Reviewerly/exHarmony huggingface dataset:
An Ensemble Based Model for Detecting Genetically Inherited Disorder

An Ensemble of Classifiers is a series of skilled classifier models whose predictions are blended to attain an ultimate decision. It is a technique to choose for prediction from the multidimensional data which can improve the classification accuracy in diverse datasets. In addition, the best method that can reduce error arising from single classifier i.e., over fitting, inaccuracy and at times complexity of train a model. Also, most individual classifiers are not suitable for large data sets and their performance tend to be slow. This study is based on Ensemble algorithms namely Voting, Stacking and Bagging by evaluations which are performed using different classifiers. Data set from Data world digest containing different attributes and dimensions will be used for the study. This study shows that detection of disorder based on ensemble model obtains better results in average accuracy of eighty three percent than other methods. The benefit of the model is an increased accuracy and a reduced run-time when performing the disorder detection.
AI-based System for Cultural Heritage Objects Identification from Real Photos

Identification a specific object that is only partially depicted in digital photography is a non-trivial task that is currently being solved by several different commercial services. These services work for unidentified photographs, depicting different types of objects (e.g. people, animals, buildings, etc.), with a very different level of reliability. To identify specific architectural monuments from the field of cultural heritage, our team created a unique dataset and designed a multi-step identification scheme using advanced machine learning methods. Based on the initial verification, it can be stated that our solution shows the ability of specific object identification.
Keep Clear of the Edges : An Empirical Study of Artificial Intelligence Workload Performance and Resource Footprint on Edge Devices

Recently, with the advent of the Internet of everything and 5G network, the amount of data generated by various edge scenarios such as autonomous vehicles, smart industry, 4K/8K, virtual reality (VR), augmented reality (AR), etc., has greatly exploded. All these trends significantly brought real-time, hardware dependence, low power consumption, and security requirements to the facilities, and rapidly popularized edge computing. Meanwhile, artificial intelligence (AI) workloads also changed the computing paradigm from cloud services to mobile applications dramatically. Different from wide deployment and sufficient study of AI in the cloud or mobile platforms, AI workload performance and their resource impact on edges have not been well understood yet. There lacks an in-depth analysis and comparison of their advantages, limitations, performance, and resource consumptions in an edge environment. In this paper, we perform a comprehensive study of representative AI workloads on edge platforms. We first conduct a summary of modern edge hardware and popular AI workloads. Then we quantitatively evaluate three categories (i.e., classification, image-to-image, and segmentation) of the most popular and widely used AI applications in realistic edge environments based on Raspberry Pi, Nvidia TX2, etc. We find that interaction between hardware and neural network models incurs non-negligible impact and overhead on AI workloads at edges. Our experiments show that performance variation and difference in resource footprint limit availability of certain types of workloads and their algorithms for edge platforms, and users need to select appropriate workload, model, and algorithm based on requirements and characteristics of edge environments.
A Trusted Distributed Crowdsourcing Framework Based on User Preferences

As a new computing paradigm to solve large-scale group collaboration problems, crowdsourcing has attracted more and more attention. However, malicious users’ participation in crowdsourcing tasks will affect the completion of crowdsourcing tasks or generate malicious evaluations that are inconsistent with the facts, which will reduce the user satisfaction of ordinary users and even lose their trust in the system. In addition, most of the existing crowdsourcing systems rely on the central server and are vulnerable to a single point of failure, affecting users’ trust in the system. To solve the above problems, this paper proposes a trusted distributed crowdsourcing framework based on user preferences. Firstly, we propose a trust model of identifying malicious users (IMU) based on reputation value, which can quickly identify all kinds of malicious users. Secondly, the framework is based on an open, transparent, and tamperproof consortium blockchain to ensure the security and reliability of transaction information, and has developed a complete service process for it. Finally, this paper also takes into account the different preferences of users, and gives priority to the tasks that best meet users’ preferences to improve user satisfaction. The proposed framework is deployed on the IBM Hyperledger Fabric. The average transaction confirmation time is 1.4424s and the average system throughput is 186tps. The experimental results show that the framework can quickly identify malicious users.
TForm-RF: An Efficient Data Augmentation for Website Fingerprinting Attack

