Reviewerly/exHarmony huggingface dataset:
NeuSub: A Neural Submodular Approach for Citation Recommendation

Citation recommendation is a task that aims to automatically select suitable references for a working manuscript. This task has become increasingly urgent as the typical pools of candidates continue to grow, in the order of tens or hundreds of thousands or more. While several approaches to citation recommendation have been proposed in the literature, they generally seem to lack principled mechanisms to ensure diversity and other global properties among the recommended citations. For this reason, in this paper we propose a novel citation recommendation approach that leverages a submodular scoring function and a deep document representation to achieve an effective trade-off between relevance to the query and diversity of the references. To optimally train the scoring function and the deep representation, we propose a novel training objective based on a structural/multiclass hinge loss and incremental recommendations. The experimental results over three popular citation datasets have showed that the proposed approach has led to remarkable accuracy improvements, with an increase of up to 1.91 pp of MRR and 3.29 pp of F1@100 score with respect to a state-of-the-art citation recommendation system.
Adapting Vehicle Detector to Target Domain by Adversarial Prediction Alignment

While recent advancement of domain adaptation techniques is significant, most of methods only align a feature extractor and do not adapt a classifier to target domain, which would be a cause of performance degradation. We propose a novel domain adaptation technique for object detection that aligns prediction output space. In addition to feature alignment, we aligned predictions of locations and class confidences of our vehicle detector for satellite images by adversarial training. The proposed method significantly improved AP score by over 5%, which shows effectivity of our method for object detection tasks in satellite images. Our code is available at https://github.com/monotaro3/vd_pred_align.
Dynamics and Control of a Magnetic Transducer Array Using Multi-Physics Models and Artificial Neural Networks

A linear mechanical oscillator is non-linearly coupled with an electromagnet and its driving circuit through a magnetic field. The resulting non-linear dynamics are investigated using magnetic circuit approximations without major loss of accuracy and in the interest of brevity. Different computational approaches to simulate the setup in terms of dynamical system response and design parameters optimization are pursued. A current source operating in baseband without modulation directly feeds the electromagnet, which consists commonly of a solenoid and a horseshoe-shaped core. The electromagnet is then magnetically coupled to a mass made of soft magnetic material and attached to a spring with damping. The non-linear system is described by a linearized steady-space representation while is examined for controllability and observability. A controller using a pole placement approach is built to stabilize the element. Drawing upon the fact that coupling works both ways, enabling estimation of the mass position and velocity (state variables) by processing the induced voltage across the electromagnet, a state observer is constructed. Accurate and fast tracking of the state variables, along with the possibility of driving more than one module from the same source using modulation, proves the applicability of the electro-magneto-mechanical transducer for sensor applications. Next, a three-layer feed-forward artificial neural network (ANN) system equivalent was trained using the non-linear plant-linear controller-linear observer configuration. Simulations to investigate the robustness of the system with respect to different equilibrium points and input currents were carried out. The ANN proved robust with respect to position accuracy.
Sunflower: an environment for standardized communication of IoMusT

The Internet of Musical Things (IoMusT) area, although recent, has well-defined aspects concerning musical practice via the network. However, several challenges are also present, from those related to musical and artistic practice, even those dealing with environmental and social issues. From a computational point of view, the main dilemmas revolve around the lack of resources to deal with heterogeneity and the lack of standard in the communication of the devices that make up this scenario. Therefore, this paper presents Sunflower, a tool inspired by the Pipes-and-Filters architecture that allows communication between different objects, and focuses on its usage protocol. Its layered structure is also presented, showing the types of data, messages, and musical things present in each one of them. After all, the tests and results that certify to the functionality of this environment are demonstrated.
Project as an effective technology for teaching students of creative specialties

We discuss the process of using project technologies to develop professional competencies of students studying in creative specialties. The specificity of the organization of the educational process using project technologies is revealed, which allows organizing the activities of students with a focus on obtaining a specific creative product, attracting alternative resources, maintaining partnerships with various organizations and structures. The features of the project-oriented approach in the interaction of traditional and innovative principles in the creative self-realization of students are highlighted. The effectiveness of the process under consideration is based on the ability to build classes with students, taking into account their acquisition of certain skills and abil-ities, among which the most significant are: thought-activity, presentation, communication, search, information. Project technologies at the present stage of student learning are considered as one of the personality-oriented technologies that integrate a problematic approach, group methods, reflexive, presentational, research, search and other methods. It is noted that project activity allows solving the problem of youth readiness for active actions in a social environment, realizing the acquired knowledge and skills. Therefore, the development of project competence is one of the most important guidelines in the system of training students of creative specialties.
A New Feature Selection Algorithm Based on Deep Q-Network

