Reviewerly/exHarmony huggingface dataset:
LDA Ensembles for Interactive Exploration and Categorization of Behaviors

We define behavior as a set of actions performed by some actor during a period of time. We consider the problem of analyzing a large collection of behaviors by multiple actors, more specifically, identifying typical behaviors and spotting anomalous behaviors. We propose an approach leveraging topic modeling techniques - LDA (Latent Dirichlet Allocation) Ensembles - to represent categories of typical behaviors by topics that are obtained through topic modeling a behavior collection. When such methods are applied to text in natural languages, the quality of the extracted topics are usually judged based on the semantic relatedness of the terms pertinent to the topics. This criterion, however, is not necessarily applicable to topics extracted from non-textual data, such as action sets, since relationships between actions may not be obvious. We have developed a suite of visual and interactive techniques supporting the construction of an appropriate combination of topics based on other criteria, such as distinctiveness and coverage of the behavior set. Two case studies on analyzing operation behaviors in the security management system and visiting behaviors in an amusement park, and the expert evaluation of the first case study demonstrate the effectiveness of our approach.
Strong bound between trace distance and Hilbert-Schmidt distance for low-rank states

The trace distance between two quantum states, $\ensuremath{\rho}$ and $\ensuremath{\sigma}$, is an operationally meaningful quantity in quantum information theory. However, in general it is difficult to compute, involving the diagonalization of $\ensuremath{\rho}\ensuremath{-}\ensuremath{\sigma}$. In contrast, the Hilbert-Schmidt distance can be computed without diagonalization, although it is less operationally significant. Here, we relate the trace distance and the Hilbert-Schmidt distance with a bound that is particularly strong when either $\ensuremath{\rho}$ or $\ensuremath{\sigma}$ is low rank. Our bound is stronger than the bound one could obtain via the norm equivalence of the Frobenius and trace norms. We also consider bounds that are useful not only for low-rank states but also for low-entropy states. Our results have relevance to quantum information theory, quantum algorithm design, and quantum complexity theory.
Hate speech harms: a social justice discussion of disabled Norwegians’ experiences

This article presents findings from a survey on hate speech reported by disabled people in Norway. Previous British and American research shows that disabled people are more likely than their non-disabled peers to be the victims of hate speech and hate crime. In our study, 38% reported at least one experience of hate speech during the previous 12 months. The respondents reported a wide range of consequences of hate speech related to psychological, social and societal issues. The findings reveal that people place restrictions on their own lives as a result of being exposed to hate speech, including raising their opinion in public debates. Drawing on the work of Nancy Fraser, we discuss the mechanisms that prevent disabled people from participating in society on equal terms. We conclude that to combat hate speech, transformative strategies need to be applied.
Dark Web and Its Impact in Online Anonymity and Privacy: A Critical Analysis and Review

The Internet as the whole is a network of multiple computer networks and their massive infrastructure.The web is made up of accessible websites through search engines such as Google, Firefox, etc. and it is known as the Surface Web.The Internet is segmented further in the Deep Web-the content that it is not indexed and cannot access by traditional search engines.Dark Web considers a segment of the Deep Web.It accesses through TOR.Actors within Dark Web websites are anonymous and hidden.Anonymity, privacy and the possibility of non-detection are three factors that are provided by special browser such as TOR and I2P.In this paper, we are going to discuss and provide results about the influence of the Dark Web in different spheres of society.It is given the number of daily anonymous users of the Dark Web (using TOR) in Kosovo as well as in the whole world for a period of time.The influence of hidden services websites is shown and results are gathered from Ahimia and Onion City Dark Web's search engines.The anonymity is not completely verified on the Dark Web.TOR dedicates to it and has intended to provide anonymous activities.Here are given results about reporting the number of users and in which place(s) they are.The calculation is based on IP addresses according to country codes from where comes the access to them and report numbers in aggregate form.In this way, indirect are represented the Dark Web users.The number of users in anonymous networks on the Dark Web is another key element that is resulted.In such networks, users are calculated through the client requests of directories (by TOR metrics) and the relay list is updated.Indirectly, the number of users is calculated for the anonymous networks.
./trilaterate

Hover, touch, and force are promising input modalities that get increasingly integrated into screens and everyday objects. However, these interactions are often limited to flat surfaces and the integration of suitable sensors is time-consuming and costly. To alleviate these limitations, we contribute Trilaterate: A fabrication pipeline to 3D print custom objects that detect the 3D position of a finger hovering, touching, or forcing them by combining multiple capacitance measurements via capacitive trilateration. Trilaterate places and routes actively-shielded sensors inside the object and operates on consumer-level 3D printers. We present technical evaluations and example applications that validate and demonstrate the wide applicability of Trilaterate.
Compact and Flexible FPGA Implementation of Ed25519 and X25519

