Reviewerly/exHarmony huggingface dataset:
KeyNet: Enhancing Cybersecurity with Deep Learning-Based LSTM on Keystroke Dynamics for Authentication

Currently, everyone accumulates, stores, and processes their sensitive data on computers which makes it essential to protect computers from intrusion. Several approaches employ biometric data such as voice, retinal scan, fingerprints, etc., to enhance user authentication. There is an added overhead of sensors needed to implement these biometric approaches. Instead, an improved and strong password authentication would be cost-effective and straightforward. Keystroke dynamics is the analysis of temporal patterns to validate user authenticity. It is a behavioral biometric that makes use of the typing style of an individual and can be used to enhance the current authentication security procedures efficiently and economically. Such a behavioral biometric system is fairly unexplored compared to other behavioral verifications models. In this study, we focus on applying and training deep learning approach based Long Short Term Memory (LSTM) algorithm in an optimized way to validate temporal keystroke patterns of users for improved password authentication. Our research shows an enhanced authentication rate for the keystroke dynamic benchmark dataset.
An Integrated Method of Semantic Parsing and Information Retrieval for Knowledge Base Question Answering

For the task of open domain Knowledge Base Question Answering (KBQA) in CCKS 2021, we propose an integrated method, which complementarily combines a more generalized information retrieval model and a more accurate semantic parsing model without manual involvement of templates. Our method achieves the averaged F1-score of 78.52% on the final test data, and ranks third in the KBQA task of CCKS 2021.
Identify Finger Rotation Angles With ArUco Markers and Action Cameras

Abstract Measuring the motions of human hand joints is often a challenge due to the high number of degrees-of-freedom. In this study, we proposed a hand tracking system utilizing action cameras and ArUco markers to continuously measure the rotation angles of hand joints during motion. Three methods were developed to estimate the joint rotation angles. The pos-based method transforms marker positions to a reference coordinate system and extracts a hand skeleton to identify the rotation angles. Similarly, the orient-x-based method calculates the rotation angles from the transformed x-orientations of the detected markers in the reference coordinate system. In contrast, the orient-mat-based method first identifies the rotation angles in each camera coordinate system using the detected orientations and then synthesizes the results regarding each joint. Experiment results indicated that the repeatability errors with one camera regarding different marker sizes were around 2.64–27.56 deg and 0.60–2.36 deg using the marker positions and orientations, respectively. With multiple cameras employed, the joint rotation angles measured by using the three methods were compared with that measured by a goniometer. Comparison results indicated that the results of using the orient-mat-based method are more stable and efficient and can describe more types of movements. The effectiveness of this method was further verified by capturing hand movements of several participants. Therefore, it is recommended for measuring joint rotation angles in practical setups.
Systems Analysis of the Digital Agent’s Role in Hybrid Social Interaction Forms

The article is devoted to the actual problems of human–computer interactions associated with the active inclusion of smart technologies and systems of artificial intelligence in the social spheres of activity. The authors turn to the study of the digital agents’ role in the organization of multi-level communication. They highlight the relevance of the problem of trust in innovations in the intelligent automation field, in particular to the functions of various assistants and consultants who are able to act autonomously in the social space, but are not endowed with responsibility, unlike a person. The conceptual basis of the system analysis of communication hybrid forms in the technosocial system is the idea of functional structures in combination with the installations of actor-network theory. The authors use methods of comparative analysis and generalization of sociological surveys that reveal the attitude of a person to the functional role of digital intermediaries in computer-mediated communication. As an empirical basis, statistical data of surveys conducted in various social groups of Russian society are used. The summarized results present a characteristic of the Russian citizens’ attitude to the prospects for the professional activity robotization and the spread of artificial intelligence systems. The positive assessment of innovations related to the automation of routine operations in solving intellectual and applied problems is emphasized. There is a tendency to strengthen the public negative attitude to the prospect of the dominance of AI, which is associated with threats to security and social order. The positive assessment of innovations related to the automation of routine operations in solving intellectual and applied problems is emphasized. There is a tendency to strengthen the public negative attitude to the prospect of the dominance of AI, which is associated with threats to security and social order.
Quantum Orbital Minimization Method for Excited States Calculation on a Quantum Computer

