Reviewerly/exHarmony huggingface dataset:
Secure ABE Scheme for Access Management in Blockchain-Based IoT

Internet of Things (IoT) has been widely used in various fields of our daily life in the past years. Obviously, in the future, the significance of its security will be more evident and enormous. However, there are still many security issues in the IoT, which makes it vulnerable to be attacked easily by some potential factors. Targeting at these issue, in this paper, we propose a secure access management scheme in IoT based on blockchain. Firstly, in our work, by using the Decisional Learning with Error (DLWE) problem, we propose a secure Key-policy Attribute-based Encryption (KP-ABE) scheme, it can implement fine-grained access control in IoT. Secondly, we use blockchain to solve the authentication and access management challenges. In addition, the smart contract in blockchain can process transactions automatically, which greatly reduces the management cost and running time for this scheme. Last but not the least, we further analyze the security of our proposed scheme. The result shows that our scheme can resist some common attacks in IoT, and it is more secure and efficient.
LiDAR-Based Navigation of Tethered Drone Formations in an Unknown Environment

The problem of navigating a formation of interconnected tethered drones, named STEM (System of TEthered Multicopters), in an unknown environment is considered. The tethers feed electrical power from a ground station to the drones and also serve as communication links. The presence of more than one interconnected drone provides enough degrees of freedom to navigate in a cluttered area. The leader drone in the formation must reach a given point of interest, while the followers must move accordingly, avoiding interference with the obstacles. The challenges are the uncertainty in the environment, with obstacles of unknown shape and position, the use of LiDAR (Light Detection And Ranging) sensors, providing only partial information of the surroundings of each drone, and the presence of the tethers, which must not impact with the obstacles and pose additional constraints to how the drones can move. To cope with these problems, a novel real-time planning algorithm based on numerical optimization is proposed: the reference position of each drone is chosen in a centralized way via a convex program, where the LiDAR scans are used to approximate the free space and the drones are moved towards suitably defined intermediate goals in order to eventually reach the point of interest. The approach is successfully tested in numerical simulations with a realistic model of the system.
Mitigating Sentiment Bias for Recommender Systems

Biases and de-biasing in recommender systems (RS) have become a research hotspot recently. This paper reveals an unexplored type of bias, i.e., sentiment bias. Through an empirical study, we find that many RS models provide more accurate recommendations on user/item groups having more positive feedback (i.e., positive users/items) than on user/item groups having more negative feedback (i.e., negative users/items). We show that sentiment bias is different from existing biases such as popularity bias: positive users/items do not have more user feedback (i.e., either more ratings or longer reviews). The existence of sentiment bias leads to low-quality recommendations to critical users and unfair recommendations for niche items. We discuss the factors that cause sentiment bias. Then, to fix the sources of sentiment bias, we propose a general de-biasing framework with three strategies manifesting in different regularizers that can be easily plugged into RS models without changing model architectures. Experiments on various RS models and benchmark datasets have verified the effectiveness of our de-biasing framework. To our best knowledge, sentiment bias and its de-biasing have not been studied before. We hope that this work can help strengthen the study of biases and de-biasing in RS.
Text Based Hate-Speech Analysis

The definition of the term “hate speech” as per Oxford is `a speech that might involve abusive or threatening words which can have or can express pre-bias against a special community / group. This pre-bias can be anything like religion, race or sexual orientation, caste.' Hatred is generally based on ethnicity, religion, disability, gender, caste, and sexual orientation the internet and social media has become a powerful tool for such propagandist to spread hate and reach new audience. The anonymity and flexibility that the internet offers allow such haters to easily and safely propagate hate without any fear. Lack of regulation and legal policy worsens the situation a bit more. The need of the hour is for automated state of the art and scalable methods for hate speech detection and classification. This paper introduces two ensemble-based models in which the first one is based on Linear SVC, Logistic Regression, Random Forest and other based on Random Forest, KNN, Logistic Regression. Also, few deep learning models using self-trained and pre-trained word embeddings have been introduced for Twitter hate speech classification systems. The proposed research work has attempted to classify the tweets & assign them in one of the 3 categories i.e., Racist, Sexist or None.
Multi-source fusion for weak target images in the Industrial Internet of Things

