Reviewerly/exHarmony huggingface dataset:
Domestic Trash Classification with Transfer Learning Using VGG16

Environmental contamination is a major issue affecting all inhabitants living in any environment. The domestic environment is engulfed with many trash items such as solid and toxic trashes, leading to severe environmental contamination and causing life-threatening diseases if not appropriately managed. Trash classification is at the heart of these issues because the inability to classify the trash leads to difficulty in recycling. Humans categorize trash based on what they understand about the trash object rather than on the recyclability status of an object, which frequently leads to incorrect classification in manual classification. Additionally, coming into contact with toxic waste directly could be physically dangerous for those involved. Few machine learning and Deep Learning (DL) techniques were proposed using benchmarked trash classification datasets. However, most benchmarked datasets used to train DL models have a transparent or white background, which leads to a lack of model generalization, particularly in the real world. In this paper, we propose a Deep Learning model based on the VGG16 Architecture that can accurately classify various types of trash objects. On the TrashNet dataset plus the images collected in the wild, we achieved an accuracy of more than 96%.
STAR

Threshold aggregation reporting systems promise a practical, privacy-preserving solution for developers to learn how their applications are used "\emph{in-the-wild}". Unfortunately, proposed systems to date prove impractical for wide scale adoption, suffering from a combination of requiring: \emph{i)} prohibitive trust assumptions; \emph{ii)} high computation costs; or \emph{iii)} massive user bases. As a result, adoption of truly-private approaches has been limited to only a small number of enormous (and enormously costly) projects. In this work, we improve the state of private data collection by proposing $\mathsf{STAR}$, a highly efficient, easily deployable system for providing cryptographically-enforced $\kappa$-anonymity protections on user data collection. The $\mathsf{STAR}$ protocol is easy to implement and cheap to run, all while providing privacy properties similar to, or exceeding the current state-of-the-art. Measurements of our open-source implementation of $\mathsf{STAR}$ find that it is $1773\times$ quicker, requires $62.4\times$ less communication, and is $24\times$ cheaper to run than the existing state-of-the-art.
Poster

Graph Neural Networks (GNNs) have achieved impressive results in various graph learning tasks. They have found their way into many applications, such as fraud detection, molecular property prediction, or knowledge graph reasoning. However, GNNs have been recently demonstrated to be vulnerable to backdoor attacks. In this work, we explore a new kind of backdoor attack, i.e., a clean-label backdoor attack, on GNNs. Unlike prior backdoor attacks on GNNs in which the adversary can introduce arbitrary, often clearly mislabeled, inputs to the training set, in a clean-label backdoor attack, the resulting poisoned inputs appear to be consistent with their label and thus are less likely to be filtered as outliers. The initial experimental results illustrate that the adversary can achieve a high attack success rate (up to 98.47%) with a clean-label backdoor attack on GNNs for the graph classification task. We hope our work will raise awareness of this attack and inspire novel defenses against it.
Internet of Things Enabled Energy Aware Metaheuristic Clustering for Real Time Disaster Management

Wireless Sensor Networks (WSNs) are a major element of Internet of Things (IoT) networks which offer seamless sensing and wireless connectivity.Disaster management in smart cities can be considered as a safety critical application.Therefore, it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted WSN.Clustering and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT networks.This article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management (EAMCR-RTDM).The proposed EAMCR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration of the features of the disaster region.To achieve this, EAMCR-RTDM technique primarily designs a yellow saddle goatfish based clustering (YSGF-C) technique to elect cluster heads (CHs) and organize clusters.In addition, enhanced cockroach swarm optimization (ECSO) based multihop routing (ECSO-MHR) approach was derived for optimal route selection.The YSGF-C and ECSO-MHR techniques compute fitness functions using different input variables for achieving improved energy efficiency and network lifetime.The design of YSGF-C and ECSO-MHR techniques for disaster management in IoT networks shows the novelty of the work.For examining the improved outcomes of the EAMCR-RTDM system, a wide range of simulations were performed and the extensive results are assessed in terms of different measures.The comparative outcomes highlighted the enhanced outcomes of the EAMCR-RTDM algorithm over the existing approaches.
Leveraging Readability and Sentiment in Spam Review Filtering Using Transformer Models