This article describes a field-programmable gate array (FPGA) cryptographic architecture, which combines the elliptic curve--based Ed25519 digital signature algorithm and the X25519 key establishment scheme in a single module. Cryptographically, these are high-security elliptic curve cryptography algorithms with short key sizes and impressive execution times in software. Our goal is to provide a lightweight FPGA module that enables them on resource-constrained devices, specifically for Internet of Things (IoT) applications. In addition, we aim at extensibility with customisable countermeasures against timing and differential power analysis side-channel attacks and fault-injection attacks. For the former, we offer a choice between time-optimised versus constant-time execution, with or without Z -coordinate randomisation and base-point blinding; and for the latter, we offer enabling or disabling default-case statements in the Finite State Machine (FSM) descriptions. To obtain compactness and at the same time fast execution times, we make maximum use of the Digital Signal Processing (DSP) slices on the FPGA. We designed a single arithmetic unit that is flexible to support operations with two moduli and non-modulus arithmetic. In addition, our design benefits in-place memory management and the local storage of inputs into DSP slices’ pipeline registers and takes advantage of distributed memory. These eliminate a memory access bottleneck. The flexibility is offered by a micro-code supported instruction-set architecture. Our design targets 7-Series Xilinx FPGAs and is prototyped on a Zynq System-on-Chip (SoC). The base design combining Ed25519 and X25519 in a single module, and its implementation requires only around 11.1K Lookup Tables (LUTs), 2.6K registers, and 16 DSP slices. Also, it achieves performance of 1.6ms for a signature generation and 3.6ms for a signature verification for a 1024-bit message with an 82MHz clock. Moreover, the design can be optimised only for X25519, which gives the most compact FPGA implementation compared to previously published X25519 implementations.
A Preliminary Design-Phase Security Methodology for Cyber–Physical Systems

Despite “cyber” being in the name, cyber–physical systems possess unique characteristics that limit the applicability and suitability of traditional cybersecurity techniques and strategies. Furthermore, vulnerabilities to cyber–physical systems can have significant safety implications. The physical and cyber interactions inherent in these systems require that cyber vulnerabilities not only be defended against or prevented, but that the system also be resilient in the face of successful attacks. Given the complex nature of cyber–physical systems, the identification and evaluation of appropriate defense and resiliency strategies must be handled in a targeted and systematic manner. Specifically, what resiliency strategies are appropriate for a given system, where, and which should be implemented given time and/or budget constraints? This paper presents two methodologies: (1) the cyber security requirements methodology and (2) a systems-theoretic, model-based methodology for identifying and prioritizing appropriate resiliency strategies for implementation in a given system and mission. This methodology is demonstrated using a case study based on a hypothetical weapon system. An assessment and comparison of the results from the two methodologies suggest that the techniques presented in this paper can augment and enhance existing systems engineering approaches with model-based evidence.
Buffer-Aware Streaming in Small-Scale Wireless Networks: A Deep Reinforcement Learning Approach

Buffer-aware video streaming, which exploits the available storage space in user device to store the prefetched video data in good channels for video data use in poor channels, has been proved to have the potential to reduce the impact of fluctuating wireless channels on user-perceived video performance. However, in practical wireless networks, due to the unknown channel state and video rate, providing buffer-aware video streaming service to wireless user is a challenging problem. In this paper, with the aim to design an autonomous wireless video streaming system, we apply the deep reinforcement learning approach to dynamic resource optimization for wireless buffer-aware video streaming under unknown channel state and video rate. Specifically, we define a reward function for buffer-aware video streaming as the effective video streaming time when neither video-playback overflow nor video-playback underflow occurs. We propose a Markov decision process based problem formulation of the joint bandwidth allocation and buffer management for maximizing the effective video streaming time of all users. The optimal bandwidth allocation and buffer management policy is learned from training a deep neural network based on a deep reinforcement learning algorithm. We simulate the proposed algorithm in Tensorflow. Simulation results verify that the proposed deep reinforcement learning approach is effective for buffer-aware video streaming in wireless networks.
Scene Categorization Model Using Deep Visually Sensitive Features

