Reviewerly/exHarmony huggingface dataset:
Generalized Born-Jordan distributions and applications

One of the most popular time-frequency representations is certainly the Wigner distribution. Its quadratic nature is, however, at the origin of unwanted interferences or artefacts. The desire to suppress these artefacts is the reason why engineers, mathematicians and physicists have been looking for related time-frequency distributions, many of them being members of the Cohen class. Among these, the Born-Jordan distribution has recently attracted the attention of many authors, since the so-called ghost frequencies are grandly damped, and the noise is, in general, reduced; it also seems to play a key role in quantum mechanics. The central insight relies on the kernel of such a distribution, which contains the sinus cardinalis sinc, the Fourier transform of the first B-spline B1. The idea is to replace the function B1 with the spline or order n, denoted by Bn, yielding the function (sinc)n when Fourier transformed, whose speed of decay at infinity increases with n. The related Cohen kernel is given by ${\Theta }^{n}(z_{1},z_{2})=\text {sinc}^{n}(z_{1}\cdot z_{2})$ , $n\in \mathbb {N}$ , and the corresponding time-frequency distribution is called generalized Born-Jordan distribution of ordern. We show that this new representation has a great potential to damp unwanted interference effects and this damping effect increases with n. Our proofs of these properties require an interdisciplinary approach, using tools from both microlocal and time-frequency analysis. As a by-product, a new quantization rule and a related pseudo-differential calculus are investigated.
A Multivariate Probit Model for Learning Trajectories: A Fine-Grained Evaluation of an Educational Intervention

Advances in educational technology provide teachers and schools with a wealth of information about student performance. A critical direction for educational research is to harvest the available longitudinal data to provide teachers with real-time diagnoses about students’ skill mastery. Cognitive diagnosis models (CDMs) offer educational researchers, policy makers, and practitioners a psychometric framework for designing instructionally relevant assessments and diagnoses about students’ skill profiles. In this article, the authors contribute to the literature on the development of longitudinal CDMs, by proposing a multivariate latent growth curve model to describe student learning trajectories over time. The model offers several advantages. First, the learning trajectory space is high-dimensional and previously developed models may not be applicable to educational studies that have a modest sample size. In contrast, the method offers a lower dimensional approximation and is more applicable for typical educational studies. Second, practitioners and researchers are interested in identifying factors that cause or relate to student skill acquisition. The framework can easily incorporate covariates to assess theoretical questions about factors that promote learning. The authors demonstrate the utility of their approach with an application to a pre- or post-test educational intervention study and show how the longitudinal CDM framework can provide fine-grained assessment of experimental effects.
Detecting, Classifying, and Mapping Retail Storefronts Using Street-level Imagery

Up-to-date listings of retail stores and related building functions are challenging and costly to maintain. We introduce a novel method for automatically detecting, geo-locating, and classifying retail stores and related commercial functions, on the basis of storefronts extracted from street-level imagery. Specifically, we present a deep learning approach that takes storefronts from street-level imagery as input, and directly provides the geo-location and type of commercial function as output. Our method showed a recall of 89.05% and a precision of 88.22% on a real-world dataset of street-level images, which experimentally demonstrated that our approach achieves human-level accuracy while having a remarkable run-time efficiency compared to methods such as Faster Region-Convolutional Neural Networks (Faster R-CNN) and Single Shot Detector (SSD).
Multimodal graph convolutional networks for high quality content recognition

With the development of the Internet, more and more creators publish articles on social media. How to automatically filter high quality content from a large number of multimedia articles is one of the core functions of information recommendation, search engine, and other systems. However, existing approaches typically suffer from two limitations: (1) They usually model content as word sequences, which ignores the semantics provided by non-consecutive phrases, long-distance word dependency, and visual information. (2) They rely on a large amount of manually annotated data to train a quality assessment model while users may only provide labels of interest in a single class for a small number of samples in reality. To address these limitations, we propose a Multimodal Graph Convolutional Networks (MGCN) to model the semantic representations in a unified framework for High Quality Content Recognition. Instead of viewing text content as word sequences, we convert them into graphs, which can model non-consecutive phrases and long-distance word dependency for better obtaining the composition of semantics. Besides, visual content is also modeled into the graphs to provide complementary semantics. A well-designed graph convolutional network is proposed to capture the semantic representations based on these graphs. Furthermore, we employ a non-negative risk estimator for high quality content recognition and the loss is back-propagated for model learning. Experiments on real datasets validate the effectiveness of our approach.
What’s in the Box? Combating Counterfeit Medications in Pharmaceutical Supply Chains with Blockchain Vigilant Information Systems

