Reviewerly/exHarmony huggingface dataset:
Multiagent Collaboration Learning: A Music Generation Test Case

In Chap. 5 I discussed how the impact of music on human decision-making can be modeled. Subsequently in Chap. 6 I discussed how this impact can be leveraged by an agent to engender better interaction with a person. However, that is only one facet of person-agent interaction in musical context. The other includes a scenario in which people and machines actively collaborate in music generation. What would such an interaction be like? An important aspect of person-agent interaction, or of agents interacting with multiple people and/or other agents, is that of reasoning about preferences. Particularly in a domain such as music generation, people’s subjective tastes play a pivotal role, and reasoning about them when trying to collaborate is critical. This train of thought leads to a deeper question: how can multiple agents reason with each other in a shared task while also maintaining individual preferences that may be at odds with the shared task and with the preferences of others? Studying the balance between shared tasks and individual preferences in multiagent interaction is a significant step in fulfillment of Contribution 4 of this book, building towards multiagent music interaction as a meaningful step towards person-agent music generation.
Improved DeepWalk Algorithm Based on Preference Random Walk

Network representation learning based on neural network originates from language modeling based on neural network. These two types of tasks are then studied and applied along different paths. DeepWalk is the most classical network representation learning algorithm, which samples the next hop nodes of the walker with an equal probability method through the random walk strategy. Node2vec improves the random walk procedures, thus improving the performance of node2vec algorithm on various tasks. Therefore, we propose an improved DeepWalk algorithm based on preference random walk (PDW), which modifies the single undirected edge into two one-way directed edges in the network, and then gives each one-way directed edge a walk probability based on local random walk algorithm. In the procedures of acquiring walk sequences, the walk probability of the paths that have been walked will be attenuated according to the attenuation coefficient. For the last hop node of the current node in the walk sequences, an inhibition coefficient is set to prevent random walker from returning to the last node with a greater probability. In addition, we introduce the Alias sampling method in order to obtain the next hop node from the neighboring nodes of current node with a non-equal probability sampling. The experimental results show that the proposed PDW algorithm possesses a stable performance of network representation learning, the network node classification performance is better than that of the baseline algorithms used in this paper.
Situation-Aware Conversational Agent with Kinetic Earables (demo)

Conversational agents are increasingly becoming digital partners of our everyday computing experiences offering a variety of purposeful information and utility services. Although rich on competency, these agents are entirely oblivious to their users' situational and emotional context today and incapable of adjusting their interaction style and tone contextually. To this end, we present a first-of-its-kind situation-aware conversational agent on kinetic earable that dynamically adjusts its conversation style, tone, volume in response to users emotional, environmental, social and activity context gathered through speech prosody, ambient sound and motion signatures.
A Hybrid Feature Selection Method for Software Fault Prediction

Fault prediction aims to identify whether a software module is defect-prone or not according to metrics that are mined from software projects. These metric values, also known as features, may involve irrelevance and redundancy, which hurt the performance of fault prediction models. In order to filter out irrelevant and redundant features, a Hybrid Feature Selection (abbreviated as HFS) method for software fault prediction is proposed. The proposed HFS method consists of two major stages. First, HFS groups features with hierarchical agglomerative clustering; second, HFS selects the most valuable features from each cluster to remove irrelevant and redundant ones based on two wrapper based strategies. The empirical evaluation was conducted on 11 widely-studied NASA projects, using three different classifiers with four performance metrics (precision, recall, F-measure, and AUC). Comparison with six filter-based feature selection methods demonstrates that HFS achieves higher average F-measure and AUC values. Compared with two classic wrapper feature selection methods, HFS can obtain a competitive prediction performance in terms of average AUC while significantly reducing the computation cost of the wrapper process.
Improving robot visual skills by means of a bio-inspired model

The following topics are dealt with: learning (artificial intelligence); mobile robots; humanoid robots; human-robot interaction; neural nets; robot vision; cognition; neurophysiology; manipulators; object detection.
Clustering interval-valued data with automatic variables weighting

Over the past few years, Symbolic Data Analysis has gained popularity providing suitable methods for managing aggregated data represented by lists, intervals, histograms or even distributions. This paper proposes a partitioning clustering algorithm for interval-valued data based on the suitable adaptive Euclidean distance that takes into account the relevance of the variables according to the boundaries. The proposed distance changes at each algorithm iteration and is different from one cluster to another. The method provides a partition and a prototype for each cluster by optimizing an adequacy criterion that measures the fitting between groups and their representatives. Experiments on synthetic and real interval-valued datasets corroborate the usefulness of the proposed algorithm.
Question Answering with Hierarchical Attention Networks

