Reviewerly/exHarmony huggingface dataset:
Text is NOT Enough

Open-domain dialogue generation in natural language processing (NLP) is by default a pure-language task, which aims to satisfy human need for daily communication on open-ended topics by producing related and informative responses. In this paper, we point out that hidden images, named as visual impressions (VIs), can be explored from the text-only data to enhance dialogue understanding and help generate better responses. Besides, the semantic dependency between an dialogue post and its response is complicated, e.g., few word alignments and some topic transitions. Therefore, the visual impressions of them are not shared, and it is more reasonable to integrate the response visual impressions (RVIs) into the decoder, rather than the post visual impressions (PVIs). However, both the response and its RVIs are not given directly in the test process. To handle the above issues, we propose a framework to explicitly construct VIs based on pure-language dialogue datasets and utilize them for better dialogue understanding and generation. Specifically, we obtain a group of images (PVIs) for each post based on a pre-trained word-image mapping model. These PVIs are used in a co-attention encoder to get a post representation with both visual and textual information. Since the RVIs are not provided during testing, we design a cascade decoder that consists of two sub-decoders. The first sub-decoder predicts the content words in response, and applies the word-image mapping model to get corresponding RVIs. Then, the second sub-decoder generates the response based on the post and RVIs. Experimental results on two open-domain dialogue datasets show that our proposed approach achieves superior performance over competitive baselines in terms of fluency, relatedness, and diversity.
Comprehensive modeling of mineral potential mapping by integration of multiset geosciences data

This work has evaluated the functionality of various fuzzy-based fusion methods in the mineral potential mapping (MPM), by which a multi-criteria decision-making problem was solved to design a layout for drilling complementary boreholes through a comprehensive analysis of geospatial datasets. The novel methods employed were fuzzy c-means clustering, fuzzy gamma operator, fuzzy inference system (FIS), fuzzy outranking, and fuzzy ordered weighted averaging (FOWA). Kahang porphyry Cu-Mo deposit in the Isfahan province of Iran was chosen as a case study to examine the performance of these fuzzy methods in MPM. Extracted geospatial indicator layers for assessing the potential of porphyry-type mineralization were derived from four criteria, namely geology (rock units and faults), remote sensing (alteration map), geochemistry (Cu, Mo, and factor maps), and geophysics (reduced to the pole and analytical signal of magnetic data). The concentration-area multifractal method was utilized to reclassify each synthesized fuzzy favorability map into five classes. To appraise and compare the efficiency of each employed method, a productivity measure assumed as a cumulative summation of Cu grade multiplied by its thickness above an economical cut-off value of 0.2% was calculated along with each drilling (totally 33 ones). According to fuzzy favorability maps derived from running all fuzzy methods, the FIS and FOWA had the highest efficiency with 80 and 78% of accuracy, respectively. Eventually, taking all fuzzy maps into account led to the delineation of some new favorable zones, whereby further exploratory investigations are envisioned for determining their mining potential.
UN sustainable development goals, good governance, and corruption: The paradox of the world's poorest economies

Abstract The first objective of this study is to demonstrate that the UN sustainable development goals (SDGs) remain beyond reach for the world's poorest economies by analyzing the trends in the worldwide governance indicators (WGIs), corruption perception index (CPI), the GDP per capita, and the human development index (HDI) using the case of 10 former European colonies (e.g., Burundi, Burkina Faso, Central African Republic, Guinea‐Bissau, Haiti, Madagascar, Niger, Sierra Leone, Togo, and the Democratic Republic of Congo). Second, this study compares three‐panel regression models (pooled OLS model, fixed‐effects model, and random effects model) to examine the influence of the WGIs on CPI, GDP per capita, and HDI and then derive the most appropriate model. Based on the results of these two objectives, a theoretical framework is drawn to provide specific recommendations to overcome some of the challenges to sustainable development in the selected countries.
DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies

Can we use reinforcement learning to learn general-purpose policies that can perform a wide range of different tasks, resulting in flexible and reusable skills? Contextual policies provide this capability in principle, but the representation of the context determines the degree of generalization and expressivity. Categorical contexts preclude generalization to entirely new tasks. Goal-conditioned policies may enable some generalization, but cannot capture all tasks that might be desired. In this paper, we propose goal distributions as a general and broadly applicable task representation suitable for contextual policies. Goal distributions are general in the sense that they can represent any state-based reward function when equipped with an appropriate distribution class, while the particular choice of distribution class allows us to trade off expressivity and learnability. We develop an off-policy algorithm called distribution-conditioned reinforcement learning (DisCo RL) to efficiently learn these policies. We evaluate DisCo RL on a variety of robot manipulation tasks and find that it significantly outperforms prior methods on tasks that require generalization to new goal distributions.
Behavior Tree Learning for Robotic Task Planning through Monte Carlo DAG Search over a Formal Grammar

We present an algorithm for learning behavior trees for robotic task planning, which alleviates the need for time-intensive or infeasible manual design of control architectures. Our method involves representing the search space of behavior trees as a formal grammar and searching over this grammar by means of a new generalization of Monte Carlo tree search (MCTS) for directed acyclic graphs (DAGs), named MCDAGS. Additionally, our method employs simulated annealing to expedite the aggregation of the most functional subtrees. We present simulated experiments for a marine target search and response scenario, and an abstract task selection problem. Our results demonstrate that the learned behavior trees compare favorably with a manually-designed tree, and outperform baseline learning methods. Overall, these results show that our method is a viable technique for the automatic design of behavior trees for robotic task planning.
Deep learning method for makeup style transfer: A survey

