Reviewerly/exHarmony huggingface dataset:
Tight Quantum Time-Space Tradeoffs for Function Inversion

In function inversion, we are given a function f:[N]→[N], and want to prepare some advice of size S, such that we can efficiently invert any image in time T. This is a well studied problem with profound connections to cryptography, data structures, communication complexity, and circuit lower bounds. Investigation of this problem in the quantum setting was initiated by Nayebi, Aaronson, Belovs, and Trevisan (2015), who proved a lower bound of ST <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =Ω̃(N) for random permutations against classical advice, leaving open an intriguing possibility that Grover's search can be sped up to time Õ(√{N/S}). Recent works by Hhan, Xagawa, and Yamakawa (2019), and Chung, Liao, and Qian (2019) extended the argument for random functions and quantum advice, but the lower bound remains ST <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =Ω̃(N). In this work, we prove that even with quantum advice, ST+ T <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =Ω̃(N), is required for an algorithm to invert random functions. This demonstrates that Grover's search is optimal for S=Õ(√N), ruling out any substantial speed-up for Grover's search even with quantum advice. Further improvements to our bounds would imply new classical circuit lower bounds, as shown by Corrigan-Gibbs and Kogan (2019). To prove this result, we develop a general framework for establishing quantum time-space lower bounds. We further demonstrate the power of our framework by proving the following results. (a) Yao's box problem: We prove a tight quantum time-space lower bound for classical advice. For quantum advice, we prove a first time-space lower bound using shadow tomography. These results resolve two open problems posted by Nayebi et al (2015). (b) Salted cryptography: We show that “salting generically provably defeats preprocessing,” a result shown by Coretti, Dodis, Guo, and Steinberger (2018), also holds in the quantum setting. In particular, we prove quantum time-space lower bounds for a wide class of salted cryptographic primitives in the quantum random oracle model. This yields the first quantum time-space lower bound for salted collision-finding, which in turn implies that PWPP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</sup> ⊈ FBQP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</sup> /qpoly relative to a random oracle O.
Performance enhancement of a dynamic K-means algorithm through a parallel adaptive strategy on multicore CPUs

The K-means algorithm is one of the most popular algorithms in Data Science, and it is aimed to discover similarities among the elements belonging to large datasets, partitioning them in K distinct groups called clusters. The main weakness of this technique is that, in real problems, it is often impossible to define the value of K as input data. Furthermore, the large amount of data used for useful simulations makes impracticable the execution of the algorithm on traditional architectures. In this paper, we address the previous two issues. On the one hand, we propose a method to dynamically define the value of K by optimizing a suitable quality index with special care to the computational cost. On the other hand, to improve the performance and the effectiveness of the algorithm, we propose a strategy for parallel implementation on modern multicore CPUs.
Dual Back-Projection-Based Internal Learning for Blind Super-Resolution

State-of-the-art super-resolution (SR) methods commonly assume that the downscaling kernel (the point spread function of the camera) is a Gaussian kernel. Therefore, the methods are particularly vulnerable to solving the problem of blind SR, which deals with a real low-resolution (LR) image that does not follow the assumption. Recently, to address this issue, several internal learning-based methods, which train an image-specific network using a single input image, have been introduced. In this approach, the blind SR is modeled as a two-stage optimization problem, which conducts downscaling kernel estimation followed by SR network training with the estimated kernel. In this letter, we assume that not only the estimated kernel can be employed for SR network training, but also the super-resolved image can contribute to downscaling kernel estimation. To that end, we propose a unified internal learning-based blind SR method that jointly trains two image-specific networks for 1) downscaling together with kernel estimation and 2) SR. More specifically, we train the two networks to minimize a dual back-projection loss; the SR network is trained to reconstruct a given input image from the LR image generated by the downscaling network, and the downscaling network is trained to downscale the high-resolution image generated by the SR network to be as close as possible to the given input image. Through the complementary training, the downscaling kernel estimation becomes more accurate, resulting in better SR performance. In the experiment, we show that the proposed method outperforms previous two-stage internal learning-based methods in terms of both SR performance and efficiency.
Autonomous Execution of Cinematographic Shots With Multiple Drones

