Reviewerly/exHarmony huggingface dataset:
Digital material value: Designing emerging technologies

In this article, we examine how the digital material value of emerging technologies is articulated in technology design contexts. We argue that here digital material value lies not in the monetisation of an artefact or product but in the opening up of possibilities for its use. Thus, to generate digital material value, a certain configuration of things and processes that enables its value to be relevant is required. We develop this through a processual theory of digital materiality, which attends to how the affordances of digital material things emerge in shifting circumstances, and a corresponding theory of value. To demonstrate how this happens in human experience and action, we draw on ethnographic research with drone developers in Australia. Studying digital materiality through emerging technologies goes beyond seeing technologies and data as finished commodities that are modified when consumed towards studying the incompleteness of the technology design process itself.
A multiple-model generalized labeled multi-Bernoulli filter based on blocked Gibbs sampling for tracking maneuvering targets

In this paper, an efficient implementation of the multiple-model generalized labeled multi-Bernoulli filter (MM-GLMB) is presented for tracking multiple maneuvering targets. To alleviate the generation of the redundant components, the original two-staged implementations of MM-GLMB filter are integrated into a single step bringing the benefit that only one truncation procedure is required per iteration. In this study, the authors take the convergence behavior of the Gibbs sampling into full consideration to improve the convergence rate. The blocked Gibbs sampling over lattice Gaussian distribution based solution to the implementation of MM-GLMB filter is proposed to greatly relax the computational load. The numerical simulations demonstrate the efficacy of the proposed algorithm with low computational cost.
An evaluation study of rural scientific and technological talents based on TODIM method with hybrid indicator

A scientific evaluation model can be effectively used for the evaluation of regional talent development level. This paper proposes a set of scientific index systems for evaluating rural science and technology talents, which can be used for understanding the development status and level of rural science and technology talents in various regions; putting forward the corresponding talent cultivation and introduction policies, and; promoting the development of rural economic construction. Moreover, in order to avoid the shortcoming of over-subjective indicator weight in analytic hierarchy process (AHP), this paper uses the entropy weight method to determine indicator weight. Furthermore, giving the fact that the evaluation experts may have individual personal preferences, this paper proposes an extended TODIM method based on hybrid index values, for achieving more scientific and effective evaluation results of rural science and technology talents. Finally, the proposed methods are evaluated on an actual case, where relevant analysis and suggestions are given.
Image stitching based on angle-consistent warping

Many warping methods for image stitching have been proposed to construct panoramic image mosaics free of artifacts. Existing methods heavily rely on coordinate correspondences between keypoints in stitching, which may not provide adequate constraints for alignment. In this paper, we discover and employ a new constraint — angle correspondences to address the above problem. The angle of a feature point represents the local directional structure of the point, which is an extension to its position and customarily ignored in image stitching. We propose to jointly consider the coordinates as well as the angles in keypoint correspondences. Such a strategy helps to generate a correct warping in the overlapping regions of the stitched image. In addition, we propose a novel constraint — mesh angle preservation to prevent undesired distortion in non-overlapping areas. Experiments in several challenging cases demonstrate that our method yields more accurate results with significantly less artifacts in comparison with state-of-the-art methods.
Capturing causality and bias in human action recognition

Human action recognition using various sensors is a mandatory component of autonomous vehicles, humanoid robots, and ambient living environments. A particular interest is the detection and recognition of falls. In this paper, we propose the use of temporal convolution networks guided by knowledge distillation for detecting falls and recognizing types of falls using accelerometer data. Tri-axial accelerometers attached to the body measure the acceleration of the body joints when an action occurs. These data are used for pattern analysis and body action recognition. We demonstrate the existence of biases caused by soft biometrics when recognizing human body actions. We introduce a causal network to capture the influences of biases on system performance and illustrate how knowledge distillation can be applied to mitigate the bias effect.
CNAME Cloaking-Based Tracking on the Web: Characterization, Detection, and Protection

