Reviewerly/exHarmony huggingface dataset:
An Efficient Approach for Super and Nested Term Indexing and Retrieval

This paper describes a new approach, called Terminological Bucket Indexing (TBI), for efficient indexing and retrieval of both nested and super terms using a single method. We propose a hybrid data structure for facilitating faster indexing building. An evaluation of our approach with respect to widely used existing approaches on several publicly available dataset is provided. Compared to Trie based approaches, TBI provides comparable performance on nested term retrieval and far superior performance on super term retrieval. Compared to traditional hash table, TBI needs 80\% less time for indexing.
Building a high performance, secure, reliable and compliant heterogeneous computing capability through the integration of core and edge processing

Heterogeneous computing and heterogeneous clouds refer to emerging architectures to build a variety of processors including CPUs, GPUs, FPGAs, and more specialized processing elements into an interconnected mesh environment to provide an ideal processing capability for a variety of complex tasks and data types. The concept expands to cover edge computing enabling preprocessing, data selection, and data conditioning/labeling to enhance the overall outcome. The combination of processors will be augmented by a variety of interconnect and memory architectures and configurations leading to customizable settings and flows meeting the changing needs of data and customers. This variety of environments, tasks, and interconnects creates a new landscape that greatly varies from our standard computing infrastructure therefore reducing our expertise in identifying threats based on network, memory, and processing based on trends resulting in an unknown trust level. The goal is to build and operate a heterogeneous platform that provides dynamic resource assignments and capability elasticity to meet standard and surge needs in an environment that provides data security, isolation and attribution. Access control also becomes an essential element of the new environment.
Histogram-based multilayer reversible data hiding method for securing secret data

In this modern age, data can be easily transferred within networks. This condition has brought the data vulnerable; so they need protection at all times. To minimize this threat, data hiding appears as one of the potential methods to secure data. This protection is done by embedding the secret into various types of data, such as an image. In this case, histogram shifting has been proposed; however, the amount of secret and the respective stego image are still challenging. In this research, we offer a method to improve its performance by performing some steps, for example removing the shifting process and employing multilayer embedding. Here, the embedding is done directly to the peak of the histogram which has been generated by the cover. The experimental results show that this proposed method has a better quality of stego image than existing ones. So, it can be one of possible solutions to protect sensitive data.
A Dynamic Evolutionary Framework for Timeline Generation based on Distributed Representations

Given the collection of timestamped web documents related to the evolving topic, timeline summarization (TS) highlights its most important events in the form of relevant summaries to represent the development of a topic over time. Most of the previous work focuses on fully-observable ranking models and depends on hand-designed features or complex mechanisms that may not generalize well. We present a novel dynamic framework for evolutionary timeline generation leveraging distributed representations, which dynamically finds the most likely sequence of evolutionary summaries in the timeline, called the Viterbi timeline, and reduces the impact of events that irrelevant or repeated to the topic. The assumptions of the coherence and the global view run through our model. We explore adjacent relevance to constrain timeline coherence and make sure the events evolve on the same topic with a global view. Experimental results demonstrate that our framework is feasible to extract summaries for timeline generation, outperforms various competitive baselines, and achieves the state-of-the-art performance as an unsupervised approach.
Continuous variable quantum steganography protocol based on quantum identity

Based on quantum identity authentication, a novel continuous variable quantum steganography protocol is proposed in this paper. It can effectively transmit deterministic secret information in the public quantum channel by taking full advantage of entanglement properties of continuous variable GHZ state. Compared with the existing quantum steganography results, this protocol has the advantages of good imperceptibility and easy implementation. Finally, the detailed performance analysis proves that the proposed protocol has not only these advantages, but also good security and information transmission efficiency, even under eavesdropping attacks, especially to the spectroscopic noise attack.
Acquiring PDF Scores for the Music Library: A Progress Report

Music librarians want to include digital scores in their collections, made available to patrons as digital files or printouts of those files. However, difficulties with the processes of discovery, cataloging and file maintenance can hinder successful collection development of the digital format, when compared to printed scores. Effective management of the workflow and a unified attempt by music libraries to establish best practices for digital scores are needed.
Self-supervised Adversarial Hashing Cross-modal Retrieval with Generative Models Based on Attention Mechanism

