Reviewerly/exHarmony huggingface dataset:
Pan-Private Uniformity Testing

A centrally differentially private algorithm maps raw data to differentially private outputs. In contrast, a locally differentially private algorithm may only access data through public interaction with data holders, and this interaction must be a differentially private function of the data. We study the intermediate model of \emph{pan-privacy}. Unlike a locally private algorithm, a pan-private algorithm receives data in the clear. Unlike a centrally private algorithm, the algorithm receives data one element at a time and must maintain a differentially private internal state while processing this stream.\n\nFirst, we show that pan-privacy against multiple intrusions on the internal state is equivalent to sequentially interactive local privacy. Next, we contextualize pan-privacy against a single intrusion by analyzing the sample complexity of uniformity testing over domain $[k]$. Focusing on the dependence on $k$, centrally private uniformity testing has sample complexity $\Theta(\sqrt{k})$, while noninteractive locally private uniformity testing has sample complexity $\Theta(k)$. We show that the sample complexity of pan-private uniformity testing is $\Theta(k^{2/3})$. By a new $\Omega(k)$ lower bound for the sequentially interactive setting, we also separate pan-private from sequentially interactive locally private and multi-intrusion pan-private uniformity testing.
An Interpretable Compression and Classification System: Theory and Applications

This study proposes a low-complexity interpretable classification system. The proposed system contains main modules including feature extraction, feature reduction, and classification. All of them are linear. Thanks to the linear property, the extracted and reduced features can be inversed to original data, like a linear transform such as Fourier transform, so that one can quantify and visualize the contribution of individual features towards the original data. Also, the reduced features and reversibility naturally endure the proposed system ability of data compression. This system can significantly compress data with a small percent deviation between the compressed and the original data. At the same time, when the compressed data is used for classification, it still achieves high testing accuracy. Furthermore, we observe that the extracted features of the proposed system can be approximated to uncorrelated Gaussian random variables. Hence, classical theory in estimation and detection can be applied for classification. This motivates us to propose using a MAP (maximum a posteriori) based classification method. As a result, the extracted features and the corresponding performance have statistical meaning and mathematically interpretable. Simulation results show that the proposed classification system not only enjoys significant reduced training and testing time but also high testing accuracy compared to the conventional schemes.
Fine-tuning BERT Models for Keyphrase Extraction in Scientific Articles

Despite extensive research, performance enhancement of keyphrase (KP) extraction remains a challenging problem in modern informatics. Recently, deep learning-based supervised approaches have exhibited state-of-the-art accuracies with respect to this problem, and several of the previously proposed methods utilize Bidirectional Encoder Representations from Transformers (BERT)-based language models. However, few studies have investigated the effective application of BERT-based fine-tuning techniques to the problem of KP extraction. In this paper, we consider the aforementioned problem in the context of scientific articles by investigating the fine-tuning characteristics of two distinct BERT models — BERT (i.e., base BERT model by Google) and SciBERT (i.e., a BERT model trained on scientific text). Three different datasets (WWW, KDD, and Inspec) comprising data obtained from the computer science domain are used to compare the results obtained by fine-tuning BERT and SciBERT in terms of KP extraction.
Numerical continuation of a physical model of brass instruments: Application to trumpet comparisons

The system formed by a trumpet player and his/her instrument can be seen as a non-linear dynamic system and modeled by physical equations. Numerical tools can then be used to study these models and clarify the influence of the model parameters. The acoustic input impedance, for instance, is strongly dependent on the geometry of the air column and is therefore of primary interest for a musical instrument maker. In this study, a method of continuation of periodic solutions based on the combination of the Harmonic Balance Method (HBM) and the Asymptotic Numerical Method (ANM) is applied to a physical model of brass instruments. It allows the study of the evolution of the system where one parameter of the model (static mouth pressure) varies. This method is used to compare different B♭ trumpets on the basis of two descriptors (hysteresis behavior and dynamic range) computed from the continuation outputs. Results show that this methodology enables the differentiation of instruments in the space of the calculated descriptors. Calculations for different values of the lip parameters are also performed to confirm that the obtained categorization is independent of variations of lip parameters.
On‐line balancing of random inputs

We consider an online vector balancing game where vectors v t , chosen uniformly at random in {− 1, + 1} n , arrive over time and a sign x t ∈ {− 1, + 1} must be picked immediately upon the arrival of v t . The goal is to minimize the L ∞ norm of the signed sum . We give an online strategy for picking the signs x t that has value O ( n 1/2 ) with high probability. Up to constants, this is the best possible even when the vectors are given in advance.
Stomach 3D Reconstruction Based on Virtual Chromoendoscopic Image Generation

