Reviewerly/exHarmony huggingface dataset:
Black Block Recorder: Immutable Black Box Logging for Robots via Blockchain

Event data recording is crucial in robotics research, providing prolonged insights into a robot's situational understanding, progression of behavioral state, and resulting outcomes. Such recordings are invaluable when debugging complex robotic applications or profiling experiments ex post facto. As robotic developments mature into production, both the roles and requirements of event logging will broaden, to include serving as evidence for auditors and regulators investigating accidents or fraud. Given the growing number of high profile public incidents involving self-driving automotives resulting in fatality and regulatory policy making, it is paramount that the integrity, authenticity and non-repudiation of such event logs are maintained to ensure accountability. Being mobile cyber-physical systems, robots present new threats, and vulnerabilities beyond traditional IT: unsupervised physical system access or postmortem collusion between robot and OEM could result in the truncation or alteration of prior records. In this letter, we address immutablization of log records via integrity proofs and distributed ledgers with special considerations for mobile and public service robot deployments.
Deep Latent Factor Model with Hierarchical Similarity Measure for recommender systems

Latent Factor Model(LFM), as an effective feature mapping method, is widely applied in recommender systems. One challenge of LFM is previous methods usually use the inner product to calculate the similarity between users and items in the latent space, which cannot characterize different impacts of various latent factors. Another challenge is the performance of LFM will be negatively affected when facing data sparsity problem. In this paper, we propose a model named DLFM-HSM(Deep Latent Factor Model with Hierarchical Similarity Measure) to overcome the challenges above. More specifically, we introduce a hierarchical similarity measure to calculate an impact score which can better represent the similarity between a user and an item than the inner product. Also, in order to ease the data sparsity problem, we extract latent representations of users and items using deep neural networks from items' content information instead of only from user-item rating records. By representing users with items they purchased, our model guarantees that users and items are mapped into a common space and thus they are directly comparable. Extensive experiments on five real-world datasets show significant improvements of DLFM-HSM over the state-of-the-art methods and demonstrate the effectiveness of our model for alleviating the data sparsity problem.
Color-Theoretic Experiments to Understand Unequal Gender Classification Accuracy From Face Images

Recent work shows unequal performance of commercial face classification services in the gender classification task across intersectional groups defined by skin type and gender. Accuracy on dark-skinned females is significantly worse than on any other group. We provide initial evidence that skin type alone is not the driver for this disparity by conducting novel stability experiments that vary an image's skin type via color-theoretic methods, namely luminance mode-shift and optimal transport. We evaluate the effect of skin type change on the gender classification decision of a pair of state-of-the-art commercial and open-source gender classifiers. The results raise the possibility that broader differences in ethnicity, as opposed to the skin type alone, are what contribute to unequal gender classification accuracy in face images.
High-Resolution Single Image Dehazing Using Encoder-Decoder Architecture

In this work we propose HR-Dehazer, a novel and accurate method for image dehazing. An encoder-decoder neural network is trained to learn a direct mapping between a hazy image and its respective clear version. We designed a special loss that forces the network to keep into account the semantics of the input image and to promote consistency among local structures. In addition, this loss makes the system more invariant to scale changes. Quantitative results on the recently released Dense-Haze dataset introduced for the NTIRE2019-Dehazing challenge demonstrates the effectiveness of the proposed method. Furthermore, qualitative results on real data show that the described solution generalizes well to different never-seen scenarios.
Random Walk Exploration for Swarm Mapping

Research in swarm robotics has shown that robot swarms are effective in the exploration of unknown environments. However, little work has been devoted to port the exploration capabilities of robot swarms into the context of mapping. Indeed, conceiving robot swarms that can map an unknown environment in a robust, scalable, and flexible way is an open issue. In this paper, we investigate a swarm mapping method in which robots first individually map the environment by random walk and then, we merge their maps into a single, global one. We focus on five variants of random walk and we compare the quality of the maps that a swarm produces when exploring the environment using these variants. Our experiments with ten e-puck robots show that, despite the individual maps being incomplete by themselves, it is possible to collectively map the environment by merging them. We found that the quality of the map depends on the exploration behavior of the individuals. Our results suggest that one of the variants of random walk, the ballistic motion, gives better mapping results for closed environments.
The SEVerESt Of Them All

