Reviewerly/exHarmony huggingface dataset:
Photon entanglement entropy as a probe of many-body correlations and fluctuations

Recent theoretical and experiments have explored the use of entangled photons as a spectroscopic probe of material systems. We develop here a theoretical description for entropy production in the scattering of an entangled biphoton state within an optical cavity. We develop this using perturbation theory by expanding the biphoton scattering matrix in terms of single-photon terms in which we introduce the photon-photon interaction via a complex coupling constant, $\xi$. We show that the von Neumann entropy provides a succinct measure of this interaction. We then develop a microscopic model and show that in the limit of fast fluctuations, the entanglement entropy vanishes whereas in the limit the coupling is homogeneous broadened, the entanglement entropy depends upon the magnitude of the fluctuations and reaches a maximum.
Dual Low-Rank Decompositions for Robust Cross-View Learning

Cross-view data are very popular contemporarily, as different viewpoints or sensors attempt to richly represent data in various views. However, the cross-view data from different views present a significant divergence, that is, cross-view data from the same category have a lower similarity than those in different categories but within the same view. Considering that each cross-view sample is drawn from two intertwined manifold structures, i.e., class manifold and view manifold, in this paper, we propose a robust cross-view learning framework to seek a robust view-invariant low-dimensional space. Specifically, we develop a dual low-rank decomposition technique to unweave those intertwined manifold structures from one another in the learned space. Moreover, we design two discriminative graphs to constrain the dual low-rank decompositions by fully exploring the prior knowledge. Thus, our proposed algorithm is able to capture more within-class knowledge and mitigate the view divergence to obtain a more effective view-invariant feature extractor. Furthermore, our proposed method is very flexible in addressing such a challenging cross-view learning scenario that we only obtain the view information of the training data while with the view information of the evaluation data unknown. Experiments on face and object benchmarks demonstrate the effective performance of our designed model over the state-of-the-art algorithms.
Trajectories as Topics: Multi-Object Tracking by Topic Discovery

This paper proposes a new approach to multi-object tracking by semantic topic discovery. We dynamically cluster frame-by-frame detections and treat objects as topics, allowing the application of the Dirichlet process mixture model. The tracking problem is cast as a topic-discovery task, where the video sequence is treated analogously to a document. It addresses tracking issues such as object exclusivity constraints as well as tracking management without the need for heuristic thresholds. Variation of object appearance is modeled as the dynamics of word co-occurrence and handled by updating the cluster parameters across the sequence in the dynamical clustering procedure. We develop two kinds of visual representation based on super-pixel and deformable part model and integrate them into the model of automatic topic discovery for tracking rigid and non-rigid objects, respectively. In experiments on public data sets, we demonstrate the effectiveness of the proposed algorithm.
Prostate Volume Segmentation in TRUS Using Hybrid Edge-Bhattacharyya Active Surfaces

Objective: We present a new hybrid edge and region-based parametric deformable model, or active surface, for prostate volume segmentation in transrectal ultrasound (TRUS) images. Methods: Our contribution is threefold. First, we develop a new edge detector derived from the radial bas-relief approach, allowing for better scalar prostate edge detection in low contrast configurations. Second, we combine an edge-based force derived from the proposed edge detector with a new region-based force driven by the Bhattacharyya gradient flow and adapted to the case of parametric active surfaces. Finally, we develop a quasi-automatic initialization technique for deformable models by analyzing the profiles of the proposed edge detector response radially to obtain initial landmark points toward which an initial surface model is warped. Results: We validate our method on a set of 36 TRUS images for which manual delineations were performed by two expert radiation oncologists, using a wide variety of quantitative metrics. The proposed hybrid model achieved state-of-the-art segmentation accuracy. Conclusion: Results demonstrate the interest of the proposed hybrid framework for accurate prostate volume segmentation. Significance: This paper presents a modular framework for accurate prostate volume segmentation in TRUS, broadening the range of available strategies to tackle this open problem.
A neural translating general hyperplane for knowledge graph embedding

For completing knowledge graph, many translation-based models, like that Trans(E and H) which embed a knowledge graph into a continuous vector space and encode relations as translation operations in that space, have achieved better performance. However, most of them have limitations in expressing complex relations for knowledge graph. In this paper, we propose a translation-neural based method NTransGH for knowledge graph completion. NTransGH combines translation mechanism for modeling relations as translation operations by generalized hyperplanes, and a neural network for capturing more complex interactions between entities and relations. We conduct experiment on two tasks link prediction and triplet classification with two datasets. Experimental results show that NTransGH has strong expression in mapping properties of complex relations, and achieves significant and consistent improvements over state-of-the-art embedding methods. This paper is an extension of our previous works [1].
Sanctorum: A lightweight security monitor for secure enclaves