We investigate hierarchical attention networks for the task of question answering. For this purpose, we propose two different approaches: in the first, a document vector representation is built hierarchically from word-to-sentence level which is then used to infer the right answer. In the second, pointer sum attention is utilized to directly infer an answer from the attention values of the word and sentence representations. We evaluate our approach on the Children's Book Test, a cloze-style question answering dataset, and analyze the generated attention distributions. Our results show that, although a hierarchical approach does not offer much improvement over a shallow baseline, it does indeed offer a large performance boost when combining word and sentence attention with pointer sum attention.
Closer to Optimal Angle-Constrained Path Planning

Planning on grids and planning via sampling are the two classical mainstreams of path planning for intelligent agents, whose respective representatives are A* and RRT, including their variants, Theta* and RRT*. However, in the nonholonomic path planning, such us being under angle constraints, Theta* and Lazy Theta* may fail to generate a feasible path because the line-of-sight check (LoS-Check) will modify the original orientation of a state, which makes the planning process incomplete (cannot visit all possible states). Then, we propose a more delayed evaluation algorithm called Late LoS-Check A* (LLA*) to relax the angle constraints. Due to the nature of random sampling, RRT* is asymptotically optimal but still not optimal, then we propose LoS-Check RRT* (LoS-RRT*). In order to solve the problems caused by improper settings of the planning resolution, we propose the LoS-Slider (LoSS) smoothing method. Through experimental comparison, it can be found that angle-constrained versions of LLA* and LoS-RRT* can both generate the near-optimal paths. Meanwhile, the experiment result shows that LLA* performs better than Theta* and Lazy Theta* under angle constraints. The planned path will be even closer to the optimal (shortest) solution after the smoothing of LoSS algorithm.
A Distributed Class-Incremental Learning Method Based on Neural Network Parameter Fusion

Class-incremental learning has received considerable attention due to the better adaptability to constantly changing characteristic of online learning. The neural network is suitable for class-incremental learning because of batch training style and fine-tuning. However, as the rapid growth of data centers and data category, data isolated island problem presents a new challenge to the current machine learning model: how to provide an accurate training method in a distributed and incremental manner. Existing solutions cannot adapt to incremental classes without aggregating data. In this paper, we propose a distributed class-incremental learning framework, called DCIL, which iteratively updates new class parameters and fixes existing class parameters to achieve high training accuracy. Especially, we propose MF, a parameter fusion method to integrate the parameters of multi-nodes into a new global parameter. In order to match the requirements of identical columns corresponding to the known classes, we propose a single node class-incremental learning, called SCIL. To evaluate the performance of DCIL, we conduct experiments on 5 datasets under various parameter settings on both 2 and 4 nodes distributed scenarios. Extensive experiments confirm that DCIL outperform the general baselines and achieve classification accuracy closes to the method of data aggregating.
AuxBlocks: Defense Adversarial Examples via Auxiliary Blocks

Deep learning models are vulnerable to adversarial examples, which poses an indisputable threat to their applications. However, recent studies have observed that gradient-masking defenses are self-deceiving methods if an attacker can realize this defense. In this paper, we propose a new defense method based on appending information. We introduce the Aux Block model to produce extra outputs as a self-ensemble algorithm and analytically investigate the robustness mechanism of Aux Block. We have empirically studied the efficiency of our method against adversarial examples in two types of white- box attacks, and found that even in the full white-box attack where an adversary can craft malicious examples from defense models, our method has a more robust performance of about 54.6% precision on Cifar10 dataset and 38.7% precision on MiniImagenet dataset. Another advantage of our method is that it is able to maintain the prediction accuracy of the classification model on clean images, and thereby exhibits its high potential in practical applications.
Total Variation Cutoff for the Transpose Top-2 with Random Shuffle

In this paper, we investigate the properties of a random walk on the alternating group $$A_n$$ generated by three cycles of the form $$(i,n-1,n)$$ and $$(i,n,n-1)$$ . We call this the transpose top-2 with random shuffle. We find the spectrum of the transition matrix of this shuffle. We show that the mixing time is of order $$\left( n-\frac{3}{2}\right) \log n$$ and prove that there is a total variation cutoff for this shuffle.
Face Recognition Using Multi-local Descriptors—A Novel Approach

