Reviewerly/exHarmony huggingface dataset:
Evolution of New Integrated Haze Removal Algorithm Based on Haze Line

Haze is a standout amongst the most essential factor that debases the outside pictures. Picture corruption relies upon the separation of the Object from the camera as the separation increment from the camera of the scene point haze additionally increases. This paper presents a haze removal technique which is relies on the Haze-line prior that is recently introduced. This prior is based on the observation that the pixel values of a hazy image can be modeled as lines in RGB space that intersect at the air- light proposed approach is known as haze line. In this paper we proposed Post processing CBMG (Contrast, Brightness, Midtone and Gama adjustment) followed by four stages: clustering the pixel into haze line, Estimation of Transmission map, Regularization, Dehazing, This method is implement by observation of two basic things first pixels in a given group are regularly non local . Second pictures with improved perceivability (or sunny morning picture) have more differentiation than the pictures stopped by terrible climate. Experimental result demonstrate that our method remove haze layer and provide qualitative result.
Online Transfer Learning: Negative Transfer and Effect of Prior Knowledge

Transfer learning is a machine learning paradigm where the knowledge from one task is utilized to resolve the problem in a related task. On the one hand, it is conceivable that knowledge from one task could be useful for solving a related problem. On the other hand, it is also recognized that if not executed properly, transfer learning algorithms could in fact impair the learning performance instead of improving it - commonly known as negative transfer. In this paper, we study the online transfer learning problems where the source samples are given in an off-line way while the target samples arrive sequentially. We define the expected regret of the online transfer learning problem, and provide upper bounds on the regret using information-theoretic quantities. We also obtain exact expressions for the bounds when the sample size becomes large. Examples show that the derived bounds are accurate even for small sample sizes. Furthermore, the obtained bounds give valuable insight on the effect of prior knowledge for transfer learning in our formulation. In particular, we formally characterize the conditions under which negative transfer occurs.
Characteristics, Applications and Use Cases of Cloud Computing

The economical of cloud computing as well as its compelling scaling provides it with an advancement motorist for small firms, especially in the establishing world. Cloud deployed venture source preparation, supply chain control apps, customer connection administration functions, medical apps and mobile functions possess the prospective to get to countless users. Within this paper, our experts check out the various principles associated with cloud computing. Leveraging our adventures on a variety of clouds, our company take a look at clouds from specialized, and also service aspects. Our experts highlight some of the possibilities in cloud computing, underscoring the relevance of clouds as well as presenting why that modern technology should be successful. This paper explains in detail about characteristics, applications and use cases of cloud computing.
High-QoE DASH Live Streaming Using Reinforcement Learning

With the live video streaming becomes more and more common in daily life such as live meeting and live video call, it is an urgent task to ensure high-quality and low-delay live video streaming service. High user quality of experience (QoE) should be ensured to satisfy the requirement of user, for which latency is one of the important factors. In this paper, a high-QoE live streaming method is proposed with reinforcement learning. Experiments are conducted to evaluate the proposed method. Results demonstrate that the proposal shows the best performance with highest QoE compared with conventional methods in three network conditions. In Ferry case, the QoE is almost twice of the QoE of other methods.
StableEmit: Selection Probability Discount for Reducing Emission Latency of Streaming Monotonic Attention ASR

While attention-based encoder-decoder (AED) models have been successfully extended to the online variants for streaming automatic speech recognition (ASR), such as monotonic chunkwise attention (MoChA), the models still have a large label emission latency because of the unconstrained end-to-end training objective. Previous works tackled this problem by leveraging alignment information to control the timing to emit tokens during training. In this work, we propose a simple alignment-free regularization method, StableEmit, to encourage MoChA to emit tokens earlier. StableEmit discounts the selection probabilities in hard monotonic attention for token boundary detection by a constant factor and regularizes them to recover the total attention mass during training. As a result, the scale of the selection probabilities is increased, and the values can reach a threshold for token emission earlier, leading to a reduction of emission latency and deletion errors. Moreover, StableEmit can be combined with methods that constraint alignments to further improve the accuracy and latency. Experimental evaluations with LSTM and Conformer encoders demonstrate that StableEmit significantly reduces the recognition errors and the emission latency simultaneously. We also show that the use of alignment information is complementary in both metrics.
Multi-Mode Transformer Transducer with Stochastic Future Context