We propose a quantum-classical hybrid variational algorithm, the quantum orbital minimization method (qOMM), for obtaining the ground state and low-lying excited states of a Hermitian operator. Given parametrized ansatz circuits representing eigenstates, qOMM implements quantum circuits to represent the objective function in the orbital minimization method and adopts a classical optimizer to minimize the objective function with respect to the parameters in ansatz circuits. The objective function has an orthogonality constraint implicitly embedded, which allows qOMM to apply a different ansatz circuit to each input reference state. We carry out numerical simulations that seek to find excited states of H2, LiH, and a toy model consisting of four hydrogen atoms arranged in a square lattice in the STO-3G basis with UCCSD ansatz circuits. Comparing the numerical results with existing excited states methods, qOMM is less prone to getting stuck in local minima and can achieve convergence with more shallow ansatz circuits.
F2-Bubbles: Faithful Bubble Set Construction and Flexible Editing

In this paper, we propose F2-Bubbles, a set overlay visualization technique that addresses overlapping artifacts and supports interactive editing with intelligent suggestions. The core of our method is a new, efficient set overlay construction algorithm that approximates the optimal set overlay by considering set elements and their non-set neighbors. Thanks to the efficiency of the algorithm, interactive editing is achieved, and with intelligent suggestions, users can easily and flexibly edit visualizations through direct manipulations with local adaptations. A quantitative comparison with state-of-the-art set visualization techniques and case studies demonstrate the effectiveness of our method and suggests that F2-Bubbles is a helpful technique for set visualization.
Enabling 3-D Object Detection With a Low-Resolution LiDAR

Light detection and ranging (LiDAR) has been widely used in autonomous vehicles for perception and localization. However, the cost of a high-resolution LiDAR is still prohibitively expensive, while its low-resolution counterpart is much more affordable. Therefore, using low-resolution LiDAR for autonomous driving is an economically viable solution, but the point cloud sparsity makes it extremely challenging. In this letter, we propose a two-stage neural network framework that enables 3-D object detection using a low-resolution LiDAR. Taking input from a low-resolution LiDAR point cloud and a monocular camera image, a depth completion network is employed to produce dense point cloud that is subsequently processed by a voxel-based network for 3-D object detection. Evaluated with KITTI dataset for 3-D object detection in bird-eye view (BEV), the experimental result shows that the proposed approach performs significantly better than directly applying the 16-line LiDAR point cloud for object detection. For both easy and moderate cases, our 3-D vehicle detection results are close to those using 64-line high-resolution LiDARs.
Fast Single Image Dehazing Using Morphological Reconstruction and Saturation Compensation

Despite having effective dehzing performance, single image dehazing methods based on the dark channel prior (DCP) still suffer from slightly dark dehazing results and oversaturated sky regions. An improved single image dehazing method, which combines image enhancement techniques with DCP model, is proposed to overcome this deficiency. Firstly, it is analyzed that the cause of darker results mainly lies in the air-light overestimation caused by bright ambient light and white objects. Then, the air-light estimation is modified by combining morphological reconstruction with DCP. Next, it is derived that appropriately increasing the saturation component can compensate for transmission underestimate, which can further alleviate the oversaturation. Finally, the image dehazed with modified air-light and transmission is further refined by linear intensity transformation to improve contrast. Extensive experiments validate the proposed method, which is on par with and even outperforms the state-of-the-art methods in subjective and objective evaluation.
Deep Fusion: Crafting Transferable Adversarial Examples and Improving Robustness of Industrial Artificial Intelligence of Things

Industry 5.0 is aimed at merging the cognitive computing capabilities of deep neural networks (DNNs) with human resourcefulness in collaborative operations. DNNs have been widely used in Industrial Artificial Intelligence of Things (Industrial AIoT) systems. However, DNNs are vulnerable to adversarial attacks, which bring a considerable risk to Industrial AIoT systems. The adversary uses adversarial examples crafted on the local ensemble model to attack black-box target of Industrial AIoT systems, resulting in catastrophic consequences. It is essential to study ensemble adversarial attack and defense strategies in black-box scenarios. Nevertheless, current ensemble attacks' performance is limited by the diversity of local models and ensemble strategies, and defensive strategies are inefficient. To solve these problems, we propose two novel deep fusion methods from both an attacker's and a defender's perspective. For initiating attacks, we propose deep fusion attack. The erosion models are applied to compensate for local models' insufficiency in diversity. We fuse erosion models in the output space, and the feature space simultaneously and continuously accumulate historical gradients to retain adversarial information, thereby improving transferability. Extensive experimental results show that our approach achieves superior performance in black-box attacks, and the average success rate of our attack reaches a compelling 87.4%. For constructing defenses, we propose deep fusion defense, using a fusion of multiple predictions with erosion models as a novel approach. We successfully increase the model's robustness by more than 90% on the ImageNet dataset.
Zebrafishtracker: A multi-zebrafish tracking algorithm can effectively solve cross occlusion

