Reviewerly/exHarmony huggingface dataset:
Tackling the Challenges of Machine Learning for Mobile Health Systems

The prominence of mobile devices and recent breakthroughs in machine learning have enabled an emerging class of new mobile health systems which hold the promise of transforming today's reactive healthcare practice to proactive, individualized care and wellbeing. However, the current mainstream machine learning approaches are largely supervised and must be trained by a large amount of labelled high-quality data. In contrast, due to small form factor and limited sensing capability, today's off-the-shelf mobile devices can only collect noisy and sporadic samples from users. Moreover, personal health data collected by on-device sensors cannot be uploaded or shared with other devices due to privacy concerns. These challenges have significantly hindered the performance and utility of mobile health systems in real-world settings. In this talk, I will first discuss new mobile systems that exploit human physiological models and innovative use of sensing modalities to achieve highly robust sensing performance without requiring extensive training. I will describe RunBuddy - the first smartphone-based system for monitoring continuous running rhythm and improving exercise efficiency. To mitigate significant environmental noise during running, RunBuddy integrates novel ambient sensing algorithms and a physiological model called Locomotor Respiratory Coupling (LRC). Based on this result, we also develop BreathCoach, a smart and unobtrusive system using smartwatch and smartphone-based VR for in-home RSA-BT (Respiratory Sinus Arrhythmia biofeedback-based Breathing Training), which is a common cardiorespiratory intervention to diseases such as asthma and an effective exercise to reduce anxiety. BreathCoach continuously monitors key bio-signals such as breathing pattern and inter-beat interval, and then recommends breathing patterns in the form of an intuitive VR game to provide an immersive training experience. In the second part of this talk, I will discuss a new approach for home activity recognition via federated learning. People spend significant portion of time in home. Characterizing home activities including both individual and family activities is important for studies of health, sociology, and home economics. For instance, risk factors of dementia and childhood obesity are strongly associated with a family's daily activities, and activity logging is proved a very effective approach to improve the self-awareness and motivate people to modify their behaviors toward a healthy lifestyle. Unfortunately, to date, there has been no unobtrusive and convenient methods to log family activities. I will present several new sensing and federated learning algorithms that enable mobile devices to collaboratively improve the accuracy of home activity sensing without sharing any sensor data, preserving user privacy.
Usage of Cloud Computing for natural resource management

In Morocco, human activities impact negatively natural resources, and the management of these resources remains difficult considering the multitude of actors, the non-coordination of users and exploiters and the non-sharing of information with actors. All this confronts us with great challenges that can lead to bad results and sometimes even to the depletion of certain resources in specific areas.
Static and Dynamic Beach Lighting Using Cloud based UAV

Beaches are one of the most visited tourist spots. Most of the beaches are not open for tourists after dusk. Due to inadequate lighting, people are skeptical to visit beaches after dark. As sand is present on beach, installing light poles is an indomitable task. An autonomous entity can serve this purpose of effective lighting. The advent of UAV technology has led us to enormous opportunities to improve existing systems and their way of working. UAVs particularly have made significant place for itself in almost every aspect of life. UAVs are omnipresent in almost every strata of life – military assistance, surveillance purpose, delivery of goods, monitoring environment, to name a few.With the help of Deep learning and efficacious wireless communication, UAVs can be used to overcome this problem. The key component of this proposal uses Deep learning’s Convolutional Neural Networks coupled with object detection in OpenCV. GSM aids in communication with UAV via Cloud Infrastructure. UAVs can be used for static lighting as well as pattern generation (dynamic) lighting using cluster of the same.
Approximate Functional Dependency Mining with Sequential Indexing Tables

Functional Dependency extraction is a useful way to find inherent rules that apply in a given dataset. However, not all of such rules are exact, datasets comprised of real-world measurements can often contain rules that are supported by a significant portion of the dataset (so-called Approximate Functional Dependencies), with only a few exceptions. Extracting such rules is also very beneficial, e.g. for estimating the achievable classification precision on a dataset. Sequential Indexing Tables (SITs) are an extended version of Lookup Tables, in which multidimensional problem space is broken down to a sequence of 1D and 2D lookup tables, where each member of the sequence forms a layer and processes the values of one given attribute. In this paper, a SIT -based dependency extractor is proposed that can be used to extract Approximate Functional Dependencies from datasets.
A Satisfiability Algorithm For The Mu-Calculus For Trees With Presburger Constraints

