Reviewerly/exHarmony huggingface dataset:
Dual Image-Based CNN Ensemble Model for Waste Classification in Reverse Vending Machine

A reverse vending machine motivates citizens to bring recyclable waste by rewarding them, which is a viable solution to increase the recycling rate. Reverse vending machines generally use near-infrared sensors, barcode sensors, or cameras to classify recycling resources. However, sensor-based reverse vending machines suffer from a high configuration cost and the limited scope of target objects, and conventional single image-based reverse vending machines usually make erroneous predictions about intentional fraud objects. This paper proposes a dual image-based convolutional neural network ensemble model to address these problems. For this purpose, we first created a prototype reverse vending machine and constructed an image dataset containing two cross-sections of objects, top and front view. Then, we chose convolutional neural network models widely used in image classification as the candidates for building an accurate and lightweight ensemble model. Considering the size and classification performance of candidates, we constructed the best-fit ensemble combination and evaluated its classification performance. The final ensemble model showed a classification accuracy higher than 95% for all target classes, including fraud objects. This result proves that our approach achieves better robustness against intentional fraud objects than single image-based models and thus can broaden the scope for target resources. The measurement results on lightweight embedded platforms also demonstrated that our model provides a short inference time that is enough to facilitate the real-time execution of reverse vending machines based on low-cost edge artificial intelligence devices.
Application of Neural ODE with embedded hybrid method for robotic manipulator control

The paper presents an approach to machine learning involving the use of a hybrid method in Neural ODE. The authors consider a way of the Neural ODE application with an embedded hybrid method for intelligent manipulator control in a non-deterministic environment. To reduce computational resources, the number of layers of the neural network using ODE is reduced, the solution of which is carried out by a hybrid transformation method. The hybrid method includes normalizing the system, linear transformation, and polynomial transformation. Using the hybrid method in Neural ODE can increase the learning and computation speed. The transformation method has high accuracy with low computational complexity of the algorithm. As a result of applying the hybrid method, the original nonlinear system is transformed to a simpler form. To solve the simplified transformed system, the scheme of the numerical Runge-Kutta method of the third order with an adaptive step is used. The results of the computational experiment show an increase in the speed and accuracy of the calculation. The presented approach to implementing the hybrid method in Neural ODE allows faster training of the neural network of the world model on limited data obtained from the real environment. After training on the data generated by the model of the world, a robotic arm always successfully reaches the object during subsequent interaction with the real environment. This solves the problem of the limited ability of the agent to interact when learning with the real environment in robotic systems.
Comparison of Models for Training Optical Matrix Multipliers in Neuromorphic PICs

We experimentally compare simple physics-based vs. data-driven neural-network-based models for offline training of programmable photonic chips using Mach-Zehnder interferometer meshes. The neural-network model outperforms physics-based models for a chip with thermal crosstalk, yielding increased testing accuracy.
Verification and Uncertainties in Self-integrating System

Self-integrating and self-improving system are required to verify their state in order to understand whether they have achieved their goal or need to adapt themselves to reach it. In this short position paper, we outline the main challenges specifically when verifying systems interacting with each other and operating under uncertainties. A short outline of the uncertainties is given as well as a brief roadmap to overcome the main challenges faced by autonomous interwoven systems, operating in an open world with incomplete knowledge.
Modeling on Steering Feedback Torque Based on Data-Driven Method

The steering feedback torque (SFT) is a key part of driving simulator and steer-by-wire system, which provides driver with desired road feel and vehicle motion dynamics. Therefore, accurately modeling of SFT is of great significance for driver to get better steering feel. Since SFT can be affected by many linear or nonlinear factors, it is appropriate to model SFT using data-driven method. In this article, we adopt artificial neural network (ANN) and Gaussian process regression (GPR) to build the SFT model, and analyze the performance. Considering the fact that the contributing factors for SFT may vary under different driving conditions, we employ K-Means to precluster the training dataset to improve the model accuracy. The model training and validation processes are mainly data-driven, and the results show that GPR and ANN can achieve similar prediction accuracy with the mean square error to be around 0.10. Since the GPR model can be trained much faster than ANN model, it is more suitable for real-time application. It is further demonstrated that using preclustered data based on K-Means for model training can significantly improve its accuracy without sacrificing its computational efficiency.
Sensor network based distributed state estimation for maneuvering target with guaranteed performances

