Reviewerly/exHarmony huggingface dataset:
Ontological Simulation of Quality Test Concepts in Seafood Domain

Measures and procedures to ensure the protection and quality of marine products are very relevant. The inherent attributes including nutritional value and fresh degree are given more importance. To track seafood quality, the food processing industries use control systems. Most of these guidelines still apply on paper, and laboratory technicians frequently refer to them for standards. This research presents the development of ontology to represent the concepts of tests used to ensure the quality in the seafood domain. It provides information on the type of tests, standard values and classifies the seafood into accepted and rejected classes. We concentrate on five types of seafood, and the case study is conducted in Oman. Protégé is the main tool used to implement the ontology. The ontology supports the laboratory technologists in knowledge discovery and information retrieval.
Inductive conformal recommender system

Traditional recommendation algorithms can be used to develop techniques that can help people choose desirable items of interest. However, in many real-world applications, it is important to quantify each recommendation’s (un)certainty, in addition to a set of recommendations. The conformal recommender system uses the experience of a user to output a set of recommendations, each associated with a precise confidence value. A significance level ɛ provides a bound ɛ on the probability of making a wrong recommendation. The conformal framework uses a key concept called nonconformity measure that measures the strangeness of an item concerning other items. One of the significant design challenges of any conformal recommendation framework is the integration of nonconformity measures with the recommendation algorithm. This paper introduces an inductive variant of a conformal recommender system. We propose and analyze different nonconformity measures in the inductive setting. In addition, we provide theoretical proofs on the error bound and time complexity. Extensive empirical analysis on seven benchmark datasets reveals that the inductive variant substantially improves the performance in computation time while preserving the accuracy. • We introduce the concept of inductive conformal recommendation. • We propose and analyze different nonconformity measures in the inductive setting. • We provide theoretical proofs on the error-bound and the time complexity. • Extensive comparative studies validate the efficiency of our algorithm.
Design Considerations for Usable Authentication in Smart Homes

Smart home devices are on the rise. To provide their rich variety of features, they collect, store and process a considerable amount of (potentially sensitive) user data. However, authentication mechanisms on such devices a) have limited usability or b) are non-existing. To close this gap, we investigated, on one hand, users’ perspectives towards potential privacy and security risks as well as how they imagine usable authentication mechanisms in future smart homes. On the other hand, we considered security experts’ perspectives on authentication for smart homes. In particular, we conducted semi-structured interviews (N=20) with potential smart home users using the story completion method and a focus group with security experts (N=10). We found what kind of devices users would choose and why, potential challenges regarding privacy and security, and potential solutions. We discussed and verified these with security experts. We derive and reflect on a set of design implications for usable authentication mechanisms for smart homes and suggest directions for future research. Our work can assist designers and practitioners when implementing appropriate security mechanisms for smart homes.
Towards the Overcome of Performance Pitfalls in Data Stream Mining Tools

Data stream mining is an essential task in today's scientific community. It allows machine learning models to be updated over time as new data becomes available. Three pillars should be accounted for when selecting an appropriate algorithm for data stream mining: accuracy, processing time, and memory consumption. To develop and assess machine learning models in streaming scenarios, different tools have been developed, where the Massive Online Analysis, written in Java, and scikit-multiflow, written in Python, are in the spotlight. Despite the ease of use of both tools, neither are focused on performance, which puts in jeopardy the usage of the computational resources. In this paper, we show that with the right tools, Python libraries reach performance comparable to C/C++. More specifically, we show how optimized implementations in scikit-multiflow using low-level languages, i.e., C++, C++ with Intel Intrinsics, and Rust; with bindings to Python vastly overcome existing tools in computational resources usage while keeping predictive performance intact.
EEG Signal Discrimination with Permutation Entropy

The information analysis of the electroencephalogram (EEG) signal is carried out by granulation and reciprocal entropy (PeEn). The analysis of the EEG signal is obtained by experimental activity. Due to its complexity and multichannel characteristic, together with granular computing (GrC) and PeEn are used to analyze the EEG signal. The EEG signal consists of 32 channels of data and the experimental data are used to discriminate patterns, with experimental focus on considering real and thinking actions. The time-series EEG signals were granularized according to the changes in the signal and analyzed by PeEn coding and Fuzzy C-Means (FCM) algorithm. Because there are two main actions, i.e., left-handed, and right-handed actions were clearly delineated. In addition, we provide the GrC algorithm to prove the boundary problem with the help of Hilbert-Huang transform. The obtained results show an advanced approach for analyzing EEG signals, which can be the basis for solving complex multichannel data analysis.
Trends in biomaterials in Japan

