Reviewerly/exHarmony huggingface dataset:
SAR Image Ship Detection Based On Deep Learning

China is a maritime power, and ship detection is particularly important under complex sea conditions. At present, deep learning plays an important role in the SAR image ship detection field. An improved yolov4-Tiny detection algorithm is proposed in this paper. The improved algorithm introduces the attention mechanism unit to enhance feature extraction and make the target feature more pro-minent. The Batch normalization optimization data set is used to increase the robustness of the training model and effectively reduce the gradient disappearance or gradient explosion. Cosine annealing is used to optimize the learning rate and speed up the fitting of deep learning model. On the basis of realizing real-time detection, the whole network further improves the detection accuracy. The experimental results show that the MAP of improved Yolov4-Tiny algorithm is 75.56%, and the FPS is 30.
Improving the cost efficiency of large-scale cloud systems running hybrid workloads - A case study of Alibaba cluster traces

The pandemic of coronavirus has dramatically disrupted the retail industry, as many stores are forced to close and people across the world are shelter-in-place with online shopping as the inevitable choice. To meet the rapidly increasing demand for e-commerce, more data centers are expected to provide new or significantly improve existing cloud services that can better support hybrid workloads (e.g. online purchase jobs and batch jobs that support ranking or recommendation systems). Successful cloud systems need to efficiently handle and quickly respond to huge volume of traffic with such hybrid workloads. Meanwhile, it is critical to reduce the total cost of ownership (TCO) for profitability. Improving system utilization is one of the effective techniques to achieve the twin goals of high performance and low TCO. This paper conducts a comprehensive analysis on the 2017 and 2018 cluster traces released by Alibaba, which provides a case study about Alibaba's best practices in improving the performance and cost efficiency of its large-scale cloud systems by consolidating time-sensitive online service jobs with time-insensitive batch jobs. Our investigation indicates that the over-subscription (causing resource waste and low utilization) and under-subscription (causing performance degradation) problems co-exist in the current Alibaba system. We develop a simulator that allows us to evaluate possible solutions to address this problem and their impact on the performance, energy consumption, and TCO. Our experiments show that the estimated TCO can be reduced by $600,000 for the 2018 trace running on over 4,000 machines without compromising performance. The TCO can decrease by nearly $68 million if similar strategy is extrapolated to Alibaba's 432,000 web facing servers.
Context-dependent Label Smoothing Regularization for Attention-based End-to-End Code-Switching Speech Recognition

Previous works utilize the context-independent (CI) label smoothing regularization (LSR) method to prevent attention-based End-to-End (E2E) automatic speech recognition (ASR) model, which is trained with a cross entropy loss function and hard labels, from making over-confident predictions. But the CI LSR method does not make use of linguistic knowledge within and between languages in the case of code-switching speech recognition (CSSR). In this paper, we propose the context-dependent (CD) LSR method. According to code-switching linguistic knowledge, the output units are classified into several categories and several context dependency rules are made. Under the guidance of the context dependency rules, prior label distribution is generated dynamically according to the category of historical context, rather than being fixed. Thus, the CD LSR method can utilize the linguistic knowledge in the case of CSSR to further improve the performance of the model. Experiments on the SEAME corpus demonstrate the effects of the proposed method. The final system with the CD LSR method achieves the best performance with 37.21% mixed error rate (MER), obtaining up to 3.7% relative MER reduction compared to the baseline system with no LSR method.
Ensemble of diluted attractor networks with optimized topology for fingerprint retrieval

The present study analyzes the retrieval capacity of an Ensemble of diluted Attractor Neural Networks for real patterns (i.e., non-random ones), as it is the case of human fingerprints. We explore the optimal number of Attractor Neural Networks in the ensemble to achieve a maximum fingerprint storage capacity. The retrieval performance of the ensemble is measured in terms of the network connectivity structure, by comparing 1D ring to 2D cross grid topologies for the random shortcuts ratio. Given the nature of the network ensemble and the different characteristics of patterns, an optimization can be carried out considering how the pattern subsets are assigned to the ensemble modules. The ensemble specialization splitting into several modules of attractor networks is explored with respect to the activities of patterns and also in terms of correlations of the subsets of patterns assigned to each module in the ensemble network.
Research on Hybrid Collaborative Filtering Recommendation Algorithm Based on the Time Effect and Sentiment Analysis

