Reviewerly/exHarmony huggingface dataset:
Object Counting using KAZE Features Under Different Lighting Conditions for Inventory Management

Inventory management in an automated industrial environment is the foremost requirement in order to shorten the gap between demand and supply. It comprises of object identification, localization, and counting. This paper introduces an approach for object counting in automated inventory management using KAZE features under different lighting conditions. Firstly, the prototype image and real-time inventory feed as the scene image are captured for detection of KAZE features. The detected features in the prototype image are subjected to density based scanning clustering algorithm. The KAZE features of each cluster obtained in the prototype image are mapped with the KAZE features of inventory feed. The mapped features in inventory feed are again subjected to density based scanning clustering algorithm. The clusters obtained in the inventory feed are then processed by Homography transform. Homography transform generates the predictions for object locations by projecting prototype corners in the inventory feed. The Homography transform projection results in rectangular box polygons in the inventory feed for the tentative location of prototype instances. Since there may be multiple predictions for a single object instance, the predicted object locations are integrated by density based scanning clustering algorithm to the centroids of these rectangular box polygons. It provides the exact location of prototype instances. Finally, the count is obtained. The graphical user interface for inventory management is also designed which exhibits user-friendly attributes. The proposed approach has also been compared with the previously developed approaches of object counting in automated inventory management. The experimental results state that the proposed approach outperforms the existing ones in the presence of different lighting conditions such as low-light or dim-light and bright light.
Use of an Automated System to Evaluate Feature Dissimilarities in Handwriting Under a Two‐Stage Evaluative Process*†

Abstract The two‐stage evaluative process is an established framework utilized by forensic document examiners (FDEs) for reaching a conclusion about the source(s) of handwritten evidence. In the second, or discrimination, stage, the examiner attempts to estimate the rarity of observations in a relevant background population. Unfortunately, control samples from a relevant background population are often unavailable, leaving the FDE to reach this determination based on subjective experience. Automated handwriting feature recognition systems are capable of performing both feature comparison and discrimination, yet these systems have not been subjected to empirical validation studies. In the present study, we repurposed a commercially available automated system to generate empirical distributions for ranking feature dissimilarity scores among pairs of handwritten phrases. The blinded results of this automated process were used to survey an international cohort of 36 FDEs regarding their strength of support for same‐ and different‐writer propositions. The survey served to cross‐validate FDE decision‐making under the two‐stage approach. Results from the survey demonstrated a clear pattern of response consistent with ground truth. Predictive regression analyses indicated that the automated feature dissimilarity scores and the log of their cumulative distribution functions accounted for 72% of the variability in FDE opinions. This study demonstrated that feature dissimilarity scores acquired using automated processes and their distributions are closely aligned with FDE decision‐making processes supporting the heuristic value of the two‐stage evaluative framework.
Möbius cancellation on polynomial sequences and the quadratic Bateman–Horn conjecture over function fields

We establish cancellation in short sums of certain special trace functions over $${\mathbb {F}}_q[u]$$ below the Pólya–Vinogradov range, with savings approaching square-root cancellation as q grows. This is used to resolve the $${\mathbb {F}}_q[u]$$ -analog of Chowla’s conjecture on cancellation in Möbius sums over polynomial sequences, and of the Bateman–Horn conjecture in degree 2, for some values of q. A final application is to sums of trace functions over primes in $${\mathbb {F}}_q[u]$$ .
Improved YOLOv3 Infrared Image Pedestrian Detection Algorithm

Security surveillance is widely used in daily life. For nighttime or complicated monitoring environments, this article proposes an infrared pedestrian monitoring based on YOLOv3. In the original YOLOv3 network structure, two aspects of optimization were made. One was to optimize the scale in the residual structure, and the rich features of the deconvolution layer were added to the original residual structure. The other was to use the DenseNet network to enhance the features. The optimization of fusion ability and delivery ability effectively improves the detection ability for small targets, and the pedestrian detection performance based on infrared images. After comparative testing, compared with YOLOv3, the overall mean average precision is improved by 4.39% to 78.86%.
A Two-way QKD Protocol Outperforming One-way Protocols at Low QBER

Two-way quantum key distribution (QKD) protocols can provide positive secret key rates for considerably higher quantum bit error rates (QBER) than one-way protocols. However, when QBER is low, only modest key rate gains have been achieved. This is one of the major obstacles for using two-way protocols. In this paper we introduce a new two-way QKD protocol which is a step towards overcoming this shortcoming. Under the assumption that the eavesdropper can only perform individual symmetric quantum attacks, our protocol performs quantum key distribution with a secret key rate that is higher than the information theoretical bound limiting the performance of any one-way protocol. This holds true also for very low QBER values.
Aligning Stakeholder Interests, Governance Requirements and Blockchain Design in Business and Government Information Sharing

