Reviewerly/exHarmony huggingface dataset:
Towards Effective and Robust Neural Trojan Defenses via Input Filtering

Trojan attacks on deep neural networks are both dangerous and surreptitious. Over the past few years, Trojan attacks have advanced from using only a single input-agnostic trigger and targeting only one class to using multiple, input-specific triggers and targeting multiple classes. However, Trojan defenses have not caught up with this development. Most defense methods still make inadequate assumptions about Trojan triggers and target classes, thus, can be easily circumvented by modern Trojan attacks. To deal with this problem, we propose two novel "filtering" defenses called Variational Input Filtering (VIF) and Adversarial Input Filtering (AIF) which leverage lossy data compression and adversarial learning respectively to effectively purify potential Trojan triggers in the input at run time without making assumptions about the number of triggers/target classes or the input dependence property of triggers. In addition, we introduce a new defense mechanism called "Filtering-then-Contrasting" (FtC) which helps avoid the drop in classification accuracy on clean data caused by "filtering", and combine it with VIF/AIF to derive new defenses of this kind. Extensive experimental results and ablation studies show that our proposed defenses significantly outperform well-known baseline defenses in mitigating five advanced Trojan attacks including two recent state-of-the-art while being quite robust to small amounts of training data and large-norm triggers.
Weakly Supervised Learning For Cell Recognition In Immunohistochemical Cytoplasm Staining Images

Cell classification and counting in immunohistochemical cytoplasm staining images play a pivotal role in cancer diagnosis. Weakly supervised learning is a potential method to deal with labor-intensive labeling. However, the inconstant cell morphology and subtle differences between classes also bring challenges. To this end, we present a novel cell recognition framework based on multi-task learning, which utilizes two additional auxiliary tasks to guide robust representation learning of the main task. To deal with misclassification, the tissue prior learning branch is introduced to capture the spatial representation of tumor cells without additional tissue annotation. Moreover, dynamic masks and consistency learning are adopted to learn the invariance of cell scale and shape. We have evaluated our framework on immunohistochemical cytoplasm staining images, and the results demonstrate that our method outperforms recent cell recognition approaches. Besides, we have also done some ablation studies to show significant improvements after adding the auxiliary branches.
X-Learner: Learning Cross Sources and Tasks for Universal Visual Representation

In computer vision, pre-training models based on large-scale supervised learning have been proven effective over the past few years. However, existing works mostly focus on learning from individual task with single data source (e.g., ImageNet for classification or COCO for detection). This restricted form limits their generalizability and usability due to the lack of vast semantic information from various tasks and data sources. Here, we demonstrate that jointly learning from heterogeneous tasks and multiple data sources contributes to universal visual representation, leading to better transferring results of various downstream tasks. Thus, learning how to bridge the gaps among different tasks and data sources is the key, but it still remains an open question. In this work, we propose a representation learning framework called X-Learner, which learns the universal feature of multiple vision tasks supervised by various sources, with expansion and squeeze stage: 1) Expansion Stage: X-Learner learns the task-specific feature to alleviate task interference and enrich the representation by reconciliation layer. 2) Squeeze Stage: X-Learner condenses the model to a reasonable size and learns the universal and generalizable representation for various tasks transferring. Extensive experiments demonstrate that X-Learner achieves strong performance on different tasks without extra annotations, modalities and computational costs compared to existing representation learning methods. Notably, a single X-Learner model shows remarkable gains of 3.0%, 3.3% and 1.8% over current pre-trained models on 12 downstream datasets for classification, object detection and semantic segmentation.
A Low-Cost Bidirectional People Counter Device for Assisting Social Distancing Monitoring for COVID-19

Accurately estimating the number of people is a useful information to monitor the occupancy level of spaces due to the COVID-19 pandemic in order to keep social distancing. Automated counters are responsible for accurately counting people movement. This paper proposes a cost-effective automatic counting system based on a microcontroller through an infrared sensor integration to monitor occupancy of indoor spaces. In order to evaluate the proposed system in real-world scenarios, experiments were carried out at the main campus of the University of Campinas—Brazil. For the first case, two prototypes were installed at both the front and rear door of a bus in order to count the number of passengers boarding and alighting on the vehicle during its operation. In the second case, three prototypes were installed in the university restaurant ticket gates in order to count the number of people entering during lunchtime. Experimental results showed an accuracy of 91.45% and 98.65% for Cases 1 and 2, respectively. The results are promising, showing that the device has potential to be used for different proposes, such as controlling access to public and indoor spaces, public transportation planning, occupancy monitoring, and security system.
Computation-Skipping Mask Generation for Super-Resolution Networks

