Reviewerly/exHarmony huggingface dataset:
A Deep Hashing Technique for Remote Sensing Image-Sound Retrieval

With the rapid progress of remote sensing (RS) observation technologies, cross-modal RS image-sound retrieval has attracted some attention in recent years. However, these methods perform cross-modal image-sound retrieval by leveraging high-dimensional real-valued features, which can require more storage than low-dimensional binary features (i.e., hash codes). Moreover, these methods cannot directly encode relative semantic similarity relationships. To tackle these issues, we propose a new, deep, cross-modal RS image-sound hashing approach, called deep triplet-based hashing (DTBH), to integrate hash code learning and relative semantic similarity relationship learning into an end-to-end network. Specially, the proposed DTBH method designs a triplet selection strategy to select effective triplets. Moreover, in order to encode relative semantic similarity relationships, we propose the objective function, which makes sure that that the anchor images are more similar to the positive sounds than the negative sounds. In addition, a triplet regularized loss term leverages approximate l1-norm of hash-like codes and hash codes and can effectively reduce the information loss between hash-like codes and hash codes. Extensive experimental results showed that the DTBH method could achieve a superior performance to other state-of-the-art cross-modal image-sound retrieval methods. For a sound query RS image task, the proposed approach achieved a mean average precision (mAP) of up to 60.13% on the UCM dataset, 87.49% on the Sydney dataset, and 22.72% on the RSICD dataset. For RS image query sound task, the proposed approach achieved a mAP of 64.27% on the UCM dataset, 92.45% on the Sydney dataset, and 23.46% on the RSICD dataset. Future work will focus on how to consider the balance property of hash codes to improve image-sound retrieval performance.
A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis

Twitter sentiment analysis is a challenging problem in natural language processing.For this purpose, supervised learning techniques have mostly been employed, which require labeled data for training.However, it is very time consuming to label datasets of large size.To address this issue, unsupervised learning techniques such as clustering can be used.In this study, we explore the possibility of using hierarchical clustering for twitter sentiment analysis.Three hierarchical-clustering techniques, namely single linkage (SL), complete linkage (CL) and average linkage (AL), are examined.A cooperative framework of SL, CL and AL is built to select the optimal cluster for tweets wherein the notion of optimal-cluster selection is operationalized using majority voting.The hierarchical clustering techniques are also compared with k-means and two state-of-the-art classifiers (SVM and Naïve Bayes).The performance of clustering and classification is measured in terms of accuracy and time efficiency.The experimental results indicate that cooperative clustering based on majority voting approach is robust in terms of good quality clusters with tradeoff of poor time efficiency.The results also suggest that the accuracy of the proposed clustering framework is comparable to classifiers which is encouraging.
Application of neural network in fault location of optical transport network

Due to the increasing variety of information and services carried by optical networks, the survivability of network becomes an important problem in current research. The fault location of OTN is of great significance for studying the survivability of optical networks. Firstly, a three-channel network model is established and analyzing common alarm data, the fault monitoring points and common fault points are carried out. The artificial neural network is introduced into the fault location field of OTN and it is used to judge whether the possible fault point exists or not. But one of the obvious limitations of general neural networks is that they receive a fixed-size vector as input and produce a fixed-size vector as the output. Not only that, these models is even fixed for mapping operations (for example, the number of layers in the model). The difference between the recurrent neural network and general neural networks is that it can operate on the sequence. In spite of the fact that the gradient disappears and the gradient explodes still exist in the neural network, the method of gradient shearing or weight regularization is adopted to solve this problem, and choose the LSTM (long-short tenn memory networks) to locate the fault. The output uses the concept of membership degree of fuzzy theory to express the possible fault point with the probability from 0 to 1. Priority is given to the treatment of fault points with high probability. The concept of F-Measure is also introduced, and the positioning effect is measured by using location time, MSE and F-Measure. The experiment shows that both LSTM and BP neural network can locate the fault of optical transport network well, but the overall effect of LSTM is better. The localization time of LSTM is shorter than that of BP neural network and the Fl-score of LSTM can reach 0.961566888396156 after 45 iterations, which meets the accuracy and real-time requirements of fault location. Therefore, it has good application prospect and practical value to introduce neural network into the fault location field of optical transport network.
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?