This paper presents a system for the execution of autonomous cinematography missions with a team of drones. The system allows media directors to design missions involving different types of shots with one or multiple cameras, running sequentially or concurrently. We introduce the complete architecture, which includes components for mission design, planning and execution. Then, we focus on the components related to autonomous mission execution. First, we propose a novel parametric description for shots, considering different types of camera motion and tracked targets; and we use it to implement a set of canonical shots. Second, for multi-drone shot execution, we propose distributed schedulers that activate different shot controllers on board the drones. Moreover, an event-based mechanism is used to synchronize shot execution among the drones and to account for inaccuracies during shot planning. Finally, we showcase the system with field experiments filming sport activities, including a real regatta event. We report on system integration and lessons learnt during our experimental campaigns.
Dual Adversarial Disentanglement and Deep Representation Decorrelation for NIR-VIS Face Recognition

The task of near-infrared and visual (NIR-VIS) face recognition refers to matching face data from different modalities, which has broad application prospects in areas such as multimedia information retrieval and criminal investigation. However, it remains a challenging task due to high intra-class variations and small-scale NIR-VIS dataset. In this paper, we propose a novel approach called Dual Adversarial Disentanglement and deep Representation Decorrelation (DADRD) to solve the NIR-VIS matching problem. In order to reduce the gap between NIR-VIS images, three key components are designed for DADRD model, including Cross-modal Margin (CmM) loss, Dual Adversarial Disentangled Variations (DADV) and Deep Representation Decorrelation (DRD). Firstly, the CmM loss captures within- and between-class information of the data, and it further reduces modality difference by a center-variation item. Secondly, the Mixed Facial Representation (MFR) layer of the backbone network is divided into three parts: the identity-related layer, the modality-related layer and the residual-related layer. The DADV is designed to reduce the intra-class variations, which consists of Adversarial Disentangled Modality Variations (ADMV) and Adversarial Disentangled Residual Variations (ADRV). Specifically, the ADMV and ADRV aim at eliminating spectrum variations and residual variations (i.e., lighting, pose, expression, occlusion, etc) respectively via an adversarial mechanism. Finally, we impose a DRD on the three decomposed features to make them irrelevant to each other, which can more effectively separate the three component information and enhance feature representations. In particular, we develop a Joint Three-stage Optimization (JTsO) strategy to effectively optimize the network. The joint formulation leads to the purification of identity information and the disentanglement of within-class variation information. Extensive experiments have been carried out on three challenging datasets, and the results demonstrate the effectiveness of our method.
Investigating Underdetermination Through Interactive Computational Handweaving

Computational handweaving combines the repeatable precision of digital fabrication with relatively high production demands of the user: a weaver must be physically engaged with the system to enact a pattern, line by line, into a fabric. Rather than approaching co-presence and repetitive labor as a negative aspect of design, we look to current practices in procedural generation (most commonly used in game design and screen-based new media art) to understand how designers can create room for suprise and emergent phenomena within systems of precision and constraint. We developed three designs for blending real-time input with predetermined pattern features. These include: using camera imagery sampled at weaving time; a 1:1 scale tool for composing patterns on the loom; and a live "Twitch'' stream where spectators determine the woven pattern. We discuss how experiential qualities of the systems led to different balances of underdetermination in procedural generation as well as how such an approach might help us think beyond an artifact/experience dichotomy in fabrication.
Sign Language Recognition Using Template Matching Technique

There is an absence of communication with deaf people in our society. To overcome this barrier the introduction of Sign Language (SL) took place. To convey meaning to normal people, sign language makes use of patterns that are visually transmitted sign patterns. Sign language is also useful for people suffering with Autism Spectrum Disorder (ASD). Normal people cannot understand the signs used by deaf, as they do not know the meaning of a particular sign. The system proposed here aims at solving this problem. This system uses a camera, which captures various gestures of the hand. Then, processing of the image takes place by using various algorithms. First, pre-processing of the image takes place. Then, determination of edges occurs by using an edge detection algorithm. Finally, a template-matching algorithm identifies the sign and display the text. As the output is text, one can easily interpret the meaning of a particular sign. This also curtails the difficulty to communicate with the deaf. The implementation of the system is by using OpenCV-Python. The system uses various libraries.
A Comparative Performance Study of Hybrid Firefly Algorithms for Automatic Data Clustering

