Reviewerly/exHarmony huggingface dataset:
Design and Comparative Analysis of New Personalized Recommender Algorithms with Specific Features for Large Scale Datasets

Nowadays, because of the tremendous amount of information that humans and machines produce every day, it has become increasingly hard to choose the more relevant content across a broad range of choices. This research focuses on the design of two different intelligent optimization methods using Artificial Intelligence and Machine Learning for real-life applications that are used to improve the process of generation of recommenders. In the first method, the modified cluster based intelligent collaborative filtering is applied with the sequential clustering that operates on the values of dataset, user′s neighborhood set, and the size of the recommendation list. This strategy splits the given data set into different subsets or clusters and the recommendation list is extracted from each group for constructing the better recommendation list. In the second method, the specific features-based customized recommender that works in the training and recommendation steps by applying the split and conquer strategy on the problem datasets, which are clustered into a minimum number of clusters and the better recommendation list, is created among all the clusters. This strategy automatically tunes the tuning parameter λ that serves the role of supervised learning in generating the better recommendation list for the large datasets. The quality of the proposed recommenders for some of the large scale datasets is improved compared to some of the well-known existing methods. The proposed methods work well when λ = 0.5 with the size of the recommendation list, |L| = 30 and the size of the neighborhood, |S| &lt; 30. For a large value of |S|, the significant difference of the root mean square error becomes smaller in the proposed methods. For large scale datasets, simulation of the proposed methods when varying the user sizes and when the user size exceeds 500, the experimental results show that better values of the metrics are obtained and the proposed method 2 performs better than proposed method 1. The significant differences are obtained in these methods because the structure of computation of the methods depends on the number of user attributes, λ, the number of bipartite graph edges, and |L|. The better values of the (Precision, Recall) metrics obtained with size as 3000 for the large scale Book-Crossing dataset in the proposed methods are (0.0004, 0.0042) and (0.0004, 0.0046) respectively. The average computational time of the proposed methods takes &lt;10 seconds for the large scale datasets and yields better performance compared to the well-known existing methods.
Synthesizing JIT Compilers for In-Kernel DSLs

Modern operating systems allow user-space applications to submit code for kernel execution through the use of in-kernel domain specific languages (DSLs). Applications use these DSLs to customize system policies and add new functionality. For performance, the kernel executes them via just-in-time (JIT) compilation. The correctness of these JITs is crucial for the security of the kernel: bugs in in-kernel JITs have led to numerous critical issues and patches. This paper presents JitSynth, the first tool for synthesizing verified JITs for in-kernel DSLs. JitSynth takes as input interpreters for the source DSL and the target instruction set architecture. Given these interpreters, and a mapping from source to target states, JitSynth synthesizes a verified JIT compiler from the source to the target. Our key idea is to formulate this synthesis problem as one of synthesizing a per-instruction compiler for abstract register machines. Our core technical contribution is a new compiler metasketch that enables JitSynth to efficiently explore the resulting synthesis search space. To evaluate JitSynth, we use it to synthesize a JIT from eBPF to RISC-V and compare to a recently developed Linux JIT. The synthesized JIT avoids all known bugs in the Linux JIT, with an average slowdown of $$1.82\times $$ in the performance of the generated code. We also use JitSynth to synthesize JITs for two additional source-target pairs. The results show that JitSynth offers a promising new way to develop verified JITs for in-kernel DSLs.
BRP-NAS: Prediction-based NAS using GCNs

Neural architecture search (NAS) enables researchers to automatically explore broad design spaces in order to improve efficiency of neural networks. This efficiency is especially important in the case of on-device deployment, where improvements in accuracy should be balanced out with computational demands of a model. In practice, performance metrics of model are computationally expensive to obtain. Previous work uses a proxy (e.g., number of operations) or a layer-wise measurement of neural network layers to estimate end-to-end hardware performance but the imprecise prediction diminishes the quality of NAS. To address this problem, we propose BRP-NAS, an efficient hardware-aware NAS enabled by an accurate performance predictor-based on graph convolutional network (GCN). What is more, we investigate prediction quality on different metrics and show that sample efficiency of the predictor-based NAS can be improved by considering binary relations of models and an iterative data selection strategy. We show that our proposed method outperforms all prior methods on NAS-Bench-101 and NAS-Bench-201, and that our predictor can consistently learn to extract useful features from the DARTS search space, improving upon the second-order baseline. Finally, to raise awareness of the fact that accurate latency estimation is not a trivial task, we release LatBench -- a latency dataset of NAS-Bench-201 models running on a broad range of devices.
Towards Interpretable Deep Learning Models for Knowledge Tracing

