Reviewerly/exHarmony huggingface dataset:
AgriERP Using Salesforce for Cloud Computing—An Application Intended for Agribusiness

Agribusiness is a sector purely intended to promote, develop and perform business based on agriculture requirement and production, which emphasizes and focuses on the farmers, farming and farming-related commercial activities. This sector and business have now become more complex and complicated with the latest regulations, government involvement and enhanced demand for the supply chain to customers directly from the buyer. Bringing ERP into agriculture business will surely going to improve the business analysis, farming capabilities, technology enables farming and raising the standard and livelihood of farmers. ERP is a way or strategic mechanism adopted by many industries to handle and combine the requirement of their working in the business process. Some ERP applications are playing a very vital role in the growth and development of the industry especially in the fields of agro farms and agribusiness. This paper proposed a cloud-based ERP application that covers key processes supported by modern ERP systems. It improves operational efficiency, accuracy and cost-effectiveness of core business activities through industry best practices and is intended especially for agriculture and farmer requirements, developed in the Salesforce. The proposed application provides numerous benefits in comparison with normal agricultural techniques.
Meta-Heuristic Algorithm for the Global Optimization: Intelligent Ice Fishing Algorithm

The Intelligent Ice Fishing Algorithm, (IIFA), is encouraged by the logic of ice fishing. The algorithm is related to the class of "tracking" algorithms when during the evolution of the agent's population (fishermen), coordinates and results of several previous tests are recorded. The IIFA algorithm implies that each fisherman has devices and equipment (e.g., a GPS navigator, binoculars, and pad) that allow determining the exact coordinates of holes, and fishing neighbors holes, to determine the amount of fish caught in the hole to form the approximate model of fish distribution in the considered area of ice to find coordinates of local and global maxima of this model. The IIFA algorithm is meta-heuristic, which allows forming on its basis a large number of specific heuristic population-based global optimization algorithms. The article describes the algorithm and presents the results of computational experiments.
Security Aspects in Cloud Tools and Its Analysis—A Study

Since the storage of information in a piece of paper or in a hard drive has become more conventional, new technologies are being developed by data science researchers to securely and efficiently store the confidential information. In this regard, the usage of cloud computing is increasing at an unprecedented rate and it is extensively used in different parts of the world. This technology uses internet as the primary source for computing and storing the data. Further, the facts and sordid resources are supplied in conformity with the user through PC and machine on-demand. Moreover, it remains as a new thought to utilize the digital assets for apportionment statistics and then it is further evolved. Yahoo and Gmail are some suitable real-time examples of cloud computing. Different industries like banking, transport, entertainment and other discipline are also drifting towards this technology. The effectiveness of the supplied with the way of dictation powered with the aid of offer as the use case model will take care of the challenges in bandwidth, information movement, transactions and storage information.
Gravitationally induced entanglement dynamics between two quantum walkers

Abstract Quantum walk is a synonym for multi-path interference and faster spread of a particle in a superposition of position space. We study the effects of a quantum mechanical interaction modeled to mimic quantum mechanical gravitational interaction between the two states of the walkers. The study has been carried out to investigate the entanglement generation between the two quantum walkers that do not otherwise interact. We see that the states do in fact get entangled more and more as the quantum walks unfold, and there is an interesting dependence of entanglement generation on the mass of the two particles performing the walks. With the introduction of noise into the dynamics, we also show the sensitivity of entanglement between the two walkers on the noise introduced in one of the walks. The signature of quantum effects due to gravitational interactions highlights the potential role of quantum systems in probing the nature of gravity.
Blockchain-Based Secure and Efficient Crowdsourcing Framework

Blockchain is an emerging Web-based technology that being distributed and decentralized, and public ledger provides the capability to build applications that are more secure, reliable and trustworthy. Crowdsourcing is one of the use cases of blockchain technology. It allows an organization or an individual to utilize the talent of individuals over the Internet in exchange for some rewards. Centralized crowdsourcing platform has many limitations, beginning from centralized storage to reward distribution for task completion. Blockchain-based approach for crowdsourcing solves many of the problems posed by centralized crowdsourcing. In this work, comparative analysis of various proposed blockchain-based crowdsourcing approaches has been done and Ethereum-based crowdsourcing platform has been proposed, that efficiently deals with Sybil attack, solution confidentiality breach, unbiased evaluators’ selection and task evaluation using techniques such as bit commitment and elliptic curve-based ElGamal cryptosystem.
Louvain-based Multi-level Graph Drawing