The μ-calculus is an expressive modal logic with fixed-point operators, well-known as a formal language to specify and verify labeled transition systems. We describe a satisfiability algorithm for the μ-calculus interpreted on tree models and extended with converse modalities and Presburger arithmetic operators. Converse modalities, together with the fixed-point operators, can be used to specify multi-directional and recursive properties in tree models. Presburger operators constrain the number of children nodes on tree models with respect to arithmetic expressions. The algorithm is based on a breadth-first search in a Fischer-Lardner construction of tree models. We also describe an implementation of the algorithm and report several experiments.
Text Segregation On Asynchronous Group Chat

Ability to successfully segregate texts around different topics can lead to successful text summarization. Though text summarization has been well researched, summarization of multi-party asynchronous chat has not been attempted. Popular systems such as Whatsapp, Telegram etc. have such kind of chat scenarios. Such chats are asynchronous as participants can respond to a thread after a long delay and not as an immediate reply to ongoing conversation. Though there are few chat data sets available, annotated data sets of such multi-party asynchronous chats are not yet available even though summarization in this particular domain presents a good use case for chat participants. With such a summarization feature, a user can quickly review the summary of past conversations during his period of inactivity. However, this is challenging as any summarization attempt on a data set like this must address three aspects i.e. threads of discussion, time window and topic/sub-topics in an inter-woven sequence of messages that does not have any usual sense of sequence. In the absence of annotated data sets and challenge of addressing these three aspects in seemingly non-sequential messages, machine learning based approaches will not work well. In this work, an innovative pipeline based on heuristics is designed to address text segregation for such a scenario. Once text segregation is achieved, text summarization may become a lesser challenge. It is observed that this approach is promising enough to warrant further research and enhancements.
Ex-Tarot: An extended Tarot-based narrative generation

Pattern-based story generation combines predefined patterns and story templates to generate stories. The Tarot cards provide a variety of meanings and interpretations which can be used as a part of such a story generation system. In this paper we describe the implementation of Ex-Tarot, a Tarot based narrative generation, which extends the expressive range of a previously built system by several orders of magnitude, by extending upon the interpretations used by the system, as well as utilizing Tarot cards in a novel way for character properties. The output of our system are short story-summaries, as could be used to advertise an upcoming movie, rendered as English text. We evaluate the stories produced by our systems in terms of consistency, perceived interest, and measure the presence of grammatical errors in the natural language output. Our system is available as open source software, and we discuss several options for improvement and future work.
Attending to Inter-sentential Features in Neural Text Classification

Text classification requires a deep understanding of the linguistic features in text; in particular, the intra-sentential (local) and inter-sentential features (global). Models that operate on word sequences have been successfully used to capture the local features, yet they are not effective in capturing the global features in long-text. We investigate graph-level extensions to such models and propose a novel architecture for combining alternative text features. It uses an attention mechanism to dynamically decide how much information to use from a sequence- or graph-level component. We evaluated different architectures on a range of text classification datasets, and graph-level extensions were found to improve performance on most benchmarks. In addition, the attention-based architecture, as adaptively-learned from the data, outperforms the generic and fixed-value concatenation ones.
A rotation invariance spatial transformation network for remote sensing image retrieval

With the development of Geospatial technology and remote sensing technology, a large number of remote sensing images come into application with assist of computers. Although convolutional networks have great performance in computer vision, features extracted by convolutional network doesn’t have the characteristic of rotation invariance, which means the current neural network methods can’t adapt to rotated objects. Considering the multiangle characteristics of remote sensing images, we proposed a Rotation Invariance Spatial transformation Network (RI-STNET) to extract the rotation invariance object features. RI-ST-NET combines convolutional neural networks and the Spatial Transformer Networks (STN) rotating the object to an angle which more easily to identify and is trained by means of Siamese network sharing the same weights of two network branches . Thus RI-ST-NET can adapt to the object features of different rotation patterns which then improved that effectively promote the accuracy of remote sensing retrieval. A Rotation Invariance Spatial Transformation Network combines the advantages of STN and tuple training which can catch the rotation of the same object when used in image retrieval task. A series of evaluation contrast experiments on chosen dataset demonstrate the performance of the proposed method.
A Smart Flight Director for Emergency Landings with Dynamical Recalculation of Stable Glide Paths

