PSNR (peak signal-to-noise ratio), best fitness value calculation, convergence maps, and box plot analysis. In addition, the experimental results of CPSOGSA are compared with standard PSO and GSA. The simulation results clearly indicate that hybrid CPSOGSA takes less computational time in finding the best threshold values of the benchmark images.
Vowel Non-Vowel Based Spectral Warping and Time Scale Modification for Improvement in Children’s ASR

Acoustic differences between children’s and adults’ speech causes the degradation in the automatic speech recognition system performance when system trained on adults’ speech and tested on children’s speech. The key acoustic mismatch factors are formant, speaking rate, and pitch. In this paper, we proposed a linear prediction based spectral warping method by using the knowledge of vowel and non-vowel regions in speech signals to mitigate the formant frequencies differences between child and adult speakers. The proposed method gives 31% relative improvement over the baseline system. We have also investigated time scale modification using RTISILA and SOLAFS algorithms and found that our proposed method performs better. Combining the proposed method with RTISILA and SOLAFS results in a further error rate reduction. The final combined system gives 49% relative improvement compared to the baseline system.
CoANE: Modeling Context Co-occurrence for Attributed Network Embedding

Attributed network embedding (ANE) is to learn low-dimensional vectors so that not only the network structure but also node attributes can be preserved in the embedding space. Existing ANE models do not consider the specific combination between graph structure and attributes. While each node has its structural characteristics, such as highly-interconnected neighbors along with their certain patterns of attribute distribution, each node's neighborhood should be not only depicted by multi-hop nodes, but consider certain clusters or social circles. To model such information, in this paper, we propose a novel ANE model, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Context Co-occurrence-aware Attributed Network Embedding</i> (CoANE). The basic idea of CoANE is to model the context attributes that each node's involved diverse patterns, and apply the convolutional mechanism to encode positional information by treating each attribute as a channel. The learning of context co-occurrence can capture the latent social circles of each node. To better encode structural and semantic knowledge of nodes, we devise a three-way objective function, consisting of positive graph likelihood, contextual negative sampling, and attribute reconstruction. We conduct experiments on five real datasets in the tasks of link prediction, node label classification, and node clustering. The results exhibit that CoANE can significantly outperform state-of-the-art ANE models.
Multi-Task Learning Based Traditional Mongolian Words Recognition

In this paper, a multi-task learning framework has been proposed for solving and improving traditional Mongolian words recognition. To be specific, a sequence-to-sequence model with attention mechanism was utilized to accomplish the task of recognition. Therein, the attention mechanism is designed to fulfill the task of glyph segmentation during the process of recognition. Although the glyph segmentation is an implicit operation, the information of glyph segmentation can be integrated into the process of recognition. After that, the two tasks can be accomplished simultaneously under the framework of multi-task learning. By this way, adjacent image frames can be decoded into a glyph more precisely, which results in improving not only the performance of words recognition but also the accuracy of character segmentation. Experimental results demonstrate that the proposed multi-task learning based scheme outperforms the conventional glyph segmentation-based method and various segmentation-free (i.e. holistic recognition) methods.
Performance Benchmarking of GPU and TPU on Google Colaboratory for Convolutional Neural Network

Convolutional Neural Network (CNN) is a sub-class of artificial intelligence deep learning technique. In this sub-class, algorithms are inspired by the structure and function of the brain called artificial neural network. CNN outperforms in image processing tasks such as image recognition, image classification, face recognition, object detection, etc. CNN consists of very large neural networks and is capable to process an enormous amount of unstructured data. To process these types of data, huge computational power is required, which should be both data intensive as well as compute intensive. For performance benchmarking of CNN, an experiment on hand written digit recognition dataset— MNIST has been performed. The main objective of this paper is a comparative study of CNN performance’s on accelerated computational power, i.e., Graphics Processing Unit (GPU) and Tensor Processing Unit (TPU). To access the GPU and TPU resources, Google colaboratory cloud platform called google Colab which has been utilized. After running CNN model for MNIST, it is observed that TPU takes comparatively less epoch time for different batch sizes as compared to GPU. This research paper is also a good source for AI researchers whose research area intersect with High-performance computing (HPC).