Due to the influence of information fusion in Industrial Internet of Things (IIoT) environments, there are many problems, such as weak intelligent visual target positioning, disappearing features, large error in visual positioning processes, and so on. Therefore, this paper proposes a weak target positioning method based on multi-information fusion, namely the "confidence interval method". The basic idea is to treat the brightness and gray value of the target feature image area as a population with a certain average and standard deviation in IIoT environments. Based on the average and the standard deviation, and using a reasonable confidence level, a critical threshold is obtained. Compared with the threshold obtained by the maximum variance method, the obtained threshold is more suitable for the segmentation of key image features in an environment in which interference is present. After interpolation and de-noising, it is applied to mobile weak target location of complex IIoT systems. Using the metallurgical industry for experimental analysis, results show that the proposed method has better performance and stronger feature resolution.
Optical configuration of an N ∶ 2<sup>N</sup> reversible decoder using a LiNbO<sub>3</sub>-based Mach–Zehnder interferometer

These days when integrated circuit (IC) designers are facing an uphill task in limiting energy/heat dissipation, reversible computing is emerging as a potential candidate with vast application in fields like nanotechnology, quantum-dot cellular automata, and low power IC. Optical reversible logics have turned up to offer high speed and low energy computations with almost no loss of input information when a certain (arithmetic or logical) operation is performed. This paper explores an optical implementation of an optimized Fredkin gate that is employed to design an <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mi>N</mml:mi> <mml:mo>:</mml:mo> <mml:msup> <mml:mn>2</mml:mn> <mml:mi>N</mml:mi> </mml:msup> </mml:math> reversible decoder. The optical designs have been carried out using the electro-optic effect of a lithium niobate ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">L</mml:mi> <mml:mi mathvariant="normal">i</mml:mi> <mml:mi mathvariant="normal">N</mml:mi> <mml:mi mathvariant="normal">b</mml:mi> <mml:mi mathvariant="normal">O</mml:mi> </mml:mrow> <mml:mn>3</mml:mn> </mml:msub> </mml:mrow> </mml:math> )-based Mach–Zehnder interferometer under the beam propagation method (BPM) with Optiwave’s OptiBPM tool. The mathematical model of output power of these designs is also performed along with its validation in MATLAB.
Inspecting the Running Process of Horizontal Federated Learning via Visual Analytics

As a decentralized training approach, horizontal federated learning (HFL) enables distributed clients to collaboratively learn a machine learning model while keeping personal/private information on local devices. Despite the enhanced performance and efficiency of HFL over local training, clues for inspecting the behaviors of the participating clients and the federated model are usually lacking due to the privacy-preserving nature of HFL. Consequently, the users can only conduct a shallow-level analysis of potential abnormal behaviors and have limited means to assess the contributions of individual clients and implement the necessary intervention. Visualization techniques have been introduced to facilitate the HFL process inspection, usually by providing model metrics and evaluation results as a dashboard representation. Although the existing visualization methods allow a simple examination of the HFL model performance, they cannot support the intensive exploration of the HFL process. In this article, strictly following the HFL privacy-preserving protocol, we design an exploratory visual analytics system for the HFL process termed HFLens, which supports comparative visual interpretation at the overview, communication round, and client instance levels. Specifically, the proposed system facilitates the investigation of the overall process involving all clients, the correlation analysis of clients' information in one or different communication round(s), the identification of potential anomalies, and the contribution assessment of each HFL client. Two case studies confirm the efficacy of our system. Experts' feedback suggests that our approach indeed helps in understanding and diagnosing the HFL process better.
Taxonomy-aware Learning for Few-Shot Event Detection

Event detection classifies unlabeled sentences into event labels, which can benefit numerous applications, including information retrieval, question answering and script learning. One of the major obstacles to event detection in reality is insufficient training data. To deal with the low-resources problem, we investigate few-shot event detection in this paper and propose TaLeM, a novel taxonomy-aware learning model, consisting of two components, i.e., the taxonomy-aware self-supervised learning framework (TaSeLF) and the taxonomy-aware prototypical networks (TaPN). Specifically, TaSeLF mines the taxonomy-aware distance relations to increases the training examples, which alleviates the generalization bottleneck brought by the insufficient data. TaPN introduces the Poincaré embeddings to represent the label taxonomy, and integrates them into a task-adaptive projection networks, which tackles problems of the class centroids distribution and the taxonomy-aware embedding distribution in the vanilla prototypical networks.
Length Matters: Fast Internet Encrypted Traffic Service Classification based on Multi-PDU Lengths

