Reviewerly/exHarmony huggingface dataset:
An Active Exploration Method for Data Efficient Reinforcement Learning

Abstract Reinforcement learning (RL) constitutes an effective method of controlling dynamic systems without prior knowledge. One of the most important and difficult problems in RL is the improvement of data efficiency. Probabilistic inference for learning control (PILCO) is a state-of-the-art data-efficient framework that uses a Gaussian process to model dynamic systems. However, it only focuses on optimizing cumulative rewards and does not consider the accuracy of a dynamic model, which is an important factor for controller learning. To further improve the data efficiency of PILCO, we propose its active exploration version (AEPILCO) that utilizes information entropy to describe samples. In the policy evaluation stage, we incorporate an information entropy criterion into long-term sample prediction. Through the informative policy evaluation function, our algorithm obtains informative policy parameters in the policy improvement stage. Using the policy parameters in the actual execution produces an informative sample set; this is helpful in learning an accurate dynamic model. Thus, the AEPILCO algorithm improves data efficiency by learning an accurate dynamic model by actively selecting informative samples based on the information entropy criterion. We demonstrate the validity and efficiency of the proposed algorithm for several challenging controller problems involving a cart pole, a pendubot, a double pendulum, and a cart double pendulum. The AEPILCO algorithm can learn a controller using fewer trials compared to PILCO. This is verified through theoretical analysis and experimental results.
Credible Information, Allowable Information and Belief Revision - Extended Abstract

In an earlier paper [Rational choice and AGM belief revision, Artificial Intelligence, 2009] a correspondence was established between the choice structures of revealed-preference theory (developed in economics) and the syntactic belief revision functions of the AGM theory (developed in philosophy and computer science).In this paper we extend the re-interpretation of (a generalized notion of) choice structure in terms of belief revision by adding: (1) the possibility that an item of "information" might be discarded as not credible (thus dropping the AGM success axiom) and ( 2) the possibility that an item of information, while not accepted as fully credible, may still be "taken seriously" (we call such items of information "allowable").We establish a correspondence between generalized choice structures (GCS) and AGM belief revision; furthermore, we provide a syntactic analysis of the proposed notion of belief revision, which we call filtered belief revision.* I am grateful to three anonymous reviewers for helpful comments. 1See, for example, [10] and [11]. 2 [1, 4] 3 Thus the intended meaning of ω ω is "alternative ω is considered to be at least as good as alternative ω ".
On the effect of age perception biases for real age regression

Automatic age estimation from facial images represents an important task in computer vision. This paper analyses the effect of gender, age, ethnic, makeup and expression attributes of faces as sources of bias to improve deep apparent age prediction. Following recent works where it is shown that apparent age labels benefit real age estimation, rather than direct real to real age regression, our main contribution is the integration, in an end-to-end architecture, of face attributes for apparent age prediction with an additional loss for real age regression. Experimental results on the APPA-REAL dataset indicate the proposed network successfully take advantage of the adopted attributes to improve both apparent and real age estimation. Our model outperformed a state-of-the-art architecture proposed to separately address apparent and real age regression. Finally, we present preliminary results and discussion of a proof of concept application using the proposed model to regress the apparent age of an individual based on the gender of an external observer.
Key Factor Not to Drop Out is to Attend Lectures

To find key factors not to drop out using learning analytics, we have added accumulated data such as the number of successes in learning check testing, the number of attendances to follow-up program classes, and etc., in addition to learning check testing ability scores performed at each lecture. Then, we have found key factors strongly related to the students at risk. They are the following. 1) Badly failed students (score range is 0-39 in the term ex-amination) tend to be absent for the regular classes and fail in learning check testing even if they attended, and they are very reluctant to attend follow-up program classes. 2) Success-ful students (score range is 60-100 in the term examination) attend classes and obtain good scores in every learning check testing. 3) Failed students but not so badly (score range is 40-59 in the term examination) reveal both sides of features appeared in score range of 0-39 and score range of 60-100. Therefore, it is crucial to attend lectures in order not to drop out. Students who failed in learning check testing more than half out of all testing times almost absolutely failed in the term examination, which could cause the drop out. Also, students who were successful to learning check testing more than two third out of all testing times took better score in the term examination.
Minimal Synthesis of String to String Functions from Examples

