Reviewerly/exHarmony huggingface dataset:
Maximizing Information Gain in Partially Observable Environments via Prediction Rewards

Information gathering in a partially observable environment can be formulated as a reinforcement learning (RL), problem where the reward depends on the agent's uncertainty. For example, the reward can be the negative entropy of the agent's belief over an unknown (or hidden) variable. Typically, the rewards of an RL agent are defined as a function of the state-action pairs and not as a function of the belief of the agent; this hinders the direct application of deep RL methods for such tasks. This paper tackles the challenge of using belief-based rewards for a deep RL agent, by offering a simple insight that maximizing any convex function of the belief of the agent can be approximated by instead maximizing a prediction reward: a reward based on prediction accuracy. In particular, we derive the exact error between negative entropy and the expected prediction reward. This insight provides theoretical motivation for several fields using prediction rewards---namely visual attention, question answering systems, and intrinsic motivation---and highlights their connection to the usually distinct fields of active perception, active sensing, and sensor placement. Based on this insight we present deep anticipatory networks (DANs), which enables an agent to take actions to reduce its uncertainty without performing explicit belief inference. We present two applications of DANs: building a sensor selection system for tracking people in a shopping mall and learning discrete models of attention on fashion MNIST and MNIST digit classification.
Rk-means: Fast Clustering for Relational Data

Conventional machine learning algorithms cannot be applied until a data matrix is available to process. When the data matrix needs to be obtained from a relational database via a feature extraction query, the computation cost can be prohibitive, as the data matrix may be (much) larger than the total input relation size. This paper introduces Rk-means, or relational k -means algorithm, for clustering relational data tuples without having to access the full data matrix. As such, we avoid having to run the expensive feature extraction query and storing its output. Our algorithm leverages the underlying structures in relational data. It involves construction of a small {\it grid coreset} of the data matrix for subsequent cluster construction. This gives a constant approximation for the k -means objective, while having asymptotic runtime improvements over standard approaches of first running the database query and then clustering. Empirical results show orders-of-magnitude speedup, and Rk-means can run faster on the database than even just computing the data matrix.
Leverage Score Sampling for Faster Accelerated Regression and ERM

Given a matrix $\mathbf{A}\in\mathbb{R}^{n\times d}$ and a vector $b \in\mathbb{R}^{d}$, we show how to compute an $\epsilon$-approximate solution to the regression problem $ \min_{x\in\mathbb{R}^{d}}\frac{1}{2} \|\mathbf{A} x - b\|_{2}^{2} $ in time $ \tilde{O} ((n+\sqrt{d\cdot\kappa_{\text{sum}}})\cdot s\cdot\log\epsilon^{-1}) $ where $\kappa_{\text{sum}}=\mathrm{tr}\left(\mathbf{A}^{\top}\mathbf{A}\right)/\lambda_{\min}(\mathbf{A}^{T}\mathbf{A})$ and $s$ is the maximum number of non-zero entries in a row of $\mathbf{A}$. Our algorithm improves upon the previous best running time of $ \tilde{O} ((n+\sqrt{n \cdot\kappa_{\text{sum}}})\cdot s\cdot\log\epsilon^{-1})$. We achieve our result through a careful combination of leverage score sampling techniques, proximal point methods, and accelerated coordinate descent. Our method not only matches the performance of previous methods, but further improves whenever leverage scores of rows are small (up to polylogarithmic factors). We also provide a non-linear generalization of these results that improves the running time for solving a broader class of ERM problems.
MMPE: A Multi-Modal Interface using Handwriting, Touch Reordering, and Speech Commands for Post-Editing Machine Translation

The shift from traditional translation to postediting (PE) of machine-translated (MT) text can save time and reduce errors, but it also affects the design of translation interfaces, as the task changes from mainly generating text to correcting errors within otherwise helpful translation proposals.Since this paradigm shift offers potential for modalities other than mouse and keyboard, we present MMPE, the first prototype to combine traditional input modes with pen, touch, and speech modalities for PE of MT.Users can directly cross out or hand-write new text, drag and drop words for reordering, or use spoken commands to update the text in place.All text manipulations are logged in an easily interpretable format to simplify subsequent translation process research.The results of an evaluation with professional translators suggest that pen and touch interaction are suitable for deletion and reordering tasks, while speech and multi-modal combinations of select & speech are considered suitable for replacements and insertions.Overall, experiment participants were enthusiastic about the new modalities and saw them as useful extensions to mouse & keyboard, but not as a complete substitute.
HAND GESTURE RECOGNITION FOR INDONESIAN SIGN LANGUAGE INTERPRETER SYSTEM WITH MYO ARMBAND USING SUPPORT VECTOR MACHINE