In this study, we focus on the problem of information expiration when using the traditional collaborative filtering algorithm and propose a new collaborative filtering algorithm by integrating the time factor (ITWCF). This algorithm considers information influence attenuation over time, introduces an information retention period based on the information half-value period, and proposes a time-weighted function, which is applied to the nearest neighbor selection and score prediction to assign different time weights to the scores. In addition, to further improve the quality of the nearest neighbor selection and alleviate the problem of data sparsity, a method of calculating users’ sentiment tendency by analysis of user review features is proposed to mine users’ attitudes about the reviewed items, which expands the score matrix. The time factor and sentiment tendency are then integrated into the K-means clustering algorithm to select the nearest neighbor. A hybrid collaborative filtering model (TWCHR) based on the improved K-means clustering algorithm is then proposed, by combining item-based and user-based collaborative filtering. Finally, the experimental results show that the proposed algorithm can address the time effect and sentiment analysis in recommendations and improve the predictive performance of the model.
Campus Placement Predictive Analysis using Machine Learning

Each student dreams of having a work offer in their hands before leaving college. A selection probability indicator lets students get an sense of where they're standing and what to do to ensure a decent selection. A placement predictor is a device that can forecast the probability or form of business that a student in the pre-final year has chances of placing. While a forecasting program could help in the academic preparation of an institution for future years. With the emergence of data mining and machine learning, through analyzing the data set of the previous student year, numerous predictive models were applied. This paper introduces a literature study for pre-final year engineering graduate students on different statistical selection models.
Artificial Intelligence based Recommendation System

Artificial Intelligence (AI) is a modern engineering method to make machines think or use their intelligence like humans by mimicking traits and by learning to take appropriate decisions and to perform assigned tasks properly. Some of the companies which have done remarkable work in the field of Artificial Intelligence (AI) are Facebook, Google, Microsoft, IBM, etc. which are investing millions and billions in this very field of AI development and research. Currently there is a huge market and need for building Intelligent Systems for Recommendation. To counter this, one of the easiest and most preferable System is Recommendation System (RS). Recommendation Systems had proved to play an important role in the field of E-Commerce websites, Online Shopping, Dating Apps, Social-Networking, Digital Marketing, Online Advertisements, etc. by providing personalized recommends and feedback to users according to their preferences and choices. The topic of this report is AI based Recommendation System. As the topic of this paper suggests we are going to discuss about various ways and approaches of Artificial Intelligence (AI) to build a Recommendation System (RS) application. There are many approaches to build a recommendation system according to one's need like Collaborative Filtering, Content based Recommendation Systems, Hybrid Systems, Artificial Neural Networks, Swarm Intelligence, Evolutionary Computing, Fuzzy Sets, etc. which will be briefly discussed in this report, but we will primarily discuss and work on Collaborative Filtering approach and about the problems like Cold start, Sparsity, Scalability and some others.
Development of integral model of speech recognition system for Uzbek language

In this paper investigates the approach to realization of recognition of Uzbek words on the basis of end-to-end models is considered. Also presented are some theoretical data on the architecture of neural networks used in the integrated model, and the results of preliminary experimental studies conducted on their basis. Deep recurrent neural networks, which combine the multiple levels of representation that have proved so effective in deep networks with the flexible use of long-range context that empowers RNNs. When trained end-to-end with suitable regularization, we find that deep BRNNs achieve a test set error of CER=49.1% on our dataset.
Analysis and Management of Motor Failures of Hexacopter in Hover

This research presents an analysis and management strategy for hovering hexacopter with one or more failing motors. Of late, multirotor drones have become particularly popular, and all drones have been increasing in popularity. Unlike a fixed-wing drone, failure of motors in a multirotor craft may cause safety problems. Numerous published articles have proposed solving this problem by redesigning the control law or control gain. This approach, however, is difficult to implement because change of control gain usually involves connecting external devices. This paper proposes to keep the control gain unchanged but reallocate the thrusts. Simulations are conducted on a hexacopter in various hovering modes. Some hovering state problems are investigated for the linearized dynamics but also numerically verified for the original nonlinear dynamics. In case some motors of a hexacopter fail in flight, an allocation matrix is proposed to redistribute required thrusts to functional motors. Seven cases of motor failure are studied. This paper analytically proves that limited controllability for emergency landing is feasible in four scenarios at the linear level, but the other three scenarios are completely uncontrollable. Numerical simulations are presented to demonstrate the validity of our algorithm. An online video of real flight also confirms our results. This paper potentially helps the design of failure management of rotors and increases the successful rate of emergent landing.
State Machine for Arbitrary Robots for Exploration and Inspection Tasks

