Reviewerly/exHarmony huggingface dataset:
Comparing web-crawled and traditional corpora

Using a multi-dimensional (MD) analysis of register variability, the study compares two corpora of Czech: Koditex, a "traditional" corpus carefully designed using various sources with rich metadata, and Araneum Bohemicum Maximum, a web-crawled corpus with an opportunistic composition representative of the "searchable" web. Both types of corpora are projected onto the space induced by the MD model, with the main objective being to find out whether they overlap in the linguistic variation they cover, or whether one introduces some specific variation which cannot be found in the other. We also document a crucial methodological point which has broader relevance for MD analyses in general, namely that texts have to be of similar lengths in order for their scores on the dimensions to be comparable. Results indicate that some traditional text categories, such as journalism or non-fiction, are characterized by language phenomena which are equally well covered by web-crawled data, though of course traditional corpora keep their edge in terms of the richness of the accompanying metadata. But overall, the range of variation in Koditex is broader as it contains texts which have no adequate substitute (i.e. texts with a comparable set of linguistic characteristics, regardless of their extratextual label) in data acquired through general-purpose web-crawling techniques. These include informal conversations, private correspondence, some types of fiction, but also user-generated content (comments on Facebook, forums etc.).
Label Specific Features-Based Classifier Chains for Multi-Label Classification

Multi-label classification tackles the problems in which each instance is associated with multiple labels.Due to the interdependence among labels, exploiting label correlations is the main means to enhance the performances of classifiers and a variety of corresponding multi-label algorithms have been proposed.Among those algorithms Classifier Chains (CC) is one of the most effective methods.It induces binary classifiers for each label, and these classifiers are linked in a chain.In the chain, the labels predicted by previous classifiers are used as additional features for the current classifier.The original CC has two shortcomings which potentially decrease classification performances: random label ordering, noise in original and additional features.To deal with these problems, we propose a novel and effective algorithm named LSF-CC, i.e.Label Specific Features based Classifier Chain for multi-label classification.At first, a feature estimating technique is employed to produce a list of most relevant features and labels for each label.According to these lists, we define a chain to guarantee that the most frequent labels that appear in these lists are top-ranked.Then, label specific features can be selected from the original feature space and label space.Based on these label specific features, corresponding binary classifiers are learned for each label.Experiments on several multi-label data sets from various domains have shown that the proposed method outperforms well-established approaches.
An Efficient and Provable Masked Implementation of qTESLA

Now that the NIST’s post-quantum cryptography competition has entered in its second phase, the time has come to focus more closely on practical aspects of the candidates. While efficient implementations of the proposed schemes are somewhat included in the submission packages, certain issues like the threat of side-channel attacks are often lightly touched upon by the authors. Hence, the community is encouraged by the NIST to join the war effort to treat those peripheral, but nonetheless crucial, topics. In this paper, we study the lattice-based signature scheme qTESLA in the context of the masking countermeasure. Continuing a line of research opened by Barthe et al. at Eurocrypt 2018 with the masking of the GLP signature scheme, we extend and modify their work to mask qTESLA. Based on the work of Migliore et al. in ACNS 2019, we slightly modify the parameters to improve the masked performance while keeping the same security. The masking can be done at any order and specialized gadgets are used to get maximal efficiency at order 1. We implemented our countermeasure in the original code of the submission and performed tests at different orders to assess the feasibility of our technique.
Predicting Content Similarity via Multimodal Modeling for Video-In-Video Advertising

Rapid development of mobile devices has led to explosive growth of videos and online platforms, which creates great demand for online advertising in videos. Existing advertising methods often aim to randomly select a time point as insertion position, which means that the video content is likely not related to the ad content, resulting in unsatisfactory user experience. While previous works have neglected to understand rich semantics as well as multimodal information in video advertising, in contrast to previous works, we present an innovative method for video-in-video advertising using multimodal modeling. First, different pre-trained models are used to extract multimodal representations. Then, through multimodal modeling, we learn the complementarity among different representations and obtain a unified video-level description. Finally, the unified representations of ads and videos are utilized to find the best matching result for each advertisement. Our method emphasizes the content similarity between ad and video, which would make the transition between video and ad more natural. Comprehensive experiments with both objective and subjective evaluations demonstrate the effectiveness and user-friendliness of our proposed video-in-video advertising framework.
Blockchain Art and Blockchain Facilitated Art Economy: Two Ways in Which Art and Blockchain Collide