While face recognition has been a topic of interest for the researchers for quite some time now, most of the advancements and superior results have come out from the field of face recognition in controlled scenarios. As we shift away from the controlled environment like passport images, driver’s license etc. to unconstrained environment like images taken from surveillance footage, images taken by bystanders etc. the recognition accuracy significantly decreases. This inconsistency is due to the fact that face images in unconstrained environment have vast variations in parameters like illumination, background detail, pose, expression, occlusion etc. At present times when important disciplines like security and forensics application depends on such systems it can prove to be very useful if a face recognition system in unconstrained environment can give comparable results to the systems in controlled environment. Our work focuses on face recognition using deep feature extraction by concatenating the features of different feature extractors to improve the recognition accuracy in unconstrained environment. We use multiple feature based methods (variants of LBP and LGS) to extract important features from the same image and combine them to form a single feature vector. For classification SVM is used and two face databases viz. ORL face database and LFWCrop (Labeled Faces in the Wild). The experimental results reveal that the proposed method improves the performance of the face recognition system.
BASIC PRINCIPLES FOR DEVELOPING AN ADAPTIVE LEARNING SYSTEM

The work focuses on the basic principles of developing an adaptive learning system. The modern concept of open education assumes multilevel character of training, and also possibility of a choice of means, a place and time of training corresponding to its inquiries by trainees . This implies the availability of alternative teaching aids (courses) and application software for their creation, support of training and adaptation to a particular student. A significant challenge is to reduce the cost of developing e-learning courses and to increase their use in online learning. In addition, despite the availability of automated tools to simplify the development of pedagogical rules, it is impossible to generate rules suitable for any situation. These reasons have led to the search for a solution to the problem of developing e-learning courses that implement adaptive learning. The principles laid down in the concept of designing such a system are formulated. The structure of the system, including the model of the subject area (educational content), the model of the user, the model of adaptation and the model of evaluation of learning outcomes are presented. Within the framework of the proposed system, it has been developed and is at the testing stage. It is justified that the successful use of such a system by students contributes to the formation of their specific competences. In the adaptive system the roles of a teacher and a trainee are changing: a teacher is an organizer of the initial activities, and the responsibility for the results is shared with students. In this system, the democratic management style, not authoritarian one, is taken into account. A student is a performer of educational process and not a passive recipient of information (an object of the process). With the help of a teacher they acquire knowledge, actively involve into thinking process and plan own educational and cognitive activity. The central place in the adaptive learning system is occupied by a student with their individual features: biological potential and abilities, specifics of the organization of the cognitive process, the level of activity and independence in practical and cognitive activity, as well as efficiency, etc. The result of this system is a qualitative change in their individual features. Active independency of a student in the process of cognitive activities promote the development of individual qualities, the formation of intellectual and spiritual growth, as well as self-knowledge of a learner’s personality. This is the way the system is adapted to the formulation of competence of individual self-improvement.
Towards Automatic Classification of Common Therapy Errors for Diabetes Therapy Support

Today, one in eleven adults is suffering from diabetes mellitus. Too high or too low blood glucose levels are dangerous. Incorrectly treated, it will lead to severe complications such as strokes, blindness, and ultimately, death. Hence, the correct medication with insulin from diabetes patients is a crucial parameter in therapy. Patients get educated regularly by diabetes experts in training sessions. These sessions contain data review by the experts to identify errors in the patients' dosage behavior. However, the review is time-consuming, since the manual error identification for a wrong dosage is nontrivial. In this paper we investigate the automatic classification of insulin dosage into three categories, representing correctly applied therapy and the most common therapy faults. We provide the experts with a pre-classified overview of the data, where the common errors are visually highlighted. This saves time in the consultation hour, enabling the expert to spend more time investigating the patients' individual problems. In our evaluation, we compare multiple classification methods based on dynamic time warping against a convolutional neural network. The results show that the convolutional neural network can achieve accuracy levels that are promising, although further improvements are required.
MATHEMATICAL MODEL OF QUALITY ASSESSMENT OF CONTACT WORK IN E-LEARNING BASED ON WEBINARS

The article deals with the problems of the quality of contact work in the implementation of training using distance educational technologies. The mathematical model of estimation of quality of the contact work realized by means of webinars is developed for a research of this problem. The analysis of the tasks arising at the organization of webinars is carried out, among which the problem of determination of the actual quality of webinars is recognized as one of essential, which is necessary for making adequate management decisions to improve the quality of contact work carried out through webinars. Three groups of the factors defining quality of distance learning in general were revealed for the solution of this problem: the quality of educational content, the professionalism of teachers, and the interaction of the teacher and student. The research of quality of webinars conducted by authors in 2018 in Siberian State University of Telecommunications and Information Science have allowed to define and formalize 31 indicators of quality of a webinar and 7 indicators of competence of a webinar leader, on the basis of which the mathematical model is developed. The model allows to carry out expert and expeditious estimation of the specified indicators, thereby to realize integrated differential approach to estimation of the aspects of quality of contact work in the form of a webinar. Also, mathematical solutions of increasing efficiency of the model implementation by applying neural network method to the estimation procedure for the majority of indicators are proposed, which allows obtaining adequate estimates of indicators without the participation of experts.
Atoms in infinite dimensional free sequence-set algebras

