Reviewerly/exHarmony huggingface dataset:
Do Simpler Models Exist and How Can We Find Them?

While the trend in machine learning has tended towards more complex hypothesis spaces, it is not clear that this extra complexity is always necessary or helpful for many domains. In particular, models and their predictions are often made easier to understand by adding interpretability constraints. These constraints shrink the hypothesis space; that is, they make the model simpler. Statistical learning theory suggests that generalization may be improved as a result as well. However, adding extra constraints can make optimization (exponentially) harder. For instance it is much easier in practice to create an accurate neural network than an accurate and sparse decision tree. We address the following question: Can we show that a simple-but-accurate machine learning model might exist for our problem, before actually finding it? If the answer is promising, it would then be worthwhile to solve the harder constrained optimization problem to find such a model. In this talk, I present an easy calculation to check for the possibility of a simpler model. This calculation indicates that simpler-but-accurate models do exist in practice more often than you might think. I then briefly overview several new methods for interpretable machine learning. These methods are for (i) sparse optimal decision trees, (ii) sparse linear integer models (also called medical scoring systems), and (iii) interpretable case-based reasoning in deep neural networks for computer vision.
Efficient Accessible Bounds to the Classical Capacity of Quantum Channels

We present a method to detect lower bounds to the classical capacity of quantum communication channels by means of few local measurements (i.e. without complete process tomography), reconstruction of sets of conditional probabilities, and classical optimisation. The method does not require any a priori information about the channel. We illustrate its performance for significant forms of noisy channels.
Private Center Points and Learning of Halfspaces

We present a private learner for halfspaces over an arbitrary finite domain $X\subset \mathbb{R}^d$ with sample complexity $mathrm{poly}(d,2^{\log^*|X|})$. The building block for this learner is a differentially private algorithm for locating an approximate center point of $m>\mathrm{poly}(d,2^{\log^*|X|})$ points -- a high dimensional generalization of the median function. Our construction establishes a relationship between these two problems that is reminiscent of the relation between the median and learning one-dimensional thresholds [Bun et al. FOCS '15]. This relationship suggests that the problem of privately locating a center point may have further applications in the design of differentially private algorithms. We also provide a lower bound on the sample complexity for privately finding a point in the convex hull. For approximate differential privacy, we show a lower bound of $m=\Omega(d+\log^*|X|)$, whereas for pure differential privacy $m=\Omega(d\log|X|)$.
Comparative Evaluation of Security and Convenience Trade-Offs in Password Generation Aiding Systems

A strong password is considered the most important feature for the security of any account credentials. In the last decades, several organizations focused on improving its strength and produced awareness initiatives and security guidelines on how to create and maintain secure passwords. However, studies found that users perceive security and convenience as a trade-off, and they often compromise password strength in favor of a key phrase that is easier to remember and type. Therefore, nowadays websites and applications implement password generation aiding systems (PGAS) that help, and even force, users to create more secure passwords. Nowadays, several types of PGAS are available, each implementing a different strategy for stimulating users in crating stronger and more secure passwords. In this paper, we present the results of a study in which we compared six different PGAS and evaluated their performance in terms of security and convenience, with the aim of suggesting the system that has the most beneficial trade-off depending on the type of application.
Isolated vertices in continuous-time quantum walks on dynamic graphs

It was recently shown that continuous-time quantum walks on dynamic graphs, i.e., sequences of static graphs whose edges change at specific times, can implement a universal set of quantum gates. This result treated all isolated vertices as having self-loops, so they all evolved by a phase under the quantum walk. In this paper, we permit isolated vertices to be loopless or looped, and loopless isolated vertices do not evolve at all under the quantum walk. Using this distinction, we construct simpler dynamic graphs that implement the Pauli gates and a set of universal quantum gates consisting of the Hadamard, $T$, and controlled-not gates, and these gates are easily extended to multiqubit systems. For example, the $T$ gate is simplified from a sequence of six graphs to a single graph, and the number of vertices is reduced by a factor of 4. We also construct a generalized phase gate, of which $Z$, $S$, and $T$ are specific instances. Finally, we validate our implementations by numerically simulating a quantum circuit consisting of layers of one- and two-qubit gates, similar to those in recent quantum supremacy experiments, using a quantum walk.
Quantum expectation-maximization algorithm

Clustering algorithms are a cornerstone of machine learning applications. Recently, a quantum algorithm for clustering based on the $k$-means algorithm has been proposed by Kerenidis, Landman, Luongo, and Prakash. Based on their work, we propose a quantum expectation-maximization algorithm for Gaussian mixture models (GMMs). The robustness and quantum speedup of the algorithm are shown. We also show numerically the advantage of GMM over $k$-means algorithm for nontrivial cluster data.
Dynamic Transfer Learning for Named Entity Recognition