In an emergency situation with total loss of thrust the pilot is forced to perform a landing just in glide. Thereby, the remaining potential energy of the aircraft’s altitude is converted into kinetic energy for the movement. The pilot has to plan the glide path so that the aircraft is reaching the emergency landing field in an appropriate altitude, speed and direction. This is a difficult task even for a well trained pilot, especially under tough conditions like strong wind or limited visibility due to clouds and fog. We implemented an Android-App called Emergency Landing Assistant (ELA) which supports the pilot by holding the aircraft on track with the best gliding speed and sink rate. We integrated a new algorithm which dynamically recalculates stable glide paths during the flight and suggest a new kind of Smart Flight Director (SFD) fitted for emergency landings. ELA is capable of supporting manual flight as well as autopilot flight. We validated it successfully in a flight simulator (X-Plane 11) and a real airplane (DV20 Katana).
National Interest vs. Online Freedom of Expression: The Discussions of Internet Users on the Blocking of ‘Wikipedia’ in Turkey

In 2017, The Information and Communication Technologies Authority (ICTA) blocked online access to the online encyclopedia Wikipedia in Turkey due to its articles and comments that mention Turkey as a country that seems to be in coordination and aligned with various terrorist groups. Since then, the blocking has been criticized by Turkish people as a violation of the right of access to knowledge and a crackdown on online freedom of expression. However, freedom of expression becomes controversial when expression appears to threaten some important national interests. This paper aims at providing a framework for resolving questions about the recognition of online information rights, by finding out the rationale behind online users’ arguments on freedom of expression. In this article, online users’ comments on the blocking of Wikipedia in Turkey are analyzed. The findings reveal that most of the users disapprove the blocking. Both opponents and approvers discussed the issue by putting a broad framework and they put forward very different arguments to justify their attitude.
Application Analysis of Artificial Intelligence Technology in Computer Network Based on Big Data Era

The rapid development of network information technology has promoted the overall progress of society and greatly improved people's living standards. Under the background of the rapid development of network technology, people's demand is also increasing. It is no longer effective to meet people's demand just to speed up and increase the transmission speed and capacity of information. As far as artificial intelligence technology is concerned, it is one of the most promising technologies in the field of computer network development. It is not only applied officially, but also most citizens are gradually applying artificial intelligence technology to their lives, which has greatly promoted the development of computer network technology. Based on the demand for intelligent development of computer network technology in the era of big data, the author analyzes the basic characteristics of big data and the advantages of artificial intelligence application, focusing on the specific application of artificial intelligence in computer network technology in the era of big data.
SolidRDP: Applying Solid Data Containers for Research Data Publishing

In the context of Open Science, researchers are encouraged to publish their research datasets in digital data repositories so that others can find and reuse it. However, this process is commonly conducted via centralized data management platforms. Research data has to be uploaded to such a platform and this imposes the risk to become dependent from the access control and data exposure capabilities of the platform provider. Semantic technologies are one approach to improve this situation and manage research datasets in a decentralized way with an interdisciplinary focus. We are particularly interested in Linked Data Platform - based approaches and how good Solid in particular fits for research data publishing (RDP) activities. In this paper, we therefore present a conceptual RDP model and we assess a container-based approach to publish research data in a Solid environment in a decentralized manner, both from a researcher and developer perspective.
Representation Learning via Adversarially-Contrastive Optimal Transport

In this paper, we study the problem of learning compact (low-dimensional) representations for sequential data that captures its implicit spatio-temporal cues. To maximize extraction of such informative cues from the data, we set the problem within the context of contrastive representation learning and to that end propose a novel objective via optimal transport. Specifically, our formulation seeks a low-dimensional subspace representation of the data that jointly (i) maximizes the distance of the data (embedded in this subspace) from an adversarial data distribution under the optimal transport, a.k.a. the Wasserstein distance, (ii) captures the temporal order, and (iii) minimizes the data distortion. To generate the adversarial distribution, we propose a novel framework connecting Wasserstein GANs with a classifier, allowing a principled mechanism for producing good negative distributions for contrastive learning, which is currently a challenging problem. Our full objective is cast as a subspace learning problem on the Grassmann manifold and solved via Riemannian optimization. To empirically study our formulation, we provide experiments on the task of human action recognition in video sequences. Our results demonstrate competitive performance against challenging baselines.
Time-Frequency Fading Algorithms Based on Gabor Multipliers

