Reviewerly/exHarmony huggingface dataset:
Establishing Software Root of Trust Unconditionally

Root-of-Trust (RoT) establishment ensures either that the state of an untrusted system contains all and only content chosen by a trusted local verifier and the system code begins execution in that state, or that the verifier discovers the existence of unaccounted for content.This ensures program booting into system states that are free of persistent malware.An adversary can no longer retain undetected control of one's local system.We establish RoT unconditionally; i.e., without secrets, trusted hardware modules and instructions, or bounds on the adversary's computational power.The specification of a system's chipset and device controllers, and an external source of true random numbers, such as a commercially available quantum RNG, is all that is needed.Our system specifications are those of a concrete Word Random Access Machine (cWRAM) model -the closest computation model to a real system with a large instruction set.We define the requirements for RoT establishment and explain their differences from past attestation protocols.Then we introduce a RoT establishment protocol based on a new computation primitive with concrete (non-asymptotic) optimal space-time bounds in adversarial evaluation on the cWRAM.The new primitive is a randomized polynomial, which has kindependent uniform coefficients in a prime order field.Its collision properties are stronger than those of a k-independent (almost) universal hash function in cWRAM evaluations, and are sufficient to prove existence of malware-free states before RoT is established.Preliminary measurements show that randomizedpolynomial performance is practical on commodity hardware even for very large k.To prove the concrete optimality of randomized polynomials, we present a result of independent complexity interest: a Hornerrule program is uniquely optimal whenever the cWRAM execution space and time are simultaneously minimized.
Sensitive Label Privacy Preservation with Anatomization for Data Publishing

Data in its original form, however, typically contain sensitive information about individuals. Directly publishing raw data will violate the privacy of people involed. Consequently, it becomes increasingly important to preserve the privacy of published data. An attacker is apt to identify an individual from the published tables, with attacks through the record linkage, attribute linkage, table linkage or probabilistic attack. Although algorithms based on generalization and suppression have been proposed to protect the sensitive attributes and resist these multiple types of attacks, they often suffer from large information loss by replacing specific values with more general ones. Alternatively, anatomization and permutation operations can de-link the relation between attributes without modifying them. In this paper, we propose a scheme Sensitive Label Privacy Preservation with Anatomization (SLPPA) to protect the privacy of published data. SLPPA includes two procedures, table division and group division. During the table division, we adopt entropy and mean-square contingency coefficient to partition attributes into separate tables to inject uncertainty for reconstructing the original table. During the group division, all the individuals in the original table are partitioned into non-overlapping groups so that the published data satisfies the pre-defined privacy requirements of our (α; β; γ; δ) model. Two comprehensive sets of real-world relationship data are applied to evaluate the performance of our anonymization approach. Simulations and privacy analysis show our scheme possesses better privacy while ensuring higher utility.
Intelligent visual object tracking with particle filter based on Modified Grey Wolf Optimizer

In a visual object tracking technology the particle filter (PF) is frequently used. The main drawback of the particle filter is that a large quantity of particles is required. This paper objectives to propose an evolutionary particle filter based upon Modified Grey Wolf Optimizer (MGWO) which will overcome the impoverishment of the sample problem in the regular PF. For this, firstly a new variant of GWO named as Modified Grey Wolf Optimizer (MGWO) is proposed. This variant works an active trigonometric sine truncated function for confirming the enhanced exploitation and exploration properties. Secondly, the MGWO algorithm is embedded in the PF structure. Before the resampling, by using MGWO, the particles in the PF are optimized. Accordingly, the more significant particles can be expanded, and the particles can estimate the actual state of the target object more precisely. Performance of proposed Modified GWO based particle filter (MGWO-PF) is evaluated on standard visual tracking benchmark databases. Also, the MGWO-PF tracker is compared with the Particle filter (PF), Particle swarm optimization based particle filter (PSO-PF), Firefly algorithm based particle filter (FAPF) and Spider monkey optimization assisted particle filter (SMO-PF). We show that visual object tracking using MGWO-PF provides more reliable and efficient tracking results than other compared methods.
Human-in-the-loop fabrication of 3D surfaces with natural tree branches

We propose a design and fabrication system leveraging the diverse shapes of natural wood. With our system, precise geometries are fabricated from non-standardized, naturally curved branches. In this way, low-valued tree branches are up-cycled to a construction material. The process is implemented as follows. Taking a set of branches and a user-defined parametric target surface, the system makes a reciprocal pattern of curves that lie on the surface. Then, it automatically matches the shapes of the scanned branches with these curves. There was a similar attempt to build a large beam structure from tree trunks using industrial equipment such as cranes and robot arms. In contrast, we target smaller surface structures from tree branches using more accessible tools (2.5D CNC) with human-in-the-loop operation. We ask the user to manually place a branch to a defined orientation with the help of audio-visual guidance. Then a CNC-machine can mill out the joint. Finally, the structure is assembled by hand without screws or adhesives.
Curate and Generate: A Corpus and Method for Joint Control of Semantics and Style in Neural NLG