The multi-level graph drawing is a popular approach to visualize large and complex graphs. It recursively coarsens a graph and then uncoarsens the drawing using layout refinement. In this paper, we leverage the Louvain community detection algorithm for the multi-level graph drawing paradigm.More specifically, we present the Louvain-based multi-level graph drawing algorithm, and compare with other community detection algorithms such as Label Propagation and Infomap clustering. Experiments show that Louvain-based multi-level algorithm performs best in terms of efficiency (i.e., fastest runtime), while Label Propagation and Infomap-based multi-level algorithms perform better in terms of effectiveness (i.e., better visualization in quality metrics).
Using hospital Admission, Discharge &amp; Transfer (ADT) data for predicting readmissions

Readmission of patients within a specific period after their discharge from a hospital is a cause of concern for the healthcare industry due to the cost involved. Most of the work done for predicting such readmissions using machine learning (ML) have been based on EHR, claims or authorization data from specific sources, which are mostly snapshot data at one static point in time and hence delayed. ADT being dynamic as the data is available instantaneous on occurrence of a medical event/visit adds value. Our goal is to utilize machine learning on unlabeled ADT data to identify patients who are at a high risk of being readmitted. We approached the problem in three parts. First, we labeled patient events using logical rules and finalized one of many readmission definitions that was more encapsulating of varied scenarios. Second, feature engineering was done which encapsulates the longitudinal timeline of each patient in a representative way considering all the contextual information. Third, we developed an automated machine learning pipeline which takes modeling inputs from the user, runs various models to generate readmission prediction, does a cross validation and returns the best model. We tried multiple combinations of models and cross-validation strategies and decided on a random forest model with specific hyper-parameter values and to be the most effective method to classify high risk patients. It had a test AUC-ROC of 72% which is better than quite a few industry standards. The model currently implemented in the client environment identifies the high-risk patients in real-time to care nurses who in turn take proper interventions to reduce their chances of readmission.
The Golden Quantizer in Complex Dimension Two

In this letter, we focus on numerical solutions to the problem of quantizing circularly symmetric complex Gaussian random variables from open symmetrized bidiscs in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {C}^{2}$ </tex-math></inline-formula> . To that end, we leverage techniques from the complex variable analysis, especially the symmetrization map. Four novel non-uniform quantizers, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> Bd1/2-GQ and EC Bd1/2-GQ, are proposed to reduce the mean square error distortion. Compared to the existing high-rate golden quantizer, entropy-coded high-rate golden quantizer, and (non)-uniform polar/rectangular quantizers, Bd1-GQ and EC Bd1-GQ achieve lower mean square error distortion. Moreover, the EC Bd2-GQ reaches better performance than the polar- and rectangular- quantizers.
A Fast-Detection and Fault-Correction Algorithm against Persistent Fault Attack

Persistent Fault Attack (PFA) is a recently proposed Fault Attack (FA) method in CHES 2018. It is able to recover full AES secret key in the Single-Byte-Fault scenario. It is demonstrated that classical FA countermeasures, such as Dual Modular Redundancy (DMR) and mask protection, are unable to thwart PFA. In this paper, we propose a fast-detection and fault-correction algorithm to prevent PFA. We construct a fixed input and output pair to detect faults rapidly. Then we build two extra redundant tables to store the relationship between the adjacent elements in the S-box, by which the algorithm can correct the faulty elements in the S-box. Our experimental results show that our algorithm can effectively prevent PFA in both Single-Byte-Fault and Multiple-Bytes-Faults scenarios. Compared with the classical FA countermeasures, our algorithm has a much better effect against PFA. Further, the time cost of our algorithm is 40% lower than the classical FA countermeasures.
Six shades lighter: a bit-serial implementation of the AES family

Recently, cryptographic literature has seen new block cipher designs such as PRESENT, GIFT or SKINNY that aim to be more lightweight than the current standard, i.e., AES. Even though AES family of block ciphers were designed two decades ago, they still remain as the de facto encryption standard, with AES-128 being the most widely deployed variant. In this work, we revisit the combined one-in-all implementation of the AES family, namely both encryption and decryption of each AES-128/192/256 as a single ASIC circuit. A preliminary version appeared in Africacrypt 2019 by Balli and Banik, where the authors design a byte-serial circuit with such functionality. We improve on their work by reducing the size of the compact circuit to 2268 GE through 1-bit-serial implementation, which achieves 38% reduction in area. We also report stand-alone bit-serial versions of the circuit, targeting only a subset of modes and versions, e.g., AES-192 and AES-256. Our results imply that, in terms of area, AES-192 and AES-256 can easily compete with the larger members of recently designed SKINNY family, e.g., SKINNY-128-256, SKINNY-128-384. Thus, our implementations can be used interchangeably inside authenticated encryption candidates such as SKINNY-AEAD/-HASH, ForkAE or Romulus in place of SKINNY.
Fast and robust interactive image segmentation in bilateral space with reliable color modeling and higher order potential