Enclaves have emerged as a particularly compelling primitive to implement trusted execution environments: strongly isolated sensitive user-mode processes in a largely untrusted software environment. While the threat models employed by various enclave systems differ, the high-level guarantees they offer are essentially the same: attestation of an enclave's initial state, as well as a guarantee of enclave integrity and privacy in the presence of an adversary.This work describes Sanctorum, a small trusted code base (TCB), consisting of a generic enclave-capable system, which is sufficient to implement secure enclaves akin to the primitive offered by Intel's SGX. While enclaves may be implemented via unconditionally trusted hardware and microcode, as it is the case in SGX, we employ a smaller TCB principally consisting of authenticated, privileged software, which may be replaced or patched as needed. Sanctorum implements a formally verified specification for generic enclaves on an in-order multiprocessor system meeting baseline security requirements, e.g., the MIT Sanctum processor and the Keystone enclave framework. Sanctorum requires trustworthy hardware including a random number generator, a private cryptographic key pair derived via a secure bootstrapping protocol, and a robust isolation primitive to safeguard sensitive information. Sanctorum's threat model is informed by the threat model of the isolation primitive, and is suitable for adding enclaves to a variety of processor systems.
Constrained matrix factorization for semi-weakly learning with label proportions

Abstract Learning with label proportions (LLP) is a weakly supervised learning problem that is conceivable in many real-world applications, where the training data is given in bags of instances, and only knowing the proportions of data points belonging to a particular category for each bag. However, how to effectively address the LLP problem with high dimensional data and some labeled samples is still a challenging problem. In this paper, we firstly propose a novel learning problem called semi-weakly learning with label proportions (SLLP), which has more extensive application scenarios. Then, we contribute a novel method based on non-negative matrix factorization, called Proportion Constrained Matrix Factorization (PCMF). It can not only effectively incorporate the label and proportion information, but also explore the local manifold structure information of training data. Moreover, the proposed method can make the data points from the same class be more likely merged together in the latent representation space, which leads to the more discriminating power. Sufficient experimental results on the benchmark datasets demonstrates its superiority over the state-of-the-art methods for LLP problem and efficiency on solving the SLLP problem.
Thunderclap: Exploring Vulnerabilities in Operating System IOMMU Protection via DMA from Untrustworthy Peripherals

DARPA I2O FA8750-10-C-0237 (CTSRD)DARPA MTO HR0011- 18-C-0016 (ECATS)Arm LtdGoogle IncThis work was also supported by EPSRC EP/R012458/1 (“IOSEC”).
Filling the Gaps: Predicting Missing Joints of Human Poses Using Denoising Autoencoders

State of the art pose estimators are able to deal with different challenges present in real-world scenarios, such as varying body appearance, lighting conditions and rare body poses. However, when body parts are severely occluded by objects or other people, the resulting poses might be incomplete, negatively affecting applications where estimating a full body pose is important (e.g. gesture and pose-based behavior analysis). In this work, we propose a method for predicting the missing joints from incomplete human poses. In our model we consider missing joints as noise in the input and we use an autoencoder-based solution to enhance the pose prediction. The method can be easily combined with existing pipelines and, by using only 2D coordinates as input data, the resulting model is small and fast to train, yet powerful enough to learn a robust representation of the low dimensional domain. Finally, results show improved predictions over existing pose estimation algorithms.
FPGA Implementation of Hand-written Number Recognition Based on CNN

Convolutional Neural Networks (CNNs) are the state-of-the-art in computer vision for different purposes such as image and video classification, recommender systems and natural language processing. The connectivity pattern between CNNs neurons is inspired by the structure of the animal visual cortex. In order to allow the processing, they are realized with multiple parallel 2-dimensional FIR filters that convolve the input signal with the learned feature maps. For this reason, a CNN implementation requires highly parallel computations that cannot be achieved using traditional general-purpose processors, which is why they benefit from a very significant speed-up when mapped and run on Field Programmable Gate Arrays (FPGAs). This is because FPGAs offer the capability to design full customizable hardware architectures, providing high flexibility and the availability of hundreds to thousands of on-chip Digital Signal Processing (DSP) blocks. This paper presents an FPGA implementation of a hand-written number recognition system based on CNN. The system has been characterized in terms of classification accuracy, area, speed, and power consumption. The neural network was implemented on a Xilinx XC7A100T FPGA, and it uses 29.69% of Slice LUTs, 4.42% of slice registers and 52.50% block RAMs. We designed the system using a 9-bit representation that allows for avoiding the use of DSP. For this reason, multipliers are implemented using LUTs. The proposed architecture can be easily scaled on different FPGA devices thank its regularity. CNN can reach a classification accuracy of 90%.
Data Trusts: Ethics, Architecture and Governance for Trustworthy Data Stewardship

