Reviewerly/exHarmony huggingface dataset:
SAT-ETL-Integrator: an extract-transform-load software for satellite big data ingestion

Satellite data are used in several environmental applications, particularly in air quality supervising, climate change monitoring, and natural disaster predictions. However, remote sensing (RS) data occur in huge volume, in near-real time, and are stored inside complex structures. We aim to prove that satellite data are big data (BD). Accordingly, we propose a software as an extract-transform-load tool for satellite data preprocessing. We focused on the ingestion layer that will enable an efficient RSBD integration. As a result, the developed software layer receives data continuously and removes ∼86 % of the unused files. This layer also eliminates nearly 20% of erroneous datasets. Thanks to the proposed approach, we successfully reduced storage space consumption, enhanced the RS data accuracy, and integrated preprocessed datasets into a Hadoop distributed file system.
Control of Impulse Systems in Conflict Situation

The sufficient conditions are obtained for hitting of conflict-controlled process, given by impulse differential system with prescribed cylindrical terminal set. The conditions are realized at different information content in the class of quasi- and stroboscope strategics based on ideas of the method of resolving functions using the inverse Minkowski functionals. Many-valued mappings and their selections represent mathematical apparatus of investigation. The specific feature of the problem which the paper deals with is that generally speaking the classic Pontryagin condition does not hold. Here special shifting functions play the role of Ponlryagin selection and instead of resolving functions the upper and the lower resolving functions of two kinds are applied that allow the convergence process to be realized in a finite time. Above mentioned innovation allows essential extension of the class of game problems which are susceptible to analysis on the basis of the resolving functions ideology under the main method constructions. In particular it becomes possible to encompass the processes with discontinuous trajectories functioning in condition of conflict and uncertainty.
Detection of hydrothermal alteration zones using ASTER data in Nimu porphyry copper deposit, south Tibet, China

Advanced space-borne thermal emission and reflection radiometer (ASTER) data were evaluated for the hydrothermal alteration mapping of the Nimu porphyry copper deposit, southern Tibet. According to the metallogenic model for porphyry copper, we chose hydrothermal alteration mineral association and then established a remote sensing model. Relative absorption-band depth (RBD) and simple band combination methods in visible near infrared (VNIR) and shortwave infrared (SWIR) bands were used to retrieve information about lithological distributions. Principal component analyses (PCA) were applied to extract the prospecting information based on the spectral information of argillization mineral association, propylitization mineral association, and iron stained (limonite) effects. Results of the study match up well with known copper occurrences and a circular structure in study area, while circular structures usually have close relationship with mineralization. Combined with field validation, ASTER data are proved able to characterise the alteration zone of these porphyry deposits. With VNIR and SWIR bands, argillic, propylitic and ferritization alterations in a single deposit can be effectively discriminated.
Security of Hedged Fiat–Shamir Signatures Under Fault Attacks

Deterministic generation of per-signature randomness has been a widely accepted solution to mitigate the catastrophic risk of randomness failure in Fiat–Shamir type signature schemes. However, recent studies have practically demonstrated that such de-randomized schemes, including EdDSA, are vulnerable to differential fault attacks, which enable adversaries to recover the entire secret signing key, by artificially provoking randomness reuse or corrupting computation in other ways. In order to balance concerns of both randomness failures and the threat of fault injection, some signature designs are advocating a "hedged" derivation of the per-signature randomness, by hashing the secret key, message, and a nonce. Despite the growing popularity of the hedged paradigm in practical signature schemes, to the best of our knowledge, there has been no attempt to formally analyze the fault resilience of hedged signatures. We perform a formal security analysis of the fault resilience of signature schemes constructed via the Fiat–Shamir transform. We propose a model to characterize bit-tampering fault attacks, and investigate their impact across different steps of the signing operation. We prove that, for some types of faults, attacks are mitigated by the hedged paradigm, while attacks remain possible for others. As concrete case studies, we then apply our results to XEdDSA, a hedged version of EdDSA used in the Signal messaging protocol, and to Picnic2, a hedged Fiat–Shamir signature scheme in Round 2 of the NIST Post-Quantum standardization process.
Blockchain and smart-contract for scalable access control in Internet of Things

