Reviewerly/exHarmony huggingface dataset:
Decentralized Device Authentication Model using the Trust Score and Blockchain Technology for Dynamic Networks

In general, applications use Trusted Third Party to authenticate users, application servers, and devices. Since Trusted Third Party based solutions are centralized, a decentralized scheme is preferred for applications, and blockchain is one of the best solutions for decentralization. Hence, this paper proposes a trust score based decentralized authentication model using the blockchain technology for dynamic networks. In our proposed model, any device can join the network at any time through existing nodes by sending the join transaction to the blockchain. A new device in the network can do messaging with other existing devices; however, the receiving device may not accept until the sender gains a full trust score in the network. The trust score of nodes is built based on its behavior and contributions to the network viz. mining, voting, introducing new node, and fact creation. The proposed model does not give incentives to the miners in the form of currency. At the same time, it should support the network, which is public, dynamic, and untrusted. Hence, we introduce a private and public nodes based blockchain model with incentives such as subsidy, green corridor, etc. We analyze the proposed model for security effectiveness, resource usage, communication overhead, and computational performance to prove lightweight devices' applicability.
Two-Stage Facial Mask Detection Model for Indoor Environments

The COVID-19 pandemic has led to many organizations around the world enforcing face mask rules for personal protection. Manual checking whether individuals entering an organization's premises are wearing masks is cumbersome and possibly confrontational. There has been relatively little work on automatic face mask rule violations thus far. We propose a system for automatic monitoring for face mask rule violations for enterprises. Our method is an efficient two-stage facial mask detection model. The first stage is based on facial landmark extraction and clustering, and the second stage analyzes the clustered nose region. A thorough accuracy evaluation on five types of sample face images (no mask, beard and mustache, single-color mask, multi-color mask, and skin-color mask) finds that the overall accuracy of the two-stage model is an excellent 97.13%, outperforming simpler single-stage models.
FLAKE: Fuzzy Graph Centrality-based Automatic Keyword Extraction

Abstract Keyword extraction is one of the most important aspects of text mining. Keywords help in identifying the document context. Many researchers have contributed their work to keyword extraction. They proposed approaches based on the frequency of occurrence, the position of words or the similarity between two terms. However, these approaches have shown shortcomings. In this paper, we propose a method that tries to overcome some of these shortcomings and present a new algorithm whose efficiency has been evaluated against widely used benchmarks. It is found from the analysis of standard datasets that the position of word in the document plays an important role in the identification of keywords. In this paper, a fuzzy logic-based automatic keyword extraction (FLAKE) method is proposed. FLAKE assigns weights to the keywords by considering the relative position of each word in the entire document as well as in the sentence coupled with the total occurrences of that word in the document. Based on the above data, candidate keywords are selected. Using WordNet, a fuzzy graph is constructed whose nodes represent candidate keywords. At this point, the most important nodes (based on fuzzy graph centrality measures) are identified. Those important nodes are selected as final keywords. The experiments conducted on various datasets show that proposed approach outperforms other keyword extraction methodologies by enhancing precision and recall.
A flexible factor analysis based on the class of mean-mixture of normal distributions

Factor analysis is a statistical technique for data reduction and structure detection that traditionally relies on the normality assumption for factors. However, due to the presence of non-normal features such as asymmetry and heavy tails in many practical situations, the first two moments cannot adequately explain the factors. An extension of the factor analysis model is introduced by assuming a generalization of the multivariate restricted skew-normal distribution for the vector of unobserved factors. An efficient and computationally tractable EM-type algorithm is adopted for computing the maximum likelihood estimates by presenting a hierarchical representation of the proposed model. Finally, the efficiency and advantages of the proposed novel methodology are demonstrated through both simulated and real benchmark datasets.
Cross-Lingual Transfer Learning for Complex Word Identification

Complex Word Identification (CWI) is a task centered on detecting hard-to-understand words, or groups of words, in texts from different areas of expertise. The purpose of CWI is to highlight problematic structures that non-native speakers would usually find difficult to understand. Our approach uses zero-shot, one-shot, and few-shot learning techniques, alongside state-of-the-art solutions for Natural Language Processing (NLP) tasks (i.e., Transformers). Our aim is to provide evidence that the proposed models can learn the characteristics of complex words in a multilingual environment by relying on the CWI shared task 2018 dataset available for four different languages (i.e., English, German, Spanish, and also French). Our approach surpasses state-of-the-art cross-lingual results in terms of macro F1-score on English (0.774), German (0.782), and Spanish (0.734) languages, for the zero-shot learning scenario. At the same time, our model also outperforms the state-of-the-art monolingual result for German (0.795 macro F1-score).
Global-Guided Selective Context Network for Scene Parsing