Exploring the blockchain as a subject, method, and medium, the world of art has embarked on a voyage of technological discovery unlike any other to date. So far, blockchain technology has been embraced by leaders in finance, computer sciences, transportation, bookkeeping and others, to bring efficiency, transparency, and added value to their products and services. The art world is exploring blockchain technology as well, experimenting with it as an art medium, creating art pieces that comment on it, and embracing it as a whole new way to revolutionize how art is being tracked, purchased and sold. In this paper we explore two vast categories in which art and blockchain collide today: (1) Blockchain art, and (2) Blockchain facilitated art economy.
Color full stokes polarization fringe projection 3D imaging

In this paper, we propose and demonstrate a new 3D imaging technique which can simultaneously provide surface shape, color, and polarization information of an object. The system employs a custom color full Stokes polarization camera and a digital color fringe projector. The color and polarization data provide additional surface details not possible using conventional fringe projection 3D imaging techniques, but important for target identification. We demonstrate that, for an object with weak spectral dependent polarization properties, it is possible to obtain surface shape, intensity, and polarization information in a snapshot, dramatically improving the imaging speed.
Enhanced RF Fingerprinting for IoT Devices with Recurrent Neural Networks

As the Internet of Things (IoT) continues to expand, there is a growing necessity for improved techniques to authenticate the identity of wireless transmitters. In this paper, we develop a physical-layer authentication technique using a neural network structure with both convolutional and recurrent components to distinguish transmissions originating from a particular target device from all others. In addition, we demonstrate strong performance in a realistic multipath channel environment, as well as show that classifier performance remains strong when presented with transmissions from devices that were never seen by the classifier during training. We explore the latter benefit in more detail via an experiment which measures the performance on unknown devices as a function of the number of devices seen during training. Next, we highlight the importance of frequency synchronization prior to fingerprint extraction by demonstrating that a network trained on unsynchronized transmissions is easily fooled by a simple frequency shift in a transmitted waveform. Finally, we increase the applicability of our approach to IoT devices by presenting a simple technique for reducing the memory footprint of trained models by 95% while maintaining strong performance.
Achieving Real-Time Sign Language Translation Using a Smartphone's True Depth Images

Sign language is used as a visual form of communication among the deaf and is considered as an official language in many countries. While there has been many efforts to achieve efficient translation between sign and verbal languages, many of these previous work can be applied in non-mobile context or exploit RGB images which can potentially invade the users' privacy. This work presents our preliminary efforts in designing a mobile device-based sign language translation system using depth-only images. Our system performs image processing on the smartphone-collected depth images to emphasize the subject's hand and upper body gestures and exploits a convolutional neural network for feature extraction. The series of features gathered from word-representing videos are passed through a Long-Short Term Memory (LSTM) model for word-level sign language translation. We train and test our system using a total of 2,200 samples collected from 26 people for 17 words. The classification accuracy of our proposed system using the self-collected data achieves 92% with an efficient image preprocessing phase.
Many-to-many Cross-lingual Voice Conversion with a Jointly Trained Speaker Embedding Network

Among various voice conversion (VC) techniques, average modeling approach has achieved good performance as it benefits from training data of multiple speakers, therefore, reducing the reliance on training data from the target speaker. Many existing average modeling approaches rely on the use of i-vector to represent the speaker identity for model adaptation. As such i-vector is extracted in a separate process, it is not optimized to achieve the best voice conversion quality for the average model. To address this problem, we propose a low dimensional trainable speaker embedding network that augments the primary VC network for joint training. We validate the effectiveness of the proposed idea by performing a many-to-many cross-lingual VC, which is one of the most challenging tasks in VC. We compare the i-vector scheme with the speaker embedding network in the experiments. It is found that the proposed system effectively improves the speech quality and speaker similarity.
Single Image Super-Resolution Using Fire Modules With Asymmetric Configuration

Recently, there have been many performance improvements in super resolution using deep learning methods. However, despite the performance improvement, it remains a challenge to reduce the amount of computation for real products. In this letter, we propose a deep network that employs modified Squeezenet's fire modules. We introduce a way to modify the original fire module for effective separation of spatial- and channel-wise learning, and describe how the modified fire modules can be arranged asymmetrically for reducing the number of parameters of the network. Our experiment results show higher PSNR and competitive processing time to other super resolution networks, but with less number of parameters.
Navigation of a Quadratic Potential with Ellipsoidal Obstacles