Gastric endoscopy is a standard clinical process that enables medical practitioners to diagnose various lesions inside a patient's stomach. If any lesion is found, it is very important to perceive the location of the lesion relative to the global view of the stomach. Our previous research showed that this could be addressed by reconstructing the whole stomach shape from chromoendoscopic images using a structure-from-motion (SfM) pipeline, in which indigo carmine (IC) blue dye-sprayed images were used to increase feature matches for SfM by enhancing stomach surface's textures. However, spraying the IC dye to the whole stomach requires additional time, labor, and cost, which is not desirable for patients and practitioners. In this paper, we propose an alternative way to achieve whole stomach 3D reconstruction without the need of the IC dye by generating virtual IC-sprayed (VIC) images based on image-to-image style translation trained on unpaired real no-IC and IC-sprayed images. We have specifically investigated the effect of input and output color channel selection for generating the VIC images and found that translating no-IC green-channel images to IC-sprayed red-channel images gives the best SfM reconstruction result.
ID Card Detection with Facial Recognition using Tensorflow and OpenCV

The main goal of computer vision is to identify and recognize different objects of various size, shape and position. The major problems faced by the computer vision is the illumination and the viewpoint of the object, Concerning this and by following multiple studies recurring to deep learning with the use of Convolution Neural Networks on detecting and recognizing objects that showed a high level of accuracy and precision on these tasks. To facilitate object detection in a college environment, the proposed work identifies the presence of a person wearing an ID card using tensor flow object detection API, detects and recognizes the face using haar cascade method of OpenCV.
Testing Chatbots with Charm

Chatbots are software programs with a conversational user interface, typically embedded in webs or messaging systems like Slack, Facebook Messenger or Telegram. Many companies are investing in chatbots to improve their customer support. This has led to a proliferation of chatbot creation platforms (e.g., Dialogflow, Lex, Watson). However, there is currently little support for testing chatbots, which may impact in their final quality. To alleviate this problem, we propose a methodology that automates the generation of coherence, sturdiness and precision tests for chatbots, and exploits the test results to improve the chatbot precision. The methodology is supported by a tool called Charm, which uses Botium as the backend for automated test execution. Moreover, we report on experiments aimed at improving Dialogflow chatbots built by third parties.
Forensic Analysis of Fitbit Versa 2 Data on Android

The increase in the adoption of wearable fitness trackers has led to their inclusion as valuable evidence used by law enforcement during investigations. The information available in these fitness trackers can be used by law enforcement to prosecute or exonerate an individual. Wearable fitness devices are constantly being released by companies, with new firmware created for each iteration. As technology developers, research and law enforcement must keep pace to take advantage of data that can be used in investigations. The Fitbit line of devices is a popular brand of wearable trackers. This study will investigate what artifacts are generated by the new Fitbit Versa 2 by investigating what data are generated and stored on the smartphone app component of the new device. The artifacts discovered will be related to areas of forensic interest that are relevant to a law enforcement officer or digital forensics practitioner. Previous research and their methodologies used for application and mobile forensics will be used to conduct this research. This study finds the Fitbit Versa 2, and by extension, the Fitbit smartphone application does not store social media message notifications pushed to the tracker by the user’s mobile device. Some credit card information, health-related data, such as heart rate, GPS locations, and other potentially identifying data were found in plaintext. While the exposed data is not enough on its own to pose an immediate serious issue, it can be used as leverage to phish a user for further details.
The Evolution of Transient-Execution Attacks

Historically, non-architectural state was considered non-observable. Side-channel attacks, in particular on caches, already showed that this is not entirely correct and meta-information, such as the cache state, can be extracted. Transient-execution attacks emerged when multiple groups discovered the exploitability of speculative execution and, simultaneously, the exploitability of deferred permission checks in modern out-of-order processors. These attacks are called transient as they exploit that the processor first executes operations that are then reverted as if they were never executed. However, on the microarchitectural level, these operations and their effects can be observed. While side-channel attacks enable and exploit direct access to meta-data from other security domains, transient-execution attacks enable and exploit direct access to actual data from other security domains. In this paper, we show how the transient-execution landscape evolved since the initial discoveries. We show that the understanding and systematic view of the field has advanced and now facilitate the discovery of new attack variants.
Ordinal Classification: Working Definition and Detection of Ordinal Structures