Recent studies on semantic segmentation are exploiting contextual information to address the problem of inconsistent parsing prediction in big objects and ignorance in small objects. However, they utilize multilevel contextual information equally across pixels, overlooking those different pixels may demand different levels of context. Motivated by the above-mentioned intuition, we propose a novel global-guided selective context network (GSCNet) to adaptively select contextual information for improving scene parsing. Specifically, we introduce two global-guided modules, called global-guided global module (GGM) and global-guided local module (GLM), to, respectively, select global context (GC) and local context (LC) for pixels. When given an input feature map, GGM jointly employs the input feature map and its globally pooled feature to learn its global contextual demand based on which per-pixel GC is selected. While GLM adopts low-level feature from the adjacent stage as LC and synthetically models the input feature map, its globally pooled feature and LC to generate local contextual demand, based on which per-pixel LC is selected. Furthermore, we combine these two modules as a selective context block and import such SCBs in different levels of the network to propagate contextual information in a coarse-to-fine manner. Finally, we conduct extensive experiments to verify the effectiveness of our proposed model and achieve state-of-the-art performance on four challenging scene parsing data sets, i.e., Cityscapes, ADE20K, PASCAL Context, and COCO Stuff. Especially, GSCNet-101 obtains 82.6% on Cityscapes test set without using coarse data and 56.22% on ADE20K test set.
Stealthy and Robust Glitch Injection Attack on Deep Learning Accelerator for Target With Variational Viewpoint

Deep neural network (DNN) accelerators overcome the power and memory walls for executing neural-net models locally on edge-computing devices to support sophisticated AI applications. The advocacy of “model once, run optimized anywhere” paradigm introduces potential new security threat to edge intelligence that is methodologically different from the well-known adversarial examples. Existing adversarial examples modify the input samples presented to an AI application either digitally or physically to cause a misclassification. Nevertheless, these input-based perturbations are not robust or surreptitious on multi-view target. To generate a good adversarial example for misclassifying a real-world target of variational viewing angle, lighting and distance, a decent number of target’s samples are required to extract the rare anomalies that can cross the decision boundary. The feasible perturbations are substantial and visually perceptible. In this paper, we propose a new glitch injection attack on DNN accelerator that is capable of misclassifying a target under variational viewpoints. The glitches injected into the computation clock signal induce transitory but disruptive errors in the intermediate results of the multiply-and-accumulate (MAC) operations. The attack pattern for each target of interest consists of sparse instantaneous glitches, which can be derived from just one sample of the target. Two modes of attack patterns are derived, and their effectiveness are demonstrated on four representative ImageNet models implemented on the Deep-learning Processing Unit (DPU) of FPGA edge and its DNN development toolchain. The attack success rates are evaluated on 118 objects in 61 diverse sensing conditions, including 25 viewing angles (−60° to 60°), 24 illumination directions and 12 color temperatures. In the covert mode, the success rates of our attack exceed existing stealthy adversarial examples by more than 16.3%, with only two glitches injected into ten thousands to a million cycles for one complete inference. In the robust mode, the attack success rates on all four DNNs are more than 96.2% with an average glitch intensity of 1.4% and a maximum glitch intensity of 10.2%.
Multi-criteria tensor model consolidating spatial and temporal information for tourism recommendation

Although spatial and temporal information has often been considered to improve recommendation performances, existing multi-criteria recommender systems often neglect to leverage spatial and temporal information. Also, it is a non-trivial task to simultaneously apply such information to recommendation services since the factors have interrelations to each other. In this paper, we propose a multi-criteria tensor model combining spatial and temporal information. The auxiliary information is categorized by several features and applied into the model. In particular, the spatial information of users’ countries is grouped into seven continents to reduce response times for learning the model. The single model enables to us keep the inherent structure of and the interrelations between multi-criteria and spatial/temporal information. To predict user preferences, tensor factorization based on Higher Order Singular Value Decomposition is exploited. Experimental results with a TripAdvisor dataset show that the proposed method outperforms other baseline methods based on a 2-dimensional rating matrix, tensor model, and other multi-criteria recommendation, in terms of RMSE and MAE. Furthermore, several experiments reveal the influences of the individual factors (i.e., multi-criteria, spatial and temporal information) and their consolidations, on restaurant recommendation. A comparative analysis of the multi-criteria elements shows that their influences relate to their correlations.
PRNU-leaks: facts and remedies

