Reviewerly/exHarmony huggingface dataset:
ST-UNet: A Spatio-Temporal U-Network for Graph-structured Time Series Modeling

The spatio-temporal graph learning is becoming an increasingly important object of graph study. Many application domains involve highly dynamic graphs where temporal information is crucial, e.g. traffic networks and financial transaction graphs. Despite the constant progress made on learning structured data, there is still a lack of effective means to extract dynamic complex features from spatio-temporal structures. Particularly, conventional models such as convolutional networks or recurrent neural networks are incapable of revealing the temporal patterns in short or long terms and exploring the spatial properties in local or global scope from spatio-temporal graphs simultaneously. To tackle this problem, we design a novel multi-scale architecture, Spatio-Temporal U-Net (ST-UNet), for graph-structured time series modeling. In this U-shaped network, a paired sampling operation is proposed in spacetime domain accordingly: the pooling (ST-Pool) coarsens the input graph in spatial from its deterministic partition while abstracts multi-resolution temporal dependencies through dilated recurrent skip connections; based on previous settings in the downsampling, the unpooling (ST-Unpool) restores the original structure of spatio-temporal graphs and resumes regular intervals within graph sequences. Experiments on spatio-temporal prediction tasks demonstrate that our model effectively captures comprehensive features in multiple scales and achieves substantial improvements over mainstream methods on several real-world datasets.
Robust labeling of human motion markers in the presence of occlusions

Human motion capture by optical sensors produces snapshots of the motion of a cloud of points that need to be labeled in order to carry out ensuing motion analysis for medical or other purposes. We generate the labeling of instantaneous captures of the cloud of points, discarding temporal correlations, in the presence of occlusions. Our approach proposes an ensemble of weak classifiers defined over geometrical features extracted from small subsets of the cloud of points. We apply an Adaboost strategy to select a minimal ensemble of weak classifiers achieving a target correct labeling detection accuracy. Furthermore, we use these features to generate the labeling of the points in the cloud even in the presence of occlusions.To deal with the occlusions of markers we search for ensembles of partial labeling solvers which can provide partial consistent labelings which cover the unoccluded markers. We test two greedy search approaches and a genetic algorithm in the search for the optimal ensemble of partial solvers We demonstrate the approach on a real dataset obtained from the measurement of gait motion of persons, with available ground truth labeling. Results are encouraging, achieving high accuracy label generation at a reduced computational cost.
Effectiveness of Installment Payment Management Using Recurring Scheduling to Cashier Performance

The purpose of writing this paper is to provide an overview of the progress of cloud-based accounting systems that are applied to Student Financial Services (cashiers) at Raharja College. Implementation of this system is due to the existence of good management in college to solve the problem. This recurring scheduling feature in the cloud-based accounting system is a new breakthrough that is useful for paying for lectures that use the installment payment system. Where, students can pay tuition twice in installments, namely the period before the midterm exam and before the final exam. This feature is useful to minimize the existence of human error because it forgets billing input, bills are inputted twice with the same nominal, and sending invoices via email is not monitored. So, with the implementation of this system, the cashier's work on students in the installment system can be more effective and have a positive impact on the cashier's performance. This research was conducted using the literature review research method, to find useful theoretical foundations as a reference for solving problems that occur.&#x0D;
Autoregressive Policies for Continuous Control Deep Reinforcement Learning.

Reinforcement learning algorithms rely on exploration to discover new behaviors, which is typically achieved by following a stochastic policy. In continuous control tasks, policies with a Gaussian distribution have been widely adopted. Gaussian exploration however does not result in smooth trajectories that generally correspond to safe and rewarding behaviors in practical tasks. In addition, Gaussian policies do not result in an effective exploration of an environment and become increasingly inefficient as the action rate increases. This contributes to a low sample efficiency often observed in learning continuous control tasks. We introduce a family of stationary autoregressive (AR) stochastic processes to facilitate exploration in continuous control domains. We show that proposed processes possess two desirable features: subsequent process observations are temporally coherent with continuously adjustable degree of coherence, and the process stationary distribution is standard normal. We derive an autoregressive policy (ARP) that implements such processes maintaining the standard agent-environment interface. We show how ARPs can be easily used with the existing off-the-shelf learning algorithms. Empirically we demonstrate that using ARPs results in improved exploration and sample efficiency in both simulated and real world domains, and, furthermore, provides smooth exploration trajectories that enable safe operation of robotic hardware.
Understanding and Predicting Private Interactions in Underground Forums