Neural natural language generation (NNLG) from structured meaning representations has become increasingly popular in recent years.While we have seen progress with generating syntactically correct utterances that preserve semantics, various shortcomings of NNLG systems are clear: new tasks require new training data which is not available or straightforward to acquire, and model outputs are simple and may be dull and repetitive.This paper addresses these two critical challenges in NNLG by: (1) scalably (and at no cost) creating training datasets of parallel meaning representations and reference texts with rich style markup by using data from freely available and naturally descriptive user reviews, and (2) systematically exploring how the style markup enables joint control of semantic and stylistic aspects of neural model output.We present YELPNLG, a corpus of 300,000 rich, parallel meaning representations and highly stylistically varied reference texts spanning different restaurant attributes, and describe a novel methodology that can be scalably reused to generate NLG datasets for other domains.The experiments show that the models control important aspects, including lexical choice of adjectives, output length, and sentiment, allowing the models to successfully hit multiple style targets without sacrificing semantics.
Cyber-Biosecurity Risk Perceptions in the Biotech Sector

The expanding digitization of the biological sciences places greater value on the data generated, information extrapolated and knowledge gained. Failing to protect data will affect a company or country's ability to position itself optimally in the forthcoming fourth industrial revolution. Further, more reliance on automation, distribution and outsourcing in biotechnology makes its infrastructure a target. The equipment and service providers that drive physical research and development are also all connected online. Failing to protect these resources from intrusion increases the risk of accidental or deliberate harm, for example by the loss of control over biological products. Robust cybersecurity measures are therefore critical for both securing the data generated by the biotechnology sector as well as securing key infrastructure. Cyber-biosecurity is emerging multidisciplinary field that combines cybersecurity, biosecurity and cyber-physical security as relates to biological systems. To better identify the perceived risks at the interface between cybersecurity and biosecurity, Biosecure conducted a pilot study surveying the views of a discrete set of biotechnology and cybersecurity leaders. The survey was carried out online from October-November 2017. Key findings of the survey showed that cyber-biosecurity risks were considered to be difficult to characterize due to variations in types of threats, targets and potential impacts, and compounded by a notable variation between the level of sophistication or maturity of mitigation and response measures. Further research is therefore necessary bringing together the different communities focusing on these issues to develop a common language, better define the threats and discuss potential ways forward in addressing risks.
Manifold-Valued Image Generation with Wasserstein Generative Adversarial Nets

Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversarial Nets (WGANs), are studied on manifold-valued images that are frequently encountered in real-world applications. To fill the gap, this paper first formulates the problem of generating manifold-valued images and exploits three typical instances: hue-saturation-value (HSV) color image generation, chromaticity-brightness (CB) color image generation, and diffusion-tensor (DT) image generation. For the proposed generative modeling problem, we then introduce a theorem of optimal transport to derive a new Wasserstein distance of data distributions on complete manifolds, enabling us to achieve a tractable objective under the WGAN framework. In addition, we recommend three benchmark datasets that are CIFAR-10 HSV/CB color images, ImageNet HSV/CB color images, UCL DT image datasets. On the three datasets, we experimentally demonstrate the proposed manifold-aware WGAN model can generate more plausible manifold-valued images than its competitors.
Evaluating Discourse in Structured Text Representations

Discourse structure is integral to understanding a text and is helpful in many NLP tasks.Learning latent representations of discourse is an attractive alternative to acquiring expensive labeled discourse data.Liu and Lapata (2018) propose a structured attention mechanism for text classification that derives a tree over a text, akin to an RST discourse tree.We examine this model in detail, and evaluate on additional discourse-relevant tasks and datasets, in order to assess whether the structured attention improves performance on the end task and whether it captures a text's discourse structure.We find the learned latent trees have little to no structure and instead focus on lexical cues; even after obtaining more structured trees with proposed model modifications, the trees are still far from capturing discourse structure when compared to discourse dependency trees from an existing discourse parser.Finally, ablation studies show the structured attention provides little benefit, sometimes even hurting performance. 1
Cybersecurity Investment Allocation for a Multi-Branch Firm: Modeling and Optimization