&lt;p&gt;&lt;em&gt;Hand gestures are the communication ways for the deaf people and the other. Each hand gesture has a different meaning. In order to better communicate, we need an automatic translator who can recognize hand movements as a word or sentence in communicating with deaf people. &lt;/em&gt;&lt;em&gt;This paper proposes a system to recognize hand gestures based on Indonesian Sign Language Standard. This system uses Myo Armband as hand gesture sensors. Myo Armband has 21 sensors to express the hand gesture data. Recognition process uses a Support Vector Machine (SVM) to classify the hand gesture based on the dataset of Indonesian Sign Language Standard. SVM yields the accuracy of 86.59% to recognize hand gestures as sign language.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;em&gt;&lt;strong&gt;Keywords&lt;/strong&gt;&lt;/em&gt;&lt;em&gt;: &lt;/em&gt;&lt;em&gt;Hand Gesture Recognition, Feature Extraction, Indonesian Sign Language, Myo Armband, Moment Invariant&lt;/em&gt;&lt;/p&gt;
Robust navigation with tinyML for autonomous mini-vehicles.

Autonomous navigation vehicles have rapidly improved thanks to the breakthroughs of Deep Learning. However, scaling autonomous driving to low-power and real-time systems deployed on dynamic environments poses several challenges that prevent their adoption. In this work, we show an end-to-end integration of data, algorithms, and deployment tools that enables the deployment of a family of tiny-CNNs on extra-low-power MCUs for autonomous driving mini-vehicles (image classification task). Our end-to-end environment enables a closed-loop learning system that allows the CNNs (learners) to learn through demonstration by imitating the original computer-vision algorithm (teacher) while doubling the throughput. Thereby, our CNNs gain robustness to lighting conditions and increase their accuracy up to 20% when deployed in the most challenging setup with a very fast-rate camera. Further, we leverage GAP8, a parallel ultra-low-power RISC-V SoC, to meet the real-time requirements. When running a family of CNN for an image classification task, GAP8 reduces their latency by over 20x compared to using an STM32L4 (Cortex-M4) or obtains +21.4% accuracy than an NXP k64f (Cortex-M4) solution with the same energy budget.
The Improved Equilibrium Optimization Algorithm

Recent studies on the nature-inspired approaches are also focusing on the improvements in addition to the construction of new algorithms. Improvements on the controlling parameters are also relevant. The important control parameter balancing the exploration and exploitation ratio was always relevant to the maximum allowed iteration times. It was very difficult for engineers in order to find the solution within a given limitation. Therefore, we paid our attention to the control parameter t to the newly raised equilibrium optimization algorithm and the improvements for the engineers. Two governing equations are proposed and simulation experiments are carried out. Results show that the improvements could be used without too much influence to the final results, and sometimes the improvements on the control parameters could be forgotten.
The Expert of Oz

We present the paradigm of Expert of Oz studies for the formative evaluation of hypothetical AI systems. These studies follow the principle of Wizard of Oz studies but use a human expert for simulating the AI. This allows the experimenter to not only investigate the user's behavior with and reaction to such a system but also to observe and analyze the performance of the expert in the situation at hand. As a consequence, we can learn both about interaction with the hypothetical AI system and about the required AI functionality at the same time and in realistic interaction scenarios.
Analysis of the Cognitive Processes Underlying Discussions in Complex Problem Solving

The efficiency of solving complex problems in groups determines the productivity of a society. Existing guidelines for these collaborations are action-focused, and the few cognitive-oriented ones require time and training to be executed accurately. This research aims to propose intuitive and light-weighted recommendations for Complex-Problem-Solving (CPS) collaborations. The underlying cognitive process in CPS discussion was explored, especially the explicit knowledge used. It was found that episodic memory functions better at expanding the conversation scope, while semantic memory appeared to be a more straightforward foundation to initiate new ideas. Since the episodic memory serves as an outstanding primer in the conversation, the results could imply that the better episodic memory is communicated, the more fluence the discussion could be.
Accurate Locations of Felt Earthquakes Using Crowdsource Detections

We present a methodology that uses crowdsource detections as an initial location to obtain fast and reliable hypocenter parameters of felt earthquakes using arrival-time data from the GEOFON Program. We derive selection criteria using a 3-year-long training set from the trial runs at the European-Mediterranean Seismological Centre (EMSC) to identify accurate event locations at a high confidence level. Since an event may have several crowd-sourced detections, we also develop a methodology dealing with multiple triggers. We validate the selection criteria using real-time processing of recent data and demonstrate that 95% of the selected events are within 50 km distance from the traditional seismic location published by the EMSC. We show that our methodology provides accurate locations much faster than those published by conventional seismic methods. On average, the EMSC CsLoc service can provide rapid and accurate locations within a minute after the occurrence of a felt earthquake.
Deep Learning Development Review