The studies on underground forums and marketplaces have significantly advanced our understandings of cybercrime workflows and underground economies. Researchers of underground economies have conducted comprehensive studies on public interactions. However, little research focuses on private interactions. The lack of the investigation on private interactions may cause misunderstandings on underground economies, as users in underground forums and marketplaces tend to share the minimal amount of information in public interactions and resort to private messages for follow-up conversations. In this paper, we propose methods to investigate the underground private interactions and we analyze a recently leaked dataset from Nulled.io. We present analyses on the contents and purposes of private messages. In addition, we design machine learning-based models that only use the publicly available information to detect if two underground users privately communicate with each other. Finally, we perform adversarial analysis to evaluate the robustness of the detector to different types of attacks.
Research on Local Dynamic Path Planning Method for Intelligent Vehicle Lane-Changing

A local dynamic path planning method is proposed to compensate for the lack of consideration of the movement state of surrounding vehicles, the poor comfort, and the low traffic efficiency when the existing vehicle changes lanes automatically. Firstly, the cubic polynomial is predefined, and the optimal track path is solved. According to the real-time information of environment perception, the model is continuously modified by acquiring real-time information in the course of path planning, and the regional safety of the vehicle is realized. The Carsim and simulink simulation results and actual vehicle verification show that, compared with the traditional nondynamic research method, this method can effectively solve the problem that the vehicle speed variation and the sudden intrusions of the vehicle leading to the compulsory operation of the vehicle during the course of lane-changing. The safety is also improved. In order to ensure the vehicle comfort and stability, the lane-changing time is shortened by 20%, and the efficiency of lane-changing is improved obviously.
Mowgli

Big Data and IoT applications require highly-scalable database management system (DBMS), preferably operated in the cloud to ensure scalability also on the resource level. As the number of existing distributed DBMS is extensive, the selection and operation of a distributed DBMS in the cloud is a challenging task. While DBMS benchmarking is a supportive approach, existing frameworks do not cope with the runtime constraints of distributed DBMS and the volatility of cloud environments. Hence, DBMS evaluation frameworks need to consider DBMS runtime and cloud resource constraints to enable portable and reproducible results. In this paper we present Mowgli, a novel evaluation framework that enables the evaluation of non-functional DBMS features in correlation with DBMS runtime and cloud resource constraints. Mowgli fully automates the execution of cloud and DBMS agnostic evaluation scenarios, including DBMS cluster adaptations. The evaluation of Mowgli is based on two IoT-driven scenarios, comprising the DBMSs Apache Cassandra and Couchbase, nine DBMS runtime configurations, two cloud providers with two different storage backends. Mowgli automates the execution of the resulting 102 evaluation scenarios, verifying its support for portable and reproducible DBMS evaluations. The results provide extensive insights into the DBMS scalability and the impact of different cloud resources. The significance of the results is validated by the correlation with existing DBMS evaluation results.
Analysis of Ethereum Smart Contracts and Opcodes

Much attention has been paid in recent years to the use of smart contracts. A smart contract is a transaction protocol that executes the terms of an agreement. Ethereum is a widely used platform for executing smart contracts, defined by using a Turing-complete language. Various studies have been performed in order to analyse smart contract data from different perspectives. In our study we gather a wide range of verified smart contracts written by using the Solidity language and we analyse their code. A similar study is carried out on Solidity compilers. The aim of our investigation is the identification of the smart contract functionalities, i.e. opcodes, that play a crucial role in practice, and single out those functionalities that are not practically relevant.
Photon Pair Condensation by Engineered Dissipation

Dissipation can usually induce detrimental decoherence in a quantum system. However, engineered dissipation can be used to prepare and stabilize coherent quantum many-body states. Here, we show that, by engineering dissipators containing photon pair operators, one can stabilize an exotic dark state, which is a condensate of photon pairs with a phase-nematic order. In this system, the usual superfluid order parameter, i.e., single-photon correlation, is absent, while the photon pair correlation exhibits long-range order. Although the dark state is not unique due to multiple parity sectors, we devise an additional type of dissipators to stabilize the dark state in a particular parity sector via a diffusive annihilation process which obeys Glauber dynamics in an Ising model. Furthermore, we propose an implementation of these photon pair dissipators in circuit-QED architecture.
Analyzing and Preventing Data Privacy Leakage in Connected Vehicle Services

