Reviewerly/exHarmony huggingface dataset:
Improved Protocols and Hardness Results for the Two-Player Cryptogenography Problem

The cryptogenography problem, introduced by Brody, Jakobsen, Scheder, and Winkler (ITCS 2014), is to collaboratively leak a piece of information known to only one member of a group 1) without revealing who was the origin of this information and 2) without any private communication, neither during the process nor before. Despite several deep structural results, even the smallest case of leaking one bit of information present at one of two players is not well understood. Brody et al. gave a 2-round protocol enabling the two players to succeed with probability 1/3 and showed the hardness result that no protocol can give a success probability of more than 3/8. In this work, we show that neither bound is tight. Our new hardness result, obtained by a different application of the concavity method used also in the previous work, states that a success probability of better than 0.3672 is not possible. Using both theoretical and numerical approaches, we improve the lower bound to 0.3384, that is, give a protocol leading to this success probability. Unfortunately, already our smallest protocol beating the previous 1/3 success probability takes up 16 rounds of communication. The protocol leading to the bound of 0.3384 even in a compact representation consists of 18248 game states. These numbers suggest that the task of finding good protocols for the cryptogenography problem as well as understanding their structure is harder than what the simple problem formulation suggests.
A Collaborative Robotic Approach to Gaze-Based Upper-Limb Assisted Reaching

Robotic treatment has been recognized as a powerful tool to face the consequences of neurological impairments such as stroke. Among several potential benefits, the possibility to program high-intensity and repetitive movements increased the popularity of this type of rehabilitation technologies for upper limb disabilities. Nevertheless, although the results are encouraging, we are still far from guaranteeing a complete recovery to all patients, and some degree of external assistance is required to enable them to execute their activities of daily living. Hence, we aim to build a system which may serve not only as a rehabilitative tool, but also as a complement to assist the users in everyday tasks. In this direction, we present a novel Human-Robot Interface (HRI), which can detect and learn repetitive user movements through gaze, and provide assistance via a collaborative robot. The robot, which provides support to the user's wrist using a comfortable interface, executes the learnt trajectories, enabling the user to perform repetitive movements with reduced effort. The experiments are carried out on ten healthy subjects. We demonstrate that the proposed HRI is not only flexible and intuitive, but also accurate in guiding the users' limb to the desired positions.
Partial Weight Adaptation for Robust DNN Inference

Mainstream video analytics uses a pre-trained DNN model with an assumption that inference input and training data follow the same probability distribution. However, this assumption does not always hold in the wild: autonomous vehicles may capture video with varying brightness; unstable wireless bandwidth calls for adaptive bitrate streaming of video; and, inference servers may serve inputs from heterogeneous IoT devices/cameras. In such situations, the level of input distortion changes rapidly, thus reshaping the probability distribution of the input. We present GearNN, an adaptive inference architecture that accommodates DNN inputs with varying distortions. GearNN employs an optimization algorithm to identify a tiny set of DNN parameters, given a memory budget. Based on the distortion level of the input, GearNN then adapts only the distortion-sensitive parameters, while reusing the rest of constant parameters across all input qualities. In our evaluation of DNN inference with dynamic input distortions, GearNN improves the accuracy (mIoU) by an average of 18.12% over a DNN trained with the undistorted dataset and 4.84% over stability training from Google, with only 1.8% extra memory overhead.
Ransp: Ranking Attention Network For Saliency Prediction On Omnidirectional Images

Various convolutional neural network (CNN)-based methods have shown the ability to boost the performance of saliency prediction on omnidirectional images (ODIs). However, these methods are limited by sub-optimal accuracy, because not all the features extracted by the CNN model are not useful for the final fine-grained saliency prediction. Features are redundant and have negative impact on the final fine-grained saliency prediction. To tackle this problem, we propose a novel Ranking Attention Network for saliency prediction (RANSP) of head fixations on ODIs. Specifically, the part-guided attention (PA) module and channel-wise feature (CF) extraction module are integrated in a unified framework and are trained in an end-to-end manner for fine-grained saliency prediction. To better utilize the channel-wise feature map, we further propose a new Ranking Attention Module (RAM), which automatically ranks and selects these maps based on scores for fine-grained saliency prediction. Extensive experiments are conducted to show the effectiveness of our method for saliency prediction of ODIs.
Automatic take-off landing quadcopter control on dynamics platform

