Reviewerly/exHarmony huggingface dataset:
Pruning Artificial Neural Networks: A Way to Find Well-Generalizing, High-Entropy Sharp Minima

Recently, a race towards the simplification of deep networks has begun, showing that it is effectively possible to reduce the size of these models with minimal or no performance loss. However, there is a general lack in understanding why these pruning strategies are effective. In this work, we are going to compare and analyze pruned solutions with two different pruning approaches, one-shot and gradual, showing the higher effectiveness of the latter. In particular, we find that gradual pruning allows access to narrow, well-generalizing minima, which are typically ignored when using one-shot approaches. In this work we also propose PSP-entropy, a measure to understand how a given neuron correlates to some specific learned classes. Interestingly, we observe that the features extracted by iteratively-pruned models are less correlated to specific classes, potentially making these models a better fit in transfer learning approaches.
A systematic review of the machine learning algorithms for the computational analysis in different domains

In this paper machine learning algorithms have been discussed and analyzed. It has been discussed considering computational aspects in different domains. These algorithms have the capability of building mathematical and analytical model. These models may be helpful in the decision-making process. This paper elaborates the computational analysis in three different ways. The background and analytical aspect have been presented with the learning application in the first phase. In the second phase detail literature has been explored along with the pros and cons of the applied techniques in different domains. Based on the literatures, gap identification and the limitations have been discussed and highlighted in the third phase. Finally, computational analysis has been presented along with the machine learning results in terms of accuracy. The results mainly focus on the exploratory data analysis, domain applicability and the predictive problems. Our systematic analysis shows that the applicability of machine learning is wide and the results may be improved based on these algorithms. It is also inferred from the literature analysis that at the applicability of machine learning algorithm has the capability in the performance improvement. The main methods discussed here are classification and regression trees (CART), logistic regression, naïve Bayes (NB), k-nearest neighbors (KNN), support vector machine (SVM) and decision tree (DT). The domain covered mainly are disease detection, business intelligence, industry automation and sentiment analysis.
Browser Forensic Investigations of WhatsApp Web Utilizing IndexedDB Persistent Storage

Digital Evidence is becoming an indispensable factor in most legal cases. However, technological advancements that lead to artifact complexity, are forcing investigators to create sophisticated connections between the findings and the suspects for admissibility of evidence in court. This paper scrutinizes whether IndexedDB, an emerging browser technology, can be a source of digital evidence to provide additional and correlating support for traditional investigation methods. It particularly focuses on the artifacts of the worldwide popular application, WhatsApp. A single case pretest–posttest quasi experiment is applied with WhatsApp Messenger and Web Application to populate and investigate artifacts in IndexedDB storage of Google Chrome. The findings are characterized and presented with their potential to be utilized in forensic investigation verifications. The storage locations of the artifacts are laid out and operations of extraction, conversion and presentation are systematized. Additionally, a proof of concept tool is developed for demonstration. The results show that WhatsApp Web IndexedDB storage can be employed for time frame analysis, demonstrating its value in evidence verification.
The Computational Complexity of Plethysm Coefficients

Abstract In two papers, Bürgisser and Ikenmeyer (STOC 2011, STOC 2013) used an adaption of the geometric complexity theory (GCT) approach by Mulmuley and Sohoni (Siam J Comput 2001, 2008) to prove lower bounds on the border rank of the matrix multiplication tensor. A key ingredient was information about certain Kronecker coefficients. While tensors are an interesting test bed for GCT ideas, the far-away goal is the separation of algebraic complexity classes. The role of the Kronecker coefficients in that setting is taken by the so-called plethysm coefficients: These are the multiplicities in the coordinate rings of spaces of polynomials. Even though several hardness results for Kronecker coefficients are known, there are almost no results about the complexity of computing the plethysm coefficients or even deciding their positivity. In this paper, we show that deciding positivity of plethysm coefficients is -hard and that computing plethysm coefficients is #-hard. In fact, both problems remain hard even if the inner parameter of the plethysm coefficient is fixed. In this way, we obtain an inner versus outer contrast: If the outer parameter of the plethysm coefficient is fixed, then the plethysm coefficient can be computed in polynomial time. Moreover, we derive new lower and upper bounds and in special cases even combinatorial descriptions for plethysm coefficients, which we consider to be of independent interest. Our technique uses discrete tomography in a more refined way than the recent work on Kronecker coefficients by Ikenmeyer, Mulmuley, and Walter (Comput Compl 2017). This makes our work the first to apply techniques from discrete tomography to the study of plethysm coefficients. Quite surprisingly, that interpretation also leads to new equalities between certain plethysm coefficients and Kronecker coefficients.
Phishing Website Detection Using Machine Learning Classifiers Optimized by Feature Selection

