Reviewerly/exHarmony huggingface dataset:
Effect of Blockchain Technology for Sustainable Performance in Supply Chain Management

This article discussed the major effect of blockchain technology for sustainable performance in Supply chain management (SCM). Blockchain technology is a technology that performs several operations without letting the information get lost. This digital technology of blockchain will show the potential to reshape the management of supply chain operations in various companies. We will investigate the impact of blockchain technology in relation to efficiency and sustainability. As an example recently the operations in the airport industry have been improving due to technological advances as blockchain technology is handling all the information in the warehouses about product delivery and services in SCM. This technology allows companies to track all types of transactions more securely and transparently and blockchain technology shows benefits, especially in terms of improving Operational Management. The technological disruptions that have an effect on operations must include A-CDM (Airport collaborative decision-making) disruption management and there should be a collaborative network where everyone shares mutual trust and common culture in order to avoid disruption. Airports are still under research in terms of managing blockchain technology efficiently and effectively. The database which is decentralized causes the transactions to be transparent, reliable, traceable, and efficient to supply chain management. This article will be useful to understand the importance of blockchain technology for maintaining sustainability in the operations of SCM along with that we will take an example of SCM in the airport industry to understand how this technology is used there.
Nested sequents for intermediate logics: the case of Gödel-Dummett logics

AbstractWe present nested sequent systems for propositional Gödel-Dummett logic and its first-order extensions with non-constant and constant domains, built atop nested calculi for intuitionistic logics. To obtain nested systems for these Gödel-Dummett logics, we introduce a new structural rule, called the linearity rule, which (bottom-up) operates by linearising branching structure in a given nested sequent. In addition, an interesting feature of our calculi is the inclusion of reachability rules, which are special logical rules that operate by propagating data and/or checking if data exists along certain paths within a nested sequent. Such rules require us to generalise our nested sequents to include signatures (i.e. finite collections of variables) in the first-order cases, thus giving rise to a generalisation of the usual nested sequent formalism. Our calculi exhibit favourable properties, admitting the height-preserving invertibility of every logical rule and the (height-preserving) admissibility of a large collection of structural and reachability rules. We prove all of our systems sound and cut-free complete, and show that syntactic cut-elimination obtains for the intuitionistic systems. We conclude the paper by discussing possible extensions and modifications, putting forth an array of structural rules that could be used to provide a sizable class of intermediate logics with cut-free nested sequent systems.Keywords: Cut-eliminationfirst-orderintermediate logicnested sequentproof theory Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingWork supported by the European Research Council (ERC) Consolidator Grant 771779.
Smartphone Applications as a Catalyst for Active Learning in Chemistry: Investigating the Ideal Gas Law

This chapter examines how interactive research-based smartphone applications can be used to engage students in hands-on chemistry learning both at school and at home. Two different smartphone applications are being discussed: the PhET (Physics Education Technology, https://phet.colorado.edu/) suite of interactive simulations and the Phyphox (Physics Phone Experiments, https://phyphox.org/) data collection and analysis smartphone application. PhET chemistry simulations let students conduct virtual experiments, while the Phyphox app allows students to collect and analyze real time data. We illustrate the pedagogical applications of these apps through an example of an ideal gas investigation. We also suggest how smartphone applications can be introduced in science teacher education to actively engage future teachers in smartphone enabled science investigations. Finally, we discuss how smartphone-based science experiments can help educators increase access to active science learning for all students and consequently reduce the educational inequality in STEM classrooms.
HDT network: A high‐resolution range profile multi‐target detection and tracking method based on neural network

Abstract In radar target detection and tracking tasks, the detection algorithm and data association algorithm are the primary technologies. The accuracy of detection, stability of tracking and processing speed are the key points to accomplishing effective high‐resolution range profile (HRRP) multi‐target tracking (MTT). Classic HRRP target tracking algorithms conduct the detection by extracting handcraft features and conduct tracking based on the data association algorithm. With the development of neural network methods, deep learning methods have been widely applied in target detection and tracking. An HRRP detection and tracking (HDT) network based on neural networks is proposed, which includes three steps: Firstly, the HRRP signal is processed by a convolutional neural network detector to extract the feature of the origin signal and generate the detection measurement. Secondly, a predicted measurement is generated by a Kalman filter, which estimates the current state of the target based on its previous states and the detection measurement. Finally, the ReID network calculates the cosine similarity between the tracks and measurements to associate them and a linear sum assignment operation is used to match the tracks and measurements. By using the proposed algorithm, the HDT network is capable of detecting and tracking multiple targets in complex cluttered environments with high accuracy. Experimental results on an HRRP dataset collected by a moving radar platform show the outstanding performance of the HDT network, including detection accuracy, track integrity and real‐time MTT.
Y00 quantum noise randomised cipher; theoretical and experimental background

