Reviewerly/exHarmony huggingface dataset:
Enhanced Edge Smoothing for SAR Data Using Image Filter Technique

The SAR is usually corrupted by some surplus speckle formed. These speckles have multiplicative noise, which appears like a grainy pattern in the SAR image. This performs an accurate interpretation of SAR images. The aim of this work was to remove the noise and to accurately classify the LULC facts with quality evolution. The SAR images play an important key role in earth observation applications using high resolution for all-weather conditions and all times. The SAR images an effect of coherent handing out of a mixture of regions and uses a variety of applications like as crop estimation, Land Use Land Cover (LULC), one of the military application is that the target detection, etc. The SAR images are high-resolution LULC facts, and still, it includes the noise. The LULC images continuously for collecting. The traditional techniques (Lee Filter, Gamma Filter) which are in use are not effective to identify the LULC facts and features of the SAR images. These never remove all the noises in SAR images especially the noise like "salt and pepper." Therefore, in this paper, the researcher proposes a new technique "Enhanced Discontinue Image Filter" which is window size based and effectively visualizes the SAR images with 89.1% accuracy to determine the ground truth value.
IIITM Face

This paper addresses the challenges of face attribute detection specifically in the Indian context. While there are numerous face datasets in unconstrained environments, none of them captures emotions in different facial orientations. Moreover, there is an under-representation of people of Indian ethnicity in these datasets since they have been scraped from popular search engines. As a result, the performance of state-of-the-art techniques can't be evaluated on Indian faces. In this work, we introduce a new dataset IIITM Face for scientific community to address these challenges. Our dataset includes 107 participants who exhibit 6 emotions in 3 different face orientations. Each of theses images are further labelled on attributes like gender, presence of moustache, beard or eyeglasses, clothes worn by the subjects and the density of their hair. Moreover, the images are captured in high resolution with specific background colors which can be easily replaced by cluttered backgrounds to simulate 'in the Wild' behavior. We demonstrate the same by constructing IIITM Face-SUE. Both IIITM Face and IIITM Face-SUE have been benchmarked across key multi-label metrics for the research community to compare their results.
Sample Transfer Optimization with Adaptive Deep Neural Network

Application-layer transfer configurations play a crucial role in achieving desirable performance in high-speed networks. However, finding the optimal configuration for a given transfer task is a difficult problem as it depends on various factors including dataset characteristics, network settings, and background traffic. The state-of-the-art transfer tuning solutions rely on real-time sample transfers to evaluate various configurations and estimate the optimal one. However, existing approaches to run sample transfers incur high delay and measurement errors, thus significantly limit the efficiency of the transfer tuning algorithms. In this paper, we introduce adaptive feed forward deep neural network (DNN) to minimize the error rate of sample transfers without increasing their execution time. We ran 115K file transfers in four different high-speed networks and used their logs to train an adaptive DNN that can quickly and accurately predict the throughput of sample transfers by analyzing instantaneous throughput values. The results gathered in various networks with rich set of transfer configurations indicate that the proposed model reduces error rate by up to 50% compared to the state-of-the-art solutions while keeping the execution time low. We also show that one can further reduce delay or error rate by tuning hyperparameters of the model to meet specific needs of user or application. Finally, transfer learning analysis reveals that the model developed in one network would yield accurate results in other networks with similar transfer convergence characteristics, alleviating the needs to run an extensive data collection and model derivation efforts for each network.
‘Take the Essence, Discard the Dregs’: A Perspective on Blockchain Technology in China

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Image Matching Research Based on Improved SIFT Algorithm

Image matching technology is the key technology for indoor vision robot positioning and map construction. The Scale Invariant Feature Transform(SIFT) algorithm has scale and rotation invariance, but there is a problem of long registration time and poor real-time performance. Aiming at this problem, a dual-threshold Featurnes From Accelerated Segment Test(FAST) algorithm is proposed to transform the feature extraction method of SIFT algorithm. At the same time, the 128-dimensional feature point descriptor of the traditional SIFT algorithm is reduced to 32-dimensional, so that local features can be extracted quickly. The experimental results show that the improved algorithm is twice as fast in registration time, which is suitable for some occasions with high real-time requirements.
TrustFS: An SGX-Enabled Stackable File System Framework

