Reviewerly/exHarmony huggingface dataset:
Quantum Private Query With Perfect Performance Universally Applicable Against Collective-Noise

Almost all of the previous quantum private query (QPQ) protocols are always based on an ideal environment without any noise. Nevertheless, due to the defects of the channel, channel noise inevitably exists in the actual transmission process, which will give rise to transmission error and reduce the probability of successful quantum transmission. An eavesdropper may disguise her actions as channel noise to avoid being detected by legitimate parties during security checks. To solve this problem, we present a QPQ protocol with perfect performance universally applicable against collective noise. Concretely, the protocol encodes bits in noiseless subspace and thus can function over a quantum channel subjected to an arbitrary degree of collective noise, as occurs, for instance, due to polarization rotation in an optical fiber. Furthermore, Bob cannot get the information about Alice's choice at all by taking fake entangled attack and by taking fake photon attack, the probability Bob can get the information about Alice's choice is only approximately 6.7%, which is far less than the probability of 25% in Yang et al.'s protocol. Moreover, the conclusiveness of the user Alice's measurement results is subject to quantum randomness rather than allowing Alice to choose by herself to obtain a conclusive result in Wei et al.'s protocol, which prevents Alice's intuitive attack and meanwhile makes it arduous for Bob to perform the joint-measurement attack. Our protocol uses only one state, which reduces the communication complexity, and is, therefore, an effective QPQ solution with the high-security level.
Improving Police Social Media Use Practices

Phenomenological study findings identified police social media site use practices that law enforcement leadership may wish to consider incorporating. Findings showed that members of the public in their lived experiences desired two-way asynchronous and synchronous interpersonal communication capabilities when accessing social media platforms maintained by law enforcement agencies. Improved police social media use practices in these areas may be a gateway to building stronger community relations and using social media as an instrument to aid in crime solving. The themes identified included crime solving, building community-relations, asynchronous versus synchronous engagement, humanistic branding, and public image.
Statistical thinking, machine learning

We read with interest “A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models” by Christodoulou et al. [ [1] Christodoulou E. Ma J. Collins G.S. Steyerberg E.W. Verbakel J.Y. Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019; 110: 12-22 Abstract Full Text Full Text PDF PubMed Scopus (604) Google Scholar ] appreciating its rigor and importance. We question, however, the conceptual dichotomy of logistic regression (LR) vs. machine learning (ML). Statistics versus machine learning: definitions are interesting (but understanding, methodology, and reporting are more important)Journal of Clinical EpidemiologyVol. 116PreviewWe thank Bian et al for their interest in our study. Since the study was published, the distinction between logistic regression and machine learning has fueled a lot of discussion. We had addressed this issue already in the initial submission but corroborated on it based on the reviewers’ comments. We state in the article that we do not believe there is a clear dichotomy but rather that algorithms lie on a continuum regarding flexibility, and reliance on the data versus subject knowledge. Nevertheless, several publications and discussions explicitly make this distinction and often conclude that machine learning leads to better predictive performance compared with traditional statistical methods. Full-Text PDF
Visual Positioning and Recognition of Gangues Based on Scratch Feature Detection

The coals and gangues in a raw coal image have similar visual features, due to the presence of coal ash on the surface.Thus, it is difficult to locate and recognize coals and gangues on the transmission line through visual recognition.To solve the problem, this paper proposes a visual positioning and recognition method for gangues based on scratch feature detection.Firstly, an image acquisition system was designed to capture the clear and suitable images.Next, scratched features were prepared on gangue surface with mechanical tools, laying the basis for visual positioning and recognition.Afterwards, the texture feature recognition method based on grey-level co-occurrence matrix (GLCM) was adopted to identify coal and scratched gangue blocks.The test results show that the GLCM correlation feature parameter is effective for scratch recognition.The parameter and the said method were proved effective through experiments.
Idea of Using Blockchain Technique for Choosingthe Best Configuration of Weights in Neural Networks

The blockchain technique is becoming more and more popular due to its advantages such as stability and dispersed nature. This is an idea based on blockchain activity paradigms. Another important field is machine learning, which is increasingly used in practice. Unfortunately, the training or overtraining artificial neural networks is very time-consuming and requires high computing power. In this paper, we proposed using a blockchain technique to train neural networks. This type of activity is important due to the possible search for initial weights in the network, which affect faster training, due to gradient decrease. We performed the tests with much heavier calculations to indicate that such an action is possible. However, this type of solution can also be used for less demanding calculations, i.e., only a few iterations of training and finding a better configuration of initial weights.
Applying Data Mining Techniques in Predicting Index and non-Index Crimes