The Japanese Society for Biomaterials (JSB) [1] was established on 4 December 1978 by the President of JSB, Prof. Takeo Yokobori (Tohoku University), together with many distinguished professors in ...
A Distributed Face Retrieval System for Large-Scale Social Networking Avatars

In order to solve the problems of poor accuracy and slow speed in face retrieval for large-scale social networking avatars, this paper proposes a real-time distributed face retrieval system supporting hundreds of millions for face images. Firstly, we detect face and learn the face feature vector by combined convolutional neural network models. Secondly, the locality sensitive hash algorithm is used to transform the high-dimensional feature into low-dimensional. Meanwhile, a distributed face feature vector database is proposed to balance the storage pressure for one single server node as well as to accelerate query speed by computing similarity score in parallel and merging sort results from all distributed server nodes. Experimental results show that the distributed system designed in this paper can efficiently, accurately and timely retrieve faces from large-scale social networking avatars.
Proficient Dual Secure Multi Keyword Search by Top-K Ranking Based on Synonym Index and DNN in Untrusted Cloud

Recent developments in cloud services have increased number of data owners to store their encrypted data in the cloud whereas equal or more data users participate to retrieve data. Secure retrieval of relevant data has become a challenging issue. In this paper, a secure ranking based multi-keyword search using semantic index is being developed. Initially, owner builds an index file by semantic representation of keyword. Security key is provided by Trusted Authority (TA) for decrypting the obtained results at the user side. TA manages dual security processes such as managing secret keys and issuing security devices to the data users. User query reaches proxy server, and it checks whether any frequent keyword matches with given query using Boolean Search. If it does not match, query enters into the main server which stores all document and index files to obtain relevant result using Deep Learning Neural Network. In deep learning neural network, the query is processed with vector space model to retrieve the relevant documents. Finally, user decrypts the relevant results obtained from deep neural network. The experimental result shows that our proposed model provides better performance in terms of recall, ranking privacy, precision, and searching time.
Ensemble-Based Graph Model and Dense Reconstruction Error for Infrared Target Detection

In this paper, Ensemble-Based Graph Model and Dense Reconstruction Error for Infrared Target Detection algorithm is proposed. Firstly, the infrared image is constructed as a closed-loop by using the super-pixel segmentation. Then the saliency map of the target area and the corresponding part of the background are extracted respectively by using the graph model-based manifold ranking algorithm and the dense reconstruction error. And the fused result of the two saliency maps is insensitive to the background clutter interference, which can clearly locate the target area. The experimental results show that the proposed algorithm can suppress the background clutter interference and maintain the integrity of the target edge.
Linear Program Powered Attack

Finding the exact robust test error is a good way to compare the robustness of neural networks, but it is a difficult task even on small networks and datasets like MNIST. However, finding reasonable lower and upper bounds is possible and can be done using either complete methods or attacks. On the one hand, complete methods such as Mixed Integer Program (MIP) give exact robust test accuracy but are time-consuming. On the other hand, attacks are usually fast but tend to perform badly against robust networks and underestimate the lower bound on the robust test error. The purpose of this paper is to present a novel attack method that is both fast and gives better lower bounds than previous attacks. This method exploits the algebraic properties of networks with piecewise linear activation functions to partition the input space in such a way that for each subset of that partition, finding the local optimal adversarial example is done by solving a linear program. Moving from one subset to another is done using classical gradient-based attack tools. To evaluate the quality of the produced adversarial examples, we compare our lower bound on the robust test error to the one previously found. The results found are satisfying in the sense that it does better than previous lower bounds on several models and finds adversarial examples that the MIP failed to expose before reaching its time limit.
A Framework for Asymmetrical DNN Modularization for Optimal Loading

The modern era of artificial intelligence is mostly driven by Deep Neural Network (DNN). As a result most of the intelligent/smart apps running on edge devices (mobile phones, televisions etc.) use DNN for their predictive ability. DNN based prediction suffers with operational overheads, which is the summation of model loading latency and inference latency. Model loading latency affects the first response of the DNN powered apps, whereas inference latency affects the subsequent responses. As apps switching has become a common practice among edge device users, so it is of utmost interest to make the switching smooth by reducing the model loading latency. In this paper asymmetrical DNN modularization is proposed as a potential solution. The proposed method solves two distinct problems (a). Improves the model loading latency by parallel loading of all the modules (child models) of given DNN model. (b). Helps in on-device training by keeping the live gradients only for last child model. The decision about modularization index and their corresponding positions are taken by reinforcement learning unit (RLU). RLU takes into account the available hardware resources on-device (eg. h/w threads), loading latency of each layer on dedicated compute units. In response, it provides the best modularization index k and their corresponding positions <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\vec{p}$</tex> specific to the DNN model and device, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P=(p_{1},p_{2},\ \ldots,p_{k})$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p_{i}$</tex> is the end position of child <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$i$</tex> . The proposed method has shown significant loading time improvement (up to 7X) on popular DNNs, used for camera use-cases. Along with improving the loading latency the proposed modularization method facilitates for On-device personalization by separating the module with trainable layers and loading them particularly while training on-device.
Towards Fine-Grained Control over Latent Space for Unpaired Image-to-Image Translation

