Reviewerly/exHarmony huggingface dataset:
Towards Private and Scalable Cross-Media Retrieval

Cross-media retrieval (CMR) is an attractive networked application where a server responds to queries with retrieval results of different modalities. Different from traditional information retrieval, CMR relies on a more enriched set of machine learning techniques to produce semantic models projecting multimodal data into a common space. A larger training dataset usually gives more accurate models, leading to a better retrieval result. Despite very promising with potential underpinnings in network analytics and multimedia applications, applying CMR in such contexts also faces severe privacy challenges, due to the fact that various data scattering among multiple parties may be sensitive and not allowed to be shared publicly. Studies jointly considering cross-media analytics, privacy protection, collaborative learning, and distributed networking contexts, are relatively sparse. In this work, we propose the first practical system for privacy-preserving cross-media retrieval by utilizing trusted processors. Our scheme enables secure aggregation of the data from distinct parties, and secure canonical correlation analysis (CCA) over collaborated data to obtain semantic models. Verification mechanisms are designed to defend against active attacks from a malicious adversary. Furthermore, to deal with large data sets, we provide a set of optimization methods to accomodate to limited trusted memory and improve the efficiency of training process in CMR. We consider issues such as data block splitting to manage memory overhead, ordering of operations as well as parameters reuse and release to simplify I/O, and parallel computation to speed up dual operations. Our experiments over both synthetic and real datasets show that our solution is very efficient in practice, outperforms the existing solutions, and performs comparably with the original CMR system.
Natural Language Inference with Monotonicity

This paper describes a working system which performs natural language inference using polaritymarked parse trees.The system handles all of the instances of monotonicity inference in the FraCaS data set, and can be easily extended to compute inferences in other sections of FraCaS.We achieve perfect precision and an accuracy comparable to previous systems on the first section of FraCaS.Except for the initial parse, it is entirely deterministic.It handles multi-premise arguments.The kind of inference performed is essentially "logical", but it goes beyond what is representable in first-order logic.In any case, the system works on surface forms and CCG parse trees rather than on logical representations of any kind.
The Titans at SemEval-2019 Task 5: Detection of hate speech against immigrants and women in Twitter

This system paper is a description of the system submitted to "SemEval-2019 Task 5" Task B for the English language, where we had to primarily detect hate speech and then detect aggressive behaviour and its target audience in Twitter.There were two specific target audiences, immigrants and women.The language of the tweets was English.We were required to first detect whether a tweet is containing hate speech.Thereafter we were required to find whether the tweet was showing aggressive behaviour, and then we had to find whether the targeted audience was an individual or a group of people.
Intention-Based Integration of Software Variants

Cloning is a simple way to create new variants of a system. While cheap at first, it increases maintenance cost in the long term. Eventually, the cloned variants need to be integrated into a configurable platform. Such an integration is challenging: it involves merging the usual code improvements between the variants, and also integrating the variable code (features) into the platform. Thus, variant integration differs from traditional soft- ware merging, which does not produce or organize configurable code, but creates a single system that cannot be configured into variants. In practice, variant integration requires fine-grained code edits, performed in an exploratory manner, in multiple iterations. Unfortunately, little tool support exists for integrating cloned variants. In this work, we show that fine-grained code edits needed for integration can be alleviated by a small set of integration intentions-domain-specific actions declared over code snippets controlling the integration. Developers can interactively explore the integration space by declaring (or revoking) intentions on code elements. We contribute the intentions (e.g., 'keep functionality' or 'keep as a configurable feature') and the IDE tool INCLINE, which implements the intentions and five editable views that visualize the integration process and allow declaring intentions producing a configurable integrated platform. In a series of experiments, we evaluated the completeness of the pro- posed intentions, the correctness and performance of INCLINE, and the benefits of using intentions for variant integration. The experiments show that INCLINE can handle complex integration tasks, that views help to navigate the code, and that it consistently reduces mistakes made by developers during variant integration.
Learning Bayesian networks by constrained Bayesian estimation

Bayesian networks (BNs) have become increasingly popular in recent years due to their wide-ranging applications in modeling uncertain knowledge.An essential problem about discrete BNs is learning conditional probability table (CPT) parameters.If training data are sparse, purely data-driven methods often fail to learn accurate parameters.Then, expert judgments can be introduced to overcome this challenge.Parameter constraints deduced from expert judgments can cause parameter estimates to be consistent with domain knowledge.In addition, Dirichlet priors contain information that helps improve learning accuracy.This paper proposes a constrained Bayesian estimation approach to learn CPTs by incorporating constraints and Dirichlet priors.First, a posterior distribution of BN parameters is developed over a restricted parameter space based on training data and Dirichlet priors.Then, the expectation of the posterior distribution is taken as a parameter estimation.As it is difficult to directly compute the expectation for a continuous distribution with an irregular feasible domain, we apply the Monte Carlo method to approximate it.In the experiments on learning standard BNs, the proposed method outperforms competing methods.It suggests that the proposed method can facilitate solving real-world problems.Additionally, a case study of Wine data demonstrates that the proposed method achieves the highest classification accuracy.
Two Factor Authentication Framework Using OTP-SMS Based on Blockchain

