Reviewerly/exHarmony huggingface dataset:
Extracting parallel fragments from comparable documents using a generative model

Although parallel corpora are essential language resources for many natural language processing tasks, they are rare or even not available for many language pairs. Instead, comparable corpora are widely available and contain parallel fragments of information that can be used in applications like statistical machine translation systems. In this research, we propose a generative latent Dirichlet allocation based model for extracting parallel fragments from comparable documents without using any initial parallel data or bilingual lexicon. The experimental results show significant improvement if the extracted fragments generated by the proposed method are used for augmenting an existing parallel corpus in an statistical machine translation system. According to the human judgment, the accuracy of the proposed method for an English-Persian task is about 59.7%. Also, the out of vocabulary error rate for the same task is reduced by 28%.
A new method (K-method) of calculating the mutual influence of nodes indirected weight complex networks

A new characteristic of paired nodes in a directed weight complex network is considered. A method (named as K-method) of the characteristics calculation for complex networks is proposed. The method is based on transforming the initial network with the subsequent application of the Kirchhoff rules. The scope of the method for sparse complex networks is proposed. The nodes of these complex networks are concepts of the real world, and the connections have a cause-effect character of the so-called "cognitive maps". Two new characteristics of concept nodes having a semantic interpretation are proposed, namely "pressure" and "influence" taking into account the influence of all nodes on each other.
The Cyber Acumen

The major purpose of this chapter is to understand average user's decision-making process in cybersecurity by reviewing and integrating several major theoretical frameworks discussed and applied in decision making processes in cybersecurity. The average users are the ones who do not realize or understand when or how to perform security-critical decisions, the ones who are unmotivated to comply with company and school cybersecurity policies and procedures due to inconvenience, and the ones who do not have sufficient knowledge in cybersecurity to make sound security decisions. It is important to discuss and understand the role of such users and their behaviors based on systematic analysis so that we can identify potential factors causing “poor” security decisions and find ways to reduce the likelihood of being victims of cyber-attacks. The ultimate goal is to provide insights and make recommendations on how to foster individual's cyber acumen and cultivate a more effective decision-making process.
Fear of Flight: Expanded Presence and Inter-sensory Gesture in Multimedia Performance

Fear of Flight investigates the intersection of surround audiovisual composition and live performance to draw meaningful conclusions about the experience of presence and the role of the performer within immersive media composition.Among the theoretical issues that contributed to Fear of Flight's development are the impact of immersion on the perception of compositional dialog, and the mechanisms by which presence may be expanded and dislocated from a performing body through mediation.
Subitizing with Variational Autoencoders

Numerosity, the number of objects in a set, is a basic property of a given visual scene. Many animals develop the perceptual ability to subitize: the near-instantaneous identification of the numerosity in small sets of visual items. In computer vision, it has been shown that numerosity emerges as a statistical property in neural networks during unsupervised learning from simple synthetic images. In this work, we focus on more complex natural images using unsupervised hierarchical neural networks. Specifically, we show that variational autoencoders are able to spontaneously perform subitizing after training without supervision on a large amount of images from the Salient Object Subitizing dataset. While our method is unable to outperform supervised convolutional networks for subitizing, we observe that the networks learn to encode numerosity as a basic visual property. Moreover, we find that the learned representations are likely invariant to object area; an observation in alignment with studies on biological neural networks in cognitive neuroscience.
Guided Unfoldings for Finding Loops in Standard Term Rewriting

In this paper, we reconsider the unfolding-based technique that we have introduced previously for detecting loops in standard term rewriting. We improve it by guiding the unfolding process, using distinguished positions in the rewrite rules. This results in a depth-first computation of the unfoldings, whereas the original technique was breadth-first. We have implemented this new approach in our tool NTI and compared it to the previous one on a bunch of rewrite systems. The results we get are promising (better times, more successful proofs).
Dynamic Adaptation on Non-stationary Visual Domains

Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of stationary domain shifts, i.e. static data sets. However, with large-scale or dynamic data sources, data from a defined domain is not usually available all at once. For instance, in a streaming data scenario, dataset statistics effectively become a function of time. We introduce a framework for adaptation over non-stationary distribution shifts applicable to large-scale and streaming data scenarios. The model is adapted sequentially over incoming unsupervised streaming data batches. This enables improvements over several batches without the need for any additionally annotated data. To demonstrate the effectiveness of our proposed framework, we modify associative domain adaptation to work well on source and target data batches with unequal class distributions. We apply our method to several adaptation benchmark datasets for classification and show improved classifier accuracy not only for the currently adapted batch, but also when applied on future stream batches. Furthermore, we show the applicability of our associative learning modifications to semantic segmentation, where we achieve competitive results.
Analyticity of Entropy Rates of Continuous-State Hidden Markov Models