Governance requirements for systems supporting information sharing between businesses and government organisations (B&G) are determined by a high variety of stakeholders with often conflicting interests. These conflicting interests can hamper the introduction and scaling-up of ICT-innovations that change their roles and authorities. We address one such innovation: the introduction of blockchain technologies in the B&G context. Who can govern data and the system depends on several elements of the design of a blockchain-based system, particularly the data structure, consensus mechanism and network topology. Design choices regarding these elements affect who can make decisions and hence we call them blockchain control points. These control points require an explicit and well-understood relationship between the design decisions and the interests of stakeholders. Yet, the literature on blockchain technology and governance does not offer such insight. Therefore, we developed a framework to assess the alignment between stakeholders interest and blockchain design choices. This framework consists of three views and their interrelationships, 1) a stakeholder view providing insight into the tensions between stakeholder's interests and governance requirements, 2), a governance view on the rights concerning the data and the system, and 3) a blockchain control view describing how design decisions on the control points affect whether governance requirements are met and how parties can exercise their rights. Making these links explicit enables an understanding of how technical design choices can trigger organizational dynamics from the stakeholder view and vice versa. Based on the framework we formulate a research agenda concerning blockchain design choices and governance.
Towards a conceptual model for promoting digital forensics experiments

Experimentation is one of the foundations for scientific evolution from the empirical point of view. Conducting experiments contributes to strengthen evidence of a given field mainly based on provided data and results, corroborated by repetitions, replications or reproducibility of an experiment, which altogether confirms or rejects pre-established hypotheses. Therefore, the proper conduction, documentation and dissemination of such experiments are essential to enable reproducibility. In this paper, we present ExperDF-CM, a conceptual model that aims to assist Digital Forensics researchers on planning, executing, analyzing and disseminating experiments. Such conceptual model was built based on almost two hundred analyzed Digital Forensics experiment papers and is mainly organized in five elements: Planning, Pre-Operation, Operation, Analysis and Interpretation, and Dissemination. We evaluated the conceptual model based on an evaluation survey and the Technology Acceptance Model (TAM) with researchers and practitioners of the Digital Forensics area. Results point out that our conceptual model is feasible for promoting reproducibility of Digital Forensics experiments, as well as it is easy to use and useful. Thus, our proposed conceptual model can contribute significantly to improve Digital Forensics experimentation and make them repeatable, replicable, and/or reproducible.
A Short-Term Forecasting Method for Photovoltaic Power Based on Ensemble Adaptive Boosting Random Forests

Accurate and reliable forecast of Photovoltaic (PV) power is crucial for power system dispatch and control. In this paper, a novel short-term PV generation forecast approach based on ensemble adaptive boosting random forests (EABRF) is proposed. The approach sets the weight distribution for training samples and bootstrap sampling is applied. By this means, different sub-training data sets with weights are generated to train the decision trees. The output of random forests is that the average value of each decision tree. Further, by calculating the error rate and assigning corresponding weight to each random forests, the weighted result of random forests can be obtained. The distinguishing features of the proposed approach are as follows: (a) The approach reduces the risk of overfitting by using the idea of ensemble learning; (b) The proposed approach can plenty mine the information in multidimensional features; (c) It has good generalization and robustness by combining adaptive boosting with random forests. PV power forecasting has been conducted in four PV plants using the proposed approach with the historical PV power and numerical weather prediction (NWP) as the inputs. The results illustrate that the proposed method is effective for forecasting of PV power generation with satisfactory forecast accuracy.
Location Wise Opinion Mining of Real Time Twitter Data Using Hadoop to reduce Cyber Crimes

The opinions of others is very important in our decision making. Twitter plays an important role on sharing user's opinion on a variety of topics, wo mining these twitter data is a very difficult task because its unstructured nature. Location plays an important role in finding opinions towards any topic because the opinions of users may vary from location to location so its very important to find opinion based on location which give in-depth knowledge where the topic is highly accepted or rejected. Opinion Mining deals with opinions or emotions of subjective elements in the textual documents. In this paper, we can take twitter data because on twitter a huge opinions are shared. In these, we can fetching a realtime data from Twitter by using flume and store these data into HDFS which is a storage component of Hadoop. Hadoop is an best open source solution for storing and processing a large amount of data. We can integrate Apache pig and hive with Hadoop and analyze the opinion on the basis of location because opinions are changes from location to location.
Efficient Maximum-Likelihood Decoding of Reed–Muller RM(m−3,m) Codes