Managing security access control has been becoming one of the open issues in Internet of Things (IoT) since the idea of IoT was introduced. The reasons being that the IoT system is built upon large numbers of constrained devices, with limited power, computing resources, and memory. Therefore, the access management needs to be highly distributed in order to ensure the scalability when many IoT nodes are connected in the system, and the cryptographic operation and other algorithms performed along with the access control mechanism have to be lightweight to fit the constrained device characteristics of IoT. The existing access controls proposed for IoT, such as Role-based Access Control (RBAC), Attribute-based Access Control (ABAC) and Capability-based Access Control (CapBAC) are not truly distributed. As for the resource limitation in the IoT devices, recent developments of more powerful microprocessors or System on Chip (SoC) allow advanced cryptographic implementation on the chip itself, e.g. public key cryptography, with the cost of performance degradation. Recently, blockchain technology which is based on distributed ledger system, gains a lot of attention from the industries and researchers. Coupled with the smart contract and various distributed consensus mechanisms, blockchain can be a good solution for providing distributed access control in IoT. This paper proposed an architecture and mechanism of blockchain and smart-contract based access control for IoT. A proof-of-concept is implemented to validate the proposed idea and its performance is evaluated to gain some preliminary sights for further developments.
SYNC—Short, Yet Novel Concise Natural Language Description: Generating a Short Story Sequence of Album Images Using Multimodal Network

Image captioning, which aims at generating automated descriptions for an image, is the large focus in current research while most of the previous works have dealt with the association between the single image and single sentences. This paper proposes to take one step further to investigate the summarized version of the narrative description for the image stream and in more generalized form for a normal user. The major challenge in the proposed work is to consider the visual variance in an ordered image collection and in preserving coherence relation among multiple sentences. Our proposed work is aimed to retrieve a coherent flow of multiple sentences that use multimodal neural architecture and ranking-based summarization to generate the summarized description of possibly larger image streams. With qualitative evaluation, the proposed work has attained significant performance improvement over traditional state-of-the-art method for text sequence generation and has captured the relevant context with syntactic meaning with respect to summarized version of the detailed descriptions.
Line Following Robot Using Arduino for Hospitals

This paper describes the line following robot using arduino for surveying, inspecting and enhancing the transportation of necessary materials inside the healthcare institutions, industries also. The proposed system spot the black path and proceed in its direction on to the ground. This system eases the work of material conveyance as well as minimizes the manpower. This technology targets on the secured, punctual and constructing transportation of goods. This paper aims to implement controlled movement of robot by tuning control parameters and thus achieve better performance. This robot is predominantly design to proceed in a predefined path. To locate this path two sensors are used. Robots like this are mainly used in industrial plants comprising of pick and place facility. This robot carries components from desired source to destination by following fixed path. Recently lot of research has been done to empower the automation in hospitals as well in industries. This robot is made to supply the essential goods such injections, medicine, etc. This paper is divided into hardware and software modules.
Improving Data Security, Interoperability, and Veracity using Blockchain for One Data Governance, Case Study of Local Tax Big Data

Presidential Decree on One Data Indonesia is intended to govern data produced by central agencies and local agencies to support planning, implementation, evaluation, and development control, including one of them is the local tax. It is a Big Data development contains a lot of data from the central and local government of Indonesia. The defining factors of data collection on Big Data are volume, velocity, variety, and veracity. Volume and velocity state how much and how soon the data is generated. Variety states the condition of the data is structured or not, while veracity speaks the level of trust in data validity. Data veracity is a big problem on Big Data Analytics and using data integrity protection feature and other methods, Blockchain can offer solutions to improve data interoperability, security, and veracity.
Minimizing Communication Overheads in Container-based Clouds for HPC Applications

Although the industry has embraced the cloud computing model, there are still significant challenges to be addressed concerning the quality of cloud services. Network-intensive applications may not scale in the cloud due to the sharing of the network infrastructure. In the literature, performance evaluation studies are showing that the network tends to limit the scalability and performance of HPC applications. Therefore, we proposed the aggregation of Network Interface Cards (NICs) in a ready-to-use integration with the OpenNebula cloud manager using Linux containers. We perform a set of experiments using a network microbenchmark to get specific network performance metrics and NAS parallel benchmarks to analyze the performance impact on HPC applications. Our results highlight that the implementation of NIC aggregation improves network performance in terms of throughput and latency. Moreover, HPC applications have different patterns of behavior when using our approach, which depends on communication and the amount of data transferring. While network-intensive applications increased the performance up to 38%, other applications with aggregated NICs maintained the same performance or presented slightly worse performance.
SparseMask: Differentiable Connectivity Learning for Dense Image Prediction

