Reviewerly/exHarmony huggingface dataset:
Finding disposable domain names: A linguistics-based stacking approach

An increasing number of Internet services tend to collect one-time information from clients via DNS queries. Notably, the uncertainty of such transient information makes these domain names be queried only once in their lifetime. This type of domain is called disposable domain. Although they do not involve any malicious activities, the efficiency of DNS infrastructures is still affected by their ever-increasing number. Existing approaches for detecting disposable domains have serious disadvantages, such as poor timeliness and high false positive rate. In this paper, we conduct an extensive measurement study of the ISP-level DNS traffic and find that the readability of domain name is suitable for identifying disposable domains. Therefore, we propose Vogers, a linguistics-based stacking model, to detect disposable domains from raw DNS traffic. Compared with the prior arts, Vogers decreases the false positive rate by more than 17%, while maintaining the true positive rate above 98.9%. In addition, Vogers generalizes quite well to unknown environments, whereby we are able to report new disposable domains. Our further application of Vogers in the real-world DNS traffic shows that filtering disposable domains can improve the efficiency of DNS infrastructures.
A Foot in the Door: An Examination of Professional Sport Internship Job Announcements

The sport management internship has been deemed a critical component of students’ academic preparation, as well as a foot in the door for many students seeking full-time employment after graduation. The number of sport management programs has grown in recent years, and the field itself remains highly competitive. Thus, it is increasingly important for sport management programs to help prepare their students for the internship hiring process. Scholarship in this area has largely focused on student perceptions of their internship experience and employer perceptions of student preparedness. But to prepare students for internship experiences in the sport industry, it is essential for faculty to understand the key skills that are sought by industry practitioners making hiring decisions, as well as the administrative requirements included. Therefore, the purpose of this study was to examine the nature of professional sport industry internship job postings in the United States by examining the content of online announcements during a 6-month period. The results indicated that digital content, sales, and operations internships were the most highly sought positions, while basic computer skills, communication skills (both oral and written), and the ability to withstand long hours were the most commonly desired skills.
What Should/Do/Can LSTMs Learn When Parsing Auxiliary Verb Constructions?

There is a growing interest in investigating what neural NLP models learn about language. A prominent open question is the question of whether or not it is necessary to model hierarchical structure. We present a linguistic investigation of a neural parser adding insights to this question. We look at transitivity and agreement information of auxiliary verb constructions (AVCs) in comparison to finite main verbs (FMVs). This comparison is motivated by theoretical work in dependency grammar and in particular the work of Tesnière ( 1959 ), where AVCs and FMVs are both instances of a nucleus, the basic unit of syntax. An AVC is a dissociated nucleus; it consists of at least two words, and an FMV is its non-dissociated counterpart, consisting of exactly one word. We suggest that the representation of AVCs and FMVs should capture similar information. We use diagnostic classifiers to probe agreement and transitivity information in vectors learned by a transition-based neural parser in four typologically different languages. We find that the parser learns different information about AVCs and FMVs if only sequential models (BiLSTMs) are used in the architecture but similar information when a recursive layer is used. We find explanations for why this is the case by looking closely at how information is learned in the network and looking at what happens with different dependency representations of AVCs. We conclude that there may be benefits to using a recursive layer in dependency parsing and that we have not yet found the best way to integrate it in our parsers.
An A⁎ search algorithm for the constrained longest common subsequence problem

The constrained longest common subsequence (CLCS) problem was introduced as a specific measure of similarity between molecules. It is a special case of the constrained sequence alignment problem and of the longest common subsequence (LCS) problem, which are both well-studied problems in the scientific literature. Finding similarities between sequences plays an important role in the fields of molecular biology, gene recognition, pattern matching, text analysis, and voice recognition, among others. The CLCS problem in particular represents an interesting measure of similarity for molecules that have a putative structure in common. This paper proposes an exact A⁎ search algorithm for effectively solving the CLCS problem. This A⁎ search is guided by a tight upper bound calculation for the cost-to-go for the LCS problem. Our computational study shows that on various artificial and real benchmark sets this algorithm scales better with growing instance size and requires significantly less computation time to prove optimality than earlier state-of-the-art approaches from the literature.
Bringing Order to Network Embedding