Reed-Muller (RM) codes, a classical family of codes known for their elegant algebraic structure, have recently been shown to achieve capacity under maximum-likelihood (ML) decoding on the binary erasure channel and this has rekindled interest in their efficient decoding. We consider the code family RM(m-3,m) and develop a new ML decoder, for transmission over the binary symmetric channel, that exploits their large symmetry group. The new decoder has lower complexity than an earlier method introduced by Seroussi and Lempel in 1983.
Phase-noise estimation using Bayesian inference for discretely modulated measurement-device-independent continuous-variable quantum key distribution

Measurement-device-independent continuous-variable quantum key distribution (MDI-CVQKD) protocol calibrates the phase reference frames by transmitting local oscillator pulses between two legitimate parties. The above implementation leaves massive loopholes; in particular, an eavesdropper can attack the system by manipulating the power of the local oscillator pulses. In this paper, a Bayesian phase-noise estimation model is proposed to estimate the phase drift and its uncertainty in the MDI-CVQKD protocol. The model employs the Bayesian estimation of the very weak quantum signal pulses and eliminates the necessity of transmitting local oscillator pulses between two legitimate parties. Moreover, the Bayesian inference algorithm has the characteristic of high robustness to noise and can achieve a well-motivated confidence interval of the estimated eigenphase. Comparing with its Gaussian counterpart, we adopt the discrete modulation in the MDI-CVQKD protocol, which allows a much better reconciliation efficiency at low signal-to-noise ratio. With simpler implementation, the proposed Bayesian phase-noise estimation model precisely avoids the security bottleneck due to the transmitted local oscillator pulses.
An Adaptive POI Recommendation Algorithm by Integrating User's Temporal and Spatial Features in LBSNs

With the rapid development of Internet technology and global positioning technology, location-based social networks (LBSNs) have emerged. Massive check-in data generated by LBSNs can be employed to explore users' behavior preferences, and enable a wide range of location-based social services. The most straightforward service is Point of Interest (POI) recommendation, which help users to discover their desired places. In order to achieve effective and precise POI recommendation, complicated high-dimensional data need to be handled, i.e., user-geographical position-visiting time-category of the visiting place. Besides, such data usually suffer from a data sparsity problem. To this end, this paper proposes a novel POI recommendation model that incorporates spatial and temporal preferences of the users. We propose a tensor factorization-based approach and a Voronoi diagram-based approach to model the impact of temporal and spatial features on users' preference, respectively. Then, a fusion framework is proposed to calculate similarities between different uses by combining both temporal and spatial preferences of the users. Finally, an adaptive collaborative filtering approach is applied to generate recommendation list. The experiments results on the Foursquare and the Gowalla datasets shows that our model has higher recommendation performance than other comparison models.
Landsat Collection-2 geometric calibration updates

The U.S. Geological Survey (USGS) changed the management and delivery of Landsat products to the public in its archive through the implementation of Collections. The Collections process ensures consistent data quality through time and across all the Landsat sensors with a few modifications to the metadata. The consistent data products from Collections are more conducive for applications such as time-series analysis, because the data can be used without a need for sensor-specific geometric or radiometric adjustments. The Collections process also allows for calibration improvements from updated reference sources, model trending, and enhanced algorithms that are grouped together and applied at one time, thus limiting the operational impacts to users. The first collection, Collection-1 was released in 2016, and Collection-2 is expected to be released in 2020. This paper addresses the geometric improvements to the Collection -2 dataset. Geometric improvements in Collection-2 include improvements to the geometric accuracy and interoperability of all Landsat products by implementing the Sentinel 2 Global Reference Image (GRI), improved elevation dataset using NASADEM and other sources of Digital Elevation Model (DEM) for terrain correction, improvements to the precision correction process for Enhanced Thematic Mapper Plus (ETM+) and Thematic Mapper (TM) sensors, changes to the Thermal Infrared Sensor (TIRS) to Operational Land Imager (OLI) alignment estimates, thermal band detector alignments and focal plane adjustments for ETM+, and improvements to the ETM+ sensor to attitude control system (ACS).
Internet of Things Anomaly Detection using Machine Learning