A. Tarski proved that the m-generated free algebra of $$\mathrm {CA}_{\alpha }$$, the class of cylindric algebras of dimension $$\alpha $$, contains exactly $$2^m$$ zero-dimensional atoms, when $$m\ge 1$$ is a finite cardinal and $$\alpha $$ is an arbitrary ordinal. He conjectured that, when $$\alpha $$ is infinite, there are no more atoms other than the zero-dimensional atoms. This conjecture has not been confirmed or denied yet. In this article, we show that Tarski’s conjecture is true if $$\mathrm {CA}_{\alpha }$$ is replaced by $$\mathrm {D}_{\alpha }$$, $$\mathrm {G}_{\alpha }$$, but the m-generated free $$\mathrm {Crs}_{\alpha }$$ algebra is atomless.
Kinect Sensor-Based Trajectory Planning Method of Collision Avoidance for Industrial Manipulator with an Dexterous Hand

This paper presents Kinect sensor-based trajectory planning method of collision avoidance for an industrial manipulator with multi-fingered dexterous hand, which is used for grasping objects by using a data glove in dynamically changing environments. Each finger's joint angles of the dexterous hand are synchronously collected by the data glove and forwarded to the trajectory planning control system that navigates the dexterous hand to the grasping poses. In order to effectively calculate the solutions to the desired configuration of the manipulator with the dexterous hand, the LazyPRM algorithm is also applied to maintain a road map for answering multiple planning queries. To maintain the dynamically changing environments, the Kinect sensor is used to detect and track the moving objects that appear in the workspace and to update the environment representation to avoid the collisions. The distances between the obstacles and the camera are obtained by the depth camera of the Kinect sensor. The experimental results have verified the effectiveness of the methods.
Incorporating Human Knowledge in Neural Relation Extraction with Reinforcement Learning

Relation Extraction (RE) aims at extracting semantic relation of entities from text and it is a crucial task in natural language processing. Deep neural network (DNN) based models have achieved excellent performance in RE. However, they still have several problems remaining to be addressed: (1) humans can hardly take measures to amend the DNN-based RE systems because it is difficult to encode human intention to guide them to capture desired patterns. (2) DNN-based RE models may suffer from not having sufficient background information for making predictions. To handle these issues, we propose an RE framework based on reinforcement learning, which can enhance existing DNN-based RE models by incorporating human knowledge including soft rules and relation evidence. The introduction of soft rules enable human to impose an effect on the RE result and correct the RE system, while the relation evidence help supplement the background information without limiting its types and sources. The experimental results show that our approach can reinforce existing DNN-based RE models effectively and outperforms state-of-the-art RE methods.
Object-aware Image Compression with Adversarial Learning

Image compression based on region of interest (ROI) can maintain high quality on specific areas and behaves well in terms of subjective quality. However, existing methods lead to a severe loss of background information as well as block effects when the bitrate goes down. In this paper, we take advantage of the generative models and develop the generative compression scheme for ROI-based image compression. The proposed method adopts different compression objectives for interested areas and background. For a target bitrate, our method tends to preserve more details for interested areas while generating the background using a perceptual metric. The generative model works on the whole image to erase the unnatural block effects. Experiments show that our method could produce visually pleasing images at very low bitrates and is superior to existing compression methods.
Hephaistos: A fast and distributed outlier detection approach for big mixed attribute data

This paper tackles a new problem in outlier detection: how to promptly detect the local outlier of a large-scale mixed attribute data in the big data era. This poses significant challenges due to a lack of access to the entire mixed attribute dataset at any individual compute machine. Proposed appr oaches firstly form a mechanism that deletes the massive clear non-noise and extracts cluster-based pre-noise set. Furthermore, we analyze pre-noise set using multi-step distributed LOF computing method on the Spark platform. Finally, the ordered LOF list is the output result. Comprehensive experiments are implemented by large-scale Benchmark datasets and the Spark platform. Extensive results show that the performance of our approaches are superior to the previous ones (4X faster than baseline LOF/2X faster than DLOF) when compared to state-of-the-art techniques, and therefore is believed to be able to give better guidance to local outlier detection of mixed attribute data.