Data confidentiality in cloud services is commonly ensured by encrypting information before uploading it. However, this approach limits the use of content-aware functionalities, such as deduplication and compression. Although this issue has been addressed individually for some of these functionalities, no unified framework for building secure storage systems exists that can leverage such operations over encrypted data. We present TrustFS, a programmable and modular stackable file system framework for implementing secure content-aware storage functionalities over hardware-assisted trusted execution environments. This framework extends the original SafeFS architecture to provide the isolated execution guarantees of Intel SGX. We demonstrate its usability by implementing an SGX-enabled stackable file system prototype while a preliminary evaluation shows that it incurs reasonable performance overhead when compared to conventional storage systems. Finally, we highlight open research challenges that must be further pursued in order for TrustFS to be fully adequate for building production-ready secure storage solutions.
A Sparse Representation Based Learning Algorithm for Denoising in Images

In the past few decades, denoising is one of the major portions in imaging analysis and it is still an ongoing research problem. Depending upon some pursuit methods an attempt has been made to denoise an image. The work comes up with a new methodology for denoising with K-SVD algorithm. Noise information has been extracted using the proposed approach. With reference to heap sort image patches are learnt using dictionary and then it is updated. Experimentation says that introduced approach reduces noise on test. The proposed approach is tested on test datasets and the proposed approach is found to be comparatively good than the existing works.
A Study on Checkpointing for Distributed Applications Using Blockchain-Based Data Storage

Blockchain and smart contract technologies are becoming increasingly common in various areas. In these days, some user-side software saves information into blockchain-based storage so that they can ensure security, traceability, and fault tolerance of their data. We expect that in the near future, more applications will use blockchain-based storage for dependability. However, there are some inconvenient points in current blockchain technologies for common use. Especially, stale blocks lead an undesirable property of blockchains, that is, a checkpoint saved into a blockchain may be invalidated later. In this article we develop a simulator for obtaining some parameters toward constructing an analytical model by using the parameters obtained.
A White-box Implementation of SEED

White-box cryptography is an implementation technique in order to protect secret keys of cryptographic algorithms in the white-box attack model, which is the setting that an adversary has full access to the implementation of the cryptographic algorithm and full control over their execution. This concept was introduced in 2002 by Chow et al., and since then, there have been many proposals for secure implementations. While there have been many approaches to construct a secure white-box implementation for the ciphers with SPN structures, there was no notable result about the white-box implementation for the block ciphers with Feistel structure after white-box DES implementation was broken. In this paper, we propose a secure white-box implementation for a block cipher SEED with Feistel structure, which can prevent the previous known attacks for white-box implementations. Our proposal is simple and practical: it is performed by only 3,376 table lookups during each execution and the total size of tables is 762.5 KB.
Virtual and Augmented Reality for Positive Social Impact

This workshop invites researchers and industry practitioners designing Virtual Reality and Augmented Reality applications for positive social impact to join us in Perth, Australia, to discuss the research and industry landscape and identify opportunities for collaboration with like-minded professionals. Participants will have an opportunity to share their experiences with other attendees, and will discuss the challenges they have experienced in working with VR and AR applications. This workshop will aim to help researchers to overcome these challenges and, as an outcome, plans will be made for future workshops to establish and promote positive social impact as a long-term research focus for VR and AR researchers in Australasia.
Study on an Extreme Classification of Cost - Sensitive Classification Algorithm

Based on the principle of cost sensitivity, this paper takes the cost sensitivity algorithm of neural network as the classifier algorithm, by using the idea of iteration, we can find a misclassification cost which can make the misclassification number of minority class samples to be zero. And compared with the evaluation indexes of some unbalanced data classification methods when the number of classification errors of minority class is non-zero, this paper hopes to realize the assumption that the minority class in the unbalanced data set will not be misclassified.
The A-Desk: A Unified Workspace of the Future

It is made from wood, projectors, and a computer running multiprojector blending software. Developed in 2008–2009 in response to an agency interest in future work environments for intelligence analysts, the immersive curved ADesk supports a variety of tasks, such as traditional office or development work, design, and even immersive telepresence. The authors discuss the A-Desk (“Analyst Desk”), a user-enveloping display system consisting of a bowl-shaped, two-dimensionally curved display and work surface. The A-Desk concept and working prototypes were developed after consultations with the U.S. National Geospatial-Intelligence Agency (NGA) and with support from the U.S. Intelligence Advanced Research Projects Activity (IARPA) (Future Analyst Workspace (A-Desk), Principal Investigators Henry Fuchs and Greg Welch, U.S. Air Force Office of Scientific Research, Agency Number FA8750-08-2-0209, IARPA Analyst Workspace for Exploitation (A-SpaceX) Program, Program Manager Dr. Jeffrey G. Morrison). The ADesk aims to address these agencies’ need for innovative display technologies for use by analysts whose complex daily tasks include visualizing intelligence and surveillance data from multiple sources comprising different modalities, developing and managing hypotheses about past and future activities, scenarios, or events, and communicating such information to, as well as discussing and processing it with, collaborators.
A Parallel AES Encryption Algorithms and Its Application