Website fingerprinting (WF) attacks have become a significant threat to users’ privacy, even against Tor, one of the most famous anonymous communication tools. However, some limitations have prevented them from applying to the real world. A severe limitation for traditional deep-learning-based WF attacks is that a large amount of training data is required to gain classification capabilities. In this paper, we considered a more practical setting where the attacker could only obtain a few traffic trace instances for each target website, called the few-shot WF problem. Unlike previous work using transfer learning and demanding additional pre-training data, we leveraged data augmentation to generate additional virtual samples from the training sample neighborhood. It can expand the support for the distribution of training data, thereby solving the data hunger problem of deep-learning-based WF attacks. Specifically, we proposed a new augmentation method called Trace-Form Based Refill (TForm-RF), which is tailored to the intrinsic properties of website traffic trace. From cell sequences, we extract TForms to figuratively represent traffic patterns and refill them randomly to generate meaningful new instances. We evaluate this idea with several reasonable experiments. We illustrated that TForm-RF is much more effective than prior HDA and has a competitive advantage over TF in both closed-world and open-world scenarios.
Video traffic identification with a distribution distance-based feature selection

The boosting of video traffic in recent years has brought severe challenges to Internet management as video traffic shows wide variety, including harmful video traffic. For example, game video traffic should be strictly supervised for teenagers. Therefore, effectively identifying video traffic has become a fundamental issue in network management, and one of the popular and difficult subjects in existing research. To this end, we propose to extract a large-scale feature set for video traffic identification in this paper. Then, a new distribution distance-based feature selection (DDFS) approach is proposed to obtain an effective feature subset. To test the effectiveness of the extracted feature and the DDFS, we designed a video traffic collection architecture to collect different video traffic data. We carried out a set of comparative experiments on the collected dataset, the experimental data suggest that the proposed approach can achieve an accuracy of over 90% for video scene traffic identification and 98% for cloud gaming video traffic identification. Additionally, the comparison of DDFS with four feature selection methods shows that DDFS is a practical feature selection technique for video traffic identification.
Automatic Polarity Identification on Twitter Using Machine Learning

This work presents a study of emotions to analyze the polarity of a set of data that was extracted from Twitter, detailing each of the resources in the different forms that a language has, and to be able to observe feelings such as irony, sarcasm, and happiness, among others. This research can help us classify the polarity of each one of them deeply in the corpus that deals with this research work. Experimental results conducted using different machine learning methods are presented: Support Vector Machines, Naïve Bayes, Logistic regression, KNN and Random Forest, with which a classification system based on cross-validation was implemented. All experiments were performed in Python. The results obtained are shown with two different Corpus; where the first set is made up of 10,653 tweets in total divided equally each with 3551 tweets with a positive, negative and neutral label; while the second set was handled with 10% of all the tweets contained in the database mentioned in the article, where the first set shows a polarity precision of 74.9%, having Logistic Regression as the best classifier using the classification scenario known as cross validation, while the second set shows an accuracy of 78.5%, also having Random Forest as the best classifier using Cross Validation as the best classification scenario.
Theoretical Perspectives Towards Culture-Centered User Engagement Design for Mobile Health in the Global South

The main objective of this article is to propose a theoretical model for uncovering users’ socio-cultural contexts for user engagement designs, as well as to present a set of consolidating concepts to guide future research in mobile health (mHealth) designs to strengthen the theoretical framing of user engagement designs. First, we present a brief discussion of the importance of employing appropriate theoretical frameworks that promote user engagement. Second, we discuss different theoretical perspectives on user engagement. Two theoretical frameworks, activity theory and the communicative ecological framework, are used to frame an understanding of users’ socio-cultural contexts and inform the design of a framework called Design Process Engagement Enhancement System (DECENT) to support designers in mHealth engagement designs. This paper addressed the research question of how to uncover the socio-cultural contexts of a user group to inform the design of engaging digital artifacts (tools) by augmenting two phases of user-centered methodology with users’ socio-cultural filtration and socio-cultural checklists for user engagement designs. The DECENT framework is an adapted user-centered design framework that uses activity theory and the communicative ecological framework as theoretical framework for understanding the socio-cultural complexity of users in the context of mHealth. The findings of the paper suggest that to fully explain and design for user engagement in mHealth, an integrated approach incorporating a variety of technological and socio-cultural factors should be required.
A transformer‐based approach for improving app review response generation