An increasing incidence of crime has led to the development and use of computer-aided diagnosis system, tools and methods in analyzing, classifying and predicting crimes.This paper clusters municipalities in Surigao del Norte using K-Means algorithm.This is instrumental in finding identical traits, patterns and values in categorizing municipalities with much, more, and most number of recorded index and non-index crimes from 2013-2017.Prediction of its occurrence for the year 2018-2022 was also provided using ARIMA(1,0,7) model.Results showed that Surigao City has the most number of recorded index and non-index crimes.Followed by the municipality of Placer, Claver, and Dapa of cluster 2 and Del Carmen of cluster 3. Further, physical injury, homicide, violation of special laws, car napping, reckless imprudence resulting to physical injury, and other non-index crimes has 26%, 25%, 25%, 24%, 24%, and 23% forecasted increase for the year 2018-2022 with the highest occurrence in years 2018, 2018, 2020, 2018, 2020, and 2020 respectively.Future researchers may utilize other data mining techniques supported by a better accuracy result.
Reversible Data Hiding in JPEG Images Using Quantized DC

Reversible data hiding in JPEG images has become an important topic due to the prevalence and overwhelming support of the JPEG image format these days. Much of the existing work focuses on embedding using AC (quantized alternating current coefficients) to maximize the embedding capacity while minimizing the distortion and the file size increase. Traditionally, DC (quantized direct current coefficients) are not used for embedding, due to the assumption that the embedding in DCs cause more distortion than embedding in ACs. However, for data analytic which extracts fine details as a feature, distortion in ACs is not acceptable, because they represent the fine details of the image. In this paper, we propose a novel reversible data hiding method which efficiently embeds in the DC. The propose method uses a novel DC prediction method to decrease the entropy of the prediction error histogram. The embedded image has higher PSNR, embedding capacity, and smaller file size increase. Furthermore, proposed method preserves all the fine details of the image.
Quantum blackjack: Advantages offered by quantum strategies in communication-limited games

We examine the advantages that quantum strategies afford in communication-limited games. Inspired by the card game blackjack, we focus on cooperative, two-party sequential games in which a single classical bit of communication is allowed from the player who moves first to the player who moves second. Within this setting, we explore the usage of quantum entanglement between the players and find analytic and numerical conditions for quantum advantage over classical strategies. Using these conditions, we study a family of blackjack-type games with varying numbers of card types, and find a range of parameters where quantum advantage is achieved. Furthermore, we give an explicit quantum circuit for the strategy achieving quantum advantage.
Interactive Structure-aware Blending of Diverse Edge Bundling Visualizations

Many edge bundling techniques (i.e., data simplification as a support for data visualization and decision making) exist but they are not directly applicable to any kind of dataset and their parameters are often too abstract and difficult to set up. As a result, this hinders the user ability to create efficient aggregated visualizations. To address these issues, we investigated a novel way of handling visual aggregation with a task-driven and user-centered approach. Given a graph, our approach produces a decluttered view as follows: first, the user investigates different edge bundling results and specifies areas, where certain edge bundling techniques would provide user-desired results. Second, our system then computes a smooth and structural preserving transition between these specified areas. Lastly, the user can further fine-tune the global visualization with a direct manipulation technique to remove the local ambiguity and to apply different visual deformations. In this paper, we provide details for our design rationale and implementation. Also, we show how our algorithm gives more suitable results compared to current edge bundling techniques, and in the end, we provide concrete instances of usages, where the algorithm combines various edge bundling results to support diverse data exploration and visualizations.
Measuring Text Complexity for Italian as a Second Language Learning Purposes

The selection of texts for second language learning purposes typically relies on teachers' and test developers' individual judgment of the observable qualitative properties of a text.Little or no consideration is generally given to the quantitative dimension within an evidence-based framework of reproducibility.This study aims to fill the gap by evaluating the effectiveness of an automatic tool trained to assess text complexity in the context of Italian as a second language learning.A dataset of texts labeled by expert test developers was used to evaluate the performance of three classifier models (decision tree, random forest, and support vector machine), which were trained using linguistic features measured quantitatively and extracted from the texts.The experimental analysis provided satisfactory results, also in relation to which kind of linguistic trait contributed the most to the final outcome.
You Talkin’ to Me? A Practical Attention-Aware Embodied Agent