In this paper, a novel state machine for mobile robots is described that enables a direct use for exploration and inspection tasks. It offers a graphical user interface (GUI) to supervise the process and to issue commands if necessary. The state machine was developed for the open-source framework Robot Operating System (ROS) and can interface arbitrary algorithms for navigation and exploration. Interfaces to the commonly used ROS navigation stack and the explore_lite package are already included and can be utilized. In addition, routines for mapping and inspection can be added freely to adapt to the area of application. The state machine features a teleoperation mode to which it changes as soon as a respective command was issued. It also implements a software emergency stop and multiplexes all movement commands to the motor controller. To show the state machine's capabilities several simulations and real-world experiments are described in which it was used.
Stochastic Gradient Descent Works Really Well for Stress Minimization

Stress minimization is among the best studied force-directed graph layout methods because it reliably yields high-quality layouts. It thus comes as a surprise that a novel approach based on stochastic gradient descent (Zheng, Pawar and Goodman, TVCG 2019) is claimed to improve on state-of-the-art approaches based on majorization. We present experimental evidence that the new approach does not actually yield better layouts, but that it is still to be preferred because it is simpler and robust against poor initialization.
Generative multiview inpainting for object removal in large indoor spaces

As interest in image-based rendering increases, the need for multiview inpainting is emerging. Despite of rapid progresses in single-image inpainting based on deep learning approaches, they have no constraint in obtaining color consistency over multiple inpainted images. We target object removal in large-scale indoor spaces and propose a novel pipeline of multiview inpainting to achieve color consistency and boundary consistency in multiple images. The first step of the pipeline is to create color prior information on masks by coloring point clouds from multiple images and projecting the colored point clouds onto the image planes. Next, a generative inpainting network accepts a masked image, a color prior image, imperfect guideline, and two different masks as inputs and yields the refined guideline and inpainted image as outputs. The color prior and guideline input ensure color and boundary consistencies across multiple images. We validate our pipeline on real indoor data sets quantitatively using consistency distance and similarity distance, metrics we defined for comparing results of multiview inpainting and qualitatively.
Global and Local Information Adjustment for Semantic Similarity Evaluation

Semantic similarity evaluation is used in various fields such as question-and-answering and plagiarism testing, and many studies have been conducted into this problem. In previous studies using neural networks to evaluate semantic similarity, similarity has been measured using global information of sentence pairs. However, since sentences do not only have one meaning but a variety of meanings, using only global information can have a negative effect on performance improvement. Therefore, in this study, we propose a model that uses global information and local information simultaneously to evaluate the semantic similarity of sentence pairs. The proposed model can adjust whether to focus more on global information or local information through a weight parameter. As a result of the experiment, the proposed model can show that the accuracy is higher than existing models that use only global information.
Platform-Dependent Computer Security Complacency: The Unrecognized Insider Threat

This article reports on a grounded theory investigation of subject response anomalies that were encountered in the course of a neurocognitive laboratory study of computer user cybersecurity behaviors. Subsequent qualitative data collection led to theoretical development in specification of three broad constructs of computer user security complacency. Theoretical insights indicate that states of security complacency can arise in the form of a naïve lack of concern about the likelihood of facing security threats (inherent complacency), from ill-advised dependence upon specific computing platforms and protective workplace technology implementations for protection (platform complacency), as well as the reliance on the guidance on advice from trusted social others in personal and workplace networks (social complacency). Elements of an emergent theory of cybersecurity complacency arising from our interpretive insights are discussed.
TDD-BPR: The topic diversity discovering on Bayesian personalized ranking for personalized recommender system

The main objective of the recommender system is to determine user behavior or preference and suggest the items of interest to the user. We notice that topic information is one of the significant factors that enables a user to choose the next items. To mimic this behavior, we design a topic diversity discovering (TDD) model to capture user preferences on topics. Unlike the previous studies applied the topic information to recommender systems, which only considered the item topics. In this paper, we not only focus on the item topic information but also consider the user topic information on the recommender system. We develop a TDD model to learn the topic information from the item, and we then utilize the transfer learning to obtain the user topic information. We then integrate the TDD model with the Bayesian personalized ranking (BPR) model to build a novel TDD-BPR recommender system. We experiment on three well-known three datasets MovieLens-1 M, Pinterest, and TMDB dataset, which achieved 71.8%, 91.2%, and 96.1% in the Hit [email protected] Empirical evidence showed that TDD-BPR discover the potential topic information to understand the user’s preferences and get better recommendation performance.
Research Perception Towards Copy-Move Image Forgery Detection: Challenges and Future Directions