Visually sensitive regions in the scene are thought to be important for scene categorization. In this paper, we propose to utilize the important visually sensitive information represented by deep features for scene categorization. Specifically, the context relationship between the objects and the surroundings is fully utilized as the main basis for judging the content of the scene, and combining with the deep convolution neural networks (CNNs), a scene categorization model based on deep visually sensitive features is constructed. First, the saliency regions of the scene images are marked according to the context-based saliency detection algorithm. Then, the original images and the corresponding visually sensitive region detection images are superimposed to obtain the visually sensitive region enhancement images. Second, the deep convolution features of the original images, the visually sensitive region detection images, and the visually sensitive region enhancement images are extracted through the deep CNNs pre-trained on the large-scale scene dataset Places. Finally, considering that the deep features extracted by different layers of the convolution network have different capabilities of discrimination, the fusion features are generated from multiple convolution layers to construct visually sensitive CNN model (VS-CNN). In order to verify the effectiveness of the proposed model, the experiments are conducted on the five standard scene datasets, i.e., LabelMe, UIUC-Sports, Scene-15, MIT67, and SUN. The experimental results show that the proposed model is effective and has good adaptability. Especially, our categorization performance is superior to many state-of the-art methods for a complex indoor scene.
Reusable Designated-Verifier NIZKs for all NP from CDH

Non-interactive zero-knowledge proofs (NIZKs) are a fundamental cryptographic primitive. Despite a long history of research, we only know how to construct NIZKs under a few select assumptions, such as the hardness of factoring or using bilinear maps. Notably, there are no known constructions based on either the computational or decisional Diffie-Hellman (CDH/DDH) assumption without relying on a bilinear map. In this paper, we study a relaxation of NIZKs in the designated verifier setting (DV-NIZK), in which the public common-reference string is generated together with a secret key that is given to the verifier in order to verify proofs. In this setting, we distinguish between one-time and reusable schemes, depending on whether they can be used to prove only a single statement or arbitrarily many statements. For reusable schemes, the main difficulty is to ensure that soundness continues to hold even when the malicious prover learns whether various proofs are accepted or rejected by the verifier. One-time DV-NIZKs are known to exist for general NP statements assuming only public-key encryption. However, prior to this work, we did not have any construction of reusable DV-NIZKs for general NP statements from any assumption under which we didn't already also have standard NIZKs. In this work, we construct reusable DV-NIZKs for general NP statements under the CDH assumption, without requiring a bilinear map. Our construction is based on the hidden-bits paradigm, which was previously used to construct standard NIZKs. We define a cryptographic primitive called a hidden-bits generator (HBG), along with a designated-verifier variant (DV-HBG), which modularly abstract out how to use this paradigm to get both standard NIZKs and reusable DV-NIZKs. We construct a DV-HBG scheme under the CDH assumption by relying on techniques from the Cramer-Shoup hash-proof system, and this yields our reusable DV-NIZK for general NP statements under CDH. We also consider a strengthening of DV-NIZKs to the malicious designated-verifier setting (MDV-NIZK) where the setup consists of an honestly generated common random string and the verifier then gets to choose his own (potentially malicious) public/secret key pair to generate/verify proofs. We construct MDV-NIZKs under the "one-more CDH" assumption without relying on bilinear maps.
Joint Transition-Based Models for Morpho-Syntactic Parsing: Parsing Strategies for MRLs and a Case Study from Modern Hebrew

Abstract In standard NLP pipelines, morphological analysis and disambiguation (MA&amp;D) precedes syntactic and semantic downstream tasks. However, for languages with complex and ambiguous word-internal structure, known as morphologically rich languages (MRLs), it has been hypothesized that syntactic context may be crucial for accurate MA&amp;D, and vice versa. In this work we empirically confirm this hypothesis for Modern Hebrew, an MRL with complex morphology and severe word-level ambiguity, in a novel transition-based framework. Specifically, we propose a joint morphosyntactic transition-based framework which formally unifies two distinct transition systems, morphological and syntactic, into a single transition-based system with joint training and joint inference. We empirically show that MA&amp;D results obtained in the joint settings outperform MA&amp;D results obtained by the respective standalone components, and that end-to-end parsing results obtained by our joint system present a new state of the art for Hebrew dependency parsing.
An Internal Validity Index Based on Density-Involved Distance

It is crucial to evaluate the quality of clustering results in cluster analysis.Although many cluster validity indices (CVIs) have been proposed in the literature, they have some limitations when dealing with non-spherical datasets.One reason is that the measure of cluster separation does not consider the impact of outliers and neighborhood clusters.In this paper, a new robust distance measure, one into which density is incorporated, is designed to solve the problem, and an internal validity index based on this separation measure is then proposed.This index can cope with both the spherical and non-spherical structure of clusters.The experimental results indicate that the proposed index outperforms some classical CVIs.
Group Signatures Without NIZK: From Lattices in the Standard Model