This paper proposes a sensor network information fusion based distributed state estimation algorithm for tracking non-cooperative maneuvering target. In order to lower the communication burden and energy consumption, an event-triggered mechanism is introduced. The distributed state estimation algorithm for maneuvering target tracking is designed by two stages, namely, local filtering stage and consensus fusion stage. The algorithm proposed in this paper can be used to obtain the high accurate state estimation of the target by introducing a multiple suboptimal fading factor even when the target makes big maneuvering. Moreover, a contribution factor is designed in the information fusion stage to improve the accuracy of maneuvering target state estimation and reduce the consensus iteration times. Besides, the stochastic boundedness of the event-triggered distributed estimation algorithm is proved by introducing a stochastic process. Finally, Monte Carlo numerical simulation example is designed to illustrate the effectiveness of the algorithm.
Lung Cancer Diagnosis via Gabor Filters and Convolutional Neural Networks

In our study, we present a lung cancer diagnosis system to classify the cancerous and healthy lung images using the latest deep learning techniques. Most of the studies have been proposed so far are based on Convolutional Neural Networks (CNN). Our model uses CNN deep learning model which aims to increase the accuracy rate by passing the images through an image pre-processing stage before training them with deep learning. In this stage, Gabor Filter method was applied with different orientation values to the images. Then the modified images were given to the deep learning architecture. Gabor Filter transform was provided with the orientation values of 60 and 90 separately for comparison. The accuracy value in case the orientation is 60 is 96.48% and the loss rate is 23.69%. On the other hand the accuracy value in case the orientation is 90, is 98.37% and the loss rate is 8.35%. The high accuracy results of the proposed study could enable experts in the field to make more accurate and faster disease detection. Thus, it is expected that the patient treatment initiation process would be minimized.
Graph Co-Attentive Session-based Recommendation

Session-based recommendation aims to generate recommendations merely based on the ongoing session, which is a challenging task. Previous methods mainly focus on modeling the sequential signals or the transition relations between items in the current session using RNNs or GNNs to identify user’s intent for recommendation. Such models generally ignore the dynamic connections between the local and global item transition patterns, although the global information is taken into consideration by exploiting the global-level pair-wise item transitions. Moreover, existing methods that mainly adopt the cross-entropy loss with softmax generally face a serious over-fitting problem, harming the recommendation accuracy. Thus, in this article, we propose a Graph Co-Attentive Recommendation Machine (GCARM) for session-based recommendation. In detail, we first design a Graph Co-Attention Network (GCAT) to consider the dynamic correlations between the local and global neighbors of each node during the information propagation. Then, the item-level dynamic connections between the output of the local and global graphs are modeled to generate the final item representations. After that, we produce the prediction scores and design a Max Cross-Entropy (MCE) loss to prevent over-fitting. Extensive experiments are conducted on three benchmark datasets, i.e., Diginetica, Gowalla, and Yoochoose. The experimental results show that GCARM can achieve the state-of-the-art performance in terms of Recall and MRR, especially on boosting the ranking of the target item.
Distributed Intelligence for Online Situational Awareness in Power Grids

This paper presents a suite of analytics that are proposed to be embedded in next-generation smart sensors in electric power grids. The proposed analytics take the electrical signals as the input and unlock the full potential in signal processing and machine learning for real-time event detection and classification. Meanwhile, a robust synchrophasor estimation mechanism is housed within the proposed sensor technology that will be triggered following a detected event and guides on the adaptive selection of the best-fit (most accurate) synchrophasor estimation algorithms at all times. Embedding such analytics within the sensor and closer to where the waveforms are captured, the proposed distributed intelligence solution technology mitigates the potential risks to communication failures and latencies as well as malicious cyber threats. Our experiments demonstrate that the introduced scheme achieves improved quality of measurements with a promising event detection and classification accuracy, collectively resulting in enhanced online situational awareness in modern power grids.
Scheduling Massive Camera Streams to Optimize Large-Scale Live Video Analytics