In this paper, a self-supervised adversarial network retrieval model (SAHCM) based on attention mechanism is proposed, which takes attention mechanism and adversarial network fusion generation process to cross-modal representation, and has the ability to capture global semantic information and local information of text and image video data at the same time. In order to learn a common representation space for multimode data, experiments show that the proposed method can accurately match image and text description in complex content, reduce the storage space of cross-modal retrieval and improve the efficiency of computation time. And the effect of state-of-the-art is obtained in the cross-modal retrieval experiment on MSCOCO dataset.
Increasing the Runs in Compressed Data Using a Dedicated Move to Front Algorithm

Compressed data is a random data with high entropy and low redundancy, so they are hard for another round of compression. Reducing the randomness of compressed data is a step towards dealing with this problem. We detail a modification to the MTF (Move-to-front) algorithm that is able to reduce the randomness of compressed data by increasing the number of runs and its corresponding quality. The experiments confirmed that our modification results in better value and increased runs among randomly selected sequences. Two formats were tested; binary and alpha-betical representation. Both reported increasing runs and qualities. A common feature was found in the alphabetical representation. The ratios of the number of each simple symbol to its file size is similar in the case of the selected compression format.
Hands-On: Experiencing Software Architecture in Virtual Reality

Recently, virtual reality (VR) and gesture-based interface devices emerged at the consumer market. Both concepts offer new visualization and interaction capabilities, which can improve the user experience when using software. In Software Engineering, exploring and comprehending software systems is often addressed via visualization techniques.In this paper, we present our VR approach to explore software systems by using a head-mounted display and two different gesture-based interaction devices. Hence, we achieve a more immersive user experience and natural interaction, which benefits the comprehension process. Our VR approach is integrated into ExplorViz, our tool for live trace visualization of large software landscapes. In order to emphasize the advantages, we apply our combined approach on a small software system, running within our web-based tool ExplorViz. In this context, we present both, the visualization and the interaction capabilities.
PNUNet: Anomaly Detection using Positive-and-Negative Noise based on Self-Training Procedure

We propose the novel framework for anomaly detection in images. Our new framework, PNUNet, is based on many normal data and few anomalous data. We assume that some noises are added to the input images and learn to remove the noise. In addition, the proposed method achieves significant performance improvement by updating the noise assumed in the inputs using a self-training framework. The experimental results for the benchmark datasets show the usefulness of our new anomaly detection framework.
A Game-centric Approach to Teaching Artificial Intelligence

Man vs. machine competitions have always been attracting much public attention and the famous defeats of human champions in chess, Jeopardy!, Go or poker undoubtedly mark important milestones in the history of artificial intelligence. In this article we reflect on our experiences with a game-centric approach to teaching artificial intelligence that follows the historical development of algorithms by popping the hood of these champion bots. Moreover, we made available a server infrastructure for playing card games in perfect information and imperfect information playing mode, where students can evaluate their implementations of increasingly sophisticated game-playing algorithms in weekly online competitions, i.e. from rule-based systems to exhaustive and heuristic search in game trees to deep learning enhanced Monte Carlo methods and reinforcement learning completely freed of human domain knowledge. The evaluation of this particular course setting revealed enthusiastic feedback not only from students but also from the university authority. What started as an experiment became part of the standard computer science curriculum after just one implementation.
Decidability of an Expressive Description Logic with Rational Grading

In this paper syntactic objects---concept constructors called part restrictions which realize rational grading are considered in Description Logics (DLs). Being able to convey statements about a rational part of a set of successors, part restrictions essentially enrich the expressive capabilities of DLs. We examine an extension of well-studied DL ALCQIHR+ with part restrictions, and prove that the reasoning in the extended logic is still decidable. The proof uses tableaux technique augmented with indices technique, designed for dealing with part restrictions.
Computing scalar products via a two-terminal quantum transmission line

The scalar product of two vectors with K real components can be computed using two quantum channels, that is, information transmission lines in the form of spin-1/2 XX chains. Each channel has its own K-qubit sender and both channels share a single two-qubit receiver. The K elements of each vector are encoded in the pure single-excitation initial states of the senders. After time evolution, a bi-linear combination of these elements appears in the only matrix element of the second-order coherence matrix of the receiver state. An appropriate local unitary transformation of the extended receiver turns this combination into a renormalized version of the scalar product of the original vectors. The squared absolute value of this scaled scalar product is the intensity of the second-order coherence which consequently can be measured, for instance, employing multiple-quantum NMR. The unitary transformation generating the scalar product of two-element vectors is presented as an example.
3D Semantic Scene Completion from a Single Depth Image using Adversarial Training

