Reviewerly/exHarmony huggingface dataset:
Verification Templates for the Analysis of User Interface Software Design

The paper describes templates for model-based analysis of usability and safety aspects of user interface software design. The templates crystallize general usability principles commonly addressed in user-centred safety requirements, such as the ability to undo user actions, the visibility of operational modes, and the predictability of user interface behavior. These requirements have standard forms across different application domains, and can be instantiated as properties of specific devices. The modeling and analysis process is carried out using the Prototype Verification System (PVS), and is further facilitated by structuring the specification of the device using a format that is designed to be generic across interactive systems. A concrete case study based on a commercial infusion pump is used to illustrate the approach. A detailed presentation of the automated verification process using PVS shows how failed proof attempts provide precise information about problematic user interface software features.
Diffeomorphic Registration of Discrete Geometric Distributions

This paper proposes a new framework and algorithms to address the problem of diffeomorphic registration on a general class of geometric objects that can be described as discrete distributions of local direction vectors. It builds on both the large deformation diffeomorphic metric mapping (LDDMM) model and the concept of oriented varifolds introduced in previous works like [Kaltenmark 2017]. Unlike previous approaches in which varifold representations are only used as surrogates to define and evaluate fidelity terms, the specificity of this paper is to derive direct deformation models and corresponding matching algorithms for discrete varifolds. We show that it gives on the one hand an alternative numerical setting for curve and surface matching but that it can also handle efficiently more general shape structures, including multi-directional objects or multi-modal images represented as distributions of unit gradient vectors.
Can prediction-based distributional semantic models predict typicality?

Recent advances in the field of computational linguistics have led to the development of various prediction-based models of semantics. These models seek to infer word representations from large text collections by predicting target words from neighbouring words (or vice versa). The resulting representations are vectors in a continuous space, collectively called word embeddings. Although psychological plausibility was not a primary concern for the developers of predictive models, it has been the topic of several recent studies in the field of psycholinguistics. That is, word embeddings have been linked to similarity ratings, word associations, semantic priming, word recognition latencies, and so on. Here, we build on this work by investigating category structure. Throughout seven experiments, we sought to predict human typicality judgements from two languages, Dutch and English, using different semantic spaces. More specifically, we extracted a number of predictor variables, and evaluated how well they could capture the typicality gradient of common categories (e.g., birds, fruit, vehicles, etc.). Overall, the performance of predictive models was rather modest and did not compare favourably with that of an older count-based model. These results are somewhat disappointing given the enthusiasm surrounding predictive models. Possible explanations and future directions are discussed.
A Meta Path Based Method for Entity Set Expansion in Knowledge Graph

Entity Set Expansion (ESE) is the problem that expands a small set of seed entities into a more complete set, entities of which have common traits. As a popular data mining task, ESE has been widely used in many applications, such as dictionary construction, query suggestion and new brand identification. Existing ESE methods mainly utilize text and Web information. That is, the intrinsic relation among entities is inferred from their occurrences in text or Web. With the surge of knowledge graph in recent years, it is possible to extend entities according to their occurrences in knowledge graph. In this paper, we consider the knowledge graph as a heterogeneous information network (HIN) that contains different types of objects and links, and propose a novel method, called MP_ESE, to extend entities in the HIN. The MP_ESE employs meta paths, a relation sequence connecting entities, in HIN to capture the implicit common traits of seed entities. In addition, an automatic meta path generation method, called SMPG, has been designed to exploit the potential relations among entities. Heuristic learning and PU learning methods are employed to learn the weights of extracted meta paths. With these generated and weighted meta paths, the MP_ESE can effectively extend entities. Comprehensive experiments on real datasets show the effectiveness and efficiency of MP_ESE.
Temporal Frame Interpolation With Motion-Divergence-Guided Occlusion Handling

We present a high-quality temporal frame interpolation (TFI) method that employs piecewise-smooth motion and handles disoccluded regions using the observation that motion discontinuities travel with the foreground object. We derive a “motion discontinuity” likelihood map from the divergence of a motion field between the input frames. Motion which is modeled at the reference frame is mapped to the target frame using a cellular-affine mapping strategy - a process during which regions of disocclusion are readily observed. This information is then used to guide the occlusion-aware, bidirectional FI process. Furthermore, we propose two computationally inexpensive texture optimizations that selectively improve the quality of the interpolated frames in regions around moving objects. The scheme produces very high-quality interpolated frames and outperforms current high-quality state-of-the-art TFI schemes by 2-2.5 dB; the method works with a very low-complexity motion estimation scheme and runs orders of magnitudes faster than its competitors.
On the Unfairness of Blockchain