In recent years, an increasing number of devices are being connected to the Internet that encompasses more than just traditional devices. Internet of Things integrates real-world sensors such as smart devices or environment sensors with the Internet allowing for real}-time monitoring of conditions. IoT devices are often constrained in their resources as the sensors involved are designed for specific purposes. Due to these constraints, typical methods of intrusion and anomaly detection cannot be used. Also, due to the amount of raw input data from these sensors, detecting anomalies among the noise and other background data can be computationally intensive. A possible solution to this is by using machine learning models that are trained on both normal and abnormal behavior to detect when anomalies occur. By using techniques such as autoencoders, models can be trained that have learned normal operating conditions. In this study, we explore the use of machine learning techniques such as autoencoders to effectively handle the high dimensionality of sensor datasets while consequently learning their normal operating conditions. Autoencoders are a type of neural network which attempts to reconstruct its input data by combining two NNs, an encoder, and a decoder network. The encoder learns its input by encoding it into a lower-dimensional space while capturing the interactions and correlations between variables. In this paper, we explore the use of techniques such as autoencoders to create a lower-dimensional representation of high dimensional sensor input. Autoencoders encode the data allowing for the network to learn the interactions between parameters in normal conditions which when reconstructed with the decoder represents non-anomalous behavior. When data containing anomalies are input into the network errors will occur within the reconstruction. The error between the reconstructions can be measured using a distance function to determine if an observation is anomalous.
Transferability of Fast Gradient Sign Method

Image classification in computer vision is a process which classifies an image depending on its content. While classifying an object is trivial for humans, robust image classification is still a challenge in computer vision applications. The robustness of such image classification models in real world applications is a major concern. Adversarial examples are specialized inputs created with the purpose of confusing a classifier, resulting in the misclassification of a given input. Some of these adversarial examples are indistinguishable to humans, but the classifier can still be tricked into outputting the wrong class. In some cases adversarial examples can be transferred: an adversarial example crafted on a target model fools another model too. In this paper we evaluate the transferability of adversarial examples crafted with Fast Gradient Sign Method across models available in the open source Tensorflow machine learning platform (using ResNetV2, DenseNet, MobileNetV2 and InceptionV3).
A Novel Supply Chain Model Based on Smart Contract

Facing on bursting of the huge quantity of trade, the problem of finance by micro and small firms, the expansion of business development of core enterprise in supply chain, the development of comprehensive finance services, it is not well worked by traditional ways. Moreover it is more and more restricting the development of supply chain. Considering the advantages of automatic executing and high efficiency of smart contract, the feature of decentralization, security, trustworthiness and anti-tampering of blockchain, it was presented a novel supply chain model based on smart contract and blockchain for solving the problem totally. The model relied on the core enterprise of supply chain, integrated the upstream and downstream industries, constructed a consortium chain, that consisted of core enterprise, financial institutions, suppliers and dealers, formed an encrypted credit named ET (Electronic Trust) based on the trustworthiness of the trust industries chain. Actually, ET is equaled to the actual credit, it could be paid for the suppliers according to the cargo value before the last settlement. So the ET could combine the virtual and actual capital. Thus it also could further solve the common pain points of supply chain.
Speech Recognition Using Neural Network for Mobile Robot Navigation

Automatic speech recognition (ASR) has gained a lot of popularity in the mobile robotics, where the commands could be provided to the robot wirelessly to maneuver. A navigation system combined with ASR is a complex system to carry out, because the system has difficulty in recognizing the voice commands when the environment involved already has disturbances like road noise, air conditioner, music, and passengers. The objective of this research is to operate a mobile robot with a single-arm manipulator, where the robot can perceive the speech and it can react to the individual speech commands provided by the operator swiftly and precisely. In order to recognize the speech, mel-frequency cepstral coefficient (MFCC) speech recognition algorithm is chosen and implemented in MATLAB. Various training and testing have been done in MFCC algorithm where it has to carry out the real-time processing of speech data and respond to it. Based on both the training and testing the voice commands collected from the five test subjects both male and female, the speech recognition system achieved 89% efficiency for the test database.
A fully-automated computer-aided breast lesion detection and classification system

This study presents an automatic computer-aided detection and diagnosis system which consists of two parts. The first part is for breast lesion characterization developed in pattern recognition framework (K-means clustering method) which is important to provide useful information for breast lesion characterization. Characterization of the detected lesion areas is done based on 6 parameters that are: (1) histogram, (2) shape, (3) gray level co-occurrence matrix, (4) gray level run length matrix, (5) neighboring gray tone difference matrix, and (6) gray level dependence matrix features. The second part of the system is developed based on machine learning algorithms and serves for the classification of localized breast lesions as benign and malignant. For classification, 4 different machine learning algorithms were investigated: (1) support vector, (2) k-nearest neighbors, (3) random forest, and (4) naïve Bayes classifiers. 84 histopathologically proven breast lesions were analyzed in the study. The proposed system compensates the motion artifacts, segments breast lesions, and classifies the lesions as benign and malignant. The results prove that the developed comprehensive system can detect and classifies breast lesions without any intervention. The best accuracy, sensitivity, specificity, and precision values to decide the tumor aggressiveness are 90.36%, 96.25%, 83.33%, and 92%, respectively.