In machine learning tasks, feature selection is an important data preprocessing step. It improves the efficiency and prediction accuracy by removing redundant and irrelevant features in a dataset. Feature selection is essentially a search process for the optimal feature subset. Although the traditional feature selection methods can search feature subset effectively, they still have the problems of large scale and low classification accuracy. In this paper, a new automatic feature selection method based on Deep Q-Network (DQN-FS) is proposed to solve the shortcomings of traditional feature selection. Firstly, the feature selection problem is formulated as a Markov decision process. Subsequently, the Q network is used to optimize the search strategy of the optimal feature subset. Finally, the parameters of the Q network are updated by stochastic gradient descent. This paper also considers an extensive study of the feature sequence setting for the proposed technique. The proposed methods are tested on 14 UCI standard datasets and then compared with mainstream feature selection methods. The results show the efficiency of DQN- FS in searching for the optimal feature subsets.
Pilot Studies on Avrora Unior Car-Like Robot Control Using Gestures

Nikiforov, Nikita Tsoy, Tatyana Safin, Ramil Bai, Yang Svinin, Mikhail Magid, EvgeniGesture recognition is not only an important communication channel in human-human interaction but it also allows a human to communicate with other intelligent devices. This paper presents a concept for controlling the car-like robot Avrora Unior locomotion using gestures. We created a list of 18 control commands that contains basic and compound commands. A group of 17 volunteers used this list to create individual control gestures independently. A small part of the obtained dataset of gestures was used with the Teachable machine service in order to preliminary evaluate a possibility of constructing a full-scale model and to train it appropriately. The obtained model demonstrated acceptable recognition rate. We also attempted to apply SURF and FLANN techniques for matching with the direct matching approach and the skeleton-based approach, but the matching results were not satisfactory.
Investigating Test Smells in JavaScript Test Code

Writing automated test cases is a challenging and demanding activity. The test case itself is software that requires proper design to ensure it can be implemented and maintained as long as the production code evolves. Like code smells, test smells may indicate violations of principles that negatively affect the quality of test code design, making it difficult to comprehend and, consequently, impairing its proper use and evolution. This work aims to investigate the occurrence of test smells in JavaScript test code and whether their presence can be correlated with test code quality. We perform an empirical study using the STEEL tool where the test suites of 11 open-source JavaScript projects from the Github repository are analyzed to detect a set of previously cataloged test smells. We then investigate: i) which ones occur more frequently; ii) whether given test smells are likely to occur together, and iii) if the presence of certain test smells is related to classical bad design indicators on the test code. We found that the Duplicate Assert, Magic Number Test, Unknown Test and Conditional Test Logic smells are the most common in JavaScript test code, whereas the Mystery Guest, Ignored Test and Resource Optimism smells are the least common. Moreover, the Conditional Test Logic, Magic Number Test, Duplicate Assert and the Exception Handling smells may often appear together. Furthermore, there is a moderate to a strong positive correlation between some smells count and quality measures in the test code. We can conclude that test smells are frequently found in JavaScript test code, and their presence may be an indicator of low design quality.
Gummy Browsers: Targeted Browser Spoofing Against State-of-the-Art Fingerprinting Techniques

We present a simple yet potentially devastating and hard-to-detect threat, called Gummy Browsers (Named after “Gummy Fingers” that can impersonate a user’s fingerprint biometrics.), whereby the browser fingerprinting information can be collected and spoofed without the victim’s awareness, thereby compromising the privacy and security of any application that uses browser fingerprinting. We design and implement the Gummy Browsers attack using three orchestration methods based on script injection, browser settings and debugging tools, and script modification, that can successfully spoof a wide variety of fingerprinting features to mimic many different browsers (including mobile browsers and the Tor browser). We then evaluate the attack against two state-of-the-art browser fingerprinting systems, FPStalker and Panopticlick. Our results show that A can accurately match his own manipulated browser fingerprint with that of any targeted victim user U’s fingerprint for a long period of time, without significantly affecting the tracking of U and when only collecting U’s fingerprinting information only once. The TPR (true positive rate) for the tracking of the benign user in the presence of the attack is larger than 0.9 in most cases. The FPR (false positive rate) for the tracking of the attacker is also high, larger than 0.9 in all cases. We also argue that the attack can remain completely oblivious to the user and the website, thus making it extremely difficult to thwart in practice.
Looper: An End-to-End ML Platform for Product Decisions

