Reviewerly/exHarmony huggingface dataset:
Quantum tetrachotomous states: Superposition of four coherent states on a line in phase space

The well-studied quantum optical Schr\"odinger cat state is a superposition of two distinguishable states, with quantum coherence between these macroscopically distinguishable states being of foundational and, in the context of quantum-information processing, practical use. We refer to these quantum-optical cat states as quantum dichotomous states, reflecting that the state is a superposition of two options, and we introduce the term quantum multichotomous state to refer to a superposition of multiple macroscopically distinguishable options. For a single degree of freedom, such as position, we construct the quantum multichotomous states as a superposition of Gaussian states on the position line in phase space. Using this nomenclature, a quantum tetrachotomous state (QTS) is a coherent superposition of four macroscopically distinguishable states. We define, analyze, and show how to create such states, and our focus on the QTSs is due to their exhibition of much richer phenomena than for the quantum dichotomous states. Our characterization of the QTS involves the Wigner function, its marginal distributions, and the photon-number distribution, and we discuss the QTS's approximate realization in a multiple-coupled-well system.
Lightweight instruction-level encryption for embedded processors using stream ciphers

Over the last 30 years, a number of secure processor architectures have been proposed to protect software integrity and confidentiality during its distribution and execution. In such architectures, encryption (together with integrity checking) is used extensively, on any data leaving a defined secure boundary. In this paper, we show how encryption can be achieved at the instruction level using a stream cipher. Thus encryption is more lightweight and efficient, and is maintained deeper in the memory hierarchy than the natural off-chip boundaries considered in most research works. It requires the control flow graph to be used and modified as part of the off-line encryption process, but thanks to the LLVM framework, it can be integrated easily in a compiler pipeline, and be completely transparent to the programmer. We also describe hardware modifications needed to support this encryption method, the latter were added to a 32-bit MIPS soft core. The synthesis performed on a Altera Cyclone V FPGA shows that encryption requires 26% of extra logic, while slowing-down execution time by an average of 48% in the best setting.
Pictionary-Style Word Guessing on Hand-Drawn Object Sketches: Dataset, Analysis and Deep Network Models

The ability of intelligent agents to play games in human-like fashion is popularly considered a benchmark of progress in Artificial Intelligence. In our work, we introduce the first computational model aimed at Pictionary, the popular word-guessing social game. We first introduce Sketch-QA, a guessing task. Styled after Pictionary, Sketch-QA uses incrementally accumulated sketch stroke sequences as visual data. Sketch-QA involves asking a fixed question (“What object is being drawn?”) and gathering open-ended guess-words from human guessers. We analyze the resulting dataset and present many interesting findings therein. To mimic Pictionary-style guessing, we propose a deep neural model which generates guess-words in response to temporally evolving human-drawn object sketches. Our model even makes human-like mistakes while guessing, thus amplifying the human mimicry factor. We evaluate our model on the large-scale guess-word dataset generated via Sketch-QA task and compare with various baselines. We also conduct a Visual Turing Test to obtain human impressions of the guess-words generated by humans and our model. Experimental results demonstrate the promise of our approach for Pictionary and similarly themed games.
Evaluating the variability of photovoltaics: A new stochastic method to generate site-specific synthetic solar data and applications to system studies

The power output of solar photovoltaics (PV) may have sharp fluctuations and its impact has to be carefully evaluated before integrating significant PV facilities into the power grid. Variability of solar resources is best measured by ground-based measurements. However, distributed ground-measured solar data is not available everywhere, and it would take considerable cost and time to obtain such data. Therefore, it is important and beneficial to model and estimate the variability of distributed PV generation even with insufficient solar data at each location. This study proposes a new methodology to generate spatially-distributed synthetic PV data based on detailed ground measurements collected at a few sites. The synthetic PV data is examined with specific criteria and the feasibility for simulating spatially-distributed PV generation is verified. A case study for Hong Kong is conducted using both the real and synthetic solar data. It is demonstrated that individual PV facilities can have significant fluctuations on a minute-by-minute basis, but the fluctuations can be significantly reduced if PV facilities are more spatially-distributed. The improvement to the estimation of solar variability with the proposed method is illustrated and the significance of its applications is discussed.
Universality in distribution of monogamy scores for random multiqubit pure states