Third-party tracking on the Web has been used for collecting and correlating user's browsing behavior. Due to the increasing use of ad-blocking and third-party tracking protections, tracking providers introduced a new technique called CNAME cloaking. It misleads Web browsers into believing that a request for a subdomain of the visited website originates from this particular website, while this subdomain uses a CNAME to resolve to a tracking-related third-party domain. This technique thus circumvents the third-party targeting privacy protections. The goals of this paper are to characterize, detect, and protect the end-user against CNAME cloaking based tracking. Firstly, we characterize CNAME cloaking-based tracking by crawling top pages of the Alexa Top 300,000 sites and analyzing the usage of CNAME cloaking with CNAME blocklist, including websites and tracking providers using this technique to track users' activities. We also point out that browsers and privacy protection extensions are largely ineffective to deal with CNAME cloaking-based tracking except for Firefox with a developer's version of the uBlock Origin extension. Secondly, we propose a supervised machine learning-based approach to detect CNAME cloaking-based tracking without the on-demand DNS lookup. We show that the proposed approach outperforms well-known tracking filter lists. Finally, to circumvent the lack of DNS API in Chrome-based browsers, we design and implement a prototype of the supervised machine learning-based browser extension to detect and filter out CNAME cloaking tracking, called CNAMETracking Uncloaker. Our evaluation shows that CNAMETracking Uncloaker is able to filter out CNAME cloaking-based tracking requests without performance degradation when compared with the vanilla setting on the Chrome browser.
Enhancing Observability of Serverless Computing with the Serverless Application Analytics Framework

To improve the observability of workload performance, resource utilization, and infrastructure underlying serverless Function-as-a-Service (FaaS) platforms, we have developed the Serverless Application Analytics Framework (SAAF). SAAF provides a reusable framework supporting multiple programming languages that developers can leverage to inspect performance, resource utilization, scalability, and infrastructure metrics of function deployments to commercial and open-source FaaS platforms. To automate reproducible FaaS performance experiments, we provide the FaaS Runner as a multithreaded FaaS client. FaaS Runner provides a programmable client that can orchestrate over one thousand concurrent FaaS function calls. The ReportGenerator is then used to aggregate experiment output into CSV files for consumption by popular data analytics tools. SAAF and its supporting tools combined can assess forty-eight distinct metrics to enhance observability of serverless software deployments. In this tutorial paper, we describe SAAF and its supporting tools and provide examples of observability insights that can be derived.
Blockchain Technology—Applicability in the Traceability of a Product Throughout the Supply Chain

Abstract Blockchain is a common, distributed, chronological, immutable, and cryptographically secure digital register that facilitates the process of recording transactions saved on multiple computers (nodes) in different locations which allow the assets to be easily tracked throughout the entire business network, regardless of their nature. Similar to a database, it can record continuously and scalable, the information added by users, as well as all the operations performed by and between users. Each new added operation or transaction that takes place in a blockchain is called a block, which as it is added, the new link is characterized by a unique identity called hash and forms an unbroken chain of information with records that can be public and verifiable. Thus, not having a central source of recording the information, they become a very difficult target for cyber attackers who want to alter or steal information about transactions, as well as in industrial espionage situations, as it can happen in companies which have in their portfolio the distribution of irrigation systems and installations, from which it is chosen to exemplify the benefits of using blockchain for the entire operational flow of the product PE 75×2 ½ compression fitting (elbow).
Social loafing prevention in agile software development teams using team expectations agreements

Social loafing is a common issue encountered by many software development teams. In practice, agile software development teams often use team expectations agreements (TEAs), which contain rules to prevent social loafing behaviour. However, few studies in the software engineering literature examine how these rules are specified in TEAs and whether they serve the purpose. The authors intend to provide a better understanding on how to specify rules in TEAs to prevent social loafing behaviour in agile software development teams. A mixed-method approach using both qualitative and quantitative data was employed. The results of our study show that agile software development teams specify the expectations of the whole team on meeting attendance and contribution, respect of tasks, roles and teammates, and collaboration with each other. These rules have the potential to prevent social loafing behaviours. However, specifying the rules in TEAs alone is not sufficient to make them effective. It needs to be complemented with team commitment to TEA rules. The work of the authors contributes to a better understanding of how to use TEAs to prevent social loafing in agile software development teams, and provides a conceptual basis for future research to investigate social loafing prevention using TEAs.
Semantic video segmentation with dynamic keyframe selection and distortion-aware feature rectification

