Reviewerly/exHarmony huggingface dataset:
HashFlow for Better Flow Record Collection

Collecting flow records is a common practice of network operators and researchers for monitoring, diagnosing and understanding a network. Traditional tools like NetFlow face great challenges when both the speed and the complexity of the network traffic increase. To keep pace up, we propose HashFlow, a tool for more efficient and accurate collection and analysis of flow records. The central idea of HashFlow is to maintain accurate records for elephant flows, but summarized records for mice flows, by applying a novel collision resolution and record promotion strategy to hash tables. We have implemented HashFlow as well as several latest flow measurement algorithms in a P4 software switch, and use traces from different operational networks to evaluate the algorithms. In these experiments, for various types of traffic analysis applications, HashFlow consistently demonstrates a clearly better performance against its state-of-the-art competitors. For example, using a small memory of 1 MB, HashFlow can accurately record around 55K flows, which is often 12.5% higher than the others. For estimating the sizes of 50K flows, HashFlow achieves a relative error of around 11.6%, while the estimation error of the best competitor is 42.9% higher. It detects 96.1% of the heavy hitters out of 250K flows with a size estimation error of 5.6%, which is 11.3% and 73.7% better than the best competitor respectively. At last, we show these merits of HashFlow come with almost no degradation of throughput.
Learning to rank hotels for search and recommendation from session-based interaction logs and meta data

Being able to provide high quality search and recommendation services can be a decisive success factor for online applications, e.g., in today's competitive e-commerce environments. Context-adaptive and personalized item suggestions can help to both improve the user experience and the provider's short-term and long-term revenue. However, automating this form of adaptation can be challenging, when no long-term preference profiles are available. In these situations, the user's preferences and short-term intent must be derived from the last few observed interactions.
An On Body Accelerometer System for streaming therapy data using COTS UHF RFID

This paper details the use of an on body Accelerometer COTS RFID system for use in determining specific therapeutic movements, and the performance of said movements. Conventional methodologies rely on power intensive Bluetooth or Wi-Fi technologies for live communications, but this paper outlines the use of ultra-low power battery assistive passive modes on UHF RFID chips to harness sub-2mW continuous streaming of Accelerometer data.
On Kernel Method–Based Connectionist Models and Supervised Deep Learning Without Backpropagation

We propose a novel family of connectionist models based on kernel machines and consider the problem of learning layer by layer a compositional hypothesis class (i.e., a feedforward, multilayer architecture) in a supervised setting. In terms of the models, we present a principled method to "kernelize" (partly or completely) any neural network (NN). With this method, we obtain a counterpart of any given NN that is powered by kernel machines instead of neurons. In terms of learning, when learning a feedforward deep architecture in a supervised setting, one needs to train all the components simultaneously using backpropagation (BP) since there are no explicit targets for the hidden layers (Rumelhart, Hinton, & Williams, 1986). We consider without loss of generality the two-layer case and present a general framework that explicitly characterizes a target for the hidden layer that is optimal for minimizing the objective function of the network. This characterization then makes possible a purely greedy training scheme that learns one layer at a time, starting from the input layer. We provide instantiations of the abstract framework under certain architectures and objective functions. Based on these instantiations, we present a layer-wise training algorithm for an l-layer feedforward network for classification, where l≥2 can be arbitrary. This algorithm can be given an intuitive geometric interpretation that makes the learning dynamics transparent. Empirical results are provided to complement our theory. We show that the kernelized networks, trained layer-wise, compare favorably with classical kernel machines as well as other connectionist models trained by BP. We also visualize the inner workings of the greedy kernelized models to validate our claim on the transparency of the layer-wise algorithm.
Discursive profile of international telecommunication regulations as institutional dialogue: a sociosemiotic perspective

ABSTRACTThis study, from a sociosemiotic perspective, sets out to examine the meaning making process of the International Telecommunication Regulations by investigating the contributions submitted by the Member States and Sector Members of the International Telecommunication Union, and two versions of the International Telecommunication Regulations (1988 version and 2012 version). The findings show that the International Telecommunication Regulations, as a sign, may be ideologically and institutionally constrained as well as historically situated. As regards its review, Member States and Sector Members have divergent views carrying distinct institutional positions, which are induced by various national interests and different technological levels. Concerning its revision, a comparative analysis of the overall articles and the modal system of the 1988 version and the 2012 version reflects that the meaning making of the International Telecommunication Regulations is inherently embedded in specific historical backgrounds, and the frequent use of soft language in the 2012 version may be conceived of as a mediational means to compromise the distinct views of member states on the controversial issues and facilitate institutional interactions. It is therefore argued that the discourse construction of the International Telecommunication Regulations is an institutional process of “interest weighing”, jurisdictional compromise and institutional dialogue among Member States.
Fast Computation of Content-Sensitive Superpixels and Supervoxels Using Q-Distances