Abstract As past works have shown, information‐theoretically secure implementations transmitters of Y00 quantum noise randomised cypher are possible. An advance to the provably secure Y00 protocol by bridging gaps between experimental results and theoretical analyses under so‐called quantum collective measurement attacks with known plaintexts is aimed. It would be the strongest attack on the Y00 protocol in the context of quantum key distribution protocols. However, recently proposed security evaluations under the attacks were too abstract to apply to experiments. Therefore, security analyses directly evaluable with the equipped Y00 transmitters under attack are offered. Thus, new security indices are proposed instead of ordinary security measures, such as the bit‐error‐rate guarantee between optical signals or a masking size. Contrarily, unsolved problems are also listed.
Personalized movie recommendations based on deep representation learning

Personalized recommendation is a technical means to help users quickly and efficiently obtain interesting content from massive information. However, the traditional recommendation algorithm is difficult to solve the problem of sparse data and cold-start and does not make reasonable use of the user-item rating matrix. In this article, a personalized recommendation method based on deep belief network (DBN) and softmax regression is proposed to address the issues with traditional recommendation algorithms. In this method, the DBN is used to learn the deep representation of users and items, and the user-item rating matrix is maximized. Then softmax regression is used to learn multiple categories in the feature space to predict the probability of interaction between users and items. Finally, the method is applied to the area of movie recommendation. The key to this method is the negative sampling mechanism, which greatly improves the effectiveness of the recommendations, as a result, creates an accurate list of recommendations. This method was verified and evaluated on Douban and several movielens datasets of different sizes. The experimental results demonstrate that the recommended performance of this model, which has high accuracy and generalization ability, is much better than typical baseline models such as singular value decomposition (SVD), and the mean absolute error (MAE) value is 98%, which is lower than the best baseline model.
Lower-temperature fabrication of airbridges by grayscale lithography to increase yield of nanowire transmons in circuit QED quantum processors

Quantum hardware based on circuit quantum electrodynamics makes extensive use of airbridges to suppress unwanted modes of wave propagation in coplanar-waveguide transmission lines. Airbridges also provide an interconnect enabling transmission lines to cross. Traditional airbridge fabrication produces a curved profile by reflowing resist at elevated temperature prior to metallization. The elevated temperature can affect the coupling energy and even yield of pre-fabricated Josephson elements of superconducting qubits, tuneable couplers and resonators. We employ grayscale lithography in place of reflow to reduce the peak airbridge processing temperature from $200$ to $150^\circ\mathrm{C}$, showing a substantial yield increase of transmon qubits with Josephson elements realized using Al-contacted InAs nanowires.
Cross-modal retrieval of remote sensing images and text based on self-attention unsupervised deep common feature space

ABSTRACTWith the increase in multimodal data, such as remote sensing images, text, and video, cross-modal retrieval is widely applied in many fields. Because different modality data are heterogeneous, the main aim of cross-modal retrieval is to bridge their ‘semantic gap’. However, there are few cross-modal retrieval studies focused on remote sensing images and text, and fine-grained information from the remote sensing images and text are not fully considered by existing methods. To improve the retrieval accuracy of remote sensing images and text, we propose a self-attention unsupervised deep common feature space cross-modal retrieval method. First, a self-attention mechanism is applied to capture the fine-grained relationships between word fragments of text and remote sensing images. Then, a deep learning method and the triplet loss of remote sensing image and text pairs are used to extract the features of remote sensing images and text, and generate a semantically consistent common feature space, in which the feature representations of remote sensing images and text exhibit a uniform distribution. The experimental results of three benchmark cross-modal datasets, including RSICD, UCM_captions, and Sydney_captions, show that the proposed method performs better than the state-of-the-art methods. For example, when compared with the best model (VSE++) tested on RSICD, the proposed model demonstrated 2.1% and 5.6% higher precision in text to image and image to text retrieval tasks, respectively, and 3.8% higher mean average precision. However, similar to the other models tested, we found that the proposed model had a weaker performance when retrieving text from an image query than retrieving images from a text query.KEYWORDS: Cross-modal retrievalself-attentionremote sensing imagecommon feature space Disclosure statementNo potential conflict of interest was reported by the authors.Data Availability statementData are available at https://github.com/201528014227051/RSICD_optimal.
Age estimation algorithm based on deep learning and its application in fall detection