In smart cities, more and more government departments will make use of live analytics of videos from surveillance cameras in their tasks, such as vehicle traffic monitoring and criminal detection. Obviously, it is costly for each individual department to deploy its own infrastructure, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , cameras and analytics system. In this paper, we consider a scenario in which a city deploys an infrastructure and departments submit requests to access and analyze videos for their own purposes. The live analytics of massive streams is computation-intensive and the tasks might be latency-critical, which makes scheduling massive streams to optimize all tasks an essential and challenging work. We exploit an end-edge-cloud architecture and propose an adaptive system to schedule the massive camera streams and tasks, which considers all factors affecting the computation and networking resource consumption, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , sharing of model computation, video quality, model partition, and task placement. Particularly, the resource consumption of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Faster R-CNN + ResNet101</i> under each partition scheme is profiled for the first time and we notice the partition must be used together with lossless compression techniques to be beneficial. Furthermore, sometimes tasks might be required to migrate because the scheduling decision made by the system changes to adapt to the changing resource supply and demand. In order to avoid the performance degradation during migration, we propose a non-destructive migration scheme and implement it in the system. Simulations demonstrate our system achieves a total utility close to the maximum and our analytics system performs better than state-of-the-art solutions.
Visual Entity Linking via Multi-modal Learning

Abstract Existing visual scene understanding methods mainly focus on identifying coarse-grained concepts about the visual objects and their relationships, largely neglecting fine-grained scene understanding. In fact, many data-driven applications on the Web (e.g., news-reading and e-shopping) require accurate recognition of much less coarse concepts as entities and proper linking them to a knowledge graph (KG), which can take their performance to the next level. In light of this, in this paper, we identify a new research task: visual entity linking for fine-grained scene understanding. To accomplish the task, we first extract features of candidate entities from different modalities, i.e., visual features, textual features, and KG features. Then, we design a deep modal-attention neural network-based learning-to-rank method which aggregates all features and maps visual objects to the entities in KG. Extensive experimental results on the newly constructed dataset show that our proposed method is effective as it significantly improves the accuracy performance from 66.46% to 83.16% compared with baselines.
Gender Identification From Community Question Answering Avatars

There are several reasons why gender recognition is vital for online social networks such as community Question Answering (cQA) platforms. One of them is progressing towards gender parity across topics as a means of keeping communities vibrant. More specifically, this demographic variable has shown to play a crucial role in devising better user engagement strategies. For instance, by kindling the interest of their members for topics dominated by the opposite gender. However, in most cQA websites, the gender field is neither mandatory nor verified when submitting and processing enrollment forms. And as might be expected, it is left blank most of the time, forcing cQA services to infer this demographic information from the activity of their users on their platforms such as prompted questions, answers, self-descriptions and profile images. There is only a handful of studies dissecting automatic gender recognition across cQA fellows, and as far as we know, this work is the first effort to delve into the contribution of their profile pictures to this task. Since these images are an unconstrained environment, their multifariousness poses a particularly difficult and interesting challenge. With this mind, we assessed the performance of three state-of-art image processing techniques, namely pre-trained neural network models. In a nutshell, our best configuration finished with an accuracy of 81.68% (Inception-ResNet-50), and its corresponding Grad-Cam maps unveil that one of its principal focus of attention is determining silhouettes edges. All in all, we envisage that our findings are going to play a fundamental part in the design of efficient multi-modal strategies.
The State of the SameSite: Studying the Usage, Effectiveness, and Adequacy of SameSite Cookies