Monogamy of quantum correlations provides a way to study restrictions on their sharability in multiparty systems. We find the critical exponent of these measures, above which randomly generated multiparty pure states satisfy the usual monogamy relation, and show that the critical power decreases with the increase in the number of parties. For three-qubit pure states, we detect that W-class states are more prone to being nonmonogamous as compared to the GHZ-class states. We also observe a different criticality in monogamy power up to which random pure states remain nonmonogamous. We prove that the "average monogamy" score asymptotically approaches its maximal value on increasing the number of parties. Analyzing the monogamy scores of random three-, four-, five- and six-qubit pure states, we also report that almost all random pure six-qubit states possess maximal monogamy score, which we confirm by evaluating statistical quantities like mean, variance and skewness of the distributions. In particular, with the variation of number of qubits, means of the distributions of monogamy scores for random pure states approach to unity -- which is the algebraic maximum -- thereby conforming to the known results of random states having maximal multipartite entanglement in terms of geometric measures.
Partially Observable Multi-Sensor Sequential Change Detection: A Combinatorial Multi-Armed Bandit Approach

This paper explores machine learning to address a problem of Partially Observable Multi-sensor Sequential Change Detection (POMSCD), where only a subset of sensors can be observed to monitor a target system for change-point detection at each online learning round. In contrast to traditional Multisensor Sequential Change Detection tasks where all the sensors are observable, POMSCD is much more challenging because the learner not only needs to detect on-the-fly whether a change occurs based on partially observed multi-sensor data streams, but also needs to cleverly choose a subset of informative sensors to be observed in the next learning round, in order to maximize the overall sequential change detection performance. In this paper, we present the first online learning study to tackle POMSCD in a systemic and rigorous way. Our approach has twofold novelties: (i) we attempt to detect changepoints from partial observations effectively by exploiting potential correlations between sensors, and (ii) we formulate the sensor subset selection task as a Multi-Armed Bandit (MAB) problem and develop an effective adaptive sampling strategy using MAB algorithms. We offer theoretical analysis for the proposed online learning solution, and further validate its empirical performance via an extensive set of numerical studies together with a case study on real-world data sets.
Optimal quantum subsystem codes in two dimensions

Given any two classical codes with parameters $[{n}_{1},k,{d}_{1}]$ and $[{n}_{2},k,{d}_{2}]$, we show how to construct a quantum subsystem code in two dimensions with parameters $[[N,K,D]]$ satisfying $N\ensuremath{\le}2{n}_{1}{n}_{2}, K=k$, and $D=min({d}_{1},{d}_{2})$. These quantum codes are in the class of generalized Bacon-Shor codes introduced by Bravyi [Phys. Rev. A 83, 012320 (2011)]. We note that constructions of good classical codes can be used to construct quantum codes that saturate Bravyi's bound $KD=O(N)$ on the code parameters of two-dimensional subsystem codes. One of these good constructions uses classical expander codes. This construction has the additional advantage of a linear time quantum decoder based on the classical Sipser-Spielman flip decoder. Finally, while the subsystem codes we create do not have asymptotic thresholds, we show how they can be gauge fixed to certain hypergraph product codes that do.
Intelligent Municipal Heritage Management Service in a Smart City: Telecommunication Traffic Characterization and Quality of Service

The monitoring of cultural heritage is becoming common in cities to provide heritage preservation and prevent vandalism. Using sensors and video cameras for this task implies the need to transmit information. In this paper, the teletraffic that cameras and sensors generate is characterized and the transmissions’ influence on the municipal communications network is evaluated. Then, we propose models for telecommunication traffic sources in an intelligent municipal heritage management service inside a smart sustainable city. The sources were simulated in a smart city scenario to find the proper quality of service (QoS) parameters for the communication network, using Valencia City as background. Specific sensors for intelligent municipal heritage management were selected and four telecommunication traffic sources were modelled according to real-life requirements and sensors datasheet. Different simulations were performed to find the proper CIR (Committed Information Rate) and PIR (Peak Information Rate) values and to study the effects of limited bandwidth networks. Packet loss, throughput, delay, and jitter were used to evaluate the network’s performance. Consequently, the result was the selection of the minimum values for PIR and CIR that ensured QoS and thus optimized the traffic telecommunication costs associated with an intelligent municipal heritage management service.
Image encryption using the random FrDCT and the chaos-based game of life

