Reviewerly/exHarmony huggingface dataset:
Anti-forensics for Double JPEG Compression Based on Generative Adversarial Network

The JPEG standard is one of the most widely used image formats. If a JPEG image is tampered and saved in JPEG format again, it would lead to double JPEG compression (DJPEG). Therefore, DJPEG detection is a research hotspot in image forensics. On the other side, tamperer would try to perform anti-forensics operation on DJPEG image to avoid detection, but there are few related reports so far. In this paper, we propose a DJPEG anti-forensics method based on a generative adversarial network called AFDJ-GAN (Anti-Forensics for Double JPEG Compression with Generative Adversarial Network). On behalf of the anti-forensics side, the generator takes the double JPEG compressed image as input, and obtains the reconstructed image removed from compression artifacts, and uses simulated JPEG compression to convert it into generated simulated Single JPEG compressed (SJPEG) image. On behalf of the forensics side, the discriminator tries to distinguish between real SJPEG image and generated simulated SJPEG image by the feature extracted by the simulated histogram layer. Via alternant updating these two networks, the generator can learn to erase the DJPEG traces. The experimental results show that the proposed method has achieved state-of-the-art anti-forensics performance against different DJPEG detectors, and outperformed existing methods with regards to image quality.
Towards Robust Autonomous Coverage Navigation for Carlike Robots

Thanks to their high carrying capacity and strong maneuverability, carlike robots which move with non-holonomic constraints, are frequently utilized in numerous coverage operation fields. In such fields, the robots need to complete the coverage task via autonomous path planning and tracking, which is referred to as a “Coverage Navigation for Carlike Robots” (CNCR) problem. When it comes to CNCR, two folds of challenges are put forward as follows. On one hand, the local path planner should not only consider the non-holonomic constraints of carlike robots but also should meet the high coverage rate required by the coverage task. On the other hand, during CNCR, there must be many U-turns in narrow areas. As a result, Reeds-Shepp-type paths will be planned inevitably. Tracking this sort of multi-segment paths stably is also an urgent problem to be solved in CNCR. To this end, this letter introduces a reference line constraint and a heuristic initialization strategy along with a cubic parametric path smoother to the time elastic band planner and designs a multi-segment-supported path tracker. Experimental results show that the proposed path planner largely reduces the average deviation off the reference line and obtains a high success rate even in complex environments. Besides, the designed path tracker achieves stable and high-precision tracking for multi-segment paths. Real-world experiments reveal that the proposed path planner and tracker are fully competent for CNCR, rendering our sweeping robot to attain an actual average coverage rate of 92.21%. To make our results fully reproducible, relevant codes and data are made available online at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/csLinZhang/cncr.git</uri> .
Assessment of Heavy Metals in Mine Soil and Tailings from Jos, Nigeria

Abstract Jos is a mining town in Nigeria; mining activities has been going on in the area for more than a 100 years. Although, the mining and other related activities have brought about economic, social growth and infrastructural development in the area. Unfortunately, mining activities has been linked to the enhance in the level of heavy metals in the soil and other geological formations in the environment. To ascertain the heavy metals (HM) burden in the soil due to tin mining activities at Rayfield-Du area of Jos, HM concentrations were evaluated in soil samples collected from the mine site. The HM concentrations in the soils were evaluated using the Energy Dispersive X-ray Florescence (EDXRF) spectrometer system. The analysis of eight HM (Cr, Co, Ni, Cu, Zn, As, Cd, and Pb) concentrations showed that they are above the Nigerian reference level except for Co and Ni. The pollution and ecological risk indices of the HM concentrations were classified as low for the mine soils.
Insincere Question Classification by Deep Neural Networks

People nowadays search for answering the questions on Q&A platforms online such as Zhihu and Quora. As many rely on these platforms, filtering controversial questions, including but not limited to hate speeches and online racism, is particularly important. While human resources are too scarce, using Artificial Intelligence to filter out some disputable and insulting questions is essential. In this work, we propose a deep learning-based classification method to analyze the sincerity of questions from Quora and achieve an overall 95.25% accuracy.
Generative and Autoencoder Models for Large-Scale Mutivariate Unsupervised Anomaly Detection