Modern software systems and products increasingly rely on machine learning models to make data-driven decisions based on interactions with users, infrastructure and other systems. For broader adoption, this practice must (i) accommodate product engineers without ML backgrounds, (ii) support finegrain product-metric evaluation and (iii) optimize for product goals. To address shortcomings of prior platforms, we introduce general principles for and the architecture of an ML platform, Looper, with simple APIs for decision-making and feedback collection. Looper covers the end-to-end ML lifecycle from collecting training data and model training to deployment and inference, and extends support to personalization, causal evaluation with heterogenous treatment effects, and Bayesian tuning for product goals. During the 2021 production deployment, Looper simultaneously hosted 440-1,000 ML models that made 4-6 million real-time decisions per second. We sum up experiences of platform adopters and describe their learning curve.
Camera-Based Security Check for Face Mask Detection Using Deep Learning

According to the World Health Organization (WHO), wearing a face mask is one of the most effective protections from airborne infectious diseases such as COVID-19. Since the spread of COVID-19, infected countries have been enforcing strict mask regulation for indoor businesses and public spaces. While wearing a mask is a requirement, the position and type of the mask should also be considered in order to increase the effectiveness of face masks, especially at specific public locations. However, this makes it difficult for conventional facial recognition technology to identify individuals for security checks. To solve this problem, Deep Learning algorithms are proposed to develop a comprehensive mask detection through a camera. CNN, AlexNet, VGG16, and Facial Recognition Pipeline with FaceNet are the Deep Learning algorithms that are used to classify the features in each scenario. This project uses multiple open datasets, Google Image Search, and simulated photos. We aim to build a comprehensive camera system that is able to cover four major issues: Mask Detection, Mask Type Classification, Mask Position Classification and Identity Recognition.
An Extended Text Combination Classification Model for Short Video Based on Albert

With the rise and rapid development of short video sharing websites, the number of short videos on the Internet has been growing explosively. The organization and classification of short videos have become the basis for the effective use of short videos, which is also a problem faced by major short video platforms. Aiming at the characteristics of complex short video content categories and rich extended text information, this paper uses methods in the text classification field to solve the short video classification problem. Compared with the traditional way of classifying and understanding short video key frames, this method has the characteristics of lower computational cost, more accurate classification results, and easier application. This paper proposes a text classification model based on the attention mechanism of multitext embedding short video extension. The experiment first uses the training language model Albert to extract sentence-level vectors and then uses the attention mechanism to study the text information in various short video extensions in a short video classification weight factor. And this research applied Google’s unsupervised data augmentation (UDA) method based on unsupervised data, creatively combining it with the Chinese knowledge graph, and realized TF-IDF word replacement. During the training process, we introduced a large amount of unlabeled data, which significantly improved the accuracy of model classification. The final series of related experiments is aimed at comparing with the existing short video title classification methods, classification methods based on video key frames, and hybrid methods, and proving that the method proposed in this article is more accurate and robust on the test set.
Illumination correction with optimized kernel extreme learning machine based on improved marine predators algorithm

Abstract Aiming at the problem of poor image illumination correction accuracy, a kernel extreme learning machine optimized based on the differential evolution‐improved marine predators algorithm is proposed to estimate the scene illumination information and restore the image. First, in order to solve the problem of randomization of the initial population of the marine predators algorithm, differential evolutions is used to provide a set of suitable initial populations to the algorithm. Then, an improved algorithm is used to optimize the weight and bias of the kernel extreme learning machine to solve the problem of the randomness of the weight and bias, so that the learning machine converges to the global optimal value and avoids the unstable predictions. Finally, after obtaining the predicted illumination information, the image model is corrected to the image effect under standard illumination through diagonal transformation. It can be seen from the experimental results that the best predictive value of the differential evolution marine predator algorithm learning machine is 0.0135915, and the quasi‐bias is only 0.001687. Compared with traditional illumination estimation algorithms such as random vector functional link and extreme learning machine, the differential evolution marine predator algorithm learning machine algorithm proposed in this article has better stability, higher accuracy of calculated predicted values, and better image restoration effect.
Blockchain-based Secure CIDS Operation