Most present-day voice-based assistants require that users utter a wake-up word to signify that they are addressing the assistant. While this may be acceptable for one-shot requests such as "Turn on the lights", it becomes tiresome when one is engaged in an extended interaction with such an assistant. To support the goal of developing low-complexity, low-cost alternatives to a wake-up word, we present the results of two studies in which users engage with an assistant that infers whether it is being addressed from the user's head orientation. In the first experiment, we collected informal user feedback regarding a relatively simple application of head orientation as a substitute for a wake-up word. We discuss that feedback and how it influenced the design of a second prototype assistant designed to correct many of the issues identified in the first experiment. The most promising insight was that users were willing to adapt to the interface, leading us to hypothesize that it would be beneficial to provide visual feedback about the assistant's belief about the user's attentional state. In a second experiment conducted using the improved assistant, we collected more formal user feedback on likability and usability and used it to establish that, with high confidence, head orientation combined with visual feedback is preferable to the traditional wake-up word approach. We describe the visual feedback mechanisms and quantify their usefulness in the second experiment.
Trifocal tensor-based 6-DOF visual servoing

This paper proposes a trifocal tensor-based approach for six-degree-of-freedom visual servoing. The trifocal tensor model among the current, desired, and reference views is constructed to describe the geometric relationship of the system. More precisely, to ensure the computation consistency of trifocal tensor, a virtual reference view is introduced by exploiting the transfer relationships between the initial and desired images. Instead of resorting to explicit estimation of the camera pose, a set of visual features with satisfactory decoupling properties are constructed from the tensor elements. Based on the selected features, a visual controller is developed to regulate the camera to a desired pose, and an adaptive update law is used to compensate for the unknown distance scale factor. Furthermore, the system stability is analyzed via Lyapunov-based techniques, showing that the proposed controller can achieve almost global asymptotic stability. Both simulation and experimental results are provided to demonstrate the effectiveness and robustness of our approach under different conditions and case studies.
Object Extraction From Very High-Resolution Images Using a Convolutional Neural Network Based on a Noisy Large-Scale Dataset

In recent years, convolutional neural networks (CNNs) have made great achievements in object extraction from very high-resolution (VHR) images. However, most existing approaches require large quantities of clean and accurate training data to achieve impressive classification results. The presence of inaccurate labels in training datasets is known to deteriorate the performance of CNNs. In this paper, we introduce a novel efficient method for improving the robustness when training CNN on the dataset with relatively noisy labels. First, we propose a feature and label noise model (FLNM) to model the noisy label distribution in the training dataset. Then, we use a multitask deep learning framework (MDLF) to integrate the FLNM into the training process of CNN. Finally, a novel loss function concerning the high-level features is introduced to efficiently train the MDLF. We evaluate our method on datasets from Massachusetts and compare this method with other state-of-the-art methods. The experimental results demonstrate the effectiveness of the proposed method in improving the classification performance of CNNs trained with noisy training dataset.
Do Nuclear Submarines Have Nuclear Captains? A Challenge Dataset for Commonsense Reasoning over Adjectives and Objects

How do adjectives project from a noun to its parts?If a motorcycle is red, are its wheels red?Is a nuclear submarine's captain nuclear?These questions are easy for humans to judge using our commonsense understanding of the world, but are difficult for computers.To attack this challenge, we crowdsource a set of human judgments that answer the English-language question "Given a whole described by an adjective, does the adjective also describe a given part?"We build strong baselines for this task with a classification approach.Our findings indicate that, despite the recent successes of large language models on tasks aimed to assess commonsense knowledge, these models do not greatly outperform simple word-level models based on pre-trained word embeddings.This provides evidence that the amount of commonsense knowledge encoded in these language models does not extend far beyond that already baked into the word embeddings.Our dataset will serve as a useful testbed for future research in commonsense reasoning, especially as it relates to adjectives and objects.
Using Customer Service Dialogues for Satisfaction Analysis with Context-Assisted Multiple Instance Learning

Customers ask questions and customer service staffs answer their questions, which is the basic service model via multi-turn customer service (CS) dialogues on E-commerce platforms.Existing studies fail to provide comprehensive service satisfaction analysis, namely satisfaction polarity classification (e.g., well satisfied, met and unsatisfied) and sentimental utterance identification (e.g., positive, neutral and negative).In this paper, we conduct a pilot study on the task of service satisfaction analysis (SSA) based on multi-turn CS dialogues.We propose an extensible Context-Assisted Multiple Instance Learning (CAMIL) model to predict the sentiments of all the customer utterances and then aggregate those sentiments into service satisfaction polarity.After that, we propose a novel Context Clue Matching Mechanism (CCMM) to enhance the representations of all customer utterances with their matched context clues, i.e., sentiment and reasoning clues.We construct two CS dialogue datasets from a top E-commerce platform.Extensive experimental results are presented and contrasted against a few previous models to demonstrate the efficacy of our model. 1
DFT-Net: Disentanglement of Face Deformation and Texture Synthesis for Expression Editing

