Reviewerly/exHarmony huggingface dataset:
The Use of Blockchain Technology for Private Data Handling for Mobile Agents in Human-Technology Interaction *

With the rising importance of automation and digitization in the fields of logistics and production systems, society is facing new challenges when it comes to human-technology interaction and the authority over personal data management. This paper proposes a method to use individual-related data for process optimization and raise of acceptance of technology and at the same time keeping the data safe and giving the individual sovereignty over the use of its personal information. In this work a multi-agent system is used for the integration of humans in technical systems, where a special software agent keeps information about the dedicated human, like abilities and properties, and acts in favour of the human. To ensure the privacy of this data, the human can decide which data he wants to expose to the system to make it adapt to his needs. This allows the system to form socio-technical system consisting of humans and robot, where multi-robot systems, like a fleet of AGVs, can interact with the individual. Furthermore, blockchain technologies and databases are used to store the personal date after usage in a secure manner. The methods to safely share, store and transfer personal information in robotics and automation application are described in the following.
Terrorism, the internet, and the threat to freedom of expression: the regulation of digital intermediaries in Europe and the United States

This article examines questions relating to the appropriate role of digital intermediaries in regulating online terrorist-related content and the extent to which proponents of human rights should b...
Distractor-Aware Tracking with Multi-Task and Dynamic Feature Learning

Recently, deep trackers based on the siamese networking are enjoying increasing popularity in the tracking community. Generally, those trackers learn a high-level semantic embedding space for feature representation but lose low-level fine-grained details. Meanwhile, the learned high-level semantic features are not updated during online tracking, which results in tracking drift in presence of target appearance variation and similar distractors. In this paper, we present a novel end-to-end trainable Convolutional Neural Network (CNN) based on the siamese network for distractor-aware tracking. It enhances target appearance representation in both the offline training stage and online tracking stage. In the offline training stage, this network learns both the low-level fine-grained details and high-level coarse-grained semantics simultaneously in a multi-task learning framework. The low-level features with better resolution are complementary to semantic features and able to distinguish the foreground target from background distractors. In the online stage, the learned low-level features are fed into a correlation filter layer and updated in an interpolated manner to encode target appearance variation adaptively. The learned high-level features are fed into a cross-correlation layer without online update. Therefore, the proposed tracker benefits from both the adaptability of the fine-grained correlation filter and the generalization capability of the semantic embedding. Extensive experiments are conducted on the public OTB100 and UAV123 benchmark datasets. Our tracker achieves state-of-the-art performance while running with a real-time frame-rate.
Image risks on transition to digital education: opinion receivers of educational services

The article reflects the aspects of digital education as a complex unstable system in the conditions of the transformation of educational processes. The image risks of a business model for the implementation of educational services based on online monitoring and analysis of the opinions of recipients of educational services in relation to the willingness to acquire educational services, the formation of adaptive and technological skills are disclosed. Identified systemic problems and risks in the activities of educational institutions associated with the lack of a clear procedure for the introduction of digital technologies, with a decrease in the academic and financial stability of the university, a narrowing of the target audience due to the loss of previous traditional and feedback. Priorities and improving factors for the technological positioning of the university during the transition to digital education are outlined. An attempt is made to reveal the elements of a business model, which includes: monitoring and expanding image perception, synchronization of IT and Edu-technologies, ensuring “transparency” of various schemes for the provision of educational services for the personalization of educational paths, targeting and adaptability to meet educational needs. There is no conflict of interests.
Probing the phonetic and phonological knowledge of tones in Mandarin TTS models

This study probes the phonetic and phonological knowledge of lexical tones in TTS models through two experiments. Controlled stimuli for testing tonal coarticulation and tone sandhi in Mandarin were fed into Tacotron 2 and WaveGlow to generate speech samples, which were subject to acoustic analysis and human evaluation. Results show that both baseline Tacotron 2 and Tacotron 2 with BERT embeddings capture the surface tonal coarticulation patterns well but fail to consistently apply the Tone-3 sandhi rule to novel sentences. Incorporating pre-trained BERT embeddings into Tacotron 2 improves the naturalness and prosody performance, and yields better generalization of Tone-3 sandhi rules to novel complex sentences, although the overall accuracy for Tone-3 sandhi was still low. Given that TTS models do capture some linguistic phenomena, it is argued that they can be used to generate and validate certain linguistic hypotheses. On the other hand, it is also suggested that linguistically informed stimuli should be included in the training and the evaluation of TTS models.
I, avatar: Towards an extended theory of selfhood in immersive VR

