Reviewerly/exHarmony huggingface dataset:
Gesture recognition by model matching of slope difference distribution features

Gesture recognition has been studied for many decades and remains an open problem. One possible reason is that the features extracted for gesture recognition are not effective enough. A trained person could recognize a variety of gestures robustly based on the contour of the hand. Accordingly, there must be a machine vision method that could recognize the same variety of gestures robustly based on the shape features. The key technique lies in how to define and extract the shape features effectively. In this paper, we propose to recognize of the gestures with the shape features defined and extracted by slope difference distribution (SDD). To calculate the SDD features, one-dimensional hand contour is defined as the distance distribution between the hand centroid and each point on the two-dimensional hand contour. To make the model matching robust, only the common intra-class SDD features are selected as the sparse representation of the hand contour. The proposed gesture recognition method was tested with three public datasets and achieved state-of-the-art recognition accuracy on two public datasets.
Density of free modules over finite chain rings

In this paper we focus on modules over a finite chain ring R of size qs. We compute the density of free modules of Rn, where we separately treat the asymptotics in n,q and s. In particular, we focus on two cases: one where we fix the length of the module and one where we fix the rank of the module. In both cases, the density results can be bounded by the Andrews-Gordon identities. We also study the asymptotic behaviour of modules generated by random matrices over R. Since linear codes over R are submodules of Rn we get direct implications for coding theory. For example, we show that random codes achieve the Gilbert-Varshamov bound with high probability.
A Compact Full Hardware Implementation of PQC Algorithm NTRU

With the emergence and development of quantum computers, the traditional public-key cryptography (PKC) is facing the risk of being cracked. In order to resist quantum attacks and ensure long-term communication security, NIST launched a global collection of Post Quantum Cryptography (PQC) standards in 2016, and it is currently in the third round of selection. There are three Lattice-based PKC algorithms that stand out, and NTRU is one of them. In this article, we proposed the first complete and compact full hardware implementation of NTRU algorithm submitted in the third round. By using one structure to complete the design of the three types of complex polynomial multiplications in the algorithm, we achieved better performance while reducing area costs.
Dynamic access control technology based on zero-trust light verification network model

With the rise of the cloud computing and services, the network environments tend to be more complex and enormous. Security control becomes more and more hard due to the frequent and various access and requests. There are a few techniques to solve the problem which developed separately in the recent years. Network Micro-Segmentation provides the system the ability to keep different parts separated. Zero Trust Model ensures the network is access to trusted users and business by applying the policy that verify and authenticate everything. With the combination of Segmentation and Zero Trust Model, a system will obtain the ability to control the access to organizations' or industrial valuable assets. To implement the cooperation, the paper designs a strategy named light verification to help the process to be painless for the cost of inspection. The strategy was found to be effective from the perspective of the technical management, security and usability.
IP Geolocation Using Traceroute Location Propagation and IP Range Location Interpolation

Many online services, including search engines, content delivery networks, ad networks, and fraud detection utilize IP geolocation databases to map IP addresses to their physical locations. However, IP geolocation databases are often inaccurate. We present a novel IP geolocation technique based on combining propagating IP location information through traceroutes with IP interpolation. Using a large ground truth set, we show that physical locations of IP addresses can be propagated along traceroute paths. We also experiment with and expand upon the concept of IP range location interpolation, where we use the location of individual addresses in an IP range to assign a location to the entire range. The results show that our approach significantly outperforms commercial geolocation by up to 31 percentage points. We open source several components to aid in reproducing our results.
A branch and bound irredundant graph algorithm for large-scale MLCS problems

Finding the multiple longest common subsequences (MLCS) among many long sequences (i.e., the large scale MLCS problem) has many important applications, such as gene alignment, disease diagnosis, and documents similarity check, etc. It is an NP-hard problem (Maier et al., 1978). The key bottle neck of this problem is that the existing state-of-the-art algorithms must construct a huge graph (called direct acyclic graph, briefly DAG), and the computer usually has no enough space to store and handle this graph. Thus the existing algorithms cannot solve the large scale MLCS problem. In order to quickly solve the large-scale MLCS problem within limited computer resources, this paper therefore proposes a branch and bound irredundant graph algorithm called Big-MLCS, which constructs a much smaller DAG (called Small-DAG) than the existing algorithms do by a branch and bound method, and designs a new data structure to efficiently store and handle Small-DAG. By these schemes, Big-MLCS is more efficient than the existing algorithms. Also, we compare the proposed algorithm with two state-of-the-art algorithms through the experiments, and the results show that the proposed algorithm outperforms the compared algorithms and is more suitable to large-scale MLCS problems.
Understanding aging and consumer fraud victimization in the Chinese context: A two-stage conceptual approach