We address the problem of information leakage from estimates of the PhotoResponse Non-Uniformity (PRNU) fingerprints of a sensor. This leakage may compromise privacy in forensic scenarios, as it may reveal information from the images used in the PRNU estimation. We propose a new way to compute the information-theoretic leakage that is based on embedding synthetic PRNUs, and presesent affordable approximations and bounds. We also propose a new compact measure for the performance in membership inference tests. Finally, we analyze two potential countermeasures against leakage: binarization, which was already used in PRNU-storage contexts, and equalization, which is novel and offers better performance. Theoretical results are validated with experiments carried out on a real-world image dataset.
A correction method of word spelling mistake for English text

Abstract Spell correction is already a mature field, the need to combine advantages of different methods for better performance arises in non-word problem. A combined spell correction method is proposed in this paper, which contains Levenshtein distance for comparation between misspelled words and correct spelled words in dictionary, improved Double Metaphone algorithm that includes vowel phoneme rule sets aimed at Chinese English learners, and global vectors(GloVe) for character representation that can generate vectors in order to obtain better suggestion lists for misspelled words. Result shows that the combined approach proposed in this paper is better than phonetic correction or edit distance method only, and a comparison with two generally implemented spell check tools is done for experiment which shows that this approach is better than them in correcting misspelled words, and the success rates of suggestion lists for spelling mistakes hit the spot.
3D-CNN Autoencoder for Plenoptic Image Compression

Recently, plenoptic image has attracted great attentions because of its applications in various scenarios. However, high resolution and special pixel distribution structure bring huge challenges to its storage and transmission. In order to adapt compression to the structural characteristic of plenoptic image, in this paper, we propose a Data Structure Adaptive 3D-convolutional(DSA-3D) autoencoder. The DSA-3D autoencoder enables up-sampling and down-samping the sub-aperture sequence along the angular resolution or spatial resolution, thereby avoiding the artifacts caused by directly compressing plenoptic image and achieving better compression efficiency. In addition, we propose a special and efficient Square rearrangement to generate sub-aperture sequence. We compare Square with Zigzag sub-aperture sequence rearrangements, and analyzed the compression efficiency of block image compression and whole image compression. Compared with traditional hybrid encoders HEVC, JPEG2000 and JPEG PLENO(WaSP), the proposed DSA-3D(Square) autoencoder achieves a superior performance in terms of PSNR metrics.
A Unified Single Image De-raining Model via Region Adaptive Coupled Network

Single image de-raining is quite challenging due to the diversity of rain types and inhomogeneous distributions of rainwater. By means of dedicated models and constraints, existing methods perform well for specific rain type. However, their generalization capability is highly limited as well. In this paper, we propose a unified de-raining model by selectively fusing the clean background of the input rain image and the well restored regions occluded by various rains. This is achieved by our region adaptive coupled network (RACN), whose two branches integrate the features of each other in different layers to jointly generate the spatial-variant weight and restored image respectively. On the one hand, the weight branch could lead the restoration branch to focus on the regions with higher contributions for de-raining. On the other hand, the restoration branch could guide the weight branch to keep off the regions with over-/under-filtering risks. Extensive experiments show that our method outperforms many state-of-the-art de-raining algorithms on diverse rain types including the rain streak, raindrop and rain-mist.
Aspect-Based Sentiment Analysis on Candidate Character Traits in Indonesian Presidential Election

This paper is part of our research that was conducted during the campaign period of the Indonesian presidential election between 2018 and 2019. We collected millions of data from Twitter to predict the election results. In this research, we focus more on aspect-based sentiment analysis of the candidate character traits. We contribute a dataset that is annotated manually which contains the tweet texts in Bahasa Indonesia and their labels. The labels are the targets which are the candidates, the aspects which are the character traits, and the sentiments itself. We also present the comparison of machine learning algorithms' performances when classifying the dataset automatically. The results show that the Support Vector Machine algorithm performs better than the Naïve Bayes and the K-Nearest Neighbor.
A 360° Video Adaptive Streaming Scheme Based on Multiple Video Qualities

As an emerging multimedia service, virtual reality (VR) video streaming is facing two challenges: extremely large bandwidth requirements and strict delay requirements. There-fore, improving the utilization of network resources is of great significance to the application and development of VR video in order to provide a better quality of experience. Currently, many VR video streaming solutions are based on 360° video, which is a compromise between restricted bandwidth and streaming delay requirement. For a tile-based 360° video streaming using HTTP2 protocol, the video is split into different tiles with multiple quality levels. In the case of insufficient bandwidth, a limited number of quality levels will lead to large quality differences between adjacent zones, which will also limit the optimization of quality level adaptation, resulting in a lower QoE. In this paper, we propose a new tile-based 360° video adaptive streaming scheme based on multiple video quality levels. The proposed method provides more video quality options/levels, dividing the 360° video into different video zones according to its viewpoint position and assign them with different video quality levels based on bandwidth conditions during the streaming time, to achieve smooth video quality distribution, and ensure high QoE with high video bitrate, low-quality switches and minimized stall time. The experimental results show that the QoE of this proposed method is improved by approximately 28% compared with the existing adaptive 360° video streaming scheme.
A Hardware Root-of-Trust Design for Low-Power SoC Edge Devices