State-of-the-art researches model the data of images and videos as low-dimensional manifolds and generate superpixels/supervoxels in a content-sensitive way, which is achieved by computing geodesic centroidal Voronoi tessellation (GCVT) on manifolds. However, computing exact GCVTs is slow due to computationally expensive geodesic distances. In this paper, we propose a much faster queue-based graph distance (called q-distance). Our key idea is that for manifold regions in which q-distances are different from geodesic distances, GCVT is prone to placing more generators in them, and therefore after few iterations, the q-distance-induced tessellation is an exact GCVT. This idea works well in practice and we also prove it theoretically under moderate assumption. Our method is simple and easy to implement. It runs 6-8 times faster than state-of-the-art GCVT computation, and has an optimal approximation ratio O(1) and a linear time complexity O(N) for N-pixel images or N-voxel videos. A thorough evaluation of 31 superpixel methods on five image datasets and 8 supervoxel methods on four video datasets shows that our method consistently achieves the best over-segmentation accuracy. We also demonstrate the advantage of our method on one image and two video applications.
Non-Markovian dynamics and quantum interference in open three-level quantum systems

The exact analytical solutions for the dynamics of the dissipative three-level V-type and $$\varLambda $$-type atomic systems in the vacuum Lorentzian environments are presented. Quantum interference between the spontaneous emissions of different decaying channels for the V-type atomic system is observed. For the dissipative $$\varLambda $$-type atomic system, however, similar phenomenon of quantum interference does not exist. We demonstrate that quantum interference can be used to protect effectively the quantum entanglement and quantum coherence. The control of the transition from Markovian to non-Markovian processes is discussed.
Do different cross‐project defect prediction methods identify the same defective modules?

Abstract Cross‐project defect prediction (CPDP) is needed when the target projects are new projects or the projects have less training data, since these projects do not have sufficient historical data to build high‐quality prediction models. The researchers have proposed many CPDP methods, and previous studies have conducted extensive comparisons on the performance of different CPDP methods. However, to the best of our knowledge, it remains unclear whether different CPDP methods can identify the same defective modules, and this issue has not been thoroughly explored. In this article, we select 12 state‐of‐the‐art CPDP methods, including eight supervised methods and four unsupervised methods. We first compare the performance of these methods in the same experiment settings on five widely used datasets (ie, NASA, SOFTLAB, PROMISE, AEEEM, and ReLink) and rank these methods via the Scott‐Knott test. Final results confirm the competitiveness of unsupervised methods. Then we perform diversity analysis on defective modules for these methods by using the McNemar test. Empirical results verify that different CPDP methods may lead to difference in the modules predicted as defective, especially when the comparison is performed between the supervised methods and unsupervised methods. Finally, we also find there exist a certain number of defective modules, which cannot be correctly identified by any of the CPDP methods or can be correctly identified by only one CPDP method. These findings can be utilized to design more effective methods to further improve the performance of CPDP.
Blockchain: Perspectives and issues

The tamper-proof digital database on which are written all exchanges between its users since its inception is named Blockchain. The blockchain is a system of storage distributed on a peer-to-peer network (p2p) using consensus mechanism, asymmetric encryption, smart contracts and other key technolog ies. In an untrusted environment, blockchain comes to associate the implementation of a mechanism for the exchange of information, transfer of value intended to ensure consistency, and integrity data is the cornerstone of the creation of value future of the Internet. Blockchain technology has the following features: decentralized, safe, reliable, open and transparent. Its characteristics make the most attractive blockchain and is the subject of all the debates at the safe level. File storage, healthcare, banking, insurance, field and other scenarios are widely used. However, the technology of the blockchain is still some problems, such as the low flow of transactions, easy disclosure of user information, the confidentiality of the data of transactions and the safe limits of algorithms encryption. His problems that must be resolved urgently in its development and its application. As an emerging technology, the blockchain still has plenty other still undiscovered problems, but it has broad prospects of development and should allow the creation of a new decentralized era and a new world of Central governance without organ of management.
On Strings in Software Model Checking

Strings represent one of the most common and most intricate data-types found in software programs, with correct string processing often being a decisive factor for correctness and security properties. This has led to a wide range of recent research results on how to analyse programs operating on strings, using methods like testing, fuzzing, symbolic execution, abstract interpretation, or model checking, and, increasingly, support for strings is also added to constraint solvers and SMT solvers. In this paper, we focus on the verification of software programs with strings using model checking. We give a survey of the existing approaches to handle strings in this context, and propose methods based on algebraic data-types, Craig interpolation, and automata learning.
More Effective Ontology Authoring with Test-Driven Development and the TDDonto2 Tool