The per-frame segmentation methods have a high computational cost, thereby, these methods are insufficient to cope with the fast inference need of semantic video segmentation. To efficaciously reuse the extracted features by feature propagation, in this paper, we present distortion-aware feature rectification and online selection of keyframes for fast and accurate video segmentation. The proposed dynamic keyframe scheduling scheme is based on the extent of temporal variations using reinforcement learning. We employ policy gradient reinforcement strategy to learn policy function for maximizing the expected reward. The policy network has two actions (key and non-key) in the action space. State information is derived from the element-wise difference frame of the current frame and the warped current frame generated by the propagated previous frame. Afterward, an adaptive partial feature rectification with distortion-aware corrections is performed for the warped features of the non-key frames. Precise feature propagation is a critical task to uphold the temporal updates in the video sequence since it enormously affects the accuracy as well as the throughput of the whole video analysis framework. The distorted feature maps are revised with the light-weight feature extractor by the guidance of the distortion map while the correctly propagated features are not influenced. Deep feature flow approach is adopted for feature propagation. We evaluate our scheme on the Cityscapes and CamVid datasets with DeepLabv3 as segmentation network and LiteFlowNet for computing flow fields. Experimental results show that the proposed method outperforms the previous state-of-the-art methods significantly both in terms of accuracy and throughput.
Context-aware Website Fingerprinting over Encrypted Proxies

Website fingerprinting (WFP) could infer which websites a user is accessing via an encrypted proxy by passively inspecting the traffic between the user and the proxy. The key to WFP is designing a classifier capable of distinguishing traffic characteristics of accessing different websites. However, when deployed in real-life networks, a well-trained classifier may face a significant obstacle of training-testing asymmetry, which fundamentally limits its practicability. Specifically, although pure traffic samples can be collected in a controlled (clean) testbed for training, the classifier may fail to extract such pure traffic samples as its input from raw complicated traffic for testing. In this paper, we are interested in encrypted proxies that relay connections between the user and the proxy individually (e.g., Shadowsocks), and design a context-aware system using built-in spatial-temporal flow correlation to address the obstacle. Extensive experiments demonstrate that our system does not only enable WFP against a popular type of encrypted proxies practical, but also achieves better performance than ideally training/testing pure samples.
A Posture Evaluation System for Fitness Videos based on Recurrent Neural Network

We present a posture evaluation system especially for fitness. Given a fitness video where a user repetitively performs a movement for fitness, we first detect human posture at each video frame. The evolution of posture in consecutive frames is then characterized by a recurrent neural network (RNN). This RNN examines this movement and outputs the degree of goodness (badness). This examination is important for users because prompt inspection of bad movement avoids injury and improves effectiveness of fitness. We demonstrate that the proposed system can accurately detect bad postures when the users perform two movements called Dumbbell Lateral Raise and Biceps Curl. We believe this work is one of the very few studies of using deep neural networks for fitness evaluation.
An improved deep learning based classification of human white blood cell images

White blood cells (WBC) are part of the immune systems which defend both infectious diseases and foreign invaders. There are various types of white blood cells in our body and each of these blood cells has a specific function in our body. The differential test is the traditional way to classify white blood cells in that it calculates the percentage of different types of white blood cells. In this test, the efficiency is low and time-consuming. Various machine learning and deep learning methods have been developed over the years that produced good results. In this work, we applied a deep learning based convolutional neural network (CNN) called “SqueezeNet” to classify white blood cells. After fine-tuning the hyperparameters, we trained our model and tested its performance in the testing dataset. Our method achieved 93.8% accuracy in the test data which is better than the existing classifiers. This proves that our method can be a useful approach for this task.
Revisiting the Evaluation Protocol of Knowledge Graph Completion Methods for Link Prediction

Completion methods learn models to infer missing (subject, predicate, object) triples in knowledge graphs, a task known as link prediction. The training phase is based on samples of positive triples and their negative counterparts. The test phase consists of ranking each positive triple with respect to its negative counterparts based on the scores obtained by a learned model. The best model ranks all positive triples first. Metrics like mean rank, mean reciprocal rank and hits at k are used to assess accuracy. Under this generic evaluation protocol, we observe several shortcomings: 1) Current metrics assume that each measurement is upper bounded by the same constant value and, therefore, are oblivious to the fact that, in link prediction, each positive triple may have a different number of negative counterparts, which alters the difficulty of ranking positive triples. 2) Benchmarking datasets contain anomalies (unrealistic redundancy) that allegedly simplifies link prediction; however, current instantiations of the generic evaluation protocol do not integrate anomalies, which are just discarded based on a user-defined threshold. 3) Benchmarking datasets have been randomly split, which typically alters the graph topology and results in the training split not resembling the original dataset. 4) A single model is typically kept based on its accuracy over the validation split using a given metric; however, since metrics aggregate ranks into a single value, there may be no significant differences among the ranks produced by several models, which must be all evaluated in the test phase. In this paper, we contribute to the evaluation of link prediction as follows: 1) We propose a variation of the mean rank that considers the number of negative counterparts. 2) We define the anomaly coefficient of a predicate and integrate such coefficient in the protocol. 3) We propose a downscaling algorithm to generate training splits that reflect the original graph topology based on a nonparametric, unpaired statistical test. 4) During validation, we discard a learned model only if its output ranks are significantly different than other ranks based on a nonparametric, paired statistical test. Our experiments over seven well-known datasets show that translation-based methods (TransD, TransE and TransH) significantly outperform recent methods, which entails that our understanding of the accuracy of completion methods for link prediction is far from perfect.
Comparative Study of Machine Learning Algorithms for Twitter Sentiment Analysis