We propose an optimization framework for interactive image segmentation (IIS) that operates in bilateral space to achieve robust object extraction and instant visual feedback. More specifically, we first resample an input image using a regular bilateral grid with a resolution that is typically coarser than the input image to reduce the complexity of subsequent IIS tasks. We then design a Markov random field energy on the vertices of the bilateral grid that can be solved efficiently using a standard graph cut label assignment. To achieve this, we introduce reliable color models to distinguish the foreground and background despite the presence of extremely difficult cases and a higher-order potential to encourage spatial consistency in segmentation. We conduct comprehensive experiments on three standard interactive segmentation datasets, MSRA 10K, IIS, and PASCAL VOC 2012 segmentation validation set. The results show that the proposed method achieves competitive performance compared with state-of-the-art methods while making the current system efficient in terms of speed.
Space-time super-resolution with motion-perceptive deformable alignment

We proposed a motion-perceptive deformable alignment network that introduces a pre-computed optical flow to improve the motion perception of the deformable alignment process. The pre-computed flows share the burden of the learned offsets for motion estimation while the flexibility of the deformable convolutional network is maintained. In addition, a motion-adaptive pyramid structure in which the features are aligned in multi-scale levels and then merged based on the motion strength among input frames is proposed. With the above structures, an innovative space-time super-resolution (STSR) network is constructed with an improved motion compensation ability. STSR aims to restore a high-resolution and high-frame-rate sequence from its corresponding low-resolution and low-frame-rate version. The proposed STSR network is trained with a Vimeo-90K dataset, and tests are conducted on the Vimeo-90K, densely-annotated video segmentation (DAVIS), and realistic and diverse scenes (REDS) datasets. Performance is evaluated using the peak signal-to-noise ratio and structural similarity index of the entire restored frame on the Y channel. Extensive experiments demonstrate the superiority of the proposed network among both the one- and two-stage STSR methods, its improved alignment ability, and its significantly improved interpolated frame synthesis.
NetScatter: Visual analytics of multivariate time series with a hybrid of dynamic and static variable relationships

The ability to capture common characteristics among complex multi-variate time series variables can profoundly impact big data analytics in uncovering valuable insights into the relationships among them and enabling a dimensionality reduction technique. Two widely used data displays include time series and scatter plots. While the former focuses on the dynamics over time, the latter deals with static relationships among variables. Motivated by these distinctive perspectives, our research aims to maximally utilize the information captured by both at the same time. This paper presents NetScatter, a visual analytic approach to characterizing changes of pairwise relationships in a high-dimensional time series. Unlike most traditional techniques that employ a single perspective of the visual display, our approach combines static perspectives of two variables in multi-variate time series into a single representation by comparing all data instances over two different time steps. The paper also introduces a list of visual features of the representation to capture how overall data evolve. We have implemented a web-based prototype that supports a full range of operations, such as ranking, filtering, and details on demand. The paper illustrates the proposed approach on data of various sizes in different domains to demonstrate its benefits.
Confidentiality of Information Stored on the Desktop of Computers in a Psychiatric Hospital

The aim of this before-after study was to evaluate the effect of an educational intervention on the confidentiality status of unprotected information stored on computer desktop in a psychiatric hospital. All switched-on computer systems were inspected in 2017-2018 at the baseline and 7 months after placing an educational poster. The log-in conditions and files stored on the desktop were reviewed using a researcher-made checklist; moreover, users answered questions about the possible mechanism of deleting information stored on the desktop. The number of files stored on desktops, the number of systems left switched on and unused, and the number of systems without a log-in password decreased in the second phase than the first phase of this study. Furthermore, automatic locking of computers after a specified period increased. The educational intervention reduced significantly the storage of information on desktops and improved security mechanisms such as using passwords and autolock. This study showed using an educational poster can encourage users to preserve the security of information, comply with security guidelines such as locking unused computers, and prevent access to confidential information.
A Processing Method of Presenting Indicators Hierarchy and Classification in Spreadsheet