Network embedding aims to automatically learn the node representations in networks. The basic idea of network embedding is to first construct a network to describe the neighborhood context for each node, and then learn the node representations by designing an objective function to preserve certain properties of the constructed context network. The vast majority of the existing methods, explicitly or implicitly, follow a pointwise design principle. That is, the objective can be decomposed into the summation of the certain goodness function over each individual edge of the context network. In this paper, we propose to go beyond such pointwise approaches, and introduce the ranking-oriented design principle for network embedding. The key idea is to decompose the overall objective function into the summation of a goodness function over a set of edges to collectively preserve their relative rankings on the context network. We instantiate the ranking-oriented design principle by two new network embedding algorithms, including a pairwise network embedding method PaWine which optimizes the relative weights of edge pairs, and a listwise method LiWine which optimizes the relative weights of edge lists. Both proposed algorithms bear a linear time complexity, making themselves scalable to large networks. We conduct extensive experimental evaluations on five real datasets with a variety of downstream learning tasks, which demonstrate that the proposed approaches consistently outperform the existing methods.
The Secret Arithmetic of Patterns: A General Method for Designing Constrained Codes Based on Lexicographic Indexing

Constrained codes are used to prevent errors from occurring in various data storage and data transmission systems. They can help in increasing the storage density of magnetic storage devices, in managing the lifetime of electronic storage devices, and in increasing the reliability of data transmission over wires. We recently introduced families of lexicographically-ordered constrained (LOCO) codes. These codes achieve capacity with simple encoding and decoding, and they are easy to reconfigure. In this paper, we generalize our work on LOCO codes by presenting a systematic method that guides the code designer to build any constrained code based on lexicographic indexing once the finite set of data patterns to forbid is known. In particular, we connect the set of forbidden patterns directly to the cardinality of the code and to the rule that uncovers the index associated with a codeword. By doing that, we reveal the secret arithmetic of patterns, and make the code design significantly easier. We design optimal (rate-wise) constrained codes for the new two-dimensional magnetic recording (TDMR) technology. We show notable performance gains as a result of solely applying the new codes. Moreover, we show how near-optimal constrained codes be designed and used to further reduce complexity.
Performance Evaluation of Adversarial Examples on Deep Neural Network Architectures

Tremendous advancement in machine learning over the recent years leads to the use of deep neural networks in many applications from weather prediction to safety critical applications like disease diagnosis. Latest work revealed that, Deep Neural Networks are possibly being attacked using the perturbed input like images/text/audio, also referred to as adversarial examples. Even a small change in considering single pixel can cause neural network models to make mistakes in predicting the output. This has resulted in increased safety concern of deployment of safety critical applications. It is observed that the adversarial examples are transferred from one neural network model to another neural network model with considering adversary knowledge either black box which corresponds to a real-life assumption with the adversary having nearly no knowledge of the framework to be attacked, or white box or gray box. Adversarial examples can be categorized by various characteristics. This paper provides a good inclination of adversarial instances in the context of computer vision with details on various types of adversarial attacks on individual types of neural network architectures and also elaborate the different metrics applied to validate the system performance. We conclude that adversarial learning is a factual threat to application of machine learning not only in the physical world but also during training the model and testing the model. There are some certain counter measures that exist but none of them can act as an individualistic solution for all the challenges. It remains an extensive challenge for the machine learning community to deal with robustness.
Applied Ontology for the Automatic Classification of Simple Sentences of the Kazakh Language

This paper introduces a knowledge base and a method for the automatic classification of given simple sentences of the Kazakh language based on the construction of their parse trees and ontological models. For this, the structure of each type of sentence is represented by the construction of corresponding parse tree. To create a knowledge base taking into account the semantics of the components of simple sentences, ontological models, which will be combined into a single ontological model of simple sentences, are developed. Then, the queries for the knowledge base are formulated and a reasoner, which automatically classifies simple sentences, is launched. The results of the work will be used to create intelligent question-answer, training, knowledge evaluating and other systems.
CIFAS: Community Inspired Firefly Algorithm with fuzzy cross-entropy for feature selection in Twitter Spam detection