Minimax optimization has found extensive applications in modern machine learning, in settings such as generative adversarial networks (GANs), adversarial training and multi-agent reinforcement learning. As most of these applications involve continuous nonconvex-nonconcave formulations, a very basic question arises — “what is a proper definition of local optima?'” Most previous work answers this question using classical notions of equilibria from simultaneous games, where the min-player and the max-player act simultaneously. In contrast, most applications in machine learning, including GANs and adversarial training, correspond to sequential games, where the order of which player acts first is crucial (since minimax is in general not equal to maximin due to the nonconvex-nonconcave nature of the problems). The main contribution of this paper is to propose a proper mathematical definition of local optimality for this sequential setting—local minimax, as well as to present its properties and existence results. Finally, we establish a strong connection to a basic local search algorithm—gradient descent ascent (GDA): under mild conditions, all stable limit points of GDA are exactly local minimax points up to some degenerate points.
GDPR Compliance Verification in Internet of Things

Data privacy in Internet of Things (IoT) applications remains a major concern of regulation bodies.The introduction of the European General Data Protection Regulation (GDPR) enables users to control how their data is accessed and processed, requiring consent from users before any data manipulation is carried out on their (personal) data by smart devices or cloud-hosted services.Blockchains provide the benefits of a distributed and immutable ledger recording digital transactions across a global network of peer nodes.Blockchain support for tracking of operations carried out by an IoT-based system provides greater confidence to a user that the IoT device is not infringing user privacy (as the Blockchain can be audited to verify which operation was carried out, by which actor).A formal model (following the privacy-bydesign approach) is proposed for supporting GDPR compliance checking for smart devices.The privacy requirements of such applications are related to GDPR obligations of device (and software systems) operators (such as user consent, data protection, right to forget etc).Three smart contracts are proposed as a practical solution to support automated verification of operations carried out by devices on user data, in accordance with GDPR rules.We evaluate the performance and scalability costs of our approach using a Blockchain test network.
The COVID-19-crisis and the information polity: An overview of responses and discussions in twenty-one countries from six continents

Governments around the world are utilizing data and information systems to manage the COVID-19-crisis. To obtain an overview of all these efforts, this global report presents the expert reports of 21 countries regarding the relation between the COVID-19-crisis and the information polity. A comparative analysis of these reports highlights that governments focus on strengthening six functions: management of information for crisis management, publishing public information for citizens, providing digital services to citizens, monitoring citizens in public space, facilitating information exchange between citizens and developing innovative responses to COVID-19. The comparative overview of information responses to the COVID-19-crisis shows that these responses cannot only be studied from a rational perspective on government information strategies but need to be studied as political and symbolic interventions.
Online Learning Through WhatsApp Group in Improving Learning Motivation in the Era and Post Pandemic COVID -19

&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; The purpose of this study is to find out the online learning process in the middle and post-pandemic Covid-19. This research employed one group pretest-posttest design. The subjects of the study were the fourth-semester students of the MPI Department of the State Islamic University of Maulana Malik Ibrahim Malang in the even semester of the academic year 2019/2020. The sample of this study was 30 students. Data obtained using a questionnaire. The data analysis technique used a paired T-test with an error rate of 5%. With online learning using WhatsApp shows that there is a correlation between the two variables of 0.776 it shows that there is a correlation between the pretest-posttest, the significance was 0.00 &amp;lt;0.05 then H0 was accepted. It further confirms that there is a significant increase in student learning outcomes before and after using WhatsApp. The results of the analysis and discussion can be concluded that there is an increase in online learning in the era and post-pandemic Covid-19 by using WhatsApp to increase learning motivation.&lt;/p&gt;&lt;strong&gt;Abstrak:&lt;/strong&gt; Tujuan dari penelitian ini adalah untuk mengetahui proses pembelajaran &lt;em&gt;online&lt;/em&gt; di tengah dan pasca pandemi Covid-19. Penelitian ini menggunakan on&lt;em&gt;e group pretest-posttest design&lt;/em&gt;. Subjek penelitian ini adalah mahasiswa semester empat Jurusan MPI Universitas Islam Negeri Maulana Malik Ibrahim Malang semester genap tahun ajaran 2019/2020. Sampel penelitian ini adalah 30 mahasiswa. Data diperoleh dengan menggunakan kuesioner. Teknik analisis data menggunakan paired T-test dengan tingkat kesalahan 5%. Pembelajaran &lt;em&gt;online&lt;/em&gt; menggunakan &lt;em&gt;whatsapp&lt;/em&gt; menunjukkan bahwa terdapat korelasi antara kedua variabel sebesar 0,776, hal ini menunjukkan bahwa terdapat korelasi antara &lt;em&gt;pretest-posttest&lt;/em&gt; dengan signifikansi 0,00 &amp;lt;0,05 maka H0 diterima. Hasil ini semakin menegaskan bahwa ada peningkatan yang signifikan dalam hasil belajar siswa sebelum dan sesudah menggunakan &lt;em&gt;WhatsApp&lt;/em&gt;. Hasil analisis dan pembahasan dapat disimpulkan bahwa terdapat peningkatan pembelajaran &lt;em&gt;online &lt;/em&gt;di era dan pasca pandemi Covid-19 dengan menggunakan &lt;em&gt;WhatsApp&lt;/em&gt; untuk meningkatkan motivasi belajar.
A Benchmark for Systematic Generalization in Grounded Language Understanding

