Reviewerly/exHarmony huggingface dataset:
Uniform Sequence Better: Time Interval Aware Data Augmentation for Sequential Recommendation

Sequential recommendation is an important task to predict the next-item to access based on a sequence of interacted items. Most existing works learn user preference as the transition pattern from the previous item to the next one, ignoring the time interval between these two items. However, we observe that the time interval in a sequence may vary significantly different, and thus result in the ineffectiveness of user modeling due to the issue of preference drift. In fact, we conducted an empirical study to validate this observation, and found that a sequence with uniformly distributed time interval (denoted as uniform sequence) is more beneficial for performance improvement than that with greatly varying time interval. Therefore, we propose to augment sequence data from the perspective of time interval, which is not studied in the literature. Specifically, we design five operators (Ti-Crop, Ti-Reorder, Ti-Mask, Ti-Substitute, Ti-Insert) to transform the original non-uniform sequence to uniform sequence with the consideration of variance of time intervals. Then, we devise a control strategy to execute data augmentation on item sequences in different lengths. Finally, we implement these improvements on a state-of-the-art model CoSeRec and validate our approach on four real datasets. The experimental results show that our approach reaches significantly better performance than the other 9 competing methods. Our implementation is available: https://github.com/KingGugu/TiCoSeRec.
nl2spec: Interactively Translating Unstructured Natural Language to Temporal Logics with Large Language Models

Abstract A rigorous formalization of desired system requirements is indispensable when performing any verification task. This often limits the application of verification techniques, as writing formal specifications is an error-prone and time-consuming manual task. To facilitate this, we present , a framework for applying Large Language Models (LLMs) to derive formal specifications (in temporal logics) from unstructured natural language. In particular, we introduce a new methodology to detect and resolve the inherent ambiguity of system requirements in natural language: we utilize LLMs to map subformulas of the formalization back to the corresponding natural language fragments of the input. Users iteratively add, delete, and edit these sub-translations to amend erroneous formalizations, which is easier than manually redrafting the entire formalization. The framework is agnostic to specific application domains and can be extended to similar specification languages and new neural models. We perform a user study to obtain a challenging dataset, which we use to run experiments on the quality of translations. We provide an open-source implementation, including a web-based frontend.
Experimental Demonstration of Fully Passive Quantum Key Distribution

The passive approach to quantum key distribution (QKD) consists of removing all active modulation from the users' devices, a highly desirable countermeasure to get rid of modulator side channels. Nevertheless, active modulation has not been completely removed in QKD systems so far, due to both theoretical and practical limitations. In this Letter, we present a fully passive time-bin encoding QKD system and report on the successful implementation of a modulator-free QKD link. According to the latest theoretical analysis, our prototype is capable of delivering competitive secret key rates in the finite key regime.
Ludomusicology and the New Drastic

In a rereading of Carolyn Abbate's seminal article “Music—Drastic or Gnostic?” this article proposes that the study of game music not only presents us with new research themes but, moreover, has the potential to inspire a major disciplinary reform. Game music studies and ludomusicology can lead to a New Drastic Musicology: an intellectual engagement with video game music that is just as rooted in immediacy, interactivity, and playfulness as the object with which it concerns itself. The New Drastic, I shall argue, can engender significant critical, epistemological, thematic, and analytical innovations in the discipline of musicology. Fundamentally, my argument is that playing with music invites “playing” with critical music theory.
The System Requirements Specification

Providing a concise and accessible overview of the design, implementation and management of medical software, this textbook will equip students with a solid understanding of critical considerations for both standalone medical software (software as a medical device/SaMD) and software that is integrated into hardware devices. It includes: practical discussion of key regulatory documents and industry standards, and how these translate into concrete considerations for medical software design; detailed coverage of the medical software lifecycle process ; accessible introduction to quality and risk management systems in the context of medical software; succinct coverage of essential topics in data science, machine learning, statistics, cybersecurity, software engineering and healthcare bring readers up-to-speed; six cautionary real-world case studies illustrate the dangers of improper or careless software processes. Accompanied by online resources for instructors, this is the ideal introduction for undergraduate students in biomedical engineering, electrical engineering and computer science, junior software engineers, and digital health entrepreneurs.
MSP: Multiple Sub-graph Query Processing using Structure-based Graph Partitioning Strategy and Map-Reduce

