Reviewerly/exHarmony huggingface dataset:
Semi-device-independent multiparty quantum key distribution in the asymptotic limit

We present a security analysis of a multiparty quantum key distribution (QKD) based on Mermin-Ardehali-Belinskiĭ-Klyshko (MABK) type multipartite nonlocality. Our analysis provides an asymptotic secret key rate of the multiparty QKD under the restriction that successive trials are completely independent. In our analysis, we construct the relation between a secret key rate of the multiparty QKD and an expectation value of MABK operator. We show that side channel attacks, which can threaten the information theoretic (IT) security analysis of multiparty QKD, are prevented in our analysis. We compare secret key rates obtained by using the IT analysis, the existing fully device-independent analysis, and our analysis. It is shown that efficiency of the multiparty QKD can be improved with increasing number of authorized parties in our analysis as well.
Cross-task weakly supervised learning from instructional videos.

In this paper we investigate learning visual models for the steps of ordinary tasks using weak supervision via instructional narrations and an ordered list of steps instead of strong supervision via temporal annotations. At the heart of our approach is the observation that weakly supervised learning may be easier if a model shares components while learning different steps: `pour egg' should be trained jointly with other tasks involving `pour' and `egg'. We formalize this in a component model for recognizing steps and a weakly supervised learning framework that can learn this model under temporal constraints from narration and the list of steps. Past data does not permit systematic studying of sharing and so we also gather a new dataset, CrossTask, aimed at assessing cross-task sharing. Our experiments demonstrate that sharing across tasks improves performance, especially when done at the component level and that our component model can parse previously unseen tasks by virtue of its compositionality.
Universal (meta-)logical reasoning: The Wise Men Puzzle (Isabelle/HOL dataset)

The authors universal (meta-)logical reasoning approach is demonstrated and assessed with a prominent riddle in epistemic reasoning: the Wise Men Puzzle. The presented solution puts a particular emphasis on the adequate modeling of common knowledge and it illustrates the elegance and the practical relevance of the shallow semantical embedding approach when utilized within modern proof assistant systems such as Isabelle/HOL. The contributed dataset provides supporting evidence for claims made in the article "Universal (meta-)logical reasoning: Recent successes" (Benzmüller, 2019).
DenseBody: Directly Regressing Dense 3D Human Pose and Shape From a Single Color Image

Recovering 3D human body shape and pose from 2D images is a challenging task due to high complexity and flexibility of human body, and relatively less 3D labeled data. Previous methods addressing these issues typically rely on predicting intermediate results such as body part segmentation, 2D/3D joints, silhouette mask to decompose the problem into multiple sub-tasks in order to utilize more 2D labels. Most previous works incorporated parametric body shape model in their methods and predict parameters in low-dimensional space to represent human body. In this paper, we propose to directly regress the 3D human mesh from a single color image using Convolutional Neural Network(CNN). We use an efficient representation of 3D human shape and pose which can be predicted through an encoder-decoder neural network. The proposed method achieves state-of-the-art performance on several 3D human body datasets including Human3.6M, SURREAL and UP-3D with even faster running speed.
Parameter Combination Framework for the Differential Evolution Algorithm

The differential evolution (DE) algorithm is a popular and efficient evolutionary algorithm that can be used for single objective real-parameter optimization. Its performance is greatly affected by its parameters. Generally, parameter control strategies involve determining the most suitable value for the current state; there is only a little research on parameter combination and parameter distribution which is also useful for improving algorithm performance. This paper proposes an idea to use parameter region division and parameter strategy combination to flexibly adjust the parameter distribution. Based on the idea, a group-based two-level parameter combination framework is designed to support various modes of parameter combination, and enrich the parameter distribution characteristics. Under this framework, two customized parameter combination strategies are given for a single-operation DE algorithm and a multi-operation DE algorithm. The experiments verify the effectiveness of the two strategies and it also illustrates the meaning of the framework.
Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives

People often share personal narratives in order to seek advice from others. To properly infer the narrator's intention, one needs to apply a certain degree of common sense and social intuition. To test the capabilities of NLP systems to recover such intuition, we introduce the new task of inferring what is the advice-seeking goal behind a personal narrative. We formulate this as a cloze test, where the goal is to identify which of two advice-seeking questions was removed from a given narrative. The main challenge in constructing this task is finding pairs of semantically plausible advice-seeking questions for given narratives. To address this challenge, we devise a method that exploits commonalities in experiences people share online to automatically extract pairs of questions that are appropriate candidates for the cloze task. This results in a dataset of over 20,000 personal narratives, each matched with a pair of related advice-seeking questions: one actually intended by the narrator, and the other one not. The dataset covers a very broad array of human experiences, from dating, to career options, to stolen iPads. We use human annotation to determine the degree to which the task relies on common sense and social intuition in addition to a semantic understanding of the narrative. By introducing several baselines for this new task we demonstrate its feasibility and identify avenues for better modeling the intention of the narrator.
Target-Aware Deep Tracking