The analyticity of the entropy and relative entropy rates of continuous-state hidden Markov models is studied here. Using the analytic continuation principle and the stability properties of the optimal filter, the analyticity of these rates is established for analytically parameterized models. The obtained results hold under relatively mild conditions and cover several useful classes of hidden Markov models. These results are relevant for several theoretically and practically important problems arising in statistical inference, system identification and information theory.
Homeomorphic Embedding Modulo Combinations of Associativity and Commutativity Axioms

The Homeomorphic Embedding relation has been amply used for defining termination criteria of symbolic methods for program analysis, transformation, and verification. However, homeomorphic embedding has never been investigated in the context of order-sorted rewrite theories that support symbolic execution methods modulo equational axioms. This paper generalizes the symbolic homeomorphic embedding relation to order–sorted rewrite theories that may contain various combinations of associativity and/or commutativity axioms for different binary operators. We systematically measure the performance of increasingly efficient formulations of the homeomorphic embedding relation modulo associativity and commutativity axioms. From our experimental results, we conclude that our most efficient version indeed pays off in practice.
A Contrast Enhancement Method for Improving Surface Defects Detection of Elevator Compensation Chain

A contrast enhancement method based on the adaptive noise threshold estimation and logarithmic function in nonsubsampled contourlet transform (NSCT) domain is proposed, which can improve the defects segmentation accuracy of the elevator compensation chain. After extracting region of interest (ROI) according to the spatial location of defects, it is transformed into NSCT domain, where the high-frequency subband coefficients corresponding to noise is suppressed by the adaptive noise threshold estimation. Then, the logarithmic function transformation is used to enhance the image edges to a variable extent. Finally, the enhanced image is segmented by watershed algorithm based on laws texture analysis. Experimental results demonstrate that the performance of the proposed method is superior to the existing methods in terms of both the quality of contrast enhancement and the segmentation accuracy.
Solving dynamic labeling problems to optimality using solution space reductions

Interactive maps are maps that allow users to execute operations that alter the state of the visualization. Two of the most common operations are rotation and scaling. As one of these operations is executed, labels must maintain their size, shape and orientation. This may cause labels that were previously disjoint to overlap. For each label, we must determine a set of active ranges (i.e., intervals of angles or scales during which the label is visible) such that no pair of overlapping labels is ever active simultaneously. The objective is to maximize the total length of the active ranges of all labels. We prove a number of properties of optimal solutions which allow us to significantly reduce the size of an integer programming formulation from the literature. These properties are independent of the particular choice of operation, which makes our reduction techniques extremely flexible. We report the results of several experiments employing operations of rotation and scaling, and using two existing benchmarks with 180 real-world instances. We obtained reductions of over 100 times in the number of variables and constraints of the formulation, which led to a decrease of up to three orders of magnitude in running times.
Proving Program Properties as First-Order Satisfiability

Program semantics can often be expressed as a (many-sorted) first-order theory $$\mathcal{S}$$ , and program properties as sentences $$\varphi $$ which are intended to hold in the canonical model of such a theory, which is often incomputable. Recently, we have shown that properties $$\varphi $$ expressed as the existential closure of a boolean combination of atoms can be disproved by just finding a model of $$\mathcal{S}$$ and the negation $$\lnot \varphi $$ of $$\varphi $$ . Furthermore, this idea works quite well in practice due to the existence of powerful tools for the automatic generation of models for (many-sorted) first-order theories. In this paper we extend our previous results to arbitrary properties, expressed as sentences without any special restriction. Consequently, one can prove a program property $$\varphi $$ by just finding a model of an appropriate theory (including $$\mathcal{S}$$ and possibly something else) and an appropriate first-order formula related to $$\varphi $$ . Beyond its possible theoretical interest, we show that our results can also be of practical use in several respects.
Error separation methods based on absolute testing of non-uniform sampling