Driven by the fast advancements of deep learning techniques, deep neural network has been recently adopted to design knowledge tracing (KT) models for achieving better prediction performance. However, the lack of interpretability of these models has painfully impeded their practical applications, as their outputs and working mechanisms suffer from the intransparent decision process and complex inner structures. We thus propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models. Specifically, we focus on applying the layer-wise relevance propagation (LRP) method to interpret RNN-based DLKT model by backpropagating the relevance from the model's output layer to its input layer. The experiment results show the feasibility using the LRP method for interpreting the DLKT model's predictions, and partially validate the computed relevance scores. We believe it can be a solid step towards fully interpreting the DLKT models and promote their practical applications.
Development of CNN-based visual recognition air conditioner for smart buildings

Demand-driven heating, ventilation, and air conditioning (HVAC) operations have become very attractive in energy-efficient smart buildings. Demand-oriented HVAC control largely relies on accurate detection of building occupancy levels and locations. So far, existing building occupancy detection methods have their disadvantages, and cannot fully meet the expected performance. To address this challenge, this paper proposes a visual recognition method based on convolutional neural networks (CNN), which can intelligently interpret visual contents of surveillance cameras to identify the number of occupants and their locations in buildings. The proposed study can detect the quantity, distance, and angle of indoor human users, which is essential for controlling air-conditioners to adjust the direction and speed of air blow. Compared with the state of the art, the proposed method successfully fulfills the function of building occupant counting, which cannot be realized when using PIR, sound, and carbon dioxide sensors. Our method also achieves higher accuracy in detecting moving or stationary human bodies and can filter out false detections (such as animal pets or moving curtains) that are existed in previous solutions. The proposed idea has been implemented and collaboratively tested with air conditioners in an office environment. The experimental results verify the validity and benefits of our proposed idea.
Quantum evolution with a large number of negative decoherence rates

Abstract We analyze the evolution of open quantum systems governed by time-local master equations beyond the Markovian semigroup. Non-Markovian effects are usually attributed to the negativity of some decoherence rates in the time-dependent generator. For the qubit dynamics, it is well known that there can be one permanently negative rate in the so-called eternally non-Markovian evolution . We show that for qudits one can have ( d − 1) 2 out of the total d 2 − 1 rates that are always negative, and the evolution is still physically legitimate—that is, represented by a completely positive, trace-preserving dynamical map.
ROS based SLAM implementation for Autonomous navigation using Turtlebot

This paper presents the autonomous navigation of a robot using SLAM algorithm.The proposed work uses Robot Operating system as a framework.The robot is simulated in gazebo and Rviz used for data visualization.Gmapping package is used for mapping by utilizing laser and odometry data from various sensors.The Turtlebot provides open source software to perform navigation.
Towards evolving robust neural architectures to defend from adversarial attacks

Neural networks are known to misclassify a class of subtly modified images known as adversarial samples. Recently, numerous defences have been proposed against these adversarial samples; however, none have improved the robustness of neural networks consistently. Here, we propose to use adversarial samples as a function evaluation to explore for robust neural architectures that can resist such attacks. Experiments on existing neural architecture search algorithms from the literature reveal that although accurate, they are not able to find robust architectures. An essential cause for this lies in their confined search space. We were able to evolve an architecture that is intrinsically accurate on adversarial samples by creating a novel neural architecture search. Thus, the results here demonstrate that more robust architectures exist as well as opens up a new range of possibilities for the development and exploration of neural networks using neural architecture search.
A neural integrator model for planning and value-based decision making of a robotics assistant

Modern manufacturing and assembly environments are characterized by a high variability in the built process which challenges human–robot cooperation. To reduce the cognitive workload of the operator, the robot should not only be able to learn from experience but also to plan and decide autonomously. Here, we present an approach based on Dynamic Neural Fields that apply brain-like computations to endow a robot with these cognitive functions. A neural integrator is used to model the gradual accumulation of sensory and other evidence as time-varying persistent activity of neural populations. The decision to act is modeled by a competitive dynamics between neural populations linked to different motor behaviors. They receive the persistent activation pattern of the integrators as input. In the first experiment, a robot learns rapidly by observation the sequential order of object transfers between an assistant and an operator to subsequently substitute the assistant in the joint task. The results show that the robot is able to proactively plan the series of handovers in the correct order. In the second experiment, a mobile robot searches at two different workbenches for a specific object to deliver it to an operator. The object may appear at the two locations in a certain time period with independent probabilities unknown to the robot. The trial-by-trial decision under uncertainty is biased by the accumulated evidence of past successes and choices. The choice behavior over a longer period reveals that the robot achieves a high search efficiency in stationary as well as dynamic environments.
Assessment of impact of relaxation in lockdown and forecast of preparation for combating COVID-19 pandemic in India using Group Method of Data Handling