With the rapid development of the Internet technology, data security is becoming more and more important. Data encryption is an important means to protect data security. AES is an important algorithm for encrypting data. However, when the amount of data needed to be encrypted is large, the traditional AES algorithm runs very slowly. This paper presents a parallel AES encryption algorithm based on MapReduce architecture, which can be applied in large-scale cluster environment. It can improve the efficiency of massive data encryption and decryption by parallelization. And the paper designs a parallel cipher block chaining mode to apply AES algorithm. Experiments show that the proposed algorithm has good scalability and efficient performance, and can be applied to the security of massive data in cloud computing environment.
Semi-supervised Video Object Segmentation with Recurrent Neural Network

Object segmentation in videos has been extensively investigated recent years. However, semi-supervised object segmentation in videos is still a challenging research topic as it is hard to modeling temporal information. Most of research treats video frames independence and lost the relationship between adjacent frames. To overcome the limitation, Semi-supervised Video Object Segmentation with Recurrent Neural Network (SVOSR) has been proposed which combines convolutional gated recurrent unit (ConvGRU) to learn the temporal information between adjacent frames. The proposed method can be treated as three main parts. First, the feature extraction part is proposed to generate spatial information from adjacent frames. Second the relation part extracts temporal information from the adjacent spatial information. Thirdly, the decoder part combines the spatiotemporal information and inference the results. We put forward the relation part and design the decoder part to better segmentation. Experiments show that our method shows achievable accuracy and has the order of magnitude faster inference time compared with OSVOS and other methods based on DAVIS dataset.
Excluding the Misleading Relatedness Between Attributes in Multi-Task Attribute Recognition Network

In the attribute recognition area, attributes that are unrelated in the real world may have a high co-occurrence rate in a dataset due to the dataset bias, which forms a misleading relatedness. A neural network, especially a multi-task neural network, trained on this dataset would learn this relatedness, and be misled when it is used in practice. In this paper, we propose Share-and-Compete Multi-Task deep learning (SCMTL) model to handle this problem. This model uses adversarial training methods to enhance competition between unrelated attributes while keeping sharing between related attributes, making the task-specific layer of the multi-task model to be more specific and thus rule out the misleading relatedness between the unrelated attributes. Experiments performed on elaborately designed datasets show that the proposed model outperforms the single task neural network and the traditional multi-task neural network in the situation mentioned above.
Author Profiles Prediction Using Syntactic and Content-Based Features

In digital forensics, the forensic analysts raised the major questions about the details of the author of a document like identity, demographic information of authors and the documents which were related these documents. To answer these questions, the researchers proposed a new research field of stylometry which uses the set of linguistic features and machine learning algorithms. Information extraction from the textual documents has become a popular research area in the last few years to know the details of the authors. In this context, author profiling is one research area concentrated by the several researchers to know the authors' demographic profiles like age, gender, and location by examining their style of writing. Several researchers proposed various types of stylistic features to analyze the style of the authors writing. In this paper, the experiment was performed with combination of syntactic features and content-based features. Various machine learning classifiers were used to evaluate the performance of the prediction of gender of reviews dataset. The proposed method achieved best accuracy for profiles prediction in author profiling.
Continuous online task planning and plan acting in a deterministic robot environment

Abstract In a deterministic environment, continuity in planning robot tasks and acting upon the generated plans is essential. This aspect does not commit the planner to generate all the required actions in advance, however; instead, the robot planner can generate the necessary abstract actions, then make the actor responsible for refining these actions into commands. Such commands are executed directly by the robot platform. In this paper, the planning and acting framework, known as RosPlanAct, is modified by including a temporal model that deals with the time interval for each action in the plan. Thus, a chronicle is associated with the abstract actions during the planning phase to act as a base for refining these actions into more detailed activities. The extended framework also has the ability to postpone the execution of any action that requires more details and to activate the monitoring function to compare what was observed with predicted results. This framework was tested in simulation and in real environments. The simulation environment was ROS, with a smart robot car used as a real testing environment. The results showed high performance in accomplishing the specified tasks with an increasing rate of success in executing the actions in the determined period of time and a low rate of failed execution. Throughout this paper, the actor was supported by being given the ability to avoid failure by online interactions between planning and acting sections in order to trigger the executing platform to perform the given task in a deterministic environment. The robot can thus refine the actions in the plan, re-planning as required in order to handle events arising.
Causality Existence Classification from Multilingual Texts Using End-to-End LSTM Models