Humans easily interpret expressions that describe unfamiliar situations composed from familiar parts (greet the pink brontosaurus by the ferris wheel). Modern neural networks, by contrast, struggle to interpret novel compositions. In this paper, we introduce a new benchmark, gSCAN, for evaluating compositional generalization in situated language understanding. Going beyond a related benchmark that focused on syntactic aspects of generalization, gSCAN defines a language grounded in the states of a grid world, facilitating novel evaluations of acquiring linguistically motivated rules. For example, agents must understand how adjectives such as 'small' are interpreted relative to the current world state or how adverbs such as 'cautiously' combine with new verbs. We test a strong multi-modal baseline model and a state-of-the-art compositional method finding that, in most cases, they fail dramatically when generalization requires systematic compositional rules.
Authorship Attribution for Neural Text Generation

In recent years, the task of generating realistic short and long texts have made tremendous advancements. In particular, several recently proposed neural network-based language models have demonstrated their astonishing capabilities to generate texts that are challenging to distinguish from human-written texts with the naked eye. Despite many benefits and utilities of such neural methods, in some applications, being able to tell the “author” of a text in question becomes critically important. In this work, in the context of this Turing Test, we investigate the so-called authorship attribution problem in three versions: (1) given two texts T1 and T2, are both generated by the same method or not? (2) is the given text T written by a human or machine? (3) given a text T and k candidate neural methods, can we single out the method (among k alternatives) that generated T? Against one humanwritten and eight machine-generated texts (i.e., CTRL, GPT, GPT2, GROVER, XLM, XLNET, PPLM, FAIR), we empirically experiment with the performance of various models in three problems. By and large, we find that most generators still generate texts significantly different from human-written ones, thereby making three problems easier to solve. However, the qualities of texts generated by GPT2, GROVER, and FAIR are better, often confusing machine classifiers in solving three problems. All codes and datasets of our experiments are available at: https://bit.ly/ 302zWdz
Trailblazing the Artificial Intelligence for Cybersecurity Discipline

Cybersecurity has rapidly emerged as a grand societal challenge of the 21st century. Innovative solutions to proactively tackle emerging cybersecurity challenges are essential to ensuring a safe and secure society. Artificial Intelligence (AI) has rapidly emerged as a viable approach for sifting through terabytes of heterogeneous cybersecurity data to execute fundamental cybersecurity tasks, such as asset prioritization, control allocation, vulnerability management, and threat detection, with unprecedented efficiency and effectiveness. Despite its initial promise, AI and cybersecurity have been traditionally siloed disciplines that relied on disparate knowledge and methodologies. Consequently, the AI for Cybersecurity discipline is in its nascency. In this article, we aim to provide an important step to progress the AI for Cybersecurity discipline. We first provide an overview of prevailing cybersecurity data, summarize extant AI for Cybersecurity application areas, and identify key limitations in the prevailing landscape. Based on these key issues, we offer a multi-disciplinary AI for Cybersecurity roadmap that centers on major themes such as cybersecurity applications and data, advanced AI methodologies for cybersecurity, and AI-enabled decision making. To help scholars and practitioners make significant headway in tackling these grand AI for Cybersecurity issues, we summarize promising funding mechanisms from the National Science Foundation (NSF) that can support long-term, systematic research programs. We conclude this article with an introduction of the articles included in this special issue.
Jerk-limited Real-time Trajectory Generation with Arbitrary Target States

We present Ruckig, an algorithm for Online Trajectory Generation (OTG) respecting third-order constraints and complete kinematic target states. Given any initial state of a system with multiple Degrees of Freedom (DoFs), Ruckig calculates a time-optimal trajectory to an arbitrary target state defined by its position, velocity, and acceleration limited by velocity, acceleration, and jerk constraints. The proposed algorithm and implementation allows three contributions: (1) To the best of our knowledge, we derive the first time-optimal OTG algorithm for arbitrary, multi-dimensional target states, in particular including non-zero target acceleration. (2) This is the first open-source prototype of time-optimal OTG with limited jerk and complete time synchronization for multiple DoFs. (3) Ruckig allows for directional velocity and acceleration limits, enabling robots to better use their dynamical resources. We evaluate the robustness and real-time capability of the proposed algorithm on a test suite with over 1,000,000,000 random trajectories as well as in real-world applications.
Coreference Resolution without Span Representations