We address the open problem of unpaired image-to-image (I2I) translation using a generative model with fine-grained control over the latent space. The goal is to learn the conditional distribution of translated images given images from a source domain without access to the joint distribution. Previous works, such as MUNIT and DRIT, which simply keep content latent codes and exchange the style latent codes, generate images of inferior quality. In this paper, we propose a new framework for unpaired I2I translation. Our framework first assumes that the latent space can be decomposed into content and style sub-spaces. Instead of naively exchanging style codes when translating, our framework uses an interpolator that guides the transformation and is able to produce intermediate results under different strengths of translation. Domain specific information, which might still exist in content codes, is excluded in our framework. Extensive experiments show that the translated images using our framework are superior than or comparable to state-of-the-art baselines. Code is available upon publication.
Crowd change detection from VHR images acquired by UAV via deep features exploitation

Images acquired by RGB cameras on Unmanned Aerial Vehicles (UAVs) can be particularly useful to detect crowd in urban areas when restrictive conditions are imposed for the sake of public safety or health , such as during the Covid 19 pandemic. Together with acquired images, opportune pattern recognition techniques have to be considered to extract useful information. In this framework, features capturing the semantic rich information encompassed in VHR images have to be computed. In particular, Deep Neural Networks (DNN) have been recently proved to be able to extract useful features from data [1]. Moreover, in a transfer learning approach, a DNN pre-trained on a data set can be used to extract opportune features, named deep-features, from another data set, belonging to a different applicative domain [1], [2]. Here, a transfer learning technique is presented to produce change maps, detecting how people gathering increases, from VHR images. It is based on deep-features computed by using some pre-trained convolutional layers of AlexNet. The proposed methodology has been tested on a data set composed of several synthetic VHR images, that simulate crowd collecting in a park, as they can be acquired by RGB camera on a UAV flying at 10 meters height from the ground. The experimental results show that the proposed technique is able to efficiently detect change due to new people incoming in the scene or people that get away, with a low computational cost and in a near-real time operative mode.
Sentimental Analysis on Sarcasm Detection with GPS Tracking

Sentimental analysis which is also known as opinion mining is one of the major tasks of natural language processing (NLP). Sentimental analysis is a technique which is used to identify a person’s sentiment, humor and their emotion. Sarcastic comments imply what a person wants to say in a conflicting manner. Sarcasm is being generally used among numerous informal communication and smaller scale blogging sites where individuals attack others which makes tricky for the person to state what it implies. For example, many sarcastic tweets which gives a positive impact like, “Technical talk right after lunch” but it describes an undesirable activity. There are number of researches done in sarcasm. In this paper feature extraction techniques were used, for instance, logistic regression, support vector machine (SVM), random forest, etc. to recognize sarcasm in tweets from the twitter gliding API. The perfect classifier is picked then joined along different pre-handling, separating methods utilizing sarcastic and non-sarcastic lexicon mapping to give the most ideal precision. A GPS tracking system is used collect the data and allocate to which location the Tweets are coming from. The sarcastic and non-sarcastic word reference being the shrewd idea introduced in this paper.
Receiving Feedback on Your Draft Manuscript

There are a great many instances in life where we do not get something right the first time, particularly if it is something in which we do not yet have much experience. An example of this would be submitting our first travel work to the editor of a desired publication outlet.
Modeling and Analysis of Critical Success Factors in the Implementation of Second Life in Virtual Classrooms for Teaching in Education Using Fuzzy Cognitive Maps

The pandemic, produced by covid-19, gave rise to a new normal, in which education is part of that change. In certain cases, the quality of education was affected, either by factors such as deficient online teaching material, teaching issues, attention, and adaptation problems on the part of students in their online courses. To help improve performance in online education, some educational institutions have chosen to use the virtual world of Second Life to implement it in their classes, offering features that benefit learning from home, making it more didactic and enjoyable for students. The objective of this article is to find and analyze the critical success factors for the implementation of Second Life in virtual classrooms, through the construction of fuzzy cognitive maps. The use of forms aimed at students is proposed, from which the results will be evaluated and the factors that influence a quality education will be determined, building a fuzzy cognitive map for their understanding.
Complex Document Layout Segmentation Based on An Encoder-Decoder Architecture