As a new branch of the machine learning, the nature of deep learning is to establish and simulate the neural network of human brain to analysis and learning. With the development of neural networks, the models are getting bigger and more complex, the network model is no longer a few layers, dozens or even hundreds of network models play a huge advantage. In recent years, various deep neural network models have achieved remarkable results in many fields, such as face recognition, voice recognition, natural language processing and so on. People called these large-scale neural networks 'deep learning'. This paper mainly reviews the development history of deep learning, and make a brief summary of the problems faced by deep learning at the end of the paper.
Designing Futures of Money and FinTech

In light of increasing cashlessness, platform economies, Open Banking APIs, financial bots and cryptocurrencies, money is on the move - once inert, money is gaining agency, becoming programmable, automated, data-driven and part of 'more than human' infrastructures. These financial futures demand that designers engage with difficult questions of economy and value, while retaining a sensibility to the many subtle and social qualities of money and our everyday economic interactions. This one-day workshop will therefore bring together practitioners and researchers to explore design challenges related to four broad themes: Designing with Transactional Data; Designing Alternative Representations of Value; Money, Automation, Power, and Control; and Financial Futures with Vulnerable Users. Developing scenarios related to these themes, the workshop will cultivate a rich design space to establish the value of design-led research in shaping our financial futures.
Development of a Market Trend Evaluation System for Policy Making

The previous economic crisis has increased the attention of government to focus their activities more on economic stability.The development of government subsidies requires an analytically based analysis, one which would identify problematic areas of regional development more precisely.However, to monitor market changes in a highly dynamic market is time consuming and inefficient.Without proper market monitoring, the level of competitiveness within regions might decrease in the long run.Thus, the goal of the article is to establish the framework of a market trend monitoring system.To achieve this goal, the research methodology consists of a scientific literature analysis, an analysis of available data infrastructure for market trend analysis, and a statistical analysis together with a machine learning approach.The authors of the publication propose a market monitoring framework which would provide an infrastructure for evidencebased policy recommendations for government institutes and might provide guidelines of how to increase their competitiveness.A case study of real estate data and macroeconomic indicators of Lithuania was conducted, during which a clustering algorithm was applied to identify groups in Lithuania.The 60 municipalities of Lithuania were grouped into 4 clusters in terms of noteworthy relationships between industrial development and population size.In 3 of the clusters, the relationship of industrial growth and population was direct.In cluster 4, however, the relationship was opposite, a result which requires a further analysis of infrastructure and industrial sectors to provide more precise policy recommendations.The theoretical contribution and findings from the case study provides grounding to develop the market monitoring system.
Towards the Edge Intelligence: Robot Assistant for the Detection and Classification of Human Emotions

Deep learning is being introduced more and more in our society. Nowadays, there are very few applications that do not use deep learning as a classification tool. One of the main application areas is focused on improving people's life quality, allowing to create personal assistants with canned benefits. More recently, with the proliferation of mobile computing and the emergence of the Internet of Things (IoT), billions of mobile and IoT devices are connected to the Internet. This allows the generation of millions of bytes of information about sensors, images, sounds, etc. Driven by this trend, there is an urgent need to push the IoT frontiers to the edge of the network, in order to decrease this massive sending of information to large exchanges for analysis. As a result of this trend, a new discipline has emerged: edge intelligence or edge AI, a widely recognised and promising solution that attracts with special interest to the community of researchers in artificial intelligence. We adapted edge AI to classify human emotions. Results show how edge AI-based emotion classification can greatly benefit in the field of cognitive assistants for the elderly or people living alone.
Why Do We Need to Be Bots? What Prevents Society from Detecting Biases in Recommendation Systems

Concerns about social networks manipulating the (general) public opinion have become a recurring theme in recent years. Whether such an impact actually exists could so far only be tested to a very limited extent. Yet to guarantee the accountability of recommendation and information filtering systems, society needs to be able to determine whether they comply with ethical and legal requirements. This paper focuses on black box analyses as methods that are designed to systematically assess the performance of such systems, but that are, at the same time, not very intrusive. We describe the conditions that must be met to allow black box analyses of recommendation systems based on an application on Facebook’s News Feed. While black box analyses have proven to be useful in the past, several barriers can easily get in the way, such as a limited possibility of automated account control, bot detection and bot inhibition. Drawing on the insights from our case study and the state of the art of research on algorithmic accountability, we formulate several policy demands that need to be met in order to allow monitoring of ADM systems for their compliance with social values.
Three Attacks on the Mediated Semi‐Quantum Key Distribution without Invoking Quantum Measurement

