Reviewerly/exHarmony huggingface dataset:
Detection of Overlapping Speech for the Purposes of Speaker Diarization

The presence of overlapping speech has a significant negative impact on the performance of speaker diarization systems. In this paper, we employ a convolutional neural network for the detection of such speech intervals and evaluate it in terms of the potential improvements to speaker diarization. We train the network on specifically-created synthetic data, while the evaluation is performed on the AMI Corpus and the SSPNet Conflict Corpus.
Single hazy image restoration using robust atmospheric scattering model

Images captured in unfavorable weather usually exhibit poor visibility, which results from scattering and absorption that the propagated light suffers in the atmosphere. To improve the quality of degraded images, multitudes of algorithms have been exploited based on traditional atmospheric scattering model. However, in the traditional model, a phenomenon is neglected that the radiance projected on scenes is uneven, which leads to low brightness in processed image. Targeted the inherent limitation of the traditional model, we propose a robust atmospheric scattering model by decomposing the real scene into incident light and reflectance component and attaching a noise term in the traditional model. Then an objective function which includes novel regularization terms for the incident light and reflectance is formulated based on the proposed model, and an alternating direction method of multipliers is adopted to jointly estimate the incident light and reflectance. Moreover, a compensation term with regard to transmission map is introduced to ameliorate over-enhancement in thick haze regions. Ultimately, comprehensive tests are implemented to compare our method with other exceptional haze removal methods. Experiments on images with different characteristics manifest excellent performance of the proposed method in terms of haze removal and brightness enhancement.
Effects of a Simple Relay Network on the Bitcoin Network

Bitcoin has a low transaction throughput. In order to allow for an increase of this throughput without increasing orphan blocks, decreasing the block propagation time is important. One of the techniques to improve its block propagation time is to utilize relay networks. However, the effects of utilizing relay networks is not apparent. Existing studies and measurements on relay networks have not focused on the effect of relay networks on the individual miners. Moreover, the relation between the degree of the effect and relay network utilization rate is unknown. Herein, we performed simulations while finely changing the proportion of nodes utilizing a relay network. Moreover we quantitatively evaluated the effect of relay networks on the entire Bitcoin network and individual miners. Results show that the propagation time decrease to approximately 77% of the original value if the utilization rate is set to 3%. This rate is close to the actual utilization rate of relay network "Falcon". We also found that the probability of blocks created by utilizing nodes to become orphan blocks is surprisingly smaller than that of the non-utilizing nodes. Even in the worst case, the value of utilizing nodes is 15% of the value of non-utilizing nodes.
Simple Proofs of Space-Time and Rational Proofs of Storage

We introduce a new cryptographic primitive: Proofs of Space-Time (PoSTs) and construct an extremely simple, practical protocol for implementing these proofs. A PoST allows a prover to convince a verifier that she spent a “space-time” resource (storing data—space—over a period of time). Formally, we define the PoST resource as a trade-off between CPU work and space-time (under reasonable cost assumptions, a rational user will prefer to use the lower-cost space-time resource over CPU work). Compared to a proof-of-work, a PoST requires less energy use, as the “difficulty” can be increased by extending the time period over which data is stored without increasing computation costs. Our definition is very similar to “Proofs of Space” [ePrint 2013/796, 2013/805] but, unlike the previous definitions, takes into account amortization attacks and storage duration. Moreover, our protocol uses a very different (and much simpler) technique, making use of the fact that we explicitly allow a space-time tradeoff, and doesn’t require any non-standard assumptions (beyond random oracles). Unlike previous constructions, our protocol allows incremental difficulty adjustment, which can gracefully handle increases in the price of storage compared to CPU work. In addition, we show how, in a crypto-currency context, the parameters of the scheme can be adjusted using a market-based mechanism, similar in spirit to the difficulty adjustment for PoW protocols.
An Adaptive Whale Optimization Algorithm Using Gaussian Distribution Strategies and Its Application in Heterogeneous UCAVs Task Allocation