Zebrafish is a model organism, which is widely used in life science and medicine research due to its high homology with human genes. How to handle cross-cases and achieve more comprehensive tracking is always a challenge. Zebrafishtracker proposed in this paper uses the instance segmentation and the skeleton stability analysis strategy to effectively solve the problem of detection error caused by the frequent crossover of multiple zebrafish. Then 2D tracking of zebrafish is realized based on identity information. The results show that in two relatively complex videos, MOTA of our algorithm in the top-view reaches more than 95%.
ELECRec: Training Sequential Recommenders as Discriminators

Sequential recommendation is often considered as a generative task, i.e., training a sequential encoder to generate the next item of a user's interests based on her historical interacted items. Despite their prevalence, these methods usually require training with more meaningful samples to be effective, which otherwise will lead to a poorly trained model. In this work, we propose to train the sequential recommenders as discriminators rather than generators. Instead of predicting the next item, our method trains a discriminator to distinguish if a sampled item is a 'real' target item or not. A generator, as an auxiliary model, is trained jointly with the discriminator to sample plausible alternative next items and will be thrown out after training. The trained discriminator is considered as the final SR model and denoted as \modelname. Experiments conducted on four datasets demonstrate the effectiveness and efficiency of the proposed approach.
Region-based Resource Management and Lexical Exception Handlers in Continuation-Passing Style

Abstract Regions are a useful tool for the safe and automatic management of resources. Due to their scarcity, resources are often limited in their lifetime which is associated with a certain scope. When control flow leaves the scope, the resources are released. Exceptions can non-locally exit such scopes and it is important that resources are also released in this case. Continuation-passing style is a useful compiler intermediate language that makes control flow explicit. All calls are tail calls and the runtime stack is not used. It can also serve as an implementation technique for control effects like exceptions. In this case throwing an exception means jumping to a continuation which is not the current one. How is it possible to offer region-based resource management and exceptions in the same language and translate both to continuation-passing style? In this paper, we answer this question. We present a typed language with resources and exceptions, and its translation to continuation-passing style. The translation can be defined modularly for resources and exceptions – the correct interaction between the two automatically arises from simple composition. We prove that the translation preserves well-typedness and semantics.
Application Analysis of Artificial Intelligence Technology in Brand Marketing Strategy

In the era of economic globalization and informatization, enterprises are facing increasingly fierce competition. The single use of traditional marketing methods by enterprises is not enough to cope with the tremendous changes brought about by informatization and globalization. If an enterprise wants to be in an advantageous position in the competition, it must adapt to changes in the surrounding environment and adjust its strategy to adapt. The development of big data in the Internet age has made artificial intelligence technology more and more mature. It has been applied to many areas of society. With the advent of the information age, brand marketing has inevitably entered the era of intelligence. The development and application of the network have made the original The traditional marketing model is no longer applicable, so that it began to develop and transform to the Internet network marketing. The purpose of this article is to study the application of artificial intelligence technology in brand marketing strategies. Based on this, this article analyzes the impact of artificial intelligence technology on marketing based on the analysis of the related concepts and theories of artificial intelligence technology and network marketing. In a nutshell, artificial intelligence technology not only affects brand marketing strategies to a certain extent, but also has an impact on the market. As the physical carrier and foundation of various new functions in the information age, artificial intelligence technology occupies an unparalleled position in the development of corporate marketing. Research shows that in recent years, the retail sales of smart marketing channels have slowly increased in total. Tmall’s sales have increased by about 10%, while the traditional marketing model has been declining year by year. It can be seen that the new the marketing channels of the era combined with artificial intelligence are gradually being recognized and used by people, and the market share of traditional marketing channels is decreasing.
HATHI 1M: Introducing a Million Page Historical Prose Dataset in English from the Hathi Trust

We present a new dataset built on prior work consisting of 1,671,370 randomly sampled pages of English-language prose roughly divided between modes of fictional and non-fictional writing and published between the years 1800 and 2000. In addition to focusing on the “page’’ as the basic bibliographic unit, our work employs a single predictive model for the historical period under consideration in contrast to prior work. Besides publication metadata, we also provide an enriched feature set of 107 features including part-of-speech tags, sentiment scores, word supersenses and more. Our data is designed to give researchers in the digital humanities large yet portable random samples of historical writing across two foundational modes of English prose writing. We present initial insights into transformations of linguistic patterns across this historical period using our enriched features as possible pointers to future work. The data can be accessed at <a href="https://doi.org/10.7910/DVN/HAKKUA" target="_blank"><em>https://doi.org/10.7910/DVN/HAKKUA</em></a>.
A Novel Null-Invariant Temporal Measure to Discover Partial Periodic Patterns in Non-uniform Temporal Databases