Abstract SAR (Search and Rescue) become an interesting topic discussed worldwide because of its urgency to save humans life. Recent natural disaster in Palu, Central Sulawesi Indonesia demonstrates the lack of mobility which can cause a problem for the SAR team reaching the victim within the isolated area. Uneasy job for the SAR team become more challenging in time response and in finding and saving disaster victims. Consider the fact, there is a need to develop more efficient and effective way to search and rescue the survivors. The using of Quadcopter can answer the problems. Unfortunately the Quadcopter still conventionally controlled by a remote and further it has to be on static ground whenever its take off or landing. This paper reports a Quadcopter fitted with dynamical autonomous take off and landing systems. Thus it can carry out the advantage from a tactical SAR vehicles or other moving platforms. Using object detection approach, The navigation system in an image-based control quadcopter or called visual servoing is the use of machine vision as a control of the closed loop position for movement. Images can be detected using YOLOv3 Real Time Object Detector, and tracks it by estimating the motion of objects in successive video frames. By using 3D reconstruction method the distance and position of the designated target and the quadcopter could be determined. Then the estimation of the relative position of the object is used as input for the control system on the quadcopter using PID controller. The controller commands the quadcopter to approach the target, while the image processing checks the relative position between the two. If the position satisfies the minimum landing parameter, then the controller commands a landing. While for take off we implement the controller to maintain its equilibrium and sufficient power to throttle.
Multi-Factor Performance Comparison of Amazon Web Services Elastic Compute Cluster and Google Cloud Platform Compute Engine

Cloud computing has rapidly become a viable competitor to on-premise infrastructure from both management and cost perspectives. This research provides insight into cluster computing performance and variability in cloud-provisioned infrastructure from two popular public cloud providers. A comparative examination of the two cloud platforms using synthetic benchmarks is provided. In this article, we compared the performance of Amazon Web Services Elastic Compute Cluster (EC2) to the Google Cloud Platform (GCP) Compute Engine using three benchmarks: STREAM, IOR, and NPB-EP. Experiments were conducted on clusters with increasing nodes from one to eight. We also performed experiments over the course of two weeks where benchmarks were run at similar times. The benchmarks provided performance metrics for bandwidth (STREAM), read and write performance (IOR), and operations per second (NPB-EP). We found that EC2 outperformed GCP for bandwidth. Both provided good scalability and reliability for bandwidth with GCP showing a slight deviation during the two-week trial. GCP outperformed EC2 in both the read and write tests (IOR) as well as the operations per second test. However, GCP was extremely variable during the read and write tests over the two-week trial. Overall, each platform excelled in different benchmarks and we found EC2 to be more reliable in general.
Chapter 13 Hazards associated with mining and mineral exploitation in Cornwall and Devon, SW England

Abstract The largest UNESCO World Heritage Site in the UK is found in Cornwall and west Devon, and its designation is based specifically on its heritage for metalliferous mining, especially tin, copper and arsenic. With a history of over 2000 years of mining, SW England is exceptional in the nature and extent of its mining landscape. The mining for metallic ores, and more recently for kaolin, is a function of the distinctive geology of the region. The mining hazards that are encountered in areas of metallic mines are a function of: the Paleozoic rocks; the predominant steeply dipping nature of mineral veins and consequent shaft mining; the great depth and complexity of some of the mines; the waste derived from processing metallic ores; the long history of exploitation; and the contamination associated with various by-products of primary ore-processing, refining and smelting, notably arsenic. The hazards associated with kaolin mining are mainly related to the volume of the inert waste products and the need to maintain stable spoil tips, and the depth of the various tailings’ ponds and pits. The extent of mining in Cornwall and Devon has resulted in the counties being leaders in mining heritage preservation and the treatment and remediation of mining-related hazards.
A Human Skeleton Data Optimization Algorithm For Multi-Kinect

As a motion capture device, Kinect can extract human skeleton data (three dimensional positions of body joints) through depth information, which has been increasingly applied in many fields such as action recognition, medical diagnosis and rehabilitation evaluation. But when collecting human skeleton data with a single Kinect, the detection range is limited and stale data exceptions or data missing might occur due to the influence of human body occlusion and environment. These invalid data will directly affect the accuracy of relevant calculation results and hinder the application of Kinect. To solve the above problems, we propose a data acquisition scheme based on multi-Kinect and an optimization algorithm for human skeleton data. First, we use the method based spatial region constraints and K-means clustering to remove the outliers in the human skeleton data collected by each Kinect. Then we convert the data from different perspectives into the same coordinate system and perform adaptive weighted fusion. We finally obtain a stable and valid data sequence, facilitating its application greatly. We design relevant experiments to compare the optimized data with the original data and the results verify the effectiveness of the algorithm.
Classification of Leukemia Microscopic Images using Blended Biogeography Optimization