Social network sites such as Facebook, Twitter, YouTube, etc., are very popular among internet users for information sharing and communication. This popularity also attracts cybercriminals to spread their malicious activities including spams using social networks. Spam may contain unsolicited information or malicious links that may harm Twitter users and make them feel insecure. So there is a crucial need for effective spam detection which requires the distinctive features that clearly portray the spammer behavior. This is accomplished by information theoretic-based feature selection methods, as these methods select the informative features that retain the original meaning. Most of the existing information theory-based feature selection methods focus on retaining the features that contain more information while removing features that contain less information for improving the performance. This may lead to the information loss. Also, the features that are individually less significant may be useful when combined with other features. So, Community Inspired Firefly Algorithm for Spam detection (CIFAS) is proposed to handle combination search for the features that provide good performance using fuzzy cross entropy as the fitness function. Fuzzy cross-entropy is used in this work, to preserve the information contained in the selected feature subset equally to the information contained in full feature set. From the experimental result, it has been proven that the proposed CIFAS offers defense against spam similar to the original feature set and performs better than the existing approaches in terms of accuracy, false-positive rate, and F measure for the two standard twitter datasets.
Analysis on Saliency Estimation Methods in High-Resolution Optical Remote Sensing Imagery for Multi-Scale Ship Detection

Ship detection is of considerable significance in both military and civilian application domains. Deep Convolutional Neural Network (DCNN) with region proposal mechanism, e.g., Faster R-CNN, performs outstandingly in ship detection with high-resolution images. However, the accuracy limitation is induced by the region proposal restricted by the training set for multi-scale target detection. Therefore, the method of multi-scale ship object detection is proposed based on saliency estimation in our work. Saliency Estimation Algorithms (SEAs) are often used to extract saliency features in images. In ship detection of high-resolution remote sensing images, these algorithms can extract information such as the scale and position of the targets, and then help the DCNN-based ship detection method to obtain better performance. To verify the effectiveness of the saliency estimation algorithm in multi-scale ship detection of high-resolution remote sensing images, and analyze the advantages of different SEAs. This paper introduces 13 classic saliency estimation algorithms and 2 DCNN-based ones to compare them by using evaluation indicators. At last, in order to demonstrate the performance of different SEAs, the extracted saliency feature maps are used to assist the DCNN-based target detection under multi-scale ships condition. In general, this framework can improve the detection accuracy of large-scale ships under scenarios of training only with small scale ships or without enough datasets.
An Analytical Study on Machine Learning Techniques

The last few months have produced a remarkable expansion in research and deep study in the field of machine learning. Machine learning is a technique in which the set of the methods are used by the computers to make prediction, improve prediction and behavior prediction based on dataset. The learning techniques can be classified as supervised and unsupervised learning. The focus is on supervised machine learning that covers all the predictions problem for which we had the dataset in which the outcome is already known. Some of the algorithm like naive bayes, linear regression, SVM, k-nearest neighbor, especially neural network have gain growth in this area. The classifiers of machine learning are completely unconstrained with the assumptions of statistical and for that they are adapted by complex data. The authors have demonstrated the application of machine learning techniques and its ethical issues.
TargetDrop: A Targeted Regularization Method for Convolutional Neural Networks

Dropout regularization has been widely used in deep learning but performs less effective for convolutional neural networks since the spatially correlated features allow dropped information to still flow through the networks. Some structured forms of dropout have been proposed to address this but are prone to result in over or under regularization as features are dropped randomly. In this paper, we propose a targeted regularization method, TargetDrop, which incorporates the attention mechanism to drop several discriminative feature units. Specifically, it masks out the target regions in the feature maps corresponding to the target channels. We conduct comprehensive experiments and demonstrate that TargetDrop outperforms the other dropout-based regularization methods.
Controllable Multi-Character Psychology-Oriented Story Generation

Story generation, which aims to generate a long and coherent story automatically based on the title or an input sentence, is an important research area in the field of natural language generation. There is relatively little work on story generation with appointed emotions. Most existing works focus on using only one specific emotion to control the generation of a whole story and ignore the emotional changes in the characters in the course of the story. In our work, we aim to design an emotional line for each character that considers multiple emotions common in psychological theories, with the goal of generating stories with richer emotional changes in the characters. To the best of our knowledge, this work is first to focuses on characters' emotional lines in story generation. We present a novel model-based attention mechanism that we call SoCP (Storytelling of multi-Character Psychology). We show that the proposed model can generate stories considering the changes in the psychological state of different characters. To take into account the particularity of the model, in addition to commonly used evaluation indicators(BLEU, ROUGE, etc.), we introduce the accuracy rate of psychological state control as a novel evaluation metric. The new indicator reflects the effect of the model on the psychological state control of story characters. Experiments show that with SoCP, the generated stories follow the psychological state for each character according to both automatic and human evaluations.
Dual Head-wise Coattention Network for Machine Comprehension with Multiple-Choice Questions