Ontology authoring is a complex process, where commonly the automated reasoner is invoked for verification of newly introduced changes, therewith amounting to a time-consuming test-last approach. Test-Driven Development (TDD) for ontology authoring is a recent test-first approach that aims to reduce authoring time and increase authoring efficiency. Current TDD testing falls short on coverage of OWL features and possible test outcomes, the rigorous foundation thereof, and evaluations to ascertain its effectiveness. We aim to address these issues in one instantiation of TDD for ontology authoring. We first propose a succinct, logic-based specification of TDD testing and present novel TDD algorithms so as to cover also any OWL 2 class expression for the TBox and for the principal ABox assertions, and prove their correctness. The algorithms use methods from the OWL API directly such that reclassification is not necessary for test execution, therewith reducing ontology authoring time. The algorithms were implemented in TDDonto2, a Protégé plugin. TDDonto2 was evaluated by users, which demonstrated that modellers make significantly fewer errors with TDDonto2 compared to the standard Protégé interface and complete their tasks better using less time. Thus, the results indicate that TDD is a promising approach in an ontology development methodology.
Writing bugchasing ethnoperformance: Creative representations of online interactions

In this piece, I discuss the creation of ethnoperformance as a method of exploring and representing online spaces. I highlight the need for writing that acknowledges the influence of technology in online communication and argue for the suitability of ethnodrama as a writing method. In particular, I focus on bugchasers – men who fetishize HIV – and their online discussions. Bugchasers’ comments to a forum, articulated as a five act ethnoperformance, serve as a case study to showcase how this form of writing allows users to discuss their negotiations of fantasy/reality while also acknowledging the uncertainty of online spaces and the mediation by technology. I finally defend the view that the visceral reactions ethnodrama produces generate interesting reflexive standpoints from which to research stigmatized practices and ideas.
Adversarial Domain Adaptation with Semantic Consistency for Cross-Domain Image Classification

In the cross-domain image classification scenario, domain adaption aims to address the challenge of transferring the knowledge obtained from the source domain to the target domain that is regarded as similar but different from the source domain. To get more reliable domain invariant representations, recent methods start to consider class-level distribution alignment across the source and target domains by adaptively assigning pseudo target labels. However, these approaches are vulnerable to the error accumulation and hence unable to preserve cross-domain category consistency. Because the accuracy of pseudo labels cannot be guaranteed explicitly. In this paper, we propose Adversarial Domain Adaptation with Semantic Consistency (ADASC) model to align the discriminative features across domains progressively and effectively, via exploiting the class-level relations between domains. Specifically, to simultaneously alleviate the negative influence of the false pseudo-target labels and get the discriminative domain invariant features, we introduce an Adaptive Centroid Alignment (ACA) strategy and a Class Discriminative Constraint (CDC) step to complement each other iteratively and alternatively in an end-to-end framework. Extensive experiments are conducted on several unsupervised domain adaptation datasets, and the results show that ADASC outperforms the state-of-the-art methods.
Digital signatures over hash-entangled chains

In this work we introduce a new approach for digital signature based on hash chains. The method is supported on the well known security principles of hash functions. Some main advantages of this public key system include security and estimated efficiency through the so called signature chains. In addition, the algorithm is a promissory alternative for digital signatures for the quantum era.
Scalable and language-independent embedding-based approach for plagiarism detection considering obfuscation type: no training phase

The efficiency and scalability of plagiarism detection systems have become a major challenge due to the vast amount of available textual data in several languages over the Internet. Plagiarism occurs in different levels of obfuscation, ranging from the exact copy of original materials to text summarization. Consequently, designed algorithms to detect plagiarism should be robust to the diverse languages and different types of obfuscation in plagiarism cases. In this paper, we employ text embedding vectors to compare similarity among documents to detect plagiarism. Word vectors are combined by a simple aggregation function to represent a text document. This representation comprises semantic and syntactic information of the text and leads to efficient text alignment among suspicious and original documents. By comparing representations of sentences in source and suspicious documents, pair sentences with the highest similarity are considered as the candidates or seeds of plagiarism cases. To filter and merge these seeds, a set of parameters, including Jaccard similarity and merging threshold, are tuned by two different approaches: offline tuning and online tuning. The offline method, which is used as the benchmark, regulates a unique set of parameters for all types of plagiarism by several trials on the training corpus. Experiments show improvements in performance by considering obfuscation type during threshold tuning. In this regard, our proposed online approach uses two statistical methods to filter outlier candidates automatically by their scale of obfuscation. By employing the online tuning approach, no distinct training dataset is required to train the system. We applied our proposed method on available datasets in English, Persian and Arabic languages on the text alignment task to evaluate the robustness of the proposed methods from the language perspective as well. As our experimental results confirm, our efficient approach can achieve considerable performance on the different datasets in various languages. Our online threshold tuning approach without any training datasets works as well as, or even in some cases better than, the training-base method.
SpaceState: Ad-Hoc Definition and Recognition of Hierarchical Room States for Smart Environments