The need for accurate medical images classification technique for assisting the clinician in disease diagnosis is increasing day by day. This research paper investigates the performance of blended biogeography based optimization for Acute Lymphoblastic Leukemia classification and the input microscopic images are categorized as abnormal and normal. In this analysis, 47 cancer cells and 26 healthy cells taken from C- NMC website are considered. The better performance of blended biogeography based optimization is established through comparison with original biogeography based optimization, particle swarm optimization, K-Means and Fuzzy C means techniques. The proposed technique yields highest accuracy of 93% when statistical features are used.
STAIDCC20: 1st International Workshop on Socio-technical AI Systems for Defence, Cybercrime and Cybersecurity

The purpose of STAIDCC20 workshop is to bring together a mixture of inter-disciplinary researchers and practitioners working in defence, cybercrime and cybersecurity application areas to discuss and explore the challenges and future research directions around socio-technical AI systems. The workshop will showcase where the state of the art is in socio-technical AI, charting a path around issues including transparency, trustworthiness, explaining bias and error, incorporating human judgment and ethical frameworks for deployment of socio-technical AI in the future.
Gender Classification Based on Face Recognition using Convolutional Neural Networks (CNNs)

Abstract Biometrics are physical or behavior characteristics of human that can be used to identify someone. One form of physical characteristics possessed by humans is fingerprints, retinal scanning, face and hand geometry, while one form of behavioral characteristics possessed by humans is handwriting, signatures, mouse usage analysis, walking patterns, etc. Basically, physical characteristics are more easily observed than behavioral characteristics. Therefore, physical characteristics are more often used in many aspects of security. One of the most common physical characteristics is face. By seeing the face, we can find out or predict how old they are, their gender and even their expression. However, there are still many mistakes in predicting a gender through person’s face. In fact, there are still many crimes in falsifying self-identity (such as gender). So, we need a method that is able to classify identity (gender) based on a person’s face appropriately. One method that can be used is Convolutional Neural Networks (CNNs). Later, CNNs will classify a person’s gender (male / female) based on a person’s face image data. And based on.
A Situation-Aware Adaptation Framework for Intelligent Transportation Systems

A transportation system is usually well-defined and operates based on a specific model defined during system design. However, the system can interact with different objects from its environment at runtime and needs to guarantee its functional and timing behavior even in the presence of adverse or failure situations through self-adaptation. Traditionally, techniques such as design analysis and testing are performed during the system design and development stage. During the adaptation process, the transportation system needs to provide assurance such that it is safe and schedulable. We present an adaptation framework, which guarantees the functional and timing behavior of the system in different situations by creating a knowledge base from mining the video stream of the monitored environment. The knowledge base provides information on system interactions with external objects, constraints imposed on the system due to interactions, and characterizes the runtime behavior. We guarantee the timing behavior by evaluating the constraints and their effects on the performance of the system. The experimental analysis of our work demonstrates that the situation-aware adaptation framework can significantly improve system performance by reducing scheduling overload and response time.
Deterministic Inference of Topic Models via Maximal Latent State Replication

Probabilistic topic models, such as latent dirichlet allocation (LDA), are often used to discover hidden semantic structure of a collection of documents. In recent years, various inference algorithms have been developed to cope with learning of topic models, among which Gibbs sampling methods remain a popular choice. In this paper, we aim to improve the inference of topic models based on the Gibbs sampling framework. We extend a state augmentation based Gibbs sampling method by maximizing the replications of latent states, and propose a new generic deterministic inference method, named maximal latent state replication (MAX), for learning of a family of probabilistic topic models. One key benefit of the proposed method lies in the deterministic nature for inference, which may help to improve its running efficiency as well as predictive perplexity. We have conducted extensive experiments on real-life publicly available datasets, and the results have validated that our proposed method MAX significantly outperforms state-of-the-art baselines for inference of existing well-known topic models.
Fast load balance parallel graph analytics with an automatic graph data structure selection algorithm

This paper investigates the performance of graph-structured analytics on large-scale shared memory systems. Graph analytics are highly demanding for efficient graph traversal due to large data set size and irregular data access patterns. In order to achieve efficient graph analytics, we consider and discuss the performance of three common types of graph data structures. Also, we demonstrate that load balance is to a large extent determined by the number of edges and number of unique vertices processed by each thread. Finally, we propose an automatic graph data structure selection algorithm and an efficient reordering as a pre-processing step to balance the number of vertices and edges together. Reordering algorithm also optimally balances edges and vertices for graphs with a power-law degree distribution and ensures an equal degree distribution across threads. The developed techniques are implemented in GraphGrind, a new shared memory graph analytics framework. Evaluation in GraphGrind, shows that this outperforms state-of-the-art graph analytics frameworks for shared memory including Ligra (Shun and Blelloch, 2013) up to 10.4×, and Polymer (Zhang et al., 2015) up to 8.3× across 8 algorithms and 6 graphs.
Adaptive distributed video surveillance system