Chromium-based browsers now restrict cookies’ scope to a same-site context by changing the default policy for cookies, thus requiring developers to adapt their websites. The extent of the adoption and effectiveness of the SameSite policy has not been studied yet, and, in this paper, we undertake one of the first evaluations of the state of the SameSite cookie policy. We conducted a set of large-scale, longitudinal, both automated and manual measurements of the Alexa top 1K, 10K, 100K, and 500K sites across the main rollout dates of the SameSite policies, covering both SameSite usage and cross-site functionality breakage caused by the new default policy. Also, we performed an extensive evaluation of threats against the new Lax-by-default policy’s effectiveness, looking at the adequacy of the coverage provided by the Lax policy and bypass caused by website developers’ mistakes.Our study shows that the growth of sites using a SameSite policy has slowed down considerably after the enforcement dates. Then, the new Lax-by-default policy has affected about 19% of the functionalities implemented via cross-site requests without an explicit SameSite policy, most of which are for online ads. Third, our study observes a significant mismatch between the request contexts covered by Lax and the ones actually used by websites in the wild, making it possible to perform XS attacks also against popular websites such as Tumblr, Twitch, SoundCloud, Mailchimp, and Pixiv. Even when using Lax or Strict policies, much of their effectiveness depends on developers’ awareness of SameSite policies’ implications, who could introduce vulnerabilities or inconsistent policies, leading to SameSite policy bypasses. For example, we identified bypass in IMDB, Paypal, and Meetup. Also, we discovered a widespread SSO IdP abuse that attackers could use to attack target websites even when using stricter SameSite policies. Finally, in this paper, we also look at SameSite implementations in popular browsers and the default configuration in web frameworks.
Contrastive Identification of Covariate Shift in Image Data

Identifying covariate shift is crucial for making machine learning systems robust in the real world and for detecting training data biases that are not reflected in test data. However, detecting covariate shift is challenging, especially when the data consists of high-dimensional images, and when multiple types of localized covariate shift affect different subspaces of the data. Although automated techniques can be used to detect the existence of covariate shift, our goal is to help human users characterize the extent of covariate shift in large image datasets with interfaces that seamlessly integrate information obtained from the detection algorithms. In this paper, we design and evaluate a new visual interface that facilitates the comparison of the local distributions of training and test data. We conduct a quantitative user study on multi-attribute facial data to compare two different learned low-dimensional latent representations (pretrained ImageNet CNN vs. density ratio) and two user analytic workflows (nearest-neighbor vs. cluster-to-cluster). Our results indicate that the latent representation of our density ratio model, combined with a nearest-neighbor comparison, is the most effective at helping humans identify covariate shift.
Organizing subject access to cultural heritage in Swedish online museums

Purpose The study aims to paint a representative picture of the current state of search interfaces of Swedish online museum collections, focussing on search functionalities with particular reference to subject searching, as well as the use of controlled vocabularies, with the purpose of identifying which improvements of the search interfaces are needed to ensure high-quality information retrieval for the end user. Design/methodology/approach In the first step, a set of 21 search interface criteria was identified, based on related research and current standards in the domain of cultural heritage knowledge organization. Secondly, a complete set of Swedish museums that provide online access to their collections was identified, comprising nine cross-search services and 91 individual museums' websites. These 100 websites were each evaluated against the 21 criteria, between 1 July and 31 August 2020. Findings Although many standards and guidelines are in place to ensure quality-controlled subject indexing, which in turn support information retrieval of relevant resources (as individual or full search results), the study shows that they are not broadly implemented, resulting in information retrieval failures for the end user. The study also demonstrates a strong need for the implementation of controlled vocabularies in these museums. Originality/value This study is a rare piece of research which examines subject searching in online museums; the 21 search criteria and their use in the analysis of the complete set of online collections of a country represents a considerable and unique contribution to the fields of knowledge organization and information retrieval of cultural heritage. Its particular value lies in showing how the needs of end users, many of which are documented and reflected in international standards and guidelines, should be taken into account in designing search tools for these museums; especially so in subject searching, which is the most complex and yet the most common type of search. Much effort has been invested into digitizing cultural heritage collections, but access to them is hindered by poor search functionality. This study identifies which are the most important aspects to improve.
The Impact of Blockchain Technology on Human Resource Management

Blockchain technology could be extended to benefit human resource (HR) practices. With the lack of definitions and best practices in the literature on the applicability of blockchain in HR, there is a need to define the terms related to blockchain technology and its possible applications in HR. In addition, it is also important to address the downside and dark side of adopting blockchain in HR practices. This chapter aims to review the developing literature on blockchain technology, relate concepts to HR practices, present a model for applying blockchain technology in transforming HR practices, and explain the limitations and disadvantages of using blockchain technology in HR management. The chapter also contains two cases that illustrate the applicability issues of blockchain technology based upon the USA's credit score systems and Turkey's e-government application.