Reviewerly/exHarmony huggingface dataset:
Keyphrase extraction from single textual documents based on semantically defined background knowledge and co-occurrence graphs

The keyphrase extraction task is a fundamental and challenging task designed to extract a set of keyphrases from textual documents. Keyphrases are essential to assist publishers in indexing documents and readers in identifying the most relevant ones. They are short phrases composed of one or more terms used to represent a textual document and its main topics. In this article, we extend our research on C-Rank, which is an unsupervised approach that automatically extracts keyphrases from single documents. C-Rank uses concept-linking to link concepts in common between single documents and an external background knowledge base. We advance our study over C-Rank by evaluating it using different concept-linking approaches - Babelfy and DBPedia Spotlight. We evaluated C-Rank on data sets composed of academic articles, academic abstracts, and news articles. Our findings indicate that C-Rank achieves state-of-the-art results extracting keyphrases from scientific documents by experimentally comparing it to existing unsupervised approaches.
Control flow equivalence method for establishing sanctity of compiling

Compiler trap doors constitute a longstanding security problem for which there is currently no good solution. This paper presents a practical approach that shifts the focus from compiler correctness in its entirety to compiler correctness for a particular software. The guarantee is that the compiled binary’s control flow is consistent with the source’s control flow for a software of interest. We present an automated method to establish the equivalence by checking whether the source and the binary control flow graphs (CFGs) are isomorphic after semantics preserving graph transformations. The automated method produces evidence that can enable and simplify manual cross-checking as required to qualify an automated tool for safety-critical applications. We believe the proposed control equivalence method and its automation would be equally useful in avionics, automotive, medical devices and other safety-critical software industries where establishing trust in the binary code is critically important.
Multiclass Imbalanced Classification of Quranic Verses Using Deep Learning Approach

This paper uses deep learning approach for the classification of Quranic verses. The dataset has an imbalance, hence first it is balanced by oversampling. This paper aims to classify the verses using Bidirectional Encoder Representation from Transformers (BERT) word embedding by considering the context of words. BERT reads a word with all its neighboring words and assigns representations accordingly. Furthermore, to ensure that the classifier remembers the most important part of the input sequence, deep learning classifiers with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are used for classification. As the BERT word embeddings of the text data are created, they are fed to 3 Neural Network (NN) models i.e. NN with LSTM which achieved f1-score and accuracy of 0.85 for uncased, and f1-score of 0.82 and accuracy of 0.83 for cased embedding; NN with GRU which achieved F1-score and accuracy of 0.89 for uncased and 0.90 for cased embedding; and fine-tuned BERT model which achieved F1-scores of 0.93 and accuracy of 0.98 for both base-uncased and base-cased embedding.
Consistency-aware Multi-modal Network for Hierarchical Multi-label Classification in Online Education System

In the online education system, predicting the knowledge of exercises is a fundamental task of many applications, such as cognitive diagnosis. Usually, experts consider this problem as Hierarchical Multi-label Classification (HMC), since the knowledge concepts exhibit a multi-level structure. However, existing methods either sacrificed knowledge consistency for classification accuracy or sacrificed classification accuracy for knowledge consistency. Maintaining the balance is difficult. To forgo this dilemma, in this paper, we develop a novel frame-work called Consistency-Aware Multi-modal Network (Cam-Net). Specifically, we develop a multi-modal embedding module to learn the representation of the multi-modal exercise. Then, we adopt a hybrid prediction method consisting of the flat prediction module and the local prediction module. The local prediction module deals with the relation between the knowledge hierarchy and the input exercise. The flat prediction module focuses on maintaining knowledge consistency. Finally, to balance classification accuracy and knowledge consistency, we combine the outputs of two modules to make a final prediction. Extensive experimental results on two real-world datasets demonstrate the high performance and the ability to reduce knowledge inconsistency of CamNet.
A Trusted Solution to Hyperledger Fabric Reordering Problem

Hyperledger Fabric consortium chain is a more reliable blockchain between members, which is widely used in a variety of applications due to its imtamperability. Fabric++ proposes optimization strategies to improve the efficiency of Hyperledger Fabric, but this leads to a decline in its credibility. In the end, we propose solutions to the fairness, stability, and availability issues of Fabric++ in terms of trust, as well as the possible impact. In our paper, we build a simulation system based on gPRC communication and verify the correctness of our solutions. Further, we implement the solution of FabricUp based on Hyperledger Fabric V2.2 and conduct a series of experiments. The experimental results show that FabricUp effectively solves the credibility problem in Fabric++.
Bottom-Up Progressive Semantic Alignment for Image-Text Retrieval