The authentication process is the main step which should be used to confirm that the user is the legitimate one and give the access only for him.Recently, Two Factor Authentication (2FA) schemes have been used by most of the applications to add an extra layer of security on the login process and solve the vulnerabilities of using only one factor for authentication.OTP-SMS is one of the most common methods which has been used in 2FA.However, attackers found a way to attack this method and gain an access to the user's account without their permission.In this paper, we proposed a new 2FA framework for OTP-SMS method to prevent different attacks, mainly Man In The Middle (MITM) attack and third party attack.The proposed framework is based on the use of Blockchain technology, which add more security and better environment for authentication process.The proposed framework uses an encrypted OTP, which generated by smart contract and uses also its hash value to send it to the application/website to complete the authentication process.We introduced a comparison between our proposed framework and other two frameworks which uses Blockchain to secure OTP-SMS.Our framework found to be secure against MITM and third party attacks and the computation time and complexity are less than other frameworks.
Don’t Blame Distributional Semantics if it can’t do Entailment

Distributional semantics has had enormous empirical success in Computational Linguistics and Cognitive Science in modeling various semantic phenomena, such as semantic similarity, and distributional models are widely used in state-of-the-art Natural Language Processing systems.However, the theoretical status of distributional semantics within a broader theory of language and cognition is still unclear: What does distributional semantics model?Can it be, on its own, a fully adequate model of the meanings of linguistic expressions?The standard answer is that distributional semantics is not fully adequate in this regard, because it falls short on some of the central aspects of formal semantic approaches: truth conditions, entailment, reference, and certain aspects of compositionality.We argue that this standard answer rests on a misconception: These aspects do not belong in a theory of expression meaning, they are instead aspects of speaker meaning, i.e., communicative intentions in a particular context.In a slogan: words do not refer, speakers do.Clearing this up enables us to argue that distributional semantics on its own is an adequate model of expression meaning.Our proposal sheds light on the role of distributional semantics in a broader theory of language and cognition, its relationship to formal semantics, and its place in computational models.
Approximate and Situated Causality in Deep Learning

Causality is the most important topic in the history of western science, and since the beginning of the statistical paradigm, its meaning has been reconceptualized many times. Causality entered into the realm of multi-causal and statistical scenarios some centuries ago. Despite widespread critics, today deep learning and machine learning advances are not weakening causality but are creating a new way of finding correlations between indirect factors. This process makes it possible for us to talk about approximate causality, as well as about a situated causality.
The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN

To address the problem of unstable training and poor accuracy in image classification algorithms based on generative adversarial networks (GAN), a novel sensor network structure for classification processing using auxiliary classifier generative adversarial networks (ACGAN) is proposed in this paper. Firstly, the real/fake discrimination of sensor samples in the network has been canceled at the output layer of the discriminative network and only the posterior probability estimation of the sample tag is outputted. Secondly, by regarding the real sensor samples as supervised data and the generative sensor samples as labeled fake data, we have reconstructed the loss function of the generator and discriminator by using the real/fake attributes of sensor samples and the cross-entropy loss function of the label. Thirdly, the pooling and caching method has been introduced into the discriminator to enable more effective extraction of the classification features. Finally, feature matching has been added to the discriminative network to ensure the diversity of the generative sensor samples. Experimental results have shown that the proposed algorithm (CP-ACGAN) achieves better classification accuracy on the MNIST dataset, CIFAR10 dataset and CIFAR100 dataset than other solutions. Moreover, when compared with the ACGAN and CNN classification algorithms, which have the same deep network structure as CP-ACGAN, the proposed method continues to achieve better classification effects and stability than other main existing sensor solutions.
Automatic Detection of NoSQL Injection Using Supervised Learning