In this paper, I argue that virtual manifestations of selfhood in VR environments have a transformative effect on the users, which in turn has spillover effects in the physical world. I will argue in favor of extending our notion of personal identity as to include VR avatars as negotiable bodies that constitute a genuine part of who we are. Recent research in VR shows that users can experience the Proteus Effect and other lasting psychological changes after being immersed in VR. An extended theory of the self, modeled after the extended mind thesis advanced by Clark and Chalmers (1998), can offer a deeper understanding of how and why immersive virtual experiences have such a transformative effect on users. The early VR scholars had a similar intuition- that “VR is a medium for the extension of body and mind” (Biocca and Delaney 1995), acting like a genuine “reality engine” (Biocca and Levy 1995).
A provisional world map of duricrust

The cartographic exercise, resulting from a collective effort and patient compilation, albeit of a provisional character, is of both scientific and pedagogic interest. It permits comparison of the areas of duricrust phenomena with major climatic zones, vegetation types, geological structures, great soil groups and relief features. The international agencies recognise two facies common to the tropical world, with Australia having a unique facies. Three phases are therefore mapped: Petric phase, Petroferric phase, and Duripan phase. In view of the limited number of general synthesis maps, frequent reference will have to be made to the FAO-UNESCO map which is regarded as the basic document. The International Atlas of West Africa, which distinguishes lithosols on duricrusts from the indurations of ferruginous and ferrallitic soils, seems to exhibit a conservative view of the extent of duricrust. The World Soil Map, the only general synthesis available for tropical America, provides very generalised, succinct information on the extent of the petric facies alone.
On Scrambling and Reconstruction

This paper aims to solve scope and binding puzzles related to clause-internal and external scrambling in Korean. In order to do this, we focus on the nature of movement which does not seem to be one unitary homogeneous operation. Following Ahn and Cho(2010), we propose that source of scope ambiguity hinges on two possible derivations: Scrambled phrases are derived either via Focus-movement or Adjunction. “Adjuncted” scrambled phrase must obligatorily undergo reconstruction, and is interpreted in situ for scope at LF. Focused QP, in contrast, remains in Spec-C at LF since it undergoes scope-taking movement. We also suggest that binding puzzles are resolved if we examine the derivational history of moved elements. Assuming that binding condition applies throughout the derivation, we resolve binding puzzles related to clause-internal/external scrambling.
Prospective validation of a machine learning algorithm to predict short-term mortality among outpatients with cancer.

2009 Background: Oncologists accurately identify only 35% of patients with cancer who will die in six months. There is an urgent need for automated, accurate prognostic systems to inform treatment and advance care planning in oncology. We assessed the prospective performance of a previously described ML algorithm (Parikh et al, JAMA Netw Open, 2019) to predict short-term mortality in a cohort of general oncology outpatients. Methods: Our prospective cohort consisted of patients aged ≥18 years who had a medical or gynecologic oncology encounter between March 1 and April 30, 2019 in either a tertiary academic practice or one of twelve community practices within a large academic cancer system. We used a retrospectively validated gradient-boosting ML algorithm, based on 559 structured electronic health record (EHR) variables, to predict 180-day mortality prior to each oncology encounter. For patients with multiple encounters, we selected the last encounter to assess performance. We assessed several performance metrics, including area under the receiver operating curve (AUC), area under the precision-recall curve (AUPRC), scaled Brier score (sBrier; a measure of calibration ranging from 0 [random] to 1 [perfect]), and positive predictive value (PPV). Results: Of 25,537 unique patients, median age was 64.4 (interquartile range 53.3 – 73.0), 76.8% were White, 56.5% were treated at a community center, and 4.1% died within 180 days. The ML algorithm had an AUC of 0.89 (95% confidence interval [CI] 0.88-0.90), AUPRC 0.34, and sBrier 0.29. At a prespecified threshold of 40%, observed 180-day mortality was 44.5% (95% CI 40.7 – 48.4%) in the high-risk group vs. 3.0% (95% CI 2.8% – 3.3%) in the low-risk group. There was an 85-fold difference in mortality (13.6% vs. 0.16%) in the top vs. bottom risk quartiles. The model was well-calibrated for mortality risks ≤40% and slightly under-calibrated for mortality risks &gt; 40%. Performance varied across cancer types in the tertiary hospital but did not vary by race or practice type (Table). Conclusions: In this prospective cohort study among outpatients with cancer, a ML prognostic algorithm based on EHR data had better discrimination and calibration that published cancer-specific models. This is one of the first ML prognostic models to be prospectively validated in oncology. [Table: see text]
Electronic Snellen Chart with Bluetooth Connection and Smartphone App