Image-text retrieval is a challenging task due to image and text are heterogeneous cross-modal data, which possess semantic gap. The key issue of image-text retrieval is how to learn a common feature space while semantic correspondence between image and text remains. Some existing works extract region feature in image and word feature in text to implement cross-modal alignment between local elements, the other works integrate relation-aware information to local elements to compute cross-modal similarity, while these methods not utilize the semantic information in different semantic-level. In order to address this issue, we propose a Bottom-up Progressive Semantic Alignment (BPSA) network, in which precise fine-grained alignment is carried out on diverse semantic-levels progressively. Specifically, the feature of the cross-modal data are extracted from bottom element to local-group, and global-representation by graph convolution and attention mechanism. We conduct extensive experiments on Flickr30K and MS-COCO datasets, compared with the related state-of-the-art methods. The results show that our network achieves competitive performance.
Fast and Memory-Efficient Planning in C-space: Modified Bi-directional RRT* Algorithm for Humanoid Robots

In this paper we address the problem of faster and memory-efficient path planning for anthropomorphic manipulators with multi-link collision avoidance. As a solution, we present a modification of the intelligent bi-directional RRT* algorithm working in a C-space, where we don’t generate excess vertices of a tree by estimating their locations. Suggested algorithm is validated for grasping task with iCub humanoid robot. Comparison with others RRT* modifications demonstrate that we find the similar-length path with the significant improvement in a planning time, reduced amount of the memory to be allocated and show 100% success rate for the cases, where others planners will likely fail.
Interconnections between Governance and Socioeconomic Conditions: Understanding Sub-Saharan African Challenges

Given that challenges facing sub-Saharan African (SSA) countries on the issue of socioeconomic development have been identified as critical to strengthening the inherent link between governance and socioeconomic conditions, this study examines the interconnections between governance and SSA socioeconomic conditions. With a focus on 25 countries between 2005 and 2019, the analysis is based on Panel-Corrected Standard Error (PCSE) and System-GMM estimations, and panel causality test. Results show that SSA seems not to have the means of effective governance to spur improved socioeconomic conditions. Findings indicate that the pervasiveness of institutional problems in many SSA countries has been responsible for the poor socioeconomic condition in the region. Furthermore, it is equally found that governance quality and socioeconomic conditions are mutually reinforcing, suggesting that they influence each other. An improvement in socioeconomic conditions could result in better governance. On the other hand, the quality of governance is viewed as a vital ingredient in achieving needed socioeconomic development outcomes. Thus, it is suggested that there is need for SSA countries to streamline governing system towards engendering improved well-being. The introduction and implementation of transformative policies through effective governance are also necessary for ensuring critical structural changes and increased social service provision. Overall, there should be a proactive identification of ineffective policies and procedures by policymakers to enhance meaningful impact in the region.
A Real-time and Robust Visual-based Autonomous Driving Model

In autonomous driving, visual-based urban driving is a challenging task. Considering that the operation of any algorithm takes time, in order to ensure its real-time performance, we propose an end-to-end model that can obtain the control parameters of the current and future four frames. Furthermore, our model includes a new ResNet50-ConvLSTM architecture. To be specific, we first trained a data intensifier through conditional imitation learning using Bird’s Eye View Semantic Segmentation (BEV-seg). The data intensifier could get different control actions (e.g., steering angle and throttle) by changing high-level commands (e.g.,turn left, turn right, follow lane and go straight). Then we use the trained data intensifier to supervise the training of a purely vision-based network. Changing commands while training enables the network to achieve robust driving strategies. Finally, the trained network can get the current and future four frames of control action by inputting the image of the front camera. We evaluated our model in the CARLA simulator. Our result shows that our model achieves better results than the baseline methods on the CARLA original and the NoCrash benchmarks.
Intelligent furniture design and function simulation based on3D technology

With the application of 5G communication technology and Internet of Things technology in China, intelligent furniture is gradually coming into our daily life with the rapid development of intelligent manufacturing technology in China. The design concept and functional characteristics of intelligent furniture must meet the requirements of modern life convenience and comfort, and also determine the practical application value of intelligent furniture. Therefore, with the support of 3D technology, the whole process of intelligent furniture design and function simulation can be intuitively displayed in front of the user, and can be creatively adjusted according to the user’s personal preferences and the actual situation of the application space, increase the user’s participation, and improve the design and use effect of intelligent furniture.
Global Fusion Capsule Network with Pairwise-Relation Attention Graph Routing