We present SpaceState, a system for designing spatial user interfaces that react to changes of the physical layout of a room. SpaceState uses depth cameras to measure the physical environment and allows designers to interactively define global and local states of the room. After designers defined states, SpaceState can identify the current state of the physical environment in real-time. This allows applications to adapt the content to room states and to react to transitions between states. Other scenarios include analysis and optimizations of work flows in physical environments. We demonstrate SpaceState by showcasing various example states and interactions. Lastly, we implemented an example application: A projection mapping based tele-presence application, which projects a remote user in the local physical space according to the current layout of the space.
Comic-guided speech synthesis

We introduce a novel approach for synthesizing realistic speeches for comics. Using a comic page as input, our approach synthesizes speeches for each comic character following the reading flow. It adopts a cascading strategy to synthesize speeches in two stages: Comic Visual Analysis and Comic Speech Synthesis. In the first stage, the input comic page is analyzed to identify the gender and age of the characters, as well as texts each character speaks and corresponding emotion. Guided by this analysis, in the second stage, our approach synthesizes realistic speeches for each character, which are consistent with the visual observations. Our experiments show that the proposed approach can synthesize realistic and lively speeches for different types of comics. Perceptual studies performed on the synthesis results of multiple sample comics validate the efficacy of our approach.
Pairwise Stereo Image Disparity and Semantics Estimation with the Combination of U-Net and Pyramid Stereo Matching Network

Stereo images are one of the most common resources for 3D reconstruction. In the pairwise semantic stereo challenge of the 2019 IEEE GRSS Data Fusion Contest, we generate the classification and the disparity map based on U-Net and Pyramid Stereo Matching Network (PSMNet), respectively. By using a dynamic and class-weighted loss function, the UNet is effectively trained with the imbalanced training samples. By voting classification results of the augmented prediction data with models trained under different epochs, we further refine the classification maps with the constraints of pseudo DSM, water index and mean-shift segmentation.
A Vision of Augmented Reality for Urban Search and Rescue

Search and rescue (SAR) operations are often nearly computer-technology-free due to the fragility and connectivity needs of current information communication technology (ICT). In this design fiction, we envision a world where SAR uses augmented reality (AR) and the surplus labor of volunteers during crisis response efforts.
Adaptive Rule Adaptation in Unstructured and Dynamic Environments

Rule-based systems have been used to augment machine learning based algorithms for annotating data in unstructured and dynamic environments. Rules can alleviate many of shortcomings inherent in pure algorithmic approaches. Rule adaptation is a challenging and error-prone task: in a rule-based system, there is a need for an analyst to adapt rules in order to keep them applicable and precise. In this paper, we present an approach for adapting data annotation rules in unstructured and constantly changing environments. Our approach offloads analysts from adapting rules and autonomically identifies the optimal modification for rules using a Bayesian multi-armed-bandit algorithm. We conduct experiments on different curation domains and compare the performance of our approach with systems relying on analysts. The experimental results show a comparative performance of our approach compared to analysts in adapting rules.
Machine Learning in Neural Networks

Evidence now suggests that precision psychiatry is becoming a cornerstone of medical practices by providing the patient of psychiatric disorders with the right medication at the right dose at the right time. In light of recent advances in neuroimaging and multi-omics, more and more biomarkers associated with psychiatric diseases and treatment responses are being discovered in precision psychiatry applications by leveraging machine learning and neural network approaches. In this article, we focus on the most recent developments for research in precision psychiatry using machine learning, deep learning, and neural network algorithms, together with neuroimaging and multi-omics data. First, we describe different machine learning approaches that are employed to assess prediction for diagnosis, prognosis, and treatment in various precision psychiatry studies. We also survey probable biomarkers that have been identified to be involved in psychiatric diseases and treatment responses. Furthermore, we summarize the limitations with respect to the mentioned precision psychiatry studies. Finally, we address a discussion of future directions and challenges.