Reviewerly/exHarmony huggingface dataset:
Topic Modeling Using LDA and BERT Techniques: Teknofest Example

This paper is a natural language processing study and includes models used in natural language processing. In this paper, topic modeling, which is one of the sub-fields of natural language processing, has been studied. In order to make topic modeling, the data set was obtained by using the data scraping method, which has been very popular in recent years, over social media. The dataset is related to Teknofest competitions. The dataset was created by utilizing the Selenium library, one of the popular libraries used for the data scraping method. In order to be able to analyze on the prepared data set and to ensure the consistency of the clustering process, the text to be used before the analysis was preprocessed. After text preprocessing, clustering was performed on the data set with natural language processing techniques such as BERT and LDA.
Exploring Graph-Structured Semantics for Cross-Modal Retrieval

We study and address the cross-modal retrieval problem which lies at the heart of visual-textual processing. Its major challenge lies in how to effectively learn a shared multi-modal feature space where the discrepancies of semantically related pairs, such as images and texts, are minimized regardless of their modalities. Most current methods focus on reasoning about cross-modality semantic relations within individual image-text pair to learn the common representation. However, they overlook more global, structural inter-pair knowledge within the dataset, i.e., the graph-structured semantics within each training batch. In this paper, we introduce a graph-based, semantic-constrained learning framework to comprehensively explore the intra- and inter-modality information for cross-modal retrieval. Our idea is to maximally explore the structures of labeled data in graph latent space, and use them as semantic constraints to enforce feature embeddings from the semantically-matched (image-text) pairs to be more similar and vice versa. It raises a novel graph-constrained common embedding learning paradigm for cross-modal retrieval, which is largely under-explored up to now. Moreover, a GAN-based dual learning approach is used to further improve the discriminability and model the joint distribution across different modalities. Our fully-equipped approach, called Graph-constrained Cross-modal Retrieval (GCR), is able to mine intrinsic structures of training data for model learning and enable reliable cross-modal retrieval. We empirically demonstrate that our GCR can achieve higher accuracy than existing state-of-the-art approaches on Wikipedia, NUS-WIDE-10K, PKU XMedia and Pascal Sentence datasets. Our code will be made publicly available. Code is available at https://github.com/neoscheung/GCR.
An Analysis of Performance Metrics for Imbalanced Classification

Numerous machine learning applications involve dealing with imbalanced domains, where the learning focus is on the least frequent classes. This imbalance introduces new challenges for both the performance assessment of these models and their predictive modeling. While several performance metrics have been established as baselines in balanced domains, some cannot be applied to the imbalanced case since the use of the majority class in the metric could lead to a misleading evaluation of performance. Other metrics, such as the area under the precision-recall curve, have been demonstrated to be more appropriate for imbalance domains due to their focus on class-specific performance. There are, however, many proposed implementations for this particular metric, which could potentially lead to different conclusions depending on the one used. In this research, we carry out an experimental study to better understand these issues and aim at providing a set of recommendations by studying the impact of using different metrics and different implementations of the same metric under multiple imbalance settings.
FKMAWCW: Categorical fuzzy k-modes clustering with automated attribute-weight and cluster-weight learning

The fuzzy k-modes (FKM) is a popular method for clustering categorical data. However, the main problem of this algorithm is that it is very sensitive to the initialization of primary clusters, so inappropriate initial cluster centers lead to poor local optima. Another problem with the FKM is the equal importance of the attributes used during the clustering process, which in real applications, the importance of the attributes are different, and some attributes are more important than others. Some versions of FKM have been presented in the literature, each of which has somehow solved one of the above problems. In this paper, we propose a new clustering method (FKMAWCW) to solve mentioned problems at the same time. In the proposed clustering process, a local attribute weighting mechanism is used to weight the attributes of each cluster properly. Also, a cluster weighting mechanism is proposed to solve the initialization sensitivity. Attribute weight and cluster weight are learned simultaneously and automatically during the clustering process. In addition, to reduce the noise sensitivity, a new distance function is proposed. So, the proposed algorithm can tolerate noisy environment. Extensive experiments on 11 benchmark datasets and an artificially generated dataset show that the proposed algorithm performs better than the state-of-the-art algorithms. This paper presents mathematical analyses to obtain updating functions, providing the convergence proof of the algorithm. The implementation source code of FKMAWCW is made publicly available at https://github.com/Amin-Golzari-Oskouei/FKMAWCW.
Slang: The Sireum Programming Language

