Reviewerly/exHarmony huggingface dataset:
Compiling with continuations, or without? whatever.

What makes a good compiler IR? In the context of functional languages, there has been an extensive debate on the advantages and disadvantages of continuation-passing-style (CPS). The consensus seems to be that some form of explicit continuations is necessary to model jumps in a functional style, but that they should have a 2nd-class status, separate from regular functions, to ensure efficient code generation. Building on this observation, a recent study from PLDI 2017 proposed a direct-style IR with explicit join points, which essentially represent local continuations, i.e., functions that do not return or escape. While this IR can work well in practice, as evidenced by the implementation of join points in the Glasgow Haskell Compiler (GHC), there still seems to be room for improvement, especially with regard to the way continuations are handled in the course of optimization. In this paper, we contribute to the CPS debate by developing a novel IR with the following features. First, we integrate a control operator that resembles Felleisen’s C , eliminating certain redundant rewrites observed in the previous study. Second, we treat the non-returning and non-escaping aspects of continuations separately, allowing efficient compilation of well-behaved functions defined by the user. Third, we define a selective CPS translation of our IR, which erases control operators while preserving the meaning and typing of programs. These features enable optimizations in both direct style and full CPS, as well as in any intermediate style with selectively exposed continuations. Thus, we change the spectrum of available options from “CPS yes or no” to “as much or as little CPS as you want, when you want it”.
Local distances preserving based manifold learning

In this paper a local manifold learning method based on the local distances preserving (LDP) is proposed. LDP focuses on extracting and preserving the local distances between the data points. In LDP, the coefficients between each data point and its neighbors are calculated based on the inverse of Euclidean distances between them. Then, a minimization function, which is in accordance with the calculated coefficients, is proposed for calculating the embedded data manifold in the low-dimensional representation space. Matching the way of calculation the coefficients and the proposed minimization function is the main significance of the proposed LDP method. In addition, the proposed LDP method shows less sensitivity to the initial parameters such as the number of neighbors and the value of variance. Also, a stochastic based extension of LDP (SLDP) manifold learning method is proposed. The proposed method is compared with the common manifold learning methods based on the achievable recognition rate and the power of the local distances preserving. The experiments have been done on two different kinds of databases: HODA Persian handwritten character database and ORL face image database. The results demonstrate the suitable performance of LDP and SLDP. Also, the results show the robustness of the proposed method to the number of neighbors and the value of variance parameter. Moreover, the proposed method of calculating the embedded data points in LDP and SLDP has less complexity than the similar local manifold learning methods, Laplacian eigenmaps (LEM) and stochastic LEM (SLEM).
Two-stage game theoretical framework for IaaS market share dynamics

In this paper, we consider the problem of cloud market share among Infrastructure as a Service (IaaS) providers in a competitive setting. The public cloud market is dominated by few large providers, which prevents a healthy competition that would benefit the end-users. We argue that to make the cloud market more competitive, new providers, even small ones, should be able to inter this market and find a share. This problem of deeply analyzing the cloud market and providing new players with mechanisms allowing them to have a market share has not been addressed yet. In fact, to make the cloud market open and increase the cloud service demand, we show in this paper that the cloud providers have to compete not only over price, but also quality. Most of the research performed in the cloud market competition focus only on pricing mechanisms, neglecting thus the cloud service quality and user’s satisfaction. However, to be consistent with the new era of cloud computing, Cloud 2.0, providers have to focus on providing value to businesses and offer higher quality services. As a solution to the aforementioned problem, we propose a conceptual, user-centric game theoretical framework that includes a two-stage game: 1) to capture the user demand preferences (optimal capacity and price), a Stackelberg game is used where IaaS providers are leaders and IaaS users are followers; and 2) to enhance the service ratings given by users in order to improve the provider position in the market and increase the future users’ demand, a differential game is proposed, which allows IaaS providers to compete over service quality (e.g., QoS, scalability and adding extra features). The proposed two-stage game model allows the new IaaS providers, even if they are small, to have a share in the market and increase user’s satisfaction through providing high quality and added-value services. To validate the theoretical analysis, experimental results are conducted using a real-world cloud service quality feedback, collected by the CloudArmor project. This research reveals that due to the fact that service customization tends to enhance the customers loyalty in today’s subscription cloud economy, the best strategy for small IaaS providers is to increase the service cost and improve the quality of their added-value solutions to prevent customers’ defection. This not only elevates the provider’s profit, but also increases the quality equilibrium that leads to a higher user satisfaction. Consequently, higher satisfaction enhances the provider’s rating and future users demand.
Discovering the influence of sarcasm in social media responses