Abstract Mobile apps are becoming an integral part of people's daily life by providing various functionalities, such as messaging and gaming. App developers try their best to ensure user experience during app development and maintenance to improve the rating of their apps on app platforms and attract more user downloads. Previous studies indicated that responding to users' reviews tends to change their attitude towards the apps positively. Users who have been replied are more likely to update the given ratings. However, reading and responding to every user review is not an easy task for developers since it is common for popular apps to receive tons of reviews every day. Thus, automation tools for review replying are needed. To address the need above, the paper introduces a Transformer‐based approach, named TRRGen, to automatically generate responses to given user reviews. TRRGen extracts apps' categories, rating, and review text as the input features. By adapting a Transformer‐based model, TRRGen can generate appropriate replies for new reviews. Comprehensive experiments and analysis on the real‐world datasets indicate that the proposed approach can generate high‐quality replies for users' reviews and significantly outperform current state‐of‐art approaches on the task. The manual validation results on the generated replies further demonstrate the effectiveness of the proposed approach.
Toward User Experience in ATC: Exploring Novel Interface Concepts for Air Traffic Control

Abstract Air traffic control (ATC) is a safety-critical, cooperative work domain, which faces usability challenges due to technology driven development in the past. In this work, we followed a user-centered design process to explore how novel interaction concepts increase user experience in ATC. Based on controllers’ needs we envisioned one unified interface together with three possible interaction concepts (the mouse interface, flight-dial and tangible interface) addressing different aspects of ATC. We prototypically implemented the interaction concepts and iterated each prototype based on feedback from 24 controllers. Qualitative data from these iterative formative evaluations indicated that controllers prefer interfaces that are efficient to use, minimalistic, customizable and context sensitive. A summative evaluation (N = 12) showed that the hedonic quality of all three concepts were higher compared to the system currently in use. Our results and insights can provide guidance and inspiration for the future design of ATC interfaces.
Underwater image restoration through regularization of coherent structures

Restoration of underwater images plays a vital role in underwater target detection and recognition, underwater robots, underwater rescue, sea organism monitoring, marine geological survey, and real-time navigation. Mostly, physics-based optimization methods do not incorporate structural differences between the guidance and transmission maps (TMs) which affect the performance. In this paper, we propose a method for underwater image restoration by utilizing a robust regularization of coherent structures. The proposed method incorporates the potential structural differences between TM and the guidance map. The optimization of TM is modeled through a nonconvex energy function which consists of data and smoothness terms. The initial TM is taken as a data term whereas the smoothness term contains static and dynamic structural priors. Finally, the optimization problem is solved using majorize-minimize (MM) algorithm. The proposed method is tested on benchmark dataset and its performance is compared with the state-of-the-art methods. The results from the experiments indicate that the proposed regularization scheme adequately improves the TM, which results in high-quality restored images.
Prototype augmented network with metric-mixed under limited samples for mechanical intelligent fault recognition

Common deep learning methods can effectively extract fault characteristics and make recognition of mechanical faults. Nevertheless, the results are undesirable when sufficient labeled training data are unavailable. Although metric learning methods can conduct fault recognition with limited samples, they cannot get satisfactory results due to the fixed relation metric function of manually selected. To address this problem we proposed a prototype augmented network with metric-mixed under limited samples (PANM). It combines fixed metric function with the network automatically learning metric, which consists of an embedded module, relation module and metric module, respectively Firstly, the class prototypes are augmented via multilayer residual network in the embedded module. Then, in the relation module, the prototypes are concatenated with training features to automatically learn the corresponding relationship for getting relation scores. Next, the metric module is used to measure the fixed spatial distance and normalize as the weights of each score. Finally, fault identification is taken according to the weighted score. The experiments in two datasets indicate that PANM cannot just identify different fault categories; it may identify new untrained classes without modifying the network parameters as well, which demonstrates practical solving ability and good generalization ability of fault recognition under limited samples.
Multiple Temporal Pooling Mechanisms for Weakly Supervised Temporal Action Localization

Recent action localization works learn in a weakly supervised manner to avoid the expensive cost of human labeling. Those works are mostly based on the Multiple Instance Learning framework, where temporal pooling is an indispensable part that usually relies on the guidance of snippet-level Class Activation Sequences (CAS) . However, we observe that previous works only leverage a simple convolutional neural network for the generation of CAS, which ignores the weak discriminative foreground action segments and the background ones, and meanwhile, the relationship between different actions has not been considered. To solve this problem, we propose multiple temporal pooling mechanisms (MTP) for a more sufficient information utilization. Specifically, with the design of the Foreground Variance Branch, Dual Foreground Attention Branch and Hybrid Attention Fine-tuning Branch, MTP can leverage more effective information from different aspects and generate different CASs to guide the learning of temporal pooling. Moreover, different loss functions are designed for a better optimization of individual branches, aiming to effectively distinguish the action from the background. Our method shows excellent results on the THUMOS14 and ActivityNet1.2 datasets.
Multiterminal Pathfinding in Practical VLSI Systems with Deep Neural Networks