In this paper, we aim at automatically searching an efficient network architecture for dense image prediction. Particularly, we follow the encoder-decoder style and focus on designing a connectivity structure for the decoder. To achieve that, we design a densely connected network with learnable connections, named Fully Dense Network, which contains a large set of possible final connectivity structures. We then employ gradient descent to search the optimal connectivity from the dense connections. The search process is guided by a novel loss function, which pushes the weight of each connection to be binary and the connections to be sparse. The discovered connectivity achieves competitive results on two segmentation datasets, while runs more than three times faster and requires less than half parameters compared to the state-of-the-art methods. An extensive experiment shows that the discovered connectivity is compatible with various backbones and generalizes well to other dense image prediction tasks.
Person-following for Telepresence Robots Using Web Cameras

Many existing mobile robotic telepresence systems have equipped with two web cameras, one is a forward-facing camera (FF camera) for video communication, and the other is a downward-facing camera (DF camera) for robot navigation. In this paper, we present a new framework of autonomous person-following for telepresence robots using the two web cameras. Based on correlation filters tracking methods, we use the FF camera to track the upper body of a person and the DF camera to localize and track the person's feet. We improve the robustness of feet trackers, consisting of a left foot tracker and a right foot tracker, by making full use of the spatial constraints of the human body parts. We conducted experiments on tracking in different environmental situations and real person-following scenario to evaluate the effectiveness of our method.
Collaborative learning based on centroid-distance-vector for wearable devices

In typical application scenarios of Internet-of-Things (IoT), human activity recognition capability of a wearable device is usually limited to the body segment on which the device is worn. We believe that an efficient approach where wearable devices collaboratively learn can lead to better performance of activity recognition. In this paper, we first propose the concept of centroid distance vector to fully exploit and express the hidden knowledge of available data. Then, we introduce our centroid-distance-vector-based collaborative learning framework which consists of two key steps: data expansion with centroid distance vector and automatic model construction. In order to achieve automatic model construction, we further propose two collaborative learning methods: a centroid-distance-vector-based label propagation method (CD-LP) and a centroid-distance-vector-based knowledge distillation method (CD-KD). Experiments have shown that by using our proposed algorithms, the human activity recognition accuracy can achieve an average improvement of 5.24% compared to the state-of-the-art machine learning methods, and 5.88 % compared to the neural network with two fully-connected hidden layers.
Improving pattern spotting in historical documents using feature pyramid networks

Pattern spotting consists of locating different instances of a given object (i.e. an image query) in a collection of historical document images. These patterns may vary in shape, size, color, context and even style because they are hand-drawn, which makes pattern spotting a difficult task. To tackle this problem, we propose a Convolutional Neural Network (CNN) approach based on Feature Pyramid Networks (FPN) as the feature extractor of our system. Using FPN allows to extract descriptors of local regions of the documents to be indexed and queries, at multiple scales with just a single forward pass. Experiments conducted on DocExplore dataset show that the proposed system improves mAP by 73% (from 0.157 to 0.272) in pattern localization compared with state-of-the-art results, even when the feature extractor is not trained with domain-specific data. Memory requirement and computation time are also decreased since the descriptor dimension used for distance computation is reduced by a factor of 16.
Learning Product Codebooks Using Vector-Quantized Autoencoders for Image Retrieval

Vector-Quantized Variational Autoencoders (VQ-VAE)[1] provide an unsupervised model for learning discrete representations by combining vector quantization and autoencoders. In this paper, we study the use of VQ-VAE for representation learning of downstream tasks, such as image retrieval. First, we describe the VQ-VAE in the context of an information-theoretic framework. Then, we show that the regularization effect on the learned representation is determined by the size of the embedded codebook before the training. As a result, we introduce a hyperparameter to balance the strength of the vector quantizer and the reconstruction error. By tuning the hyperparameter, the embedded bottleneck quantizer is used as a regularizer that forces the output of the encoder to share a constrained coding space. With that, the learned latent features better preserve the similarity relations of the data space. Finally, we incorporate the product quantizer into the bottleneck stage of VQ-VAE and use it as an end-to-end unsupervised learning model for image retrieval tasks. The product quantizer has the advantage of generating large and structured codebooks. Fast retrieval can be achieved by using lookup tables that store the distance between any pair of sub-codewords. State-of-the-art retrieval results are achieved by the proposed codebooks.
Towards a Deeper Understanding of How a Pathologist Makes a Diagnosis: Visualization of the Diagnostic Process in Histopathology

