Reviewerly/exHarmony huggingface dataset:
Cis-Hetero-Misogyny Online

This article identifies five genres of anti-queer hate speech found in The Australian’s Facebook comments sections, exposing and analyzing the ways in which such comments are used to derogate cisgender and (often) heterosexual women. One may be tempted to think of cis-het women as third-party victims of queerphobia; however, this article argues that these genres of anti-queer speech are, in fact, misogynistic. Specifically, it argues that these are instances of cis-hetero-misogynistic hate speech. Cis-hetero-misogyny functions as the “law enforcement branch” of a cis-hetero-patriarchal gender order. Given the existence of such an order, it is clear that cis-het women’s liberation is inextricable from queer liberation (and vice versa). This article argues that to facilitate allyship and challenge this gender order—the order that elicits such hate speech acts—we need an epistemological revolution in the way we recognize and re-cognize human difference.
Massively Parallel ANS Decoding on GPUs

In recent years, graphics processors have enabled significant advances in the fields of big data and streamed deep learning. In order to keep control of rapidly growing amounts of data and to achieve sufficient throughput rates, compression features are a key part of many applications including popular deep learning pipelines. However, as most of the respective APIs rely on CPU-based preprocessing for decoding, data decompression frequently becomes a bottleneck in accelerated compute systems. This establishes the need for efficient GPU-based solutions for decompression. Asymmetric numeral systems (ANS) represent a modern approach to entropy coding, combining superior compression results with high compression and decompression speeds. Concepts for parallelizing ANS decompression on GPUs have been published recently. However, they only exhibit limited scalability in practical applications. In this paper, we present the first massively parallel, arbitrarily scalable approach to ANS decoding on GPUs, based on a novel overflow pattern. Our performance evaluation on three different CUDA-enabled GPUs (V100, TITAN V, GTX 1080) demonstrates speedups of up to 17 over 64 CPU threads, up to 31 over a high performance SIMD-based solution, and up to 39 over Zstandard's entropy codec. Our implementation is publicly available at https://github.com/weissenberger/multians.
SIGIR 2019 Tutorial on Explainable Recommendation and Search

Explainable recommendation and search attempt to develop models or methods that not only generate high-quality recommendation or search results, but also intuitive explanations of the results for users or system designers, which can help to improve the system transparency, persuasiveness, trustworthiness, and effectiveness, etc. This is even more important in personalized search and recommendation scenarios, where users would like to know why a particular product, web page, news report, or friend suggestion exists in his or her own search and recommendation lists. The tutorial focuses on the research and application of explainable recommendation and search algorithms, as well as their application in real-world systems such as search engine, e-commerce and social networks. The tutorial aims at introducing and communicating explainable recommendation and search methods to the community, as well as gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.
Flat ORAM: A Simplified Write-Only Oblivious RAM Construction for Secure Processors

Oblivious RAM (ORAM) is a cryptographic primitive which obfuscates the access patterns to a storage, thereby preventing privacy leakage. So far in the current literature, only ‘fully functional’ ORAMs are widely studied which can protect, at a cost of considerable performance penalty, against the strong adversaries who can monitor all read and write operations. However, recent research has shown that information can still be leaked even if only the write access pattern (not reads) is visible to the adversary. For such weaker adversaries, a fully functional ORAM turns out to be an overkill, causing unnecessary overheads. Instead, a simple ‘write-only’ ORAM is sufficient, and, more interestingly, is preferred as it can offer far better performance and energy efficiency than a fully functional ORAM. In this work, we present Flat ORAM: an efficient write-only ORAM scheme which outperforms the closest existing write-only ORAM called HIVE. HIVE suffers from performance bottlenecks while managing the memory occupancy information vital for correctness of the protocol. Flat ORAM introduces a simple idea of Occupancy Map (OccMap) to efficiently manage the memory occupancy information resulting in far better performance. Our simulation results show that, compared to HIVE, Flat ORAM offers 50 % performance gain on average and up to 80 % energy savings.
Deep Representation Learning Characterized by Inter-Class Separation for Image Clustering

