Reviewerly/exHarmony huggingface dataset:
LAPAS: Latency-Aware Playback-Adaptive Streaming

Live video streaming is one of the fastest growing applications in the mobile Internet. In addition to video quality and streaming performance, live video typically requires shorter user viewing latency or else the service quality will degrade significantly (e.g., live sports). This poses a significant challenge as latency and quality-of-experience (QoE) are inherently conflicting objectives. This work tackles this challenge by developing a novel LAPAS algorithm that employs playback rate adaptation in addition to bitrate adaptation to: (a) enable the service provider to explicitly control the tradeoff between latency and QoE; (b) reduce latency while maintaining reasonably high QoE. Extensive trace-driven simulation results showed that LAPAS can reduce latency by 21.8% to 75.1% at the same or higher QoE compared to leading algorithms. Moreover, it is the only algorithm that can achieve viewing latency shorter than 4 seconds and can work well even for latency as short as 2 seconds. Last but not least, LAPAS's performance is robust with respect to network locations, mobile operators, and time. It can be implemented entirely at the client side and is thus readily deployable in today's mobile Internet.
Sentiment Analysis of Restaurant Review with Classification Approach in the Decision Tree-J48 Algorithm

In the increasingly fierce restaurant business competition, many restaurants compete to provide the best quality for consumers. The quality of the restaurant includes food and drink, environment, place and service. The condition affects the restaurant brand image that is marked whether consumers are satisfied or not. One of review restaurant website called TripAdvisor is chosen as the data source because it contains User-Generated Content features (UGC). The advantage of using UGC is to ensure the authenticity of consumer comment data. English texts classification is used in this study to determine dissatisfaction (negative) and satisfaction (positive) of consumers based on their comment or review. This paper utilises Decision Tree-J48 as the classification method in this study. Data retrieval is done by crawling data using WebHarvy. The overall performance of the Decision Tree-J48 method is the average value of Precision 48.7%, Recall 36.8%, F-Measure 41.40% and accuracy 45.6%. The benefit of this classification results is as a recommendation for consumers to choose the best restaurant.
Action4D: Online Action Recognition in the Crowd and Clutter

Recognizing every person's action in a crowded and cluttered environment is a challenging task in computer vision. We propose to tackle this challenging problem using a holistic 4D ``scan'' of a cluttered scene to include every detail about the people and environment. This leads to a new problem, i.e., recognizing multiple people's actions in the cluttered 4D representation. At the first step, we propose a new method to track people in 4D, which can reliably detect and follow each person in real time. Then, we build a new deep neural network, the Action4DNet, to recognize the action of each tracked person. Such a model gives reliable and accurate results in the real-world settings. We also design an adaptive 3D convolution layer and a novel discriminative temporal feature learning objective to further improve the performance of our model. Our method is invariant to camera view angles, resistant to clutter and able to handle crowd. The experimental results show that the proposed method is fast, reliable and accurate. Our method paves the way to action recognition in the real-world applications and is ready to be deployed to enable smart homes, smart factories and smart stores.
Goldilocks: Adaptive Resource Provisioning in Containerized Data Centers

Power management in data centers is challenging because of fluctuating workloads and strict task completion time requirements. Recent resource provisioning systems, such as Borg and RC-Informed, pack tasks on servers to save power. However, current power optimization frameworks based on packing leave very little headroom for spikes, and the task completion times are compromised. In this paper, we design Goldilocks, a novel resource provisioning system for optimizing both power and task completion time by allocating tasks to servers in groups. Tasks hosted in containers are grouped together by running a graph partitioning algorithm. Containers communicating frequently are placed together, which improves the task completion times. We also leverage new findings on power consumption of modern-day servers to ensure that their utilizations are in a range where they are power-proportional. Both testbed implementation measurements and large-scale trace-driven simulations prove that Goldilocks outperforms all the previous works on data center power saving. Goldilocks saves power by 11.7%-26.2% depending on the workload, whereas the best of the implemented alternatives, Borg, saves 8.9%-22.8%. The energy per request for the Twitter content caching workload in Goldilocks is only 33% of RC-Informed. Finally, the best alternative in terms of task completion time, E-PVM, has 1.17-3.29 times higher task completion times than Goldilocks across different workloads.
A Comprehensive Ontology for Internet of Things (COIoT)