In this study, we propose a neural model for extracting causal sentences from both English and Japanese documents. Causal knowledge extraction is an important topic in the area of natural language processing. However, numerous studies concerning the extraction of causal knowledge target only one language. Therefore, in this study, we propose a multilingual model for extracting causal knowledge. Our model employs end-to-end architecture to deal with multilingual documents by using one model. Through the experiment, the effectiveness of our model is revealed.
Graph-Based Motion Planning Networks

Differentiable planning network architecture has shown to be powerful in solving transfer planning tasks while it possesses a simple end-to-end training feature. Much related work that has been proposed in literature is inspired by a design principle in which a recursive network architecture is applied to emulate backup operations of a value iteration algorithm. However, existing frameworks can only learn and plan effectively on domains with a lattice structure, i.e., regular graphs embedded in a particular Euclidean space. In this paper, we propose a general planning network called Graph-based Motion Planning Networks (GrMPN). GrMPN will be able to i) learn and plan on general irregular graphs, hence ii) render existing planning network architectures special cases. The proposed GrMPN framework is invariant to task graph permutation, i.e., graph isomorphism. As a result, GrMPN possesses an ability that is data-efficient and strong at generalization strength. We demonstrate the performance of two proposed GrMPN methods against other baselines on three domains ranging from 2D mazes (regular graph), path planning on irregular graphs, and motion planning (an irregular graph of robot configurations).
Goals and Suggestions in Knowledge Map Development using SNA: A Literature Review

Abstract As a non-ministerial government institution in charge of statistics, BPS-Statistics Indonesia must be able to provide quality statistical data through integrated and international standard statistical activities. Integrated statistical activities require a process of statistical activities that does not silo thinking. This silo thinking can be detrimental to BPS-Statistics Indonesia, especially in terms of overcoming the problem of statistical activities that have actually occurred in previous statistical activities. From these problems it is necessary to identify important knowledge that is useful to be able to be accessed and used as a lesson learned by other statistical activities. In Knowledge Management there is a tool to help identify important knowledge, namely Knowledge Map, which is assisted by analysis using the Social Network Analysis (SNA) technique. To solve the problem, BPS-Statistics Indonesia needs to conduct a study on the application of this knowledge maps with SNA technique. In this study, a study literature on knowledge maps with SNA was carried out. This literature study is in the form of Systematic Literature Review (SLR). From this literature study, an overview of the fields used as research subjects on knowledge maps with SNA, description of objectives and research suggestions currently used by researchers. This study found that the most used fields of research were non-business fields, especially in the fields of education and research. Findings regarding the purpose of the study are divided into making a design / framework or analyzing a problem. From the synthesis of SLR papers it was found that research on Knowledge Map with SNA, the purpose mostly to analyzing a problem. In this study it was also found that the suggestions for most subsequent research were suggestions for modification or refinement of research methods on knowledge maps with SNA. From these findings, BPS-Statistics Indonesia is expected to be able to take advantage and can make this literature study as the basis for the implementation of knowledge maps with SNA to solve the problem of silo thinking.
Into the Reverie: Exploration of the Dream Market

Since the emergence of the Silk Road market in the early 2010s, dark web `cryptomarkets' have proliferated and offered people an online platform to buy and sell illicit drugs, relying on cryptocurrencies such as Bitcoin for anonymous transactions. However, recent studies have highlighted the potential for de-anonymization of bitcoin transactions, bringing into question the level of anonymity afforded by cryptomarkets. We examine a set of over 100,000 product reviews from several cryptomarkets collected in 2018 and 2019 and conduct a comprehensive analysis of the markets, including an examination of the distribution of drug sales and revenue among vendors, and a comparison of incidences of opioid sales to overdose deaths in a US city. We explore the potential for de-anonymization of vendors by implementing a Naïve-Bayes classifier to predict the vendor from a given product review, and attempt to link vendors' sales to specific Bitcoin transactions. On the buyer side, we evaluate the efficacy of hierarchical agglomerative clustering for grouping together transactions corresponding to the same buyer. We find that the high degree of specialization among the small subset of high-revenue vendors may render these vendors susceptible to de-anonymization. Further research is necessary to confirm these findings, which are restricted by the scarcity of ground-truth data for validation.