Advancements in Artificial Intelligence (AI) and Machine Learning (ML) are enabling new diagnostic capabilities. In this paper we argue that the very first step before introducing AI/ML into diagnostic workflows is a deep understanding of how pathologists work. To contribute to a deeper understanding of the diagnostic process in histopathology, we developed a visualization concept, including: (a) the sequence of the views observed by the pathologist (Observation Path), (b) the sequence of the spoken comments and statements of the pathologist (Dictation Path), (c) the underlying knowledge and experience of the pathologist (Knowledge Path), (d) information about the current phase of the diagnostic process and (e) the current magnification factor of the microscope chosen by the pathologist. We implemented the proofof-concept prototype as HTML5/CSS3/JavaScript application.
ParaOpt: Automated Application Parameterization and Optimization for the Cloud

The variety of instance types available on cloud platforms offers enormous flexibility to match the requirements of applications with available resources. However, selecting the most suitable instance type and configuring an application to optimally execute on that instance type can be complicated and time-consuming. For example, application parallelism flags must match available cores and problem sizes must be tuned to match available memory. As the search space of application configurations can be enormous, we propose an automated approach, called ParaOpt, to automatically explore and tune application configurations on arbitrary cloud instances. ParaOpt supports arbitrary applications, enables use of custom optimization methods, and can be configured with different optimization targets such as runtime and cost. We evaluate ParaOpt by optimizing genomics, molecular dynamics, and machine learning applications with four types of optimizers. We show with as few as 15 parameterized executions of an application, representing between 1.2%-26.7% of the search space, that ParaOpt is able to identify the optimal configuration in 32.7% of experiments and a near-optimal configuration in 83.2% of cases. As a result of using near-optimal configurations, ParaOpt reduces overall execution time by up to 85.8% when compared with using the default configuration.
SENTI2POP: Sentiment-Aware Topic Popularity Prediction on Social Media

Topic popularity prediction is an important task on social media, which aims at predicting the ongoing trends of topics according to logged historical text-based records. However, only limited existing approaches apply sentiment analysis to facilitate popularity prediction. Public sentiment is worth taking into consideration because the topics with strong sentiment tend to spread faster and broader on social media. In this paper, we propose a novel framework, SENTI2POP, to predict topic popularity utilizing sentiment information. We first adapt a state-of-art popularity quantification method to capture the topic popularity, and then design a novel tree-like network (Tree-Net) combining Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) for sentiment analysis. In addition, we propose a sentiment-aware time series prediction approach based on Dynamic Time Warping (DTW) and Autoregressive Integrated Moving Average model (ARIMA) to predict topic popularity. We prove by experiments that SENTI2POP outperforms the existing popularity prediction models on a real-world Twitter dataset by reducing the prediction error. Experimental results also show that SENTI2POP could be applied to improve the accuracy of most non-sentiment popularity prediction models.
Brno Mobile OCR Dataset

We introduce the Brno Mobile OCR Dataset (B-MOD) for document Optical Character Recognition from low-quality images captured by handheld mobile devices. While OCR of high-quality scanned documents is a mature field where many commercial tools are available, and large datasets of text in the wild exist, no existing datasets can be used to develop and test document OCR methods robust to non-uniform lighting, image blur, strong noise, built-in denoising, sharpening, compression and other artifacts present in many photographs from mobile devices. This dataset contains 2 113 unique pages from random scientific papers, which were photographed by multiple people using 23 different mobile devices. The resulting 19 728 photographs of various visual quality are accompanied by precise positions and text annotations of 500k text lines. We further provide an evaluation methodology, including an evaluation server and a testset with non-public annotations. We provide a state-of-the-art text recognition baseline build on convolutional and recurrent neural networks trained with Connectionist Temporal Classification loss. This baseline achieves 2 %, 22 % and 73 % word error rates on easy, medium and hard parts of the dataset, respectively, confirming that the dataset is challenging. The presented dataset will enable future development and evaluation of document analysis for low-quality images. It is primarily intended for line-level text recognition, and can be further used for line localization, layout analysis, image restoration and text binarization.
Generation of Multiple Key Based on Monitoring the User Behavior

Cloud computing is the recent technology used to share and store the computer resources rather than having resources in local server to maintain the application. Though cloud is used for a large amount of storage, there is no security in cloud. In general, all the groups have data owners and data members each should have user name, key, and group key. If a user shifts from one group to another, they can easily access the information from another group. It leads to a security problem. In order to increase security and confidentiality, author generates the new group key via Email using Diffie-Hellman algorithm. In case of a new user is added or an existing user leaves themselves from the group. The data members have to get permission from the data owners in case of any data updation. If the user misbehaves, i.e., (DDOS) attack, data owner or cloud terminates the user from the group. The updated key is sent to the users through Email. This mechanism significantly improves security in cloud computing.