Abstract Snellen chart is a tool to measure a person’s visual acuity consisting of a series of black capitalized letters on a whiteboard arranged in rows. Snellen charts commonly used in many hospitals today are still in the form of posters, which are inefficient because to show the letters on the Snellen board, we still have to input it manually. Therefore, an electronic Snellen chart tool, which is considered to be more efficient and practical is made. The control device is an application on a cellphone connected via Bluetooth by utilizing LED as lighting while showing letters on the Snellen chart. It will facilitate ophthalmologists to perform examinations, and also show the letters by pressing a button on the cellphone so that they can install a trial lens without having to return to the Snellen board to indicate the letters. The testing method used in this study tested the Bluetooth connection and the tool’s performance with seven questions on a questionnaire testing method to determine the eligibility of the instrument. The feasibility test results by using a questionnaire show that it has 82.8% feasibility percentage with 0.9 validity rate and 0.87 reliability; moreover, the results of 20 trials of Bluetooth connection testing have a 100% success rate. The questionnaire results showed that the tool is eligible and proper to use because the installation of the application is easy to do.
Evaluating Word Embeddings for Indonesian-English Code-Mixed Text Based on Synthetic Data.

Code-mixed texts are abundant, especially in social media, and poses a problem for NLP tools, which are typically trained on monolingual corpora. In this paper, we explore and evaluate different types of word embeddings for Indonesian–English code-mixed text. We propose the use of code-mixed embeddings, i.e. embeddings trained on code-mixed text. Because large corpora of code-mixed text are required to train embeddings, we describe a method for synthesizing a code-mixed corpus, grounded in literature and a survey. Using sentiment analysis as a case study, we show that code-mixed embeddings trained on synthesized data are at least as good as cross-lingual embeddings and better than monolingual embeddings.
Continual Rare-Class Recognition with Emerging Novel Subclasses

Given a labeled dataset that contains a rare (or minority) class of of-interest instances, as well as a large class of instances that are not of interest, how can we learn to recognize future of-interest instances over a continuous stream? We introduce RaRecognize, which (i) estimates a general decision boundary between the rare and the majority class, (ii) learns to recognize individual rare subclasses that exist within the training data, as well as (iii) flags instances from previously unseen rare subclasses as newly emerging. The learner in (i) is general in the sense that by construction it is dissimilar to the specialized learners in (ii), thus distinguishes minority from the majority without overly tuning to what is seen in the training data. Thanks to this generality, RaRecognize ignores all future instances that it labels as majority and recognizes the recurrent as well as emerging rare subclasses only. This saves effort at test time as well as ensures that the model size grows moderately over time as it only maintains specialized minority learners. Through extensive experiments, we show that RaRecognize outperforms state-of-the art baselines on three real-world datasets that contain corporate-risk and disaster documents as rare classes.
Removing European Language Barriers with Innovative Machine Translation Technology

This paper presents our progress towards deploying a versatile communication platform in the task of highly multilingual live speech translation for conferences and remote meetings live subtitling. The platform has been designed with a focus on very low latency and high flexibility while allowing research prototypes of speech and text processing tools to be easily connected, regardless of where they physically run. We outline our architecture solution and also briefly compare it with the ELG platform. Technical details are provided on the most important components and we summarize the test deployment events we ran so far.
Timing Covert Channels Detection Cases via Machine Learning

Currently, packet data networks are widespread. Their architectural features allow constructing covert channels that are able to transmit covert data under the conditions of using standard protection measures. However, encryption or packets length normalization, leave the possibility for an intruder to transfer covert data via timing covert channels (TCCs). In turn, inter-packet delay (IPD) normalization leads to reducing communication channel capacity. Detection is an alternative countermeasure. At the present time, detection methods based on machine learning are widely studied. The complexity of TCCs detection based on machine learning depends on the availability of traffic samples, and on the possibility of an intruder to change covert channels parameters. In the current work, we explore the cases of TCCs detection via machine learning and study the possibility to implement learning machines algorithms for detecting TCCs under conditions of varying covert channel characteristics: flow capacity and encoding scheme.
A proposal of skill evaluation method for production systems digital design with production simulation

Since we recognize needs for official training of personnel for the digital age, we expect a skill evaluation method of training skills for personnel in production systems design fields. To address this situation, we propose a skill evaluation method for production systems digital design with production simulation. The proposed method was developed by the subcommittee of JSME (the Japan Society of Mechanical Engineers). The subcommittee consists of experts from industry, the government and universities. The proposed method will promote the training of personnel for the digital age as a standard across different industries, taking advantage of production simulation.
Human-Human Health Coaching via Text Messages: Corpus, Annotation, and Analysis