Counterfeit medications, medication overprescribing and a slow antiquated process encompassed in recalling batches of medications represent serious supply chain concerns for the pharmaceutical industry. Blockchain, the technology underpinning the Bitcoin cryptocurrency, has been touted as a possible solution and panacea for the pharmaceutical industry to overcome the aforementioned concerns. Vigilant information systems enable organisations to make quick decisions in real-time in dynamic supply chain environments. Currently, the concept of blockchain vigilant information systems and their impact on supply chain decision making has not been researched in the pharmaceutical industry. Consequently, using an inductive grounded theory approach, we investigate pharmaceutical blockchain use cases and present an emergent multi-layer pharmaceutical blockchain vigilant information system (PBVIS) model. The various capabilities of each layer for pharmaceutical supply chain stakeholders are discussed. Research implications and fruitful avenues for future research studies are also presented in this chapter.
Research on Sentiment Tendency and Evolution of Public Opinions in Social Networks of Smart City

With the rapid development of mobile Internet, the social network has become an important platform for users to receive, release, and disseminate information. In order to get more valuable information and implement effective supervision on public opinions, it is necessary to study the public opinions, sentiment tendency, and the evolution of the hot events in social networks of a smart city. In view of social networks’ characteristics such as short text, rich topics, diverse sentiments, and timeliness, this paper conducts text modeling with words co-occurrence based on the topic model. Besides, the sentiment computing and the time factor are incorporated to construct the dynamic topic-sentiment mixture model (TSTS). Then, four hot events were randomly selected from the microblog as datasets to evaluate the TSTS model in terms of topic feature extraction, sentiment analysis, and time change. The results show that the TSTS model is better than the traditional models in topic extraction and sentiment analysis. Meanwhile, by fitting the time curve of hot events, the change rules of comments in the social network is obtained.
Modeling and Simulation of 6 DOF Robotic Arm Based on Gazebo

This paper proposes a method for robotic arms in realistic physical environment based on Gazebo. Parallel-Axis Theorem is used to estimate the links' moment of inertia. Controllers of the joints and the path planning algorithm are the important parts of the system. ROS is chosen to organize task architecture considering its easier hardware abstraction. The simulation system is able to analysis and compare robotic arm's responses under different algorithms and different target pose. When several targets given at the same time, the system can analysis which is optimal concerning the path's length and velocity's change smoothness.
Camouflaged Optical Encryption Based on Compressive Ghost Imaging

A camouflaged encryption method based on ghost imaging is proposed. During the encryption process, a light source illuminates a camouflaged image with specific modulated patterns, and a generated sequence of intensity is transmitted to the receiver as a ciphertext. The ciphertext is captured by authorized receivers and potential eavesdroppers. Only authorized receivers with keys can obtain the secret image, while eavesdroppers can obtain camouflaged images only even if they steal the ciphertext and keys. The secret image is hidden by the camouflaged image, which protects the security of the secret image by confusing the eavesdropper. The feasibility and safety of this method in practical applications were verified by a series of simulations and experiments.
Experimental analysis and modeling of weather condition effects on photovoltaic systems’ performance: Tehran case study

Increased reliability in grid-independent photovoltaic systems is an important factor in the prevalence and greater use of solar systems. The present study was conducted in Iran. It is one of the countries with high potential in the field of solar energy with average radiation of 5–5.5 kWh/m2 intensity. On the other hand, due to the wide desert areas, it is important to investigate the effect of dust deposition on photovoltaic panels. An experimental study on the performance of photovoltaic panels over a 90-day period from May 25 to August 22, 2018, in Tehran, and under real conditions, to investigate the effect of dust deposition and climate change on their output power was performed. Because of its large population, political and economic conditions, Tehran has the highest importance amongst other cities in Iran. The results showed that the dirty panels produced 10% less power than the clean panels. Utilization of the data achieved from the experiment, the output power of the clean and dirty panels considering the air temperature and solar radiation is modeled using the MLR method. In order to establish the relationship between the reductions of the output power of the panel, due to the deposition of walnut dust, the soiling ratio (SR) is used. This ratio is gained by dividing the output power of the dirty panel by the output power of the clean panel to obtain a coefficient to determine the reduction of the output power of the desired photovoltaic panel in terms of air temperature and solar radiation. The coefficient earned with the correlation coefficient of 0.9 (R2) and the error of the mean root squares 7.94 (RMSE) can well predict the loss of power output of the panel due to dust sediment on the surface of the panel. The uncertainty range of experimental results is between of 7.83% to 8.08%.
Mining User Opinions to Support Requirement Engineering: An Empirical Study