With the advancement in big data, NoSQL databases are enjoying ever-growing popularity. The increasing use of this technology in large applications also brings security concerns to the fore. Historically, SQL injection has been one of the major security threats over the years. Recent studies reveal that NoSQL databases also have become vulnerable to injections. However, NoSQL security is yet to receive the attention it deserves from the industry or academia. In this work, we develop a tool for detecting NoSQL injections using supervised learning. To the best of our knowledge, our developed training dataset on NoSQL injection is the first of its kind. We manually design important features and apply various supervised learning algorithms. Our tool has achieved 0.93 F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -score as established by 10-fold cross-validation. We also apply our tool to a NoSQL injection generating tool, NoSQLMap and find that our tool outperforms Sqreen, the only available NoSQL injection detection tool, by 36.25% in terms of detection rate. The proposed technique is also shown to be database-agnostic achieving similar performance with injection on MongoDB and CouchDB databases.
Two-Step Trajectory Spoofing Algorithm for Loosely Coupled GNSS/IMU and NIS Sequence Detection

Satellite navigation spoofing has become a central issue of jamming technology research because of its serious threat and ability to conceal itself. Increasingly, targets are equipped with more robust GNSS/IMU systems and normalized innovation squared (NIS) is used to detect interference. Therefore, it is harder to implement covert trajectory spoofing on a GNSS/IMU system than a GNSS-only target. In practice, spoofing is needed to control unknown targets. Therefore, covert trajectory spoofing for GNSS/IMU targets is an important issue. Hence, using the information fusion of a GNSS/IMU system, the influence of spoofing on loosely coupled GNSS/IMU positioning is derived. To avoid ill-posed equations when introducing a measurement deviation, a Kalman gain matrix local regularization method is proposed to accurately determine the measurement deviation. To avoid triggering the NIS detection alarm, the range that enables the introduced measurement deviation to remain concealed is calculated. Then, a two-step trajectory guidance algorithm is proposed to quickly guide the target onto the spoofing trajectory. The simulation results show that the proposed trajectory spoofing algorithm can guide a loosely coupled GNSS/IMU target along a spoofing trajectory without triggering the NIS detection alarm. The proposed method can remain concealed and has good theoretical and practical application value.
On-Line Devanagari Handwritten Character Recognition Using Moments Features

Now a days recognizing the handwritten character is receiving high significance because of numerous applications like Educational Software, On-line Signature Verification, Bank Cheque Processing, postal code recognition, Electronic library etc. Very less work is accounted in the research of Devanagari handwritten character recognition (HWDCR), so that there is a large scope of research in this area. In this paper we proposed a HWDCR system that recognizes Devanagari handwritten characters, the most popular script in India. Using pen tablet handwritten character is inputted and its on-line features are extracted like sequence of (x, y) coordinates, stroke and pressure information which are passed to classifier for classification. We have used MLP-BP Neural Network Classifier for classification. The average recognition accuracy is achieved by the proposed HWDCR system is 90% using on-line data.
Investigating the Dynamics Between Price Volatility, Price Discovery, and Criminality in Cryptocurrency Markets

This paper identifies several stylised facts relating to the volatility and price discovery process from eight cryptocurrencies utilising an empirical analysis of intra-day trading data to uncover four main results.First, cryptocurrencies exhibit weekend-volatility effects while intra-day volatility is found to be influenced by international trading times, periods of substantial volatility in the markets for oil, and GBP/USD and cybercrime events.Secondly, a thorough investigation of recent cybercriminality identifies that cryptocurrency hacks are found to increase both the volatility of the currency hacked and the correlations across the hacked currency and other cryptocurrencies.Thirdly, hacks significantly reduce price discovery sourced within the hacked currency relative to other cryptocurrencies.Finally, there are abnormal returns associated with the hacks observed in the hours prior to the actual hacking event, which reverts to zero at the time of the public announcement of the hack.
Peningkatan Minat Belajar Siswa Melalui Media Pembelajaran Berbasis Teknologi Informasi

This study aims to see an increase in learning outcomes through information technology-based learning media in class X IPA 3 students at 9 Bone State High School.This research is a Class Room Assessment.The instruments used to collect data are the implementation observation observation sheets (teacher activities), student activity observation sheets, student response questionnaires, and learning outcomes tests in each cycle.The data obtained were analyzed using quantitative descriptive analysis and qualitative analysis.Quantitative descriptive analysis is used to determine student learning outcomes, while qualitative analysis is used to determine the results of observations of student activities.The results of the study indicate that learning using information technology-based media can increase the learning interest of class X students of Science 3 in Bone 9 High School.This can be seen from the average learning outcomes of class X students of IPA 3SMA 9 Watampone in the first cycle of 67.34% and in the second cycle the average student learning outcomes increased to 83.19%.Student activities in the learning process also experienced an increase, although based on the student activity sheet in the first cycle there were still some obstacles but in the second cycle there was a significant increase.Questionnaire responses of students in the first cycle there were 15 items with an overall percentage of 77.36% and in the second cycle 81.53%.
Transport and entanglement for single photons in optical waveguide ladders