This paper put forward a method of presenting indicators hierarchy and classification in spreadsheet. using indicator framework construction module, according to the user's personalized choice, the indicators and their layers, the indicators classification description and its layers, the application scenarios of indicators and indicators classification description as well as other hidden attributes are presented in the form of a spreadsheet, so that the indicators expression is simpler and more readable, and ability to achieve accurate representation of two-dimensional table data, rendering tables easier to understand; Based on this, it can also display the indicators and indicator classification description flexibly and diversely, so that the results presented are more diversified, flexible and personalized.
Successive Information Bottleneck and Applications in Deep Learning

Information Bottleneck (IB) method studies the trade-off between compression and prediction: extracting relevant information from the input variable X while preserving relevant information about another random variable Y , the resulting representation is another random variable Z. Motivated by the deep neural networks implementations, this paper studies a novel variation of the N-layer IB problem where the layers are assumed to be encoded in a successive fashion. We propose a method for performing an N-Layer IB in a greedy fashion and analyze numerical results obtained over a set of synthetic experiments.
A Pitfall of Learning from User-generated Data: In-Depth Analysis of Subjective Class Problem

Research in the supervised learning algorithms field implicitly assumes that training data is labeled by domain experts or at least semi-professional labelers accessible through crowdsourcing services like Amazon Mechanical Turk. With the advent of the Internet, data has become abundant and a large number of machine learning based systems are being trained with user-generated data, where categorical data is used as labels. However, little work has been done in the area of supervised learning with user-defined labels where users are not necessarily experts and might be unable to provide correct labels to some data or the labels might contain significant human bias. In this article, we propose two types of classes in user-defined labels: subjective class and objective class - showing that the objective classes are as reliable as if they were provided by domain experts, whereas the subjective classes are subject to error and bias. We name this a subjective class problem and propose a Normalized Feature Indicative Score that can be effective in detecting subjective classes in a dataset without querying oracle. This score provides early detection of subjective classes in the data, saving time for data mining practitioners working with data that might contain errors and biases.
Know-What and Know-Who: Document Searching and Exploration using Topic-Based Two-Mode Networks

This paper proposes a novel approach for analyzing search results of a document collection, which can help support know-what and know-who information seeking questions. Search results are grouped by topics, and each topic is represented by a two-mode network composed of related documents and authors (i.e., biclusters). We visualize these biclusters in a 2D layout to support interactive visual exploration of the analyzed search results, which highlights a novel way of organizing entities of biclusters. We evaluated our approach using a large academic publication corpus, by testing the distribution of the relevant documents and of lead and prolific authors. The results indicate the effectiveness of our approach compared to traditional 1D ranked lists. Moreover, a user study with 12 participants was conducted to compare our proposed visualization, a simplified variation without topics, and a text-based interface. We report on participants' task performance, their preference of the three interfaces, and the different strategies used in information seeking.
A GPU-based ray tracing approach for the prediction of multireflections on measurement objects and the a priori estimation of low-reflection measurement poses

For the automated optical inspection of manufactured components with complex geometries or highly reflective surfaces, a suitable selection of measurement poses and the associated planning of the measurement trajectory is crucial. This is especially important for active triangulation measurement methods like fringe projection. Due to complex measurement object geometries or poor alignment of the measuring system the influence of multiple reflections can potentially lead to incorrect or incomplete 3-D reconstruction of the specimen surface. This paper introduces a simulative GPU-based inverse ray tracing approach to identify low-reflection measurement poses for active optical measurement systems. Starting from the virtual camera origin, rays are emitted from each camera pixel and the reflection at the measurement objects surface is calculated using the Torrence- Sparrow BRDF. With an additional approach based on Whitted raytracing, the influence of multiple reflections and the reflection depth on the rendered camera image is taken into account. By calculating the summed reflection depth of each rendered measurement sequence, a height map of the reflection frequency distribution is created. By sampling a predefined surface point on the path of a limited sphere, the comparability of possible measurement poses is achieved. Thus, local minima can be identified and the poses with the lowest reflection influence can be selected to perform a suitable trajectory planning. This a priori knowledge can also be transferred into application and used for the estimation of image areas, which captured multiple reflections. Thus for these areas specific masks are generated and can be applied in real measurements to reconstruct multiple reflection free surfaces.