To overcome the defect of whale optimization algorithm (WOA) being easily fallen into local optimum caused by the ill-distribution of solutions, this paper explores an adaptive WOA variant using Gaussian distribution strategies (GDSs), named GDS-WOA.In GDS-WOA, by means of one GDS, named the Gaussian estimation of distribution method, the superior population information is used to evolve the distribution scope and modify the evolution direction.Moreover, an adaptive framework is adopted to integrate the Gaussian estimation of distribution method and WOA together, in which each individual can update its position using Gaussian estimation of distribution method or WOA according to an adaptive probability parameter.When the algorithm falls into stagnation, another GDS, named Gaussian random walk, is activated to enrich the population diversity and help the algorithm get rid of the local optimum.Additionally, the greedy strategy is carried out to select the offspring from the parents and the generated candidates to fully retain the promising solutions.The GDS-WOA is benchmarked on CEC 2014 test suite, and the performance of GDS-WOA is evaluated by comparing with WOA and its promising variant IWOA, as well as other five state-of-the-art evolutionary algorithms, i.e., COA, VCS, CoBiDE, HFPSO and GWO.The statistical results demonstrate that GDS-WOA outperforms other competitors in terms of convergence efficiency and accuracy.Finally, GDS-WOA is applied to solve the optimal task allocation problem of heterogeneous unmanned combat aerial vehicles (UCAVs).To address this constrained real-world optimizing problem efficiently, the mathematical model of heterogeneous UCAVs task allocation is described with the operational effectiveness value as the objective.The validity and practicauility of the model as well as the performance of GDS-WOA for solving constrained optimization problem are demonstrated by the experimental results.
Recursive Integrity Monitoring for Mobile Robot Localization Safety

This paper presents a new methodology to quantify robot localization safety by evaluating integrity risk, a performance metric widely used in open-sky aviation applications that has been recently extended to mobile ground robots. Here, a robot is localized by feeding relative measurements to mapped landmarks into an Extended Kalman Filter while a sequence of innovations is evaluated for fault detection. The main contribution is the derivation of a sequential chi-squared integrity monitoring methodology that maintains constant computation requirements by employing a preceding time window and, at the same time, is robust against faults occurring prior to the window. Additionally, no assumptions are made on either the nature or shape of the faults because safety is evaluated under the worst possible combination of sensor faults.
CCA Security and Trapdoor Functions via Key-Dependent-Message Security

We study the relationship among public-key encryption (PKE) satisfying indistinguishability against chosen plaintext attacks (IND-CPA security), that against chosen ciphertext attacks (IND-CCA security), and trapdoor functions (TDF). Specifically, we aim at finding a unified approach and some additional requirement to realize IND-CCA secure PKE and TDF based on IND-CPA secure PKE, and show the following two main results. As the first main result, we show how to achieve IND-CCA security via a weak form of key-dependent-message (KDM) security. More specifically, we construct an IND-CCA secure PKE scheme based on an IND-CPA secure PKE scheme and a secret-key encryption (SKE) scheme satisfying one-time KDM security with respect to projection functions (projection-KDM security). Projection functions are very simple functions with respect to which KDM security has been widely studied. Since the existence of projection-KDM secure PKE implies that of the above two building blocks, as a corollary of this result, we see that the existence of IND-CCA secure PKE is implied by that of projection-KDM secure PKE. As the second main result, we extend the above construction of IND-CCA secure PKE into that of TDF by additionally requiring a mild requirement for each building block. Our TDF satisfies adaptive one-wayness. We can instantiate our TDF based on a wide variety of computational assumptions. Especially, we obtain the first TDF (with adaptive one-wayness) based on the sub-exponential hardness of the constant-noise learning-parity-with-noise (LPN) problem.
Optimization of the design and manufacture of a solar-wind hybrid street light

The demand for electricity has escalated and cannot be fulfilled by conventional energy sources alone. There has been a rising demand to seek new renewable energy sources. Although solar and wind energy are the most cost effective renewable energy sources, they are unreliable due to the sporadic nature of their occurrence, if implemented as standalones. In Zimbabwe, solar street lighting has been implemented since 2014 as a solution to the erratic power supplies and outages. Wind potential in Zimbabwe has been identified at elevated heights, with Gweru having the maximum power density of 115 W/m2 at 50 m hub height. This paper presents the optimization of the design of a hybrid renewable energy system (HRES) of solar and wind energy to power a 160W streetlight. The system consisted of a wind turbine, photovoltaic modules, charge controller, battery bank and lights. The system sizing was done in Excel using wind and solar data obtained from the database, HOMER Software Package and PVSyst. The 3D streetlight was modelled using Inventor Professional and a working prototype was manufactured. The results showed that the HRES reduced the energy storage requirements by 38.75% with an overall cost reduction of 14.4%, relative to a standalone solar streetlight. The diffuser effect to the turbine was experimentally assessed, showing 69.3% increase in turbine power output and a 50% decrease in energy storage requirements. Further research can be carried to improve the reliability for standalone systems.
All-optical generation of quantum entangled states with strictly constrained ultrafast laser pulses