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;The rapid development of connected and automated vehicle technologies together with cloud-based mobility services are revolutionizing the transportation industry. As a result, huge amounts of data are being generated, collected, and utilized, hence providing tremendous business opportunities. However, this big data poses serious challenges mainly in terms of data privacy. The risks of privacy leakage are amplified by the information sharing nature of emerging mobility services and the recent advances in data analytics. In this paper, we provide an overview of the connected vehicle landscape and point out potential privacy threats. We demonstrate two of the risks, namely additional individual information inference and user de-anonymization, through concrete attack designs. We also propose corresponding countermeasures to defend against such privacy attacks. We evaluate the feasibility of such attacks and our defense strategies using real world vehicular data.&lt;/div&gt;&lt;/div&gt;
Client-friendly continuous-variable blind and verifiable quantum computing

We present a verifiable and blind protocol for assisted universal quantum computing on continuous-variable (CV) platforms. This protocol is experimentally friendly to the client, as it only requires Gaussian-operation capabilities from the latter. Moreover, the server does not require universal quantum-computational power either, its only function being to supply the client with copies of a single-mode non-Gaussian state. Universality is attained based on state injection of the server's non-Gaussian supplies. The protocol is automatically blind because the non-Gaussian resource requested to the server is always the same, regardless of the specific computation. Verification, in turn, is possible thanks to an efficient non-Gaussian state fidelity test where we assume identical state preparation by the server. It is based on Gaussian measurements by the client on the injected states, which is potentially interesting on its own. The division of quantum hardware between client and server assumed here is in agreement with the experimental constraints expected in realistic schemes for CV cloud quantum computing.
A Hybrid Retrieval-Generation Neural Conversation Model

Intelligent personal assistant systems that are able to have multi-turn conversations with human users are becoming increasingly popular. Most previous research has been focused on using either retrieval-based or generation-based methods to develop such systems. Retrieval-based methods have the advantage of returning fluent and informative responses with great diversity. However, the performance of the methods is limited by the size of the response repository. On the other hand, generation-based methods can produce highly coherent responses on any topics. But the generated responses are often generic and not informative due to the lack of grounding knowledge. In this paper, we propose a hybrid neural conversation model that combines the merits of both response retrieval and generation methods. Experimental results on Twitter and Foursquare data show that the proposed model outperforms both retrieval-based methods and generation-based methods (including a recently proposed knowledge-grounded neural conversation model) under both automatic evaluation metrics and human evaluation. We hope that the findings in this study provide new insights on how to integrate text retrieval and text generation models for building conversation systems.
Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression

Heatmap regression with a deep network has become one of the mainstream approaches to localize facial landmarks. However, the loss function for heatmap regression is rarely studied. In this paper, we analyze the ideal loss function properties for heatmap regression in face alignment problems. Then we propose a novel loss function, named Adaptive Wing loss, that is able to adapt its shape to different types of ground truth heatmap pixels. This adaptability penalizes loss more on foreground pixels while less on background pixels. To address the imbalance between foreground and background pixels, we also propose Weighted Loss Map, which assigns high weights on foreground and difficult background pixels to help training process focus more on pixels that are crucial to landmark localization. To further improve face alignment accuracy, we introduce boundary prediction and CoordConv with boundary coordinates. Extensive experiments on different benchmarks, including COFW, 300W and WFLW, show our approach outperforms the state-of-the-art by a significant margin on various evaluation metrics. Besides, the Adaptive Wing loss also helps other heatmap regression tasks. Code will be made publicly available at https://github.com/protossw512/AdaptiveWingLoss.
Teacher and Pupil: Happiness of Interaction

Happiness is a universal category that describes processes of human existence. Getting education, acquiring knowledge is an important process of personal and civic development. The research problem posed in the article: is it possible to feel happiness and pleasure during the process of learning? The authors analyze this problem from the theoretical positions of philosophy, axiology, and sociology of culture. Overview of the theories and techniques of happiness reveals the methodical helplessness and invalidity of Humanities in relation to the research of happiness as a natural right of a man. The authors believe that the formulation and justification of the category of “feeling”, and its introduction into the scientific terminology of social studies and Humanities are in demand in modern scientific practice. Modern pedagogical practice, which is limited by standards and instructions, is far from being a happy process of acquiring knowledge and self-disclosure by a student and a teacher. It does not only involve the release of feelings, but also poses threat to the social order by violating the social position of the status of the “teacher – student” interaction, “freezing” the meanings of the process of social inheritance due to the requirements of conformity with performance indicators, thus transferring the educational process from the domain of culture onto the plane of market relations. Value of knowledge is included into the axiological system of society and does not need to be evaluated. To realize the value potential of knowledge means to create conditions for happy interaction of the main participants of this process – the carriers of the “teacher” and “pupil” statuses, but not to control the amount of this process. Knowledge may have terminal and instrumental meanings, but its main purpose is to serve the formation of personality, and not to serve the existence and needs of controllers.
Explaining Deep Classification of Time-Series Data with Learned Prototypes