Abstract Semi‐quantum key distribution is a very interesting new branch of quantum key distribution. It can be implemented when one or more participants are restricted to operate quantum states only on the quantum computational basis. Very recently, a mediated semi‐quantum key distribution protocol without invoking two participants' quantum measurement has been proposed. The protocol allows two “classical” participants without sophisticated quantum capability to establish a shared secret key under an untrusted third party. It is claimed that the protocol is secure against several well‐known attacks. However, in this paper, it is first pointed out that there exist three attacks “Measurement Attack, Modification Attack, and Collective Attack” on the mediated semi‐quantum key distribution protocol without invoking quantum measurement. By proposed attacks, a malicious third party can obtain the secret key without being noticed by legitimated participants.
Semantics-Driven Conversational Interfaces for Museum Chatbots

This work addresses the challenges of creating usable and personalized conversational interfaces for broad, yet applicable, domains that require user engagement and learning, such as museum chatbots. Whether the chatbots are standalone or coupled with virtual agents or real-life robots, the functional requirements for interaction that targets specific learning aspects would be expected to be more or less similar. This work reports on experimental semantics-driven conversational interface design for chatbots in museum settings, targeting visitors to converse about exhibits and learn information about their style, the artists, the era, and other aspects related to them. Depending on the semantics (presentation, learning, exploration), chatbot scenarios were designed and evaluated by participants in a formative evaluation. The evaluation show that user requirement perception manifests in expectations on the semantic level, instead of just the technical level. The results between the scenarios are compared to see how the semantics considered for the design transferred to the implementation and to the user perception.
SiamBOMB: A Real-time AI-based System for Home-cage Animal Tracking, Segmentation and Behavioral Analysis

Biologists often need to handle numerous video-based home-cage animal behavior analysis tasks that require massive workloads. Therefore, we develop an AI-based multi-species tracking and segmentation system, SiamBOMB, for real-time and automatic home-cage animal behavioral analysis. In this system, a background-enhanced Siamese-based network with replaceable modular design ensures the flexibility and generalizability of the system, and a user-friendly interface makes it convenient to use for biologists. This real-time AI system will effectively reduce the burden on biologists.
Personalized Tourist Guide for People with Autism

Cultural Heritage exploration is interesting for the development of inclusive tourist guides because it exposes visitors to different types of challenges, from steering content recommendation to visitors' interests and cognitive capabilities, to the suggestion of places that can be effectively reached and visited under different types of constraints: e.g., temporal and physical ones. In this work we are interested in the needs of people with Autism in order to support them in the exploration of a geographic area. Specifically, this paper presents a mobile tourist guide that we are developing to help people in visiting new places. The app is an evolution of PIUMA (Personalised Interactive Urban Maps for Autism), conceived to help autistic citizens in their everyday movements. It shows a map tailored to users with Autism Spectrum Disorder. In particular, it presents a personalized selection of safe Points of Interest, i.e., places that are, at the same time, interesting for the user and have "safe" characteristics from the sensory point of view, such as being quiet, scarcely crowded, or with smooth lights. In this paper, we present how we intend to extend PIUMA to support tourists.
Image Copy Detection Based on Local Binary Pattern and SVM Classifier

Abstract Due to the availability of a large number of image editing software, it is very easy to find duplicate copies of original images. In such a situation, there is a need to develop a robust technique that can be used for the identification of duplicate copies apart from differentiating it from different images. In this paper, we have proposed an image hashing technique based on uniform Local Binary Pattern (LBP). Here, the input image is initially pre-processed before calculating the Local Binary Pattern (LBP) which is used for image identification. Experiments show that proposed hashing gives excellent performance against the Histogram equalization attack. The Receiver Operating Curve (ROC) indicates that the proposed hashing also performs better in terms of robustness and discrimination. Support Vector Machine (SVM) classifier shows that generated features can easily classify images into a set of similar and different images, and can classify new data with a high level of accuracy.
Implementation and Optimization of Particle Effects based on Unreal Engine 4

Abstract The wide application of virtual reality and augmented reality makes the engine of developing virtual reality develop towards more realistic rendering, more convenient operation and better performance. In the realization of virtual reality scene, the three-dimensional particle effects play an excellent performance, and enhance the immersion of the experiencer. However, the production and optimization of three-dimensional particle effects have always been a complicated task for developers. Based on the visual blueprint script of Unreal Engine 4 and the modular particle editor, the author elaborates the method of making particle effects in detail, calculates the cost of particle system in real time by using the console of Unreal Engine 4, and then analyses the reasons that affect the performance of particle effects according to the data. Three methods, GPU launcher, material instruction reduction and LOD application, are used to optimize the performance of particle effects, and data are used to show the results of optimization.