Itika Gupta, Barbara Di Eugenio, Brian Ziebart, Aiswarya Baiju, Bing Liu, Ben Gerber, Lisa Sharp, Nadia Nabulsi, Mary Smart. Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue. 2020.
Non-Markovian dynamics of quantum systems coupled with several mixed fermionic-bosonic heat baths

For the fermionic or bosonic oscillator fully coupled to several heat baths with mixed statistics, the analytical expressions for the occupation numbers are derived within the non-Markovian quantum Langevin approach. Employing two or three heat baths and the Ohmic dissipation with Lorenzian cutoffs, the role of statistics of the system and heat baths in the dynamics of the system is studied.
Unsupervised variational auto-encoder hash algorithm based on multi-channel feature fusion

Hashing technology is widely used to solve the problem of large-scale Remote Sensing (RS) image retrieval due to its high speed and low memory. Among the existing hashing algorithm, the unsupervised method is widely used in largescale RS image retrieval. However, the existing unsupervised RS image retrieval methods do not consider the multichannel properties of multi-spectral RS images and the discriminability in the local preservation mapping process adequately, which make it difficult to satisfy the retrieval performance of RS data. To solve these problems, we propose an unsupervised Variational Auto-Encoder Hashing algorithm based on multi-channel feature fusion (VAEH). MultiChannel Feature Fusion (MCFF) is used to extract the feature information of image, which fully considers the multichannel properties of the multi-spectral RS image. In order to enhance the discriminability in the local preservation mapping process, variational construction process and automatic encoder are added into the learning process of hashing function, and the KL distance of the Variational Auto-Encoder (VAE) is used to constrain the hashing code. Experiments on two large public RS image data sets (i.e. SAT-4 and SAT-6) have shown that our VAEH method outperforms the state of the art.
Learning Mixtures of Graphs from Epidemic Cascades

We consider the problem of learning the weighted edges of a balanced mixture of two undirected graphs from epidemic cascades. While mixture models are popular modeling tools, algorithmic development with rigorous guarantees has lagged. Graph mixtures are apparently no exception: until now, very little is known about whether this problem is solvable. To the best of our knowledge, we establish the first necessary and sufficient conditions for this problem to be solvable in polynomial time on edge-separated graphs. When the conditions are met, i.e., when the graphs are connected with at least three edges, we give an efficient algorithm for learning the weights of both graphs with optimal sample complexity (up to log factors). We give complimentary results and provide sample-optimal (up to log factors) algorithms for mixtures of directed graphs of out-degree at least three, for mixture of undirected graphs of unbalanced and/or unknown priors.
Low-Variance and Zero-Variance Baselines for Extensive-Form Games

Extensive-form games (EFGs) are a common model of multi-agent interactions with imperfect information. State-of-the-art algorithms for solving these games typically perform full walks of the game tree that can prove prohibitively slow in large games. Alternatively, sampling-based methods such as Monte Carlo Counterfactual Regret Minimization walk one or more trajectories through the tree, touching only a fraction of the nodes on each iteration, at the expense of requiring more iterations to converge due to the variance of sampled values. In this paper, we extend recent work that uses baseline estimates to reduce this variance. We introduce a framework of baseline-corrected values in EFGs that generalizes the previous work. Within our framework, we propose new baseline functions that result in significantly reduced variance compared to existing techniques. We show that one particular choice of such a function --- predictive baseline --- is provably optimal under certain sampling schemes. This allows for efficient computation of zero-variance value estimates even along sampled trajectories.
A Structured Latent Variable Recurrent Network With Stochastic Attention For Generating Weibo Comments

Building intelligent agents to generate realistic Weibo comments is challenging. For such realistic Weibo comments, the key criterion is improving diversity while maintaining coherency. Considering that the variability of linguistic comments arises from multi-level sources, including both discourse-level properties and word-level selections, we improve the comment diversity by leveraging such inherent hierarchy. In this paper, we propose a structured latent variable recurrent network, which exploits the hierarchical-structured latent variables with stochastic attention to model the variations of comments. First, we endow both discourse-level and word-level latent variables with hierarchical and temporal dependencies for constructing multi-level hierarchy. Second, we introduce a stochastic attention to infer the key-words of interest in the input post. As a result, diverse comments can be generated with both discourse-level properties and local-word selections. Experiments on open-domain Weibo data show that our model generates more diverse and realistic comments.