A multiterminal obstacle-avoiding pathfinding approach is proposed. The approach is inspired by deep image learning. The key idea is based on training a conditional generative adversarial network (cGAN) to interpret a pathfinding task as a graphical bitmap and consequently map a pathfinding task onto a pathfinding solution represented by another bitmap. To enable the proposed cGAN pathfinding, a methodology for generating synthetic dataset is also proposed. The cGAN model is implemented in Python/Keras, trained on synthetically generated data, evaluated on practical VLSI benchmarks, and compared with state-of-the-art. Due to effective parallelization on GPU hardware, the proposed approach yields a state-of-the-art-like wirelength and a better runtime and throughput for moderately complex pathfinding tasks. However, the runtime and throughput with the proposed approach remain constant with an increasing task complexity, promising orders of magnitude improvement over state-of-the-art in complex pathfinding tasks. The cGAN pathfinder can be exploited in numerous high throughput applications, such as, navigation, tracking, and routing in complex VLSI systems. The last is of particular interest to this work.
Strings from linear recurrences and permutations: A Gray code

Each positive increasing integer sequence {an}n≥0 can serve as a numeration system to represent each non-negative integer by means of suitable coefficient strings. We analyse the case of k-generalized Fibonacci sequences leading to the binary strings avoiding 1k. We prove a bijection between the set of such strings of length n and the set Sn+1(321,312,23…(k+1)1) which is a subset of the symmetric group Sn+1 of the permutations of length n+1 avoiding the permutation patterns 321,312, and 234…(k+1)1, where 234…(k+1) stands for the sequence of increasing integers from 2 to k+1. Finally, basing on a known Gray code for those strings, we define a Gray code for Sn+1(321,312,234…(k+1)1), where two consecutive permutations differ by an adjacent transposition.
Feature and instance selection through discriminant analysis criteria

Feature selection and instance selection are two data preprocessing methods widely used in data mining and pattern recognition. The main goal is to reduce the computational cost of many learning tasks. Recently, joint feature and instance selection has been approached by solving some global optimization problems using meta-heuristics. This approach is not only computationally expensive, but also does not exploit the fact that the data usually have a structured manifold implicitly hidden in the data and its labels. In this paper, we address joint feature and instance selection using scores derived from discriminant analysis theory. We present three approaches for joint feature and instance selection. The first scheme is a wrapper technique, while the other two schemes are filtering techniques. In the filtering approaches, the search process uses a genetic algorithm where the evaluation criterion is mainly given by the discriminant analysis score. This score depends simultaneously on the feature subset candidate and the best corresponding subset of instances. Thus, the best feature subset and the best instances are determined by finding the best score. The performance of the proposed approaches is quantified and studied using image classification with Nearest Neighbor and Support Vector Machine Classifiers. Experiments are conducted on five public image datasets. We compare the performance of our proposed methods with several state-of-the-art methods. The experiments performed show the superiority of the proposed methods over several baseline methods.
Jordan Types of Triangular Matrices over a Finite Field

Let $$\lambda $$ be a partition of an integer n and $${\mathbb F}_q$$ be a finite field of order q. Let $$P_\lambda (q)$$ be the number of strictly upper triangular $$n\times n$$ matrices of the Jordan type $$\lambda $$ . It is known that the polynomial $$P_\lambda $$ has a tendency to be divisible by high powers of q and $$Q=q-1$$ , and we put $$P_\lambda (q)=q^{d(\lambda )}Q^{e(\lambda )}R_\lambda (q)$$ , where $$R_\lambda (0)\ne 0$$ and $$R_\lambda (1)\ne 0$$ . In this article, we study the polynomials $$P_\lambda (q)$$ and $$R_\lambda (q)$$ . Our main results: an explicit formula for $$d(\lambda )$$ (an explicit formula for $$e(\lambda )$$ is known, see Proposition 3.3 below), a recursive formula for $$R_\lambda (q)$$ (a similar formula for $$P_\lambda (q)$$ is known, see Proposition 3.1 below), the stabilization of $$R_\lambda $$ with respect to extending $$\lambda $$ by adding strings of 1's, and an explicit formula for the limit series $$R_{\lambda 1^\infty }$$ . Our studies are motivated by projected applications to the orbit method in the representation theory of nilpotent algebraic groups over finite fields.