Reviewerly/exHarmony huggingface dataset:
The $$\aleph $$-Calculus

A novel model of reversible computing, the $$\aleph $$ -calculus, is introduced. It is declarative, reversible-Turing complete, and has a local term-rewriting semantics. Unlike previously demonstrated reversible term-rewriting systems, it does not require the accumulation of history data. Terms in the $$\aleph $$ -calculus, in combination with the program definitions, encapsulate all program state. An interpreter was also written.
Computer Vision and Image Understanding

In this work, we study a complete framework of Video Compressive Sensing (VCS), from capturing a sequence of video frames in one single compressed measurement to reconstructing the original frames. To our best knowledge, we present the first end-to-end sampling and recovery network built upon video Transformers, widely explored in vision related tasks, to capture long-range spatio-temporal relations. Our proposed Video Transformer for Snapshot Compressive Imaging recovery (ViT-SCI) is based on Spatio-temporal Convolutional Multi-Head Attention (ST-ConvMHA) which is an extended version of the fully-connected attention adapted for vision problems. Our comprehensive qualitative and quantitative experiments on several datasets demonstrate that ViT-SCI outperforms previous state-of-the-art methods with much faster reconstruction capacities, which pave the way for applying our algorithm in real-time applications. Indeed, ViT-SCI achieves high quality reconstruction on 64 × 64 video frames at the unprecedented rate of 1 frame per ms. In addition, an important ablation study on the Transformer network is provided to inspire future research works aiming to test the abilities of Transformers in vision tasks.
Algeo: An Algebraic Approach to Reversibility

We present Algeo, a functional logic programming language based on the theory of infinite dimensional modules. Algeo is reversible in the sense that every function has a generalised inverse, an adjoint, which can be thought of as an inverse execution of the forward function. In particular, when the given function is invertible, the adjoint is guaranteed to coincide with the inverse. Algeo generalises “ordinary” forward-backward deterministic reversible programming by permitting relational and probabilistic features. This allows functions to be defined in a multitude of ways, which we summarise by the motto that “all definitions are extensional characterisations; all extensional characterisations are definitions.” We describe the syntax, type system, and the axiomatic semantics of Algeo, and showcase novel features of the language through examples.
Cross-Modal Language Modeling in Multi-Dynamic-Informed Context for Lip Reading

We observe that for lip reading, the language is locally transformed, instead of globally transformed, i.e., speaking and writing follow the same basic grammar rules. In this work, we present cross-modal language modeling in multi-dynamic-informed contexts to tackle the lip-reading challenge on silent videos. We present a method to distribute attention blocks over multiple lip-dynamic representations corresponding to the output of stacked convolutions. In this way, multi-dynamic-informed contexts composed of multiple lip-dynamic or sequence representations from different subspaces are used to guide decoding via the source-target attention mechanism. To further leverage the framework, we use a piece-wise pre-training strategy inspired by multi-task learning to pre-train a visual module to generate required multi-dynamic-informed contexts for cross-modality and pre-train a decoder to generate texts for language modeling. Our final large-scale model outperforms baseline models on four datasets: LRS2, LRS3, LRW, and GRID. We will open our source code on GitHub.
Particle Stochastic Map Filters

Filtering in nonlinear state-space models is known to be a challenging task due to the posterior distribution being either intractable or expressed in a complex form. One of the most successful methods, particle filtering (PF), although generally outperforming traditional filters, suffers from sample degeneracy. Drawing from optimal transport theory, the stochastic map filter (SMF) accommodates a solution to this problem, but its performance is influenced by the limited-flexibility of nonlinear transport maps. We propose a hybrid filter which combines the PF and SMF, and hence call it PSMF. Specifically, the PSMF splits the likelihood into two parts with a splitting factor. Both parts are then updated by PF and SMF, respectively. The proposed approach adopts systematic resampling and smoothing to break the particle degeneracy caused by the PF. In order to investigate the influence of the nonlinearity of transport maps, we introduce two variants of the proposed filter, the PSMF-L and PSMF-NL, which are based on linear and nonlinear maps, respectively. The PSMF is tested on various nonlinear state-space models and a nonlinear non-Gaussian target tracking model.The proposed linear PSMF-L outperforms all the reference models for medium-to-large numbers of particles, whilst the PSMF-NL shows better resilience to parameter changes.
Improving Cohesion of a Software System by Performing Usage Pattern Based Clustering