Automatic speech recognition (ASR) models make fewer errors when more surrounding speech information is presented as context. Unfortunately, acquiring a larger future context leads to higher latency. There exists an inevitable trade-off between speed and accuracy. Naively, to fit different latency requirements, people have to store multiple models and pick the best one under the constraints. Instead, a more desirable approach is to have a single model that can dynamically adjust its latency based on different constraints, which we refer to as Multi-mode ASR. A Multi-mode ASR model can fulfill various latency requirements during inference -- when a larger latency becomes acceptable, the model can process longer future context to achieve higher accuracy and when a latency budget is not flexible, the model can be less dependent on future context but still achieve reliable accuracy. In pursuit of Multi-mode ASR, we propose Stochastic Future Context, a simple training procedure that samples one streaming configuration in each iteration. Through extensive experiments on AISHELL-1 and LibriSpeech datasets, we show that a Multi-mode ASR model rivals, if not surpasses, a set of competitive streaming baselines trained with different latency budgets.
Ontological Concept Structure Aware Knowledge Transfer for Inductive Knowledge Graph Embedding

Conventional knowledge graph embedding methods mainly assume that all entities at reasoning stage are available in the original training graph. But in real-world application scenarios, newly emerged entities are always inevitable, which results in the severe problem of out-of-knowledge-graph entities. Existing efforts on this issue mostly either utilize additional resources, e.g., entity descriptions, or simply aggregate in-knowledge-graph neighbors to embed these new entities inductively. However, high-quality additional resources are usually hard to obtain and existing neighbors of new entities may be too sparse to provide enough information for modeling these entities. Meanwhile, they may fail to integrate the rich information of ontological concepts, which provide a general figure of instance entities and usually remain unchanged in knowledge graph. To this end, we propose a novel inductive framework namely CatE to solve the sparsity problem with the enhancement from ontological concepts. Specifically, we first adopt the transformer encoder to model the complex contextual structure of the ontological concepts. Then, we further develop a template refinement strategy for generating the target entity embedding, where the concept embedding is used to form a basic skeleton of the target entity and the individual characteristics of the entity will be enriched by its existing neighbors. Finally, extensive experiments on public datasets demonstrate the effectiveness of our proposed model compared with state-of-the-art baseline methods.
REEFT-360

With 360° video streaming, the user's field of view (a.k.a. viewport) is at all times determined by the user's current viewing direction. Since any two users are unlikely to look in the exact same direction as each other throughout the viewing of a video, the frame-by-frame video sequence displayed during a playback session is typically unique. This complicates the direct comparison of the perceived Quality of Experience (QoE) using popular metrics such as the Multiscale-Structural Similarity (MS-SSIM). Furthermore, there is an absence of light-weight emulation frameworks for tiled-based 360° video streaming that allow easy testing of different algorithm designs and tile sizes. To address these challenges, we present REEFT-360, which consists of (1) a real-time emulation framework that captures tile-quality adaptation under time-varying bandwidth conditions and (2) a multi-step evaluation process that allows the calculation of MS-SSIM scores and other frame-based metrics, while accounting for the user's head movements. Importantly, the framework allows speedy implementation and testing of alternative head-movement prediction and tile-based prefetching solutions, allows testing under a wide range of network conditions, and can be used either with a human user or head-movement traces. The developed software tool is shared with the paper. We also present proof-of-concept evaluation results that highlight the importance of including a human subject in the evaluation.
Intelligent Waste Sorting Bin for Recyclable Municipal Solid Waste

This paper describes about a sorting bin of Recyclable Municipal Solid Waste. This bin is described as intelligent due to automatic source separation method has been implemented in this bin. A method called Convolutional Neural Network (CNN) has been implemented in waste sorting process. CNN is one kind of deep learning process. CNN is very fast, efficient and reliable method for object classification. For this reason, this method is implemented for waste sorting. There are many algorithms have already been developed for object detection. Among these algorithms the Inception-ResNet V2 is used. Inception-ResNet V2 is simply the combination of the Inception and the Residual Network structure. This model is trained by images of PET and LDPE about 300 of each sample. PET bottles and chips packet has been considered as sample. Training has been done by Google Collaboratory platform. Accuracy of detection is measured on the basis of 100 observations and accuracy is 84 CSS for manual feeding of image and decision making. This technique may increase recyclable MSW (PET LDPE) collection rate, reduce environment pollution, can help recycling plants economically.
Patch vs. Global Image-Based Unsupervised Anomaly Detection in MR Brain Scans of Early Parkinsonian Patients