Successful navigation of a convex quadratic potential in a space with ellipsoidal obstacles can be attained with Rimon-Koditschek artificial potentials in spaces where the ellipsoids are not too eccentric (flat). This paper proposes a modification to gradient dynamics that allows successful navigation of an environment with a quadratic cost and ellipsoidal obstacles regardless of their eccentricity. This is accomplished by altering gradient dynamics with the addition of a second order curvature correction that is intended to imitate worlds with spherical obstacles in which Rimon-Koditschek potentials are known to work. Convergence to the goal is proven for all environments with a single obstacle. In worlds with multiple obstacles convergence is guaranteed in cases when the obstacles are not tightly packed around the agent's target. Results are numerically verified with a discretized version of the proposed flow dynamics.
Sentence Simplification using Syntactic Parse trees

Text simplification is one of the domains in Natural Language Processing which offers great promise for exploration. Simplifying sentences offer better results, as compared dealing with complex/compound sentences, in many language processing applications as well. Recently, Neural Networks have been used in simplifying texts, be it by state of the art LSTM's and GRU cells or by Reinforcement learning models. In contrast, in this work, we present a classical approach consisting of two separate algorithms, for simplification of complex and compound sentences to their corresponding simple forms.
#Confused and beyond

Students' confusion is a barrier for learning, contributing to loss of motivation and to disengagement with course materials. However, detecting students' confusion in large-scale courses is both time and resource intensive. This paper provides a new approach for confusion detection in online forums that is based on harnessing the power of students' self-reported affective states (reported using a set of pre-defined hashtags). It presents a rule for labeling confusion, based on students' hashtags in their posts, that is shown to align with teachers' judgement. We use this labeling rule to inform the design of an automated classifier for confusion detection for the case when there are no self-reported hashtags present in the test set. We demonstrate this approach in a large scale Biology course using the Nota Bene annotation platform. This work lays the foundation to empower teachers with better support tools for detecting and alleviating confusion in online courses.
Measuring the Tangle of Three-Qubit States

We present a quantum circuit that transforms an unknown three-qubit state into its canonical form, up to relative phases, given many copies of the original state. The circuit is made of three single-qubit parametrized quantum gates, and the optimal values for the parameters are learned in a variational fashion. Once this transformation is achieved, direct measurement of outcome probabilities in the computational basis provides an estimate of the tangle, which quantifies genuine tripartite entanglement. We perform simulations on a set of random states under different noise conditions to asses the validity of the method.
Detecting and Tracking Small Moving Objects in Wide Area Motion Imagery (WAMI) Using Convolutional Neural Networks (CNNs)

This paper proposes an approach to detect moving objects in Wide Area Motion Imagery (WAMI), in which the objects are both small and well separated. Identifying the objects only using foreground appearance is difficult since a 100&#x2013;pixel vehicle is hard to distinguish from objects comprising the background. Our approach is based on background subtraction as an efficient and unsupervised method that is able to output the shape of objects. In order to reliably detect low contrast and small objects, we configure the background subtraction to extract foreground regions that might be objects of interest. While this dramatically increases the number of false alarms, a Convolutional Neural Network (CNN) considering both spatial and temporal information is then trained to reject the false alarms. In areas with heavy traffic, the background subtraction yields merged detections. To reduce the complexity of multi-target tracker needed, we train another CNN to predict the positions of multiple moving objects in an area. Our approach shows competitive detection performance on smaller objects relative to the state-of-the-art. We adopt a GM-PHD filter to associate detections over time and analyse the resulting performance.
Deep Learning Technique for Sentiment Analysis of Hindi-English Code-Mixed Text using Late Fusion of Character and Word Features

Enormous presence of Code-Mixed text in social media provides an important research area for Natural Language Processing (NLP). This paper proposes a novel deep learning based techniques for Sentiment Analysis of Code-Mixed Hindi-English text. The proposed Deep Learning technique uses joint learning from Character and Word features. This paper also implements a technique for preparing corpus for training Word Embedding of Code-Mixed text. This embedding is further used in proposed architecture for robust classification. The proposed approach exceeds baseline accuracy. Additionally this paper shares experiments, which are conducted on the proposed architecture using different loss functions and optimizers.
Secure Authentication Mechanism for Resistance to Password Attacks

Authentication is a process that provides access control of any type of computing applications by inspecting the user's identification with the database of authorized users. Passwords play the vital role in authentication mechanism to ensure the privacy of the information and avert from the illicit access. Password based authentication mechanism suffers from many password attacks such as shoulder surfing, brute forcing and dictionary attacks that crack the password of authentication schema by the adversary. Key Stroke technique, Click Pattern technique, Graphichical Password technique and Authentication panel are the several authentication techniques used to resist the password attacks in the literature. This research study critically reviews the types of password attacks and proposes a matrix based secure authentication mechanism which includes three phases namely, User generation phase, Matrix generation phase and Authentication phase to resist the existing password attacks. The performance measure of the proposed method investigates the results in terms existing password attacks and shows the good resistance to password attacks in any type of computing applications.
Conceptualizing Requirements Using User Stories and Use Cases: A Controlled Experiment