We present a quantum optimal control theory study combined with theoretical analysis to determine a pulsed laser field, capable of generating a maximally entangled state in two trapped two-level atoms. By expanding the time-dependent unitary operator to the first leading term of Magnus expansion, we reexamine the pulse area theorem for the trapped atoms driven by an arbitrarily temporary field. Due to the dipole-dipole interaction blockade, we find that the two trapped atoms described by a three-level ladder system can be reduced into an equivalent two-level model by using narrow-bandwidth pulses, leading to an analytical solution for generating the maximally entangled state. We also solve a highly constrained optimization problem to search for optimal laser pulses with broad bandwidths. A zero pulse-area constraint is employed to remove the dc offset of the optimized laser pulses, and a fixed fluence limitation combined with a constant pulse-area constraint at the resonant frequency of the equivalent two-level system are utilized to restrict the unitary evolution of quantum systems by the first leading term of Magnus expansion. This work provides a potentially useful approach to find all-optical control schemes for generating the maximally entangled state by using ultrafast laser pulses while satisfying multiple strict limitations.
Decentralization of Multiagent Policies by Learning What to Communicate

Effective communication is required for teams of robots to solve sophisticated collaborative tasks. In practice it is typical for both the encoding and semantics of communication to be manually defined by an expert; this is true regardless of whether the behaviors themselves are bespoke, optimization based, or learned. We present an agent architecture and training methodology using neural networks to learn task-oriented communication semantics based on the example of a communication-unaware expert policy. A perimeter defense game illustrates the system's ability to handle dynamically changing numbers of agents and its graceful degradation in performance as communication constraints are tightened or the expert's observability assumptions are broken.
Plug-and-Play: Improve Depth Prediction via Sparse Data Propagation

We propose a novel plug-and-play (PnP) module for improving depth prediction with taking arbitrary patterns of sparse depths as input. Given any pre-trained depth prediction model, our PnP module updates the intermediate feature map such that the model outputs new depths consistent with the given sparse depths. Our method requires no additional training and can be applied to practical applications such as leveraging both RGB and sparse LiDAR points to robustly estimate dense depth map. Our approach achieves consistent improvements on various state-of-the-art methods on indoor (i.e., NYU-v2) and outdoor (i.e., KITTI) datasets. Various types of LiDARs are also synthesized in our experiments to verify the general applicability of our PnP module in practice.
Characterizing the Variability in Face Recognition Accuracy Relative to Race

Many recent news headlines have labeled face recognition technology as "biased" or "racist". We report on a methodical investigation into differences in face recognition accuracy between African-American and Caucasian image cohorts of the MORPH dataset. We find that, for all four matchers considered, the impostor and the genuine distributions are statistically significantly different between cohorts. For a fixed decision threshold, the African-American image cohort has a higher false match rate and a lower false non-match rate. ROC curves compare verification rates at the same false match rate, but the different cohorts achieve the same false match rate at different thresholds. This means that ROC comparisons are not relevant to operational scenarios that use a fixed decision threshold. We show that, for the ResNet matcher, the two cohorts have approximately equal separation of impostor and genuine distributions. Using ICAO compliance as a standard of image quality, we find that the initial image cohorts have unequal rates of good quality images. The ICAO-compliant subsets of the original image cohorts show improved accuracy, with the main effect being to reducing the low-similarity tail of the genuine distributions.
Effects of 3D Rotational Jitter and Selection Methods on 3D Pointing Tasks

3D pointing is an integral part of Virtual Reality interaction. Typical pointing devices rely on 3D trackers and are thus subject to fluctuations in the reported pose, i.e., jitter. In this work, we explored how different levels of rotational jitter affect pointing performance and if different selection methods can mitigate the effects of jitter. Towards this, we designed a Fitts' Law experiment with three selection methods. In the first method, subjects used a single controller to position and select the object. In the second method, subjects used the controller in their dominant hand to point at objects and the trigger button of a second controller, held in their non-dominant hand, to select objects. Finally, subjects used the controller in their dominant hand to point the objects and pressed the space bar on a keyboard to select the object in the third condition. During the pointing task we added five different levels of jitter: no jitter, ±0.5°, ±1°, and ±2° uniform noise, as well as White Gaussian noise with 1° standard deviation. Results showed that the Gaussian noise and ±2° of jitters significantly reduced the throughput of the participants. Moreover, subjects made fewer errors when they performed the experiment with two controllers. Our results inform the design of 3D user interfaces, input devices and interaction techniques.
Clustering by finding prominent peaks in density space

Recently, density peaks clustering (DPC) has received much attention. The key step in DPC is to find the nearest denser point (NDP) for each point. If a point and its NDP are close, they are in the same cluster; otherwise, they are in different clusters. However, the density gap between a point and its NDP is not considered in DPC. Once the point and its NDP are close, they are assigned to the same cluster, even if the density gap between them is large, which could result in poor clustering results. In this study, we propose a clustering algorithm based on finding prominent peaks (prominent peak clustering, PPC). Its main concept is to divide points into multiple potential clusters and then merge clusters whose density peaks are not prominent to obtain accurate clustering results. The prominence of cluster density peaks is measured by the density gap. Experimental results on low- and high-dimensional datasets demonstrate that PPC is competitive with other clustering techniques.
Deep Multi-Sensory Object Category Recognition Using Interactive Behavioral Exploration