Increasing the software design quality is a key research challenge in object-oriented software development system. Cohesion is one of the key spect that helps to evaluate the quality and modularity of a software system at the design level. It helps to create software components that are directly reusable to the industry because of their less dependence on other components. In this paper, a new cohesion metric for object-oriented software, named as Usage Pattern Based Cohesion (UPBC), is proposed which is computed at the module level. This paper considers class as a module initially and subsequently group of classes (i.e. a package) is considered as a module with an aim of improving overall cohesion. This metric utilizes the Frequent Usage Patterns (FUP) extracted from different member functions interactions to capture the cohesiveness of the module. Further, the measured cohesion value is used to perform clustering of modules in order to increase cohesion and decrease coupling among modules simultaneously. The clustering is performed by using a newly proposed clustering algorithm called FUPClust (Frequent Usage Pattern based Clustering) based on FUP interactions among modules. The proposed approach is applied to two Java software systems and the results obtained show a significant improvement in the cohesiveness of the software system.
Cost-Based Virtual Machine Scheduling for Data-as-A-Service

Data-as-a-Service (DaaS) is a branch of cloud computing that supports “querying the Web”. Due to its ultrahigh scale, it is essential to establish rules when pricing resources and guidelines for infrastructure investments. Those decisions should prioritize compliance with SLA requirements, minimize the incidence of agreement breaches that compromise the performance of cloud services and optimize resources’ usage and services’ cost. This work aims to address the pricing problem of DaaS by developing a cost model that optimizes the costs of querying distributed data sources over virtual machines spread across multisite data centers.
Application of Quantum Computing in Discrete Portfolio Optimization

This article proposes a new, more efficient quantum algorithm for portfolio optimization and uses quantum combinatorial optimization (QCO) techniques. The recent construction developed in 2021 has sparked the field of financial portfolio optimization through the Quantum Walk Optimization Algorithm (QWOA). We study the complexity and efficiency of quantum optimization algorithms with a special interest in QWOA. The objective is to minimize the risk of investing by having a good combination of assets in the portfolio. We also focus on reducing the number of iterations yet attaining a high-quality resolution through contraction of the solution space that eases computations. We extend QWOA’s idea by constructing a newly outperforming scheme known as ’Quantum Mix Optimization Algorithm (QMOA)’. QMOA algorithm codes are provided for the implementation and simulation of numerical results. In addition, QMOA’s efficiency (better than the existing QCO algorithms) is discussed. For instance, the least QWOA number of computations required to execute the initial state equation is p > 2 while this value is p ≥ 2 in the proposed QMOA.
Real-Time AI-Based Inference of People Gender and Age in Highly Crowded Environments

Gender identity is one of life’s most fundamental aspects. Automatic gender identification is becoming more widely used in fields such as security, marketing, and social robots. The focus of this paper is to address the challenges of gender and age identification in very crowded/noisy environments where faces are not clear and/or people are moving in relatively random directions. It presents an end-to-end real-time intelligent video analytics solution for instantaneous people counting, gender and age estimation in crowded and open environments. The proposed solution includes a full pipeline for training vision deep learning models and deploying them to edge devices that are connected to a distributed streaming analytics server. Our final Deep Learning architecture is a modified version of FairMOT, a Multi-object tracking model, with two additional layers for gender multi-class classification and age regression. The training phase is carried out using an enhanced and enriched version of the CrowdHuman dataset, a public dataset for human detection, with gender and age annotations added. The overall system has been validated for various movies and has shown state-of-the-art performance in terms of people tracking, gender and age inference.
Applying Fuzzy Association Rule Mining and Blockchain–Iot for Process Control and Analytics

The quality management (QM) literature assumes that product and process performance data are authentic and readily accessible. Such a perception is often correct but can be erroneous because data authenticity and timeliness cannot be guaranteed in many circumstances. Inaccurate, incomplete, and inaccessible data often occur in supply chains, which prevent QM from achieving its aim: the assurance of product and process quality. This study sheds light on resolving data problems in QM from the perspective of supply chain visibility by proposing a platform that integrates the blockchain technology, Internet of Things, and big data analytics. Simulations and fuzzy association rule mining were performed to demonstrate how the platform can overcome data quality and timeliness problems. This study also delineates the mechanisms that automatically execute process control and quality analytics. Furthermore, the contributions of the integrated platform to QM are discussed from four theoretical perspectives.
Proposed Intelligent Learning Algorithm to Improve the Diagnosis and Classification of Breast Cancer Masses