The introduction of pretrained language models has reduced many complex task-specific NLP models to simple lightweight layers.An exception to this trend is coreference resolution, where a sophisticated task-specific model is appended to a pretrained transformer encoder.While highly effective, the model has a very large memory footprint -primarily due to dynamically-constructed span and span-pair representations -which hinders the processing of complete documents and the ability to train on multiple instances in a single batch.We introduce a lightweight end-to-end coreference model that removes the dependency on span representations, handcrafted features, and heuristics.Our model performs competitively with the current standard model, while being simpler and more efficient.
CoTexT: Multi-task Learning with Code-Text Transformer

We present CoTexT, a pre-trained, transformerbased encoder-decoder model that learns the representative context between natural language (NL) and programming language (PL).Using self-supervision, CoTexT is pretrained on large programming language corpora to learn a general understanding of language and code.CoTexT supports downstream NL-PL tasks such as code summarizing/documentation, code generation, defect detection, and code debugging.We train CoTexT on different combinations of available PL corpus including both "bimodal" and "unimodal" data.Here, bimodal data is the combination of text and corresponding code snippets, whereas unimodal data is merely code snippets.We first evaluate CoTexT with multi-task learning: we perform Code Summarization on 6 different programming languages and Code Refinement on both small and medium size featured in the CodeXGLUE dataset.We further conduct extensive experiments to investigate Co-TexT on other tasks within the CodeXGlue dataset, including Code Generation and Defect Detection.We consistently achieve SOTA results in these tasks, demonstrating the versatility of our models.
A Large-Scale Open Motion Dataset (KFall) and Benchmark Algorithms for Detecting Pre-impact Fall of the Elderly Using Wearable Inertial Sensors

Research on pre-impact fall detection with wearable inertial sensors (detecting fall accidents prior to body-ground impacts) has grown rapidly in the past decade due to its great potential for developing an on-demand fall-related injury prevention system. However, most researchers use their own datasets to develop fall detection algorithms and rarely make these datasets publicly available, which poses a challenge to fairly evaluate the performance of different algorithms on a common basis. Even though some open datasets have been established recently, most of them are impractical for pre-impact fall detection due to the lack of temporal labels for fall time and limited types of motions. In order to overcome these limitations, in this study, we proposed and publicly provided a large-scale motion dataset called “KFall,” which was developed from 32 Korean participants while wearing an inertial sensor on the low back and performing 21 types of activities of daily living and 15 types of simulated falls. In addition, ready-to-use temporal labels of the fall time based on synchronized motion videos were published along with the dataset. Those enhancements make KFall the first public dataset suitable for pre-impact fall detection, not just for post-fall detection. Importantly, we have also developed three different types of latest algorithms (threshold based, support-vector machine, and deep learning), using the KFall dataset for pre-impact fall detection so that researchers and practitioners can flexibly choose the corresponding algorithm. Deep learning algorithm achieved both high overall accuracy and balanced sensitivity (99.32%) and specificity (99.01%) for pre-impact fall detection. Support vector machine also demonstrated a good performance with a sensitivity of 99.77% and specificity of 94.87%. However, the threshold-based algorithm showed relatively poor results, especially the specificity (83.43%) was much lower than the sensitivity (95.50%). The performance of these algorithms could be regarded as a benchmark for further development of better algorithms with this new dataset. This large-scale motion dataset and benchmark algorithms could provide researchers and practitioners with valuable data and references to develop new technologies and strategies for pre-impact fall detection and proactive injury prevention for the elderly.
HTC+ for SAR Ship Instance Segmentation

Existing instance segmentation models mostly pay less attention to the targeted characteristics of ships in synthetic aperture radar (SAR) images, which hinders further accuracy improvements, leading to poor segmentation performance in more complex SAR image scenes. To solve this problem, we propose a hybrid task cascade plus (HTC+) for better SAR ship instance segmentation. Aiming at the specific SAR ship task, seven techniques are proposed to ensure the excellent performance of HTC+ in more complex SAR image scenes, i.e., a multi-resolution feature extraction network (MRFEN), an enhanced feature pyramid net-work (EFPN), a semantic-guided anchor adaptive learning network (SGAALN), a context ROI extractor (CROIE), an enhanced mask interaction network (EMIN), a post-processing technique (PPT), and a hard sample mining training strategy (HSMTS). Results show that each of them offers an observable accuracy gain, and the instance segmentation performance in more complex SAR image scenes becomes better. On two public datasets SSDD and HRSID, HTC+ surpasses the other nine competitive models. It achieves 6.7% higher box AP and 5.0% higher mask AP than HTC on SSDD. These are 4.9% and 3.9% on HRSID.