Reviewerly/exHarmony huggingface dataset:
Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters.

Bayesian Optimization (BO) is a common approach for hyperparameter optimization (HPO) in automated machine learning. Although it is well-accepted that HPO is crucial to obtain well-performing machine learning models, tuning BO's own hyperparameters is often neglected. In this paper, we empirically study the impact of optimizing BO's own hyperparameters and the transferability of the found settings using a wide range of benchmarks, including artificial functions, HPO and HPO combined with neural architecture search. In particular, we show (i) that tuning can improve the any-time performance of different BO approaches, that optimized BO settings also perform well (ii) on similar problems and (iii) partially even on problems from other problem families, and (iv) which BO hyperparameters are most important.
Generalized Controllers in POMDP Decision-Making

We present a general policy formulation for partially observable Markov decision processes (POMDPs) called controller family policies that may be used as a framework to facilitate the design of new policy forms. We prove how modern approximate policy forms: point-based, finite state controller (FSC), and belief compression, are instances of this family of generalized controller policies. Our analysis provides a deeper understanding of the POMDP model and suggests novel ways to design POMDP solutions that can combine the benefits of different state-of-the-art methods. We illustrate this capability by creating a new customized POMDP policy form called the belief-integrated FSC (BI-FSC) tailored to overcome the shortcomings of a state-of-the-art algorithm that uses non-linear programming (NLP). Specifically, experiments show that for NLP the BI-FSC offers improved performance over a vanilla FSC-based policy form on benchmark domains. Furthermore, we demonstrate the BI-FSC’s execution on a real robot navigating in a maze environment. Results confirm the value of using the controller family policy as a framework to design customized policies in POMDP robotic solutions.
Using EDA-Based Local Search to Improve the Performance of NSGA-II for Multiobjective Semantic Web Service Composition

Service-oriented computing is a computing paradigm that creates reusable modules over the Internet, often known as Web services. Web service composition aims to accomplish more complex functions by loosely coupling web services. Researchers have been proposing evolutionary computation (EC) techniques for efficiently building up composite services with optimized non-functional quality (i.e., QoS). Some of these techniques employ multi-objective EC algorithms to handle conflict qualities in QoS for fully automated service composition. One recent state-of-art work hybridizes NSGA-II and MOEA/D, which allows the multi-objective service composition problem to be decomposed into many scalar optimization subproblems, where a simple form of local search can be easily applied. However, their local search is considered to be less effective and efficient because it is randomly applied to a predefined large number of subproblems without focusing on the most suitable candidate solutions. In this paper, we propose a memetic NSGA-II with probabilistic model-based local search based on Estimation of Distribution Algorithm (EDA). In particular, a clustering technique is employed to select suitable Pareto solutions for local search. Each selected solution and its belonged cluster members are used to learn a distribution model that samples new solutions for local improvements. Besides that, a more challenging service composition problem that optimizes both functional and non-functional quality is considered. Experiments have shown that our method can effectively and efficiently produce better Pareto optimal solutions compared to other state-of-art methods in the literature.
Towards Diverse and Accurate Image Captions via Reinforcing Determinantal Point Process.

Although significant progress has been made in the field of automatic image captioning, it is still a challenging task. Previous works normally pay much attention to improving the quality of the generated captions but ignore the diversity of captions. In this paper, we combine determinantal point process (DPP) and reinforcement learning (RL) and propose a novel reinforcing DPP (R-DPP) approach to generate a set of captions with high quality and diversity for an image. We show that R-DPP performs better on accuracy and diversity than using noise as a control signal (GANs, VAEs). Moreover, R-DPP is able to preserve the modes of the learned distribution. Hence, beam search algorithm can be applied to generate a single accurate caption, which performs better than other RL-based models.
A Bayesian based Hyper-Heuristic approach for global optimization

Several metaheuristics have been developed for global optimization. Most of them are designed for solving a specific problem at hand, and their use on a new implementation is a challenging task. Hyper-heuristics are strategies that support these issues, combining a metaheuristic in a high-level for selecting or generating simple heuristics from a low-level. The aim is to find nearoptimal solutions based on the feedback received during the search. Estimation of Distribution Algorithms (EDAs) have been applied as hyper-heuristics, using a Probabilistic Graphical Model (PGM) to extract and represent interactions between its low-level heuristics to provide high-valued problem solutions. In this paper, we consider an EDA based on Bayesian networks as PGM on a hyper-heuristic context which encompasses a heuristic selection approach to find the best combinations of different known simple heuristics. We compare our proposed approach named Hyper-heuristic approach based on Bayesian Optimization Algorithm (HHBOA) using CEC'05 benchmark functions among 9 optimization algorithms. The experimental results show that HHBOA is competitive, outperforming the other approaches, especially in terms of convergence, on most of the functions considered in this paper.
Towards Identifying De-anonymisation Risks in Distributed Health Data Silos