This study introduces a computation-skipping mask (CSM) generation framework to reduce redundant computations of Super-Resolution (SR) neural networks. For a layer in a given network, the CSM framework adds a tiny module to predict all zero locations in its output feature maps, so that their related operations can be skipped during inference. The experimental results show that CSM reduces the computational cost up to 58%, with negligible performance degradation for various SR models.
UAV Image Small Object Detection Based on Composite Backbone Network

Small objects in traffic scenes are difficult to detect. To improve the accuracy of small object detection using images taken by unmanned aerial vehicles (UAV), this study proposes a feature-enhancement detection algorithm based on a single shot multibox detector (SSD), named composite backbone single shot multibox detector (CBSSD), which uses a composite connection backbone to enhance feature representation. First, to enhance the detection effect of small objects, the lead backbone network, VGG16, is kept constant, and ResNet50 is added as an assistant backbone network, and the residual structure in ResNet50 is used to obtain lower feature information. The obtained lower feature information is then fused to the lead network through feature fusion, allowing the lead network to retain rich lower feature information. Finally, the lower feature information in the prediction layer increases. The experimental results show that CBSSD has a significantly higher recognition rate and a lower false detection rate than conventional algorithms, and it still maintains a good detection effect under low illumination. This is of great significance to small object detection using images taken by UAVs in traffic scenes. Furthermore, a method to improve the SSD algorithm is proposed.
A Dynamic Approach for Live Virtual machine Migration using OU Detection Algorithm

Cloud computing is the new trend and the services of platform, infrastructure and software provided by CC have profited the users. While providing these services the issue of load balancing is introduced. To achieve the load balancing, the migration of VMs is done so as to balance the load and the traffic of network. Live migration helps us in performing the task of VM migration from source to the destined host without shutting down the server. An OU (Overload-Underload) detection virtual machine algorithm is proposed to perform live migration that helps us save power, energy and also reduce the downtime. The algorithm first checks the overload and underload condition of the host and takes the step according to the situation. Then the process of migration takes place in stages. After completing these stages, the total migration time is calculated. Our proposed methodology reduces the downtime and total migration time in comparison with other algorithms as it first checks the condition of host reducing the re-migration step, hence making it energy and power efficient.
Tourist Experiences Through Mobile Augmented Reality

The paper is aimed at identifying the potential as well as the correct approach for the implementation of augmented reality (AR) in the tourism sector, which is considered to be one of the highest contributing sectors in the global economy. Augmented reality technology is disruptive and has the potential to completely revolutionize the entire tourism sector if applied properly. It can also enhance the competitiveness of the tourism sector. AR in combination with mobile devices has the highest potential of generating huge interest among tourists which has been the case in other industries as well. AR can effectively enhance the experience of a tourist and at the same time enable tourism sector stakeholders to design and implement new tourism products at a very considerable cost. However, before adopting the AR technology tourism companies should first understand the nature of the technology and the demands that are generally associated with AR. This study will analyze both the potential and the limitations of the technology so that the reader can have a brief overview of the implementation of AR in tourism.
Post-Quantum Cryptography Coprocessor for RISC-V CPU Core

The importance of PQC (Post-Quantum Cryptography) is highly emphasized according to the advancement of quantum computing technology and emergence of Shor's algorithm. Various PQC algorithms are developed but their high computational complexity makes implementation challenging. Dedicated hardware accelerator lacks flexibility to new algorithms and software implementation requires high execution time. We propose a PQC coprocessor with RISC-V ISA(Instruction Set Architecture) extension supporting not only round-3 candidates in NIST(National Institute of Standards and Technology) PQC standardization process including CRYSTALS-KYBER, CRYSTALS-DILITHIUM, FrodoKEM, SABER, NTRU and Falcon, but also upcoming new algorithms. Proposed architecture supports Keccak, NTT (Number Theoretic Transform), sampling and arithmetic operations including conditional addition and subtraction. The proposed PQC ISA extension includes RISC-V scalar cryptography and bit-manipulation extension. The coprocessor can be attached to baseline RISC-V CPU core through coprocessor interface. PQC instruction considered invalid by CPU core is offloaded through coprocessor interface.
Ontology Evaluation Tools: Current and Future Research