In digital images, Copy-Move Forgery is a general kind of forgery techniques. The process of replicating one part of the image within the same image is termed as copy-move forgery. An effective and reliable approach needs to be developed for identifying these forgeries for restoring the image trustworthiness. The main intent of this paper is to sort out the diverse copy-move image forgery detection models. This survey makes an effective literature analysis on a set of literal works from the past 10 years. The analysis is focused on categorizing the models based on transformation models, machine learning algorithms, and other advanced techniques. The main contribution and limitations of the works are clearly pointed out. In addition, the types of datasets and the simulation platforms utilized by different copy-move forgery detection (CMFD) models are analyzed. The performance measures evaluated by different contributions have been observed for making a concluding decision. The utilization of optimization algorithms on copy-move image forgery detection has also been studied. Finally, the research gaps and challenges with future direction are discussed, which is helpful for researchers in developing an efficient CMFD that could attain high performance.
A Novel Nucleus Detection on Pap Smear Image Using Mathematical Morphology Approach

The fourth most common form of cancer among women is cervical cancer with 569,847new cases and 311,365 reported deaths worldwide in 2018. Cervical cancer is classified as the third leading cause of cancer among women in Malaysia, with approximately 1,682 new cervical cases and about 944 deaths occurred in 2018. Cervical cancer can be detected early by cervical cancer screening. Papanicolaou test, also known as Pap smear test is conducted to detect cancer or precancer in the cervix. The disadvantage of this conventional method is that the sample of microscopic images will risk blurring effects, noise, shadow, lighting and artefact problems. The diagnostic microscopic observation performed by a microbiologist is normally time-consuming and may produce inaccurate results even by experienced hands. Thus, correct diagnosis information is essential to assist physicians to analyze the condition of the patients. In this study, an automatedsegmentation system is proposed to be used as it is more accurate and faster compared to the conventional technique. Using the proposed method in this paper, the image was enhanced by applying a median filter and Partial Contrast Stretching. A segmentation method based on mathematical morphology was performed to segment the nucleus in the Pap smear images. Image Quality Assessment (IQA) which measures the accuracy, sensitivity and specificity were used to prove the effectiveness of the proposed method. The results of the numerical simulation indicate that the proposed method shows a higher percentage of accuracy and specificity with 93.66% and 95.54% respectively compared to Otsu, Niblack and Wolf methods. As a conclusion, the percentage of sensitivity is slightly lower, with 89.20% compared to Otsu and Wolf methods. The results presented here may facilitate improvements in the detection performance in comparison to the existing methods.
Questions and Answers on Legal Texts Based on BERT-BiGRU

Abstract Using text question and answer technology to improve the efficiency of judicial personnel in the process of case handling can greatly reduce labour costs. This paper proposes a hybrid neural network model that combines pre-training model BERT and Bi-GRU. The model first uses the pre-training model to learn powerful semantic capabilities, then combines Bi-GRU to learn the semantic information between the text and the question, finally gets the answer corresponding to the legal text. The experimental results on the CJRC data set show that compared with the basic baseline model, the algorithm in this paper can effectively improve the accuracy and F1 value.
Social Modeling Meets Virtual Reality: An Immersive Implication

The development of novel techniques for social modeling in the context of surveillance applications has significantly reduced manual processing of large and continuous video data. These techniques for social modeling widely cover crowd motion analysis since the impact of social modeling on crowd is significant. However, existing crowd motion analysis methods face a number of problems including limited availability of crowd data representing a specific behavior and weaknesses of proposed models to explore the underlying patterns of crowd behavior. To cope with these problems, we propose a novel method based on energy modeling and social interaction of individual particles in crowd to detect unusual behavior. Our method describes collective dissipative interactions among particles in a crowd scene. We reveal the changing patterns about the crowd behavior states, to support the conversion between different social behaviors during evolution. To further improve the performance of our method, virtual reality can be considered to consolidate the acquisition of data associated with a particular behavior. Therefore, we provide theoretical background of immersive implication considering virtual reality that can expose individuals to virtual crowds and acquire useful data on human motion and behaviors in crowds. The experimental evaluation of our energy and social interaction driven method shows convincing results.
Content-Based Image Retrieval and the Semantic Gap in the Deep Learning Era

Content-based image retrieval has seen astonishing progress over the past decade, especially for the task of retrieving images of the same object that is depicted in the query image. This scenario is called instance or object retrieval and requires matching fine-grained visual patterns between images. Semantics, however, do not play a crucial role. This brings rise to the question: Do the recent advances in instance retrieval transfer to more generic image retrieval scenarios?To answer this question, we first provide a brief overview of the most relevant milestones of instance retrieval. We then apply them to a semantic image retrieval task and find that they perform inferior to much less sophisticated and more generic methods in a setting that requires image understanding. Following this, we review existing approaches to closing this so-called semantic gap by integrating prior world knowledge. We conclude that the key problem for the further advancement of semantic image retrieval lies in the lack of a standardized task definition and an appropriate benchmark dataset.