Grating lateral shearing interferometry is one of important technique to test the system wavefront aberration from lithographic lens. In order to achieve the calibration in ultra-high-precision, we must eliminate the system errors from the grating lateral shearing interferometer. Therefore, a three-step average algorithm and a weighed-three-step average algorithm are proposed to remove the rotationally asymmetric system errors from our shearing setup. The research results show that the RMS of the accuracy of three-step average algorithm centered on 45° can approach 0.71 nm, and the RMS of the accuracy of its weighed algorithm can approach 0.54 nm.
Holoentropy measures for image stitching of scenes acquired under CAMERA unknown or arbitrary positions

Image stitching of similar scenes is a challenging task when scenes are captured under varying illuminations between the scenes, varying camera positions, varying orientations either in axial or azimuth. In this paper, we explore a seamless image stitching algorithm to address the above-said issues by applying techniques of dehazing on the acquired scenes and before identifying the image features and holoentropy aided feature matching on the Scale Invariant Feature Transform (SIFT) based features for the image. Experimentation of the proposed system is compared with the existing image stitching methods using squared distance, Minkowski and pairwise Euclidean distance for feature matching. The proposed seamless stitching method is evaluated based on the metrics, horizontal square gradient value (HSGV) and vertical square gradient value (VSGV). The obtained results are shown to be feasible for stitching the nonuniform or illumination variation multiple images. The exploration of above said stitching algorithm is intended to reduce the number of computations and inconsistencies in the stitched results.
Exploring the Application of Homomorphic Encryption to a Cross Domain Solution

A Cross Domain Solution (CDS) is a means of secure information exchange that provides the ability to access or transfer digital data between varying security domains. Most existing CDS methods focus on risk management policies that rely on using protected or trusted parties to process the information in order to solve this problem. A CDS that is able to function in the presence of untrusted parties is a challenge. We apply the concepts of homomorphic encryption (HE) to explore a new solution to the CDS problem. We built a practical software case study application using the Yet Another Somewhat Homomorphic Encryption Scheme (YASHE) around the specific challenge of evaluating the gateway bypass condition on encrypted data. We assess the feasibility of such an application through performance and memory profiling in order to find a parameter selection that ensures proper homomorphic evaluation. The correctness of the application was assured for 64-, 72-, 96-, and 128-bit security parameter selections of YASHE resulting in high latency performance. The computing time required by our proof-of-concept implementation may be high but this approach allows the manual process employed in current systems to be eliminated.
A Real-Time Court Detection and Tracking System to Tennis Videos

Abstract To analyze a tennis video sequence at a higher semantic level, the first step is to determine the position of the court. Thus, it is required to track the court in the scene. In this article, we proposed a new automatic system for tennis court detection and tracking in real time. Our system comprises a structural analysis system that extracted the court view images to be processed by classifying the tennis video to non-court view or court view frame. Then, the initialization step is dedicated to detecting the court in the first frame. It consists of extracting the tennis court line using the Probabilistic Hough Transform and estimating the homography between the tennis court model and the detected court lines. To ensure the accuracy of the obtained homography, it should pass a rejection test. We used the camera motion as a supplementary information, and we tracked the court only in case where the camera is moving, which improves the stability of our system. In addition, for each court view frame, we updated the homography, and we fitted the projected model based on the algorithm of Levenberg–Marquardt.
PABO: Mitigating congestion via packet bounce in data center networks

In today’s data center, a diverse mix of throughput-sensitive long flows and delay-sensitive short flows are commonly presented. However, commodity switches used in a typical data center network are usually shallow-buffered for the sake of reducing queueing delay and deployment cost. The direct outcome is that the queue occupation by long flows could potentially block the transmission of delay-sensitive short flows, leading to degraded performance. Congestion can also be caused by the synchronization of multiple TCP connections for short flows, as typically seen in the partition/aggregate traffic pattern. The congestion is usually transient and any end-device intervention through the timeout-based pathway would result in suboptimal performance. While multiple end-to-end transport-layer solutions have been proposed, none of them has tackled the real challenge: reliable transmission in the network. In this paper, we fill this gap by presenting PABO — a novel link-layer design that can mitigate congestion by temporarily bouncing packets to upstream switches. PABO’s design fulfills the following goals: (i) providing per-flow based flow control on the link layer, (ii) handling transient congestion without the intervention of end devices, and (iii) gradually back propagating the congestion signal to the source when the network is not capable to handle the congestion. We present the detailed design of PABO and complete a proof-of-concept implementation. We discuss the impact of system parameters on packet out-of-order delivery and conduct extensive experiments to prove the effectiveness of PABO. We examine the basic properties of PABO using a tree-based topology, and further evaluate the overall performance of PABO using a realistic Fattree topology for data center networks. Experiment results show that PABO can provide prominent advantage of mitigating transient congestions and can achieve significant gain on flow completion time.
Extensions to hybrid code networks for FAIR dialog dataset