This paper presents design goals, development approaches, and applications for Slang – a subset of the Scala programming language designed for engineering high assurance safety/security-critical systems. Rationale is given for specializing Scala for Slang so as to retain Scala's synergistic blend of imperative, functional, and object-oriented features while omitting and tailoring features that make formal verification and other analyses difficult. Strategies for enhancing the usability of Slang are discussed including integration with the broader Scala/JVM ecosystem, compilers, and development environments. A number of accompanying Slang tools are described including Slang scripting, meta-programming support, and translators to Javascript and native code that enable support for a wide range of deployment platforms. To support deployment on constrained embedded platforms, the Slang Embedded subset and an accompanying C translator generate efficient implementations that avoid garbage-collection and other aspects that hinder deployment and safety/security assurances. We conclude with a discussion of how our experiences with Slang may provide suggestions for the future of programming and programming language design for engineering critical systems.
Towards Fast and High-Quality Sign Language Production

Sign Language Production (SLP) aims to automatically translate a spoken language description to its corresponding sign language video. The core procedure of SLP is to transform sign gloss intermediaries into sign pose sequences (G2P). Most existing methods for G2P are based on sequential autoregression or sequence-to-sequence encoder-decoder learning. However, by generating target pose frames conditioned on the previously generated ones, these models are prone to bringing issues such as error accumulation and high inference latency. In this paper, we argue that such issues are mainly caused by adopting autoregressive manner. Hence, we propose a novel Non-AuToregressive (NAT) model with a parallel decoding scheme, as well as an External Aligner for sequence alignment learning. Specifically, we extract alignments from the external aligner by monotonic alignment search for gloss duration prediction, which is used by a length regulator to expand the source gloss sequence to match the length of the target sign pose sequence for parallel sign pose generation. Furthermore, we devise a spatial-temporal graph convolutional pose generator in the NAT model to generate smoother and more natural sign pose sequences. Extensive experiments conducted on PHOENIX14T dataset show that our proposed model outperforms state-of-the-art autoregressive models in terms of speed and quality.
NAS-FCOS: Efficient Search for Object Detection Architectures

Neural Architecture Search (NAS) has shown great potential in effectively reducing manual effort in network design by automatically discovering optimal architectures. What is noteworthy is that as of now, object detection is less touched by NAS algorithms despite its significant importance in computer vision. To the best of our knowledge, most of the recent NAS studies on object detection tasks fail to satisfactorily strike a balance between performance and efficiency of the resulting models, let alone the excessive amount of computational resources cost by those algorithms. Here we propose an efficient method to obtain better object detectors by searching for the feature pyramid network as well as the prediction head of a simple anchor-free object detector, namely, FCOS (Tian et al. in FCOS: Fully convolutional one-stage object detection, 2019), using a tailored reinforcement learning paradigm. With carefully designed search space, search algorithms, and strategies for evaluating network quality, we are able to find top-performing detection architectures within 4 days using 8 V100 GPUs. The discovered architectures surpass state-of-the-art object detection models (such as Faster R-CNN, RetinaNet and, FCOS) by 1.0 to 5.4% points in AP on the COCO dataset, with comparable computation complexity and memory footprint, demonstrating the efficacy of the proposed NAS method for object detection. Code is available at https://github.com/Lausannen/NAS-FCOS .
Dynamic Movement Primitive based Motion Retargeting for Dual-Arm Sign Language Motions

We aim to develop an efficient programming method for equipping service robots with the skill of performing sign language motions. This paper addresses the problem of transferring complex dual-arm sign language motions characterized by the coordination among arms and hands from human to robot, which is seldom considered in previous studies of motion retargeting techniques. In this paper, we propose a novel motion retargeting method that leverages graph optimization and Dynamic Movement Primitives (DMPs) for this problem. We employ DMPs in a leader-follower manner to parameterize the original trajectories while preserving motion rhythm and relative movements between human body parts, and adopt a three-step optimization procedure to find deformed trajectories for robot motion planning while ensuring feasibility for robot execution. Experimental results of several Chinese Sign Language (CSL) motions have been successfully performed on ABB’s YuMi dual-arm collaborative robot (14-DOF) with two 6-DOF Inspire-Robotics’ multi-fingered hands, a system with 26 DOFs in total.
Robust Monocular Visual-Inertial Depth Completion for Embedded Systems