Existing deep trackers mainly use convolutional neural networks pre-trained for generic object recognition task for representations. Despite demonstrated successes for numerous vision tasks, the contributions of using pre-trained deep features for visual tracking are not as significant as that for object recognition. The key issue is that in visual tracking the targets of interest can be arbitrary object class with arbitrary forms. As such, pre-trained deep features are less effective in modeling these targets of arbitrary forms for distinguishing them from the background. In this paper, we propose a novel scheme to learn target-aware features, which can better recognize the targets undergoing significant appearance variations than pre-trained deep features. To this end, we develop a regression loss and a ranking loss to guide the generation of target-active and scale-sensitive features. We identify the importance of each convolutional filter according to the back-propagated gradients and select the target-aware features based on activations for representing the targets. The target-aware features are integrated with a Siamese matching network for visual tracking. Extensive experimental results show that the proposed algorithm performs favorably against the state-of-the-art methods in terms of accuracy and speed.
When a Tweet is Actually Sexist. A more Comprehensive Classification of Different Online Harassment Categories and The Challenges in NLP

Sexism is very common in social media and makes the boundaries of freedom tighter for feminist and female users. There is still no comprehensive classification of sexism attracting natural language processing techniques. Categorizing sexism in social media in the categories of hostile or benevolent sexism are so general that simply ignores the other types of sexism happening in these media. This paper proposes a more comprehensive and in-depth categories of online harassment in social media e.g. twitter into the following categories, "Indirect harassment", "Information threat", "sexual harassment", "Physical harassment" and "Not sexist" and address the challenge of labeling them along with presenting the classification result of the categories. It is preliminary work applying machine learning to learn the concept of sexism and distinguishes itself by looking at more precise categories of sexism in social media.
AuCPace: Efficient verifier-based PAKE protocol tailored for the IIoT

Increasingly connectivity becomes integrated in products and devices that previously operated in a stand-alone setting. This observation holds for many consumer applications in the so-called of Things (IoT) as well as for corresponding industry applications (IIoT), such as industrial process sensors. Often the only practicable means for authentication of human users is a password. The security of password-based authentication schemes frequently forms the weakest point of the security infrastructure. In this paper we first explain why a tailored protocol designed for the IIoT use case is considered necessary. The differences between IIoT and the conventional Internet use-cases result in largely modified threats and require special procedures for allowing both, convenient and secure use in the highly constrained industrial setting. Specifically the use of a verifier-based password-authenticated key-exchange (V-PAKE) protocol as a hedge against public-key-infrastructure (PKI) failures is considered important. Availability concerns for the case of failures of (part of) the communication infrastructure makes local storage of access credentials mandatory. The larger threat of physical attacks makes it important to use memory-hard password hashing. This paper presents a corresponding tailored protocol, AuCPace, together with a security proof within the Universal Composability (UC) framework considering fully adaptive adversaries. We also introduce a new security notion of partially augmented PAKE that provides specific performance advantages and makes them suitable for a larger set of IIoT applications. We also present an actual instantiation of our protocol, AuCPace25519, and present performance results on ARM Cortex-M0 and Cortex-M4 microcontrollers. Our implementation realizes new speed-records for PAKE and X25519 Diffie-Hellman for the ARM Cortex M4 architecture.
Stereo camera visual odometry for moving urban environments

This work proposes a system designed to estimate the ego-motion of a synchronized calibrated stereo camera in scenes containing a moderate number of moving objects. This is particularly useful in busy road scenes and populated urban areas. The key novelty of the proposed approach is that it estimat es the motion of clusters of pixels between stereo frames, which allows it to explicitly reject clusters in motion. This is in contrast to current state-of-the-art algorithms, that tend to treat moving elements as outliers, which are removed using strategies such as RANSAC or M-estimators. Unfortunately treating moving pixels as outliers can give poor performance when the motion represents a significant portion of pixels. The proposed approach overcomes this, if the motion is due to many independently moving objects (such as people or cars). Our experiments show promising results in a variety of urban environments.
A semi-supervised deep network embedding approach based on the neighborhood structure