Encryption of network traffic has become an inevitable trend. As an important link to Internet encrypted traffic analysis, encrypted traffic service classification can provide support for the coarse-grained network service traffic management and security supervision. But traditional DPI method cannot be effectively applied in an encrypted traffic environment, and the existing methods based on machine learning have two problems in feature selection. One is the complex feature classification over costing problem, the other is the TLS-1.2 suited method is no longer applicable to TLS-1.3 handshake encryption. To solve these problems, in this paper, we consider the differences among encryption network protocol stacks and propose a method of encrypted traffic service classification combining with capsule neural network in a multi-protocol environment by using multi-PDU lengths as the features, making full use of Markov property between PDU length sequences and being suitable to TLS1.3 environment. The feature makes our method much faster than others in feature extraction. Our control experiments on ISCX VPN-nonVPN dataset show that our method achieves a satisfactory performance (0.9860 Pr, 0.9856 Rc, 0.9855 F1), which is superior to the state-of-the-art methods.
UGRec: Modeling Directed and Undirected Relations for Recommendation

The recommender systems, which merely leverage user-item interactions for user preference prediction (such as the collaborative filtering-based ones), often face dramatic performance degradation when the interactions of users or items are insufficient. In recent years, various types of side information have been explored to alleviate this problem. Among them, knowledge graph (KG) has attracted extensive research interests as it can encode users/items and their associated attributes in the graph structure to preserve the relation information. In contrast, less attention has been paid to the item-item co-occurrence information (i.e., co-view), which contains rich item-item similarity information. It provides information from a perspective different from the user/item-attribute graph and is also valuable for the CF recommendation models. In this work, we make an effort to study the potential of integrating both types of side information (i.e., KG and item-item co-occurrence data) for recommendation. To achieve the goal, we propose a unified graph-based recommendation model (UGRec), which integrates the traditional directed relations in KG and the undirected item-item co-occurrence relations simultaneously. In particular, for a directed relation, we transform the head and tail entities into the corresponding relation space to model their relation; and for an undirected co-occurrence relation, we project head and tail entities into a unique hyperplane in the entity space to minimize their distance. In addition, a head-tail relation-aware attentive mechanism is designed for fine-grained relation modeling.
Using Residual Networks and Cosine Distance-Based K-NN Algorithm to Recognize On-Line Signatures

As a result of the urgent need to immediately identify individuals through the Internet, especially given the Coronavirus (COVID-19) pandemic at present, the recognition of online handwritten signatures has quickly evolved to become an urgent and necessary matter.However, signature identification remains challenging in the pattern recognition field due to intra-class variability and inter-class similarity.Intra-class variability is a characteristic of human behavioural activities, particularly in handwriting where no two handwritten signatures of any person can exactly coincide.The inter-class similarity is also a characteristic of human movement-based activities such as handwritten signatures particularly when the number of writers is large.In this research, an optimized transfer-learning-based architecture is proposed as a highly accurate identification technique for online-signatures using ResNet18 as a feature extraction deeplearning module.The X-Y time-series signals of the signatures were initially converted into images and used in retraining the ResNet18 model to achieve relatively high accuracy.The retrained ResNet18 model was then used to extract features that possess high discriminative distances among different classes of handwritten signatures.The model's deep layers were searched to determine the best layer that provided the most discriminative features when using a 1-nearest neighbour learning algorithm based on the cosine distance.By using an ensemble of five models trained on rotated versions of the original signatures and using only three training samples from each writer, the classification accuracy achieved 100% when applied on the genuine signatures of public datasets such as SVC 2004 TASK1 and TASK2, and a new proprietary dataset composed of 120 genuine users.When the abovementioned technique was tested on the aggregated version of the aforementioned datasets, the resultant accuracy was still above 99%.Moreover, the robustness of the technique was proven by testing the generated models trained with one dataset with the other two datasets resulting in accuracy above 99% for all combinations.
AdaPDP: Adaptive Personalized Differential Privacy

Users usually have different privacy demands when they contribute individual data to a dataset that is maintained and queried by others. To tackle this problem, several personalized differential privacy (PDP) mechanisms have been proposed to render statistical information of the entire dataset without revealing individual privacy. However, existing mechanisms produce query results with low accuracy, which leads to poor data utility. This is primarily because (1) some users are over protected; (2) utility is not explicitly included in the design objective. Poor data utility impedes the adoption of PDP in the real-world applications. In this paper, we present an adaptive personalized differential privacy framework, called AdaPDP. Specifically, to maximize data utility in different cases, AdaPDP adaptively selects underlying noise generation algorithms and calculates the corresponding parameters based on the type of query functions, data distributions and privacy settings. In addition, AdaPDP performs multiple rounds of utility-aware sampling to satisfy different privacy requirements for users. Our privacy analysis shows that the proposed framework renders rigorous privacy guarantee. We conduct extensive experiments on synthetic and real-world datasets to demonstrate the much less utility losses of the proposed framework over various query functions.
Unsupervised Extractive Text Summarization with Distance-Augmented Sentence Graphs