In today's world, intelligent medical diagnostic systems, with their remarkable advances, have greatly contributed to the medical world and have accelerated and increased the accuracy of many diagnoses, including the system for diagnosing and classifying breast cancer masses . Unfortunately, breast cancer is one of the most dangerous diseases and has led to many deaths among women. Early detection can, firstly, increase treatment options and, secondly, increase life expectancy in women with earlier treatment. Early detection methods include mammography, MRI and ultrasound. Early detection methods for breast cancer, include mammography, MRI, and ultrasound. In all diagnostic methods, image processing and artificial intelligence methods can be significantly effective in the process of noise detection and reduction, tumor area detection and classification. In this paper, first we reduce the noise in mammographic images using Directional filters, then extract the tumor area by combining the support vector machine and the Evolutionary Algorithms, and finally, using deep learning, To classify the type of mass . The proposed method has better performance compared to previous methods and offers 99.1% accuracy, 98.4% sensitivity and specificity with 100%. in diagnosis and classification.
Reversible Computation in Integrated Photonics

Sold-state microphotonics allows to build circuits which transform w light beams in a unitary way. Such a circuit transforms a w-dimensional complex vector by means of a unitary $$w \times w$$ matrix. For the synthesis of the optical circuit, we can learn a lot from properties of quantum circuits, subject to adaptation of the interpretation of the matrix decompositions.
Stability Analysis for Uncertain Switched Fractional Delayed Neural Networks

We consider a fractional order uncertain switch neural networks with time delay model in this manuscript. The stability analysis of the equilibrium points of the model have been analyzed. Numerical simulations have been done by using the Predictor-Corrector method to establish the adequacy of the hypothetical outcomes corresponding to the model and the derived results have been illustrated graphically using MATLAB.
Directed Graph Encoding in Quantum Computing Supporting Edge-Failures

Graphs are one of the most common data structures in classical computer science and graph theory has been widely used in complexity and computability. Recently, the use of graphs in application domains such as routing, network analysis and resource allocation has become crucial. In these areas, graphs are often evolving in time: for example, connection links may fail due to temporary technical issues, meaning that edges of the graph cannot be traversed for some time interval and alternative paths have to be followed. In classical computation, where graphs are represented as adjacency matrices or lists, these problems are well studied and ad-hoc visit procedures have been developed. For specific problems quantum computation, through superpositions and entanglement has provided faster algorithms than their classical counterpart. However, in this model, only reversible operations are allowed and this poses the quest of augmenting a graph in order to be able to reverse edge traversals. In this paper we present a novel graph representation in quantum computation supporting dynamic connectivity typical of real-world network applications. Our proposal has the advantage of being closer than others in literature to the adjacency matrix of the graph. This makes easy dynamic edge-failure modeling. We introduce optimal algorithms for computing our graph encoding and we show the effectiveness of our proposal with some examples.
Memory Efficient Deep Neural Network Training

Recently Artificial Intelligence (AI) has demonstrated a huge progress in solving complex problems such as image classification, text generation, translation... Its success is due to a development of hardware and algorithms making possible the emergence of Deep Neural Networks (DNNs). Such DNNs are composed of a number of operations from numerical linear algebra, whose order is defined with a Directed Acyclic Graph (DAG). These DNNs operate at the limit of the computational resources, and to go to deeper and more complex neural networks it is necessary either to design more powerful computational resources or optimize their usage. In our work, we address in particular the problem of lowering memory usage during the training of DNNs.
Exploring the Impact of Node Failures on the Resource Allocation for Parallel Jobs

Increasing the size and complexity of modern HPC systems also increases the probability of various types of failures. Failures may disrupt application execution and waste valuable system resources due to failed executions. In this work, we explore the effect of node failures on the completion times of MPI parallel jobs. We introduce a simulation environment that generates synthetic traces of node failures, assuming that the times between failures for each node are independently distributed, following the same distribution but with different parameters. To highlight the importance of failure-awareness for resource allocation, we compare two failure-oblivious resource allocation approaches with one that considers node failure probabilities before assigning a partition to a job: a heuristic that randomly selects the partition for a job, and Slurm’s linear resource allocation policy. We present results for a case study that assumes a 4D-torus topology and a Weibull distribution for each node’s time between failures, and considers several different traces of node failures, capturing different failure patterns. For the synthetic traces explored, the benefit is more prominent for longer jobs, up to 82% depending on the trace, when compared with Slurm and a failure-oblivious heuristic. For shorter jobs, benefits are noticeable for systems with more frequent failures.
High Resolution Photovoltaic Power Generation Potential Assessments of Rooftop in China