In this paper, we address a particular instance of time-frequency filter design, which we call Time-Frequency Fading (TFF). In TFF the only available information concerns the time-frequency localization of the component to be filtered out or attenuated: the signal of interest is supposed to be spread out in the time-frequency plane, whereas the perturbation signal is concentrated within a specified time-frequency region Ω. The problem is formulated as an optimization problem designed to fade out the perturbation with accurate control on the fading level. The corresponding objective function involves a data fidelity term that aims to match the TF coefficients of the estimated signal to those of the observed signal outside the perturbation support. It also involves a penalty term that controls the energy of the reconstructed signal, within that region. We obtain the closed-form solution of the problem which involves Gabor multipliers, i.e. linear operators of the pointwise product by a time-frequency transfer function called a mask. We study the TF localization properties of dominant eigenvectors of these Gabor multipliers, with particular attention to the case where the region Ω is a disjoint union of several sub-regions. The decay properties of eigenvalues naturally lead to reduced-rank approximations, and further approximations are obtained in the multiply connected region case. Also, we exploit random projection methods to speed up eigenvalue decompositions and rank reduction. This is implemented in two TFF algorithms, that cover the cases of single or multiple regions. The efficiency of the proposed approach is demonstrated on several audio signals where the perturbations are filtered while leading to a good quality of signal reconstruction.
Classification of Nuclear Explosion Earthquake Signal based on Machine Learning Approach

Identification and classification of the nuclear explosion signals have long been an important issue for some reason. The impact of the nuclear tests upon the environment is range out within minute affectation fatalities that may last for a long time. Signal identification generated by a nuclear test had been analysis based on machine learning wavelet. The goal of this study to classify and detect the waveform of nuclear explosion signals. The result is then discussed in the context of the potential use of the proposed for a new perspective for the classification of nuclear explosion phases in vertical channel signals data collected within tele-seismic recording stations in Indonesia. The machine learning wavelet-based presents a useful way of observing the time-frequency component of a nuclear explosion to identifying the phase of Primer (P) wave is clear and simultaneous.
Multi-stability in a Dynamic Model of Language Development

Development is a serial changes within a complex dynamic system. During development, children reach and pass different stages while their dynamical development systems change their states. We hypothesize that in a developmental process, each stage can be accounted as a stable point and the system can be considered multi-stable due to different stages of development. We propose a dynamical model for language development which can explain some developmental disorders in this field, such as pragmatics impairment in autistic children.
Fianet: Video Object Detection Via Joint Feature-Level And Instance-Level Aggregation

Video object detection task is challenging due to the nonrigid and rigid appearance deformations in videos. Most of the typical competitive methods are to enhance per-frame features through aggregating lots of previous and future frames. But feature-level aggregation isn't robust to rigid deformations such as occlusion and rare postures. In this paper, we propose an online video object detection method with joint feature-level aggregation and instance-level aggregation network (FIANet). Besides feature-level aggregation, we design a spatial-temporal instance calibration module (STIC) to aggregate the instance as a whole, which can reduce the interference of local distorted and missed pixels. Joint featurelevel and instance-level aggregation can work collaboratively to overcome different deformations. Only using less previous frames, our method can achieve 81.6% mAP with relatively high speed on ImageNet VID, which is state-of-the-art compared with causal and non-causal methods.
From Sets to Multisets: Provable Variational Inference for Probabilistic Integer Submodular Models

Submodular functions have been studied extensively in machine learning and data mining. In particular, the optimization of submodular functions over the integer lattice (integer submodular functions) has recently attracted much interest, because this domain relates naturally to many practical problem settings, such as multilabel graph cut, budget allocation and revenue maximization with discrete assignments. In contrast, the use of these functions for probabilistic modeling has received surprisingly little attention so far. In this work, we firstly propose the Generalized Multilinear Extension, a continuous DR-submodular extension for integer submodular functions. We study central properties of this extension and formulate a new probabilistic model which is defined through integer submodular functions. Then, we introduce a block-coordinate ascent algorithm to perform approximate inference for those class of models. Finally, we demonstrate its effectiveness and viability on several real-world social connection graph datasets with integer submodular objectives.
Order-Dependent Event Models for Agent Interactions

In multivariate event data, the instantaneous rate of an event's occurrence may be sensitive to the temporal sequence in which other influencing events have occurred in the history. For example, an agent’s actions are typically driven by preceding actions taken by the agent as well as those of other relevant agents in some order. We introduce a novel statistical/causal model for capturing such an order-sensitive historical dependence, where an event’s arrival rate is determined by the order in which its underlying causal events have occurred in the recent past. We propose an algorithm to discover these causal events and learn the most influential orders using time-stamped event occurrence data. We show that the proposed model fits various event datasets involving single as well as multiple agents better than baseline models. We also illustrate potentially useful insights from our proposed model for an analyst during the discovery process through analysis on a real-world political event dataset.