In cluster analysis, the goal has always been to extemporize the best possible means of automatically determining the number of clusters.However, because of lack of prior domain knowledge and uncertainty associated with data objects characteristics, it is challenging to choose an appropriate number of clusters, especially when dealing with data objects of high dimensions, varying data sizes, and density.In the last few decades, different researchers have proposed and developed several nature-inspired metaheuristic algorithms to solve data clustering problems.Many studies have shown that the firefly algorithm is a very robust, efficient and effective nature-inspired swarm intelligence global search technique, which has been successfully applied to solve diverse NP-hard optimization problems.However, the diversification search process employed by the firefly algorithm can lead to reduced speed and convergence rate for large-scale optimization problems.Thus this study investigates the application of four hybrid firefly algorithms to the task of automatic clustering of high density and large-scaled unlabelled datasets.In contrast to most of the existing classical heuristic-based data clustering analyses techniques, the proposed hybrid algorithms do not require any prior knowledge of the data objects to be classified.Instead, the hybrid methods automatically determine the optimal number of clusters empirically and during the program execution.Two well-known clustering validity indices, namely the Compact-Separated and Davis-Bouldin indices, are employed to evaluate the superiority of the implemented firefly hybrid algorithms.Furthermore, twelve standard ground truth clustering datasets from the UCI Machine Learning Repository are used to evaluate the robustness and effectiveness of the algorithms against those of the classical swarm optimization algorithms and other related clustering results from the literature.The experimental results show that the new clustering methods depict high superiority in comparison with existing standalone and other hybrid metaheuristic techniques in terms of clustering validity measures.
A critical overview of privacy-preserving approaches for collaborative forecasting

Cooperation between different data owners may lead to an improvement in forecast quality—for instance, by benefiting from spatiotemporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection concerns, however, said data owners might be unwilling to share their data. Interest in collaborative privacy-preserving forecasting is thus increasing. This paper analyzes the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing vector autoregressive models. The methods are divided into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as (i) the necessary trade-off between privacy and forecasting accuracy, empirically evaluated through simulations and real-world experiments based on solar data; and (ii) iterative model fitting processes, which reveal data after a number of iterations.
Site-Adaptation of Modeled Solar Radiation Data: The SiteAdapt Procedure

The adaptation of modeled solar radiation data with coincident ground measurements has become a standard practice of the industry, typically requested by financial institutions in the detailed solar resource assessments of solar projects. This practice mitigates the risk of solar projects, enhancing the adequate solar plant design and reducing the uncertainty of its yield estimates. This work presents a procedure for improving the accuracy of modeled solar irradiance series through site-adaptation with coincident ground-based measurements relying on the use of a regression preprocessing followed by an empirical quantile mapping (eQM) correction. It was tested at nine sites in a wide range of latitudes and climates, resulting in significant improvements of statistical indicators of dispersion, distribution similarity and overall performance: relative bias is reduced on average from −1.8% and −2.3% to 0.1% and 0.3% for GHI and DNI, respectively; relative root mean square deviation is reduced on average from 17.9% and 34.9% to 14.6% and 29.8% for GHI and DNI, respectively; the distribution similarity is also improved after the site-adaptation (KSI is 3.5 and 3.9 times lower for GHI and DNI at hourly scale, respectively). The methodology is freely available as supplementary material and downloadable as R-package from SiteAdapt.
Can connected technologies improve sleep quality and safety of older adults and care-givers? An evaluation study of sleep monitors and communicative robots at a residential care home in Japan

A sheet-shaped body vibrometer (SBV) is a type of assistive technology which offers a constant and noninvasive method of recording and monitoring the physical condition and sleep patterns of care recipients. With the aim of creating a safer environment for both care recipients and caregivers, we connected the SBV to a communicative robot (com-robot), to function as an integrated system. The robot has a sensor which activates when a care recipient tries to stand up, whereupon it sends an alert to care staff and speaks to the care recipient. The combined technologies offer an enhanced sense of security, as they watch over older people during the night, visualise sleep patterns and alert care staff. As proof of concept, this study examines the usefulness of this connected system by testing its effectiveness among two types of users (care recipients and professionals) in a residential care home in Japan. For the former, sleep parameters were investigated to see if there was any change over time in and impact on an older person's quality of life. As a measurement of quality of life, the interRAI method was used as a comprehensive assessment tool, based on which a care plan was also created for each care recipient. The interRAI is a nursing care evaluation and nursing care plan creation guideline package that provides unbroken care that can be used at home, in facilities or in the community For the latter, the study tests the level of fatigue among care professionals during night shifts before and after the intervention. For triangulation of data, semi-structured interviews and usability tests were carried out. Despite a few points for improvement, the results highlight multiple benefits for care recipients and professionals of using the SBV and com-robot integrated system in a residential care home.
Detecting and Tracking Criminals in the Real World through an IoT-Based System