The relationship between aging and consumer fraud victimization is mixed in the literature. Most studies based on survey data have found older consumers less likely to be fraud victims, while a few studies have found older consumers more likely to be victimized, especially with certain fraud types. We developed a two-stage conceptual framework to differentiate fraud exposure from fraud victimization once exposed. Using nationally representative Chinese data and controlling for confounders, we found that consumers between 65 and 74 face similar risks of being targeted by perpetrators compared to younger groups, while consumers 75+ and older are less likely to be exposed to fraud. However, once exposed, both groups of older consumers are significantly more likely to become fraud victims. In the Chinese context, these two opposing effects led to an overall higher risk of consumer financial fraud victimization by older consumers.
Forecasting of COVID 19 Cases in Karnataka State using Artificial Neural Network (ANN)

COVID-19 is a pandemic that has caused lot of deaths and infections in the last 2 months and is showing an increasing trend not only in the number of infections and deaths, but also in the recovery rate. Accurate prediction models are very much essential to make proper forecasts and take necessary actions. This study demonstrates the capability of Multilayer Perceptron (MLP), an Artificial Neural network (ANN) model for forecasting the number of infected cases in the state of Karnataka in India. It is trained using a fast training algorithm namely, Extreme Learning machine to reduce the training time required. The parameters required for the forecasting model have been selected using partial autocorrelation function, which is a conventional method, and its performance has been compared with parameters selected using cuckoo search (CS) algorithm, which is a very popular metaheuristic optimization algorithm. The testing of the forecasting model has been done, and comparison between the two parameter selection methods as well as with MLP with conventional backpropagation has been carried out. Use of CS algorithm has resulted in a better forecasting performance based on mean absolute percentage error (MAPE), with a value of 6.62% on training data and 7.03% on the test data. Further to check the efficacy of the model, the data of COVID-19 cases of Hungary from 4 March to 19 April 2020 have been used, which resulted in a MAPE of 1.55%, thereby establishing the robustness of the proposed ANN model for forecasting COVID-19 cases for the state of Karnataka.
PAUL: Procrustean Autoencoder for Unsupervised Lifting

Recent success in casting Non-rigid Structure from Motion (NRSfM) as an unsupervised deep learning problem has raised fundamental questions about what novelty in NRSfM prior could the deep learning offer. In this paper we advocate for a 3D deep auto-encoder framework to be used explicitly as the NRSfM prior. The framework is unique as: (i) it learns the 3D auto-encoder weights solely from 2D projected measurements, and (ii) it is Procrustean in that it jointly resolves the unknown rigid pose for each shape instance. We refer to this architecture as a Procustean Autoencoder for Unsupervised Lifting (PAUL), and demonstrate state-of-the-art performance across a number of benchmarks in comparison to recent innovations such as Deep NRSfM [21] and C3PDO [32].
Learning to Track Instances without Video Annotations

Tracking segmentation masks of multiple instances has been intensively studied, but still faces two fundamental challenges: 1) the requirement of large-scale, frame-wise annotation, and 2) the complexity of two-stage approaches. To resolve these challenges, we introduce a novel semisupervised framework by learning instance tracking networks with only a labeled image dataset and unlabeled video sequences. With an instance contrastive objective, we learn an embedding to discriminate each instance from the others. We show that even when only trained with images, the learned feature representation is robust to instance appearance variations, and is thus able to track objects steadily across frames. We further enhance the tracking capability of the embedding by learning correspondence from unlabeled videos in a self-supervised manner. In addition, we integrate this module into single-stage instance segmentation and pose estimation frameworks, which significantly reduce the computational complexity of tracking compared to two-stage networks. We conduct experiments on the YouTube-VIS and PoseTrack datasets. Without any video annotation efforts, our proposed method can achieve comparable or even better performance than most fullysupervised methods <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Multiple Dynamic Outlier-Detection from a Data Stream by Exploiting Duality of Data and Queries

Real-time outlier detection from a data stream has become increasingly important in the current hyperconnected world. This paper focuses on an important yet unaddressed challenge in continuous outlier detection: the multiplicity and dynamicity of queries. This challenge arises from various contexts of outliers evolving over time, but the state-of-the-art algorithms cannot handle the challenge effectively, as they can only process a fixed set of outlier detection queries for each data point separately. In this paper, we propose a novel algorithm, abbreviated as MDUAL, based on a new idea called duality-based unified processing. The underlying rationale is to exploit the duality of data and queries so that a group of similar data points are processed together by a group of similar queries incrementally. Two main techniques embodying the idea, data-query grouping and prioritized group processing, are employed. Comprehensive experiments showed that MDUAL runs 216 to 221 times faster while consuming 11 to 13 times less memory than the state-of-the-art algorithms through its efficient and effective handling of the multiplicity-dynamicity challenge.
LOMA: Fast Auto-Scheduling on DNN Accelerators through Loop-Order-based Memory Allocation