Network embedding is a very important task to represent the high-dimensional network in a lowdimensional vector space, which aims to capture and preserve the network structure.Most existing network embedding methods are based on shallow models.However, actual network structures are complicated which means shallow models cannot obtain the high-dimensional nonlinear features of the network well.The recently proposed unsupervised deep learning models ignore the labels information.To address these challenges, in this paper, we propose an effective network embedding method of Structural Labeled Locally Deep Nonlinear Embedding (SLLDNE).SLLDNE is designed to obtain highly nonlinear features through utilizing deep neural network while preserving the label information of the nodes by using a semi-supervised classifier component to improve the ability of discriminations.Moreover, we exploit linear reconstruction of neighborhood nodes to enable the model to get more structural information.The experimental results of vertex classification on two real-world network datasets demonstrate that SLLDNE outperforms the other state-of-the-art methods.
Web Feature Deprecation: A Case Study for Chrome

Deprecation - signaling that a function is becoming obsolete in the near future - is necessary for the web ecosystem. However, an unsuccessful deprecation can lead to pain for both developers and end-users. In this paper, we analyze web feature deprecations with the goal of improving Chrome's deprecation process by balancing the need for deprecation and minimizing user pain. We produce a taxonomy of reasons why developers want to deprecate web features and a deprecation methodology for deciding when it is safe to deprecate a web feature. We also discuss the challenges faced during the deprecation process.
The Effect of Teaching Methods and Learning Styles on Students’ English Achievement (An Experimental Study at Junior High School 1 Pasangkayu)

&lt;p&gt;The objectives of the research are to determine the effects of teaching methods (STAD and jigsaw) and learning styles (visual, auditory, and kinesthetic) on students’ English achievement. This research is an experimental study conducted at Junior High School Pasangkayu in 2014 with 213 sample which is selected stratified-randomly (n = 68). The results of the research are as follow: (1) English achievement of students taught with STAD is better than those of taught with jigsaw; (2) there is no significant difference in English achievement among visual, auditory, and kinesthetic students; (3) there is any significant effect of interaction among teaching method and learning styles on students’ learning English achievement. The research also find out that for visual students, studying English achievement of students taught with STAD is better than that of students taught with jigsaw; for auditory students, learning English achievement of students taught with jigsaw is better than that of students taught with STAD; and for kinesthetic students, English achievement of students taught with STAD is better than that of students taught with jigsaw. To sum up, STAD is more effective than jigsaw in improving students’ English achievement. STAD is suitable to improve English achievement of visual and kinesthetic students, and jigsaw is suitable to improve English achievement of auditory students.&lt;/p&gt;
Experimental study on parallel and analog optical reservoir computing with delayed feedback system for physical implementation

Optical reservoir computing (RC) with delayed feedback is expected to achieve high-speed data processing. However, in the parallel RC framework, the digital pre-process limits the actual processing speed. An analog-based, simple pre-processing method was developed and implemented in an optical RC architecture to overcome the bottleneck, and the performance in calculating two types of parallel task was evaluated. One was two independent benchmark tasks; the other was an integrative multi-input and multi-output odor identification task. We successfully demonstrated that these two parallel tasks can be physically processed with the network parameters optimized to maximize the RC performance. These results strongly suggest the potential of the optical reservoir system for future high-speed multimodal data processing applications.
Reinforced Imitation in Heterogeneous Action Space

Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse. In this paper, we consider a challenging setting where an agent and an expert use different actions from each other. We assume that the agent has access to a sparse reward function and state-only expert observations. We propose a method which gradually balances between the imitation learning cost and the reinforcement learning objective. In addition, this method adapts the agent's policy based on either mimicking expert behavior or maximizing sparse reward. We show, through navigation scenarios, that (i) an agent is able to efficiently leverage sparse rewards to outperform standard state-only imitation learning, (ii) it can learn a policy even when its actions are different from the expert, and (iii) the performance of the agent is not bounded by that of the expert, due to the optimized usage of sparse rewards.
Exposing GAN-synthesized Faces Using Landmark Locations

Generative adversary networks (GANs) have recently led to highly realistic image synthesis results. In this work, we describe a new method to expose GAN-synthesized images using the locations of the facial landmark points. Our method is based on the observations that the facial parts configuration generated by GAN models are different from those of the real faces, due to the lack of global constraints. We perform experiments demonstrating this phenomenon, and show that an SVM classifier trained using the locations of facial landmark points is sufficient to achieve good classification performance for GAN-synthesized faces.
Class of genuinely high-dimensionally-entangled states with a positive partial transpose