Network interconnection and information sharing among firms and their departments expose them to cybersecurity breaches. Traditional cybersecurity studies have paid little attention to the reallocation of security investment within firms. This paper proposes a mathematical model for optimal allocation of cybersecurity investment among headquarters and branches with budget constraints. The differences in size of information sets and system interconnection have been taken into account. The responses of optimal allocation to internal and external factors, such as the portion of branch information set, the propagation probability, the budget constraints, and the intrinsic vulnerability, have been studied in deep both theoretically and numerically. Analysis results indicate that the group will give priority to protecting headquarters when the total budget is small and intrinsic vulnerability is high. The security investment allocated to each branch increases with budget, propagation probability and portion of information set, but never exceeds 1 / ( n + 1 ) of total budget. Numerical simulations also verify that security information sharing among headquarters and branches can help improve the efficiency of security investment in the whole system. Furthermore, the findings of this paper will draw attention to the reallocation of cybersecurity investment within a business group and help cybersecurity managers to develop investment allocation strategies and policies.
A Secure Image Encryption Algorithm Based on Composite Chaos Theory

This paper introduces chaotic image encryption to realize the secure transmission of digital images, and combines 1D and 3D chaotic systems into an image encryption algorithm.Firstly, two sets of logistic chaotic sequences were generated iteratively for pixel permutation, so were the initial values of the Bao system.Then, the chaotic sequence for pixel permutation and diffusion was generated based on the plaintext pixel value and Bao system.After that, the plaintext image was encrypted and decrypted by encryption and decryption formulas.Finally, the encryption process was simulated, and the algorithm performance was analyzed in terms of key space and correlation of adjacent pixels.The results show that our algorithm achieved better security than the traditional methods.
Computer aided tool for automatic detection and delineation of nucleus from oral histopathology images for OSCC screening

Characterization of nucleus from oral tissue histology images is important for oral squamous cell carcinoma (OSCC) diagnosis. Therefore, the accurate delineation of nucleus is the foremost task to initiate the quantification of its various properties such as morphology, texture, intensity, etc. In this paper, we propose a two-stage computer-aided tool for automatic detection (the first stage) and delineation of the detected nucleus (second stage) from oral histological images to aid clinician in OSCC diagnosis. In the first stage, 81×81 patches were extracted from our image database followed by the wavelet-based downsampling process to generate 21×21 patches, which were used to train the deep convolution neural network (CNN) architecture. This approach was used to automatically detect nucleus from OSCC images and achieved 88.87% recall and 82.03% precision. After detection, 81×81 image patches were extracted from those detected nucleus regions and further processed through the second stage of proposed pipeline to segment the exact boundary of nucleus using active contour (Chan-Vese model) after applying edge enhancing nonsubsampled contourlet transform. The proposed segmentation methodology performed well with 94.22% Dice coefficient, 89.38% Jaccard index, 97.56% precision, and 91.58% recall. We view this work as the first attempt on oral tissue histology image computation for joint nucleus detection and segmentation to diagnose OSCC to the best of our knowledge.
Machine-Assisted Learning in Highly-Interdisciplinary Media Fields: A Multimedia Guide on Modern Art

Art and technology have always been very tightly intertwined, presenting strong influences on each other. On the other hand, technological evolution led to today’s digital media landscape, elaborating mediated communication tools, thus providing new creative means of expression (i.e., new-media art). Rich-media interaction can expedite the whole process into an augmented schooling experience though art cannot be easily enclosed in classical teaching procedures. The current work focuses on the deployment of a modern-art web-guide, aiming at enhancing traditional approaches with machine-assisted blended-learning. In this perspective, “machine” has a two-folded goal: to offer highly-interdisciplinary multimedia services for both in-class demonstration and self-training support, and to crowdsource users’ feedback, as to train artificial intelligence systems on painting movements semantics. The paper presents the implementation of the “Istoriart” website through the main phases of Analysis, Design, Development, and Evaluation, while also answering typical questions regarding its impact on the targeted audience. Hence, elaborating on this constructive case study, initial hypotheses on the multidisciplinary usefulness, and contribution of the new digital services are put into test and verified.
NLPR@SRPOL at SemEval-2019 Task 6 and Task 5: Linguistically enhanced deep learning offensive sentence classifier

The paper presents a system developed for the SemEval-2019 competition Task 5 hat-Eval Basile et al. ( 2019) (team name: LU Team) and Task 6 OffensEval Zampieri et al. (2019b) (team name: NLPR@SRPOL), where we achieved 2 nd position in Subtask C. The system combines in an ensemble several models (LSTM, Transformer, OpenAI's GPT, Random forest, SVM) with various embeddings (custom, ELMo, fastText, Universal Encoder) together with additional linguistic features (number of blacklisted words, special characters, etc.).The system works with a multi-tier blacklist and a large corpus of crawled data, annotated for general offensiveness.In the paper we do an extensive analysis of our results and show how the combination of features and embedding affect the performance of the models.
Learning to Write Stories with Thematic Consistency and Wording Novelty