Abstract: In recent years, the increasing interest in ontologies resulted in the developing and publishing of many ontologies in the same or different domains. When users try to reuse the existing ontologies in their applications, they may encounter problems determining the most appropriate ontology based on their needs. Ontology evaluation is a process that helps users to examine the quality of the ontology based on different attributes. Many accessible and usable tools for ontology evaluation have been studied in the literature. However, finding an efficient ontology evaluation tool, following ontology specifications and their requirements (advantages/disadvantages), is still missing, limiting the researchers from determining possible future research. This paper aims to help new researchers and practitioners identify appropriate ontology evaluation tools based on their requirements and provide guidelines for future research directions on the same topic. This paper provides a detailed description of the different types of ontologies and classifies the available ontology evaluation tools into two categories, namely domain dependant ontology evaluation tools and domain-independent ontology evaluation tools.
Distributed Evolution Strategies for Black-Box Stochastic Optimization

This work concerns the evolutionary approaches to distributed stochastic black-box optimization, in which each worker can individually solve an approximation of the problem with nature-inspired algorithms. We propose a distributed evolution strategy (DES) algorithm grounded on a proper modification to evolution strategies, a family of classic evolutionary algorithms, as well as a careful combination with existing distributed frameworks. On smooth and nonconvex landscapes, DES has a convergence rate competitive to existing zeroth-order methods, and can exploit the sparsity, if applicable, to match the rate of first-order methods. The DES method uses a Gaussian probability model to guide the search and avoids the numerical issue resulted from finite-difference techniques in existing zeroth-order methods. The DES method is also fully adaptive to the problem landscape, as its convergence is guaranteed with any parameter setting. We further propose two alternative sampling schemes which significantly improve the sampling efficiency while leading to similar performance. Simulation studies on several machine learning problems suggest that the proposed methods show much promise in reducing the convergence time and improving the robustness to parameter settings.
Conceptual Model of a Quality Assurance System in Higher Education

Higher education institutions operate in an extremely dynamic and competitive environment. They are subject to numerous regulations on which their status, rating and funding depend. The use of a specialized software system to support external and internal quality assurance procedures provides a significant advantage. The article proposes a model of software ecosystem for quality assurance of higher education. It is based on the successful experience of the authors in building COMPASS software application, successfully used in many accreditation procedures at the University of Plovdiv. The proposed model expands COMPASS by adding new components - Register of Services, University Document Repository and Register of Units and Users. They aim to unify the process of automated extraction and analysis of quality data from existing university software systems with guaranteed access control.
Combining Container Orchestration and Machine Learning in the Cloud: a Systematic Mapping Study

Containerization is a virtualization technology that facilitates the deployment of applications. Container Orchestration is the process of automating the deployment, management, scaling and networking of containers. In this systematic mapping study, we are presenting the analysis of recent scientific papers that deal with containerization and container orchestration in the cloud, combined with machine learning, and how these are utilized to solve problems in different application areas. Currently new challenges arise related to the processing of big data, but also the optimized management of increasing amount of heterogeneous workloads in a cloud environment. The analysis results from the publications of recent years show the growing interest in the scientific community in these evolving technologies - container orchestration from one hand and utilizing machine learning on the other. The emphasis of the study are the trends and innovations, the orchestration technologies and strategies, the machine learning algorithms. Evaluating the relevance of the proposed solutions and ideas for future research are also outlined.
Whispered Speech Detection Using Glottal Flow-Based Features

Recent studies have reported that the performance of Automatic Speech Recognition (ASR) technologies designed for normal speech notably deteriorates when it is evaluated by whispered speech. Therefore, the detection of whispered speech is useful in order to attenuate the mismatch between training and testing situations. This paper proposes two new Glottal Flow (GF)-based features, namely, GF-based Mel-Frequency Cepstral Coefficient (GF-MFCC) as a magnitude-based feature and GF-based relative phase (GF-RP) as a phase-based feature for whispered speech detection. The main contribution of the proposed features is to extract magnitude and phase information obtained by the GF signal. In the GF-MFCC, Mel-frequency cepstral coefficient (MFCC) feature extraction is modified using the estimated GF signal derived from the iterative adaptive inverse filtering as the input to replace the raw speech signal. In a similar way, the GF-RP feature is the modification of the relative phase (RP) feature extraction by using the GF signal instead of the raw speech signal. The whispered speech production provides lower amplitude from the glottal source than normal speech production, thus, the whispered speech via Discrete Fourier Transformation (DFT) provides the lower magnitude and phase information, which make it different from a normal speech. Therefore, it is hypothesized that two types of our proposed features are useful for whispered speech detection. In addition, using the individual GF-MFCC/GF-RP feature, the feature-level and score-level combination are also proposed to further improve the detection performance. The performance of the proposed features and combinations in this study is investigated using the CHAIN corpus. The proposed GF-MFCC outperforms MFCC, while GF-RP has a higher performance than the RP. Further improved results are obtained via the feature-level combination of MFCC and GF-MFCC (MFCC&amp;GF-MFCC)/RP and GF-RP(RP&amp;GF-RP) compared with using either one alone. In addition, the combined score of MFCC&amp;GF-MFCC and RP&amp;GF-RP gives the best frame-level accuracy of 95.01% and the utterance-level accuracy of 100%.
Synthesized rain images for deraining algorithms