The scheduling or temporal mapping of a neural network (NN) on a given hardware (HW) accelerator strongly impacts its execution energy and latency. Unfortunately the mapping space is huge and varies a lot in function of the NN-HW combination. Many design space exploration (DSE) frameworks aim at automatically exploring this vast mapping space. Yet, SotA frameworks suffer from being slow (e.g. exhaustive search), inflexible across a wide range of HW architectures (e.g. no support for uneven mapping), or can't guarantee global optimality (e.g. from relying on user-defined constraints or on random sampling). Moreover, existing frameworks are typically unable to predict the required CPU run-time and peak CPU memory requirements in advance and as such are unable to trade-off search time with optimality in a deterministic manner. This work proposes LOMA, a fast auto-scheduling methodology through loop-order-based memory allocation, which overcomes above bottlenecks. LOMA's capabilities are demonstrated at scale in finding the optimal schedule of complete MobileNetV3, resp. NASNet.
Laser-Based Algorithms Meeting Privacy in Surveillance: A Survey

Privacy of people is a key factor in surveillance systems. Video camera brings us well-off color information. How would the privacy be secured then? Besides, privacy protection should not create a hindrance for finding of objects or people under specific cases. Laser scanner takes way affluent color information. It functions with eye-safe and invisible laser beam. Yet, it provides us robust object recognition map. Images can be interpreted by humans, but laser-based systems need software applications to explain the data. Camera-based surveillance system does not focus on the problem of private life conservation. On the contrary, laser-based surveillance system ensures privacy of people inherently, as it does not record real world videos except laser scanned data points. In this paper, first, the privacy issues of people for both surveillance systems have been compared to realize their significance. Second, a qualitative performance comparison between laser-based and RGB camera-based systems has been made to hint that laser-based algorithms should be used instead of common RGB cameras. Third, a succinct survey of laser-based detection and tracking algorithms of movers has been conducted. Final, a superiority measure of the leading laser-based people-vehicles related algorithms has been performed on the basis of statistical test scores deeming the ineffectualness metrics (e.g., errors and failures) of each algorithm.
DeepCuts: a deep learning optimization framework for versatile GPU workloads

Widely used Deep Learning (DL) frameworks, such as TensorFlow, PyTorch, and MXNet, heavily rely on the NVIDIA cuDNN for performance. However, using cuDNN does not always give the best performance. One reason is that it is hard to handle every case of versatile DNN models and GPU architectures with a library that has a fixed implementation. Another reason is that cuDNN lacks kernel fusion functionality that gives a lot of chances to improve performance. In this paper, we propose a DL optimization framework for versatile GPU workloads, called DeepCuts. It considers both kernel implementation parameters and GPU architectures. It analyzes the DL workload, groups multiple DL operations into a single GPU kernel, and generates optimized GPU kernels considering kernel implementation parameters and GPU architecture parameters. The evaluation result with various DL workloads for inference and training indicates that DeepCuts outperforms cuDNN/cuBLAS-based implementations and the state-of-the-art DL optimization frameworks, such as TVM, TensorFlow XLA, and TensorRT.
Nonlinear input transformations are ubiquitous in quantum reservoir computing

Abstract The nascent computational paradigm of quantum reservoir computing presents an attractive use of near-term, noisy-intermediate-scale quantum processors. To understand the potential power and use cases of quantum reservoir computing, it is necessary to define a conceptual framework to separate its constituent components and determine their impacts on performance. In this manuscript, we utilize such a framework to isolate the input encoding component of contemporary quantum reservoir computing schemes. We find that across the majority of schemes the input encoding implements a nonlinear transformation on the input data. As nonlinearity is known to be a key computational resource in reservoir computing, this calls into question the necessity and function of further, post-input, processing. Our findings will impact the design of future quantum reservoirs, as well as the interpretation of results and fair comparison between proposed designs.
Comonadic semantics for guarded fragments

In previous work ([1], [2], [3]), it has been shown how a range of model comparison games which play a central role in finite model theory, including Ehrenfeucht-Fraïssé, pebbling, and bisimulation games, can be captured in terms of resource-indexed comonads on the category of relational structures. Moreover, the coalgebras for these comonads capture important combinatorial parameters such as tree-width and tree-depth.The present paper extends this analysis to quantifier-guarded fragments of first-order logic. We give a systematic account, covering atomic, loose and clique guards. In each case, we show that coKleisli morphisms capture winning strategies for Duplicator in the existential guarded bisimulation game, while back-and-forth bisimulation, and hence equivalence in the full guarded fragment, is captured by spans of open morphisms. We study the coalgebras for these comonads, and show that they correspond to guarded tree decompositions. We relate these constructions to a syntax-free setting, with a comonad on the category of hypergraphs.
Dilated U-Block for Lightweight Indoor Depth Completion With Sobel Edge