Online reviews significantly influence decision-making in many aspects of society.The integrity of internet evaluations is crucial for both consumers and vendors.This concern necessitates the development of effective fake review detection techniques.The goal of this study is to identify fraudulent text reviews.A comparison is made on shill reviews vs. genuine reviews over sentiment and readability features using semi-supervised language processing methods with a labeled and balanced Deceptive Opinion dataset.We analyze textual features accessible in internet reviews by merging sentiment mining approaches with readability.Overall, the research improves fake review screening by using various transformer models such as Bidirectional Encoder Representation from Transformers (BERT), Robustly Optimized BERT (Roberta), XLNET (Transformer-XL) and XLM-Roberta (Cross-lingual Language model-Roberta).This proposed research extracts and classifies features from product reviews to increase the effectiveness of review filtering.As evidenced by the investigation, the application of transformer models improves the performance of spam review filtering when related to existing machine learning and deep learning models.
An online collision-free trajectory generation algorithm for human–robot collaboration

The premise of human–robot collaboration is that robots have adaptive trajectory planning strategies in hybrid work cell. The aim of this paper is to propose a new online collision avoidance trajectory planning algorithm for moderate dynamic environments to insure human safety when sharing collaborative tasks. The algorithm contains two parts: trajectory generation and local optimization. Firstly, based on empirical Dirichlet Process Gaussian Mixture Model (DPGMM) distribution learning, a neural network trajectory planner called Collaborative Waypoint Planning network (CWP-net) is proposed to generate all key waypoints required for dynamic obstacle avoidance in joint space according to environmental inputs. These points are used to generate quintic spline smooth motion trajectories with velocity and acceleration constraints. Secondly, we present an improved Stochastic Trajectory Optimization for Motion Planning (STOMP) algorithm which locally optimizes the generated trajectories of CWP-net by constraining the trajectory optimization range and direction through the DPGMM model. Simulations and real experiments from an industrial use case of human–robot collaboration in the field of aircraft assembly testing show that the proposed algorithm can smoothly adjust the nominal path online and effectively avoid collisions during the collaboration.
Deep neural network incorporating domain and resolution transformations model for histopathological image classification

• Present a combination of heterogeneous and homogeneous transfer learning. • Novel transfer learning by incorporating domain and resolution transformations. • Automatic classification at different magnifications of the BreaKHis dataset. • Classifying the breast abnormality into benign and malignant cancers. To accurately diagnose breast cancer, pathologists take biopsy and perform microscopic examination. However, this procedure is inconvenient, time-consuming, and requires high cost. To overcome these challenges, a deep neural network is used to offer a computer-based observation to categorize breast cancer as benign or malignant. In this study, the binary classification of breast cancer from histopathological images is proposed using multistage transfer learning incorporating domain and resolution transformations. We trained our models with a publicly available BreaKHis dataset. We modified and fine-tuned the well-known architectures, VGG19, ResNet (50 and 101), InceptionV3, Xception, Inception-ResNetV2, and NASNet-Large to classify 40 × magnification histopathology images as benign or malignant, then transferred the pre-trained weights for 100 × , 200 × , and 400 × magnification classifications. Translation of the proposed CNN based multistage transfer learning to vision transformer was also performed. Our approach achieved a remarkable performance by demonstrating a test accuracy of 93.97%100% and an F 1-score of 0.911.00. The results from our study depict the use of multistage transfer learning improves the breast histopathological images classification performance. Therefore, the proposed approach might be applied in a clinical setting to offer improved breast cancer diagnosis.
Characterizing and orchestrating VM reservation in geo-distributed clouds to improve the resource efficiency

Cloud providers often build a geo-distributed cloud from multiple datacenters in different geographic regions, to serve tenants at different locations. The tenants that run large scale applications often reserve resources based on their peak loads in the region close to the end users to handle the ever changing application load, wasting a large amount of resources. We therefore characterize the VM request patterns of the top tenants in our production public geo-distributed cloud, and open-source the VM request traces in four months from the top 20 tenants of our cloud. The characterization shows that the resource usage of large tenants has various temporal and spatial patterns on the dimensions of time series, regions, and VM types, and has the potential of peak shaving between different tenants to further reduce the resource reservation cost. Based on the findings, we propose a resource reservation and VM request scheduling scheme named ROS to minimize the resource reservation cost while satisfying the VM allocation requests. Our experiments show that ROS reduces the overall deployment cost by 75.4% and the reservation resources by 60.1%, compared to the tenant-specified reservation strategy.
Pisces

Federated learning (FL) is typically performed in a synchronous parallel manner, and the involvement of a slow client delays the training progress. Current FL systems employ a participant selection strategy to select fast clients with quality data in each iteration. However, this is not always possible in practice, and the selection strategy has to navigate a knotty tradeoff between the speed and the data quality.
Towards an Initial Understanding of the Design of Playful Water Experiences Through Flotation