Security is one of the most actual topics in the online world. Lists of security threats are constantly updated. One of those threats are phishing websites. In this work, we address the problem of phishing websites classification. Three classifiers were used: K-Nearest Neighbor, Decision Tree and Random Forest with the feature selection methods from Weka. Achieved accuracy was 100% and number of features was decreased to seven. Moreover, when we decreased the number of features, we decreased time to build models too. Time for Random Forest was decreased from the initial 2.88s and 3.05s for percentage split and 10-fold cross validation to 0.02s and 0.16s respectively.
Semantic Complexity in End-to-End Spoken Language Understanding

End-to-end spoken language understanding (SLU) models are a class of model architectures that predict semantics directly from speech. Because of their input and output types, we refer to them as speech-to-interpretation (STI) models. Previous works have successfully applied STI models to targeted use cases, such as recognizing home automation commands, however no study has yet addressed how these models generalize to broader use cases. In this work, we analyze the relationship between the performance of STI models and the difficulty of the use case to which they are applied. We introduce empirical measures of dataset semantic complexity to quantify the difficulty of the SLU tasks. We show that near-perfect performance metrics for STI models reported in the literature were obtained with datasets that have low semantic complexity values. We perform experiments where we vary the semantic complexity of a large, proprietary dataset and show that STI model performance correlates with our semantic complexity measures, such that performance increases as complexity values decrease. Our results show that it is important to contextualize an STI model's performance with the complexity values of its training dataset to reveal the scope of its applicability.
Differential Attacks on CRAFT Exploiting the Involutory S-boxes and Tweak Additions

CRAFT is a lightweight tweakable block cipher proposed at FSE 2019, which allows countermeasures against Differential Fault Attacks to be integrated into the cipher at the algorithmic level with ease. CRAFT employs a lightweight and involutory S-box and linear layer, such that the encryption function can be turned into decryption at a low cost. Besides, the tweakey schedule algorithm of CRAFT is extremely simple, where four 64-bit round tweakeys are generated and repeatedly used. Due to a combination of these features which makes CRAFT exceedingly lightweight, we find that some input difference at a particular position can be preserved through any number of rounds if the input pair follows certain truncated differential trails. Interestingly, in contrast to traditional differential analysis, the validity of this invariant property is affected by the positions where the constant additions take place. We use this property to construct “weak-tweakey” truncated differential distinguishers of CRAFT in the single-key model. Subsequently, we show how the tweak additions allow us to convert these weak-tweakey distinguishers into ordinary secret-key distinguishers based on which key-recovery attacks can be performed. Moreover, we show how to construct MILP models to search for truncated differential distinguishers exploiting this invariant property. As a result, we find a 15-round truncated differential distinguisher of CRAFT and extend it to a 19-round key-recovery attack with 260.99 data, 268 memory, 294.59 time complexity, and success probability 80.66%. Also, we find a 14-round distinguisher with probability 2−43 (experimentally verified), a 16-round distinguisher with probability 2−55, and a 20-round weak-key distinguisher (2118 weak keys) with probability 2−63. Experiments on round-reduced versions of the distinguishers show that the experimental probabilities are sometimes higher than predicted. Finally, we note that our result is far from threatening the security of the full CRAFT.
Multi-Label Arabic Text Classification: An Overview

There is a massive growth of text documents on the web.This led to the increasing need for methods that can organize and classify electronic documents (instances) automatically.Multi-label classification task is widely used in real-world problems and it has been applied on different applications.It assigns multiple labels for each document simultaneously.Few and insufficient research studies have investigated the multi-label text classification problem in the Arabic language.Therefore, this survey paper aims to present an extensive review of the existing multi-label classification methods and techniques that can deal with multi-label problem.Besides, we focus on Arabic language by covering the relevant applications of multi-label classification on the Arabic text, and identify the main challenges faced by these studies.Furthermore, this survey presents an experimental comparisons of different multi-label classification methods applied for the Arabic context and points out some baseline results.We found that further investigations are also needed to improve the multi-label classification task in the Arabic language, especially the hierarchical classification task.
Support Vector Machine (SVM) Optimization Using Grid Search and Unigram to Improve E-Commerce Review Accuracy