Abstract Sarcasm in verbal and nonverbal communication is known to attract higher attention and create deeper influence than other negative responses. Many people are adept at including sarcasm in written communication thus sarcastic comments have the potential to stimulate the virality of social media content. Although diverse computational approaches have been used to detect sarcasm in social media, the use of text mining to explore the influential role of sarcasm in spreading negative content is limited. Using tweets during a service disruption of a leading Australian organization as a case study, we explore this phenomenon using a text mining framework with a combination of statistical modeling and natural language processing (NLP) techniques. Our work targets two main outcomes: the quantification of the influence of sarcasm and the exploration of the change in topical relationships in the conversations over time. We found that sarcastic expressions during the service disruption are higher than on regular days and negative sarcastic tweets attract significantly higher social media responses when compared to literal negative expressions. The content analysis showed that consumers initially complaining sarcastically about the outage tended to eventually widen the negative sarcasm in a cascading effect towards the organization's internal issues and strategies. Organizations could utilize such insights to enable proactive decision‐making during crisis situations. Moreover, detailed exploration of these impacts would elevate the current text mining applications, to better understand the impact of sarcasm by stakeholders expressed in a social media environment, which can significantly affect the reputation and goodwill of an organization. This article is categorized under: Technologies &gt; Data Preprocessing Ensemble Methods &gt; Text Mining Application Areas &gt; Industry Specific Applications Fundamental Concepts of Data and Knowledge &gt; Big Data Mining
Developing a Data Infrastructure for Enabling Breast Cancer Women to BOUNCE Back

Breast cancer is the most common cancer disease in women and is rapidly becoming a chronic illness due recent advances in treatment methods. As such, coping with cancer has become a major socio-economic challenge leading to an increasing need for predicting resilience of women to the variety of stressful experiences and practical challenges they face. In this paper, we present the data infrastructure developed for this purpose, demonstrating the various components that will contribute to the developing the resilience trajectory predictor. Special emphasis is given to the semantic tier, presenting the project solution already implemented for effectively collecting, ingesting, cleaning, modelling and processing data that will be used throughout the lifetime of the project.
Filtering for a Class of Quantum Systems With Classical Stochastic Disturbances

We present filtering equations for a class of hybrid quantum-classical systems where a quantum optical cavity system is continuously disturbed by a classical linear stochastic process. Another quantum cavity system is employed to describe the classical stochastic process as an analog. The initial hybrid quantum-classical system consisting of both a classical signal and a quantum cavity system is then modeled by a combined quantum system with two quantum cavity systems. The stochastic master equations, or filtering equations, are then derived using quantum filtering theory. A quantum extended Kalman filter method is adopted as a convenient solution to this filtering problem. The effectiveness of both methods is illustrated by numerical examples.
Modeling of Cumulative QoE in On-Demand Video Services: Role of Memory Effect and Degree of Interest

The growing demand on video streaming services increasingly motivates the development of a reliable and accurate models for the assessment of Quality of Experience (QoE). In this duty, human-related factors which have significant influence on QoE play a crucial role. However, the complexity caused by multiple effects of those factors on human perception has introduced challenges on contemporary studies. In this paper, we inspect the impact of the human-related factors, namely perceptual factors, memory effect, and the degree of interest. Based on our investigation, a novel QoE model is proposed that effectively incorporates those factors to reflect the user’s cumulative perception. Evaluation results indicate that our proposed model performed excellently in predicting cumulative QoE at any moment within a streaming session.
A type theory for cartesian closed bicategories (Extended Abstract)

We construct an internal language for cartesian closed bicategories. Precisely, we introduce a type theory modelling the structure of a cartesian closed bicategory and show that its syntactic model satisfies an appropriate universal property, thereby lifting the Curry-Howard-Lambek correspondence to the bicategorical setting. Our approach is principled and practical. Weak substitution structure is constructed using a bicategori-fication of the notion of abstract clone from universal algebra, and the rules for products and exponentials are synthesised from semantic considerations. The result is a type theory that employs a novel combination of 2-dimensional type theory and explicit substitution, and directly generalises the Simply-Typed Lambda Calculus. This work is the first step in a programme aimed at proving coherence for cartesian closed bicategories.
Model-Based Hierarchical Clustering for Categorical Data