Entangled states with a positive partial transpose (so-called PPT states) are central to many interesting problems in quantum theory. On the one hand, they are considered to be weakly entangled, since no pure state entanglement can be distilled from them. On the other hand, it has been shown recently that some of these PPT states exhibit genuinely high-dimensional entanglement, i.e., they have a high Schmidt number. Here we investigate the ($d\ifmmode\times\else\texttimes\fi{}d$)-dimensional PPT states for $d\ensuremath{\ge}4$ discussed recently by E. Sindici and M. Piani [Phys. Rev. A 97, 032319 (2018)]. By generalizing their methods to the calculation of Schmidt numbers, we show that a linear $d/2$ scaling of its Schmidt number in the local dimension $d$ can be attained.
Actual Evapotranspiration Model Based on the Irrigation Volume of the Maize Fields on Alfisols

&lt;p&gt;Evapotranspiration data are considered important to determine volume and schedule of the irrigation. The purpose of this study is to determine the actual evapotranspiration model based on the volume of the irrigation to obtain an accurate evapotranspiration value on Alfisols with maize plantation. This research is conducted in the experimental field Jumantono subdistrict, Karanganyar regency by the experiment of the maize (&lt;em&gt;Zea mays&lt;/em&gt;) on Alfisols. The evapotranspiration model uses the soil correction factor (x) and the irrigation volume (% ETc). The soil correction factor (X) is calculated by linear regression on actual evapotranspiration (ETa) with crop evapotranspiration (ETc). ETc using reference evapotranspiration (ETo) using the &lt;em&gt;Penman-Monteith&lt;/em&gt; model. The results showed that ETa was smaller than ETc in all treatments. The models that can be produced in this study are 3 models. All models applied to produce a determination coefficient &amp;gt; 90%, which all models have a positive relationship. The best actual evapotranspiration model was in total model uses ETa = {0.0403 + (0.0085 × Irrigation volume)} × ETc, for daily estimation and total one planting estimation; weekly estimation using the weekly model using ETa = {0.4428 + (0.0054 ×Irrigation volume)}× ETc. The errors of both models are ± 1%.&lt;/p&gt;
Adaptive Learning Expert System for Diagnosis and Management of Viral Hepatitis

Viral hepatitis is the regularly found health problem throughout the world among other easily transmitted diseases, such as tuberculosis, human immune virus, malaria and so on.Among all hepatitis viruses, the uppermost numbers of deaths are result from the long-lasting hepatitis C infection or long-lasting hepatitis B. In order to develop this system, the knowledge is acquired using both structured and semi-structured interviews from internists of St.Paul Hospital.Once the knowledge is acquired, it is modeled and represented using rule based reasoning techniques.Both forward and backward chaining is used to infer the rules and provide appropriate advices in the developed expert system.For the purpose of developing the prototype expert system SWI-prolog editor also used.The proposed system has the ability to adapt with dynamic knowledge by generalizing rules and discover new rules through learning the newly arrived knowledge from domain experts adaptively without any help from the knowledge engineer.
STC Speaker Recognition Systems for the VOiCES From a Distance Challenge

This paper presents the Speech Technology Center (STC) speaker recognition (SR) systems submitted to the VOiCES From a Distance challenge 2019. The challenge's SR task is focused on the problem of speaker recognition in single channel distant/far-field audio under noisy conditions. In this work we investigate different deep neural networks architectures for speaker embedding extraction to solve the task. We show that deep networks with residual frame level connections outperform more shallow architectures. Simple energy based speech activity detector (SAD) and automatic speech recognition (ASR) based SAD are investigated in this work. We also address the problem of data preparation for robust embedding extractors training. The reverberation for the data augmentation was performed using automatic room impulse response generator. In our systems we used discriminatively trained cosine similarity metric learning model as embedding backend. Scores normalization procedure was applied for each individual subsystem we used. Our final submitted systems were based on the fusion of different subsystems. The results obtained on the VOiCES development and evaluation sets demonstrate effectiveness and robustness of the proposed systems when dealing with distant/far-field audio under noisy conditions.
BMAR – Blockchain for Medication Administration Records

Medication Administration Records are crucial documents in the care and quality offered to service users. Audits will inspect MAR sheets and these will form a significant impact on the outcome of Medication Management. This impact will be combined with inspections of registered hospitals, care and residential homes across the healthcare profession. The proposal is to build a prototype using blockchain technology to implement MAR sheets, essentially building a blockchain application that stores electronic health records (EHR). The use of permissioned blockchain technology provides confidentiality and trust with the auditors (e.g., CQC). The prototype is tested on two scenarios and results are encouraging. The results indicate that reminders can be sent to healthcare professionals and other consequences of the implementation of EHR and permissioned blockchain.