We study the problem of synthesizing string to string transformations from a set of input/output examples. The transformations we consider are expressed using a particular class of transducers: functional non-deterministic Mealy machines (f-NDMM). These are machines that read input letters one at a time, and output one letter at each step. The functionality constraint ensures that, even though the machine is locally non-deterministic, each input string is mapped to exactly one output string by the transducer. We suggest that, given a set of input/output examples, the smallest f-NDMM consistent with the examples is a good candidate for the transformation the user was expecting. We therefore study the problem of, given a set of examples, finding a minimal f-NDMM consistent with the examples and satisfying the functionality and totality constraints mentioned above. We prove that, in general, the decision problem corresponding to that question is $$\textsf {NP}$$ -complete, and we provide several $$\textsf {NP}$$ -hardness proofs that show the hardness of multiple variants of the problem. Finally, we propose an algorithm for finding the minimal f-NDMM consistent with input/output examples, that uses a reduction to SMT solvers. We implemented the algorithm, and used it to evaluate the likelihood that the minimal f-NDMM indeed corresponds to the transformation expected by the user.
Sketching and layering graffiti primitives

We present a variant of the skeletal strokes algorithm aimed at mimicking the appearance of hand made graffiti art. It includes a unique fold-culling process that stylizes folds rather than eliminating them. We demonstrate how the stroke structure can be exploited to generate non-global layering and self-overlap effects like the ones that are typically seen in graffiti art and other related art forms like traditional calligraphy. The method produces vector output with no artificial artwork splits, patches or masks to render the non-global layering; each path of the vector output is part of the desired outline. The method lets users interactively generate a wide variety of stylised outputs.
Substantive Legal Software Quality

Readily available interactive programs dispense substantive legal guidance, often including bespoke documents. These are found across a wide spectrum of commercial and non-commercial contexts. Consumers are coming to rely on them as alternatives to expensive lawyer services. Yet their quality is uneven and difficult to assess. We are in danger of serious harm being done to unwitting users. How can we avoid an epidemic of artificial misinformation, systematic inaccuracy, and mechanical malpractice? This paper reviews how those dangers play out in real-world application contexts and explores ways in which the AI & Law community might help address them.
The number of rational points of hyperelliptic curves over subsets of finite fields

We prove two related concentration inequalities concerning the number of rational points of hyperelliptic curves over subsets of a finite field. In particular, we investigate the probability of a large discrepancy between the numbers of quadratic residues and non-residues in the image of such subsets over uniformly random hyperelliptic curves of given degrees. We find a constant probability of such a high difference and show the existence of sets with an exceptionally large discrepancy.
Universal (and Existential) Nulls

Incomplete Information research is quite mature when it comes to so called existential nulls, where an existential null is a value stored in the database, representing an unknown object. For some reason universal nulls, that is, values representing all possible objects, have received almost no atte ntion. We remedy the situation in this paper, by showing that a suitable finite representation mechanism, called Star Cylinders, handling universal nulls can be developed based on the Cylindric Set Algebra of Henkin, Monk and Tarski. We provide a finitary version of the cylindric set algebra, called Cylindric Star Algebra, and show that our star-cylinders are closed under this algebra. Moreover, we show that any First Order Relational Calculus query over databases containing universal nulls can be translated into an equivalent expression in our cylindric star-algebra, and vice versa. All cylindric star-algebra expressions can be evaluated in time polynomial in the size of the database. The representation mechanism is then extended to Naive Star Cylinders, which are star-cylinders allowing existential nulls in addition to universal nulls. For positive queries (with universal quantification), the well known naive evaluation technique can still be applied on the existential nulls, thereby allowing polynomial time evaluation of certain answers on databases containing both universal and existential nulls. If precise answers are required, certain answer evaluation with universal and existential nulls remains in coNP. Note that the problem is coNP-hard, already for positive existential queries and databases with only existential nulls. If inequalities ¬(xi ≈ xj) are allowed, reasoning over existential databases is known to be ∏2p -complete, and it remains in ∏2p when universal nulls and full first order queries are allowed.
Quantum algorithms for conformal bootstrap

With the help of recent developments in quantum algorithms for semidefinite programming, we discuss the possibility for quantum speedup for the numerical conformal bootstrap in conformal field theory. We show that quantum algorithms may have significant improvement in the computational performance for several numerical bootstrap problems.
A Survival Analysis-Based Prioritization of Code Checker Warning: A Case Study Using PMD

StaticAman, Hirohisa code analysis tools (code checkers) scanAmasaki, Sousuke sourceYokogawa, Tomoyuki programsKawahara, Minoru and issue warnings to potentially-problematic parts. Programmers can utilize a code checker whenever they change their source code to make sure that their code changes do not carry high risks of decreasing the code quality. Although code checkers would be helpful to detect risky code changes as early as possible, there is a practical problem which prevents an active utilization of such tools in the real: code checkers tend to produce a lot of false-positive warnings, i.e., such a tool outputs many warnings, but the majority of them are not attractive to the programmer. Toward an efficient utilization of code checkers, this paper proposes an application of the survival analysis method to prioritize code checker warnings. The proposed method estimates a warning’s lifetime with using the real trend of warnings through code changes; the brevity of warning means its importance because severe warnings are related to problematic parts which programmers would fix sooner. This paper conducts a large-scale case study of 6,927,432 warnings (259 types of warnings) appeared in 100 open source software projects. The results show that only 30 types of warnings are practically important for programmers in terms of the brevity, and the proposed method can drastically reduce the number of really needed warnings.
Exploring bigram character features for Arabic text clustering