Ever since the outbreak of novel coronavirus in December 2019, lockdown has been identified as the only effective measure across the world to stop the community spread of this pandemic. India implemented a complete shutdown across the nation from March 25, 2020 as lockdown I and went on to extend it by giving timely partial relaxations in the form of lockdown II, III & IV. This paper statistically analyses the impact of relaxation during Lockdown III and IV on coronavirus disease (COVID) spread in India using the Group Method of Data Handling (GMDH) to forecast the number of active cases using time series analysis and hence the required medical infrastructure for the period of next six months. The Group Method of Data Handling is a novel self organized data mining technique with data driven adaptive learning capability which grasps the auto correlative relations between the samples and gives a high forecasting accuracy irrespective of the length and stochasticity of a time series. The GMDH model has been first validated and standardized by forecasting the number of active and confirmed cases during lockdown III-IV with an accuracy of 2.58% and 2.00% respectively. Thereafter, the number of active cases has been forecasted for the rest of 2020 to predict the impact of lockdown relaxation on spread of COVID-19 and indicate preparatory measures necessary to counter it.
Market share of the largest publishers in Journal Citation Reports based on journal price and article processing charge

Purpose: There are growing questions about the market share of the largest publishers. Although evaluations based on Journal Citation Reports (JCR) are important, librarians are more interested in journal costs. Therefore, this study was conducted with the aim of estimating the market share of the largest publishers listed in JCR using the journal subscription price (journal price) and article processing charge (APC). Methods: The top 10 publishers were selected based on six indicators in JCR 2014 to 2018, and then their journal prices and APCs were investigated according to list prices. Other prior studies were also compared to estimate their market share more realistically because list and actual prices are not identical. Results: The estimated average price of subscription journals in JCR was 2,300 US dollars and the average APC for an article was 2,652 US dollars. The APC per article was more expensive than the average journal price. Based on journal price and APC, the market influence of the top three publishers was 48.0%, but their market share was estimated to be 55.2% when annual reports and other studies were combined with this study. The difference was due to Elsevier’s journal costs, as Elsevier’s market share was higher than its market influence. Conclusion: APCs require additional budgetary resources from institutions, but are another revenue source for publishers. Librarians need to reflect APC spending in journal subscription negotiations with the largest publishers. To clarify the market share more accurately, it is necessary to share information on subscription and APC costs paid by institutions.
Bee inspired secured protocol for routing in cognitive radio ad hoc networks

Background: Enhancing security and minimizing the delay is a significant task present in all modern networks.Dynamic emerging problems like node failure, route failure, intrusions, and security attacks lead the network to failure.Hence, there exist needs for development of a protocol that detects the intrusions and find the better route to destination.Objectives: The main objectives of this research is to understand the issues and challenges in routing that rise dynamically in cognitive radio ad hoc network and propose a bio-inspired routing protocol to enhance security and routing efficiency which will result in reduced delay cum energy consumption.Methods: This study proposes Bee Inspired Secured Protocol (BISP) for routing in cognitive radio ad hoc networks that focuses on increasing the security before sending data packets and decreasing the overall delay.An instinctive characteristic of Bees towards searching for food is utilized to design the proposed routing protocol, which selects the better path to the destination.To enrich the security during data transmission, Rivest Shamir Adelman algorithm is applied.The proposed protocol analyzes the security level of the route and neighbor node energy level before sending the data.Findings: NS2.35 simulator used to evaluate the performance of BISP.Simulation results indicate that BISP has better performance than the existing protocol (i.e., WPIP and GRP) in terms of throughput, packet delivery ratio, reduced delay and enhanced security.Novelty : Comprehensive analysis indicates that BISP have superior performance in classifying the intruding nodes, enhancing the security of data getting transmitted, and reducing the delay.
High Capacity Reversible Data Hiding Based on the Compression of Pixel Differences