Electronic Commerce (E-Commerce) is distributing, buying, selling, and marketing goods and services over electronic systems such as the Internet, television, websites, and other computer networks. E-commerce platforms such as amazon.com and Lazada.co.id offer products with various price and quality.&nbsp;Sentiment&nbsp;analysis used to understand the product’s popularity based on customers’ reviews. There are some approaches in sentiment analysis including machine learning. The part of machine learning that focuses on text processing called text mining. One of the techniques in text mining is classification and Support Vector Machine (SVM) is one of the frequently used algorithms to perform classification. Feature and parameter selection in SVM significantly affecting the classification accuracy. In this study, we chose unigram as the feature extraction and grid search as parameter optimization to improve SVM classification accuracy. Two customer review datasets with different language are used which is Amazon reviews that written in English and Lazada reviews in the Indonesian language. 10-folds cross validation and confusion matrix are used to evaluating the experiment results. The experiment results show that applying unigram and grid search on SVM algorithm can improve Amazon review accuracy by 26,4% and Lazada reviews by 4,26%.
A SIMPLE SOLUTION FOR THE TAXONOMY ENRICHMENT TASK: DISCOVERING HYPERNYMS USING NEAREST NEIGHBOR SEARCH

In this paper, we present the system we used in the Taxonomy Enrichment for the Russian Language evaluation campaign. The goal of this challenge is to predict hypernyms for the words not included in the taxonomy. Our approach was to generate and score candidate hypernyms by word embedding similarity of the input words and concepts already in the taxonomy. Despite being very simple, our system was ranked first on the verbs track.
Towards a Conceptual Model for Data Narratives

Data narration is the activity of producing stories supported by facts extracted from data analysis, possibly using interactive visualizations. In spite of the increasing interest in data narration in several communities (e.g. journalism, business, e-government), there is no consensual definition of data narrative, let alone a conceptual or logical model of it. In this paper, we propose a conceptual model of data narrative for exploratory data analysis. It is based on four layers that reflect the transition from raw data to the visual rendering of the data story: factual, intentional, structural and presentational. This model aims to support the entire lifecycle of building a data narrative, starting from an intentional goal: fetch and explore data, bring out highlights, derive important messages, structure the plot of the data narrative, and render it in a visual manner. Our contributions include a description of the model and its instantiation for several real examples showing that it covers data narration needs.
BERT for Monolingual and Cross-Lingual Reverse Dictionary

Reverse dictionary is the task to find the proper target word given the word description.In this paper, we tried to incorporate BERT into this task.However, since BERT is based on the byte-pair-encoding (BPE) subword encoding, it is nontrivial to make BERT generate a word given the description.We propose a simple but effective method to make BERT generate the target word for this specific task.Besides, the cross-lingual reverse dictionary is the task to find the proper target word described in another language.Previous models have to keep two different word embeddings and learn to align these embeddings.Nevertheless, by using the Multilingual BERT (mBERT), we can efficiently conduct the crosslingual reverse dictionary with one subword embedding, and the alignment between languages is not necessary.More importantly, mBERT can achieve remarkable cross-lingual reverse dictionary performance even without the parallel corpus, which means it can conduct the cross-lingual reverse dictionary with only corresponding monolingual data.
Classifying Syntactic Errors in Learner Language

We present a method for classifying syntactic errors in learner language, namely errors whose correction alters the morphosyntactic structure of a sentence.The methodology builds on the established Universal Dependencies syntactic representation scheme, and provides complementary information to other error-classification systems.Unlike existing error classification methods, our method is applicable across languages, which we showcase by producing a detailed picture of syntactic errors in learner English and learner Russian.We further demonstrate the utility of the methodology for analyzing the outputs of leading Grammatical Error Correction (GEC) systems.* First two authors contributed equally.
ptype: probabilistic type inference

Abstract Type inference refers to the task of inferring the data type of a given column of data. Current approaches often fail when data contains missing data and anomalies, which are found commonly in real-world data sets. In this paper, we propose ptype , a probabilistic robust type inference method that allows us to detect such entries, and infer data types. We further show that the proposed method outperforms existing methods.
Progressive Transfer Learning for Person Re-identification

Model fine-tuning is a widely used transfer learning approach in person Re-identification (ReID) applications, which fine-tuning a pre-trained feature extraction model into the target scenario instead of training a model from scratch. It is challenging due to the significant variations inside the target scenario, e.g., different camera viewpoint, illumination changes, and occlusion. These variations result in a gap between the distribution of each mini-batch and the distribution of the whole dataset when using mini-batch training. In this paper, we study model fine-tuning from the perspective of the aggregation and utilization of the global information of the dataset when using mini-batch training. Specifically, we introduce a novel network structure called Batch-related Convolutional Cell (BConv-Cell), which progressively collects the global information of the dataset into a latent state and uses this latent state to rectify the extracted feature. Based on BConv-Cells, we further proposed the Progressive Transfer Learning (PTL) method to facilitate the model fine-tuning process by joint training the BConv-Cells and the pre-trained ReID model. Empirical experiments show that our proposal can improve the performance of the ReID model greatly on MSMT17, Market-1501, CUHK03 and DukeMTMC-reID datasets. The code will be released later on at \url{https://github.com/ZJULearning/PTL}
CRANK: A Hybrid Model for User and Content Sentiment Classification Using Social Context and Community Detection