In their report on the development of the UK AI industry, Wendy Hall and Jerome Pesenti recommend the establishment of data trusts, “proven and trusted frameworks and agreements” that will “ensure exchanges [of data] are secure and mutually beneficial” by promoting trust in the use of data for AI. This paper defends the following thesis: A data trust works within the law to provide ethical, architectural and governance support for trustworthy data processing. Data trusts are therefore both constraining and liberating. They constrain: they respect current law, so they cannot render currently illegal actions legal. They are intended to increase trust, and so they will typically act as further constraints on data processors, adding the constraints of trustworthiness to those of law. Yet they also liberate: if data processors are perceived as trustworthy, they will get improved access to data. The paper addresses the areas of: trust and trustworthiness; ethics; architecture; legal status.
Data clustering using proximity matrices with missing values

In most applications of data clustering the input data includes vectors describing the location of each data point, from which distances between data points can be calculated and a proximity matrix constructed. In some applications, however, the only available input is the proximity matrix, that is, the distances between each pair of data point. Several clustering algorithms can still be applied, but if the proximity matrix has missing values no standard method is directly applicable. Imputation can be done to replace missing values, but most imputation methods do not apply when only the proximity matrix is available. As a partial solution to fill this gap, we propose the Proximity Matrix Completion (PMC) algorithm. This algorithm assumes that data is missing due to one of two reasons: complete dissimilarity or incomplete observations; and imputes values accordingly. To determine which case applies the data is modeled as a graph and a set of maximum cliques in the graph is found. Overlap between cliques then determines the case and hence the method of imputation for each missing data point. This approach is motivated by an application in plant breeding, where what is needed is to cluster new experimental seed varieties into sets of varieties that interact similarly to the environment, and this application is presented as a case study in the paper. The applicability, limitations and performance of the new algorithm versus other methods of imputation are further studied by applying it to datasets derived from three well-known test datasets.
“Development and preliminary validation of an image-based instrument to assess depressive symptoms”

Depression has high social and economic costs, making the reducing of potential barriers to screening of utmost importance. The use of non-verbal, image-based items might help to widen accessibility to depression screenings due to their potentially increased ease of interpretation and language-free nature. In this view, the paper presents two studies exploring the feasibility of assessing depressive symptoms using a set of image-based items consisting of 36 emoji. In study 1, 430 online-recruited young adults participated to investigate whether they ever felt in the way depicted by each emoji during the last week. Results showed that 33 emoji had significant, theoretically coherent correlations with the 10-item version of the Center for Epidemiologic Studies Depression Scale. Next, a subset of 10 emoji were selected for potential inclusion in a brief depression assessment. In study 2, using a sample of 482 young adults, the 10-item emoji-based assessment showed acceptable internal consistency, and theoretically consistent convergent and divergent validity with depressive symptoms, and big-5 personality traits. Further, the emoji-based screening instrument showed remarkable accuracy in identifying individuals showing depression symptoms. Overall, results indicate that the selected emoji represent a promising alternative to text-based items when assessing depressive symptoms among young adults.
Blockchain analytics for intraday financial risk modeling

Blockchain offers the opportunity to use the transaction graph for financial governance, yet properties of this graph are understudied. One key question in this direction is the extent to which the transaction graph can serve as an early-warning indicator for large financial losses. In this article, we demonstrate the impact of extreme transaction graph activity on the intraday volatility of the Bitcoin prices series. Specifically, we identify certain sub-graphs (‘chainlets’) that exhibit predictive influence on Bitcoin price and volatility and characterize the types of chainlets that signify extreme losses. Using bars ranging from 15 min up to a day, we fit GARCH models with and without the extreme chainlets and show that the former exhibit superior value-at-risk backtesting performance.
A New Model of Russian Doctoral Education: Problems and Prospects

On November 16, 2018, as a part of its Advanced Training Program, the National Research Lobachevsky State University of Nizhny Novgorod (UNN) hosted a round table organized by the UNN Institute of Postgraduate and Doctoral Studies jointly with the editorial board of the journal “Higher Education in Russia”. The topic of the round table discussion was “A New Model of Russian Doctoral Education: Problems and Prospects for Development”. The Program is being implemented in accordance with the UNN’s “Roadmap” for 2018 within the framework of the Russian Academic Excellence Project (the “5-100” Project). The event was attended by 48 administrative, managerial and academic staff members from 24 cities, representing 37 universities and research organizations of the Russian Federation (including NUST MISIS, National Research University Higher School of Economics, UNN, Tomsk Polytechnic University, Samara National Research University, Plekhanov Russian University of Economics, Belgorod National Research University, Ogarev Mordovia State University, Tyumen National Research University, etc.). Moderators of the round table: Director of the UNN Institute of Postgraduate and Doctoral Studies Prof. B.I. Bednyi and the Сhief Editor of the journal «Higher Education in Russia» Prof. M.B. Sapunov.
TEXTUAL DATA AUGMENTATION FOR NEURAL NETWORKS