The success of Bitcoin relies on the perception of a fair underlying peer-to-peer protocol: blockchain. Fairness here means that the reward (in bitcoins) given to any participant that helps maintain the consistency of the protocol by mining, is proportional to the computational power devoted by that participant to the mining task. Without such perception of fairness, honest miners might be disincentivized to maintain the protocol, leaving the space for dishonest miners to reach a majority and jeopardize the consistency of the entire system. We prove that blockchain is unfair, even in a distributed system of only two honest miners. In a realistic setting where message delivery is not instantaneous, the ratio between the (expected) number of blocks committed by two miners is actually lower bounded by a term exponential in the product of the message delay and the difference between the two miners’ hashrates. To obtain our result, we model the growth of blockchain, which may be of independent interest. We also apply our result to explain recent empirical observations and vulnerabilities.
Animation Plans for Before-and-After Satellite Images

Before-and-after image pairs show how entities in a given region have evolved over a specific period of time.Satellite images are a major source of such data, that capture how natural phenomena or human activity impact a geographical area.These images are used both for data analysis and to illustrate the resulting findings to diverse audiences.The simple techniques used to display them, including juxtaposing, swapping and monolithic blending, often fail to convey the underlying phenomenon in a meaningful manner.We introduce Baia, a framework to create advanced animated transitions, called animation plans, between before-and-after images.Baia relies on a pixel-based transition model that gives authors much expressive power, while keeping animations for common types of changes easy to create thanks to predefined animation primitives.We describe our model, the associated animation editor, and report on two user studies.In the first study, advanced transitions enabled by Baia were compared to monolithic blending, and perceived as more realistic and better at focusing viewer's attention on a region of interest than the latter.The second study aimed at gathering feedback about the usability of Baia's animation editor.
Mitigating Bottlenecks in Wide Area Data Analytics via Machine Learning

Over the past decade, we have witnessed exponential growth in the density (petabyte-level) and breadth (across geo-distributed datacenters) of data distribution. It becomes increasingly challenging but imperative to minimize the response times of data analytic queries over multiple geo-distributed datacenters. However, existing scheduling-based solutions have largely been motivated by pre-established mantras (e.g., bandwidth scarcity). Without data-driven insights into performance bottlenecks <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">at runtime</i> , schedulers might blindly assign tasks to workers that are suffering from unidentified bottlenecks. In this paper, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lube</i> , a system framework that minimizes query response times by detecting and mitigating bottlenecks at runtime. Lube monitors geo-distributed data analytic queries in real-time, detects potential bottlenecks, and mitigates them with a bottleneck-aware scheduling policy. Our preliminary experiments on a real-world prototype across Amazon EC2 regions have shown that Lube can detect bottlenecks with over 90 percent accuracy, and reduce the median query response time by up to 33 percent compared to Spark's built-in locality-based scheduler.
An improved nonlocal maximum likelihood estimation method for denoising magnetic resonance images with spatially varying noise levels

Magnetic resonance images (MRI) reconstructed with parallel MRI (pMRI) techniques generally have spatially varying (non-stationary) noise levels. However, most of the existing MRI denoising methods rely on a stationary noise model and end with suboptimal results when applied to pMRI images. To address this problem, this paper proposes an improved nonlocal maximum likelihood (NLML) estimation method. In the proposed method, a noise map is computed with a robust noise estimator before the ML estimation of the underlying signal. Also, a similarity measure based on local frequency descriptors (LFD) is introduced to find the nonlocal samples for ML estimation. The experiments on simulated and real magnetic resonance (MR) data demonstrate that the proposed technique has superior filtering capabilities in terms of subjective and quantitative assessments when compared with other state-of-the-art methods.
Unsupervised Adaptation of a Person-Specific Manifold of Facial Expressions

In order to analyze expressions that are different from the prototypic expressions defined by Ekman, manifold learning has been proposed to build person-specific continuous representations of facial expressions. Yet, it is still a challenging problem to build such a manifold with no prior knowledge on the morphology of the subject. Here, we propose a method to build a person-specific manifold of facial expressions able to adapt to the morphology of the subject in an unsupervised manner. The manifold is initialized with the facial landmarks of the neutral face and 5 synthesized basic expressions. Our first contribution is to detect automatically the neutral face of the subject so that we can build the manifold in an unsupervised manner. Our second and main contribution is to adapt in an unsupervised manner the initialized manifold to the morphology of the subject by detecting the real basic expressions of the subject while maintaining constraints in the manifold. Our third contribution is to perform the adaptation on spontaneous expressions with typical head pose variation for human-computer interaction. The experiments show that the adaptation works well on posed expressions and that the constraints for the adaptation on spontaneous expressions is efficient when head pose variation is considered.
XHX – A Framework for Optimally Secure Tweakable Block Ciphers from Classical Block Ciphers and Universal Hashing