The emergence of deep learning networks raises a need for explainable AI so that users and domain experts can be confident applying them to high-risk decisions. In this paper, we leverage data from the latent space induced by deep learning models to learn stereotypical representations or during training to elucidate the algorithmic decision-making process. We study how leveraging prototypes effect classification decisions of two dimensional time-series data in a few different settings: (1) electrocardiogram (ECG) waveforms to detect clinical bradycardia, a slowing of heart rate, in preterm infants, (2) respiration waveforms to detect apnea of prematurity, and (3) audio waveforms to classify spoken digits. We improve upon existing models by optimizing for increased prototype diversity and robustness, visualize how these prototypes in the latent space are used by the model to distinguish classes, and show that prototypes are capable of learning features on two dimensional time-series data to produce explainable insights during classification tasks. We show that the prototypes are capable of learning real-world features - bradycardia in ECG, apnea in respiration, and articulation in speech - as well as features within sub-classes. Our novel work leverages learned prototypical framework on two dimensional time-series data to produce explainable insights during classification tasks.
EIQ: EEG based IQ test using wavelet packet transform and hierarchical extreme learning machine

The use of electroencephalography has been perpetually incrementing and has numerous applications such as clinical and psychiatric studies, social interactions, brain computer interface etc. Intelligence has baffled us for centuries, and we have attempted to quantify using EEG signals.This paper aims at devising a novel non-invasive method of measuring human intelligence. A newly devised scoring scheme is used to ultimately generate a score for the subjects. Wavelet packet transform approach for feature extraction is applied to 5 channel EEG data. This approach uses db-8 as the mother wavelet. Hierarchical extreme learning machine is used for classification of the EEG signals.80.00% training accuracy and 73.33% testing accuracy was measured for the classifier. The average sensitivity and specificity across all three classes was measured to be 0.8133 and 0.8923 respectively. An aggregate score was determined from the classification of EEG data. The power spectral analysis of the EEG data was conducted and regions of the brain responsible for various activities was confirmed. In the memory test, theta and beta bands exhibit high power, for arithmetic test, alpha and beta bands are strong, whereas in linguistic test, theta, alpha and beta bands are equally strong.The traditional IQ test determines intelligence indirectly, based on the score obtained from Wechsler test. In this paper an attempt is made to measure intelligence based on various brain activities - memory, arithmetic, linguistic.A new method to measure intelligence using direct approach by classifying the EEG signals is proposed.
Learning families of algebraic structures from informant

We combine computable structure theory and algorithmic learning theory to study learning of families of algebraic structures. Our main result is a model-theoretic characterization of the learning type InfEx≅, consisting of the structures whose isomorphism types can be learned in the limit. We show that a family of structures is InfEx≅-learnable if and only if the structures can be distinguished in terms of their Σ2inf-theories. We apply this characterization to familiar cases and we show the following: there is an infinite learnable family of distributive lattices; no pair of Boolean algebras is learnable; no infinite family of linear orders is learnable.
Evaluating the usage of predefined interactive behaviors for writing user stories: an empirical study with potential product owners

Behavior-driven development (BDD) has gained popularity in agile software development as a means of specifying user requirements through user stories and scenarios for interactive systems under construction. Templates for guiding the writing of such stories are widely employed and can be helpful to ensure that consistent information about the requirements is provided. With the aim of getting preliminary results about how product owners (POs) write their own user stories under a predefined template, we conducted a case study with potential POs at the department in charge of business trips in a French research institute. The participants were invited to write their own user stories to describe a feature they are used to perform. The resultant stories have been analyzed to check their adherence to a template including common interactive behaviors defined by an ontology for interactive systems. Although the participants have followed different specification strategies, we observed an overall high level of adherence to the proposed template (62.26%). The results also pointed out to a wide use of domain-dependent behaviors, with the interactive behaviors defined by the ontology being, to some extent, reproduced by the participants even without prior training in the adopted vocabulary.