In this work, we introduce a hardware root-of-trust architecture for low-power edge devices. An accelerator-based SoC design that includes the hardware root-of-trust architecture is developed. An example application for the device is presented. We examine attacks based on physical access given the significant threat they pose to unattended edge systems. The hardware root-of-trust provides security features to ensure the integrity of the SoC execution environment when deployed in uncontrolled, unattended locations. E-fused boot memory ensures the boot code and other security critical software is not compromised after deployment. Digitally signed programmable instruction memory prevents execution of code from untrusted sources. A programmable finite state machine is used to enforce access policies to device resources even if the application software on the device is compromised. Access policies isolate the execution states of application and security-critical software. The hardware root-of-trust architecture saves energy with a lower hardware overhead than a separate secure enclave while eliminating software attack surfaces for access control policies.
Twitter Hate Speech Detection using Stacked Weighted Ensemble (SWE) Model

Online Social Media has expanded the freedom of expression in the internet, which has become a disturbing problem if it has an impact on the situation or the interest of a country. Hate speech refers to the use of hostile, abusive or offensive language, directed at a certain group of people who share common property, whether it is their gender, ethnicity or race (i.e. racism), faith and religion. Therefore, auto detection of hate speeches has an increased importance in Online Social Media for filtering any message that has hatred language before posting it to the network. In this paper, a Stacked Weighted Ensemble (SWE) model is proposed for the detection of hate speeches. The model ensembles five standalone classifiers: Linear Regression, Naïve Bayes', Random Forest, Hard Voting and Soft Voting. The experimental results on a Twitter® dataset has shown an accuracy of 95.54% in binary classification of tweets into hateful speech and an improved performance is noted compared to the standalone classifiers.
BCoV: a covergence of blockchain and IoV

The information generated by the internet of vehicles during normal operation involves personal privacy. Due to the lack of appropriate information security mechanisms in IoV architecture, IoV has the risk of privacy leakage. However, the emergence of blockchain brings opportunities to solve these problems. The combination of blockchain and IoV can make full use of the advantages of the blockchain (such as distributed storage, and smart contracts) to solve some problems in the IoV. In this paper, we investigate the combination of blockchain and IoV and name this structure as BCoV. Firstly, we introduce the structure of IoV and discuss the challenges of IoV. Secondly, we outline the blockchain structure, consensus algorithm, and smart contract. Thirdly, we discuss the convergence of blockchain and IoV and propose the architecture of BCoV, and make a comparison with IoV. Fourthly, we introduce some applications of BCoV. Finally, we present the challenges of BCoV.
Performance Evaluation of TPC-C Benchmark on Various Cloud Providers

With the hardware costs becoming cheaper day by day and with industry giants focus, Cloud computing has exploded and reached leaps and bounds in the last 15 years. With the power of creating, using and destroying virtual machines in the cloud at the tip of mouse click, industries have started moving their core applications to the cloud. This has reduced the hassle for industries to maintain the hardware by themselves. Tech giants like Amazon, Microsoft and Google are head of the game and the fierce competition between them has led to astonishing innovation. With so many players in the cloud market, it is essential for cloud users to know how each of the services provided by these cloud service providers are performing against each other. In this paper we have evaluated the performance of famous OLTP benchmark TPC-C on these cloud providers. It is observed that Amazon's AWS has performed better than Microsoft Azure and Google Cloud Platform in terms of the number of transactions/orders per second, and I/O reads/writes. We have done the extended comparison with respect to transaction throughput, database throughput and Machine throughput.
Ensemble based Data Imputation at the Edge

Edge Computing (EC) offers an infrastructure that acts as the mediator between the Cloud and the Internet of Things (IoT). The goal is to reduce the latency that we face when relying on Cloud. IoT devices interact with their environment to collect data relaying them towards the Cloud through the EC. Various services can be provided at the EC for the immediate management of the collected data. One significant task is the management of missing values. In this paper, we propose an ensemble based approach for data imputation that takes into consideration the spatio-temporal aspect of the collected data and the reporting devices. We propose to rely on the group of IoT devices that resemble to the device reporting missing data and enhance its data imputation process. We continuously reason on the correlation of the reported streams and efficiently combine the available data. Our aim is to `aggregate' the local view on the appropriate replacement with the `opinion' of the group. We adopt widely known similarity techniques and a statistical modelling methodology to deliver the final outcome. We provide the description of our model and evaluate it through a high number of simulations adopting various experimental scenarios.