Tweakable block ciphers are important primitives for designing cryptographic schemes with high security. In the absence of a standardized tweakable block cipher, constructions built from classical block ciphers remain an interesting research topic in both theory and practice. Motivated by Mennink's $$\widetilde{F}[2]$$ publication from 2015, Wang et al. proposed 32 optimally secure constructions at ASIACRYPT'16, all of which employ two calls to a classical block cipher each. Yet, those constructions were still limited to n-bit keys and n-bit tweaks. Thus, applications with more general key or tweak lengths still lack support. This work proposes the XHX family of tweakable block ciphers from a classical block cipher and a family of universal hash functions, which generalizes the constructions by Wang et al. First, we detail the generic XHX construction with three independently keyed calls to the hash function. Second, we show that we can derive the hash keys in efficient manner from the block cipher, where we generalize the constructions by Wang et al.; finally, we propose efficient instantiations for the used hash functions.
Learning on Hypergraphs with Sparsity

Hypergraph is a general way of representing high-order relations on a set of objects. It is a generalization of graph, in which only pairwise relations can be represented. It finds applications in various domains where relationships of more than two objects are observed. On a hypergraph, as a generalization of graph, one wishes to learn a smooth function with respect to its topology. A fundamental issue is to find suitable smoothness measures of functions on the nodes of a graph/hypergraph. We show a general framework that generalizes previously proposed smoothness measures and also gives rise to new ones. To address the problem of irrelevant or noisy data, we wish to incorporate sparse learning framework into learning on hypergraphs. We propose sparsely smooth formulations that learn smooth functions and induce sparsity on hypergraphs at both hyperedge and node levels. We show their properties and sparse support recovery results. We conduct experiments to show that our sparsely smooth models have benefits to irrelevant and noisy data, and usually give similar or improved performances compared to dense models.
Classical Verification of Quantum Computations

We present the first protocol allowing a classical computer to interactively verify the result of an efficient quantum computation. We achieve this by constructing a measurement protocol, which enables a classical verifier to use a quantum prover as a trusted measurement device. The protocol forces the prover to behave as follows: the prover must construct an $n$ qubit state of his choice, measure each qubit in the Hadamard or standard basis as directed by the verifier, and report the measurement results to the verifier. The soundness of this protocol is enforced based on the assumption that the learning with errors problem is computationally intractable for efficient quantum machines.
Derivation of quantum propagator for coupled harmonic oscillator with uniform electric field in a single harmonic oscillator environment using white noise functional approach

The use of white noise analysis is utilized in this study. We consider an open quantum system of coupled harmonic oscillator with uniform two dimensional electric field in a single harmonic oscillator environment. We obtain the quantum propagator for the said system by considering the Feynman path integral as a white noise functional. In addition, the resulting quantum propagator was consistent with the previous literature and also, this study gives more general form due to the inclusion of the electric field.
Integrative Double Kaizen Loop (IDKL): Towards a Culture of Continuous Learning and Sustainable Improvements for Software Organizations

In the past decades, software organizations have been relying on implementing process improvement methods to advance quality, productivity, and predictability of their development and maintenance efforts. However, these methods have proven to be challenging to implement in many situations, and when implemented, their benefits are often not sustained. Commonly, the workforce requires guidance during the initial deployment, but what happens after the guidance stops? Why do not traditional improvement methods deliver the desired results? And, how do we maintain the improvements when they are realized? In response to these questions, we have combined social and organizational learning methods with Lean's continuous improvement philosophy, Kaizen, which has resulted in an IDKL model that has successfully promoted continuous learning and improvement. The IDKL has evolved through a real-life project with an industrial partner; the study employed ethnographic action research with 231 participants and had lasted for almost 3 years. The IDKL requires employees to continuously apply small improvements to the daily routines of the work-procedures. The small improvements by themselves are unobtrusive. However, the IDKL has helped the industrial partner to implant continuous improvement as a daily habit. This has led to realizing sustainable and noticeable improvements. The findings show that on average, Lead Time has dropped by 46 percent, Process Cycle Efficiency has increased by 137 percent, First-Pass Process Yield has increased by 27 percent, and Customer Satisfaction has increased by 25 percent.
Synthesis of Mathematical Programming models with one-class evolutionary strategies