Agglomerative hierarchical clustering methods based on Gaussian probability models have recently shown to be efficient in different applications. However, the emerging of pattern recognition applications where the features are binary or integer-valued demand extending research efforts to such data types. This paper proposes a hierarchical clustering framework for clustering categorical data based on Multinomial and Bernoulli mixture models. We have compared two widely used density-based distances, namely; Bhattacharyya and KullbackLeibler. The merits of our proposed framework have been shown through extensive experiments on clustering text and images using the bag of visual words model.
Revisiting Jump-Diffusion Process for Visual Tracking: A Reinforcement Learning Approach

In this paper, we revisit the classical stochastic jump-diffusion process and develop an effective variant for estimating visibility statuses of objects while tracking them in videos. Dealing with partial or full occlusions is a long standing problem in computer vision but largely remains unsolved. In this paper, we cast the above problem as a Markov decision process and develop a policy-based jump-diffusion method to jointly track object locations in videos and estimate their visibility statuses. Our method employs a set of jump dynamics to change visibility statuses of objects and a set of diffusion dynamics to track objects in videos. Different from the traditional jump-diffusion process that stochastically generates dynamics, we utilize deep policy functions to determine the best dynamic for the present state and learn the optimal policies using reinforcement learning methods. Our method is capable of tracking objects with full or partial occlusions in crowded scenes. We evaluate the proposed method over challenging video sequences and compare it to alternative tracking methods. Significant improvements are made particularly for videos with frequent interactions or occlusions.
fABElous: An Attribute-Based Scheme for Industrial Internet of Things

The Internet of Things (IoT) is a technological vision in which constrained or embedded devices connect together through the Internet. This enables common objects to be empowered with communication and cooperation capabilities. Industry can take an enormous advantage of IoT, leading to the so-called Industrial IoT. In these systems, integrity, confidentiality, and access control over data are key requirements. An emerging approach to reach confidentiality and access control is Attribute-Based Encryption (ABE), which is a technique able to enforce cryptographically an access control over data. In this paper, we propose fABElous, an ABE scheme suitable for Industrial IoT applications which aims at minimizing the overhead of encryption on communication. fABElous ensures data integrity, confidentiality, and access control, while reducing the communication overhead of 35% with respect to using ABE techniques naively.
Collaborative Deep Reinforcement Learning for Image Cropping

An automatic photo composition method based on collaborative deep reinforcement learning(called CDRL-RC) is proposed in this paper. Our method models photo composition as a markov decision-making process by reinforcement learning and generates cropping result through a series of moving and zooming actions. Emotional attention information is added to the composition task, which was trained by eye-tracking datasets to consider the relationship and importance between objects. In order to sufficiently use the emotional attention map and original image for image cropping, they are processed as inputs to two collaborative agents. For the collaborative composition of two agents, we design an information interaction module, which allows inter-agents to exchange information and give advice to each other, and finally predict the action together. In addition, we add attention weight to the traditional IoU to efficiently evaluate the cropping quantity in the reward function. Experiment results show that our CDRL-RC model achieved the state-of-the-art photo composition performance on a variety of datasets.
Building Accurate Physics-based Face Models from Data

The human face is an anatomical system exhibiting heterogenous and anisotropic mechanical behavior. This leads to complex deformations even in a neutral facial expression due to external forces such as gravity. We start by building a volumetric model from magnetic resonance images of a neutral facial expression. To obtain data on facial deformations we capture and register 3D scans of the face with different gravity directions and with various facial expressions. Our main contribution consists in solving an inverse physics problem where we learn mechanical properties of the face from our training data (3D scans). Specifically, we learn heterogenous stiffness and prestrain (which introduces anisotropy). The generalization capability of our resulting physics-based model is tested on 3D scans. We demonstrate that our model generates predictions of facial deformations more accurately than recent related physics-based techniques.
CMOS Annealing Machine: an In-memory Computing Accelerator to Process Combinatorial Optimization Problems

A new computing architecture, an annealing machine, which is specialized to solve combinatorial optimization problems, is proposed. The annealing machine maps optimization problems to an Ising model and solves the optimization problems by its own convergence property. We proposed a CMOS implementation of the annealing machine, which is a type of an in-memory computing. We constructed two prototypes of the CMOS annealing machine. The 1st-generation prototype confirmed the power efficiency is 1800times higher than that of the conventional von-Neumann computers. The FPGA 2md-generation prototype is used to explore its applications and is also confirmed its multiple-chip operation.
Entanglement detection under coherent noise: Greenberger-Horne-Zeilinger-like states