Anomaly detection is a major problem that has been well studied in various fields of research and fields of application. In this paper, we present several methods that can be built on existing deep learning solutions for unsupervised anomaly detection, so that outliers can be separated from normal data in an efficient manner. We focus on approaches that use generative adversarial networks (GAN) and autoencoders for anomaly detection. By using these deep anomaly detection techniques, we can overcome the problem that we need to have a large-scale anomaly data in the learning phase of a detection system. So, we compared various methods of machine based and deep learning anomaly detection with its application in various fields. This article used seven available datasets. We report the results on anomaly detection datasets, using performance metrics, and discuss their performance on finding clustered and low density anomalies.
Feature-based Method for Nurse Care Complex Activity Recognition from Accelerometer Sensor

As the number of patients in hospitals is increasing day by day, proper monitoring and hospital care towards patients must be ensured. In this regard, Nurse care activity recognition can play a significant role in improving the existing healthcare system. Research in this domain is very challenging because nursing activities are very complex and troublesome than other normal activities. Nursing activities are dependent not only on nurses but also on the patients’ various states of illness. As a result, each activity has a high intra-class variation. We have participated in ’3rd Nurse Care Activity Recognition Challenge 2021’ and proposed a simple machine learning approach to recognize nursing activities. After data pre-processing and feature engineering, we have used several machine learning algorithms. Among them, we have achieved our best results in the Random Forest model. Using this model, We have obtained 72 percent validation accuracy classifying several challenging activities.
Design and Implementation of an Integrated Platform for Multi-language Methods Based on Javaweb

In view of the diversified characteristics of programming languages used by many current data processing algorithms, we have built an integrated platform based on Javaweb whose methods are developed by multiple programming languages. In this article, we discussed the application of some programming languages in data processing algorithms, and how to build a mixed programming environment of Java and other languages. By adopting Java and other multi-language mixed programming methods, we have carried out the design of the business logic layer of the integrated platform for the realization of multiple programming languages, which overcomes the lack of support of the Java programming language in data processing algorithm programming. It meets the needs of users who only need to use the integrated platform to quickly run different methods written in multiple languages. Application examples and results show that the method of using Java and other languages to build integrated Web projects is efficient and feasible.
OntoJudy: A Ontology Approach for Content-Based Judicial Recommendation Using Particle Swarm Optimisation and Structural Topic Modelling

Under the history of the Judicial Reform of India, major data of judicial cases are commonly used to address the issue of judicial study. Similarity review in legal trials is the foundation of wisdom judicature. The analysis of the Indian Judicial cases in a established format is a central concern by giving necessity for eradicating incompetent information extracting appropriate rules and conditions from the vivid document. Hence, this paper proposes a method to recommend judicial cases to the user, based on the content of the cases. The proposed OntoJudy model uses Static Judicial Domain Ontology with Structural Topic Modelling. The semantic similarity is computed using Particle Swarm Optimization with Jaccard Similarity and SemantoSim. Hybridizing all these helps in yielding better accuracy. To assimilate users preferences for the content recommendations, the CAIL2018 dataset is used which is then classified using Random Forest Classification with the help of extracted query word from the user information. The proposed model has achieved an Accuracy of 95.89% and tends to do better than the other baseline models by attenuating the resilience of the traditional content recommender systems.
IoT Enabled Low Cost Distributed Angle Measurement Fault Detection System for LFR Plants

The advances in renewable solar energy generation have led to the development of Linear Fresnel Reflector (LFR) technology, in which solar energy is focused by means of a set of longitudinal mirrors onto a tubular receiver. The efficiency in the energy generation process is mainly given by the relation between the angle of incidence of incoming sunlight rays and the fixed location of the elevated tubular receiver. In order to maximize efficiency, the tilt angles for each one of the longitudinal mirrors should vary as a function of solar position tracking. The angular variation process is generally based on the use of encoders implementing a closed-loop feedback system to provide information to the controller in relation with actual mirror angular position. An alternative that has recently emerged is the use of inclinometers in order to obtain angular information, providing a cost-effective solution in order to employ angular information for mirror position control. In this work, a low cost monitoring system for LFR plants is presented, based on the use of a distributed configuration to obtain information related to mirror inclination angles. The proposed system is based on the use of accelerometers coupled to low cost wireless transceivers, enabling distributed real time data collection. The system allows fault or damage detection at all monitored mirror sections, avoiding long periods of efficiency loss of the LFR plant. The configuration and location of the wireless sensor nodes has been analyzed by means of deterministic 3D Ray Launching channel modeling, in order to optimize coverage/capacity relations within the scenario under test. The proposed system has been implemented enabling remote LFR facility monitoring by means of cloud-based infrastructure. In addition, the system is highly scalable, allowing the inclusion of other interesting applications such as intruder detection for security.
Improving classification of low-resource COVID-19 literature by using Named Entity Recognition