In a distributed environment, the volume of graph database increases quickly because graphs emerge from several autonomous sources. Sub-graph query processing is a challenging problem in distributed environment. Centralized approaches proposed many algorithms, they mine frequent subgraphs from the graph database and construct an index which is very expensive. These algorithms require more number of database scans to mine frequent subgraphs and they use filter and verify approach, which requires many subgraph isomorphism tests. In this paper, we design a novel Map-Reduce based multiple subgraph query processing framework, namely MSP. MSP processes multiple graph queries using distributed index. The framework completely relies on the graph partition and indexing. Moreover, in order to improve its performance, we propose several solutions to balance the workload and reduce the size of Integrated Graph Index. We propose a structure-based partitioning technique and distributed way of building Integrated Graph Index. This work uses two Map-Reduce rounds, the first Map-Reduce round partitions the graphs and creating index for each partition, second Map-Reduce round processes sub-graph queries and index maintenance. A good partitioning will reduce the index size by distributing the load equally to the machines in the cluster and improves the performance of query evaluation. This graph partition and Integrated Graph Index reduces the search space of query graphs. Our approach allows to add data graphs incrementally to Integrated Graph Index while doing query processing. We experimentally show that our approach decreases remarkably the execution time and scales the subgraph query processing to large graph databases.
Groupwise Multichannel Image Registration

Multichannel image registration is an important challenge in medical image analysis. Multichannel images result from modalities such as dual-energy CT or multispectral microscopy. Besides, multichannel feature images can be derived from acquired images, for instance, by applying multiscale feature banks to the original images to register. Multichannel registration techniques have been proposed, but most of them are applicable to only two multichannel images at a time. In the present study, we propose to formulate multichannel registration as a groupwise image registration problem. In this way, we derive a method that allows the registration of two or more multichannel images in a fully symmetric manner (i.e., all images play the same role in the registration procedure), and therefore, has transitive consistency by definition. The method that we introduce is applicable to any number of multichannel images, any number of channels per image, and it allows to take into account correlation between any pair of images and not just corresponding channels. In addition, it is fully modular in terms of dissimilarity measure, transformation model, regularisation method, and optimisation strategy. For two multimodal datasets, we computed feature images from the initially acquired images, and applied the proposed registration technique to the newly created sets of multichannel images. MIND descriptors were used as feature images, and we chose total correlation as groupwise dissimilarity measure. Results show that groupwise multichannel image registration is a competitive alternative to the pairwise multichannel scheme, in terms of registration accuracy and insensitivity towards registration reference spaces.
Contrapuntal Aspects of the Mystic Chord and Scriabin’s Piano Sonata No. 5

We present statistical evidence for the importance of the “mystic chord” in Scriabin’s Piano Sonata No. 5, Op. 53, from a computational and mathematical counterpoint perspective. More specifically, we compute the effect sizes and perform $$\chi ^{2}$$ tests with respect to the distributions of counterpoint symmetries in the Fuxian, mystic, Ionian and representatives of the other three possible counterpoint worlds in two passages of the work, which provide evidence of a qualitative change between the Fuxian and the mystic worlds in the sonata.
Unicyclic strong permutations

In this paper, we study some properties of a certain kind of permutation σ over $\mathbb {F}_{2}^{n}$, where n is a positive integer. The desired properties for σ are: (1) the algebraic degree of each component function is n − 1; (2) the permutation is unicyclic; (3) the number of terms of the algebraic normal form of each component is at least 2n− 1. We call permutations that satisfy these three properties simultaneously unicyclic strong permutations. We prove that our permutations σ always have high algebraic degree and that the average number of terms of each component function tends to 2n− 1. We also give a condition on the cycle structure of σ. We observe empirically that for n even, our construction does not provide unicylic permutations. For n odd, n ≤ 11, we conduct an exhaustive search of all σ given our construction for specific examples of unicylic strong permutations. We also present some empirical results on the difference tables and linear approximation tables of σ.
SALSA-TEXT: Self Attentive Latent Space Based Adversarial Text Generation

Inspired by the success of self attention mechanism and Transformer architecture in sequence transduction and image generation applications, we propose novel self attention-based architectures to improve the performance of adversarial latent code-based schemes in text generation. Adversarial latent code-based text generation has recently gained a lot of attention due to its promising results. In this paper, we take a step to fortify the architectures used in these setups, specifically AAE and ARAE. We benchmark two latent code-based methods (AAE and ARAE) designed based on adversarial setups. In our experiments, the Google sentence compression dataset is utilized to compare our method with these methods using various objective and subjective measures. The experiments demonstrate the proposed (self) attention-based models outperform the state-of-the-art in adversarial code-based text generation.
On collaborative predictive blacklisting