Despite significant advances in clustering methods in recent years, the outcome of clustering of a natural image dataset is still unsatisfactory due to two important drawbacks. Firstly, clustering of images needs a good feature representation of an image and secondly, we need a robust method which can discriminate these features for making them belonging to different clusters such that intra-class variance is less and inter-class variance is high. Often these two aspects are dealt with independently and thus the features are not sufficient enough to partition the data meaningfully. In this paper, we propose a method where we discover these features required for the separation of the images using deep autoencoder. Our method learns the image representation features automatically for the purpose of clustering and also select a coherent image and an incoherent image simultaneously for a given image so that the feature representation learning can learn better discriminative features for grouping the similar images in a cluster and at the same time separating the dissimilar images across clusters. Experiment results show that our method produces significantly better result than the state-of-the-art methods and we also show that our method is more generalized across different dataset without using any pre-trained model like other existing methods.
A Hierarchical WDM-Based Scalable Data Center Network Architecture

Massive data centers are at the heart of the Internet. The rapid growth of Internet traffic and the abundance of rich data-driven applications have raised the need for enormous network bandwidth. Towards meeting this growing traffic demand, optical interconnects have gained significant attention, as they can provide high throughput, low latency, and scalability. In particular, optical Wavelength Division Multiplexing (WDM) provides the possibility to build data centers comprising of millions of servers, while providing hundreds of terabits per second bandwidth. In this paper, we propose a WDM-based Reconfigurable Hierarchical Optical Data Center Architecture (RHODA) that can satisfy future Internet traffic demands. To improve scalability, our DCN architecture is hierarchical, as it groups server racks into clusters. Cluster membership is reconfigurable through the use of optical switches. Each cluster enables heavy-traffic communication among the racks within. To support varying traffic patterns, the inter-cluster network topology and link capacities are also reconfigurable, which is achieved through the use of optical space switches and Wavelength Selective Switches (WSSs). Our simulation results demonstrate that in terms of average hop distance, RHODA outperforms OSA, FatTree and WaveCube by up to 81%, 66% and 60%, respectively.
Dynamic Load Balancing in Hybrid Switching Data Center Networks with Converters

Today's data centers rely on scale-out architectures like fat-tree, BCube, VL2, etc. to connect a large number of commodity servers. It's important to balance the traffic load across the available links. Since the traditional electrical network cannot perfectly respond to the traffic variations in data centers, a growing trend is to introduce converters with adjustable optical links instead of adding more wiring links. However, little is known today about how to fully exploit the potential of the flexibility from the converters: the joint optimization on adjusting the optical links inside the converters and the routing in the whole network remains algorithmically challenging. In this paper, we initiate the study of dynamic load balancing problem (DLBP) in hybrid switching data center networks with converters. We design a set of specific converters for Diamond, VL2, BCube topologies to introduce more flexibility. Based on it, the connections of the optical links inside the converter and the route for each flow needs to be jointly optimized to minimize the maximum link utilization in the whole network. We formulate DLBP as an optimization program and prove that it's not only NP-hard, but also ρ-inapproximation. Further, we design a greedy algorithm to solve it. Extensive experiments show that our algorithm can reduce the traffic congestion by 12% on average.
Fundamental Limits of Invisible Flow Fingerprinting

Network flow fingerprinting can be used to de-anonymize communications on anonymity systems such as Tor by linking the ingress and egress segments of anonymized connections. Assume Alice and Bob have access to the input and the output links of an anonymous network, respectively, and they wish to collaboratively reveal the connections between the input and the output links without being detected by Willie who protects the network. Alice generates a codebook where each codeword is a unique fingerprint indicating a sequence of interpacket delays, and shares it only with Bob. To trace each flow, Alice selects a fingerprint and manipulates the packet timings of the flow to follow the packet timings suggested by the fingerprint, and Bob extracts the fingerprints from it after it passes through the network. We model the network as parallel M/M/1 queues where each queue is shared by a flow f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> from Alice to Bob and other flows independent of f <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</sub> . Packet timings of the flows are governed by independent Poisson processes. Assuming all input flows have equal packet rates and that Bob observes only flows with fingerprints, we first present two scenarios: 1) Alice fingerprints all the flows and 2) Alice fingerprints a subset of the flows, unknown to Willie. Then, we extend the construction and analysis to the case of arbitrary flow rates and the case where Bob observes flows with and without fingerprints. For each scenario, we derive the number of flows that Alice and Bob can trace by fingerprinting.
Borders and gateways