Automatic document classification for highly interrelated classes is a demanding task that becomes more challenging when there is little labeled data for training. Such is the case of the coronavirus disease 2019 (COVID-19) Clinical repository-a repository of classified and translated academic articles related to COVID-19 and relevant to the clinical practice-where a 3-way classification scheme is being applied to COVID-19 literature. During the 7th Biomedical Linked Annotation Hackathon (BLAH7) hackathon, we performed experiments to explore the use of named-entity-recognition (NER) to improve the classification. We processed the literature with OntoGene's Biomedical Entity Recogniser (OGER) and used the resulting identified Named Entities (NE) and their links to major biological databases as extra input features for the classifier. We compared the results with a baseline model without the OGER extracted features. In these proof-of-concept experiments, we observed a clear gain on COVID-19 literature classification. In particular, NE's origin was useful to classify document types and NE's type for clinical specialties. Due to the limitations of the small dataset, we can only conclude that our results suggests that NER would benefit this classification task. In order to accurately estimate this benefit, further experiments with a larger dataset would be needed.
Single-photon sources based on asymmetric spatial multiplexing with optimized inputs

We develop a statistical theory describing the operation of multiplexed single-photon sources equipped with photon-number-resolving detectors that includes the potential use of different input mean photon numbers in each of the multiplexed units. This theory accounts for all relevant loss mechanisms and allows for the maximization of the single-photon probabilities under realistic conditions by optimizing the different input mean photon numbers unit-wise and the detection strategy that can be defined in terms of actual detected photon numbers. We apply this novel description to analyze periodic single-photon sources based on asymmetric spatial multiplexing realized with general asymmetric routers. We show that optimizing the different input mean photon numbers results in maximal single-photon probabilities higher than those achieved by using optimal identical input mean photon numbers in this setup. We identify the parameter ranges of the system for which the enhancement in the single-photon probability for the various detection strategies is relevant. An additional advantage of the unit-wise optimization of the input mean photon numbers is that it can result in the decrease of the optimal system size needed to maximize the single-photon probability. We find that the highest single-photon probability that our scheme can achieve in principle when realized with state-of-the-art bulk optical elements is 0.935. This is the highest one to our knowledge that has been reported thus far in the literature for experimentally realizable single-photon sources.
Towards Unified Modeling for NoSQL Solution Based on Mapping Approach

Today NoSQL databases have become unavoidable that more and more companies are turning to these solutions. The variety of non-relational databases resides in their data storage structure to face the big amount of data and their modeling as well. Hence, our paper presents a unified schema modeling for NoSQL solutions based on a mapping approach. Indeed, NoSQL refers to a family of database management systems (DBMS) that departs from the traditional paradigm of relational databases. These systems remain efficient in a continuous scalability case. Among the NoSQL solutions our paper deals with Columns, Document and Graph oriented databases to test the feasibility of our proposed approach.
Success and Hindrance Factors of AHA-Oriented Open Service Platforms

In the past years, there has been a flourishing of platforms dedicated to Active Assisted Living (AAL) and Active and Healthy Ageing (AHA). Most of them feature as their core elements intelligent systems for the analysis of multisource and multimodal data coming from sensors of various nature inserted in suitable IoT ecosystems. While progress in signal processing and artificial intelligence has shown how these platforms may have a great potential in improving the daylife of seniors or frail subjects, there are still several technological and non-technological barriers that should be torn down before full uptake of the existing solutions. In this paper, we address specifically this issue describing the outcome and creation process of a methodology aimed at evaluating the successful uptake of existing platforms in the field of AHA. We propose a pathway (as part of an overarching methodology) to define and select for Key Performance Indicators (KPIs), taking into account an extensive amount of parameters related to success, uptake and evolution of platforms. For this, we contribute a detailed analysis structured along with the 4 main actions of mapping, observing, understanding, and defining. Our analysis focuses on Platforms, defined as operating environments, under which various applications, agents and intelligent services are designed, implemented, tested, released and maintained. By following the proposed pathway, we were able to define a practical and effective methodology for monitoring and evaluating the uptake and other success indicators of AHA platforms. Besides, by the same token, we were able to provide guidelines and best practices for the development of the next-generation platforms in the AHA domain.
The First Fifteen Years of the Verified Software Project

chapter Share on The First Fifteen Years of the Verified Software Project Authors: Peter Müller Search about this author , Natarajan Shankar Search about this author Authors Info & Claims Theories of Programming: The Life and Works of Tony HoareOctober 2021 Pages 93–124https://doi.org/10.1145/3477355.3477362Online:02 October 2021Publication History 1citation16DownloadsMetricsTotal Citations1Total Downloads16Last 12 Months16Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Policy invariant explicit shaping: an efficient alternative to reward shaping