Sentiment Analysis is important to understand various aspects of human emotions through different modes, the modes can be, either by understanding the text or analyzing it for obtaining the desired outputs. The three algorithms considered for sentiment analysis are Logistic Regression, Multinomial Naïve Bayes & Random Forest on the Uber & Ola datasets. The number of tweets extracted from Twitter is 3000. These tweets are cleaned & tokenized using python. The main factor of this paper is Google word2Vec, as the tokenized tweets are transformed with vocabulary from Google Word2Vec. Using this immense dataset of words, helped tokenized words to create a better vocabulary and understanding. Finally, the accuracy and the Mean Cross-Validation Accuracy (MCVA) was generated for all the three algorithms which are used to check if it was giving proper results to the trained data. Visualization was created for understanding the accuracy of three algorithms, which in turn helped to select the most accurate algorithm among others. The programming language used in this for pre-processing & analysis is Python.
Research on Multi-UAV Path Planning and Obstacle Avoidance Based on Improved Artificial Potential Field Method

The traditional APF algorithm is limited to the trajectory planning of a single UAV, and usually can't guarantee obstacle avoidance and reach the target. In this paper, an improved potential field function with distance factor and a dynamic step-size adjustment method are proposed to solve the common problems such as unreachable target, easy to fall into local minimum and jitter problem. At the same time, this method also considers the influence of the force between UAV to ensure a certain distance between UAV. Finally, in the three-dimensional simulation environment, multi-UAV can avoid obstacles and reach the target position.
OSPREY: Recovery of Variable and Data Structure via Probabilistic Analysis for Stripped Binary

Recovering variables and data structure information from stripped binary is a prominent challenge in binary program analysis. While various state-of-the-art techniques are effective in specific settings, such effectiveness may not generalize. This is mainly because the problem is inherently uncertain due to the information loss in compilation. Most existing techniques are deterministic and lack a systematic way of handling such uncertainty. We propose a novel probabilistic technique for variable and structure recovery. Random variables are introduced to denote the likelihood of an abstract memory location having various types and structural properties such as being a field of some data structure. These random variables are connected through probabilistic constraints derived through program analysis. Solving these constraints produces the posterior probabilities of the random variables, which essentially denote the recovery results. Our experiments show that our technique substantially outperforms a number of state-of-the-art systems, including IDA, Ghidra, Angr, and Howard. Our case studies demonstrate the recovered information improves binary code hardening and binary decompilation.
Amalgamation of Neural Network and Genetic Algorithm for Efficient Workload Prediction in Data Center

Prediction is one among the major challenging tasks in today’s research area for predicting dynamic workload in cloud data center. Prediction is more tedious because of huge fluctuations in workloads coming to servers. Neural network is the best technology which is used in prediction using its various flavors like recurrent neural network, genetic regression neural network, etc., for prediction. Recently, bio-inspired algorithm like particle swarm algorithm, differential evolution, covariant matrix adaptation, Evolutionally algorithm, spider monkey algorithm, etc., are playing significant and consequential role in optimizing neural network for updating its weights. In this paper, we have combined neural network with genetic algorithm for better optimization of algorithm for prediction. Many researchers have ignored the resource parameters by considering only CPU as resource constraint for prediction. In this work, we have considered CPU, memory and disk storage of Google server utilization to improve the accuracy of prediction. The work is carried out using real world workload traces from Google. Comparison is done between NN and proposed hybrid load prediction algorithm (HLPA). The proposed algorithm reduces the mean square error up to 0.002 compared to neural network.