Reviewerly/exHarmony huggingface dataset:
Proof Nets, Coends and the Yoneda Isomorphism

Proof nets provide permutation-independent representations of proofs and are used to investigate coherence problems for monoidal categories.We investigate a coherence problem concerning Second Order Multiplicative Linear Logic (MLL2), that is, the one of characterizing the equivalence over proofs generated by the interpretation of quantifiers by means of ends and coends.We provide a compact representation of proof nets for a fragment of MLL2 related to the Yoneda isomorphism.By adapting the "rewiring approach" used in coherence results for * -autonomous categories, we define an equivalence relation over proof nets called "rewitnessing".We prove that this relation characterizes, in this fragment, the equivalence generated by coends.1 In [15] the definition of proof structures is based on two conditions: (1) that any ∀ link has a distinct eigenvariable and (2) that the conclusions of a proof structures have no free variable (in particular, new constants x are introduced to eliminate free variables).Moreover, in the definition of the correctness criterion any ∀-link of eigenvariable X can jump on any formula in which X occurs free.In [21] conditions (1) and (2) are replaced by the equivalent condition that the conclusions of the proof structure plus the witnesses of existential links must form a clean sequent and the correctness criterion is modified by demanding that a ∀-link of eigenvariable X can jump on any ∃-link whose witness formula contains free occurrences of X.Here we will consider this formulation.
Research on Facial Expression Recognition Based on Neural Network

For facial expression recognition, this paper proposes a cross-connected AlexNet improved convolutional neural network model. In general, due to lack of image information and noise interference, traditional machine learning methods lack robustness and poor recognition rate. Based on the advantages of deep learning in feature extraction, this paper adds a convolution layer and a pooling layer to the original AlexNet network structure, and uses the cross-connect method to extract low-level features from the network structure and combines advanced functions to construct a Classifier. The trainable features are used to extract feature convolution kernels, and then the pooling layer performs downsampling to eliminate unimportant information, reduce the amount of information calculation, and finally use the Softmax classifier for classification and recognition. Conduct comparative tests before and after the improvement to verify the performance improvement of the improved method.
Off-grid and decentralized hybrid renewable electricity systems data analysis platform (OSDAP)

Off-grid and decentralized Hybrid Renewable Electricity Systems (OHRES) play a promising role in providing sustainable energy access to 860 million people around the globe with no access to electricity. However, the diversity of global geographies, lack of performance analysis standardization, and modeling limitations hinder the scalability of OHRES. This study outlines the creation of a data analysis platform (OSDAP) and irradiance forecasting models for OHRES that can be used to support the provision of global energy access. The paper presents the technical layout of OHRES in two case studies in Canada and Uganda and outlines an assessment and modeling platform that can be used to assess OHRES performance across widely varying geographic locations. Two irradiance forecasting models are developed using artificial neural networks, which can be used to increase solar energy utilization in OHRES.
A Smart System Based on Digital Object Architecture to Verify the Diploma Certificates

Digital Object Architecture (DOA), is the architecture of a distributed storage system, location, and searching the Internet for information. Despite the efforts made to eliminate the phenomenon of fake and forged certificates in the labor market, the phenomenon is still present and disturbs society in most countries of the world. According to specialists, the damage of fake or fraudulent testimony, in the Arab world and the world as a whole, is not limited to high unemployment rates and depriving of job opportunities but extends to economic and social damages. In this paper, we developed a fast and smart system based on DOA technology for the certification verification system. The proposed system is characterized by a speedy verification process that identifies fraudulent activities from producing false or illegal certificates. It also contributes to the process of the digital transformation in the certification system, and promotes further research in DOA technology. The proposed model in this paper will improve tackling counterfeit academic degrees worldwide.
Single-photon scattering controlled by an imperfect cavity*

We study the single-photon transport in the coupled-resonator waveguide (CRW) controlled by an imperfect cavity. A Lorentzian spectrum is introduced to describe the dissipation. We find that the probability current conservation can be broken, although the imperfect cavity is a Hermitian system. The coupling strength between the imperfect cavity and the CRW has significant influences near the resonant frequency. With the increase of the coupling strength, the transmission coefficient becomes smaller. The spectral width plays a dominant role under the off-resonant condition, where the transmission coefficient is greatly suppressed with the increase of the spectral width. We also observe an abrupt jump of the transmission and reflection coefficients when the hopping amplitude is large enough. All the distinctive behaviors are closely related to the complex effective potential induced by the imperfect cavity.
Video Classification Technology Based on Deep Learning