“Rare item problem” is a fundamental problem in pattern mining. It represents the inability of a pattern mining model to discover the knowledge about frequent and rare items in a database. In the literature, researchers advocated the usage of null-invariant measures as they disclose genuine correlations without being influenced by the object co-absence in the database. Since the existing null-invariant measures consider only an item’s frequency and disregard its temporal occurrence information, they are inadequate to address the rare item problem faced by the partial periodic pattern model. This paper proposes a novel null-invariant measure, called relative periodic-support, to find the patterns containing both frequent and rare items in non-uniform temporal databases. We also introduce an efficient pattern-growth algorithm to find all desired patterns in a database. Experimental results demonstrate that our algorithm is efficient.
Large-scale Face Clustering Method Research Based on Deep Learning

Face clustering is a pre-processing stage for face detection and face recognition tasks, alleviating the heavy manual labeling work. The goodness of clustering depends on the data features and clustering algorithm. The current data is so complex and diverse that traditional machine learning clustering algorithms can no longer handle such large-scale and high latitude data. Therefore, this paper introduces two types of face clustering algorithms based on deep learning, face clustering algorithms based on deep subspace and face clustering algorithms based on graph convolutional networks, and introduces each algorithm and compares its advantages and shortcomings.
Conversational Intelligent Tutoring Systems: The State of the Art

This chapter describes the latest developments in Conversational Intelligent Tutoring Systems (CITS), which are e-learning systems that adopt Computational Intelligence approaches to mimic a human tutor. CITS deliver a personalized, human-like tutorial via natural language. Like expert human tutors, CITS automatically profile a learner's knowledge and skills and also profile and adapt to an individual's affective traits (such as mood or personality) to improve their learning. The chapter explores the challenges for CITS design that prevent them from moving into the mainstream and highlights scalability issues that the research community must address. Two cutting-edge CITS are described. Oscar CITS dynamically profiles learning styles using learner behaviour throughout the tutorial conversation, and adapts at a question level, resulting in significantly better learning gain. Hendrix 2.0 CITS uses a bank of artificial neural networks to automatically profile a learner's comprehension using webcam images, and intervenes to help improve learner motivation and avoid impasse. The considerable issue of trust in AI is discussed, and a study of public opinion on ethical use of AI in education showed that more needs to be done in educating the public on the benefits and risks of AI in education.
A Survey of the Hybrid Exact String Matching Algorithms

The matching of search string patterns has become a bigger issue of concern because biological sequence databases are growing rapidly at overwhelming proportions so string matching algorithm is an essential scientific tool to assist in solving composite string search problems. At present, the shift of focus is geared towards hybrid algorithms as they provide better performance in various evaluation metrics. Thus, researchers and experts view a hybrid algorithm as the way forward for resolving string search difficulties. Therefore, the concentration will be on the various kinds of hybrid algorithms that have been implemented, their processes, and their performances. This research has performed a systematic literature review by reviewing over 30 papers and identifying 15 string matching hybrid algorithms. When reviewing these hybrid algorithms, it is found that by combining different original algorithms, the hybrid algorithm can outperform the individual algorithms in different evaluation metrics. The purpose, enhanced concepts, and results of these algorithms have been highlighted in the article. This article has critically reviewed different string matching hybrid algorithms and provided useful information for experts and researchers that can utilize strong-matching hybrid algorithms in their systems.
Online Voting System Using Blockchain

In an electronic information system, blockchain technology has already been touted as a way to support the need for trust between transactions. To a certain extent, electronic democracy has addressed the problems of helpless anonymity and low competency associated with conventional democracy. Electronic democracy. We can’t ignore the problems it poses in overseeing the vote counting, or the fact that it necessitates the presence of a reliable third party. Blockchain technology’s decentralisation, anonymity, and non-tampering properties have made it a better contender for overcoming electronic voting’s concerns in the recent years. In this research, we propose a Blockchain-based online voting system. In contrast to traditional and online voting systems, we are able to cast our ballots anywhere in the world using a mobile device.