The success of cloud computing has shown that the cost and convenience benefits of outsourcing infrastructure, platform, and software resources outweigh concerns about confidentiality. Still, many businesses and individuals resist moving private data to cloud providers due to intellectual property and privacy reasons. A recent wave of hardware virtualization technologies aims to alleviate these concerns by offering encrypted virtualization features that support data confidentiality of guest virtual machines (e.g., by transparently encrypting memory) even when running on top untrusted hypervisors. We introduce two new attacks that can breach the confidentiality of protected enclaves. First, we show how a cloud adversary can judiciously inspect the general purpose registers to unmask the computation that passes through them. Specifically, we demonstrate a set of attacks that can precisely infer the executed instructions and eventually capture sensitive data given only indirect access to the CPU state as observed via the general purpose registers. Second, we show that even under a more restrictive environment - where access to the general purpose registers is no longer available - we can apply a different inference attack to recover the structure of an unknown, running, application as a stepping stone towards application fingerprinting. We demonstrate the practicality of these inference attacks by showing how an adversary can identify different applications and even distinguish between versions of the same application and the compiler used, recover data transferred over TLS connections within the encrypted guest, retrieve the contents of sensitive data as it is being read from disk by the guest, and inject arbitrary data within the guest. Taken as a whole, these attacks serve as a cautionary tale of what can go wrong when the state of registers (e.g., in AMD's SEV) and application performance data (e.g. in AMD's SEV-ES) are left unprotected. The latter is the first known attack that was designed to specifically target SEV-ES.
Y-DWMS: A Digital Watermark Management System Based on Smart Contracts

With the development of information technology, films, music, and other publications are inclined to be distributed in digitalized form. However, the low cost of data replication and dissemination leads to digital rights problems and brings huge economic losses. Up to now, existing digital rights management (DRM) schemes have been powerless to deter attempts of infringing digital rights and recover losses of copyright holders. This paper presents a YODA-based digital watermark management system (Y-DWMS), adopting non-repudiation of smart contract and blockchain, to implement a DRM mechanism to infinitely amplify the cost of infringement and recover losses copyright holders suffered once the infringement is reported. We adopt game analysis to prove that in Y-DWMS, the decision of non-infringement always dominates rational users, so as to fundamentally eradicate the infringement of digital rights, which current mainstream DRM schemes cannot reach.
Planning Approximate Exploration Trajectories for Model-Free Reinforcement Learning in Contact-Rich Manipulation

Recent progress in deep reinforcement learning has enabled simulated agents to learn complex behavior policies from scratch, but their data complexity often prohibits real-world applications. The learning process can be sped up by expert demonstrations but those can be costly to acquire. We demonstrate that it is possible to employ model-free deep reinforcement learning combined with planning to quickly generate informative data for a manipulation task. In particular, we use an approximate trajectory optimization approach for global exploration based on an upper confidence bound of the advantage function. The advantage is approximated by a network for Q-learning with separately updated streams for state value and advantage that allows ensembles to approximate model uncertainty for one stream only. We evaluate our method on new extensions to the classical peg-in-hole task, one of which is only solvable by active usage of contacts between peg tips and holes. The experimental evaluation suggests that our method explores more relevant areas of the environment and finds exemplar solutions faster-both on a real robot and in simulation. Combining our exploration with learning from demonstration outperforms state-of-the-art model-free reinforcement learning in terms of convergence speed for contact-rich manipulation tasks.
Deep Asymmetric Metric Learning via Rich Relationship Mining

Learning effective distance metric between data has gained increasing popularity, for its promising performance on various tasks, such as face verification, zero-shot learning, and image retrieval. A major line of researches employs hard data mining, which makes efforts on searching a subset of significant data. However, hard data mining based approaches only rely on a small percentage of data, which is apt to overfitting. This motivates us to propose a novel framework, named deep asymmetric metric learning via rich relationship mining (DAMLRRM), to mine rich relationship under satisfying sampling size. DAMLRRM constructs two asymmetric data streams that are differently structured and of unequal length. The asymmetric structure enables the two data streams to interlace each other, which allows for the informative comparison between new data pairs over iterations. To improve the generalization ability, we further relax the constraint on the intra-class relationship. Rather than greedily connecting all possible positive pairs, DAMLRRM builds a minimum-cost spanning tree within each category to ensure the formation of a connected region. As such there exists at least one direct or indirect path between arbitrary positive pairs to bridge intra-class relevance. Extensive experimental results on three benchmark datasets including CUB-200-2011, Cars196, and Stanford Online Products show that DAMLRRM effectively boosts the performance of existing deep metric learning approaches.
Using Conditional Restricted Boltzmann Machines to Model Complex Consumer Shopping Patterns