In the Internet of Things (IoT), interoperability among heterogeneous entities is an important issue. Most existing solutions offer to address the interoperability by proposing a horizontal service platform to enable communication interoperability between machines. Without realizing the true meaning of data, interoperability is restricted to addressing protocols and message formats. Semantics enable interoperability in the true sense by allowing applications to be autonomic. However, most of the current ontologies for IoT mainly focus on modelling resources, services, and location information and/or define completely new ontologies defeating the purpose of using them for interoperability. Introduction of new ontologies is causing more fragmentation in building a consistent and comprehensive knowledge base for IoT. Careful reuse of existing specialized ontologies can help in developing an interoperable semantic IoT system. Comprehensive Ontology for IoT (COIoT) reuses core concepts from existing ontologies like SSN, iot-lifecycle, PowerOnt, OWL-S, GeoNames, DUL and complements concepts like policy, context, services and monitoring. Using COIoT, it is demonstrated that an interoperable semantic knowledge base for IoT environments can be built and used.
A Quad Edge-Based Grid Encoding Model for Content-Aware Image Retargeting

In this paper, we present a novel grid encoding model for content-aware image retargeting. In contrast to previous approaches such as vertex-based and axis-aligned grid encoding models, our approach takes each horizontal/vertical distance between two adjacent vertices as an optimization variable. Upon this difference-based encoding scheme, every vertex position of a target grid is subsequently determined after optimizing the one-dimensional values. Our quad edge-based grid model has two major advantages for image retargeting. First, the model enables a grid optimization problem to be developed in a simple quadratic program while ensuring the global convexity of objective functions. Second, due to the independency of variables, spatial regularizations can be applied in a locally adaptive manner to preserve structural components. Based on this model, we propose three quadratic objective functions. Note that, in our work, their linear combination guides a grid deformation process to obtain a visually comfortable retargeting result by preserving salient regions and structural components of an input image. Comparative evaluations have been conducted with ten existing state-of-the-art image retargeting methods, and the results show that our method built upon the quad edge-based model consistently outperforms other previous methods both on qualitative and quantitative perspectives.
Enhancing Interpretability in Factor Analysis by Means of Mathematical Optimization

Exploratory Factor Analysis (EFA) is a widely used statistical technique to discover the structure of latent unobserved variables, called factors, from a set of observed variables.EFA exploits the property of rotation invariance of the factor model to enhance factors' interpretability by building a sparse loading matrix.In this paper, we propose an optimizationbased procedure to give meaning to the factors arising in EFA by means of an additional set of variables, called explanatory variables, which may include in particular the set of observed variables.A goodness-of-fit criterion is introduced which quantifies the quality of the interpretation given this way.Our methodology also exploits the rotational invariance of EFA to obtain the best orthogonal rotation of the factors, in terms of the goodness-of-fit, but making them match to some of the explanatory variables, thus going beyond traditional rotation methods.Therefore, our approach allows the analyst to interpret the factors not only in terms of the observed variables, but in terms of a broader set of variables.Our experimental results demonstrate how our approach enhances interpretability in EFA, first in an empirical dataset, concerning volumes of reservoirs in California, and second in a synthetic data example.
A Performance Evaluation of Machine Learning Techniques for Breast Ultrasound Classification

In this paper, a performance comparison of seven machine learning (ML) approaches for classifying breast lesions on ultrasound is presented. From a dataset with 2032 cases (1341 benign and 691 malignant), 137 morphological and texture features were extracted, aiming to describe the BI-RADS lexicon for masses. Support vector machine (SVM), k -nearest neighbor ( kNN), radial basis function network (RBFN), linear discriminant analysis (LDA), multinomial logistic regression (MLR), random forest (RF), and AdaBoost (Ada) were evaluated in terms of sensitivity (SEN), specificity (SPE), accuracy (ACC), and area under the ROC curve (AUC). The results revealed that LDA obtained the best classification performance with ACC=0.89,SEN=0.82, SPE=0.93, and AUC=0.95. Contrarily, the kNN obtained the lowest classification performance with ACC=0.87, SEN=0.76, SPE=0.93 and AUC=0.91. This results point out that the LDA classifier can be convenient to be used in a CAD system because it is simple to implement, and it does not require the tuning of hyperparameters.
TagAttention: Mobile Object Tracing without Object Appearance Information by Vision-RFID Fusion

We propose to study mobile object tracing, which allows a mobile system to report the shape, location, and trajectory of the mobile objects appearing in a video camera and identifies each of them with its cyber-identity (ID), even if the appearances of the objects are not known to the system. Existing tracking methods either cannot match objects with their cyber-IDs or rely on complex vision modules pre-learned from vast and well-annotated datasets including the appearances of the target objects, which may not exist in practice. We design and implement TagAttention, a vision-RFID fusion system that archives mobile object tracing without the knowledge of the target object appearances and hence can be used in many applications that need to track arbitrary un-registered objects. TagAttention adopts the visual attention mechanism, through which RF signals can direct the visual system to detect and track target objects with unknown appearances. Experiments show TagAttention can actively discover, identify, and track the target objects while matching them with their cyber-IDs by using commercial sensing devices, in complex environments with various multipath reflectors. It only requires around one second to detect and localize a new mobile target appearing in the video aWe thank the anonymous reviewers for their suggestions and comments.nd keeps tracking it accurately over time.
Variational autoencoder-based outlier detection for high-dimensional data