Abstract In our work, we propose an end-to-end encoder-decoder network for complex document layout segmentation. The proposed multi-scale feature extraction network with two parallel branches is applied to further process the feature maps, where one branch enriches the multi-scale information of the feature maps by building feature pyramids, another branch is introduced to capture the dependencies between different locations and integrate long-range context information. Moreover, we merge the outputs of the two branches to enhance the feature representation so as to further improve segmentation accuracy. Experimental results on datasets of PubLayNet and DSSE-200 demonstrate the effectiveness of our proposed method, which yields pixel-wise accuracy of above 99%.
Synthesis of a Two-Parameter Controller for the Maximum Response Rate of a Control System with an Interval Characteristic Polynom

This article considers the application of mathematical programming for the synthesis of robust controllers. These methods are proposed for the synthesis of stationary control systems. They are based on the criterion of maximizing the degree of stability. The article solves the problem of providing the maximum response rate for the two-parameter regulator. This regulator is used in the system with interval-indefinite coefficients of the characteristic polynomial. For this, algebraic conditions are obtained. Under these conditions, the degree of stability will be maximum. Also, the algorithm of regulator synthesis is given. Based on these conditions and algorithms, the limiting degree of stability of the third-order interval system is found.
A Co-Melting Reduction Auto-Encoder

The auto-encoder has been widely used in deep learning techniques as one of the unsupervised feature extraction algorithms. This paper proposes a Co-Melting Reduction Auto-Encoder (CMRAE) model to enrich extracted and release the burden of model parameters in image feature extraction. Firstly, a co-melting reduction structure is designed to realize feature fusion in the encoder, and it can reduce feature loss and model parameters in the decoder. Secondly, a multi-reconstruction loss function introduces reconstruction loss between feature layers, which can strengthen the connection between the feature layers and avoid the model premature effectively. The experiments prove that CMRAE can improve the feature extraction ability and efficiency of the model. The classification accuracy of features extracted by CMRAE is better than the state-of-the-art results on the datasets including Cats vs. Dogs (CvD), lung CT images, and multi-class weather (MWD).
TranFuzz: An Ensemble Black-Box Attack Framework Based on Domain Adaptation and Fuzzing

A lot of research effort has been done to investigate how to attack black-box neural networks. However, less attention has been paid to the challenge of data and neural networks all black-box. This paper fully considers the relationship between the challenges related to data black-box and model black-box and proposes an effective and efficient non-target attack framework, namely TranFuzz. On the one hand, TranFuzz introduces a domain adaptation-based method, which can reduce data difference between the local (or source) and target domains by leveraging sub-domain feature mapping. On the other hand, TranFuzz proposes a fuzzing-based method to generate imperceptible adversarial examples of high transferability. Experimental results indicate that the proposed method can achieve an attack success rate of more than 68% in a real-world CVS attack. Moreover, TranFuzz can also reinforce both the robustness (up to 3.3%) and precision (up to 5%) of the original neural network performance by taking advantage of the adversarial re-training.
CHECKER: Detecting Clickbait Thumbnails with Weak Supervision and Co-teaching

Clickbait thumbnails on video-sharing platforms (e.g., YouTube, Dailymotion) are small catchy images that are designed to entice users to click to view the linked videos. Despite their usefulness, the landing videos after click are often inconsistent with what the thumbnails have advertised, causing poor user experience and undermining the reputation of the platforms. In this work, therefore, we aim to develop a computational solution, named as CHECKER, to detect clickbait thumbnails with high accuracy. Due to the fuzziness in the definition of clickbait thumbnails and subsequent challenges in creating high-quality labeled samples, the industry has not coped with clickbait thumbnails adequately. To address this challenge, CHECKER shares a novel clickbait thumbnail dataset and codebase with the industry, and exploits: (1) the weak supervision framework to generate many noisy-but-useful labels, and (2) the co-teaching framework to learn robustly using such noisy labels. Moreover, we also investigate how to detect clickbaits on video-sharing platforms with both thumbnails and titles, and exploit recent advances in vision-language models. In the empirical validation, CHECKER outperforms five baselines by at least 6.4% in F1-score and 4.2% in AUC-ROC. The codebase and dataset from our paper are available at: https://github.com/XPandora/CHECKER.