We develop and apply a model based on conditional restricted Boltzmann machines to analyze intertemporal crossproduct purchase patterns in enormous consumer purchase data sets.
Lithological mapping in the Central African Copper Belt using Random Forests and clustering: Strategies for optimised results

The Trident project is located in the Domes region of the Central African Copper Belt and hosts a number of mineralised systems including the Sentinel (Ni) and Enterprise (Cu) deposits. The project has received extensive systematic geochemical soil sampling in addition to high resolution airborne geophysical coverage. This data-rich environment enables experimentation with machine learning strategies which aim to produce or refine geological maps from limited direct observations. In this study we present a series of three case studies that test lithological classification using the supervised Random Forests algorithm. These studies inform the situations encountered in mineral exploration including early stage lithology mapping and more mature stage map refinement. We also present a fourth study, using the unsupervised algorithms k-means and Self-Organising Maps, to identify clusters, potentially associated with lithology in absence of a priori geological information. Our case studies are most relevant to the situation where the geology of a prospect is largely concealed beneath extensive cover rocks, with some rock types being poorly expressed or even absent in outcrop. We find that sampling from limited outcrop produces a RF lithology prediction that is likely to be incorrect. We demonstrate that balancing sample size through a combination of decimation and bootstrapping can improve results. Additionally, we identify some important indicators in both the predicted geology and uncertainty metrics which could alert an explorer to an inability of their training data to make accurate predictions and to the presence of lithological classes not expressed in outcrop. Sampling from a mature lithology map enables further map refinement and acts as an objective audit of the existing product. Information entropy (H) is calculated as a metric to describe quantitatively the uncertainty associated with classification, provide valuable information on the geological complexity of the mapped region and highlight areas which are potentially misclassified. Clusters obtained using the k-means algorithm produced a result more consistent with lithology in this instance and was faster; however Self Organising Maps remains attractive due to the production of additional metrics to assess algorithm performance. Clustering could be used either in the development of a first pass interpretation, or in the critical appraisal and subsequent refinement of existing interpretations.
Breast Cancer Diagnosis Using Image Processing and Machine Learning

Breast cancer is one of the second leading causes of cancer death in women. Despite the fact that cancer is preventable and curable in primary stages, a huge number of patients are diagnosed with cancer very late. Conventional methods of detecting and diagnosing cancer mainly depend on skilled physicians, with the help of medical imaging, to detect certain symptoms that usually appear in the later stages of cancer. Therefore, we present here the computerized method for cancer detection in its early stage within a very short time. Here, we have used Machine learning to train a model using the predicted features of the nuclei of cells. A comparative study of two different algorithms KNN and SVM is conducted where the accuracy of each classifier is measured. After this, we analyze a digital image of a fine needle aspirate (FNA) of breast tissue using image processing to find out the features of nuclei of the cells. We then apply the feature values to our trained model to find whether the tumor developed is benign or malignant.
Webpage Fingerprinting using Only Packet Length Information

Encrypted web traffic can reveal sensitive information of a user, such as their browsing histories. Existing studies on encrypted traffic analysis attacks usually focus on traffic fingerprinting of different websites rather than that of webpages from a same website. Fine-grained webpage fingerprinting allows exploiting more private information of users, e.g., their interests within a news website or an online shopping website. Since webpages from a same website usually have very similar features (e.g., statistical information) that make them indistinguishable, existing solutions may end up with low accuracy. In this paper, we propose a novel webpage fingerprinting method based on a simple and comprehensible idea. We make an observation that the length information of packets in bidirectional interaction between clients and servers can be a distinctive feature in webpage fingerprinting. Then, we extract the cumulative length of a sequence of packets to represent the fingerprint of a specific webpage. More precisely, only the first 100 packets in the loading process of a webpage is considered, thus enabling early-stage fingerprinting. The experimental results with real-world datasets demonstrate that our method is superior to other state-of-the-art approaches in terms of classification accuracy and time complexity. To the best of our knowledge, this is the first work on fine-grained webpage fingerprinting.
Detecting Malicious Attacks Exploiting Hardware Vulnerabilities Using Performance Counters