In this work we augment our prior state-of-the-art visual-inertial odometry (VIO) system, OpenVINS [1], to produce accurate dense depth by filling in sparse depth estimates (depth completion) from VIO with image guidance – all while focusing on enabling real-time performance of the full VIO+depth system on embedded devices. We show that noisy depth values with varying sparsity produced from a VIO system can not only hurt the accuracy of predicted dense depth maps, but also make them considerably worse than those from an image-only depth network with the same underlying architecture. We investigate this sensitivity on both an outdoor simulated and indoor handheld RGB-D dataset, and present simple yet effective solutions to address these shortcomings of depth completion networks. The key changes to our state-of-the-art VIO system required to provide high quality sparse depths for the network while still enabling efficient state estimation on embedded devices are discussed. A comprehensive computational analysis is performed over different embedded devices to demonstrate the efficiency and accuracy of the proposed VIO depth completion system.
Robot Path Planning via Neural-Network-Driven Prediction

This article presents a novel heuristic method named <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">neural-network-driven prediction (NEED)</i> for robot path planning problems. Different from classical heuristic methods, NEED is not designed for some specific environments, which can be applied to various environments. NEED has an encoder–decoder structure, which consists of three modules: convolutional neural network backbone, spatial pooling module, and decoder module. By learning from a number of successful path planning cases, NEED can learn to analyze the environment structure and predict the promising search region for the new path planning problem. This predicted region serves as a heuristic to guide the search direction of path planning algorithms. A series of simulation experiments are conducted to demonstrate that NEED significantly improves the algorithm performance on solving new path planning problems. Meanwhile, NEED can also be applied to highly dynamic environments since it can output the prediction results at a speed of over 100 Hz on a laptop.
On the human evaluation of universal audio adversarial perturbations

Human-machine interaction is increasingly dependent on speech communication, mainly due to the remarkable performance of Machine Learning models in speech recognition tasks. However, these models can be fooled by adversarial examples, which are inputs intentionally perturbed to produce a wrong prediction without the changes being noticeable to humans. While much research has focused on developing new techniques to generate adversarial perturbations, less attention has been given to aspects that determine whether and how the perturbations are noticed by humans. This question is relevant since high fooling rates of proposed adversarial perturbation strategies are only valuable if the perturbations are not detectable. In this paper we investigate to which extent the distortion metrics proposed in the literature for audio adversarial examples, and which are commonly applied to evaluate the effectiveness of methods for generating these attacks, are a reliable measure of the human perception of the perturbations. Using an analytical framework, and an experiment in which 36 subjects evaluate audio adversarial examples according to different factors, we demonstrate that the metrics employed by convention are not a reliable measure of the perceptual similarity of adversarial examples in the audio domain.
Focusing on Persons

In industry, there exist plenty of scenarios where old gray photos need to be automatically colored, such as video sites and archives. In this paper, we present the HistoryNet focusing on historical person's diverse high fidelity clothing colorization based on fine grained semantic understanding and prior. Colorization of historical persons is realistic and practical, however, existing methods do not perform well in the regards. In this paper, a HistoryNet including three parts, namely, classification, fine grained semantic parsing and colorization, is proposed. Classification sub-module supplies classifying of images according to the eras, nationalities and garment types; Parsing sub-network supplies the semantic for person contours, clothing and background in the image to achieve more accurate colorization of clothes and persons and prevent color overflow. In the training process, we integrate classification and semantic parsing features into the coloring generation network to improve colorization. Through the design of classification and parsing subnetwork, the accuracy of image colorization can be improved and the boundary of each part of image can be more clearly. Moreover, we also propose a novel Modern Historical Movies Dataset (MHMD) containing 1,353,166 images and 42 labels of eras, nationalities, and garment types for automatic colorization from 147 historical movies or TV series made in modern time. Various quantitative and qualitative comparisons demonstrate that our method outperforms the state-of-the-art colorization methods, especially on military uniforms, which has correct colors according to the historical literatures.
A quantitative content analysis of Turkish state universities’ official websites in terms of their preparedness and actions during emergency distance education in the early phase of the COVID-19 pandemic period