Abstract Reinforcement learning (RL) is a powerful learning paradigm in which agents can learn to maximize sparse and delayed reward signals. Although RL has had many impressive successes in complex domains, learning can take hours, days, or even years of training data. A major challenge of contemporary RL research is to discover how to learn with less data. Previous work has shown that domain information can be successfully used to shape the reward; by adding additional reward information, the agent can learn with much less data. Furthermore, if the reward is constructed from a potential function, the optimal policy is guaranteed to be unaltered. While such potential-based reward shaping (PBRS) holds promise, it is limited by the need for a well-defined potential function. Ideally, we would like to be able to take arbitrary advice from a human or other agent and improve performance without affecting the optimal policy. The recently introduced dynamic potential-based advice (DPBA) was proposed to tackle this challenge by predicting the potential function values as part of the learning process. However, this article demonstrates theoretically and empirically that, while DPBA can facilitate learning with good advice, it does in fact alter the optimal policy. We further show that when adding the correction term to “fix” DPBA it no longer shows effective shaping with good advice. We then present a simple method called policy invariant explicit shaping (PIES) and show theoretically and empirically that PIES can use arbitrary advice, speed-up learning, and leave the optimal policy unchanged.
A novel data classification-based scheme for cloud data security using various cryptographic algorithms

Abstract Digital technologies had an effect on people's lives. The majority of these digital devices rely on cloud storage to meet their memory needs. Hundreds of thousands of images, videos, and audio files are being transferred to cloud storage. Thousands of people around the world access these media every second. Unauthorized access to these media must be avoided. One of the weak points for data breaches is the user-end encryption. This paper suggests a strategy for improving cloud data protection by combining the AES and blowfish encryption and decryption algorithms. AES-256 is used as the first layer, followed by blowfish as the second layer, in the hybrid solution. The output of the first layer is input to the second layer and the final result is analyzed. The proposed method also discusses other combined approaches such as AES with other traditional algorithms but the proposed method gives significant results compared to other approaches.
Classical Machine Learning Approach for Human Activity Recognition Using Location Data

The Sussex-Huawei Locomotion-Transportation (SHL) recognition Challenge 2021 was a competition to classify 8 different activities and modes of locomotion performed by three individual users. There were four different modalities of data (Location, GPS, WiFi, and Cells) which were recorded from the phones of the users in their hip position. The train set came from user-1 and the validation set and test set were from user-2 and user-3. Our team ’GPU Kaj Kore Na’ used only location modality to give our predictions in test set of this year’s competition as location data was giving more accurate predictions and the rest of the modalities were too noisy as well as not contributing much to increase the accuracy. In our method, we used statistical feature set for feature extraction and Random Forest classifier to give prediction. We got validation accuracy of 78.138% and a weighted F1 score of 78.28% on the SHL Validation Set 2021.
A Didactic Framework for Analyzing Learning Activities to Design InfoVis Courses

Data visualization is a powerful tool to cope with the demands of our current information age. In order to understand and be able to develop visualizations for specific use cases, data visualization activities (vis activities) have been proposed in recent years. These highly effective tools focus on practical relevance, reflection, and discussion in order to teach data visualization knowledge in a variety of contexts. However, the conscious selection of one or more vis activities for learners in comprehensive courses remains difficult. We aim to support this process by proposing a didactic vis framework. Based on Bloom's revised learning taxonomy, we decompose vis activities into distinct learning activities with their specific learning goals. By assigning the learning goals to the cognitive process and knowledge dimensions, a didactic course structure can be planned and evaluated. To demonstrate this didactic vis framework, we conducted several workshops based on an existing interface construction kit.
Real-Time Text &amp; Speech Translation Using Sequence To Sequence Approach

The following paper discusses the process of research and development of a realtime application that helps people from different linguistics to interact with each other fluently without any language barriers. We aim to improve the current traditional 3-tiered convolutional architecture of translation and propose a sequence-to-sequence improvisation without relying on the intermediate text representation length. We propose an application where there is availability for chat/audio/video translations for the users.