&lt;abstract&gt; &lt;p&gt;With the continuous development and progress of society, age estimation based on deep learning has gradually become a key link in human-computer interaction. Widely combined with other fields of application, this paper performs a gradient division of human fall behavior according to the age estimation of the human body, a complete priority detection of the key population, and a phased single aggregation backbone network VoVNetv4 was proposed for feature extraction. At the same time, the regional single aggregation module ROSA module was constructed to encapsulate the feature module regionally. The adaptive stage module was used for feature smoothing. Consistent predictions for each task were made using the CORAL framework as a classifier and tasks were divided in binary. At the same time, a gradient two-node fall detection framework combined with age estimation was designed. The detection was divided into a primary node and a secondary node. In the first-level node, the age estimation algorithm based on VoVNetv4 was used to classify the population of different age groups. A face tracking algorithm was constructed by combining the key point matrices of humans, and the body processed by OpenPose with the central coordinates of the human face. In the secondary node, human age gradient information was used to detect human falls based on the AT-MLP model. The experimental results show that compared with Resnet-34, the MAE value of the proposed method decreased by 0.41. Compared with curriculum learning and the CORAL-CNN method, MAE value decreased by 0.17 relative to the RMSE value. Compared with other methods, the method in this paper was significantly lower, with a biggest drop of 0.51.&lt;/p&gt; &lt;/abstract&gt;
Distances from Fuzzy Implications

In the literature, there have been a few works studying distance functions from fuzzy logic connectives (FLCs), such as t-norms, t-conorms, copulas, and quasi-copulas. Recently, Nanavati et al. defined a distance function $$d_I$$ using a fuzzy implication I. They characterised fuzzy implications that would yield a metric and showed that $$ d_I$$ is a pseudo-monometric w.r.t. the usual order on [0, 1]. In this work, we generalise the definition of $$d_I$$ with the aid of any t-conorm S. Denoting it by $$d_{I,S}$$ , we investigate the conditions under which $$d_{I,S}$$ yields metrics and pseudo-monometrics for the major t-conorms. We thus expand our armoury of practical distance functions using FLCs such as t-conorms and fuzzy implications.
Go-Kart Simulation in HoloLens

With increased computing power, there are various advancements in the field of augmented reality (AR), simulation technology and visualization. Manufacturing a car is one of the potential areas where mixed reality is applied. Various famous industries like NASA, Volvo, Lowe’s, Audi and Volkswagen are implementing HoloLens for several reasons. A few reasons for entrancing simulation in HoloLens for exploration of hardware components, self-driving car model, etc. For example, Volvo is at the vanguard of exploring automation to put Microsoft’s HoloLens mixed reality into its manufacturing toolkit. The particular set of Volvo vehicles is the main example of their scalable product architecture (SPA) platform being manufactured and tested in HoloLens. Simulations were designed and modified several times before the first XC90 or S90. Ford is operating HoloLens with designers to quickly prototype changes to cars. This allows designers and customers to see the enhancement on top of a current physical vehicle instead of the conventional clay model approach to designing a car. Devices like HoloLens, Google Glass, Apple Glass and Google ARCore are used to generate these applications. While the mixed reality is a major tool, artificial intelligence (AI) and machine learning (ML) can also help change car automation and manufacturing methods. The focus is to develop an application which can offer advantages to car industries for both producers and consumers through combining AI, mixed reality and ML technologies. Hence, this paper focuses on making a simulated Go-Kart system in HoloLens through a mixed reality application in which we can see the detailed version of a chosen car and choose a race track or a city line area to which we can implement autonomous driving.
A Modified LSB Steganography Algorithm to Store Images of Large Size