State-of-the-art named entity recognition (NER) systems have been improving continuously using neural architectures over the past several years. However, many tasks including NER require large sets of annotated data to achieve such performance. In particular, we focus on NER from clinical notes, which is one of the most fundamental and critical problems for medical text analysis. Our work centers on effectively adapting these neural architectures towards low-resource settings using parameter transfer methods. We complement a standard hierarchical NER model with a general transfer learning framework consisting of parameter sharing between the source and target tasks, and showcase scores significantly above the baseline architecture. These sharing schemes require an exponential search over tied parameter sets to generate an optimal configuration. To mitigate the problem of exhaustively searching for model optimization, we propose the Dynamic Transfer Networks (DTN), a gated architecture which learns the appropriate parameter sharing scheme between source and target datasets. DTN achieves the improvements of the optimized transfer learning framework with just a single training setting, effectively removing the need for exponential search.
Measurement-induced randomness and structure in controlled qubit processes

When an experimentalist measures a time series of qubits, the outcomes constitute a classical stochastic process. We show that projective measurement induces high complexity in these processes in two specific senses: They are inherently random (finite Shannon entropy rate) and they require infinite memory for optimal prediction (divergent statistical complexity). We identify nonorthogonality of the quantum states as the mechanism underlying the resulting complexities and examine the influence that measurement choice has on the randomness and structure of measured qubit processes. We introduce quantitative measures of this complexity and provide efficient algorithms for their estimation.
Investigating Direct Manipulation of Graphical Encodings as a Method for User Interaction

We investigate direct manipulation of graphical encodings as a method for interacting with visualizations. There is an increasing interest in developing visualization tools that enable users to perform operations by directly manipulating graphical encodings rather than external widgets such as checkboxes and sliders. Designers of such tools must decide which direct manipulation operations should be supported, and identify how each operation can be invoked. However, we lack empirical guidelines for how people convey their intended operations using direct manipulation of graphical encodings. We address this issue by conducting a qualitative study that examines how participants perform 15 operations using direct manipulation of standard graphical encodings. From this study, we 1) identify a list of strategies people employ to perform each operation, 2) observe commonalities in strategies across operations, and 3) derive implications to help designers leverage direct manipulation of graphical encoding as a method for user interaction.
A POS Tagging Model for Vietnamese Social Media Text Using BiLSTM-CRF with Rich Features

This paper deals with the task of part-of-speech (POS) tagging for Vietnamese social media text, which poses several challenges compared with tagging for conventional text. We introduce a POS tagging model that takes advantages of deep learning and manually engineered features to overcome the challenges of the task. The main part of the model consists of several bidirectional long short-term memory (BiLSTM) layers that are used to learn intermediate representations of sentences from features extracted at both the character and the word levels. Conditional random field (CRF) is then used on top of those BiLSTM layers, at the inference layer, to predict the most suitable POS tags. We leverage various types of manually engineered features in addition to automatically learned features to capture the characteristics of Vietnamese social media data and therefore improve the performance of the model. Experimental results on a public POS tagging corpus for Vietnamese social media text show that our model outperforms previous work [4] by a large margin, reaching 91.9% accuracy with 27% error rate reduction. The results also reveal the effectiveness of using both automatically learned and manually designed features in a deep learning framework when only a limited amount of training data is available.
Seamless Stitching Dual Fisheye Images For 360° Free View

We demonstrate a seamless stitching technology for dual-fisheye video cameras that combines two images/frames of ultra-wide field of view into a 360° image/frame. Our technology generates high-quality 360° free view without the disjoint appearance and ghost artifact that plague most 360° cameras in the market today. Our technology is efficient, robust, and stunning. Target customers include VR device manufactures, telco operators, broadcasters, surveillance service providers, and immersive free view users.
Connecting and Comparing Language Model Interpolation Techniques

In this work, we uncover a theoretical connection between two language model interpolation techniques, count merging and Bayesian interpolation. We compare these techniques as well as linear interpolation in three scenarios with abundant training data per component model. Consistent with prior work, we show that both count merging and Bayesian interpolation outperform linear interpolation. We include the first (to our knowledge) published comparison of count merging and Bayesian interpolation, showing that the two techniques perform similarly. Finally, we argue that other considerations will make Bayesian interpolation the preferred approach in most circumstances.
Fast Face Tracking-by-Detection Algorithm for Secure Monitoring