Internet topology reflects economic and political constraints that change over time. Although autonomous systems (AS) topology has been measured and modeled for many years, focusing primarily on economic relationships, earlier studies have not quantified how topology is changing with respect to nation-state boundaries. National boundaries are natural points of control for surveillance, censorship, tariffs and data localization. This paper introduces a measure, national choke-point potential (NCP), to characterize how a country's AS topology is organized in terms of BGP paths that can carry traffic across international borders. To study country-level chokepoints, we developed BGP-SAS, an open source, cross platform, efficient set of tools for simulating BGP routing and calculating national chokepoint measures. We use these tools to assess how AS topologies have changed over a ten-year span, finding significant variability among countries, with some increasing their chokepoint potential and others remaining constant, fluctuating, and in some cases declining. Overall, however, most national Internet boundaries have either become more pronounced or remained constant, despite new infrastructure buildouts and increased Internet usage. When compared to independent measures of Internet freedom, we find statistically significant relationships between NCP and Internet freedom.
Modelling the interplay of security, privacy and trust in sociotechnical systems: a computer-aided design approach

Personal data have become a central asset for multiple enterprise applications and online services offered by private companies, public organisations or a combination of both. The sensitivity of such data and the continuously growing legislation that accompanies their management dictate the development of methods that allow the development of more secure, trustworthy software systems with focus on privacy protection. The contribution of this paper is the definition of a novel requirements engineering method that supports both early and late requirements specification, giving emphasis on security, privacy and trust. The novelty of our work is that it provides the means for software designers and security experts to analyse the system-to-be from multiple aspects, starting from identifying high-level goals to the definition of business process composition, and elicitation of mechanisms to fortify the system from external threats. The method is supported by two CASE tools. To demonstrate the applicability and usefulness of our work, the paper shows its applications to a real-world case study.
Debugging Static Analysis

Static analysis is increasingly used by companies and individual code developers to detect and fix bugs and security vulnerabilities. As programs grow more complex, the analyses have to support new code concepts, frameworks and libraries. However, static-analysis code itself is also prone to bugs. While more complex analyses are written and used in production systems every day, the cost of debugging and fixing them also increases tremendously. To understand the difficulties of debugging static analysis, we surveyed 115 static-analysis writers. From their responses, we determined the core requirements to build a debugger for static analyses, which revolve around two main issues: abstracting from both the analysis code and the code it analyses at the same time, and tracking the analysis internal state throughout both code bases. Most tools used by our survey participants lack the capabilities to address both issues. Focusing on those requirements, we introduce Visuflow, a debugging environment for static data-flow analysis. Visuflow features graph visualizations and custom breakpoints that enable users to view the state of an analysis at any time. In a user study on 20 static-analysis writers, Visuflow helped identify 25 and fix 50 percent more errors in the analysis code compared to the standard Eclipse debugging environment.
Design of Controllers Applied to Autonomous Unmanned Aerial Vehicles Using Software In The Loop

This paper presents the design of the altitude controller of Unmanned Aerial Vehicle (UAV) based on the data acquired from the Software in the Loop (SITL). More specifically, the developed methodology permits to describe a test procedure where an algorithm or even a control strategy can be designed and validated based on the physical model of the system. In this way, the PID controller with a technique known as Successive Loop Closure is designed. The paper also shows how to establish a remote communication with the quadcopter using all the versatility of the Software in the Loop (SITL) in combination with a Ground Control Station and Dronekit developing tool. Finally, the flight simulation tests show that the designed controller was able to reach a stable flight with its performance evaluated through non-intrusive performance indexes and analysis of the system response in the time domain.
Computing quartet distance is equivalent to counting 4-cycles

The quartet distance is a measure of similarity used to compare two unrooted phylogenetic trees on the same set of n leaves, defined as the number of subsets of four leaves related by a different topology in both trees. After a series of previous results, Brodal et al. [SODA 2013] presented an algorithm that computes this number in O(ndlogn) time, where d is the maximum degree of a node. For the related triplet distance between rooted phylogenetic trees, the same authors were able to design an O(nlogn) time algorithm, that is, with running time independent of d. This raises the question of achieving such complexity for computing the quartet distance, or at least improving the dependency on d.
Selecting Discriminative Terms for Relevance Model

Pseudo-relevance feedback based on the relevance model does not take into account the inverse document frequency of candidate terms when selecting expansion terms. As a result, common terms are often included in the expanded query constructed by this model. We propose three possible extensions of the relevance model that address this drawback. Our proposed extensions are simple to compute and are independent of the base retrieval model. Experiments on several TREC news and web collections show that the proposed modifications yield significantly better MAP, precision, NDCG, and recall values than the original relevance model as well as its two recently proposed state-of-the-art variants.
Message Transmission Over Classical Quantum Channels With a Jammer With Side Information: Message Transmission Capacity and Resources