Accessing distributed and isolated data repositories such as medical research and treatment data in a privacy-preserving manner is a challenging problem. Furthermore, in the context of high-dimensional datasets, adhering to strict privacy legislation can be projected to a W[2]-complete problem whereby all privacy violating attribute combinations must be identified. While traditional anonymisation algorithms incur high levels of information loss when applied to high-dimensional data, they often do not guarantee privacy, which defeats the purpose of anonymisation. In this paper, we extend our previous work and address these issues by using Bayesian networks to handle data transformation for anonymisation [29]. By computing conditional probabilities linking attribute pairs for all attribute pair combinations the privacy exposure risk can be assessed. Attribute pairs differing by a high conditional probability indicate a high risk of de-anonymisation, similar to quasi-identifiers in syntactic anonymisation schemes, and can be separated instead of deleted. Attribute compartmentation removes the risk of privacy exposure, and deletion avoidance results in a significant reduction in information loss. In other words, assimilating the conditional probability of outliers directly in the adjacency matrix in a greedy fashion is efficient and privacy-preserving. Further, we offer deeper evaluation insights for optimising Bayesian networks with multigrid solver for aggregating state space explosion.
How Augmented Reality Affects People’s Perceptions: Adoption of AR in Product Display Improves Consumers’ Product Attitude

Abstract Augmented reality has emerged as a novel communication tool which adds virtual computer-generated information to viewers’ real life. Although a wealth of research has investigated the effect of AR technology on consumers’ attitude, little is known about the underlying mechanism of how it influences consumers’ attitude. The purpose of this paper is to address the problem. Two experiments were performed to test the effect of AR based product display on consumer’s attitude and the underlying mechanism of the effect. Specifically, in Experiment 1, the authors presented a bottle to 92 participants (43 women, 39 men, mean age = 25.78) via three different methods: AR based display, non-AR based display without background, and non-AR based display with a background to verify the main effect. Additionally, another 100 volunteers (65 women, 35 men, mean age = 24.16) participated in Experiment 2 to verify the mediating effect. The results have demonstrated that the effect of AR based display on consumers’ product attitude is mediated by Self-referencing reality simulation. The current research suggests that managers should develop and adopt AR technology in their product display which can improve consumers’ product attitude.
Improved Hourglass Structure for high Performance Facial Landmark Detection

A robust facial landmark detection framework is proposed in this paper, which can be trained in an end-to-end fashion and has achieved promising detection accuracy in Grand Challenge of 106-p Facial Landmark Localization. Firstly, in order to deal with challenging cases (e.g. large pose, exaggerated expression, non-uniform lighting and occlusion), a four-stage hourglass (HGs) structure is used as the backbone while a novel hierarchical block is designed to replace the standard residual block in the original HGs. Then in order to prevent the accuracy loss by the coordinates quantization, a novel function named dual soft argmax is designed for mapping the heatmap response to final coordinates. Besides, for data augmentation cutout is used and proved to be effective to the partially occluded cases. The model is trained from the beginning, and finally the best result 83.076% for AUC is achieved on the validation set.
Augmented Concentration: Concentration Improvement by Visual Noise Reduction with a Video See-Through HMD

We propose a concentration improvement technique by using a video see-through head mounted display (HMD). Our technique reduces the visual noise or disturbing visual stimulus, such as moving objects near the central visual field, by lowering its visual saliency in real-time. Earphones and noise cancelling headphones are often used to shutdown auditory noise from surroundings when we need to concentrate on the job. Several studies have proven the effectiveness of such noise reduction on improving the concentration and learning efficiencies. We apply this analogy to the visual noise and propose a visual noise reduction HMD to improve concentration. In this article, we report on two preliminary user studies we conducted to investigate the effectiveness of our method. In the prototype system, we manually specify a part of the visual field as the working area and apply grayscale and blur filters outside it in real-time. We compared two conditions “no effect” and “strong blur” (for Exp 1) or “weak blur” (for Exp 2), using a simple math task. Our preliminary results show that visual noise reduction improves the task completion time by 10% for Exp 1 and 7.9% for Exp 2 on average.
Research on Library Information Visualization Retrieval Technology Based on Readers’ Needs