Since most of the rainy scene datasets used for training single image rain removal (SIRR) algorithms are constructed by blending artificial rain streaks with source images, it is difficult for a machine trained with such datasets to understand the patterns of real or realistic rain streaks. So, several studies have been attempted to build a real rainy scene dataset. However, since collecting real rainy scenes itself requires significant costs, the real rainy scene datasets provided by some studies cover only very limited rainy environment(s). This paper presents a new approach to synthesize realistic rainy scenes using GAN, which is a world-first attempt as far as we know. The proposed method builds a representation space to which rain streaks of multiple styles are smoothly mapped by learning the distributions of various rain datasets. The representation space allows control over the generated rain streaks. Also, the proposed method can synthesize multiple rainy scenes per clean (source) scene simultaneously, thereby a synthesized rain image dataset (SyRa) (Dataset can be found here: https://github.com/jaewoong1/SyRa-Synthesized_Rain_dataset) consisting of 11 K clean images and 55 K rainy images was constructed. Finally, this paper provides benchmarking results of several SIRR methods trained with SyRa. This result will be very useful for developing SIRR algorithms that can cope well with the actual rain environment.
Investigation of Pre-service Teachers’ Conceptions of the Nature of Science Based on the LDA Model

This study used the Latent Dirichlet Allocation (LDA) topic model to analyze pre-service teachers' views on the nature of science (NOS). This approach can be used to automate the classification of documents, and at the same time, the researcher does not need to deduce with a NOS framework prior to evaluation. Participants were 155 pre-service teachers studying at the Shandong Normal University in China. To gather our data, we used an open questionnaire, namely, the Views of Nature of Science Questionnaire-Form C (VNOS-C). LDA topic modeling was used to classify the document, which was divided into 12 topics. By comparing the LDA topic modeling results with the theoretical framework behind the VNOS-C questionnaire, we categorized these 12 topics into eight descriptive aspects of the NOS: The Empirical Nature of Scientific Knowledge, Observation, Inference, and Theoretical Entities in Science, Scientific Theories and Laws, The Theory-Laden Nature of Scientific Knowledge, The Social and Cultural Embeddedness of Scientific Knowledge, The Myth of The Scientific Method, The Tentative Nature of Scientific Knowledge, and The Nature of Scientific Theory. The results show that pre-service teachers usually hold naive or mixed views of the NOS. In addition, each aspect of NOS is not independent of each other but interrelated and influencing each other. In the future, more consideration can be given to the relationship between each aspect of NOS.
Traffic Light Detection using Attention-Guided Continuous Conditional Random Fields

Traffic light detection plays a central role in Advanced Driver Assistance System (ADAS). For an autonomous vehicles to move smoothly and safely on the road, not only it is crucial for an ego vehicle to detect all presented traffic light candidates, but also to not yield any false positives. It is necessary for Convolutional Neural Networks (CNNs) to attend more focus on important features and suppress non-useful details through activations. To carry out such task, we propose a novel network that employs continuous Conditional Random Fields (CRFs) to fuse multi-scale information from different layers of a CNN, guided by attention modules. Extensive experiments are conducted on traffic light detection dataset, which we have acquired with own our autonomous driving platform SNUver. Results indicate that by incorporating attention-guided CRF module inside the network, the network focuses more on regions with traffic lights and thereby improves accuracy and recall rate.
Advanced Electronic Signatures and eIDAS – Analysis of the Concept

We discuss the model of electronic signatures as described by the European eIDAS Regulation from the perspective of common understanding of electronic signatures in the cryptographic community. We show that these two perspectives do not present the same picture. The discrepancies between them may become opportunities as well as barriers for rapid deployment of electronic signatures and seals in business and administration. We focus particularly on validation of advanced electronic signatures and its interplay with the data protection requirements of GDPR. We show that by tweaking the existing technical standards one can reduce the number of problems and achieve compliance with both GDPR and eIDAS. Among others, we wish to bring attention to the evolving regulatory framework that without any doubt will have a substantial impact on the ecosystem of electronic signatures.