App reviews provide a rich source of user opinions that can support requirement engineering activities. Analysing them manually to find these opinions, however, is challenging due to their large quantity and noisy nature. To overcome the problem, automated approaches have been proposed for so-called opinion mining. These approaches facilitate the analysis by extracting features discussed in app reviews and identifying their associated sentiments. The effectiveness of these approaches has been evaluated using different methods and datasets. Unfortunately, replicating these studies to confirm their results and to provide benchmarks of different approaches is a challenging problem. We address the problem by extending previous evaluations and performing a comparison of these approaches. In this paper, we present an empirical study in which, we evaluated feature extraction and sentiment analysis approaches on the same dataset. The results show these approaches achieve lower effectiveness than reported originally, and raise an important question about their practical use.
Correcting Decalibration of Stereo Cameras in Self-Driving Vehicles

Camera systems in autonomous vehicles are subject to various sources of anticipated and unanticipated mechanical stress (vibration, rough handling, collisions) in real-world conditions. Even moderate changes in camera geometry due to mechanical stress decalibrate multi-camera systems and corrupt downstream applications like depth perception. We propose an on-the-fly stereo recalibration method applicable in real-world autonomous vehicles. The method is comprised of two parts. First, in optimization step, external camera parameters are optimized with the goal to maximise the amount of recovered depth pixels. In the second step, external sensor is used to adjust the scaling of the optimized camera model. The method is lightweight and fast enough to run in parallel with stereo estimation, thus allowing an on-the-fly recalibration. Our extensive experimental analysis shows that our method achieves stereo reconstruction better or on par with manual calibration. If our method is used on a sequence of images, the quality of calibration can be improved even further.
Initializing photonic feed-forward neural networks using auxiliary tasks

Photonics is among the most promising emerging technologies for providing fast and energy-efficient Deep Learning (DL) implementations. Despite their advantages, these photonic DL accelerators also come with certain important limitations. For example, the majority of existing photonic accelerators do not currently support many of the activation functions that are commonly used in DL, such as the ReLU activation function. Instead, sinusoidal and sigmoidal nonlinearities are usually employed, rendering the training process unstable and difficult to tune, mainly due to vanishing gradient phenomena. Thus, photonic DL models usually require carefully fine-tuning all their training hyper-parameters in order to ensure that the training process will proceed smoothly. Despite the recent advances in initialization schemes, as well as in optimization algorithms, training photonic DL models is still especially challenging. To overcome these limitations, we propose a novel adaptive initialization method that employs auxiliary tasks to estimate the optimal initialization variance for each layer of a network. The effectiveness of the proposed approach is demonstrated using two different datasets, as well as two recently proposed photonic activation functions and three different initialization methods. Apart from significantly increasing the stability of the training process, the proposed method can be directly used with any photonic activation function, without further requiring any other kind of fine-tuning, as also demonstrated through the conducted experiments.
Smart Agriculture Using Supply Chain Management Based On Hyperledger Blockchain

Abstract Indonesia is an agricultural country, and one of the regions is Semarang Regency which well known as chili producers. Unfortunately, common issues occurred such as the fluctuation of the stock price and chili, the low exchange values of farmers in prices because of the complicated flow distribution chain. Furthermore, chili is quickly decomposed, but the demand is high enough. The solutions are to predict the demand for supply chain management, to make payment transactions of agricultural needs of farmers by analyzing stocks, raw materials, price to the chain of distribution, also to build trade links among farmers, consumers and Semarang District Agriculture Service. This journal discussed the flow of supply chain and logistics. We recommend to use the latest technology called Hyperledger Blockchain and IOT so that they can make it easier to carry out the transaction process because it is simple, low cost, transparent, faster. It also has high data security, and simplify ecosystems can be easy to exchange information quickly through Hyperledger Blockchain transactions so that the potential of this technology can bring benefits to farmers and consumers by creating the construction of a trusted distribution network that will bring a data transparency solution so that it can become a suggestion for the Semarang District Agriculture Service.
Immersive visualization with bar graphics