In this paper, we proposed a novel reversible data hiding method in encrypted image (RDHEI), which is based on the compression of pixel differences. In the proposed method, at the content owner’ side the image is divided into non-overlapping blocks, and a block-level image encryption scheme is used to generate the encrypted image, which partially retains spatial correlation in the blocks. Due to the spatial correlation, in each block the pixels are highly likely to be similar. Therefore, the pixel differences in all blocks are concentrated in a small range and can be compressed. By the compression of pixel differences, the data hider can vacate the room to accommodate secret data in the encrypted image without losing information. At the receiver’s side, the receiver can obtain secret data or retrieve the original image using different keys with no error. The experimental results demonstrate that, compared with existing methods, the proposed method can achieve a higher capacity and visual quality.
Progressive sample mining and representation learning for one-shot person re-identification

In this paper, we aim to tackle the one-shot person re-identification problem where only one image is labelled for each person, while other images are unlabelled. This task is challenging due to the lack of sufficient labelled training data. To tackle this problem, we propose to iteratively guess pseudo labels for the unlabelled image samples, which are later used to update the re-identification model together with the labelled samples. A new sampling mechanism is designed to select unlabelled samples to pseudo labelled samples based on the distance matrix, and to form a training triplet batch including both labelled samples and pseudo labelled samples. We also design an HSoften-Triplet-Loss to soften the negative impact of the incorrect pseudo label, considering the unreliable nature of pseudo labelled samples. Finally, we deploy an adversarial learning method to expand the image samples to different camera views. Our experiments show that our framework achieves a new state-of-the-art one-shot Re-ID performance on Market-1501 (mAP 42.7%) and DukeMTMC-Reid dataset (mAP 40.3%). Code is available on https://github.com/detectiveli/PSMA.
Empirical Evaluation of Pretraining Strategies for Supervised Entity Linking

In this work, we present an entity linking model which combines a Transformer architecture with large scale pretraining from Wikipedia links. Our model achieves the state-of-the-art on two commonly used entity linking datasets: 96.7% on CoNLL and 94.9% on TAC-KBP. We present detailed analyses to understand what design choices are important for entity linking, including choices of negative entity candidates, Transformer architecture, and input perturbations. Lastly, we present promising results on more challenging settings such as end-to-end entity linking and entity linking without in-domain training data.
Developing an Augmented Reality Application in an Undergraduate DNA Precipitation Experiment to Link Macroscopic and Submicroscopic Levels of Chemistry

An augmented reality (AR) application that complements a DNA precipitation experiment has been developed for use on mobile devices. The AR experience provides atomistic detail on the reasoning behind the experimental observations. The development of the application is described, as are the download data. In a pilot survey, the students were asked about their use or intended use of the application. More students used the application after the laboratory compared with before or during the laboratory. These results suggest that students prefer to use this type of technology as a reflective tool to consolidate their laboratory experience.
Integration of Internet of Things and blockchain toward portability and low‐energy consumption

Abstract The modern era of information and technology leverages connectivity and information sharing for enhancing applications and reducing human intervention. The proliferation of Internet of Things (IoT) in almost all aspects of modern life raises major challenges of security and privacy in health care services and the pharmaceutical industry. Blockchain has emerged as a secure technology in a trustless network without central governance. The inherent attributes it provides can solve the security and privacy challenges related to IoT and extend its capabilities. However, traditional instances of Blockchain are not suited for the IoT environment for reasons such as computationally expensive consensus process. Proof of authentication is a lightweight consensus algorithm that can be implemented in the IoT environment. This paper presents an IoT‐friendly Blockchain scheme implemented to evaluate the feasibility of medical supply and drug transportation to aid security and mitigate privacy issues which are fault‐tolerant, transparent, and traceable. The proof of concept implementation on portable single board computer demonstrates proof of authentication consensus algorithm executed with block validation and block addition in 30 and 40 ms while consuming 45 and 60mJ, respectively. Additionally, the network performance of the proposed architecture was presented and evaluated in the application context.
Analysis of Firefly–Fuzzy Hybrid Algorithm for Navigation of Quad-Rotor Unmanned Aerial Vehicle

In the present scenario for the development of the unmanned aerial vehicle (UAV), artificial intelligence plays an important role in path planning and obstacle detection. Due to different environments, it is always a task to achieve the proper moment for achieving the target goal while avoiding obstacles with minimum human interference. To achieve the goal with the avoidance of obstacles, individual optimization techniques with metaheuristic algorithms such as fuzzy, particle swarm optimization (PSO), etc. were implemented in various configurations. However, the optimal solution was not attained. Thus, in order to achieve an optimal solution, a hybrid model was developed by using the firefly algorithm and the fuzzy algorithm, establishing multiple features of the individual controller. The path and time optimization were achieved by a standalone controller and a hybrid firefly–fuzzy controller in different conditions, whereby the results of the controller were validated by simulation and experimental results, highlighting the advantages of the hybrid controller over the single controller.