Abstract With the advancement of social informatization and the widespread application of network, the resources accumulated by libraries are becoming larger and larger. In addition to the need to store, transmit, retrieve and classify massive data, there is an urgent need to understand the relationship between data and its development trend. With the continuous development of modern information technology, digital libraries have also made great progress. And to introduce visual information retrieval technology into digital libraries is an inevitable trend in the future development. This paper first analyses the characteristics of readers’ information retrieval needs in the new period, and then introduce library information visual presentation technologies to design the library information resource visualization retrieval system. The system can visually display the collection structure, popular books, readers’ interests and so on, to truly reflect the structure, coverage, utilization and other indicators of books resource, and provide convenience for the visual sharing of library information resources.
Privacy Protected Recognition of Activities of Daily Living in Video

This paper proposes a new method to protect the privacy while retaining the ability to accurately recognise the activities of daily living for video-based monitoring in ambient assisted living applications. The proposed method obfuscates the human appearance by modelling the temporal saliency in the monitoring video sequences. It mimics the functionality of neuromorphic cameras and explores the temporal saliency to generate a mask to anonymise the human appearance. Since the anonymising masks encapsulate the temporal saliency with respect to motion in the sequence, they provide a good basis for further utilisation in activity recognition, which is achieved by representing the HOG features on privacy masks. The proposed method has resulted in excellent anonymising performances compared using the cross correlation measures. In terms of activity recognition, the proposed method has resulted in 5.6% and 5.4% improvements of accuracies over other anonymisation methods for Weizmann and DHA datasets, respectively.
User Needs in Smart Homes: Changing Needs According to Life Cycles and the Impact on Designing Smart Home Solutions

Smart home solutions are gaining popularity but hold a large untapped potential. This paper presents smart home needs from a user point of view. Based on co-creation workshops with residents of different life cycles in Belgium, it draws conclusions about key issues related to user needs. Starting from these insights we can describe characteristics of inhabitants according to their life cycle. We conclude that their needs for smart solutions and the way they want to equip their homes differ. The younger generation looks for modular systems with possibilities to expand or move them. They are moment oriented and buy Internet of Things (IoT) appliances according to their current needs and budget. The older generation, by contrast, searches for quality and all-in-one solutions. They want a complete solution that does not require any updates or extras and is proof for their future needs. The results of this study in the form of eight guidelines can be considered when designing smart home solutions.
High Quality Dataset for Machine Learning in the Business Intelligence Domain

This paper is aimed at showing the relevance and importance of high quality dataset in machine learning within the field of economic intelligence. As open source dataset flourish and algorithm are trained with different and often very narrow data of various kind, in the field of economic intelligence it is important to train machines with proper and high value data to avoid or reduce at maximum false positives, errors and biases of various kind. We propone the case study and the solution offered by Bureau Van Dijk, where economic data are carefully evaluated, organized and its API and REST services could matter in the very near future in the field of data mining and machine learning for economic intelligence purposes.
Identity Resolution in Ontology Based Data Access to Structured Data Sources

Earlier work has proposed a notion of referring expressions and types in first order knowledge bases as a way of more effectively answering conjunctive queries in ontology based data access (OBDA). We consider how PTIME description logics can be combined with referring expressions to provide a more effective virtual front-end to nested relational data sources via OBDA. In particular, we consider replacing the standard notion of an assertion box, or ABox, with a more general notion of a concept box, or CBox, and show how this can serve as a front-end to such data sources.
Network Anomaly Detection Using Header Information With Greedy Algorithm

Network anomaly detection is an important and rapidly growing area. In this paper, we propose a new network anomaly detection method based on the probability distributions of header information. The distances between the distributions of headers are calculated to reflect the main characteristics of the network. These are calculated using Greedy algorithm which eliminates some requirements associated with Kullback-Leibler divergence such as having the same rank of the probability distributions. Then, Support Vector Machine classifier is used in the detection phase to reduce false alarm rates and to make the system adaptive for different networks. This algorithm is tested on the real data collected from Boğaziçi University network and MIT Darpa 2000 dataset.
Person Detection in Thermal Videos Using YOLO