Comparing with traditional convolutional neural networks, the recent capsule networks based on routing-by-agreement have shown their robustness and interpretability. However, the routing mechanisms lead to huge computations and parameters, significantly reducing the effectiveness and efficiency of capsule networks towards complex and large-scale data. Moreover, the capsule network and its variants only explore the PrimaryCaps layer for various reasoning tasks while ignoring the specific information learned by the low-level convolutional layers. In this paper, we propose a Graph Routing based on Multi-head Pairwise-relation Attention (GraMPA) that could thoroughly exploit both the semantic and location similarity of capsules in the form of multi-head graphs, to improve the robustness of the capsule network with much fewer parameters. Moreover, a Global Fusion Capsule Network (GFCN) architecture based on the Multi-block Attention (MBA) module and Multi-block Feature Fusion (MBFF) module is designed, aiming to fully explore detailed low-level signals and global information to enhance the quality of final representations especially for complicated images. Exhaustive experiments show that our method can achieve better classification performance and robustness on CIFAR10 and SVHN datasets with fewer parameters and computations. The source code of GraMPA is available at https://github.com/SWU-CS-MediaLab/GraMPA.
An Overview of Adversarial Sample Attacks and Defenses for Graph Neural Networks

Graph-structured data has been widely used. Graph neural network can be used to analyze graph-structured data well. However, the existence of adversarial samples indicates that the prediction results of graph neural networks can be deliberately manipulated. This affects the feasibility of applying deep learning methods to critical situations. Study on graph neural network adversarial sample attack methods and defense techniques can help to strengthen our understanding of graph neural network and build a more robust graph neural network model. It is of great significance to promote the feasibility and security of relevant algorithms in practical applications. This paper analyzes the current graph neural network adversarial sample attack and defense techniques, which has a guiding significance for future research work.
Transparent Network Memory Storage for Efficient Container Execution in Big Data Clouds

This paper presents a transparent Container Network Memory storage device, coined as CNetMem, aiming to address the open problem of unpredictable performance degradation of containers when the working set of an application no longer fits in container memory. First, CNetMem will enable application tenants running in a container to park their working set memory/file to a faster network memory storage by organizing a group of remote memory nodes as remote memory donors. This allows CNetMem to take advantage of remote idle memory on a cluster before resorting to a slow local I/O subsystem like local disk without any modification of host OS or application. Second, CNetMem provides a hybrid batching technique to remove or alleviate performance bottlenecks in the I/O performance critical path for remote memory read/write with replication or disk backup for fault tolerance. Third, CNetMem introduces a rank-based node selection algorithm to find the optimal node for placing remote memory blocks across cluster. This helps CNetMem to reduce the performance impact due to remote memory eviction. Extensive experiments are conducted on three big data applications and four machine learning workloads. The results show that CNetMem achieves up to 172× throughput improvements compared to vanilla Linux and up to 5.9× completion time improvements over existing approaches in big data and ML workload.
E-Learn Detector: Smart Behaviour Monitoring System to Analyze Student Behaviours During Online Educational Activities

With the rise of online education more attention is being paid to the deficiencies in online learning platforms. Online Learning environments aim to deliver efficacious instructions, but rarely take providing a conventional classroom experience to the students into consideration. Efficient detection of students' learning situations can provide information to teachers to help them identify students having trouble in real-time. This idea has been exploited several times for Intelligent Tutoring Systems, but not yet in other types of learning environments that are less structured. "E-Learn Detector is a web application solution to these existing issues in online learning which consists of unique features such as verifying the user during logging procedure and throughout an examination, detecting suspicious behaviors and presence of multiple users during online examinations and detecting low engagement levels of students during online lectures. "E-Learn Detector" is developed with the aim to provide guidance to students to improve their academic performance and behavior during classroom activities and to induce the best out of the educational activities.
A Study on ECG Denoising Using Wiener Filter

The electrocardiogram (ECG) is an essential signal in the medical field, with the help of which doctors manage to identify the electrical functions of the heart. From the interpretation of the ECG signal it is possible to diagnose and measure the presence of abnormal heart rhythms, as well as the presence of indicators for future abnormalities. The captured ECG signal is usually contaminated with noise. Therefore, it is important to eliminate the noise to prevent any error in the subsequent analysis of the signal. This paper presents a study of ECG noise reduction via adaptive filtering using the Wiener filter. Simulation results obtained in Matlab provide a numeric assessment of the approach.
Correction Cubature GM-PHD Filter for nonlinear Multi-target tracking

So as to improve the accurate multi-target tracking, This paper proposes a Correction Cubature GM-PHD (CKF-CGMPHD) algorithm. The algorithm introduces the measurement auxiliary weight correction into the GM-PHD algorithm, and uses the estimated value to correct the updated Gaussian weights to fix the Gaussian weights, so as to further improve the accuracy of the filtering algorithm, and reduce the calculation at the same time; The CKF method is introduced into the GM-PHD algorithm, and uses cubature point to predict and update the GM-PHD, so as to improve the nonlinearity and stability of the filtering algorithm. The simulation results show that compared with the traditional algorithm, CKF-CGMPHD can obtain more accurate target tracking results under different clutter environments, and the computational complexity is smaller.
Design of Fitness Pedometer Based on Infrared Photoelectric Sensor