[Context and motivation] Notations for expressing requirements are often proposed without explicit consideration of their suitability for specific tasks. Consequently, practitioners may choose a sub-optimal notation, thereby affecting task performance. [Question/problem] We investigate the adequacy of two well-known notations: use cases and user stories, as a starting point for the manual derivation of a static conceptual model. In particular, we examine the completeness and correctness of the derived conceptual model. [Principal ideas/results] We conducted a two-factor, two-treatment controlled experiment with 118 subjects. The results indicate that for deriving conceptual models, user stories fit better than use cases. It seems that the repetitions in user stories and their conciseness contribute to these results. [Contribution] The paper calls for evaluating requirements notations in the context of various requirements engineering tasks and for providing evidence regarding the aspects that need to be taken into account when selecting a requirement notation.
Fine-Grained Access Control in the Era of Cloud Computing: An Analytical Review

Access control is one of the imperative defense frontlines for digital computing environments especially for pervasive computing environments such as cloud computing and the internet of things (IoT) characterized by heterogeneous and distributed services and resources. Although, numerous access control models (ACM) have been implemented and used to prevent unauthorized access to cloud services and resources, these models may not prove efficacious to prevent overexposure and underexposure of data. More granular access control models or fine-grained access control therefore are developed to enable more itemized authorization policies. This paper presents a comparative analysis of different ACM used in the cloud with a focus on fine-grained access control models. First, we discuss the existing ACMs then and present the state-of-the-art research efforts carried out in the domain. Finally, we provide our analysis of the existing cloud-based access control methods and identify the gaps in the field for future work. This survey aims to improve the understanding and application of ACMs in the cloud and pave the road for future works in this area.
Estimation Accuracy and Classification of Polymetallic Nodule Resources Based on Classical Sampling Supported by Seafloor Photography (Pacific Ocean, Clarion-Clipperton Fracture Zone, IOM Area)

The amount and accuracy of nodule resources estimation in the Pacific Ocean are among the main factors conditioning the future exploitation. The estimates are based on the results of classical, direct seafloor sampling. Due to the large distance between sampling sites, the accuracy of assessing nodule resources in small parts of the deposit is low. The accuracy can be increased by using a large number of seafloor photographs taken along the route of the research vessel performing classic sampling. The study conducted for a part of the area administered by Interoceanmetal Joint Organization (IOM) included: (i) determining a model of the relationship between nodule abundance and seafloor nodule coverage using statistical methods, (ii) assessing the accuracy of nodule resources estimation using a geostatistical kriging procedure, (iii) proposing a preliminary classification of resources referring to International Seabed Authority (ISA) classification standards as material for further discussion. It was found that achievement of high accuracy in the estimation of nodule resources (with relative standard error &lt;5%) in blocks planned for annual exploitation based on direct sampling is difficult. While the use of seafloor photographs increases the accuracy of estimating nodule resources, this improvement is not radical due to the unfavorable, preferential arrangement of photographic data.
Criminal Activity Detection in Social Network by Text Mining: Comprehensive Analysis

Criminal activity detection in social network by text mining is the process of finding criminal activity by the criminals and help law text mining technique, the ability to detect hidden text from corpus documents. Text mining is process of transforming data from unstructured text to structured text which is easily perceived and processed by humans, but hard for machines to understand without designing algorithms, tools and methods in order to effectively process, such enforcing agencies to keep control of the prevailing crimes Text mining is method deriving high-quality information from raw data through the pattern devising and statistical pattern learning. Text mining is field a multidisciplinary field that relies on data mining, information retrieval, statistics, machine learning, and computational linguistics. The main thing in text mining process of analyzing and exploring is natural language processing, information retrieval, information extraction, content analysis, text clustering, and text classification. All that processes are wanted after you complete a step, the preprocess task. The importance of pre-processing task is to reduce the volume of the corpus textual documents and the tasks involved in that step are text boundary determinant, natural language specific stemming stop-word, elimination, and tokenization to remove unwanted data and handling missing data. Among this, doing the most important work is tokenization. Tokenization assist to divide the text data to individual words, open source tools become available for those interested such as spacy, NLTK with python, Gensim and many other. After that define model architecture to fit the model on the training data and evaluate this model on test simple data in order to predict values.