A novel image encryption algorithm using the random fractional DCT (RFrDCT) and the chaos-based Game of Life (GoL) is presented here. Firstly the plaintext image is transformed by the reality-preserving RFrDCT into the RFrDCT domain, and a preliminary encryption output is obtained. And then a combination of the confusion and diffusion (C&D) processes is performed in the transform domain, where the confusion process is carried out by GoL, and the diffusion process is carried out by an XOR operation. Therefore a more random-like encryption result can be obtained after further encryption by the C&D. Both the GoL and XOR operation are based on chaos whose control parameters serve as cipher keys in order to enlarge cipher space and enhance key sensitivity. Theoretical analysis and simulation experiments for the proposed algorithm are described, the desirable encryption performance is obtained, and it is secure against various common attacks.
High-Quality Immunohistochemical Stains Through Computational Assay Parameter Optimization

Accurate profiling of tumors using immunohistochemistry (IHC) is essential in cancer diagnosis. The inferences drawn from IHC-stained images depend to a great extent on the quality of immunostaining, which is in turn affected strongly by assay parameters. To optimize assay parameters, the available tissue sample is often limited. Moreover, with current practices in pathology, exploring the entire assay parameter space is not feasible. Thus, the evaluation of IHC stained slides is conventionally a subjective task, in which diagnoses are commonly drawn on images that are suboptimal. In this work, we introduce a framework to analyze IHC staining quality and its sensitivity to process parameters. To that extent, first histopathological sections are segmented automatically. Then, machine learning techniques are employed to extract disease-specific staining quality metrics (SQMs) targeting a quantitative assessment of staining quality. Finally, an approach to efficiently analyze the parameter space is introduced to infer sensitivity to process parameters. We present results on microscale IHC tissue samples of five breast tumor classes, based on disease state and protein expression. A disease-type classification F1-score of 0.82 and a contrast-level classification F1-score of 0.95 were achieved. With the proposed SQMs, an area under the curve of 0.85 was achieved on average over different disease types. Our methodology provides a promising step in automatically evaluating and quantifying staining quality of IHC stained tissue sections, and it can potentially standardize immunostaining across diagnostic laboratories.
A Serious Game for Introducing Software Engineering Ethics to University Students

This paper presents a game based on storytelling, in which the players are faced with ethical dilemmas related to software engineering specific issues. The players’ choices have consequences on how the story unfolds and could lead to various alternative endings. This Ethics Game was used as a tool to mediate the learning activity and it was evaluated by 144 students during a Software Engineering Course on the 2017–2018 academic year. This evaluation was based on a within-subject pre-post design methodology and provided insights on the students learning gain (academic performance), as well as on the students’ perceived educational experience. In addition, it provided the results of the students’ usability evaluation of the Ethics Game. The results indicated that the students did improve their knowledge about software engineering ethics by playing this game. Also, they considered this game to be a useful educational tool and of high usability. Female students had statistically significant higher knowledge gain and higher evaluation scores than male students, while no statistically significant differences were measured in groups based on the year of study.
A Survey of Computational Approaches and Challenges in Multimodal Sentiment Analysis

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Applying the Fractal Analysis Methods for the Study of the Mechanisms of Deformation and Destruction of Polymeric Material Samples Affected by Tensile Stresses

The paper proposes an approach to study the mechanism of deformation of epoxy polymers based on the methods of fractal calculations. The advantages of the method for determining the fractality index of the deformation curves of polymer samples using the minimum coverage method are shown. An algorithm for the quantitative determination of the location of the "critical" points of the deformation curves of polymer samples under tension is proposed. It is shown that the use of the developed methodology for the fractal analysis of time series on the basis of a set of points of deformation curves of epoxy polymer samples under tension, provides valuable information about the processes occurring in the structure of composite materials affected by mechanical loads and various aggressive factors.
A Weighted Word Embedding Model for Text Classification

Neural bag-of-words models (NBOW) have achieved great success in text classification. They compute a sentence or document representation by mathematical operations such as simply adding and averaging over the word embedding of each sequence element. Thus, NBOW models have few parameters and require low computation cost. Intuitively, considering the important degree of each word and the word-order information for text classification are beneficial to obtain informative sentence or document representation. However, NBOW models hardly consider the above two factors when generating a sentence or document representation. Meanwhile, term weighting schemes assigning relatively high weight values to important words have exhibited successful performance in traditional bag-of-words models. However, it is still seldom used in neural models. In addition, n-grams capture word-order information in short context. In this paper, we propose a model called weighted word embedding model (WWEM). It is a variant of NBOW model introducing term weighting schemes and n-grams. Our model generates informative sentence or document representation considering the important degree of words and the word-order information. We compare our proposed model with other popular neural models on five datasets in text classification. The experimental results show that our proposed model exhibits comparable or even superior performance.
End-to-End Learning of Semantic Grid Estimation Deep Neural Network with Occupancy Grids