In a group signature scheme, users can anonymously sign messages on behalf of the group they belong to, yet it is possible to trace the signer when needed. Since the first proposal of lattice-based group signatures in the random oracle model by Gordon, Katz, and Vaikuntanathan (ASIACRYPT 2010), the realization of them in the standard model from lattices has attracted much research interest, however, it has remained unsolved. In this paper, we make progress on this problem by giving the first such construction. Our schemes satisfy CCA-selfless anonymity and full traceability, which are the standard security requirements for group signatures proposed by Bellare, Micciancio, and Warinschi (EUROCRYPT 2003) with a slight relaxation in the anonymity requirement suggested by Camenisch and Groth (SCN 2004). We emphasize that even with this relaxed anonymity requirement, all previous group signature constructions rely on random oracles or NIZKs, where currently NIZKs are not known to be implied from lattice-based assumptions. We propose two constructions that provide tradeoffs regarding the security assumption and efficiency: Technically, we obtain the above schemes by combining a secret key encryption scheme with additional properties and a special type of attribute-based signature (ABS) scheme, thus bypassing the utilization of NIZKs. More specifically, we introduce the notion of indexed ABS, which is a relaxation of standard ABS. The above two schemes are obtained by instantiating the indexed ABS with different constructions. One is a direct construction we propose and the other is based on previous work.
Privacy preserving publication of relational and transaction data: Survey on the anonymization of patient data

Past two decades have seen a growing interest in methods for providing privacy preserving data publishing. Several models, algorithms and system designs have been proposed in the literature to protect identities in the published data. A number of these have been implemented and are successfully in use by diverse applications such as medical, supermarkets and e-commerce. This work presents a comprehensive survey of the previous research done to develop techniques for ensuring privacy while publishing data. We have identified and described in detail the concepts, models and algorithms related to this problem. The emphasis of the present work, in particular, is upon preserving privacy of patient data that includes demographics data, diagnosis codes and the data containing both demographics and diagnosis codes. Finally, we have summarized some of the major open problems in privacy preserving data publishing and discussed the possible directions for future work in this domain.
TED Multilingual Discourse Bank (TED-MDB): a parallel corpus annotated in the PDTB style

TED-Multilingual Discourse Bank, or TED-MDB, is a multilingual resource where TED-talks are annotated at the discourse level in 6 languages (English, Polish, German, Russian, European Portuguese, and Turkish) following the aims and principles of PDTB. We explain the corpus design criteria, which has three main features: the linguistic characteristics of the languages involved, the interactive nature of TED talks—which led us to annotate Hypophora, and the decision to avoid projection. We report our annotation consistency, and post-annotation alignment experiments, and provide a cross-lingual comparison based on corpus statistics.
Knowledge Distillation for Recurrent Neural Network Language Modeling with Trust Regularization

Recurrent Neural Networks (RNNs) have dominated language modeling because of their superior performance over traditional N-gram based models. In many applications, a large Recurrent Neural Network language model (RNNLM) or an ensemble of several RNNLMs is used. These models have large memory footprints and require heavy computation. In this paper, we examine the effect of applying knowledge distillation in reducing the model size for RNNLMs. In addition, we propose a trust regularization method to improve the knowledge distillation training for RNNLMs. Using knowledge distillation with trust regularization, we reduce the parameter size to a third of that of the previously published best model while maintaining the state-of-the-art perplexity result on Penn Treebank data. In a speech recognition N-best rescoring task, we reduce the RNNLM model size to 18.5% of the baseline system, with no degradation in word error rate (WER) performance on Wall Street Journal data set.
A class imbalance-aware Relief algorithm for the classification of tumors using microarray gene expression data

DNA microarray data has been widely used in cancer research due to the significant advantage helped to successfully distinguish between tumor classes. However, typical gene expression data usually presents a high-dimensional imbalanced characteristic, which poses severe challenge for traditional machine learning methods to construct a robust classifier performing well on both the minority and majority classes. As one of the most successful feature weighting techniques, Relief is considered to particularly suit to handle high-dimensional problems. Unfortunately, almost all relief-based methods have not taken the class imbalance distribution into account. This study identifies that existing Relief-based algorithms may underestimate the features with the discernibility ability of minority classes, and ignore the distribution characteristic of minority class samples. As a result, an additional bias towards being classified into the majority classes can be introduced. To this end, a new method, named imRelief, is proposed for efficiently handling high-dimensional imbalanced gene expression data. imRelief can correct the bias towards to the majority classes, and consider the scattered distributional characteristic of minority class samples in the process of estimating feature weights. This way, imRelief has the ability to reward the features which perform well at separating the minority classes from other classes. Experiments on four microarray gene expression data sets demonstrate the effectiveness of imRelief in both feature weighting and feature subset selection applications.