The vector space model (VSM) is an algebraic model that is widely used for data representation in text mining applications. However, the VSM poses a critical challenge, as it requires a high-dimensional feature space. Therefore, many feature selection techniques, such as employing roots or stems (i.e. words without infixes and prefixes, and/or suffixes) instead of using complete word forms, are proposed to tackle this space challenge problem. Recently, the literature shows that one more basic unit feature can be used to handle the textual features, which is the twoneighboring character form that we call microword. To evaluate this feature type, we measure the accuracy of the Arabic text clustering using two feature types: the complete word form and the microword form. Hence, the microword is two consecutive characters which are also known as the Bigram character feature. In the experiment, the principal component analysis (PCA) is used to reduce the feature vector dimensions while the k-means algorithm is used for the clustering purposes. The testing set includes 250 documents of five categories. The entire corpus contains 54,472 words, whereas the vocabulary contains 13,356 unique words. The experimental results show that the complete word form score accuracy is 97.2% while the two-character form score is 96.8%. In conclusion, the accuracies are almost the same; however, the two-character form uses a smaller vocabulary as well as less PCA subspaces. The study experiments might be a significant indication of the necessity to consider the Bigram character feature in the future text processing and natural language processing applications.
Development of an Optical Multispectral Remote Sensing System for Measuring Volcanic Surface Phenomena – Promotion Project for Next Generation Volcano Research B2 (Subtopic 2-2)

In 2016, we launched the “Promotion Project for Next Generation Volcano Research B2 (Theme B: Development of Cutting-edge Volcano Observation Technology, subtheme 2: Development of Remote Sensing Techniques for Volcano Observation), subtopic 2-2 (Development of Remote Sensing Techniques for Surface Phenomena of Volcano)” under the “Integrated Program for Next Generation Volcano Research and Human Resources Development” [1], aiming at the development of an optical multispectral remote sensing system for measuring volcanic surface phenomena. With subtopic 2-2, we are planning to develop a new observation device called a surface phenomena imaging camera (SPIC), which is technically superior to current remote sensing techniques, i.e., optical remote observation techniques used to observe volcanic surface phenomena from aircrafts or ground. We are also aiming at applying the developed observation system to quantify volcanic activities and determine volcanic eruption potentials (degrees of urgency) or branching of event trees for volcanic crises with high accuracy, contributing to better predictions of volcanic eruption transitions. To achieve the above-mentioned aims, we started the development of the SPIC by equipping it with camera-type sensors, based on preliminary analyses of the experimental observations made with the airborne spectral imaging system ARTS-SE, which consists of a pushbroom scanner and a camera system, developed by the National Research Institute for Earth Science and Disaster Resilience in FY 2015. We have already developed its components, such as the prototype filter-type multiband cameras SPIC-UC, a prototype uncooled infrared camera, SPIC-C, a cooled camera, and SPIC-SS, a visible-light camera. The SPIC-UC is a two-band camera with the function of visualizing temperature and SO 2 gas concentration distributions. The SPIC-C has the function of measuring temperatures between 2 and 1075 ◦ C with high accuracy (noise equivalent temperature difference, NETD: 16 mK); it is equipped with a sensor and a filter wheel that work in the middle wave infrared region (MWIR). The SPIC-SS is a six-lens multiband camera system that estimates the measured images from multiband spectra (6 bands) to hyper spectra (300 bands). Further, we studied a method to estimate digital surface model with a ∼30-m error. As our plan has progressed as scheduled, we intend to complete the prototype SPIC by 2020.
Dipole–dipole interactions enhance non-Markovianity and protect information against dissipation*

Preserving non-Markovianity and quantum entanglement from decoherence effect is of theoretical and practical significance in the quantum information processing technologies. In this context, we study a system S that is initially correlated with an ancilla A , which interacts with the environment E via an amplitude damping channel. We also consider dipole-dipole interactions (DDIs) between the system and ancilla, which are responsible for strong correlations. We investigate the impact of DDIs and detuning on the non-Markovianity and information exchange in different environments. We show that DDIs are not only better than detuning at protecting the information (without destroying the memory effect) but also induce memory by causing a transition from Markovian to non-Markovian dynamics. In contrast, although detuning also protects the information, it causes a transition from non-Markovian to the Markovian dynamics. In addition, we demonstrate that the non-Markovianity grows with increasing DDI strength and diminishes with increasing detuning. We also show that the effects of negative detuning and DDIs can cancel out each other, causing a certain loss of coherence and information.
Providing Targeted Digital Signage: Possible Solutions