Collaborative predictive blacklisting (CPB) allows to forecast future attack sources based on logs and alerts contributed by multiple organizations. Unfortunately, however, research on CPB has only focused on increasing the number of predicted attacks but has not considered the impact on false positives and false negatives. Moreover, sharing alerts is often hindered by confidentiality, trust, and liability issues, which motivates the need for privacy-preserving approaches to the problem. In this paper, we present a measurement study of state-of-the-art CPB techniques, aiming to shed light on the actual impact of collaboration. To this end, we reproduce and measure two systems: a non privacy-friendly one that uses a trusted coordinating party with access to all alerts [12] and a peer-to-peer one using privacy-preserving data sharing [8]. We show that, while collaboration boosts the number of predicted attacks, it also yields high false positives, ultimately leading to poor accuracy. This motivates us to present a hybrid approach, using a semi-trusted central entity, aiming to increase utility from collaboration while, at the same time, limiting information disclosure and false positives. This leads to a better trade-off of true and false positive rates, while at the same time addressing privacy concerns.
Joint attention is intact even when visuospatial working memory is occupied

The gaze of another modulates and shifts an observer's spatial attention toward this gaze direction. This phenomenon is termed joint attention. Although previous studies found joint attention was induced during a spatial working memory task, it remains an open question whether spatial working memory interferes with joint attention. To address this question, we conducted a dual task paradigm consisting of gaze cuing and spatial working memory tasks. The gaze cuing task requires that participants respond to the location of an abrupt onset of a target while ignoring the gaze of a facial image located at the center of the display. In the spatial working memory task, participants memorized two or three locations of small dots and judged whether these locations changed in the probe screen. In the dual task condition, while participants were maintaining spatial information of the spatial working memory task, they performed the gaze cuing task. In Experiment 1, maintaining spatial information did not impair the magnitude of joint attention. In Experiment 2, we increased load of spatial working memory task, and still observed results similar to Experiment 1. In Experiment 3, we replicated the findings in Experiment 1 when an identification task was conducted. In addition, we measured electrooculographic signals to investigate eye movements of participants during the task. The study provides that joint attention is intact even if spatial working memory is occupied.
High Sensitive Readout Circuit for Capacitance Touch Panel With Large Size

A readout circuit for large sized capacitive touch screens is presented in this letter. Being different from the conventional readout circuit, the phase conversion module is inserted between the differential sensing structure and the integrating amplifier, to take advantage of both the positive and negative edge of the touch sensing signal for the following integrator. Thus, the sensing output value can be increased as the touch signals are integrated by twice for every clock cycle. Both simulation and measurement results verify that, thanks to the insertion of the proposed phase conversion module, the amplitude of touch sensing circuit’s output is increased from 1.4 to 2.8 V. In addition, compared with the frequency increasing method, the proposed scheme features an increase of 40% in the sensing output voltage.
Robust Noisiness Measure Based Improved Generalized Fuzzy Peer Group for Removal of Mixed Noise From Color Image

In this letter, a novel method has been proposed to estimate noisiness of a pixel in an image. The proposed noisiness is based on a robust noisiness measure, which is evaluated using noise and edge information of locality of the pixel. Furthermore, this noisiness is used to develop a fuzzy peer group for removing mixed Gaussian and impulse noises from color images. Also, an iterative approach to the proposed method is presented. The proposed approach is then compared with the state-of-the-art algorithms. Results shows that the proposed method outperform existing algorithms significantly.
Detecting and Mapping Video Impairments

Automatically identifying the locations and severities of video artifacts without the advantage of an original reference video is a difficult task. We present a novel approach to conducting no-reference artifact detection in digital videos, implemented as an efficient and unique dual-path (parallel) excitatory/inhibitory neural network that uses a simple discrimination rule to define a bank of accurate distortion detectors. The learning engine is distortion-sensitized by pre-processing each video using a statistical image model. The overall system is able to produce full-resolution space-time distortion maps for visualization, providing global distortion detection decisions that represent the state of the art in performance. Our model, which we call the video impairment mapper (VIDMAP), produces a first-of-a-kind full-resolution map of artifact detection probabilities. The current realization of this system is able to accurately detect and map eight of the most important artifact categories encountered during streaming video source inspection: aliasing, video encoding corruptions, quantization, contours/banding, combing, compression, dropped frames, and upscaling artifacts. We show that it is either competitive with or significantly outperforms the previous state of the art on the whole-image artifact detection task. A software release of VIDMAP that has been trained to detect and map these artifacts is available online: http://live.ece.utexas.edu/research/quality/VIDMAP_release.zip for public use and evaluation.
Two Birds With One Stone: A Coupled Poisson Deconvolution for Detecting and Describing Topics From Multimodal Web Data