We address the task of 3D semantic scene completion, i.e. , given a single depth image, we predict the semantic labels and occupancy of voxels in a 3D grid representing the scene. In light of the recently introduced generative adversarial networks (GAN), our goal is to explore the potential of this model and the efficiency of various important design choices. Our results show that using conditional GANs outperforms the vanilla GAN setup. We evaluate these architecture designs on several datasets. Based on our experiments, we demonstrate that GANs are able to outperform the performance of a baseline 3D CNN in case of clean annotations, but they suffer from poorly aligned annotations.
ROI Regularization for Semi-supervised and Supervised Learning

We propose ROI regularization (ROIreg) as a semi-supervised learning method for image classification. ROIreg focuses on the maximum probability of a posterior probability distribution g(x) obtained when inputting an unlabeled data sample x into a convolutional neural network (CNN). ROIreg divides the pixel set of x into multiple blocks and evaluates, for each block, its contribution to the maximum probability. A masked data sample x_ROI is generated by replacing blocks with relatively small degrees of contribution with random images. Then, ROIreg trains CNN so that g(x_ROI ) does not change as much as possible from g(x). Therefore, ROIreg can be said to refine the classification ability of CNN more. On the other hand, Virtual Adverserial Training (VAT), which is an excellent semi-supervised learning method, generates data sample x_VAT by perturbing x in the direction in which g(x) changes most. Then, VAT trains CNN so that g(x_VAT ) does not change from g(x) as much as possible. Therefore, VAT can be said to be a method to improve CNN's weakness. Thus, ROIreg and VAT have complementary training effects. In fact, the combination of VAT and ROIreg improves the results obtained when using VAT or ROIreg alone. This combination also improves the state-of-the-art on "SVHN with and without data augmentation" and "CIFAR-10 without data augmentation". We also propose a method called ROI augmentation (ROIaug) as a method to apply ROIreg to data augmentation in supervised learning. However, the evaluation function used there is different from the standard cross-entropy. ROIaug improves the performance of supervised learning for both SVHN and CIFAR-10. Finally, we investigate the performance degradation of VAT and VAT+ROIreg when data samples not belonging to classification classes are included in unlabeled data.
Watermark retrieval from 3D printed objects via synthetic data training

We present a deep neural network based method for the retrieval of watermarks from images of 3D printed objects. To deal with the variability of all possible 3D printing and image acquisition settings we train the network with synthetic data. The main simulator parameters such as texture, illumination and camera position are dynamically randomized in non-realistic ways, forcing the neural network to learn the intrinsic features of the 3D printed watermarks. At the end of the pipeline, the watermark, in the form of a two-dimensional bit array, is retrieved through a series of simple image processing and statistical operations applied on the confidence map generated by the neural network. The results demonstrate that the inclusion of synthetic DR data in the training set increases the generalization power of the network, which performs better on images from previously unseen 3D printed objects. We conclude that in our application domain of information retrieval from 3D printed objects, where access to the exact CAD files of the printed objects can be assumed, one can use inexpensive synthetic data to enhance neural network training, reducing the need for the labour intensive process of creating large amounts of hand labelled real data or the need to generate photorealistic synthetic data.
Domain Adaptive Person Re-Identification via Camera Style Generation and Label Propagation

Unsupervised domain adaptation in person re-identification resorts to labeled source data to promote the model training on target domain, facing the dilemmas caused by large domain shift and large camera variations. The non-overlapping labels challenge that source domain and target domain have entirely different persons further increases the re-identification difficulty. In this paper, we propose a novel algorithm to narrow such domain gaps. We derive a camera style adaptation framework to learn the style-based mappings between different camera views, from the target domain to the source domain, and then we can transfer the identity-based distribution from the source domain to the target domain on the camera level. To overcome the non-overlapping labels challenge and guide the person re-identification model to narrow the gap further, an efficient and effective soft-labeling method is proposed to mine the intrinsic local structure of the target domain through building the connection between GAN-translated source domain and the target domain. Experiment results conducted on real benchmark datasets indicate that our method gets state-of-the-art results.
Efficient Transformer-Based Sentence Encoding for Sentence Pair Modelling