It takes a large amount of time for artificial video classification and analysis, and the processing efficiency is low, so it is difficult to better meet the practical needs for the era of big data. Therefore, it is extremely urgent to form an intelligent video analysis and classification method, which is worth exploring. In this paper, experiments on feature extraction, feature fusion and similarity measurement are carried out to determine methods to improve video classification accuracy. The results show that the classification accuracy decreases with the increase of pyramid layers. The performance of the cascade SRU method is better, which can improve the classification accuracy. In addition, two-level cascading coding can better complete the extraction of effective information in depth characteristics and promote the accuracy of classification. The classification accuracy of the network increases, after the characteristic aggregation is accomplished by means of average pooling.
Orthonormal, moment preserving boundary wavelet scaling functions in Python

In this paper we derive an orthonormal basis of wavelet scaling functions for $$L^2([0, 1])$$ motivated by the need for such a basis in the field of generalized sampling. A special property of this basis is that it includes carefully constructed boundary functions and it can be constructed with arbitrary smoothness. This construction makes assumptions about the signal outside the interval unnecessary. Furthermore, we provide a Python package implementing this wavelet decomposition. Wavelets defined on a bounded interval are widely used for signal analysis, compression, and for numerical solution of differential equations. We show that for many cases using the basis that we derive results in smaller error than the commonly used alternative.
A machine learning approach for socialbot targets detection on Twitter

In online social networks (OSNs), socialbots are responsible for various malicious activities, and they are mainly programmed to imitate human-behavior to bypass the existing detection systems. The socialbots are generally successful in their malicious intent due to the existence of OSN users who follow them and thereby increase their reputation in the network. Analysis of the socialbot networks and their users is vital to comprehend the socialbot problem from target users’ perspective. In this paper, we present a machine learning-based approach for characterizing and detecting socialbot targets, i.e., users who are susceptible to be trapped by the socialbots. We model OSN users based on their identity and behavior information, representing the static and dynamic components of their personality. The proposed approach classifies socialbot targets into three categories viz. active, reactive, and inactive users. We evaluate the proposed approach using three classifiers over a dataset collected from a live socialbot injection experiment conducted on Twitter. We also present a comparative evaluation of the proposed approach with a state-of-the-art method and show that it performs significantly better. On feature ablation analysis, we found that network structure and user intention and personality related dynamic features are most discriminative, whereas static features show the least impact on the classification. Additionally, following rate, multimedia ratio, and follower rate are most relevant to segregate different categories of the socialbot targets. We also perform a detailed topical and behavioral analysis of socialbot targets and found active users to be suspicious. Further, joy and agreeableness are the most dominating personality traits among the three categories of the users.
The Potential for Nonparametric Joint Latent Class Modeling of Longitudinal and Time-to-Event Data

Joint latent class modeling (JLCM) of longitudinal and time-to-event data is a parametric approach of particular interest in clinical studies. JLCM has the flexibility to uncover complex data-dependent latent classes, but it suffers high computational cost, and it does not use time-varying covariates in modeling time-to-event and latent class membership. In this work, we explore in more detail both the strengths and weaknesses of JLCM. We then discuss the sort of nonparametric joint modeling approach that could address some of JLCM’s weaknesses. In particular, a tree-based approach is fast to fit, and can use any type of covariates in modeling both the time-to-event and the latent class membership, thus serving as an alternative method for JLCM with great potential.
Invisible QR Code Generator Using Convolutional Neural Network

The authors aim to embed arbitrary information in arbitrary images and restore them using CNN. To achieve this goal, we propose a model consisting of two CNNs with different roles. In the proposed method, it is used as an information medium for embedding a QR code. The QR code error correction function is expected to restore the embedded information without error. Existing research has shown that embedding a QR code in a sharp color image does not restore the QR code correctly. This paper modified the CNN configuration to address this issue. The authors hope this technology can be used to integrate QR codes into human living space and hide information without upset. We learned how to embed a QR code image in a color image using the CNN model proposed this time. As a result, the authors were able to embed the QR code image without degrading the quality of the input color image. Current methods have drawbacks. Blur the image with the embedded QR code. Then, there is a problem that the embedded QR code cannot be restored. We will solve this problem in the future.
Pricing Research on Cloud Manufacturing Service Based on Game Theory

Abstract The pricing of the cloud manufacturing service was critical for the success of service transaction. Firstly, based on the hotelling model, this paper discussed the pricing and revenue of providers on the cloud platform for service trading. In the cloud service transaction, the cloud platform is generally the leader of service providers. Therefore, we further proposed the stackelberg game model, in which the pricing of the manufacturing service was determined by the profit ratio of the cloud platform. Thirdly, basing on the utilization rate of the manufacturing service, we adjusted the determined pricing of manufacturing service dynamically to cope with unpredictable service demand. Finally, we compared the price and benefit between the proposed model and the traditional hotelling model. The result shows that the price and revenue of the manufacturing service are improved by introducing cloud platform. In the future, we should consider demanders’ revenue and other factors on the price.
Challenges Encountered in Building A Fast and Efficient Surveillance System: An Overview