Automatic story generation is a challenging task, which involves automatically comprising a sequence of sentences or words with a consistent topic and novel wordings. Although many attention has been paid to this task and prompting progress has been made, there still exists a noticeable gap between generated stories and those created by humans, especially in terms of thematic consistency and wording novelty. To fill this gap, we propose a cache-augmented conditional variational autoencoder for story generation, where the cache module allows to improve thematic consistency while the conditional variational autoencoder part is used for generating stories with less common words by using a continuous latent variable. For combing the cache module and the autoencoder part, we further introduce an effective gate mechanism. Experimental results on ROCStories and WritingPrompts indicate that our proposed model can generate stories with consistency and wording novelty, and outperforms existing models under both automatic metrics and human evaluations.
Advanced redox zonation of the San Pedro Sula alluvial aquifer (Honduras) using data fusion and multivariate geostatistics

The incorrect wastewater management and the land use distribution lead to severe environmental problems, creating heavy eutrophication condition in surface-water; when surface-water/groundwater relationships exist, the organic matter transferred to the aquifer oxidizes and triggers redox processes (i.e. Terminal Electron Accepting Processes, TEAPs), that provoke severe groundwater quality modifications and complicate its exploitation and management. For this reason, the definition of the redox zonation within an aquifer is an effective tool for the identification of the contamination sources and for the conceptual model refinement, when remediation strategies need to be planned. Although the redox processes are dynamic reactions, the aquifer redox zonation is generally aimed to identify homogenous zones, characterized by a predominant TEAP. To overcome this methodological approach, the Multi-Collocated Factorial Kriging (MCFK) was applied to redox-related physico-chemical parameters, which allowed identifying their spatial relationships at different scales, transferring this method from precision agriculture and soil science to hydrogeochemistry. The selected study area is the San Pedro Sula aquifer (Honduras), a multi-layer alluvial aquifer characterized by well-known surface-water/groundwater interactions and heavy eutrophicated streams. Here, high concentrations of Mn and Fe were found in the aquifer. The MCFK results identified a short-range (2300 m) factor, highlighting a strong relation between Mn concentrations and anoxic conditions, due to the organic matter transfer from eutrophicated surface-water into the aquifer. Simultaneously, the relationship between Fe and turbidity is related to a fine Fe(III) oxi-hydroxide colloidal phase, developed when different redox conditions of groundwater mix up in the wells. The long-range (6000 m) factor points out that Fe is related to redox processes at a wider scale, especially in the northern San Pedro Sula alluvial plain. These results are supported by both the Principal Component Analysis and the hydrogeochemical numerical modeling. As a result, different TEAPs occur simultaneously in contaminated areas, acting at multiple scales.
Towards Efficient and Scalable Machine Learning-Based QoS Traffic Classification in Software-Defined Network

Internet Service Provider (ISP) has the responsibility to fulfill the Quality of Service (QoS) of various types of applications. The centralized network controller in Software Defined Networking (SDN) provides the chance to instil intelligence in managing network resources based on QoS requirements. A fined-grained QoS Traffic Engineering can be realized by identifying different traffic flow types and categorizing them according to various application/classes. Previous methods include port-based classification and Deep Packet Inspection (DPI), which have been found non-accurate and highly computational. Thus, machine learning (ML) based traffic classifier has gained much attention from the research community, which can be seen from an increase number of works being published. This paper identifies the issues in ML-based traffic classification (TC) in order to devised the best solution; i.e. the TC framework should be scalable to accommodate network expansion, can accurately identify flows according to their source applications/classes, while maintaining an efficient run-time and memory requirement. Therefore, based on these findings, this work proposed a TC engine comprises of Training and Feature Selection Module and Classifier Model, which is placed at the data plane. The training and feature selection will be done offline and regularly to keep the Classifier Model updated. In the proposed solution, the SDN switch forwards the packets the Classifier Model, which classify the packets with accurate applications and send them to the control plane. Finally, the controller will perform resource and queue management according to the labeled packets and updates the flow tables via the switch. The proposed solution will be the starting point in solving efficiency and scalability issues in SDN-ISP TC.
Reachable Space Characterization of Markov Decision Processes with Time Variability

We propose a solution to a time-varying variant of Markov Decision Processes which can be used to address decision-theoretic planning problems for autonomous systems operating in unstructured outdoor environments.We explore the time variability property of the planning stochasticity and investigate the state reachability, based on which we then develop an efficient iterative method that offers a good trade-off between solution optimality and time complexity.The reachability space is constructed by analyzing the means and variances of states' reaching time in the future.We validate our algorithm through extensive simulations using ocean data, and the results show that our method achieves a great performance in terms of both solution quality and computing time.