Organizing multimodal Web pages into hot topics is the core step to grasp trends on the Web. However, the less-constrained social media generate noisy user-generated content, which makes a detected topic be less coherent and less interpretable. In this paper, we address this problem by proposing a coupled Poisson deconvolution to jointly handle topic detection and topic description. For the topic detection, the interestingness of a topic is estimated from the similarities refined by the description of topics; for the topic description, the interestingness of topics is leveraged to describe topics. Two processes cyclically detect interesting topics and generate the multimodal description of topics. This is the innovation of this paper, which just likes killing two birds with one stone. Experiments not only show the significantly improved accuracies for the topic detection but also demonstrate the interpretable descriptions for the topic description on two public data sets.
Non-negative and local sparse coding based on l2-norm and Hessian regularization

Abstract Due to the efficiency of representing visual data and reducing dimension of complex structure, methods of sparse coding have been widely investigated and achieved ideal performance in image classification. These sparse coding methods learn both a dictionary and the sparse codes from the original data together under the constraint to l1-norm. However, the introduction of l1-norm tends to choose small number of atoms from the relevant bases in process of dictionary learning, abandoning other high-related bases, which results in the neglect of group effect and weak generalization of the model. In this paper, we propose a novel sparse coding model which introduces the l2-norm constraint and the second-order Hessian energy in the optimization function. This model eliminates the restrictions on the number of selected base vectors in the dictionary learning, and makes better use of the topological structure information as well, thus the intrinsic geometric characteristics of the data is described more accurately. In addition, our model is extended with a non-negative local constraint, which ensures similar features to share their local bases. Extensive experimental results on the real-world datasets show that the proposed model extraordinarily outperforms several state-of-the-art image representative methods.
Performance analysis of moving object detection using BGS techniques in visual surveillance

Over the last decennium, the object detection is the pivotal step in any machine vision and image processing application.It is the initial step applied to extract most informative pixel from the video stream.Many algorithms are available in literature for extraction of visual information or foreground object from video sequence.This paper also provides a detailed overview of both conventional and traditional approaches used for detection of object.This paper explores various related methods, major challenges, applications, resources such as datasets, web-sources, etc.This paper presents a detailed overview of a moving object detection using background subtraction techniques in the video surveillance system that provide safety in cities, towns or home when video sequence is captured using IP cameras.The experimental work of this paper is performed over change detection, I2R, and wallflower datasets.The experimental work also depicts a comparative analysis of some of the peer methods.This work demonstrates several performance metrics to check robustness of the compared state-of-the-art methods.
Activity Archetypes in Question-and-Answer (Q8A) Websites—A Study of 50 Stack Exchange Instances

Millions of users on the Internet discuss a variety of topics on Question-and-Answer (Q8A) instances. However, not all instances and topics receive the same amount of attention, as some thrive and achieve self-sustaining levels of activity, while others fail to attract users and either never grow beyond being a small niche community or become inactive. Hence, it is imperative to not only better understand but also to distill deciding factors and rules that define and govern sustainable Q8A instances. We aim to empower community managers with quantitative methods for them to better understand, control, and foster their communities, and thus contribute to making the Web a more efficient place to exchange information. To that end, we extract, model, and cluster a user activity-based time series from 50 randomly selected Q8A instances from the Stack Exchange network to characterize user behavior. We find four distinct types of user activity temporal patterns, which vary primarily according to the users’ activity frequency. Finally, by breaking down total activity in our 50 Q8A instances by the previously identified user activity profiles, we classify those 50 Q8A instances into three different activity profiles. Our parsimonious categorization of Q8A instances aligns with the stage of development and maturity of the underlying communities, and can potentially help operators of such instances: We not only quantitatively assess progress of Q8A instances, but we also derive practical implications for optimizing Q8A community building efforts, as we, e.g., recommend which user types to focus on at different developmental stages of a Q8A community.
Circular Derivative Local Binary Pattern Feature Description for Facial Expression Recognition

This paper presents a novel feature extraction technique called circular derivative local binary pattern (CD-LBP) for Facial Expression Recognition (FER). Motivated by uniform local bi ...