Steganography deals with the hiding of a secret message within an ordinary message and extracting it at its destination. The concept of digital steganography is gaining importance as privacy, and security is becoming a key aspect in the current-day communication. In this work, we propose variants of the naive least significant bit (LSB) algorithm for image steganography that improves certain aspects such as security and study their performance when run serially, and parallelly on a graphical processing unit (GPU). Image similarity measurement metric has been used to find how similar the image is after steganography when compared with the original image. By modifying the three LSB bits, we achieved the image similarity 92.88% using sequential approach and 99.05% using the proposed approach. In the case of five LSB bits steganography approach, the similarity with the proposed approach is around 96.04%, whereas the sequential approach is at 61.90%. There is an around 55% increase in the proposed approach.
Precipitation Forecasting: LSTM Modeling in Visual Analytic Framework

Precipitation prediction is the main feature to be considered to run any planning-based activities such as agriculture, logistic management and any sort of business. Rainfall is influenced by atmospheric factors which in turn are affected by pollution factors. This research work focuses on precipitation forecasts in taking account of ground-level atmospheric features and pollution features using visual analytic framework. Visual analytic approach relies on the right data model and aids visualizations at every stage to assess the performance of the model; thus, helps in decision making. The long short-term memory (LSTM) model is constructed to remember the past day recordings that aids improved precision in forecasting. Building LSTM as an analytical model in visual analytic approach provides better understanding, performance, recalling capability, accuracy of forecast, aided with side-by-side visual representations.
Deep Learning-Based Approach for Outlier Detection in Wireless Sensor Network

The sensor nodes of wireless sensor networks are prone to malicious attacks during transmission by way of the sensor nodes which are placed remotely in hostile environment. As a result, the data generated are sometimes unreliable and inaccurate which cause outlier. This paper presents a deep learning approach for outlier detection, namely generative adversarial network (GAN) with robust continuous clustering. In GAN, the discriminator (D) network and the generator (G) network operate through adversarial mechanisms, with the discriminator model estimating the chance that a sample came from training data and the ability to distinguish between actual and outlier data. Then, the outlier data are replaced by a single bit by the cluster head and transmitted to a relay cluster head or the sink node along with the node identification number. The result improves detection accuracy compared to existing state-of-the-art techniques.
URL Weight-Based Round Robin Load Balancing in Cloud Environment

Cloud load balancing Algorithm Round Robin is used to assign each task a particular time slice for its completion by the load balancer to the servers in a round robin fashion (circularly). A more advanced type of Round Robin Algorithm is Weighted Round Robin which is used in the current market. In the Round Robin Weighted cloud Load Balancing Algorithm, the tasks/jobs are assigned particular weights to servers. Generally, higher-weighted servers are assigned more tasks. In URL weight-based Round Robin Cloud Load Balancing, every request is classified into two categories. They were requested for pages and a request for database operation. Here requests for pages and database operations are given values 1 and 2. Here instead of using a regular time slice for a request, we use the time slice as 1 and 2 for its completion by the load balancer to the servers in a round robin fashion.
Depth Maps-Based 3D Convolutional Neural Network and 3D Skeleton Information with Time Sequence for HAR

The traditional methodology is to design a handcrafted feature descriptor to represent the object. In the last decade, with the development of computer software and hardware technology, the deep learning method has been applied in various research and application domains, such as artificial intelligence and computer vision in the real world. Unlike the handcrafted methods to design the feature descriptor for human action recognition, the deep learning-based approaches directly extract feature maps after the neural network processing. Compared with the RGB video, depth sequences are more advantageous in lighting changes and information collection. In this paper, we study depth maps based on deep learning and corresponding skeleton joint information for human activity recognition. The 3D convolutional neural network (3DCNN) directly learns spatial–temporal features from depth maps sequence. Geometric information where skeleton joint point distance, angle information and time sequences of skeleton point changes in different frames are extracted to merge deep learning features map. The merged features descriptor was put into SVM for activity classification. The MSR-Action3D benchmark dataset 1/3 was divided into testing data, training is the rest. The experimental result is compared with the superior performance on the MSR-Action3D benchmark dataset. Our proposed technique proves that it has very promising classification accuracy...
A Survey on Efficient Neural Network Compression Techniques

We have seen substantial advancements in the performance and accuracy of neural networks (NNs)-based models for a wide range of issues over the last decade. Highly over-parameterized models are frequently used to attain this improvement. The size of the model grows as the number of parameters grows, making it impractical for use in many resource-constrained applications. With the increase in deep learning-based IoT applications demanding fast real-time inference, with low consumption of energy and high accuracy, the need for compressing these neural networks arises. In this paper, we have summarized and discussed some of the main techniques to compress the neural network by maintaining its accuracy.