This paper presents a novel deep architecture DFT-Net that combines the advantages of Generative Adversarial Networks (GANs) and warp mechanisms for expression editing. Recent generative models leverage Action Units as annotations and show more flexible expression manipulation than previous approaches using other guiding information. However, those methods bring inevitable artifacts where facial components deform (e.g. eyes from open to close), for the structural defect in modeling shape variations without geometric guidance such as facial landmarks. Our approach explicitly disentangles face deformations and appearance details by constructing two parallel networks, one that learns an appearance flow for 2D warps and the other generates corresponding texture and hallucinates hidden regions such as mouth interiors. Experimental results show our method outperforms the state-of-the-art on various expression editing tasks.
Curio SmartChat : A system for Natural Language Question Answering for Self-Paced K-12 Learning

During learning, students often have questions which they would benefit from responses to in real time.In class, a student can ask a question to a teacher.During homework, or even in class if the student is shy, it can be more difficult to receive a rapid response.In this work, we introduce Curio SmartChat, an automated question answering system for middle school Science topics.Our system has now been used by around 20,000 students who have so far asked over 100,000 questions.We present data on the challenge created by students' grammatical errors and spelling mistakes, and discuss our system's approach and degree of effectiveness at disambiguating questions that the system is initially unsure about.We also discuss the prevalence of student "small talk" not related to science topics, the pluses and minuses of this behavior, and how a system should respond to these conversational acts.We conclude with discussions and point to directions for potential future work.
Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment

This paper presents a multi-task learning approach to natural language inference (NLI) and question entailment (RQE) in the biomedical domain.Recognizing textual inference relations and question similarity can address the issue of answering new consumer health questions by mapping them to Frequently Asked Questions on reputed websites like the NIH 1 .We show that leveraging information from parallel tasks across domains along with medical knowledge integration allows our model to learn better biomedical feature representations.Our final models for the NLI and RQE tasks achieve the 4 th and 2 nd rank on the shared-task leaderboard respectively.
Cross-Sentence N-ary Relation Extraction using Lower-Arity Universal Schemas

Most existing relation extraction approaches exclusively target binary relations, and n-ary relation extraction is relatively unexplored.Current state-of-the-art n-ary relation extraction method is based on a supervised learning approach and, therefore, may suffer from the lack of sufficient relation labels.In this paper, we propose a novel approach to cross-sentence n-ary relation extraction based on universal schemas.To alleviate the sparsity problem and to leverage inherent decomposability of n-ary relations, we propose to learn relation representations of lower-arity facts that result from decomposing higher-arity facts.The proposed method computes a score of a new nary fact by aggregating scores of its decomposed lower-arity facts.We conduct experiments with datasets for ternary relation extraction and empirically show that our method improves the n-ary relation extraction performance compared to previous methods.
PaLM: A Hybrid Parser and Language Model

We present PaLM, a hybrid parser and neural language model.Building on an RNN language model, PaLM adds an attention layer over text spans in the left context.An unsupervised constituency parser can be derived from its attention weights, using a greedy decoding algorithm.We evaluate PaLM on language modeling, and empirically show that it outperforms strong baselines.If syntactic annotations are available, the attention component can be trained in a supervised manner, providing syntactically-informed representations of the context, and further improving language modeling performance.
Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices

Recurrent Neural Networks (RNNs) capture long dependencies and context, and 2 hence are the key component of typical sequential data based tasks. However, the sequential nature of RNNs dictates a large inference cost for long sequences even if the hardware supports parallelization. To induce long-term dependencies, and yet admit parallelization, we introduce novel shallow RNNs. In this architecture, the first layer splits the input sequence and runs several independent RNNs. The second layer consumes the output of the first layer using a second RNN thus capturing long dependencies. We provide theoretical justification for our architecture under weak assumptions that we verify on real-world benchmarks. Furthermore, we show that for time-series classification, our technique leads to substantially improved inference time over standard RNNs without compromising accuracy. For example, we can deploy audio-keyword classification on tiny Cortex M4 devices (100MHz processor, 256KB RAM, no DSP available) which was not possible using standard RNN models. Similarly, using SRNN in the popular Listen-Attend-Spell (LAS) architecture for phoneme classification [4], we can reduce the lag inphoneme classification by 10-12x while maintaining state-of-the-art accuracy.