Analysis of high-dimensional data often suffers from the curse of dimensionality and the complicated correlation among dimensions. Dimension reduction methods often are used to alleviate these problems. Existing outlier detection methods based on dimension reduction usually only rely on reconstruct ion error to detect outlier or apply conventional outlier detection methods to the reduced data, which could deteriorate the performance of outlier detection as only considering part of the information from data. Few studies have been done to combine these two strategies to do outlier detection. In this paper, we proposed an outlier detection method based on Variational Autoencoder (VAE), which combines low-dimensional representation and reconstruction error to detect outliers. Specifically, we first model the data use VAE, then extract four outlier scores from VAE model, finally propose an ensemble method to combine the four outlier scores. The experiments conducted on six real-world datasets show that the proposed method performs better than or at least comparable to state of the art methods.
Hide and Seek: Waveform Emulation Attack and Defense in Cross-Technology Communication

The exponentially increasing number of heterogeneous Internet of Things (IoT) devices result in severe spectrum shortage and interference in the already crowded ISM band. Cross-Technology Communication (CTC) is dedicated to achieving direct communication among wireless devices with different radios and modulation schemes, which serves as an effective approach to address the above challenges. Nevertheless, CTC also provides opportunities for adversaries to manipulate IoT devices. In this paper, we identify a new attack. Built on CTC, WiFi devices are able to hide the pre-intercepted ZigBee message into their transmitted waveforms, achieving the objective of directly controlling ZigBee devices. To defend against the attack, we analyze possible strategies and consider constellation higher-order statistic analysis as the countermeasure. Extensive simulations and experiments with commodity devices (CC26x2R1) and USRP-based prototypes show the existence of the newly identified attack, and further, validate the effectiveness of the proposed defensive approach.
Challenges associated with the use of information and communication technologies in information sharing by fish farmers in the Southern highlands of Tanzania

Purpose This paper aims to examine the challenges facing fish farmers in the use of information and communication technology (ICT) in information sharing on fish farming. Design/methodology/approach This study used both quantitative and qualitative methods. It involved 240 fish farmers who were randomly selected. Questionnaires, focus group discussions (FGDs), observation and key informant’s interviews were used as methods of data collection. Both descriptive and inferential statistics were used to analyse quantitative data, while content analysis was used for qualitative data. Findings It was found that the most frequently used ICTs by fish farmers in sharing agricultural information were mobile phones, radio and television. Also, the study revealed that major challenges facing fish farmers in sharing information include unfavourable radio or television broadcasting time, high cost of acquiring and maintenance of ICT facilities, lack of training on ICT, poor network connectivity and low level of literacy. Moreover, it was further found that there was negative significant relationship ( P &lt; 0.05) between challenges associated with the use and degree of ICT usage by fish farmers. Originality/value The study is original with the exception of areas where citations have been made. Besides, it provides awareness and understanding of the challenges facing fish farmers in ICT usage in information sharing on fish farming, and this will enable improvement of timely provision and access to relevant information and hence improved fish farming production.
Depth estimation from stereo cameras through a curved transparent medium

In this paper, we propose a novel method for estimating depth values by stereo cameras through a curved transparent medium that causes refraction. Our method takes both the surface shape of the medium and the refraction into account. We model that the rays from the stereo cameras are refracted by a curved transparent medium whose inner surface is represented by a parametric model, assuming that the medium has constant thickness. The parameters of the model are estimated using a constrained optimization simply by attaching several markers on the inner surface. The depth value is then estimated by the triangulation considering the refraction based on the model. The experimental results show that our method yields consistently high error reduction rates with respect to the baseline method without considering the refraction caused by the medium. In addition, our method provides satisfactory estimates for various shapes of the medium.
Robust Motion Segmentation From Pairwise Matches

In this paper we consider the problem of motion segmentation, where only pairwise correspondences are assumed as input without prior knowledge about tracks.The problem is formulated as a two-step process.First, motion segmentation is performed on image pairs independently.Secondly, we combine independent pairwise segmentation results in a robust way into the final globally consistent segmentation.Our approach is inspired by the success of averaging methods.We demonstrate in simulated as well as in real experiments that our method is very effective in reducing the errors in the pairwise motion segmentation and can cope with large number of mismatches.
MAC ID Spoofing-Resistant Radio Fingerprinting