Ordinal classification (OC) is an important niche of supervised pattern recognition, in which the classes constitute an ordinal structure.In general, the ordinal structure can be identified, either according to the natural occurrence of the current task (e.g.healthy -mild condition -moderate condition -severe condition), or by extracting expert knowledge.However, we assume that many multi-class classification tasks might have a hidden ordinal structure, which, once identified, can facilitate and hence leverage the classification process.Therefore, we propose a working definition for OC tasks, which is based on the decision boundaries of standard binary Support Vector Machines.Moreover, resulting from our proposed definition, we introduce a simple algorithm for the detection of ordinal structures.Our proposed definition is easy to interpret and reflects an intuitive understanding of ordinal structures.Another main advantage is that our proposed definition is easy to apply.Therefore, there is no more dependence on expert knowledge for the identification of (non-intuitive) ordinal class structures.In the current study, we include ten benchmark data sets from the field of OC to experimentally evaluate and hence to confirm the validity of our proposed definition.Additionally, we analyse our proposed definition based on a small set of traditionally non-ordinal multi-class classification tasks.Furthermore, we provide an analysis of the computational cost of our proposed detection algorithm, and discuss the limitations of our proposed working definition.
Research on basketball players’ action recognition based on interactive system and machine learning

The difficulty of sports gesture recognition is the effective cooperation of hardware and software. Moreover, there are few studies on machine learning in the capture of the details of sports athletes’ gesture recognition. Therefore, based on the learning technology, this study uses the sensor with gesture recognition algorithm to analyze the detailed motion capture of sports athletes. At the same time, this study selects inertial sensor technology as the gesture recognition hardware through comparative analysis. In addition, by analyzing the actual needs of athletes’ gesture recognition, the Kalman filter algorithm is used to solve the athlete’s posture, construct a virtual human body model, and perform sub-regional processing, so as to facilitate the effective identification of different limbs. Finally, in order to verify the validity of the algorithm model, the basketball exercise is taken as an example for experimental analysis. The research results show that the basketball gesture recognition method used in this paper is quite satisfactory.
Aerial infrared target tracking based on a Siamese network and traditional features

Due to the low signal-to-noise ratios and fast motion of aerial targets, tracking aircraft based on infrared sequences is still a challenging task. To develop a robust and fast aerial infrared target tracking method, a new framework based on a Siamese network is proposed. Traditional features, namely, the aspect ratio and mean contrast, are used to improve the performance of the tracker. We performed experiments on three simulated infrared sequences to compare the proposed method with nine state-of-the-art tracking algorithms. To make the experimental results more credible, we also performed experiments on six real shot sequences that were not used when training the network. These experiments clearly demonstrate that our algorithm can reliably track an aerial infrared target while running at a real-time speed. The accuracy of our approach is improved by 136.3% compared with CFNet.
Prediction for Chaotic Time Series-Based AE-CNN and Transfer Learning

It has been a hot and challenging topic to predict the chaotic time series in the medium-to-long term. We combine autoencoders and convolutional neural networks (AE-CNN) to capture the intrinsic certainty of chaotic time series. We utilize the transfer learning (TL) theory to improve the prediction performance in medium-to-long term. Thus, we develop a prediction scheme for chaotic time series-based AE-CNN and TL named AE-CNN-TL. Our experimental results show that the proposed AE-CNN-TL has much better prediction performance than any one of the following: AE-CNN, ARMA, and LSTM.
Orbital Battleship: A Multiplayer Guessing Game in Immersive Virtual Reality

Orbital Battleship, published in 2016 in this Journal, is a hands-on competitive exercise which has been shown to be an efficient tool to reinforce the knowledge of the atomic structure. We decided to give it a more futuristic look and are presenting VR Orbital Battleship, a completely immersive collaborative experience which, in contrast to the pen-and-paper version, guarantees that students are unable to fill in an erroneous electron configuration thanks to the special control elements of the game. Hence, they are motivated to do their best in order to apply Madelung’s rule and Hund’s rule right to be able to start the game. The game design makes full use of what the immersive virtual reality has to offer: the headset completely immerses the player in the process of the game, resulting in less distraction compared to the classic version. Furthermore, the game’s design is user-friendly and intuitive, and thus, it adds to the overall immersive experience. The game is recommended for use as an interactive addition to school and university classes that are dedicated to the atomic structure. VR Orbital Battleship is available for free worldwide in both a portable mobile platform (Oculus Go) and a PC version by downloading the game (see the Supporting Information of this paper).
Quantum entanglement and teleportation in superposition of multiple-photon-added two-mode squeezed vacuum state

In this paper, a new state called superposition of multiple-photon-added two-mode squeezed vacuum state (SMPA-TMSVS) is introduced by adding the multiple photons to both modes of a two-mode squeezed vacuum state (TMSVS). We explicitly investigate the degree of quantum entanglement, the Einstein–Podolsky–Rosen (EPR) correlation and the quantum steering in the SMPA-TMSVS. The results show in the SMPA-TMSVS that the degree of entanglement and the EPR correlation can be enhanced by nonlocal adding photons to a TMSVS. The quantum steering appears in the SMPA-TMSVS in case the superposition of single-photon addition [Formula: see text], in which mode [Formula: see text] can steer mode [Formula: see text]. By using the SMPA-TMSVS as an entangled resource, the quantum teleportation process is studied in detail based on the Vaidman–Braunstein–Kimble (VBK) protocol.
A Linked Open Data-Oriented Sustainable System for Transparency and Open Access to Government Data: A Case Study of the Public’s Response to Women’s Driving in Saudi Arabia