Video surveillance systems are a major multipurpose video data source for modern emergency management solutions. As their complexity and image quality grows, the requirements for data exchange channels, storage and processing units are becoming much more demanding.Simultaneously, a wide range of neural networks becomes a universal instrument for image analysis. Such algorithms require deployment of powerful HPC-class server equipment.In this article, we propose a new adaptive approach to decentralized environment for video data storage and event-driven video processing for modern emergency management solution.The proposed solutions have been tested on different arrays of video data collected from various sources. These sources include city video surveillance systems, supermarket video surveillance systems, etc.For each of the listed examples, a discrete dynamic model has been developed based on video processing and event management.Our research resulted in a new data collection and analysis approach based on distributed video processing, reconstruction of 3D scenes and tactical situations detection.
Robust Adaptable Segmentation-Based Copy Move Forgery Detection Method

The availability of the most advanced image correction tools along with the latest highly classy capturing devices has made the tampering of the images more easier. Copy move method of tampering happens when a particular section in the digital image is copy pasted on a new section on the same image for guarding or else hiding offensive areas. Digital forgery detection methods on images target the discovery of fake regions or the duplicated parts. Many pre- or post-operations are done on the images by persons who do the tampering. A novel method integrating the block-based and key-point-based methods is suggested in this paper. Initially, it adaptively segments the input host picture into non-overlapping and unequal sized slabs and features and thus mined from each block is harmonized with each further to discover labeled feature points that point out doubted fake regions approximately. In order to make it further precise, an extraction algorithm is proposed that substitutes the feature points by minor superpixels as slabs of feature which later fuses with the adjacent blocks which show like local color features to create that detected forgery sections. Lastly, morphological operations are performed on these fused areas to produce the spotted tampered areas. Experiments show that this method of detection achieves better outputs under several perplexing conditions compared with the latest CMFD algorithms.
Secure Communication Scheme Based on Synchronization of Non-Identical Hyperchaotic Systems

In this work, a secure communication scheme based on the synchronization of non-identical hyperchaotic systems is considered. Using hyperchaotic Lorenz and L u as prototype oscillators, a secure communication scheme based on synchronization of different hyperchaotic systems with unknown parameters is presented. The communication scheme consists of a transmitter, which comprises the hyperchaotic carrier and modulator, and a receiver, which comprises the hyperchaotic response and a demodulator. Appropriate controllers and parameter update laws are designed to achieve synchronization between the hyperchaotic drive and hyperchaotic response systems and achieve the estimate of unknown parameters simultaneously. The message signal is recovered by the identified parameter and the corresponding demodulation method. Numerical simulations were performed to show the validity and feasibility of the designed secure communication scheme.
A Highly Efficient and Robust Method For NNF-Based Template Matching

Template matching is widely used in many applications such as arbitrary object detection and tracking. In this paper, we propose a highly efficient and robust method for template matching. The state-of-the-art nearest neighbor field (NNF)- based algorithms construct the NNF by searching the nearest neighbor of the target points. Instead, our method utilize the inverse NNF by searching the nearest neighbor of template points, which is more robust than original NNF. As for similarity metric, our method utilizes the diversity on the inverse NNF to calculate a global probability image and makes use of the integral image to calculate the similarity score, which makes our method highly efficient. Furthermore, our method is a non-parametric model and has non-explicit assumptions about data. Experiments on challenging public datasets demonstrate the superiority of our method to state- of-the-art methods both in robustness and efficiency.
Study on the optimal Zernike order in the phase parameterization for global phase retrieval algorithm

Phase retrieval algorithm is an effective method to reconstruct the surface distortions for reflector antennas. As the traditional Fourier iterative algorithms usually stagnate at local minima, we previously proposed a global phase retrieval algorithm, named CMAES-HIO, based on the hybridization of hybrid-input-output algorithm and covariance matrix adaptation evolution strategy. We address the problem of selection of the Zernike order used in the phase parameterization for CMAES-HIO algorithm in this paper. By introducing a hybrid evaluation parameter, which combines the algorithm accuracy and time consumption, we utilize the Monte-Carlo method to simulate the algorithm performance under different random surface distortions. Simulation results show that for an unknown surface distortion, a Zernike order of 5 or 6 is probably the optimum for the comprehensive algorithm performance with respect to time and accuracy.