Goal-oriented dialog systems require a different approach from chit-chat conversational systems in that they should perform various subtasks as well as continue the conversation itself. Since these systems typically interact with an external knowledge base that changes over time, it is desirable to incorporate domain knowledge to deal with such changes, yet with minimum human effort. This paper presents an extended version of the Hybrid Code Network (HCN) developed for the Facebook AI research (FAIR) dialog dataset used in the Sixth Dialog System Technology Challenge (DSTC6). Compared to the original HCN, the system was more adaptable to changes in the knowledge base due to the modules that are extended to be learned from data. Using the proposed learning scheme with fairly elementary domain-specific rules, the proposed model achieved 100% accuracy in all test datasets.
Reference Type Logic Variables in Constraint-Logic Object-Oriented Programming

Constraint-logic object-oriented programming, for example using Muli, facilitates the integrated development of business software that occasionally involves finding solutions to constraint-logic problems. The availability of object-oriented features calls for the option to use objects as logic variables as well, as opposed to being limited to primitive type logic variables. The present work contributes a concept for reference type logic variables in constraint-logic object-oriented programming that takes arbitrary class hierarchies of programs written in object-oriented languages into account. The concept discusses interactions between constraint-logic object-oriented programs and reference type logic variables, particularly invocations on and access to logic variables, type operations, and equality. Furthermore, it proposes approaches as to how these interactions can be handled by a corresponding execution environment.
The Research Core Dataset (KDSF) in the Linked Data context

This paper describes our efforts to implement the Research Core Dataset (“Kerndatensatz Forschung”; KDSF) as an ontology in VIVO. KDSF is used in VIVO to record the required metadata on incoming data and to produce reports as an output. While both processes need an elaborate adaptation of the KDSF specification, this paper focusses on the adaptation of the KDSF basic data model for recording data in VIVO. In this context, the VIVO and KDSF ontologies were compared with respect to domain, syntax, structure, and granularity in order to identify correspondences and mismatches. To produce an alignment, different matching approaches have been applied. Furthermore, we made necessary modifications and extensions on KDSF classes and properties.
Qualitative and quantitative efficacy measurement of TERI library and information centre resources

Purpose The purpose of the paper is to evaluate print and electronic resources of TERI’s Library and Information Centre (LIC) with an aim to maximize the net marginal benefits and minimize net marginal costs, without compromising the quality of the library resources. Design/methodology/approach The parameters considered for analyzing the value of the library resources for this exercise were resource access costs, strategic value of the resource based on subject area coverage, frequency of use, citations, direct and indirect benefits to users. The data regarding these parameters were provided from wide range of sources (both tangible and intangible), to come out with the qualitative and quantitative assessment through an optimization and simulation based model. Findings Out of the total holdings in TERI LIC that were analyzed, 85 percent of book collections and 63.5 percent of journals were found to be useful for the researchers. The least-used books and journals were identified for weeding to optimize the value of library for users and make space for new and topical library collections. Research limitations/implications A sample of data sources out of the total library collections was defined for the evaluation. Practical implications The paper demonstrates the value of library resources that is of critical importance to libraries for an effective and efficient delivery of services for generating future knowledge. Evaluating the value of libraries resources has implications both for librarians as well as library users. Originality/value The evaluation exercise established the efficacy of the TERI library holdings for research and academic purposes in the domain of sustainable development. The library collection was found to be cost effective and beneficial to meet the future demand from the user community.
Critical IT Project Management Competencies

Academic computing curricula generally focus on teaching the specific technological skills expected of new graduates in their disciplines. Yet when it comes to hiring these graduates, behavioral skills (also called soft skills) such as communication and personal integrity are almost always rated as being more important than the technological skills. This mixed-method research project adds to the understanding of skill expectations required for new hires by providing information from a global sample of project management professionals. Both the quantitative and qualitative results are in accord with the vast majority of the extant literature in that behavioral skills were seen as more critical than technical skills. Implications and recommendations for educators, curriculum developers, and prospective graduates are discussed.