In this paper, the task of automatic person detection in thermal images using convolutional neural network-based models originally intended for detection in RGB images is investigated. The performance of the standard YOLOv3 model is compared with a custom trained model on a dataset of thermal images extracted from videos recorded at night in clear weather, rain and fog, at different ranges and with different types of movement – running, walking and sneaking. The experiments show excellent results in terms of average precision for all tested scenarios, and a significant improvement of performance for person detection in thermal imaging with a modest training set.
SENTIMENT ANALYSIS IN TURKISH FILM COMMENTS

Analysing the sentiments in text files, movie website's user comment is an important research fields both for companies and the academics due to the challenges and opportunities it possesses. In this study, we applied lexicon based method and Machine learning algorithms that are support vector machine, naive bayes, logistic regression and decision tree methods to various sized Turkish datasets. As a result, we observed that the Logistic Regression algorithm gave the best result with an accuracy of %77.35.
Custom OpenAI Reinforcement Learning Environments

For our final chapter, we will be focusing on Open AI’s gym package, but more importantly trying to understand how we can create our own custom environments so we can tackle more than the typical use cases. Most of this chapter will focus around what I would suggest regarding programming practices for Open AI as well as recommendations on how I would generally write most of this software. Finally, after we have completed creating an environment, we will move on to focusing on solving the problem. For this instance, we will focus on trying to create and solve a new video game.
Adversarial Fisher Vectors for Unsupervised Representation Learning

We examine Generative Adversarial Networks (GANs) through the lens of deep Energy Based Models (EBMs), with the goal of exploiting the density model that follows from this formulation. In contrast to a traditional view where the discriminator learns a constant function when reaching convergence, here we show that it can provide useful information for downstream tasks, e.g., feature extraction for classification. To be concrete, in the EBM formulation, the discriminator learns an unnormalized density function (i.e., the negative energy term) that characterizes the data manifold. We propose to evaluate both the generator and the discriminator by deriving corresponding Fisher Score and Fisher Information from the EBM. We show that by assuming that the generated examples form an estimate of the learned density, both the Fisher Information and the normalized Fisher Vectors are easy to compute. We also show that we are able to derive a distance metric between examples and between sets of examples. We conduct experiments showing that the GAN-induced Fisher Vectors demonstrate competitive performance as unsupervised feature extractors for classification and perceptual similarity tasks. Code is available at \url{https://github.com/apple/ml-afv}.
Compositional Plan Vectors

Autonomous agents situated in real-world environments must be able to master large repertoires of skills. While a single short skill can be learned quickly, it would be impractical to learn every task independently. Instead, the agent should share knowledge across behaviors such that each task can be learned efficiently, and such that the resulting model can generalize to new tasks, especially ones that are compositions or subsets of tasks seen previously. A policy conditioned on a goal or demonstration has the potential to share knowledge between tasks if it sees enough diversity of inputs. However, these methods may not generalize to a more complex task at test time. We introduce compositional plan vectors (CPVs) to enable a policy to perform compositions of tasks without additional supervision. CPVs represent trajectories as the sum of the subtasks within them. We show that CPVs can be learned within a one-shot imitation learning framework without any additional supervision or information about task hierarchy, and enable a demonstration-conditioned policy to generalize to tasks that sequence twice as many skills as the tasks seen during training. Analogously to embeddings such as word2vec in NLP, CPVs can also support simple arithmetic operations -- for example, we can add the CPVs for two different tasks to command an agent to compose both tasks, without any additional training.
Locally Private Learning without Interaction Requires Separation

We consider learning under the constraint of local differential privacy (LDP). For many learning problems known efficient algorithms in this model require many rounds of communication between the server and the clients holding the data points. Yet multi-round protocols are prohibitively slow in practice due to network latency and, as a result, currently deployed large-scale systems are limited to a single round. Despite significant research interest, very little is known about which learning problems can be solved by such non-interactive systems. The only lower bound we are aware of is for PAC learning an artificial class of functions with respect to a uniform distribution (Kasiviswanathan et al., 2008). We show that the margin complexity of a class of Boolean functions is a lower bound on the complexity of any non-interactive LDP algorithm for distribution-independent PAC learning of the class. In particular, the classes of linear separators and decision lists require exponential number of samples to learn non-interactively even though they can be learned in polynomial time by an interactive LDP algorithm. This gives the first example of a natural problem that is significantly harder to solve without interaction and also resolves an open problem of Kasiviswanathan et al.~(2008). We complement this lower bound with a new efficient learning algorithm whose complexity is polynomial in the margin complexity of the class. Our algorithm is non-interactive on labeled samples but still needs interactive access to unlabeled samples. All of our results also apply to the statistical query model and any model in which the number of bits communicated about each data point is constrained.