Human-Computer Interaction (HCI) researchers are increasingly captivated by water interactions and hence explored interactive devices to support aquatic activities in different settings (e.g., mixed realities in water parks). However, our understanding of the user experience in interacting with water and technology is still underdeveloped. To begin closing this gap, we explore flotation tanks as a water setting for playful interactive experiences. The goal of the associated somaesthetic design approach was to sensitize the body of the designer (the first author) by engaging with her experiences interacting with water and create meaningful interactions. This preliminary work presents four different user experiences that can facilitate play through a defamiliarization analysis of water interaction with the body mediated by technology. We offer HCI insights for design researchers interested in creating playful experiences in water settings, while also providing industry with initial strategies on how to enrich flotation tank sessions.
Towards Understanding the Design of Body-Actuated Play

Bodily games often use players’ physiology as input to provide output via screen-based modalities. Game design researchers could extend the use of the body as input and output (I/O) by using body-actuating technologies such as Electrical Muscle Stimulation (EMS). EMS works by passing a small amount of electricity via electrodes attached to the player’s body, contracting their muscles to actuate involuntary body movements. Our work explores this bodily I/O by creating three "body-actuated play" systems ranging from single-player to social game experiences. Ultimately, by studying the associated user experiences of these systems, we will deduce a prescriptive design framework for designing bodily games in which humans can use their bodies as input and output.
Counteracting software integrity attacks on IoT devices with remote attestation: a prototype

Internet of Things (IoT) devices are increasingly deployed nowadays in various security-sensitive contexts, e.g., inside homes or in critical infrastructures. The data they collect is of interest to attackers as it may reveal living habits, personal data, or the operational status of specific targets. This paper presents an approach to counter software manipulation attacks against running processes, data, or configuration files on an IoT device, by exploiting trusted computing techniques and remote attestation. We have used a Raspberry Pi 4 single-board computer device equipped with Infineon Trusted Platform Module (TPM) v2, acting as an attester. A verifier node continuously monitors the attester and checks its integrity through remote attestation protocol and TPM-enabled operations. We have exploited the Keylime framework from MIT Lincoln Laboratories as remote attestation software. Through tests, we show that remote attestation can be performed within short time (in order of seconds), allowing to restrict the window of exposure of such devices to attacks against the running software and/or hosted data.
Leakage Inversion

Searchable encryption (SE) provides cryptographic guarantees that a user can efficiently search over encrypted data while only disclosing patterns about the data, also known as leakage. Recently, the community has developed leakage-abuse attacks that shed light on what an attacker can infer about the underlying sensitive information using the aforementioned leakage. A glaring missing piece in this effort is the absence of a systematic and rigorous method that quantifies the privacy guarantees of SE.
Assessment of academic achievements in m-learning

The paper analyzes the teaching experiment regarding the use of mobile apps in the assessment of student learning. The study involved survey and self-description methods offered to students and instructors to assess the effectiveness of using mobile apps in learning. The study was conducted among 320 s-year students of the I.M. Sechenov First Moscow State Medical University and 8 instructors between September 1 and December 28, 2021, during the academic semester. Socrative and Plickers apps have been found to contribute to: better learning (φ* = 8.94, p ≤ 0.01); developing cognitive motivation among students resulting from diverse learning resources used with apps (φ* = 7.6, p ≤ 0.01); convenience of learning activities because students can work with the app both in the classroom and at home (φ* = 13.66, p ≤ 0.01); improved digital competence (φ* = 2.4, p ≤ 0.05). The research findings can be used by instructors in universities and secondary schools. Since didactic opportunities offered by mobile apps are quite significant, and considering other positive results of their use, this technology may be considered as an important tool for training young people and strengthening their cognitive motivation.
Machine Learning Based Approach for Automated Cervical Dysplasia Detection Using Multi-Resolution Transform Domain Features

Pattern detection and classification of cervical cell dysplasia can assist with diagnosis and treatment. This study aims to develop a computational model for real-world applications for cervical dysplasia that has the highest degree of accuracy and the lowest computation time. Initially, an ML framework is created, which has been trained and evaluated to classify dysplasia. Three different color models, three multi-resolution transform-based techniques for feature extraction (each with different filters), two feature representation schemes, and two well-known classification approaches are developed in conjunction to determine the optimal combination of “transform (filter) ⇒ color model ⇒ feature representation ⇒ classifier”. Extensive evaluations of two datasets, one is indigenous (own generated database) and the other is publicly available, demonstrated that the Non-subsampled Contourlet Transform (NSCT) feature-based classification performs well, it reveals that the combination “NSCT (pyrexc,pkva), YCbCr, MLP” gives most satisfactory framework with a classification accuracy of 98.02% (average) using the F1 feature set. Compared to two other approaches, our proposed model yields the most satisfying results, with an accuracy in the range of 98.00–99.50%.
Multi-Guidance CNNs for Salient Object Detection