Although neural networks have proven very successful in a number of medical image analysis applications, their use remains difficult when targeting subtle tasks such as the identification of barely visible brain lesions, especially given the lack of annotated datasets. Good candidate approaches are patch-based unsupervised pipelines which have both the advantage to increase the number of input data and to capture local and fine anomaly patterns distributed in the image, while potential inconveniences are the loss of global structural information. We illustrate this trade-off on Parkinson's disease (PD) anomaly detection comparing the performance of two anomaly detection models based on a spatial auto-encoder (AE) and an adaptation of a patch-fed siamese auto-encoder (SAE). On average, the SAE model performs better, showing that patches may indeed be advantageous.
Extrapolation Behavior Comparison of Nonlinear State Space Models

The extrapolation behavior and its consequences of two different state space models is studied. More specifically, the extrapolation behavior of the local model state space network (LMSSN) is compared to the polynomial nonlinear state space model (PNLSS). Both approaches represent favorable alternatives in the light of their results on recent benchmark studies. The models are compared with regards to their static extrapolation behavior and their tendency to erratic behavior if test amplitudes grow larger than training amplitudes. It is shown on a Hammerstein and Wiener artificial test process that the PNLSS shows severe shortcomings concerning stability when operated close to or outside the boundaries in which it was trained. The LMSSN instead shows more favorable behavior in extrapolation and does only exhibit minor erratic behavior. If the process is inherently polynomial in some way, the PNLSS outperforms other models in extrapolation, as shown on the Silverbox benchmark test.
A Bio-Inspired and Heuristic-Based Hybrid Algorithm for Effective Performance With Load Balancing in Cloud Environment

In a cloud computing environment, effective scheduling policies and load balancing have always been the aim. An efficient task scheduler must be proficient in a dynamically distributed environment and to the policy of efficient scheduling of jobs based upon the workload. In this research, a novel hybrid heuristic algorithm is developed for balancing the load among cloud nodes. This is achieved by hybridizing the existing ant colony optimization (ACO), artificial bee colony algorithm (ABC), and AHP (analytical hierarchy process) algorithm. The AHP algorithm and the artificial bee colony (ABC) algorithm is used for figuring out the best servers suitable for a particular job, and the ant colony algorithm is used to find the most efficient path to that particular server. The proposed algorithm is better in resource utilization. It also performs better load balancing, which keeps on improving with time. The result analysis shows better average response time and better average makespan time compared to other two existing algorithms.
A Comprehensive Survey of Phishing Email Detection and Protection Techniques

E-Mails are commonly used as a medium of communication for personal and pro- fessional purposes. Information shared via mail is also sensitive and private, such as financial information, credit records, login data, and so on. This makes them useful to cyber attackers who can exploit this knowledge for illegal purposes. Phishing is a technique used by fraudsters to gain confidential data from users by alleging to be from known sources. In a phished e-mail, the sender can encourage users to provide private in- formation under false premises. Phishing e-mails have also been frequently used in financial institutions and consumer fraud. This paper discusses an overview of dif- ferent techniques for phishing e-mail detection and protection currently used in e-mail filtering. A comparative analysis and review of these techniques are carried out. This survey provides an understanding of the phishing detection issue, its present space for a solution, and its future directions for study.
A novel approach for human activity recognition using object interactions and machine learning

Recognising human activity can be advantageous in a number of different scenarios including elder care, healthcare or for training purposes. It can be of direct use to support humans in doing different activities, but is still a challenge for systems to correctly classify the activity in a way that is valuable for the user, as they often times lack the robustness or simplicity for day-to-day use. In this paper an approach for human activity recognition based on object interactions is presented. The proposed system consists of a wireless sensor network, with each sensor node measuring the received signal strength indication (RSSI) to its neighbouring nodes. The accumulated RSSI data is then analyzed by a machine learning algorithm which tries to infer one of several cooked dishes from that data. Experimental studies demonstrate promising results and therefore potential for this technology for recognising human activity in the form of cooking, but its generalised approach makes it suitable for other environments, too.
Dynamic latency control of serverless applications operated on AWS lambda and greengrass

Cloud native programming and the serverless paradigm can revolutionize software development and the operation of distributed applications. However, latency sensitive applications pose additional challenges to the underlying networks and cloud platforms. Moving compute resources to the edge is an inevitable step but further mechanisms and novel components are also required to enable such services in a serverless environment. In this demonstration, we present a novel system providing soft latency control for serverless applications and we showcase our proof-of-concept prototype supervising microservices operated on Amazon Web Services, and its edge extension, called Greengrass. Our main objective is the cost optimal operation while meeting the average latency requirements which is achieved by dynamically changing the software layout and serverless artifacts based on live monitoring.
A post COVID Machine Learning approach in Teaching and Learning methodology to alleviate drawbacks of the e-whiteboards