Due to the wide-ranging development of data-oriented sustainable systems in the government and the public sectors, the development of such sustainable systems is replete with potential. The ultimate focus of developing these sustainable systems is to provide citizens with transparency, accountability, awareness as well as a single point of query for asking integrated and smart queries. In view of these benefits, the Saudi government has taken the initiative to publish and develop sustainable open data-oriented information systems. However some major challenges in the Saudi Government Open Data are that the (1) data are published and available in different formats such as Excel sheets, CSV files (Comma Separated Values), images, scanned documents and social media sources such as Twitter, (2) datasets from different government departments are not linked with each other or to existing datasets in Linked Open Data Cloud (even though they have strong links with each other), and (3) there is no SPARQL Endpoint that can be used to pose smart semantic-based queries to Saudi Government Data. This paper is part of an ongoing research project to present a framework that can be used to transfer the government data from different sources to RDF format. The framework can also be used to clean and classify/map the data according to the Saudi Government Ontology. We also describe our approach for semiautomatically linking Saudi Government Datasets with one another as well as with other existing open datasets, thus resulting in the Saudi Linked Open Government Data Cloud (SLOGDC). Finally, taking the topic “Public’s Response to Women’s Driving in Saudi Arabia” as a case study, we demonstrate the SLOGD SPARQL Endpoint as a data-oriented system by executing different queries and analyzing results of these queries. This work also contributes new insights into women’s driving in Saudi Arabia using the SLOGDC, thus suggesting the way forward in shaping policies for decision-making.
Baidu Kunlun An AI processor for diversified workloads

This article consists only of a collection of slides from the author's conference presentation.
The seven deadly sins of psychology a manifesto for reforming the culture of scientific practice

There is growing consensus that the scientific disciplines of psychology and cognitive neuroscience are in a state of crisis because a staggeringly high number of experimental studies have proven d...
S2IGAN: Speech-to-Image Generation via Adversarial Learning

An estimated half of the world’s languages do not have a written form, making it impossible for these languages to benefit from any existing text-based technologies. In this paper, a speech-to-image generation (S2IG) framework is proposed which translates speech descriptions to photo-realistic images without using any text information, thus allowing unwritten languages to potentially benefit from this technology. The proposed S2IG framework, named S2IGAN, consists of a speech embedding network (SEN) and a relation-supervised densely-stacked generative model (RDG). SEN learns the speech embedding with the supervision of the corresponding visual information. Conditioned on the speech embedding produced by SEN, the proposed RDG synthesizes images that are semantically consistent with the corresponding speech descriptions. Extensive experiments on datasets CUB and Oxford-102 demonstrate the effectiveness of the proposed S2IGAN on synthesizing high-quality and semantically-consistent images from the speech signal, yielding a good performance and a solid baseline for the S2IG task.
TangibleSphere – Interaction Techniques for Physical and Virtual Spherical Displays

Tangible interaction is generally assumed to provide benefits compared to other interaction styles due to its physicality. We demonstrate how this physicality can be brought to VR by means of TangibleSphere – a tracked, low-cost physical object that can (a) be rotated freely and (b) is overlaid with a virtual display. We present two studies, investigating performance in terms of efficiency and usability: the first study (N=16) compares TangibleSphere to a physical spherical display regarding accuracy and task completion time. We found comparable results for both types of displays. The second study (N=32) investigates the influence of physical rotation in more depth. We compare a pure VR condition to TangibleSphere in two conditions: one that allows actual physical rotation of the object and one that does not. Our findings show that physical rotation significantly improves accuracy and task completion time. These insights are valuable for researchers designing interaction techniques and interactive visualizations for spherical displays and for VR researchers aiming to incorporate physical touch into the experiences they design.
GeoGraph: Graph-Based Multi-view Object Detection with Geometric Cues End-to-End

In this paper we propose an end-to-end learnable approach that detects static urban objects from multiple views, re-identifies instances, and finally assigns a geographic position per object. Our method relies on a Graph Neural Network (GNN) to, detect all objects and output their geographic positions given images and approximate camera poses as input. Our GNN simultaneously models relative pose and image evidence, and is further able to deal with an arbitrary number of input views. Our method is robust to occlusion, with similar appearance of neighboring objects, and severe changes in viewpoints by jointly reasoning about visual image appearance and relative pose. Experimental evaluation on two challenging, large-scale datasets and comparison with state-of-the-art methods show significant and systematic improvements both in accuracy and efficiency, with 2–6% gain in detection and re-ID average precision as well as 8x reduction of training time.