Most universities worldwide had to temporarily interrupt face-to-face education and start Emergency Distance Education (EDE) due to the COVID-19 outbreak. It is useful to identify the difficulties and problems that universities faced in this process in order that they can carry out a similar process more efficiently in the future. Therefore, this study aimed to conduct an in vivo examination of EDE from the institutional perspective within the scope of universities during the pandemic period in Turkey. To this end, all state university websites were searched according to 27 criteria which were determined to set out the EDE readiness status of universities. The results revealed that higher education institutions had difficulty in providing sufficient pedagogical and technical guidance to academic staff due to the rapid transition. With respect to students, distance course contents, activities and announcements were not equitably accessible for students from foreign countries and those with disabilities owing to the lack of information and communications technology (ICT) tools, internet connection, translation, etc. A remarkable number of higher education institutions preferred the features of distance education tools such as cost, familiarity, stability, availability of technical support, and short preparation time rather than their pedagogical affordances.
FAST DATA: A Fair, Secure, and Trusted Decentralized IIoT Data Marketplace Enabled by Blockchain

As the world calls it, data is the new oil. With vast installments of Industrial Internet of Things (IIoT) infrastructure, data is produced at a rate like never before. Similarly, artificial intelligence (AI) and machine learning (ML) solutions are getting integrated to numerous services, making them “smarter.” However, the data remain fragmented in individual organizational silos inhibiting data value extraction to its full potential. Digital marketplaces are emerging to allow data owners to monetize these data. Yet concerns, such as privacy, security, and unfair payment settlement deter adoption of such platforms. In addition, the state-of-the-art platforms are under the control of large multinational corporations with no transparency between the buyer and seller in terms of payment details, listing, data discovery, and storage. In this work, a novel decentralized platform of a digital data marketplace for IoT data has been proposed. The platform leverages a decentralized data streaming network to host IoT data in a reliable and fault-tolerant manner. The platform ensures fair trading, data storage, and delivery in a privacy-preserving manner and trust metric calculation for actors in the network. In order to study the feasibility of the proposed platform, an opensource library is developed using Hyperledger Fabric and data network layer built on VerneMQ, the library is deployed on a real-time Google cloud platform. The library is tested and results are analyzed for throughput, overheads, and scalability.
Adaptive Temporal Difference Learning With Linear Function Approximation

This paper revisits the temporal difference (TD) learning algorithm for the policy evaluation tasks in reinforcement learning. Typically, the performance of TD(0) and TD( λ) is very sensitive to the choice of stepsizes. Oftentimes, TD(0) suffers from slow convergence. Motivated by the tight link between the TD(0) learning algorithm and the stochastic gradient methods, we develop a provably convergent adaptive projected variant of the TD(0) learning algorithm with linear function approximation that we term AdaTD(0). In contrast to the TD(0), AdaTD(0) is robust or less sensitive to the choice of stepsizes. Analytically, we establish that to reach an ϵ accuracy, the number of iterations needed is [Formula: see text] in the general case, where ρ represents the speed of the underlying Markov chain converges to the stationary distribution. This implies that the iteration complexity of AdaTD(0) is no worse than that of TD(0) in the worst case. When the stochastic semi-gradients are sparse, we provide theoretical acceleration of AdaTD(0). Going beyond TD(0), we develop an adaptive variant of TD( λ), which is referred to as AdaTD( λ). Empirically, we evaluate the performance of AdaTD(0) and AdaTD( λ) on several standard reinforcement learning tasks, which demonstrate the effectiveness of our new approaches.
Phishing Email Detection using Persuasion Cues

Phishing is an attempt to acquire sensitive information from an unsuspecting victim by malicious means. Recent studies have shown that phishers often use persuasion techniques to get positive responses from the recipients. Still missing from this literature are studies assessing effectiveness of persuasion cues in phishing email detection. Specifically focusing on gain and loss persuasion cues, we address the following research questions: In detecting phishing emails, (1) how effective are the gain persuasion cues, (2) how effective are the loss persuasion cues, and (3) how effective is an integrated model of gain and loss persuasion In order to address the research questions, we create three machine learning models, with relevant gain persuasion cues, loss persuasion cues, and combined gain and loss persuasion cues respectively, and compare the estimates with a baseline model that does not account for the persuasion cues. The results show that the three phishing detection models with relevant persuasion cues significantly outperform the baseline model by approximately 5% to 20% percent in terms of F-score, thus representing reliable methods for phishing email detection.
Natural language-guided programming

In today’s software world with its cornucopia of reusable software libraries, when a programmer is faced with a programming task that they suspect can be completed through the use of a library, they often look for code examples using a search engine and then manually adapt found examples to their specific context of use. We put forward a vision based on a new breed of developer tools that have the potential to largely automate this process. The key idea is to adapt code autocompletion tools such that they take into account not only the developer’s already-written code but also the intent of the task the developer is trying to achieve next, formulated in plain natural language. We call this practice of enriching the code with natural language intent to facilitate its completion natural language-guided programming.