Augmented reality (AR) enables the creation of immersive situated visualizations. These visualizations blend with the real world so that the user perceives the virtual objects as physically present. We propose two novel immersive situated visualizations for geospatial quantitative point data that link AR bar graphics in the real environment with a virtual AR chart or map. The Egocentric Bar Chart combines virtual bars placed in a real landscape with a virtual 360° bar chart arranged around the user. The Circular Map with Bars combines bars in a real landscape with a 360° virtual map showing bar graphics. These visualizations are designed for inspecting the location and attributes of individual points, as well as understanding the spatial and non-spatial patterns of a set of points. We conducted a study with 12 participants to evaluate reading accuracy and efficiency of the two visualizations, with a view from a skyscraper at the center of the Melbourne metropolitan area. To better control confounding factors, the study used a virtual environment instead of an AR setup. We found that the two suggested visualizations performed better than bars placed in a landscape, and received positive feedback from study participants. The Egocentric Bar Chart visualization was most accurate when estimating and comparing values, and we identified promising improvements for the Circular Map with Bars visualization. Our results demonstrate the potential of AR technology for the communication of geospatial point data with immersive situated bar graphics in an outdoor environment.
A Survey on Sequential Recommendation

In modern e-commerce, the user's sequential behavior contains a wealth of information. According to the user's historical interaction behavior information, modeling the user's preferences to predict the continuous items that the user may interact with is the main task of the sequential recommendation system. In this article, we conducted a systematic and comprehensive review of sequential recommendation systems based on user interaction behavior. Specifically, first of all, we briefly introduce the existing general recommendation methods, and point out the shortcomings of the general recommendation method, and then systematically expound the sequence recommendation, and finally summarize and forecast.
Two-stage sentiment classification based on user-product interactive information

Document-level review sentiment classification aims to predict the sentiment category for given review documents written by users for products. Most of the existing methods focus on generating a good review document representation and classifying the review document directly. However, on the one hand, as document-level review sentiment classification usually includes many sentiment categories and the difference between these sentiment categories is not obvious, it may be difficult to obtain satisfying result by direct classification. On the other hand, this once classification process with review representation may fail to well interpret how the results are achieved. In addition, although some information such as user preference and product characteristics are incorporated when building models, the interactive information between user and product are usually ignored. In this paper, inspired by the deductive reasoning strategy of human doing multiple choice questions, we are motivated to propose a Two-Stage Sentiment Classification (TSSC) model to classify review documents in two stages: (1) Coarse classification stage, where model mainly adopts user-product interactive information to pre-judge the sentiment tendency of the review document without considering the review information; (2) Fine classification stage, where model uses text information of the review document for further classification based on the sentiment tendency obtained in coarse classification stage. Finally, the sentiment classification task is accomplished by combining both the results of coarse classification and fine classification. The experimental results demonstrate that our TSSC model significantly outperforms most of the related models (e.g., Trigram and NSC+UPA) on IMDB and Yelp datasets in terms of classification accuracy. When compared with the state-of-the-art HUAPA model, our TSSC model not only achieves slightly more accurate performance, but also has lower time complexity and stronger interpretability.
Automatic Source Code Documentation using Code Summarization Technique of NLP

Source code documentation is an important process for software project maintenance and management. The documentation process always consumes a lot of time and effort from human experts. The software project document should be concise and clear without any ambiguity. Existing source code documentation tools like JavaDoc are very limited in the market. Also, tools can identify only the predefined methods of corresponding programming languages. In this paper, a novel system is proposed to automate the source code documentation process for C programming language using the source code summarization technique of NLP. The core component of this proposed system i.e., Software Word Usage Model (SWUM) build using Context-Free Grammars and NLP preprocessing techniques. This system can successfully generate the documentation for a C program along with predefined and user-defined methods using Natural Language Generation technique. This proposed system can document a program in two major formats; method-based, abstract level and statement-based, detailed level. The proposed system efficiency is evaluated by comparing system-generated source code documentation with an expert generated documentation. Results obtained from that comparison shows that the proposed system can give better performance for small and medium-size software projects.