Entanglement is an essential resource in many quantum information tasks and entanglement witness is a widely used tool for its detection. In experiments the prepared state generally deviates from the target state due to some noise. Normally the white noise model is applied to quantifying such derivation and in the same time reveals the robustness of the witness. However, there may exist other kind of noise, in which the coherent noise can dramatically "rotate" the prepared state. In this way, the coherent noise is likely to lead to a failure of the detection, even though the underlying state is actually entangled. In this work, we propose an efficient entanglement detection protocol for $N$-partite Greenberger-Horne-Zeilinger (GHZ)-like states. The protocol can eliminate the effect of the coherent noise and in the same time feedback the corresponding noise parameters, which are beneficial to further improvements on the experiment system. In particular, we consider two experiment-relevant coherent noise models, one is from the unconscious phase accumulation on $N$ qubits, the other is from the rotation on the control qubit. The protocol effectively realizes a family of entanglement witnesses by postprocessing the measurement results from $N+2$ local measurement settings, which only adds one more setting than the original witness specialized for the GHZ state. Moreover, by considering the trade-off between the detection efficiency and the white-noise robustness, we further reduce the number of local measurements to 3 without altering the performance on the coherent noise. Our protocol can enhance the entanglement detection under coherent noises and act as a benchmark for the state-of-the-art quantum devices.
An Improved Edge Detection Method based on Median Filter

To preserve the edges from corrupted noise, median filter is a popular choice for filtering algorithms. The salt and pepper noise is one of the main problems present while capturing an image. To solve this problem the edges were detected, using different edge detection operators which include Roberts, Sobel, Prewitt and Canny, prior to processing by a median filter. Several simulations were carried out over a number of digital images. The peak signal to noise ratio (PSNR) is higher using Prewitt operator than Sobel operator. Similarly, results also confirm the better performance of Canny edge detection algorithm over others for edge detection however computational cost is high in it compared to other methods.
Rough segmentation of coherent local intensity for bias induced 3-D MR brain images

Segmentation of brain MR volumes into different meaningful tissue classes is an essential prerequisite for many clinical analyses. However, intensity inhomogeneity or bias field, present in MR volumes, considerably degrades the quality of segmentation. In this regard, the paper presents a new segmentation algorithm, termed as CoLoRS (Coherent Local Intensity Rough Segmentation), for brain MR volumes corrupted with bias field artifact. It judiciously integrates the merits of coherent local intensity clustering and the theory of rough sets for simultaneous segmentation and bias field correction of brain MR volumes. The proposed algorithm partitions the entire image space into a number of small overlapping neighborhood regions. The bias, in each neighborhood region, is assumed to be constant. For each individual region, an objective function is defined for coherent local intensity rough segmentation. The voxels near the center point have similar influences on local objective function. In addition, the smaller distance between center and neighboring voxels yields more contribution on the voxel of interest. The proposed algorithm uses the dual-region concept to represent the neighborhood structure more efficiently. It makes possible of separate modeling of the voxels within neighborhood, according to their locations. Each region is considered to have several tissue classes, where each tissue class consists of a core region and an overlapping region. The segmentation in fuzzy approximation spaces provides an effective mean for brain MR volume analysis, as it handles overlapping partitions and addresses vagueness in tissue class definition. The effectiveness of the proposed algorithm, along with a comparison with existing approaches, is demonstrated on several publicly available brain MR data.
Explaining Multi-label Black-Box Classifiers for Health Applications

Today the state-of-the-art performance in classification is achieved by the so-called "black boxes", i.e. decision-making systems whose internal logic is obscure. Such models could revolutionize the health-care system, however their deployment in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of transparency. The typical classification problem in health-care requires a multi-label approach since the possible labels are not mutually exclusive, e.g. diagnoses. We propose MARLENA, a model-agnostic method which explains multi-label black box decisions. MARLENA explains an individual decision in three steps. First, it generates a synthetic neighborhood around the instance to be explained using a strategy suitable for multi-label decisions. It then learns a decision tree on such neighborhood and finally derives from it a decision rule that explains the black box decision. Our experiments show that MARLENA performs well in terms of mimicking the black box behavior while gaining at the same time a notable amount of interpretability through compact decision rules, i.e. rules with limited length.