Over the past decades, the major objectives of computer design have been to improve performance and to reduce cost, energy consumption, and size, while security has remained a secondary concern. Meanwhile, malicious attacks have rapidly grown as the number of Internet-connected devices, ranging from personal smart embedded systems to large cloud servers, have been increasing. Traditional antivirus software cannot keep up with the increasing incidence of these attacks, especially for exploits targeting hardware design vulnerabilities. For example, as DRAM process technology scales down, it becomes easier for DRAM cells to electrically interact with each other. For instance, in Rowhammer attacks, it is possible to corrupt data in nearby rows by reading the same row in DRAM. As Rowhammer exploits a computer hardware weakness, no software patch can completely fix the problem. Similarly, there is no efficient software mitigation to the recently reported attack Spectre. The attack exploits microarchitectural design vulnerabilities to leak protected data through side channels. In general, completely fixing hardware-level vulnerabilities would require a redesign of the hardware which cannot be backported. In this paper, we demonstrate that by monitoring deviations in microarchitectural events such as cache misses, branch mispredictions from existing CPU performance counters, hardware-level attacks such as Rowhammer and Spectre can be efficiently detected during runtime with promising accuracy and reasonable performance overhead using various machine learning classifiers.
Inter-hour direct normal irradiance forecast with multiple data types and time-series

Boosted by a strong solar power market, the electricity grid is exposed to risk under an increasing share of fluctuant solar power. To increase the stability of the electricity grid, an accurate solar power forecast is needed to evaluate such fluctuations. In terms of forecast, solar irradiance is the key factor of solar power generation, which is affected by atmospheric conditions, including surface meteorological variables and column integrated variables. These variables involve multiple numerical time-series and images. However, few studies have focused on the processing method of multiple data types in an inter-hour direct normal irradiance (DNI) forecast. In this study, a framework for predicting the DNI for a 10-min time horizon was developed, which included the nondimensionalization of multiple data types and time-series, development of a forecast model, and transformation of the outputs. Several atmospheric variables were considered in the forecast framework, including the historical DNI, wind speed and direction, relative humidity time-series, and ground-based cloud images. Experiments were conducted to evaluate the performance of the forecast framework. The experimental results demonstrate that the proposed method performs well with a normalized mean bias error of 0.41% and a normalized root mean square error (nRMSE) of 20.53%, and outperforms the persistent model with an improvement of 34% in the nRMSE.
A Successor Rule Framework for Constructing $k$ -Ary de Bruijn Sequences and Universal Cycles

We present a simple framework for constructing <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -ary de Bruijn sequences, and more generally, universal cycles, via successor rules. The framework is based on the often used method of joining disjoint cycles. It generalizes several previously known de Bruijn sequence constructions based on the pure cycling register and is applied to derive a new construction that is perhaps the simplest of all successors. Furthermore, it generalizes an algorithm to construct binary de Bruijn sequences based on any arbitrary nonsingular feedback function. The framework is applied to derive and prove the correctness of successors to efficiently construct 1) universal cycles for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -ary strings of length <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> whose weight is bounded by some <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$w$ </tex-math></inline-formula> and 2) universal cycles for permutations. It has also been subsequently applied to find the first universal cycle constructions for weak orders.
Mapping of QoS/QoE in 5G Networks

5G technology will dramatically change the way of living. With a flexible combination of developing existing technology solutions and new radio concepts, and meeting the requirements for efficiency and scalability, the 5G network can respond to new user scenarios followed by a large increase in traffic and number of connected devices. This coming era of 5G network is demanding dynamic resource sharing and context sharing with seamless high speed connectivity everywhere. The challenge for operators is to understand and manage both the quality and performance of the services to fulfill on the technical quality of service (QoS) as well as on the quality of experience (QoE) level. Quality of experience is the subjective acceptability of the quality of a telecommunication service perceived by the user. This contrasts with quality of service, which concerns objective and technical metrics at network and application level. The identification of service-relevant QoE metrics and modeling of how these are affected by the different 5G QoS metrics is discussed in this paper. The impact of QoS parameters on QoE and the possible scenario of QoS/QoE mapping process are presented.