For large, intricate, and multi-layered networks like that of Industrial IoT, an individual instance of intrusion detection system cannot efficiently work against advanced attack strategies. The reason is that it would not be aware of the overall context, environment, and relevant incidents in other networks. This necessitates a collaborative intrusion detection system that allows multiple intrusion detection systems to communicate with each other and share information on emerging cyber-attack incidents. Thus, immunizing themselves and preventing the attack from escalating. However, the main challenge here is to manage the trust among the peers, where an insider attacker may input false attack signatures to the network, thus degrading the performance. Hence, we propose a blockchain-based trustfree collaborative intrusion detection system, in which threat alert messages will only be propagated in the network after network consensus.
Dataset Knowledge Transfer for Class-Incremental Learning without Memory

Incremental learning enables artificial agents to learn from sequential data. While important progress was made by exploiting deep neural networks, incremental learning remains very challenging. This is particularly the case when no memory of past data is allowed and catastrophic forgetting has a strong negative effect. We tackle class-incremental learning without memory by adapting prediction bias correction, a method which makes predictions of past and new classes more comparable. It was proposed when a memory is allowed and cannot be directly used without memory, since samples of past classes are required. We introduce a two-step learning process which allows the transfer of bias correction parameters between reference and target datasets. Bias correction is first optimized offline on reference datasets which have an associated validation memory. The obtained correction parameters are then transferred to target datasets, for which no memory is available. The second contribution is to introduce a finer modeling of bias correction by learning its parameters per incremental state instead of the usual past vs. new class modeling. The proposed dataset knowledge transfer is applicable to any incremental method which works without memory. We test its effectiveness by applying it to four existing methods. Evaluation with four target datasets and different configurations shows consistent improvement, with practically no computational and memory overhead.
A Factoid based Question Answering System based on Dependency Analysis and Wikidata

Over the last years, the use and the need for automated question answering systems have become more important than ever. The main reasons for this relate to the constant increase of the information that is available in textual form as well as the need to facilitate users in getting information they seek in a precise, fast, and easy way. In this work, we deal with the problem of the open domain factoid-based answering of questions, where the answer to a question is in the form of a word or a small phrase. We present an efficient methodology for analyzing questions and specifying proper answers with the use of knowledge bases. Initially, given a specific question that a user sets, a high-quality linguistic analysis of the question is performed. The dependencies of the question are specified, and the main triplets of the question are created. The system creates a SPARQL query to get the right answer for the question via the API from the Wikidata Query Service. The results are quite encouraging and highlight the quite good performance of our methodology.
Smartphone-Based Lifelogging: Toward Realization of Personal Big Data

The technological advancements have turned smartphones into powerful lifelogging devices. Smartphone-based lifelogging system captures and stores information about peoples’ daily life activities, behaviors, interactions, and contexts into rich personal big data archives. The personal big data is of potential interest to the information sciences researchers and policy and decision makers in governments and organizations because of the availability of information, which would be impossible otherwise. Despite its potential, the smartphone-based lifelogging has been limitedly been explored from the big data perspective. This paper aims to provide a close-up view of the smartphone-based lifelogging as the source of personal big data. First, the smartphone-based lifelogging is reviewed to demonstrate its technological capabilities for personal big data generation and conformance to big data characteristics, alongside key personal big data applications. Second, a generalized architecture is presented for smartphone-based lifelog personal big data systems using big data systems design principals to advance the research in this space. Third, several challenges are highlighted regarding data capture, storage, analysis, visualization, privacy, and security. To address these concerns, several recommendations are suggested to improve personal big data generation, management, and usability.
Freyja: A Full Multirotor System for Agile &amp; Precise Outdoor Flights

Several independent approaches exist for state estimation and control of multirotor unmanned aerial systems (UASs) that address specific and constrained operational conditions. This work presents a complete end-to-end pipeline that enables precise, aggressive and agile maneuvers for multirotor UASs under real and challenging outdoor environments. We leverage state-of-the-art optimal methods from the literature for trajectory planning and control, such that designing and executing dynamic paths is fast, robust and easy to customize for a particular application. The complete pipeline, built entirely using commercially available components, is made open-source and fully documented to facilitate adoption. We demonstrate its performance in a variety of operational settings, such as hovering at a spot under dynamic wind speeds of up to 5– 6m/s (12–15mi/h) while staying within 12cm of 3D error. We also characterize its capabilities in flying high-speed trajectories outdoors, and enabling fast aerial docking with a moving target with planning and interception occurring in under 8s.