This work proposes a fast face tracking-by-detection (FFTD) algorithm that can perform tracking, face detection and discrimination tasks. On the basis of using the kernelized correlation filter (KCF) as the basic tracker, multitask cascade convolutional neural networks (CNNs) are used to detect the face, and a new tracking update strategy is designed. The update strategy uses the tracking result modified by detector to update the filter model. When the tracker drifts or fails, the discriminator module starts the detector to correct the tracking results, which ensures the out-of-view object can be tracked. Through extensive experiments, the proposed FFTD algorithm is shown to have good robustness and real-time performance for video monitoring scenes.
Segmented character recognition using curvature-based global image feature

Character recognition in natural scene images is a fundamental prerequisite for many text-based image analysis tasks. Generally, local image features are employed widely to recognize characters segmented from natural scene images. In this paper, a curvature-based global image feature and description for segmented character recognition is proposed. This feature is entirely dependent on the curvature information of the image pixels. The proposed feature is employed for segmented character recognition using Chars74k dataset and ICDAR 2003 character recognition dataset. From the two datasets, 1068 and 540 images of characters, respectively, are randomly chosen and 573-dimensional feature vector is synthesized per image. Quadratic, linear and cubic support vector machines are trained to examine the performance of the proposed feature. The proposed global feature and two well-known local feature descriptors called scale invariant feature transform (SIFT) and histogram of oriented gradients (HOG) are compared in terms of classification accuracy, computation time, classifier prediction and training time. Experimental results indicate that the proposed feature yielded higher classification accuracy (%65.3) than SIFT (%53), performed better than HOG and SIFT in terms of classifier training time, and achieved better prediction speed than HOG and less computational time than SIFT.
Attacking quantum key distribution by light injection via ventilation openings

Quantum cryptography promises security based on the laws of physics with proofs of security against attackers of unlimited computational power. However, deviations from the original assumptions allow quantum hackers to compromise the system. We present a side channel attack that takes advantage of ventilation holes in optical devices to inject additional photons that can leak information about the secret key. We experimentally demonstrate light injection on an ID~Quantique Clavis2 quantum key distribution platform and show that this may help an attacker to learn information about the secret key. We then apply the same technique to a prototype quantum random number generator and show that its output is biased by injected light. This shows that light injection is a potential security risk that should be addressed during the design of quantum information processing devices.
GraVF-M

Due to the irregular nature of connections in most graph datasets, partitioning graph analysis algorithms across multiple computational nodes that do not share a common memory inevitably leads to large amounts of interconnect traffic. Previous research has shown that FPGAs can outcompete software-based graph processing in shared memory contexts, but it remains an open question if this advantage can be maintained in distributed systems. In this work, we present GraVF-M, a framework designed to ease the implementation of FPGA-based graph processing accelerators for multi-FPGA platforms with distributed memory. Based on a lightweight description of the algorithm kernel, the framework automatically generates optimized RTL code for the whole multi-FPGA design. We exploit an aspect of the programming model to present a familiar message-passing paradigm to the user, while under the hood implementing a more efficient architecture that can reduce the necessary inter-FPGA network traffic by a factor equal to the average degree of the input graph. A performance model based on a theoretical analysis of the factors influencing performance serves to evaluate the efficiency of our implementation. With a throughput of up to 5.8GTEPS (billions of traversed edges per second) on a 4-FPGA system, the designs generated by GraVF-M compare favorably to state-of-the-art frameworks from the literature and reach 94% of the projected performance limit of the system.
Double-slit interferometry as a lossy beam splitter

We cast diffraction-based interferometry in the framework of post-selected unitary description towards enabling it as a platform for quantum information processing. We express slit-diffraction as an infinite-dimensional transformation and truncate it to a finite-dimensional transfer matrix by post-selecting modes. Using such a framework with classical fields, a customized double-slit setup is effectively a lossy beam splitter in a post-selected sense. Diffraction optics provides a robust alternative to conventional multi-beam interferometry with scope for miniaturization, and also has applications in matter wave interferometry. In this work, the classical treatment sets the stage for quantization of fields and implementing higher dimensional quantum information processing like that done with other platforms such as orbital angular momentum.
Towards electronic lexicography for the Kurdish language

This project has received funding from the European Union’s Horizon 2020 researchand innovation programme under grant agreement No 731015.
The Role of Additional Information in Obtaining Information

One of the basic components of the smart city concept is the infocommunication infrastructure.The purpose of the infocommunication system is, among others, to transmit information.Generally, besides the necessary information provided in a transmission, some additional information is displayed as well.In public communication the destination of the information is not a specific person or object.Therefore, additional information can be obtained by anyone.Thus, such additional information facilitates the unwanted acquisition of information and its later use.The present study illustrates the role of additional information in obtaining information through an example of an online game.This game is public, therefore, it is an open source of information.The technology of Open Source Intelligence is one of the basic elements of the social engineering information palette.
Human-Agent Communication