Presenting information to a wide audience through digital signage has now become a very popular way in both public areas (shopping centers, exhibitions) and areas accessible by limited groups of people (condominiums, office buildings). This method of information delivery can be used both for advertising and for non-commercial information. Although targeted information delivery to one person (for example, banner ads on web pages) is already very well developed, the design of digital signage systems that provide targeted information has not been paid enough attention. The paper proposes an approach to providing information through targeted digital signage. The research method used in this work is based on the design science research (DSR) approach. This is an approach to problem solving, motivated and initiated by a specific business problem and trying to solve this problem by creating and verifying information technology artifacts, such as prototypes, models, methods, or architectures. Based on this method, the problem was refined, in particular, from the point of view of the housing industry, which led to the development of a new solution to support business processes of stakeholder groups in the industry. Verification of the draft decision showed that additional technological solutions are needed, such as user identification support, search for common preferences for a group of users, ensuring confidentiality of interests and preferences of individual users. The paper also proposes technologies for user identification and finding common interests and preferences.
Pervasive Tracking for Time-Dependent Acute Patient Flow: A Case Study in Trauma Management

The problem of tracking has gained a central role in healthcare research since it enables the acquisition of the information needed for improving healthcare management and efficiency, alongside patient safety. In literature, it is mainly discussed as an allocation problem that must deal with limited resources (rooms, physicians, equipment) to optimise workflows, and Real-Time Location Systems have been introduced with the main goal of locating and identifying assets and personnel in a healthcare facility. In this paper, we propose a novel perspective of pervasive tracking into Hospital 4.0, devised explicitly for time-dependent acute patient flow. The goal is to develop a tracking system that acquires not only the time and location of entities, exploiting state-of-the-art techniques, but also the main clinical events occurred. As an example application we describe TraumaTracker, a system developed to support the accurate and complete documentation of trauma resuscitation processes from pre-hospital care.
Automated labelling and severity prediction of software bug reports

Our main aim is to develop an intelligent classifier that is capable of predicting the severity and label (type) of a newly submitted bug report through a bug tracking system. For this purpose, we build two datasets that are based on 350 bug reports from the open-source community (Eclipse, Mozilla, and Gnome). These datasets are characterised with various textual features. Based on this information, we train variety of discriminative models that are used for automated labelling and severity prediction of a newly submitted bug report. A boosting algorithm is also implemented for an enhanced performance. The classification performance is measured using accuracy and a set of other measures. For automated labelling, the accuracy reaches around 91% with the AdaBoost algorithm and cross validation test. On the other hand, for severity prediction, the classification accuracy reaches around 67% with the AdaBoost algorithm and cross validation test. Overall, the results are encouraging.
Denoising Adversarial Networks for Rain Removal and Reflection Removal

This paper presents a novel adversarial scheme to perform image denoising for the tasks of rain streak removal and reflection removal. Similar to several previous works, the proposed method first estimates a prior image and then uses it to guide the inference of noise-free image. The novelty of our approach is to jointly learn the gradient and noise-free image based on an adversarial scheme. More specifically, we use the gradient map as the prior image. The inferred noise-free image guided by an estimated gradient is regarded as a negative sample, while the noise-free image guided by the ground truth of a gradient is taken as a positive sample. With the anchor defined by the ground truth of noise-free image, we play a min-max game to jointly train two optimizers for the estimation of the gradient and the inference of noise-free images. We show that both prior image and noise-free image can be accurately obtained under this adversarial scheme. Our state-of-the-art performance achieved on two public benchmark datasets validate the effectiveness of our approach.
An intelligent fuzzy-induced recommender system for cloud-based cultural communities

The rapid development of communication technologies and web-based services generate a large amount of information. In recent years, recommender systems (RS) emerge as an effective mechanism to tackle the information overloading problems. By exploiting the cloud computing paradigm, RS discovers interesting new cultural items based on user preferences and interests. Recent investigations on RS reveal that employing social network data can yield enhanced personalised recommendations with better prediction accuracy. Since users tend to visit only conventional monuments, and many charming cultural items are hidden from them due to lack of awareness about the cultural sites. This article proposes a personalised recommendation model in the field of cultural heritage (CH) with the help of the cloud computing environment. The experimental results obtained demonstrate the improved performance of developed RS in the area of cultural heritage tourism services.