Most often a prominent IoT application in a residential area and office spaces for simple surveillance. Challenge wrests in the legal systems nowadays to any developing nations, especially in those nations where the density of populations is more than normal. Here a huge task-force is required to monitor and maintain law and order situations across the loop and corner of the cities and villages. CCTV's is deployed to at-least capture some part of the busy streets and at a server room; it is being monitored by some officials. With a large number of data being produced as part of criminals, suspects, missing profiles, etc., it would be difficult for any officials to keep track of those data sets manually. Hence if a smart system can be used to process the videos as and when it is captured, one can say 90% of the task of monitoring the busy streets can be achieved. In this paper, it is possible to review the works/algorithms based on four categories for building fast, efficient, and accurate Surveillance Systems, i.e., face detection, face recognition, video/image processing, pattern matching in a video surveillance system. The investigation in these recent advancements will help one to build a better surveillance system.
Towards Dependable Object Detection

A high confidence in object detection is very crucial for object detector modules to be used in real world applications. Though the confidence scores in object detection can be improved by using better and larger training data set and using more robust architectures, which is an approach from the computer vision side, this paper aims to improve the detection confidence by finding the effective viewpoints which makes a dependable use of the available object detectors. In particular, we investigate the effect of viewing distance on detection confidence of an object detector, which can further be used to control a camera mounted on a mobile robot to approach a better viewing position. We consider the cases with both fixed focal length and variable focal length camera. Experimental results are presented to demonstrate the efficacy and validity of the techniques presented in this work.
Anomalous Crowd Behavior Detection Using Raspberri PI

Anomalous crowd behavior detection with proper accuracy is an important aspect all over the world. To detect the abnormal activity, suspicious persons, suspicious objects, any accident, bomb explosion in crowded area like station, fair, bus stand and like others are huge concern for us. The aim of this paper is the fast detection of an anomalous event by the use of TensorFlow algorithm in python software and after detecting the anomalous event there is a raspberry pi hardware interfacing which will create an alarming sound to aware everyone about the unexpected situation. This system can be affordable by all easily. To calculate the accuracy and sensitivity of a given video, PETS2009 datasets have been used in this paper. The proposed work finds out a reasonably good accuracy and high sensitivity.
RCAR Framework

Regularized Class Association Rules (RCAR) is an algorithm which produces rules based classifier in a categorical data space. The main goal of RCAR algorithm is to build classifiers which are as accurate as the state of the art algorithms, while improving the interpretability and allowing end-users to maintain and understand its outcome easily and without statistical modeling background. In this work, first, we introduce the RCAR framework, second, we provide the main functions which extract Class Association Rules (CARs), prune irrelevant rules, and rank the conserved CARs according to a set of weights calculated for each CAR. The RCAR framework also consists of multiple visualization techniques that traces the steps of the model building according to its parameters, which facilitates the model elaboration and tuning parameter for simple users. Eventually, we implemented the RCAR algorithm in the RCAR's R package.
Relative and Incomplete Time Expression Anchoring for Clinical Text

Extracting and modeling temporal information in clinical text is an important element for developing timelines and disease trajectories.Time information in written text varies in preciseness and explicitness, posing challenges for NLP approaches that aim to accurately anchor temporal information on a timeline.Relative and incomplete time expressions (RI-Timexes) are expressions that require additional information for their temporal anchor to be resolved, but few studies have addressed this challenge specifically.In this study, we aimed to reproduce and verify a classification approach for identifying anchor dates and relations in clinical text, and propose a novel relation classification approach for this task.
First-Class Subtypes

First class type equalities, in the form of generalized algebraic data types (GADTs), are commonly found in functional programs. However, first-class representations of other relations between types, such as subtyping, are not yet directly supported in most functional programming languages. We present several encodings of first-class subtypes using existing features of the OCaml language (made more convenient by the proposed modular implicits extension), show that any such encodings are interconvertible, and illustrate the utility of the encodings with several examples.
Time-frequency Overlapped Signals Intelligent Modulation Recognition in Underlay CRN