When identifying an object and its properties, humans use features from multiple sensory modalities produced when manipulating the object. Motivated by this cognitive process, we propose a deep learning methodology for object category recognition which uses visual, auditory, and haptic sensory data coupled with exploratory behaviors (e.g., grasping, lifting, pushing, etc.). In our method, as the robot performs an action on an object, it uses a Tensor-Train Gated Recurrent Unit network to process its visual data, and Convolutional Neural Networks to process haptic and auditory data. We propose a novel strategy to train a single neural network that inputs video, audio and haptic data, and demonstrate that its performance is better than separate neural networks for each sensory modality. The proposed method was evaluated on a dataset in which the robot explored 100 different objects, each belonging to one of 20 categories. While the visual information was the dominant modality for most categories, adding the additional haptic and auditory networks further improves the robot's category recognition accuracy. For some of the behaviors, our approach outperforms the previous published baseline for the dataset which used handcrafted features for each modality. We also show that a robot does not need the sensory data from the entire interaction, but instead can make a good prediction early on during behavior execution.
Deep Learning for American Sign Language Fingerspelling Recognition System

Sign language has always been a major tool for communication among people with disabilities. In this paper, a sign language fingerspelling alphabet identification system would be developed by using image processing technique, supervised machine learning and deep learning. In particular, 24 alphabetical symbols are presented by several combinations of static gestures (excluding 2 motion gestures J and Z). Histogram of Oriented Gradients (HOG) and Local Binary Pattern (LBP) features of each gesture will be extracted from training images. Then Multiclass Support Vector Machines (SVMs) will be applied to train these extracted data. Also, an end-to-end Convolutional Neural Network (CNN) architecture will be applied to the training dataset for comparison. After that, a further combination of CNN as feature descriptor and SVM produces an acceptable result. The Massey Dataset is implemented in the training and testing phases of the whole system.
In-hand Object Scanning via RGB-D Video Segmentation

This paper proposes a technique for 3D object scanning via in-hand manipulation, in which an object reoriented in front of a video camera with multiple grasps and regrasps. In-hand object tracking is a significant challenge under fast movement, rapid appearance changes, and occlusions. This paper proposes a novel video-segmentation-based object tracking algorithm that tracks arbitrary in-hand objects more effectively than existing techniques. It also describes a novel RGB-D in-hand object manipulation dataset consisting of several common household objects. Experiments show that the new method achieves 6% increase in accuracy compared to top performing video tracking algorithms and results in noticeably higher quality reconstructed models. Moreover, testing with a novice user on a set of 200 objects demonstrates relatively rapid construction of complete 3D object models.
Adversarial Attack Type I: Cheat Classifiers by Significant Changes

Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations. In this paper, we propose another type of adversarial attack that can cheat classifiers by significant changes. For example, we can significantly change a face but well-trained neural networks still recognize the adversarial and the original example as the same person. Statistically, the existing adversarial attack increases Type II error and the proposed one aims at Type I error, which are hence named as Type II and Type I adversarial attack, respectively. The two types of attack are equally important but are essentially different, which are intuitively explained and numerically evaluated. To implement the proposed attack, a supervised variation autoencoder is designed and then the classifier is attacked by updating the latent variables using gradient information. Besides, with pre-trained generative models, Type I attack on latent spaces is investigated as well. Experimental results show that our method is practical and effective to generate Type I adversarial examples on large-scale image datasets. Most of these generated examples can pass detectors designed for defending Type II attack and the strengthening strategy is only efficient with a specific type attack, both implying that the underlying reasons for Type I and Type II attack are different.
Cohort-based T-SSIM Visual Computing for Radiation Therapy Prediction and Exploration

We describe a visual computing approach to radiation therapy (RT) planning, based on spatial similarity within a patient cohort. In radiotherapy for head and neck cancer treatment, dosage to organs at risk surrounding a tumor is a large cause of treatment toxicity. Along with the availability of patient repositories, this situation has lead to clinician interest in understanding and predicting RT outcomes based on previously treated similar patients. To enable this type of analysis, we introduce a novel topology-based spatial similarity measure, T-SSIM, and a predictive algorithm based on this similarity measure. We couple the algorithm with a visual steering interface that intertwines visual encodings for the spatial data and statistical results, including a novel parallel-marker encoding that is spatially aware. We report quantitative results on a cohort of 165 patients, as well as a qualitative evaluation with domain experts in radiation oncology, data management, biostatistics, and medical imaging, who are collaborating remotely.