Criminals and related illegal activities represent problems that are neither trivial to predict nor easy to handle once they are identified. The Police Forces (PFs) typically base their strategies solely on their intra-communication, by neglecting the involvement of third parties, such as the citizens, in the investigation chain which results in a lack of timeliness among the occurrence of the criminal event, its identification, and intervention. In this regard, a system based on IoT social devices, for supporting the detection and tracking of criminals in the real world, is proposed. It aims to enable the communication and collaboration between citizens and PFs in the criminal investigation process by combining app-based technologies and embracing the advantages of an Edge-based architecture in terms of responsiveness, energy saving, local data computation, and distribution, along with information sharing. The proposed model as well as the algorithms, defined on the top of it, have been evaluated through a simulator for showing the logic of the system functioning, whereas the functionality of the app was assessed through a user study conducted upon a group of 30 users. Finally, the additional advantage in terms of intervention time was compared to statistical results.
Facial micro-expression recognition: A machine learning approach

Micro-expression recognition is a growing research area owing to its application in revealing subtle intention of humans, especially while under high stake conditions. With the rapid increase in security issues all over the world, the use of micro-expressions to understand one's state of mind has received major interest. Micro-expressions are characterized by short duration and low intensity, hence, efforts to train humans in recognizing them have resulted in very low performances. Automatic recognition of micro-expressions using machine learning techniques thus promises a more effective result and saves time and resources. In this study, we explore the use of Extreme Learning Machine (ELM) for micro-expression recognition because of its fast learning ability and higher performance when compared with other models. Support Vector Machine (SVM) is used as a baseline model and its recognition performance and its training time compared with ELM training time. Feature extraction is performed on apex micro-expression frames using Local Binary Pattern (LBP) and on micro-expression videos divided into image sequences using a spatiotemporal feature extraction technique called Local Binary Pattern on Three Orthogonal Planes (LBP-TOP). Evaluation of the two models is performed on spontaneous facial micro-expression samples acquired from Chinese Academy of Sciences (CASME II). Results obtained from the experiments show that ELM produces a higher recognition performance than SVM in terms of accuracy, precision, recall and F-score when temporal features are used. Comparison between SVM and ELM training time also shows that ELM learns faster than SVM. An average training time of 0.3405 seconds is achieved for SVM while an average training time of 0.0409 seconds is achieved for ELM for the five selected micro-expression classes. This study shows that automatic recognition of micro-expressions is produces a better result when temporal features and a machine learning algorithm with fast learning speed are used.
A PSO-based algorithm for mining association rules using a guided exploration strategy

Association rule mining is one of the most important and active research areas in data mining. In the literature, several association rule miners have been proposed; among them, those based on particle swarm optimization (PSO) have reported the best results. However, these algorithms tend to prematurely fall into local solutions, avoiding a wide exploration that could produce even better results. In this paper, an algorithm based on PSO, called PSO-GES, for mining association rules using a Guided Exploration Strategy is introduced. Our experiments, over real-world transactional databases, show that our proposed algorithm mines better quality association rules than the most recent PSO-based algorithms for mining association rules of the state of the art.
High-Speed Optical 3D Measurement Sensor for Industrial Application

3D optical sensors are becoming more and more popular in vision guidance of industrial robots and other industrial automation applications. Although research on high-speed 3D shape measurement (or imaging) has experienced tremendous growth over the past decades, simultaneously achieving high-speed and high-accuracy performance remains a major challenge in industrial practice. This paper presents a new FPGA architecture for the PMP algorithm and designs an high-speed embedded binocular structured light 3D measurement sensor. The sensor mainly contains a DLP projector, two cameras and a Xilinx Zynq-7000 UltraScale which make real-time computation possible. Experiments showed the time consuming of processing a set of 9 ×2 images with a resolution of 2048 ×1536 using the sensor proposed in this paper is 31.47 ms that has a better performance than 255ms on GPU. To the best of our knowledge, this is the first high-speed FPGA-based embedded binocular structured light 3D measurement sensor. This proposed 3D sensor can obtain ultra high quality 3D information in real time, it can be widely applied to robot random pin-picking and other industrial applications.
Parallels, Tangents, and Loops

While attention in Research Through Design (RtD) is often on the findings, in this pictorial, we choose to attend to the 'through' part of RtD in order to reveal the messy stories of how those insights were arrived at-stories that are often untold, truncated, or streamlined. We use a yearlong RtD project on human-data entanglements in the home as a case study to explore the contours of this process. We detail how our messy lines of inquiry crossed, dead ended, wove together, and looped. Grounded in illustrations of lines, we offer practical reflections on experiences we encountered while navigating these scribbly lines.