Recent works have shown that sentiment analysis on social media can be improved by fusing text with social context information. Social context is information such as relationships between users and interactions of users with content. Although existing works have already exploited the networked structure of social context by using graphical models or techniques such as label propagation, more advanced techniques from social network analysis remain unexplored. Our hypothesis is that these techniques can help reveal underlying features that could help with the analysis. In this work, we present a sentiment classification model (CRANK) that leverages community partitions to improve both user and content classification. We evaluated this model on existing datasets and compared it to other approaches.
Demystifying the delayed-choice quantum eraser

The delayed-choice quantum eraser has long been a subject of controversy, and has been looked at as being incomprehensible to having retro-causal effect in time. Here the delayed-choice quantum eraser is theoretically analyzed using standard quantum mechanics. Employing Mach-Zehnder interferometer, instead of a conventional two-slit interference, brings in surprising clarity. Some common mistakes in interpreting the experiment are pointed out. It is demonstrated that in the delayed mode there is no which-way information present after the particle is registered on the screen or the final detectors, contrary to popular belief. However, it is shown that another kind of path information is present even after the particle is registered in the final detectors. The registered particle can be used to predict the results of certain yet to be made measurements on the which-way detector. This novel correlation can be tested in a careful experiment. It is consequently argued that there is no big mystery in the experiment, and no retro-causal effect whatsoever.
Hitachi at MRP 2020: Text-to-Graph-Notation Transducer

This paper presents our proposed parser for the shared task on Meaning Representation Parsing (MRP 2020) at CoNLL, where participant systems were required to parse five types of graphs in different languages.We propose to unify these tasks as a text-to-graph-notation transduction in which we convert an input text into a graph notation.To this end, we designed a novel Plain Graph Notation (PGN) that handles various graphs universally.Then, our parser predicts a PGN-based sequence by leveraging Transformers and biaffine attentions.Notably, our parser can handle any PGN-formatted graphs with fewer frameworkspecific modifications.As a result, ensemble versions of the parser tied for 1st place in both cross-framework and cross-lingual tracks.* Contributed equally.Ozaki mainly developed PGN parser.Morio mainly developed neural models.
Entangling power of multipartite unitary gates

Abstract We study the entangling properties of multipartite unitary gates with respect to the measure of entanglement called one-tangle . Putting special emphasis on the case of three parties, we derive an analytical expression for the entangling power of an n -partite gate as an explicit function of the gate, linking the entangling power of gates acting on the n -partite Hilbert space of dimension to the entanglement of pure states in the Hilbert space of dimension . Furthermore, we evaluate its mean value averaged over the unitary and orthogonal groups, analyze the maximal entangling power and relate it to the absolutely maximally entangled (AME) states of a system with 2 n parties. Finally, we provide a detailed analysis of the entangling properties of the three-qubit unitary and orthogonal gates.
Towards deeper graph neural networks with differentiable group normalization

34th Conference on Neural Information Processing Systems (NeurIPS 2020), Vancouver, Canada, 2020
Tighter constraints of multiqubit entanglement in terms of Rényi-α entropy*

Quantum entanglement plays essential roles in quantum information processing. The monogamy and polygamy relations characterize the entanglement distributions in the multipartite systems. We present a class of monogamy inequalities related to the μ -th power of the entanglement measure based on Rényi- α entropy, as well as polygamy relations in terms of the μ -th power of Rényi- α entanglement of assistance. These monogamy and polygamy relations are shown to be tighter than the existing ones.
Accessible coherence in open quantum system dynamics

Quantum coherence generated in a physical process can only be cast as a potentially useful resource if its effects can be detected at a later time. Recently, the notion of non-coherence-generating-and-detecting (NCGD) dynamics has been introduced and related to the classicality of the statistics associated with sequential measurements at different times. However, in order for a dynamics to be NCGD, its propagators need to satisfy a given set of conditions for all triples of consecutive times. We reduce this to a finite set of $d(d-1)$ conditions, where $d$ is the dimension of the quantum system, provided that the generator is time-independent. Further conditions are derived for the more general time-dependent case. The application of this result to the case of a qubit dynamics allows us to elucidate which kind of noise gives rise to non-coherence-generation-and-detection.