Modelling a pair of sentences is important for many NLP tasks such as textual entailment (TE), paraphrase identification (PI), semantic relatedness (SR) and question answer pairing (QAP). Most sentence pair modelling work has looked only at the local context to generate a distributed sentence representation without considering the mutual information found in the other sentence. The proposed attentive encoder uses the representation of one sentence generated by a multi-head transformer encoder to guide the focussing on the most semantically relevant words from the other sentence using multi-branch attention. Evaluating this novel sentence encoder on the TE, PI, SR and QAP tasks shows notable improvements over the standard Transformer encoder as well as other current state-of-the-art models.
Geometric Measures of Information for Quantum State Characterization

We analyze the geometry of a joint distribution over a set of discrete random variables. We briefly review Shannon's entropy, conditional entropy, mutual information and conditional mutual information. We review the entropic information distance formula of Rokhlin and Rajski. We then define an analogous information area. We motivate this definition and discuss its properties. We extend this definition to higher-dimensional volumes. We briefly discuss the potential utility for these geometric measures in quantum information processing.
Sampling from Stochastic Finite Automata with Applications to CTC Decoding

Stochastic finite automata arise naturally in many language and speech processing tasks. They include stochastic acceptors, which represent certain probability distributions over random strings. We consider the problem of efficient sampling: drawing random string variates from the probability distribution represented by stochastic automata and transformations of those. We show that path-sampling is effective and can be efficient if the epsilon-graph of a finite automaton is acyclic. We provide an algorithm that ensures this by conflating epsilon-cycles within strongly connected components. Sampling is also effective in the presence of non-injective transformations of strings. We illustrate this in the context of decoding for Connectionist Temporal Classification (CTC), where the predictive probabilities yield auxiliary sequences which are transformed into shorter labeling strings. We can sample efficiently from the transformed labeling distribution and use this in two different strategies for finding the most probable CTC labeling.
Irreducibility criterion and 2-pisot series in positive character

Let Fq((X?1)) be the field of formal power series over a finite field Fq. We characterize a pair of roots that lies outside the unit disc while all remaining conjugates have a modulus strictly less than 1. In particular, we provide a sufficient condition for a pair of formal power series to be a 2-Pisot series. We also give an irreducibility criterion over Fq [X].
An Essay on Optimization Mystery of Deep Learning

Despite the huge empirical success of deep learning, theoretical understanding of neural networks learning process is still lacking. This is the reason, why some of its features seem "mysterious". We emphasize two mysteries of deep learning: generalization mystery, and optimization mystery. In this essay we review and draw connections between several selected works concerning the latter.
On some generalized Fermat curves and chords of an affinely regular polygon inscribed in a hyperbola

Let $\mathcal{G}$ be the projective plane curve defined over $\mathbb{F}_q$ given by $$aX^nY^n-X^nZ^n-Y^nZ^n+bZ^{2n}=0,$$ where $ab\notin\{0,1\}$, and for each $s\in\{2,\ldots,n-1\}$, let $\mathcal{D}_s^{P_1,P_2}$ be the base-point-free linear series cut out on $\mathcal{G}$ by the linear system of all curves of degree $s$ passing through the singular points $P_1=(1:0:0)$ and $P_2=(0:1:0)$ of $\mathcal{G}$. The present work determines an upper bound for the number $N_q(\mathcal{G})$ of $\mathbb{F}_q$-rational points on the nonsingular model of $\mathcal{G}$ in cases where $\mathcal{D}_s^{P_1,P_2}$ is $\mathbb{F}_q$-Frobenius classical. As a consequence, when $\mathbb{F}_q$ is a prime field, the bound obtained for $N_q(\mathcal{G})$ improves in several cases the known bounds for the number $n_P$ of chords of an affinely regular polygon inscribed in a hyperbola passing through a given point $P$ distinct from its vertices.
Text Classification with Few Examples using Controlled Generalization

Training data for text classification is often limited in practice, especially for applications with many output classes or involving many related classification problems. This means classifiers must generalize from limited evidence, but the manner and extent of generalization is task dependent. Current practice primarily relies on pre-trained word embeddings to map words unseen in training to similar seen ones. Unfortunately, this squishes many components of meaning into highly restricted capacity. Our alternative begins with sparse pre-trained representations derived from unlabeled parsed corpora; based on the available training data, we select features that offers the relevant generalizations. This produces task-specific semantic vectors; here, we show that a feed-forward network over these vectors is especially effective in low-data scenarios, compared to existing state-of-the-art methods. By further pairing this network with a convolutional neural network, we keep this edge in low data scenarios and remain competitive when using full training sets.
Understanding Cyber Collateral Damage

We leverage existing collateral damage methodologies in order to explore methods for assessing collateral damage from cyber incidents.