We explore the resistance of deep learning methods for radio fingerprinting to MAC ID spoofing. We demonstrate that classifying transmission slices enables classification of a transmission with a fixed-length input deep classifier, enhances shift-invariance, and, most importantly, makes the classifier resistant to MAC ID spoofing. This is a consequence of the fact that the classifier does not learn to use the MAC ID to classifying among transmissions, but relies on other inherent discriminating signals, e.g., device imperfections. We demonstrate this via experiments on transmissions generated using two protocols, namely, WiFi and ADS-B.
Multi-agent parallel hierarchical path finding in navigation meshes (MA-HNA*)

One of the main challenges in video games is to compute paths as efficiently as possible for groups of agents. As both the size of the environments and the number of autonomous agents increase, it becomes harder to obtain results in real time under the constraints of memory and computing resources. Hierarchical approaches, such as HNA* (Hierarchical A* for Navigation Meshes) can compute paths more efficiently, although only for certain configurations of the hierarchy. For other configurations, the method suffers from a bottleneck in the step that connects the Start and Goal positions with the hierarchy. This bottleneck can drop performance drastically. In this paper we present two approaches to solve the HNA* bottleneck and thus obtain a performance boost for all hierarchical configurations. The first method relies on further memory storage, and the second one uses parallelism on the GPU. Our comparative evaluation shows that both approaches offer speed-ups as high as 9x faster than A*, and show no limitations based on hierarchical configuration. Finally we show how our CUDA based parallel implementation of HNA* for multi-agent path finding can now compute paths for over 500K agents simultaneously in real-time, with speed-ups above 15x faster than a parallel multi-agent implementation using A*.
The Standardization of the Higher Engineering Education in Ukraine in the Light of American Experience

The paper deals with the analysis of the problematic issues of the creation of the system for the provision of the high quality of higher education in Ukraine at the state level. The system of engineering program accreditation in the educational establishments of the USA is shown. The fundamental differences between the engineering and engineering technology training programs are analyzed. The reasonability of such separation and the necessity for the determination and use of different criteria during the accreditation of these programs are stated. Attention is drawn to the necessity for the development of professional standards at the state level and the subsequent creation of higher education standards for the formation of the policy in the field of training a specialist who will be competitive at the labor market.
Visualizing Hierarchical Performance Profiles of Parallel Codes Using CallFlow

Calling context trees (CCTs) couple performance metrics with call paths, helping understand the execution and performance of parallel programs. To identify performance bottlenecks, programmers and performance analysts visually explore CCTs to form and validate hypotheses regarding degraded performance. However, due to the complexity of parallel programs, existing visual representations do not scale to applications running on a large number of processors. We present CallFlow, an interactive visual analysis tool that provides a high-level overview of CCTs together with semantic refinement operations to progressively explore CCTs. Using a flow-based metaphor, we visualize a CCT by treating execution time as a resource spent during the call chain, and demonstrate the effectiveness of our design with case studies on large-scale, production simulation codes.
"Privacy is not a concept, but a way of dealing with life"

Privacy scholarship has shown how norms of appropriate information flow and information regulatory processes vary according to environment, which change as the environment changes, including through the introduction of new technologies. This paper describes findings from a qualitative research study that examines practices and perceptions of privacy in Cambodia as the population rapidly moves into an online environment (specifically Facebook, the most popular Internet tool in Cambodia today). We empirically demonstrate how the concept of privacy differs across cultures and show how the Facebook platform, as it becomes popular worldwide, catalyzes change in norms of information regulation. We discuss how the localization of transnational technology platforms provides a key site in which to investigate changing cultural ideas about privacy, and to discover misalignments between different expectations for information flow. Finally, we explore ways that insufficient localization effort by transnational technology companies puts some of the most marginalized users at disproportionate information disclosure risk when using new Internet tools, and offer some pragmatic suggestions for how such companies could improve privacy tools for users who are far -geographically or culturally - from where the tools are designed.
Comparative analysis on Facebook post interaction using DNN, ELM and LSTM

This study presents a novel research approach to predict user interaction for social media post using machine learning algorithms. The posts are converted to vector form using word2vec and doc2vec model. These two methods are used to analyse the best approach for generating word embeddings. The generated word embeddings of post combined with other attributes like post published time, type of post and total interactions are used to train machine learning algorithms. Deep neural network (DNN), Extreme Learning Machine (ELM) and Long Short-Term Memory (LSTM) are used to compare the prediction of total interaction for a particular post. For word2vec, the word vectors are created using both continuous bag-of-words (CBOW) and skip-gram models. Also the pre-trained word vectors provided by google is used for the analysis. For doc2vec, the word embeddings are created using both the Distributed Memory model of Paragraph Vectors (PV-DM) and Distributed Bag of Words model of Paragraph Vectors (PV-DBOW). A word embedding is also created using PV-DBOW combined with skip-gram.