Data augmentation is one of the ways of dealing with labeled data scarcity and overfitting. Both these problems are crucial for modern deep learning algorithms which require massive amounts of data. The problem is better explored in the context of image analysis than for text. This work is a step forward to close this gap. We propose a method for augmenting textual data when training convolutional neural networks for sentence classification. The augumentation is based on the substitution of words using a thesaurus as well as the Princeton WordNet. Our method improves upon the baseline in almost all cases. In terms of accuracy the best of the variants is 1.2% (pp.) better than the baseline.
Asymmetrical and Biometric Encrypted Optical Scanning Holography (ABE-OSH)

In this article, we report an optical scanning holography (OSH) system that is equipped with biometric and the asymmetrical Rivest-Shamir-Adleman (RSA) encryption. Past research works have demonstrated that either encryption alone is vulnerable to certain types of attacks if both the encrypted and source data are available. Essentially, given sufficient time and computation power, it is possible to deduce the encryption key from these two datasets. Our proposed method can be divided into two stages. In the first stage, biometric encryption is incorporated in the OSH system to convert the image of a physical object into a biometric encrypted hologram. In the second stage, the biometric encrypted hologram is encoded with differential pulse code modulation (DPCM), and the polarity of the DPCM data is encrypted with the RSA algorithm. Due to the cascade of these two stages, it is not possible to identify both the source and encrypted data in each stage, and use them to hack the decryption key. We have demonstrated fast capturing and encryption of physical objects with our proposed method, and shown that that when either the biometric or the RSA encryption key is missing, the hologram cannot be recovered and the reconstructed image is incomprehensible. While we have demonstrated our idea with OSH, the proposed technique is applicable to any digital holographic systems.
Happy parents’ tweets: An exploration of Italian Twitter data using sentiment analysis

BACKGROUNDDemographers are increasingly interested in connecting demographic behaviour and trends with 'soft' measures, i.e., complementary information on attitudes, values, feelings, and intentions. OBJECTIVEThe aim of this paper is to demonstrate how computational linguistic techniques can be used to explore opinions and semantic orientations related to parenthood. METHODSIn this article we scrutinize about three million filtered Italian tweets from 2014.First, we implement a methodological framework relying on Natural Language Processing techniques for text analysis, which is used to extract sentiments.We then run a supervised machine-learning experiment on the overall dataset, based on the annotated set of tweets from the previous stage.Consequently, we infer to what extent social media users report negative or positive affect on topics relevant to the fertility domain. RESULTSParents express a generally positive attitude towards being and becoming parents, but they are also fearful, surprised, and sad.They also have quite negative sentiments about their children's future, politics, fertility, and parental behaviour.By exploiting geographical information from tweets we find a significant correlation between the
Datasets of RT spoofing attacks on MIL-STD-1553 communication traffic

The datasets in this article are produced to evaluate the ability of MIL-STD-1553 intrusion detection systems to detect attacks that emulate normal non-periodical messages, at differing attack occurrence rates. And different data representations. We present three streams of simulated MIL-STD-1553 traffic containing both normal and attack messages corresponding to packets that were injected into the bus by a malicious remote terminal. The implemented attacks emulate normal non-periodical communication so detecting them with a low false positive rate is non-trivial. Each stream is separated into a training set of normal messages and a test set of both normal and attack messages. The test sets differ by the occurrence rate of attack messages (0.01%, 0.10%, and 1.00%). Each stream is also preprocessed into a dataset of message sequences so that it can be used for sequential anomaly detection analysis. The sequential test sets differ by the occurrence rate of attack sequences (0.14%, 1.26%, and 11.01%). All dataset files can be found in Mendeley Data, doi:10.17632/jvgdrmjvs3.3.
Publication Metrics as a Tool for Measuring Research Productivity and Their Relation to Motivation

The article presents an analytical review of literature on publication metrics as a tool of performance management in academia. Issues of quantitative re­search assessment are investigated in the light of modern views of motivation, in particular through the lens of self-determination theory. The article provi­des an insight into empirical studies on the effects of intrinsic and extrinsic motivation on publication productivity, research quality and subjective well­being. Accumulated international experience in performance management is used as a basis for developing recommendations on how to improve academic governance.