Deep learning has paved the way for critical and revolutionary applications in almost every field of life in general. Ranging from engineering to healthcare, machine learning and deep learning has left its mark as the state-of-the-art technology application which holds the epitome of a reasonable high benchmarked solution. Incorporating neural network architectures into applications has become a common part of any software development process. In this paper, we perform a comparative analysis on the different transfer learning approaches in the domain of hand-written digit recognition. We use two performance measures, loss and accuracy. We later visualize the different results for the training and validation datasets and reach to a unison conclusion. This paper aims to target the drawbacks of the electronic whiteboard with simultaneous focus on the suitable model selection procedure for the digit recognition problem. © 2021 Tamkang University. All Rights Reserved.
Multi-oriented scene text detection by fixed-width multi-ratio rotation anchors

Scene text detection plays an important role in many real-world applications. In this paper, we propose a multi-oriented scene text detection framework, which includes three main modules. We utilize a deep residual network in the front of the framework to learn text representations. A set of fixed-width, multi-ratio rotation anchors is introduced to slide over convolutional feature maps and generate the text proposals with orientation information. An in-network recurrent architecture is then seamlessly connected, where the sequential context of proposals is encoded in order to facilitate the construction of text lines. Extensive experiments are conducted on two ICDAR benchmarks to demonstrate the effectiveness of our approach.
Friend Relationships Recommendation Algorithm in Online Education Platform

With the development and application of online education, mining friend relationships between learners can improve interactive communication, enhance collaborative learning, and motivate learners to make mutual progress. However, existing methods only recommend well-known members through the number of likes and fans, which fail to consider the hidden interest points and content topics of members in the text. To address this problem, we propose an Evaluation Latent Dirichlet Allocation (EvaluationLDA) algorithm to recommend suitable friends for learners in online education. The EvaluationLDA algorithm clusters learners with similar learning interests to obtain Top-N friend recommendation sequences based on constructing learner document datasets, calculating learner similarity, and modeling the friend topic. We conduct experiments to demonstrate the effectiveness of our EvaluationLDA algorithm. The result shows that our EvaluationLDA algorithm can effectively recommend Top-N friend sequences in the online education platform.
Towards Deep Learning-Based Image Steganalysis: Practices and Open Research Issues

Steganalysis is the technique of identifying secret messages concealed in cover objects using newly developed content-adaptive steganographic algorithms. Steganography has been explored by many anti-social elements and attackers as it permits for secret communication without making third parties to notice. As a result, uncovering those behaviours becomes a crucial task that may be accomplished using steganalysis, which is a method of determining whether or not a cover object contains hidden message. In the recent past, the deep learning-based approaches have shown the prominent performance compared to handcrafted–based counterparts. This paper presents a study and analysis of various state-of-the-art deep learning-based image steganalysis techniques. In addition, we have identified the various open research challenges that may provide a future direction in this active field of image steganalysis. Our analysis indicates the paucity of large-scale data for training Convolutional Neural Networks (CNN) models that is a major hurdle for deep learning-based approaches. The arbitrary size of images, cover mismatch problem still remains the open research issues.
Spatial co-location pattern mining based on the improved density peak clustering and the fuzzy neighbor relationship

Spatial co-location pattern mining discovers the subsets of spatial features frequently observed together in nearby geographic space. To reduce time and space consumption in checking the clique relationship of row instances of the traditional co-location pattern mining methods, the existing work adopted density peak clustering to materialize the neighbor relationship between instances instead of judging the neighbor relationship by a specific distance threshold. This approach had two drawbacks: first, there was no consideration in the fuzziness of the distance between the center and other instances when calculating the local density; second, forcing an instance to be divided into each cluster resulted in a lack of accuracy in fuzzy participation index calculations. To solve the above problems, three improvement strategies are proposed for the density peak clustering in the co-location pattern mining in this paper. Then a new prevalence measurement of co-location pattern is put forward. Next, we design the spatial co-location pattern mining algorithm based on the improved density peak clustering and the fuzzy neighbor relationship. Many experiments are executed on the synthetic and real datasets. The experimental results show that, compared to the existing method, the proposed algorithm is more effective, and can significantly save the time and space complexity in the phase of generating prevalent co-location patterns.