Multiple-choice Machine Comprehension (MC) is an important and challenging nature language processing (NLP) task where the machine is required to make the best answer from candidate answer set given particular passage and question. Existing approaches either only utilize the powerful pre-trained language models or only rely on an over complicated matching network that is design supposed to capture the relationship effectively among the triplet of passage, question and candidate answers. In this paper, we present a novel architecture, Dual Head-wise Coattention network (called DHC), which is a simple and efficient attention neural network designed to perform multiple-choice MC task. Our proposed DHC not only support a powerful pre-trained language model as encoder, but also models the MC relationship as attention mechanism straightforwardly, by head-wise matching and aggregating method on multiple layers, which better model relationships sufficiently between question and passage, and cooperate with large pre-trained language models more efficiently. To evaluate the performance, we test our proposed model on five challenging and well-known datasets for multiple-choice MC: RACE, DREAM, SemEval-2018 Task 11, OpenBookQA, and TOEFL. Extensive experimental results demonstrate that our proposal can achieve a significant increase in accuracy comparing existing models based on all five datasets, and it consistently outperforms all tested baselines including the state-of-the-arts techniques. More remarkably, our proposal is a pluggable and more flexible model, and it thus can be plugged into any pre-trained Language Models based on BERT. Ablation studies demonstrate its state-of-the-art performance and generalization.
Zero-Shot Learning for Gesture Recognition

Zero-Shot Learning (ZSL) is a new paradigm in machine learning that aims to recognize the classes that are not present in the training data. Hence, this paradigm is capable of comprehending the categories that were never seen before. While deep learning has pushed the limits of unseen object recognition, ZSL for temporal problems such as unfamiliar gesture recognition (referred to as ZSGL) remain unexplored. ZSGL has the potential to result in efficient human-machine interfaces that can recognize and understand the spontaneous and conversational gestures of humans. In this regard, the objective of this work is to conceptualize, model and develop a framework to tackle ZSGL problems. The first step in the pipeline is to develop a database of gesture attributes that are representative of a range of categories. Next, a deep architecture consisting of convolutional and recurrent layers is proposed to jointly optimize the semantic and classification losses. Lastly, rigorous experiments are performed to compare the proposed model with respect to existing ZSL models on CGD 2013 and MSRC-12 datasets. In our preliminary work, we identified a list of 64 discriminative attributes related to gestures' morphological characteristics. Our approach yields an unseen class accuracy of (41%) which outperforms the state-of-the-art approaches by a considerable margin. Future work involves the following: 1. Modifying the existing architecture in order to improve the ZSL accuracy, 2. Augmenting the database of attributes to incorporate semantic properties, 3. Addressing the issue of data imbalance which is inherent to ZSL problems, and 4. Expanding this research to other domains such as surgeme and action recognition.
On the Potential for Discrimination via Composition

The success of platforms such as Facebook and Google has been due in no small part to features that allow advertisers to target ads in a fine-grained manner. However, these features open up the potential for discriminatory advertising when advertisers include or exclude users of protected classes---either directly or indirectly---in a discriminatory fashion. Despite the fact that advertisers are able to compose various targeting features together, the existing mitigations to discriminatory targeting have focused only on individual features; there are concerns that such composition could result in targeting that is more discriminatory than the features individually.
Preliminary design of a multipurpose UAV situational awareness platform based on novel computer vision and machine learning techniques

There has been an increased trend in the use of Unmanned Aerial Vehicles (UAVs) providing essential assistance and enhanced situational awareness in various application domains. Considering the sensing and intelligent processing capabilities of UAV platforms, operational managers are able to make smart decisions by having a more complete picture in real-time about the state of the managed event. In this paper, a preliminary design of a multipurpose UAV based situational awareness platform is proposed by exploiting advanced on-board processing capabilities and efficient computer vision, image processing and machine learning techniques. The proposed design consists of aerial and terrestrial surveillance assets able to provide rapid and reliable real-time event information of areas of interests in emergency operations. Experimental results are provided using RGB and thermal video datasets and applying novel object detection and tracking algorithms. The results show the utility and the potentials of the proposed design, while future directions for extension and optimization of the multipurpose UAV based situational awareness system are presented.