In underlay cognitive radio networks (CRN), a novel intelligent modulation recognition method of time-frequency overlapped MPSK/MQAM signals based on convolutional neural network (CNN) is proposed in this paper. Aiming to achieve better modulation recognition performance of the time-frequency overlapped signals, the contour maps of cyclic spectrum of the received signals as the initial features are extracted, and then the CNN is used to extract further features as the final features. After preprocessing the contour maps to serve as input data for the CNN, the CNN is constructed and optimized to complete the training of the CNN. Finally, the well trained CNN is used for modulation recognition of the time-frequency overlapped MPSK/MQAM signals. Simulation results show that the average recognition rate of the proposed method is 90 % when the signal-to-noise ratio (SNR) is 2dB, and it is robust to the power ratio and spectrum overlapped rate of the component signals.
Molecular Augmented Reality for Design and Engineering (MADE): Effectiveness of AR Models on Discovery, Learning, and Education

The design and manipulation of chemical systems involves understanding the form or morphology of chemical structures. An understand of the form of chemical structure includes an understanding of the components of chemical structure, the functions of the forms and sub-components, and changes in the structure of chemical systems during interaction, maturation, or chemical processes. Viewed from a computer graphic viewpoint these chemical processes can be described and modelled as three-dimensional structures, changing shape, and interacting with other 3D structures. Furthermore, our intuition was that the visualization should be as embodied as possible and open for collaboration. In this project we seek to create a tool for collaborative, embodied visualization of biomolecules. To achieve this interaction with targeted for hand on visualizations allowing for biomolecular exploration and scientific visualization within immersive augmented reality platforms. We anticipate a tool where components can assist both in (1) biomolecule discovery and design and a subset applicable for (2) education in biomolecules. We conducted some formative research to analyze user value and requirement. For the prototype we focused on the visualization of DNA binding protein, called Zip Proteins. These proteins are transcription factors. This system is implemented across two devices that support AR capabilities: head mount display (HMD) and the mobile phone. Key development is the porting of these molecules to immersive augmented reality environment for direct interaction. Describing the advantage of the platform for this application at the broadest level, we can say that augmented reality platforms allow for full embodied interaction with the structures at any scale and contextualized by the physical background. We also discuss future plans for this platform.
Hyperdoctrines and the Ontology of Stratified Semantics

I present a version of Kit Fine’s stratified semantics for the logic RWQ and define a natural family of related structures called RW hyperdoctrines. After proving that RWQ is sound with respect to RW hyperdoctrines, we show how to construct, for each stratified model, a hyperdoctrine that verifies precisely the same sentences. Completeness of RWQ for hyperdoctrinal semantics then follows from completeness for stratified semantics, which is proved in an appendix. By examining the base category of RW hyperdoctrines, we find reason to be worried about the ontology of stratified models.
Characterizing Performance Variation of Genomic Data Analysis Workflows on the Public Cloud

Public Infrastructure-as-a-Service (IaaS) clouds abstract the physical hardware implementation of resources provided to users. Users are not informed about the exact physical location of their virtual machines (VMs), the specific hardware used, the number of co-resident VMs they reside with, or the workloads that co-resident VMs are running. Detecting when VMs underperform can help identify resource contention from co-resident VMs to spur their replacement. In addition, resource utilization metrics may help classify performance of runs for use in VM performance model datasets that sample the distribution of performance outcomes. VM performance models are key to optimizing the cost of bioinformatics analyses in the public cloud. In this paper, we investigate performance variation of running big data genomics workflows in the public cloud. We examine causes of performance variation including VM provisioning, CPU heterogeneity, and resource contention. We leverage Amazon Elastic Compute Cloud placement groups, a feature designed to help influence VM placement on Amazon EC2 to help examine how VM placement impacts performance variation. As a use case, we investigate the performance of a multi-stage bioinformatics RNA sequencing (RNA-seq) analytical workflow consisting of four distinct phases, executing in ~90 minutes on average on 8-core public cloud VMs. In addition, we investigate whether Linux resource utilization metrics collected by profiling workflow runs can help identify performance variations.
Intelligent Wheelchair Control System Based on Finger Pose Recognition

In the old day, wheelchairs are moved manually by using hand or with the assistance of someone else. Users of this wheelchair get tired quickly if they have to walk long distances. The electric wheelchair emerged as a form of innovation and development for the manual wheelchair. This paper presented the control system of the electric wheelchair based on finger poses using the Convolutional Neural Network (CNN). The camera is used to take pictures of five-finger poses. Images are selected only in certain sections using Region of Interest (ROI). The five-finger poses represent the movement of the electric wheelchair to stop, right, left, forward, and backward. The experimental results indicated that the accuracy of the finger pose detection is about 93.6%. Therefore, the control system using CNN can be a potential solution for an electric wheelchair.