Transfer and scattering matrix theories are derived for studying single-photon (SP) transport in optical waveguide ladders (OWLs). The OWLs consist of two one-dimensional waveguides connected by Jaynes-Cummings emitters (JCEs) and have two input and two output channels. The von Neumann entropy is introduced to describe the entanglement between the transmitted states from the two output channels. Two types of the OWLs are studied, i.e., the OWLs with two identical waveguides (i-OWLs) and those with two different waveguides (d-OWLs). When the OWLs contain only one JCE, the SP transport behavior in the i-OWLs is the same as that in the d-OWLs. When two JCEs are introduced, the quantum interference among the JCE-scattered waves can lead to the SP jumping with a 100% chance between the waveguides for the i-OWLs, while this is hard for the d-OWLs. As a result, the i-OWLs can serve as a SP router with respect to the d-OWLs. When the number of JCEs increases to a large value (e.g., 16), the transmission probabilities of the two output channels both tend to be 0.25 for the i-OWLs, but zero for d-OWLs. Correspondingly, the entanglements approximate a constant of 1 for the i-OWLs, but of zero for the d-OWLs. It shows that a large number of JCEs can suppress the influence of other system parameters including the SP frequency and JCE loss. Therefore, the i-OWLs with a large number of the JCEs show potential for a SP splitter and entanglement generator.
Radar Waveform Recognition Based on Multiple Autocorrelation Images

Radar signal waveform recognition, as a key component of radar target recognition, has always been a research topic of great concern in the field of electronic countermeasures.In this paper, aiming at the contradiction between improving recognition rate and reducing the sample size, we propose a multiple autocorrelation joint decision models, which achieves higher waveform recognition rate while requires lower data volume for the original sampled data.The key point of the model is to perform multiple autocorrelations on the signal and use time-frequency transform for each times autocorrelation result to obtain multiple time-frequency feature images that can characterize the same signal.Then, the model adapts to the input of multiple feature images and gets the pre-classification results of each feature image.Finally, using the pre-classification results, this paper designs an inference machine module based on a fully connected structure to achieve a better signal classification result.This paper simulates six types of the signals and generates training sets and test sets, using two data sets to achieve hyper parameter optimization, training, and testing of the model.The simulation results compared with the literature show that the proposed model not only has a high recognition rate at a high signal-to-noise ratio (SNR) but also better adapts to waveform recognition at low SNR environment.At -9dB SNR, the recognition rate of six types of signals is more than 74%.
Boolean Tensor Decomposition for Conjunctive Queries with Negation

We propose an approach for answering conjunctive queries with negation, where the negated relations have bounded degree. Its data complexity matches that of the InsideOut and PANDA algorithms for the positive subquery of the input query and is expressed in terms of the fractional hypertree width and the submodular width respectively. Its query complexity depends on the structure of the conjunction of negated relations; in general it is exponential in the number of join variables occurring in negated relations yet it becomes polynomial for several classes of queries.This approach relies on several contributions. We show how to rewrite queries with negation on bounded-degree relations into equivalent conjunctive queries with not-all-equal (NAE) predicates, which are a multi-dimensional analog of disequality (!=). We then generalize the known color-coding technique to conjunctions of NAE predicates and explain it via a Boolean tensor decomposition of conjunctions of NAE predicates. This decomposition can be achieved via a probabilistic construction that can be derandomized efficiently.
Dictionary-Guided Editing Networks for Paraphrase Generation

An intuitive way for a human to write paraphrase sentences is to replace words or phrases in the original sentence with their corresponding synonyms and make necessary changes to ensure the new sentences are fluent and grammatically correct. We propose a novel approach to modeling the process with dictionary-guided editing networks which effectively conduct rewriting on the source sentence to generate paraphrase sentences. It jointly learns the selection of the appropriate word level and phrase level paraphrase pairs in the context of the original sentence from an off-the-shelf dictionary as well as the generation of fluent natural language sentences. Specifically, the system retrieves a set of word level and phrase level paraphrase pairs derived from the Paraphrase Database (PPDB) for the original sentence, which is used to guide the decision of which the words might be deleted or inserted with the soft attention mechanism under the sequence-to-sequence framework. We conduct experiments on two benchmark datasets for paraphrase generation, namely the MSCOCO and Quora dataset. The automatic evaluation results demonstrate that our dictionary-guided editing networks outperforms the baseline methods. On human evaluation, results indicate that the generated paraphrases are grammatically correct and relevant to the input sentence.