Depth completion has proven to be the key to obtaining high-precision depth maps from a sparse set of measurements and a single RGB image. However, state-of-the-art depth completion algorithms are based on rather complex deep networks that are unfriendly for multiple applications with limited computational resources and energy. In this paper, we consider the task of lightweight indoor depth completion. We propose an efficient convolution block, called Dilated U-Block (DUB), for multi-level feature extraction and integration. The DUBs and an auxiliary Sobel edge prediction network are used to decrease the number of parameters and model complexity. Comprehensive experiments are performed on the indoor NYU-Depth-v2 dataset. The results show that our proposed approach achieves similar accuracy while requiring only about 5% of parameter size and 20% model complexity compared to the state-of-the-art methods.
Alps: Adaptive Quantization of Deep Neural Networks with GeneraLized PositS

In this paper, a new adaptive quantization algorithm for generalized posit format is presented, to optimally represent the dynamic range and distribution of deep neural network parameters. Adaptation is achieved by minimizing the intra-layer posit quantization error with a compander. The efficacy of the proposed quantization algorithm is studied within a new low-precision framework, ALPS, on ResNet-50 and EfficientNet models for classification tasks. Results assert that the accuracy and energy dissipation of low-precision DNNs using generalized posits outperform other well-known numerical formats, including standard posits.
Control of a virtual vehicle influences postural activity and motion sickness in pre-adolescent children

Among adults, persons in control of a vehicle (i.e., drivers) are less likely to experience motion sickness compared to persons in the same vehicle who do not control it (i.e., passengers). This "driver-passenger effect" is well-known in adults, but has not been evaluated in children. Using a yoked-control design with seated pre-adolescent children, we exposed dyads to a driving video game. In each dyad, one child (the driver) drove the virtual vehicle. Their performance was recorded, and later shown to the other child (the passenger). Thus, visual motion stimuli were identical for the members of each dyad. During exposure to the video game, we monitored the quantitative kinematics of head and torso movements. Participants were instructed to discontinue participation immediately if they experienced any symptoms of motion sickness, however mild. Accordingly, the movements that we recorded preceded the onset of motion sickness. Results revealed that Passengers (73.08%) were more likely than Drivers (42.31%) to state that they were motion sick. Drivers tended to move more than passengers, and with a greater degree of multifractality. The magnitude of movement was greater among participants who later reported motion sickness than among those who did not. In addition, for the multifractality of movement a statistically significant interaction revealed that postural precursors of motion sickness differed qualitatively between Drivers and Passengers. Overall, the results reveal that control of a virtual vehicle reduces the risk of motion sickness among pre-adolescent children.
Learning via variably scaled kernels

We investigate the use of the so-called variably scaled kernels (VSKs) for learning tasks, with a particular focus on support vector machine (SVM) classifiers and kernel regression networks (KRNs). Concerning the kernels used to train the models, under appropriate assumptions, the VSKs turn out to be more expressive and more stable than the standard ones. Numerical experiments and applications to breast cancer and coronavirus disease 2019 (COVID-19) data support our claims. For the practical implementation of the VSK setting, we need to select a suitable scaling function. To this aim, we propose different choices, including for SVMs a probabilistic approach based on the naive Bayes (NB) classifier. For the classification task, we also numerically show that the VSKs inspire an alternative scheme to the sometimes computationally demanding feature extraction procedures.
Postdigital Humans: Taking Evolution in Own Hands

This chapter provides a brief overview of implicit and explicit references to human beings in postdigital literature. It identifies a range of viewpoints, approaches, and fields of inquiry which have shaped postdigital thinking over the past 20 years and establishes theoretical underpinnings for inquiry into postdigital humans. Building on the understanding of humanity as a normative category, the chapter moves on to explore three ‘performance standards’ for inclusion into humanity: life, consciousness, and behaviour. The concluding part synthesizes philosophical approaches and postdigital theories and builds a postdisciplinary understanding of the postdigital human. Postdigital humans may exist in various carbon-based and non-carbon-based forms, and the exact demarcation between postdigital humans and postdigital non-humans is a constantly moving target. However, postdigital theory is more concerned with in-between configurations and bio-machine entanglements from which postdigital humans can be made. Postdigital theory can contribute actively to studies of human beings with its convenient positioning between the analog and the digital and its radical postdisciplinary approaches. However, the central tenet of postdigital theory reaches beyond the question: Who is a postdigital human? It focuses on inquiry into humankind’s active contribution to own evolution, and poses questions such as: What kind of postdigital humans do we want to become? What kind of future do we want for our postdigital children?