Conversational agents (e.g., chatbots, virtual agents) are becoming part of our everyday lives, from personal assistants, social companions to team support. In recent years, there has been a wave of research on this topic in both the HCI and AI communities, though the two communities approach the research and development of conversational agents with different foci and distinct methodologies. In this panel, we bring scholars in the HCI and AI communities together to share their perspectives and identify research topics that the two communities can collaborate on. We invite the CSCW community to reflect on the phenomena, challenges and research opportunities in human-agent communication, as well as the roles of agents in improving our personal, social and organizational lives.
Logical measurement-based quantum computation in circuit-QED

Abstract We propose a new scheme of measurement-based quantum computation (MBQC) using an error-correcting code against photon-loss in circuit quantum electrodynamics. We describe a specific protocol of logical single-qubit gates given by sequential cavity measurements for logical MBQC and a generalised Schrödinger cat state is used for a continuous-variable (CV) logical qubit captured in a microwave cavity. To apply an error-correcting scheme on the logical qubit, we utilise a d -dimensional quantum system called a qudit. It is assumed that a three CV-qudit entangled state is initially prepared in three jointed cavities and the microwave qudit states are individually controlled, operated, and measured through a readout resonator coupled with an ancillary superconducting qubit. We then examine a practical approach of how to create the CV-qudit cluster state via a cross-Kerr interaction induced by intermediary superconducting qubits between neighbouring cavities under the Jaynes-Cummings Hamiltonian. This approach could be scalable for building 2D logical cluster states and therefore will pave a new pathway of logical MBQC in superconducting circuits toward fault-tolerant quantum computing.
Evaluation of enhancement events of global horizontal irradiance due to clouds at Patras, South-West Greece

Cloudiness is the dominant factor that affects the transfer of solar irradiance through the atmosphere. Especially the type of clouds, their high spatial and temporal variability, velocity and position relatively to the Sun, could cause events ranging from total reduction to substantial enhancements of surface solar irradiance. In study, the enhancements of Global Horizontal Irradiance (GHI) at South Greece for 2 years are examined. We use 1–min averages of GHI, sunshine duration and synchronized estimations of cloud coverage and type from a digital imaging system. A radiative transfer model is used to estimate GHI under clear skies. For solar zenith angles lower than 80°, 4% of total GHI measurements are considered as enhancements. It is shown that the effect of thick clouds on the GHI enhancements is significantly higher when compared to the thin ones. We also examine cases that correspond to instantaneous clearness index values higher than 1 (Kt > 1): 228 extreme enhancement events are found including 5 cases with GHI values higher than 1367 W/m2 and a maximum value of 1420 W/m2. These results suggest that such events are common and should be included in the design and simulation of the performance of solar energy systems.
On the Security of Privacy-Preserving Attribute-Based Keyword Search in Shared Multi-owner Setting

Recently in the IEEE Transactions on Dependable and Secure Computing (doi: 10.1109/TDSC.2019.28976752019), Miao et al. proposed a novel construction of Privacy-Preserving Attribute-Based Keyword Search in Shared Multi-owner Setting (ABKS-SM), which can delegate keyword search tasks to cloud server provider (CSP) without revealing any useful information. Although the authors claimed that the offline keyword guessing attacks can be resisted in ABKS-SM scheme, we show that this scheme indeed suffers from four types of offline keyword guessing attacks and hence fails to gain the claimed security property, which is an important goal to be achieved in searchable encryption schemes. Specifically, given the concrete attacks, we demonstrate that the underlying keyword information can be extracted from both encrypted keyword indexes and trapdoors by any malicious user and any adversarial CSP. We hope that the similar security vulnerabilities could be avoided in the future design of related searchable encryption schemes.
Orientation Adaptive YOLOv3 for Object Detection in Remote Sensing Images

Object detection in remote sensing images is full of challenges. The large scale of detection region, and the difficulty of dense object detection are the main problems needed to solve. Although YOLOv3 perform better and faster, it is not suitable to the orientation which means it is not friendly to NMS operation on dense objects area. This drawback would lead to an increase in miss rate in dense object detection. In this paper, we modified YOLOv3 based on the oriented bounding box (OBB) for object detection in remote images to solve the problems above. This model is based on YOLOv3 which performs better on small target compared with YOLOv2. We modified the architecture of YOLOv3 to predict oriented bounding box. In this way, we can obtain bounding boxes more suitable for large aspect ratio objects. Moreover, in a dense area such as parking lot, it can also achieve a good performance.