We propose an Evolutionary Strategy-based One Class Constraint Synthesis (ESOCCS), a novel method for computer-assisted synthesis of constraints for Mathematical Programming models. ESOCCS synthesizes constraints of Linear Programming and Non-Linear Programming types using solely examples of feasible states of the modeled entity. This is a crucial feature from the viewpoint of modeling real-world business processes from data, as acquisition of examples of feasible states is straightforward for a normally operating process, while infeasible states corresponding to errors and faults are avoided in practice and thus uncommon. ESOCCS is verified on a suite of synthetic benchmarks having known model representations and synthesizes models with noticeable fidelity to the known representations in terms of syntax and semantics. We employ ESOCCS and an off-the-shelf solver in a fully automated setup for modeling and optimization of a real-world business process of rice production. The resulting optimal parameters of production are validated in the context of three exemplary rice farms. Likely increase in profit thanks to applying those parameters is concluded.
Creating Rich and Representative Personas by Discovering Affordances

During the last decade, information system designers have used the persona technique to put user needs and preferences at the center of all development decisions. Persona development teams draw on qualitative data, quantitative data or a combination of both to develop personas that are representative of the target users. Despite the benefits of both approaches, qualitative methods are limited by the cognitive capabilities of the experts, whereas quantitative methods lack contextual richness. To gain the advantages of both approaches, this article suggests a mixed qualitative-quantitative approach to create user personas based on the patterns of the affordances they actualize rather than merely the actions they take. It enriches personas by referring to the purposes fulfilled through affordance actualizations, and it grounds personas in readily available objective log data. This study illustrates the practical value of the proposed methodology by empirically creating personas based on real user data. Furthermore, it demonstrates its value by having practitioners compare the suggested method to that of qualitative-only and quantitative-only methods.
Multiobjective Evolutionary Map Design for <i>Cube 2: Sauerbraten</i>

Map design is a major challenge in the development of a successful multiplayer first-person shooter. In fact, it has a large impact on the game dynamics and deeply affects the player experience. In this paper, we present a search-based procedural content generation approach to the map design problem in Cube 2: Sauerbraten , an open-source first-person shooter. Extending previous works introduced in the literature, we propose several design objectives to evaluate the maps as well as a novel methodology to compute them. To test our approach, we designed two different design problems that require to deal with conflicting objectives (i.e., balancing, pacing, and achievement of long kill streaks) and with players using different playing styles. The results are promising as they show that our approach, exploiting multiobjective evolution, is able to explore effectively the map design space and to provide maps that feature interesting tradeoffs between the conflicting objectives.
Quantum steganography over noiseless channels: Achievability and bounds

Quantum steganography is the study of hiding secret quantum information by encoding it into what an eavesdropper would perceive as an innocent-looking message. Here we study an explicit steganographic encoding for Alice to hide her secret message in the syndromes of an error-correcting code, so that the encoding simulates a given noisy quantum channel. We calculate achievable rates of steganographic communication over noiseless quantum channels using this encoding. We give definitions of secrecy and reliability for the communication process, and with these assumptions derive upper bounds on the amount of steganographic communication possible, and show that these bounds match the communication rates achieved with our encoding. This gives a steganographic capacity for a noiseless channel emulating a given noisy channel.
Parallelizing and optimizing neural Encoder–Decoder models without padding on multi-core architecture

Scaling up Artificial Intelligence (AI) algorithms for massive datasets to improve their performance is becoming crucial. In Machine Translation (MT), one of most important research fields of AI, models based on Recurrent Neural Networks (RNN) show state-of-the-art performance in recent years, and many researchers keep working on improving RNN-based models to achieve better accuracy in translation tasks. Most implementations of Neural Machine Translation (NMT) models employ a padding strategy when processing a mini-batch to make all sentences in a mini-batch have the same length. This enables an efficient utilization of caches and GPU/SIMD parallelism but leads to a waste of computation time. In this paper, we implement and parallelize batch learning for a Sequence-to-Sequence (Seq2Seq) model, which is the most basic model of NMT, without using a padding strategy. More specifically, our approach forms vectors which represent the input words as well as the neural network’s states at different time steps into matrices when it processes one sentence, and as a result, the approach makes a better use of cache and optimizes the process that adjusts weights and biases during the back-propagation phase. Our experimental evaluation shows that our implementation achieves better scalability on multi-core CPUs. We also discuss our approach’s potential to be used in other implementations of RNN-based models.