We propose semantic grid, a spatial 2D map of the environment around an autonomous vehicle consisting of cells which represent the semantic information of the corresponding region such as car, road, vegetation, bikes, etc. It consists of an integration of an occupancy grid, which computes the grid states with a Bayesian filter approach, and semantic segmentation information from monocular RGB images, which is obtained with a deep neural network. The network fuses the information and can be trained in an end-to-end manner. The output of the neural network is refined with a conditional random field. The proposed method is tested in various datasets (KITTI dataset, Inria-Chroma dataset and SYNTHIA) and different deep neural network architectures are compared.
Tackling Permutation-based Optimization Problems with an Algebraic Particle Swarm Optimization Algorithm

Particle Swarm Optimization (PSO), though originally introduced for continuous search spaces, has been increasingly applied to combinatorial optimization problems. In this paper, we focus on the PSO applications to permutation-based problems. As far as we know, the most popular and general PSO sche mes for permutation solutions are those based on random key techniques. After highlighting the main criticalities of the random key approach, we introduce a discrete PSO variant for permutation-based optimization problems. By simulating search moves through a vector space, the proposed algorithm, Algebraic PSO (APSO), allows the original PSO design to be applied to the permutation search space. APSO directly represents both particle positions and velocities as permutations. The APSO search scheme is based on a general algebraic framework for combinatorial optimization based on strong mathematical foundations. However, in order to make this new scheme viable, some challenges have to be overcome: the choice of the order of the velocity terms, and the rationale behind the PSO inertial move. Design solutions have been proposed for both the issues. Furthermore, an alternative geometric interpretation of classical PSO dynamics allows to introduce a major APSO variant based on a novel concept of convex combination between permutation objects. In total, four APSO schemes have been introduced. Experiments have been held to compare the performances of the APSO schemes with respect to the random key based PSO schemes in literature. Widely adopted benchmark instances of four popular permutation problems have been considered. The experimental results clearly show that, with high statistical evidence, APSO outperforms its competitors and it reaches results comparable with state-of-the-art on most of the instances considered.
Deep Learning with Convolutional Neural Networks for Histopathology Image Analysis

In theHistopathology recent years, deep learningDeep learning based methods and, in particular, convolutional neural networksConvolutional neural network, have been dominating the arena of medical image analysis. This has been made possible both with the advent of new parallel hardware and the development of efficient algorithms. It is expected that future advances in both of these directions will increase this domination. The application of deep learningDeep learning methods to medical image analysis has been shown to significantly improve the accuracy and efficiency of the diagnoses. In this chapter, we focus on applications of deep learningDeep learning in microscopy image analysis and digital pathologyDigital pathology, in particular. We provide an overview of the state-of-the-art methods in this area and exemplify some of the main techniques. Finally, we discuss some open challenges and avenues for future work.
Manipulating the Difficulty of C-Tests

We propose two novel manipulation strategies for increasing and decreasing the difficulty of C-tests automatically. This is a crucial step towards generating learner-adaptive exercises for self-directed language learning and preparing language assessment tests. To reach the desired difficulty level, we manipulate the size and the distribution of gaps based on absolute and relative gap difficulty predictions. We evaluate our approach in corpus-based experiments and in a user study with 60 participants. We find that both strategies are able to generate C-tests with the desired difficulty level.
Using SWISH to Realize Interactive Web-based Tutorials for Logic-based Languages

Programming environments have evolved from purely text based to using graphical user interfaces, and now we see a move towards web based interfaces, such as Jupyter. Web based interfaces allow for the creation of interactive documents that consist of text and programs, as well as their output. The output can be rendered using web technology as, e.g., text, tables, charts or graphs. This approach is particularly suitable for capturing data analysis workflows and creating interactive educational material. This article describes SWISH, a web front-end for Prolog that consists of a web server implemented in SWI-Prolog and a client web application written in JavaScript. SWISH provides a web server where multiple users can manipulate and run the same material, and it can be adapted to support Prolog extensions. In this paper we describe the architecture of SWISH, and describe two case studies of extensions of Prolog, namely Probabilistic Logic Programming (PLP) and Logic Production System (LPS), which have used SWISH to provide tutorial sites.