Feature refinement and feature fusion are two key steps in convolutional neural networks–based salient object detection (SOD). In this article, we investigate how to utilize multiple guidance mechanisms to better refine and fuse extracted multi-level features and propose a novel multi-guidance SOD model dubbed as MGuid-Net. Since boundary information is beneficial for locating and sharpening salient objects, edge features are utilized in our network together with saliency features for SOD. Specifically, a self-guidance module is applied to multi-level saliency features and edge features, respectively, which aims to gradually guide the refinement of lower-level features by higher-level features. After that, a cross-guidance module is devised to mutually refine saliency features and edge features via the complementarity between them. Moreover, to better integrate refined multi-level features, we also present an accumulative guidance module, which exploits multiple high-level features to guide the fusion of different features in a hierarchical manner. Finally, a pixelwise contrast loss function is adopted as an implicit guidance to help our network retain more details in salient objects. Extensive experiments on five benchmark datasets demonstrate our model can identify salient regions of an image more effectively compared to most of state-of-the-art models.
Retentive multimodal scale-variable anomaly detection framework with limited data groups for liquid rocket engine

Anomaly detection (AD) plays a significant role in the safe and stable operation of liquid rocket engine (LRE). Facing the fact that LRE operating data are in multiple modalities and extremely scarce, existing methods, which ignore the modal properties, cannot detect anomalies effectively. With a limited number of data groups, they hinder the AD during LRE research and development (R&D) especially in the early stage. A retentive multimodal scale-variable framework is proposed for AD with limited LRE operating data groups. Then the framework utilizes multimodal scale-variable structure to extract and fuse the distinct features of different modalities, which pays more attention to modal properties. To suppress the overfitting and get stronger generalization ability, the framework constructs a memory matrix to fuse it with test data at feature level, which has a retentive memory of samples encountered. To verify the effectiveness of this framework, three experimental cases are designed for research and discussion according to different stages of R&D process. Results show that this framework could recognize the state of LRE with F1-scores over 0.977 in three experimental cases, verifying it has reliable performance and strong potential of AD in the whole R&D cycle of LRE.
A Comprehensive Framework for Online Job Portals for Job Recommendation Strategies Using Machine Learning Techniques

The employment market in today’s modern society is growing increasingly active, which makes choosing a clear opportunity for yourself a difficult endeavor, particularly for newcomers who are unfamiliar with the numerous possible professions. As a result, the need for employment recommendation systems has been steadily increasing. Many systems employ suggestions to provide consumers with personalized solutions. By examining job recommendation articles, we are taking into account various machine learning algorithms as well as models provided in this study. The information in the student’s résumé is compared to the specifications of the job opportunities. Users’ abilities, knowledge, past previous employment, demographic data, as well as other necessary details are extracted from recommendation apps. The applicant is presented with fresh positions that are unrelated to the one being sought based on the extraction of information. We discovered that by using content-based filtering to unsupervised based on deep learning classification methods such as SVM, KNN, and randomized forest, the random forest approach delivers the highest outcomes for our applications. Python is used to construct the recommendation engine.
Graph pooling via Dual-view Multi-level Infomax

Graph pooling is an essential component to improve the representation ability of graph neural networks. Existing pooling methods typically select a subset of nodes to generate an induced subgraph as the representation of the entire graph. However, they ignore the potential value of augmented views and cannot exploit the multi-level dependencies between representations. To address these problems, we propose Dual-view Multi-level Infomax Pooling (DMIPool), which can obtain and maximize the multi-level mutual information across dual-view representations. First, we generate a dual-view framework for graph pooling through data augmentation to encode hierarchical information and optimize representations. Next, we design a dynamic fusion mechanism to exploit both node features and graph topology information in dual views, and achieve comprehensive node-level and graph-level representations. Finally, to evaluate and further optimize the dual-view representations, we propose multi-level infomax, which maximizes mutual information of the representations across dual views at the graph–graph level, node–node level, and graph–node level. DMIPool can learn robust representations and preserve representative nodes by leveraging multi-level dependencies between dual-view representations. Comprehensive experimental results on 9 graph classification benchmark datasets demonstrated the superior performance of DMIPool compared with 10 advanced graph pooling methods.
Optimum Bayesian thresholds for rebalanced classification problems using class-switching ensembles

Asymmetric label switching is an effective and principled method for creating a diverse ensemble of learners for imbalanced classification problems.This technique can be combined with other rebalancing mechanisms, such as those based on cost policies or class proportion modifications.In this study, and under the Bayesian theory framework, we specify the optimal decision thresholds for the combination of these mechanisms.In addition, we propose using a gating network to aggregate the learners contributions as an additional mechanism to improve the overall performance of the system.