Rooftop photovoltaic system plays an important role in solar energy power generation especially in urban. In this paper, we present an assessment method for the PV power generation potential of rooftop in China. Using machine learning model processes the big data that consists of the gross domestic product, building footprint, road length and population, at a high geographic resolution of 10 km by 10 km. The result shows that the rooftop generation potential in China is 3.27×10 9 MWh annually, which is closed to half of the total electricity generation of China mainland in 2020, and will contribute to 2.41×10 9 tons of CO 2 emission reduction per year. The regional results are counted by provinces and cities, showing Shandong province is the one with highest potential of 0.275×10 9 MWh. On the whole, the western region covers a large area with sufficient solar radiation, while the eastern region has greater photovoltaic power generation potential because of its available roof resources. The development potential of first tier, new first tier and second tier cities is generally greater than that of third and fourth tier cities.
Detecting ShadowsocksR User Based on Intelligence of Cyber Entities

ShadowsocksR (SSR), as a typical emerging anonymous communication tool, may record user information on the SSR client or server, leading to the theft of the user’s privacy, and may be used by attackers to anonymize their internal network environment and organization, which will cause serious damage to data security and bring severe challenges to security defense and threat assessment within organizations. To solve the problem of accurately and effectively discovering SSR users within an organization in a real traffic environment, in this paper, we propose an SSR user detection method based on network entity intelligence as follows: (1) According to the communication characteristics of SSR users, relevant network entity intelligence information from inside and outside the organization is obtained, such as the distribution of IP addresses within and outside the organization, and the differences between SSR and non-SSR users are analyzed to construct a feature space. (2) The communication behaviors of SSR and non-SSR users are further analyzed and features are extracted from the perspective of traffic behavior analysis, and the feature space of the SSR user detection model is expanded. (3) A data-driven machine-learning-based approach is designed and implemented to provide suggestions for the automatic identification of SSR users based on the extracted feature vectors. Results show that the detection method proposed in this paper has a detection accuracy of more than 95% for SSR users in the experimental environment, can accurately distinguish between SSR communication and normal communication, and can achieve accurate SSR user detection.
Localization Verifiability Under Outlier Measurements With Linear Programming

The problem of localizing a set of nodes from relative pairwise measurements appears in different fields, such as computer vision, sensor networks, and robotics. In practice, the measurements might be contaminated by noise and outliers that lead to erroneous localization. Previous work has empirically shown that robust algorithms can, in some situations, almost completely cancel the effect of outliers. However, there is a theoretical gap in answering the following question: Under what conditions on the number, magnitude, and arrangement of the outlier measurements can we guarantee that a robust algorithm will recover the ground truth locations from the relative measurements alone? We denote this concept as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">verifiability</i> , and answer the question for the case of an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> -norm robust optimization formulation, with translation measurements that are affected only by large-magnitude outliers and no small-magnitude noise. We prove that verifiability depends only on the topology of the graph, the location of the edges affected by the outliers, and the sign of the outliers, while it is independent of the ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> unknown) true location of the nodes, and the magnitude of the outliers. We present an algorithm based on the dual simplex algorithm that checks the verifiability of a problem, and, if not verifiable, completely characterizes the space of equivalent solutions that are consistent with the given pairwise measurements. Our theory and algorithms can be used to compute the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> probability of recovering a solution congruent or equivalent to the ground truth, without having access to the true locations.
Combine Supervised Edge and Semantic Supplement for Instance Segmentation

Two-stage instance segmentation method outperforms the one-stage counterpart on complex occasions. However, we found that the RoIAlign operation identifies the feature map to smaller size, and the convolution or up-sampling causes the loss of detailed information. All these make it difficult to achieve precise segmentation. To circumvent the issue, we propose a simple and efficient anchor-free model for instance segmentation. We name it as CSAS because it combines the detection-based and segmentation-based idea. The CSAS adopts the two-stage paradigm, which mainly includes detection and segmentation. The box head not only considers the location accuracy into confidence score but calculates the IoU loss of regression, which leads to a gain of 1.5%. The mask head adopts the multi-task learning to accomplish precise segmentation, and it grows 1.7 points. Using the ResNet-50-FPN, a single CSAS obtains 1.6% improvement over the Mask R-CNN. Our result demonstrates that CSAS is capable of gaining the complete mask of instance. We conclude that the detailed feature information is essential for precise segmentation, the idea is available for other segmentation tasks.