Supervised summarization has made significant improvements in recent years by leveraging cutting-edge deep learning technologies. However, the true success of supervised methods relies on the availability of large quantity of human-generated summaries of documents, which is highly costly and difficult to obtain in general. This paper proposes an unsupervised approach to extractive text summarization, which uses an automatically constructed sentence graph from each document to select salient sentences for summarization based on both the similarities and relative distances in the neighborhood of each sentences. We further generalize our approach from single-document summarization to a multi-document setting, by aggregating document-level graphs via proximity-based cross-document edges. In our experiments on benchmark datasets, the proposed approach achieved competitive or better results than previous state-of-the-art unsupervised extractive summarization methods in both single-document and multi-document settings, and the performance is competitive to strong supervised baselines.
Graph Based Structure Binary Pattern for Face Analysis

This work presents novel LBP variant so-called Graph Based Structure Binary Pattern (GBSBP) in different challenges. By considering 3 × 5 & 5 × 3 pixel windows, 4 graphs are created & each graph structure is injected with 3 different edges (→, ↔ and ―) which corresponds to single edge (any one) from one pixel position to another pixel position. Each injected edge specifies different measures & produces different values by utilizing 2 pixels. From both the windows 8 & 6 values are produced after applying different edge measures for each pixel position. By using these values 4 transformed images are produced. Then extracted histograms (from images transformed) are integrated to make the GBSBP feature size. Further enhancement in accuracy is achieved when GBSBP features are incorporated with LPQ features.
Survey Paper: Whale optimization algorithm and its variant applications

Whale Optimization Algorithm (WOA) proposed by Seyedali Mirjalili and Andrew Lewis in 2016 is popular and powerful metaheuristic algorithm to search the global solution of optimization problems. WOA is nature-inspired, metaheuristic (randomization and deterministic) algorithm, which has been widely used to solve various single objective, multi objective and multidimensional optimization problems. WOA and its variant have been introduced in engineering applications, bioinformatics, multi-level image segmentation, clustering applications, design of low pass filter, Email classification, Diabetes classification, heterogeneous networks, machine learning etc. WOA is gradient free, easy to represent, capable to explore, exploit the search space and able to avoid local optima. This paper presents overview of WOA, its variants and applications. The performance of WOA is enhanced by introducing hybridization of other methods with WOA such as WOA-PSO, WOA-Levy, WOA-BAT, WOA-ANN, WOA-SVM etc. Objective of metaheuristic algorithm is tofind best position or leader position X* which is near to optimal solution for target prey over successive iteration. Objective function could be based on minimization or maximization approach.
DC2: Delay-aware Compression Control for Distributed Machine Learning

Distributed training performs data-parallel training of DNN models which is a necessity for increasingly complex models and large datasets. Recent works are identifying major communication bottlenecks in distributed training. These works seek possible opportunities to speed-up the training in systems supporting distributed ML workloads. As communication reduction, compression techniques are proposed to speed up this communication phase. However, compression comes at the cost of reduced model accuracy, especially when compression is applied arbitrarily. Instead, we advocate a more controlled use of compression and propose DC2, a delay-aware compression control mechanism. DC2 couples compression control and network delays in applying compression adaptively. DC2 not only compensates for network variations but can also strike a better trade-off between training speed and accuracy. DC2 is implemented as a drop-in module to the communication library used by the ML toolkit and can operate in a variety of network settings. We empirically evaluate DC2 in network environments exhibiting low and high delay variations. Our evaluation of different popular CNN models and datasets shows that DC2 improves training speed-ups of up to 41× and 5.3 × over baselines with no-compression and uniform compression, respectively.
Shifting to Emergency Remote Teaching Due to the COVID-19 Pandemic: An Investigation of Greek Teachers’ Beliefs and Experiences

During schools' closure, due to the pandemic of COVID-19, teachers around the globe were forced to transfer their instruction on-line. They were facing a range of barriers and difficulties to plan and provide online remote teaching to their students. This paper reports on a study exploring Greek primary and secondary education teachers' views about emergency remote teaching and e-learning. The survey conducted in May 2020, just after schools' reopening in Greece. A total of 694 K-12 teachers responded to an online questionnaire. The preliminary findings of data analysis showed that the majority of the participants perceived the pandemic as a turning point with regards to the role of digital technologies and e-learning in the schools. On the other hand, we identified teachers' needs for professional development and support, in terms of learning design abilities necessary to integrate synchronous and asynchronous learning in both, online and physical, classrooms.