In order to increase the accuracy of step counting value and the interest of running in fitness exercise, a embedded pedometer for fitness running system based on infrared photoelectric sensor, OLED display, MP3 module and STM8L embedded microprocessor is designed. In addition, the functions of hardware circuit and software program are realized. In this system, transmitter of infrared photoelectric sensor is electrified to continuously emit infrared light, and the receiver receives infrared light and converts it into electrical signal. The electrical signal is collected and counted by single chip microcomputer, and the result is displayed on the display screen. In addition, MP3 module is added to realize the function of playing music and voice prompt, which increases the entertainment nature of the system. The experimental results in different light conditions and stride frequency show that the system can achieve accurate counting values, and satisfy the normal use for jogging users.
An Adaptive Logarithm Quantization Method for DNN Compression

The size and complexity of Neural Network models grow rapidly in recent years, which makes the inference of these models require more computational and memory resources. To reduce the required resources, quantization is one of the promising methods. Logarithm quantization can both reduce the model size and the computational complexity because the time-consuming multiplication operation can be replaced with the addition operation in logarithm domain. However, the previous logarithm quantization methods use a fixed logarithm base. Therefore, they cannot adapt according to the distribution of data and bit-width budgets, which causes performance degradation. To address such a problem, we propose an adaptive quantization method to optimize the quantization function. Our method first finds an optimized weight quantization function by minimizing the quantization loss of the model’s weight data under a given bit-width budget. Then we use a zero-shot way to find an optimized quantization function for activation data. Finding the optimized parameters is time-consuming. We propose a heuristic algorithm to solve the optimization problem fast. Compared to the previous logarithm quantization methods, our method can achieve up to 72.53% higher Top-1 accuracy under the same bit-width constraint.
Hindi Correspondence of Bengali Nominal Suffixes

One bottleneck of Bengali to Hindi transfer based machine translation system is the translation of suffixes of noun.The appropriate translation of a nominal suffix often depends on the semantic role of the corresponding noun chunk in the sentence.With the availability of a high performance Bengali morphological analyzer and a basic Bengali parser it is possible to identify the role of each noun chunk.This information may be used for building rules for translating the ambiguous nominal suffixes.As there are some similarities between the uses of Bengali and Hindi nominal suffixes we find that the rules may be identified by linguistically analyzing corpus data.In this paper, we identify rules for the ambiguous four Bengali nominal suffixes from corpus data and evaluate their performances.This set of rules is able to resolve a majority of the nominal suffix ambiguities in Bengali to Hindi transfer based machine translation system.Using the rules, we are able to translate 98.17% Bengali nouns correctly which is much better than the baseline ILMT system's accuracy of 62.8%.
Experimental evidence on the cognitive effectiveness of diagrams

Diagrams continue to play an important role in different areas of computer science. In this paper, we discuss how the cognitive processing of such diagrams can be conceptually understood. To this end, we refer to the CogniDia framework, a recent framework that describes the consecutive phases of cognitive processing. We discuss experimental results for the four categories of the framework. We conclude with an outlook on future research.
An adaptive attack on 2-SIDH

We present a polynomial-time adaptive attack on the 2-SIDH protocol. The 2-SIDH protocol is a special instance of the countermeasure proposed by Azarderakhsh, Jao and Leonardi to perform isogeny-based key exchange with static keys in the presence of an adaptive attack. This countermeasure has also been recently explicitly proposed by Kayacan. Our attack extends the adaptive attack by Galbraith, Petit, Shani and Ti (GPST) to recover a static secret key using malformed points. The extension of GPST is non-trivial and requires learning additional information. In particular, the attack needs to recover intermediate elliptic curves in the isogeny path, and points on them. We also discuss how to extend the attack to k-SIDH when k>2 and explain that the attack complexity is exponential in k.
Introduction to Quantum Gates : Implementation of Single and Multiple Qubit Gates

A quantum gate or quantum logic gate is an elementary quantum circuit working on a small number of qubits. It means that quantum gates can grasp two primary feature of quantum mechanics that are entirely out of reach for classical gates : superposition and entanglement. In simpler words quantum gates are reversible. In classical computing sets of logic gates are connected to construct digital circuits. Similarly, quantum logic gates operates on input states that are generally in superposition states to compute the output. In this paper the authors will discuss in detail what is single and multiple qubit gates and scope and challenges in quantum gates.