In this paper, a new model for arbitrarily varying classical-quantum channels is proposed. In this model, a jammer has side information. The communication scenario in which a jammer can select only classical inputs as a jamming sequence is considered in the first part of the paper. This situation corresponds to the standard model of arbitrarily varying classical-quantum channels. Two scenarios are considered. In the first scenario, the jammer knows the channel input, while in the second scenario the jammer knows both the channel input and the message. The transmitter and receiver share a secret random key with a vanishing key rate. The capacity for both average and maximum error criteria for both scenarios is determined in this paper. A strong converse is also proved. It is shown that all these corresponding capacities are equal, which means that additionally revealing the message to the jammer does not change the capacity. The communication scenario with a fully quantum jammer is considered in the second part of the paper. A single letter characterization for the capacity with secret random key as assistance for both average and maximum error criteria is derived in the paper.
Formal-Verification of Smart-Contract Languages: A Survey

A blockchain is a peer-to-peer electronic ledger of transactions that may be publicly or privately distributed to all users. Apart from unique consensus mechanisms, their success is also obliged to smart contracts. Also, These programs let on distrusting parties to enter reconciliation that are executed autonomously. Although a number of studies focus on security of introducing new programming languages., However, there is no comprehensive survey on the smart-contract language in suitability and expressiveness concepts and properties that recognize the interaction between people in organizations and technology in workplaces. To fill this gap, we conduct a systematic analysis about smart-contract language properties that focus on e-contractual and pattern-based exploration. In particular, this paper gives smart-contract language taxonomy, introducing technical challenges of languages as well as recent solutions in tackling the challenges. Moreover, this paper also represents the future research direction in the introducing new smart-contract language.
Modeling the motion of a space manipulation robot using position control

In this article the modeling of robot motion for the case of transferring a robot to a given point in the phase space and for transferring it to a program trajectory is under study. The equations of motion of the robot in the form of Lagrange's equations of the 2nd kind were derived. The positional control in the form of feedbacks is constructed. The graphs of changes in the components of the deviation vector and the components of the control vector for its translation onto the program trajectory are given.
Active learning with error-correcting output codes

In many real-world classification problems, while there is a large amount of unlabeled data, labeled data is usually hard to acquire. One way to solve these problems is active learning. It aims to select the most valuable instances for labeling and construct a superior classifier. Most existing active learning algorithms are designed for binary classification problems, only a few algorithms can deal with multi-class cases. Moreover, as most multi-class active learning methods are directly extended from binary active learning methods, it is difficult for them to fuse the output results of binary cases. In this paper, we propose a novel multi-class active learning algorithm to tackle the above problems and select the most informative instances, called active learning with error-correcting output codes (ECOCAL). We create a codeword for each class and then obtain a test code for each unlabeled instance by error-correcting output codes (ECOC) framework, which is a powerful tool to combine multiple binary classifiers to address multi-class classification problems. By calculating the variance of the distance between a test code and all codewords, the proposed algorithm is able to measure the uncertainty across multiple classes. Extensive experimental results show that the proposed method outperforms several state-of-the-art active learning methods on both binary and multi-class datasets.
An Innovations Approach to Viterbi Decoding of Convolutional Codes

We introduce the notion of innovations for Viterbi decoding of convolutional codes. First, we define a kind of innovation corresponding to the received data, i.e., the input to a Viterbi decoder. Then, the structure of a scarce-state-transition (SST) Viterbi decoder is derived in a natural manner. It is shown that the newly defined innovation is just the input to the main decoder in an SST Viterbi decoder and generates the same syndrome as the original received data does. A similar result holds for quick-look-in codes as well. In this case, however, the precise innovation is not defined. We see that this innovation-like quantity is related to the linear smoothed estimate of the information. The essence of innovations approach to a linear filtering problem is first to whiten the observed data, and then to treat the resulting simpler white-noise observations problem. In our case, this corresponds to the reduction of decoding complexity in the main decoder in an SST Viterbi decoder. We show that the distributions related to the main decoder (i.e., the input distribution and the state distribution in the code trellis for the main decoder) are much biased under moderately noisy conditions. We see that these biased distributions actually lead to the complexity reduction in the main decoder. Furthermore, it is shown that the proposed innovations approach can be extended to maximum-likelihood decoding of block codes as well.