Makeup transfer is one of the applications of image style transfer, which refers to transfer the reference makeup to the face without makeup, and maintaining the original appearance of the plain face and the makeup style of the reference face. In order to understand the research status of makeup transfer, this paper systematically sorts out makeup transfer technology. According to the development process of the method of makeup transfer, our paper first introduces and analyzes the traditional methods of makeup transfer. In particular, the methods of makeup transfer based on deep learning framework are summarized, covering both disadvantages and advantages. Finally, some key points in the current challenges and future development direction of makeup transfer technology are discussed.
Key drivers of cybersecurity audit effectiveness: the neo-institutional perspective

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An Efficient Audio Encryption Scheme Based on Finite Fields

Finite fields are well-studied algebraic structures with enormous efficient properties which have applications in the fields of cryptology and coding theory.In this study, we proposed a lossless binary Galois field extension-based efficient algorithm for digital audio encryption.The proposed architecture hired a special type of curve in the diffusion module which depends on efficient elliptic curve arithmetic operations.So, it generates good quality pseudo-random numbers (PRN) and with slight computational efforts, it produces optimum diffusion in the encrypted audio files.For the confusion module, a novel construction mechanism of block cipher has been employed which includes prominent arithmetic operations of binary Galois field inversion and multiplication operations.The suggested scheme generates multiple substitution boxes (S-boxes) by using a higher-order Galois field.Thus, the replacement with multiple S-boxes generates effective perplexity in the data and provides additional security to the ciphered audio.The investigational outcomes through different analyses and time complexity demonstrated the ability of the technique to counter various attacks.Furthermore, as a consequence of a rapid and simple application of the binary finite field in hardware and software, the proposed scheme is more appropriate to be applied for data security.
Dark Patterns

The term Dark Pattern has been used to define design patterns intentionally created to deceive users or favor the interests of parties other than users. Dark Patterns present socio-technical characteristics as something that happens when humans interact with technical artifacts in a given environment, causing annoyance, frustration, anger, and other emotions in users. Comprehending Dark Patterns require us to look at them from different perspectives, in a multifocal way, considering social and technical aspects in an intertwined way. In this paper, we apply the Semiotic Framework to analyze and discuss Dark Patterns from a socio-technical perspective. We use a popular Dark Pattern called Roach Motel to exemplify our discussion, unraveling it with the Semiotic Framework. Results show the complexity of the topic and exemplify the potential of a socio-technical approach to understand, characterize and advance our knowledge on it.
Wing deformation measurement using the stereo-vision methods in the presence of camera movements

The stereo-vision methods have been developed and used for measuring the wing deformation while the aircraft is in flight. However, it is difficult to achieve accurate measurement in flight test since inevitable camera movements often occur due to elasticity in the camera support and the surrounding structure, especially during heavy turbulence and large maneuver flight. Any slight camera movements within a stereo-vision system can directly influence the calibrated extrinsic parameters, then decrease 3D reconstruction accuracy, and even cause the measurement to fail. This study proposes a method to correct the effect of the camera movements to enable accurate wing deformation measurement using a stereo-vision system. A reference coordinate system is established based on the control points to unify the changing camera coordinate system. A dynamic extrinsic parameters calibration algorithm is developed to estimate the instantaneous position and orientation of the stereo cameras. To verify the presented method, an experiment in which the stereo cameras occurring relative movement was performed. The results show that the proposed method has effectively corrected the effects of camera movements. Furthermore, the proposed method was applied to the practical wing deformation measurement of a UAV in fieldwork. The maximum error of length and displacement is less than 0.8 mm when the stereo cameras remained unchanged. The actual accuracy of the overall measurement system is about 0.01% of the observed area (e.g. 0.8 mm in 6 m scale) under the conditions of flat viewing angle and outdoor scenes. While camera movements occur during the test, the corrected RMS error of displacement is reduced to about 1.5 mm and has decreased by 50% compared with that before correction. The enhanced precision and robustness of wing deformation measurement are well realized by the proposed method of correcting camera movements.
Rethinking Content and Style: Exploring Bias for Unsupervised Disentanglement

Content and style (C-S) disentanglement intends to decompose the underlying explanatory factors of objects into two independent subspaces. From the unsupervised disentanglement perspective, we rethink content and style and propose a formulation for unsupervised C-S disentanglement based on our assumption that different factors are of different importance and popularity for image reconstruction, which serves as a data bias. The corresponding model inductive bias is introduced by our proposed C-S disentanglement Module (C-S DisMo), which assigns different and independent roles to content and style when approximating the real data distributions. Specifically, each content embedding from the dataset, which encodes the most dominant factors for image reconstruction, is assumed to be sampled from a shared distribution across the dataset. The style embedding for a particular image, encoding the remaining factors, is used to customize the shared distribution through an affine transformation. The experiments on several popular datasets demonstrate that our method achieves the state-of-the-art unsupervised C-S disentanglement, which is comparable or even better than supervised methods. We verify the effectiveness of our method by downstream tasks: image-to-image translation and single-view 3D reconstruction. Project page at https://github.com/xrenaa/CS-DisMo.
A Study of Teacher Performance in English for Academic Purposes Course: Evaluating Efficiency

Identifying significant factors in teaching-learning performance affecting the efficiency of teachers at university is an indispensable factor in upholding the quality of the teaching in English for Academic Purposes (EAP) courses. This study aims to evaluate teachers’ performance in EAP courses using the students’ course-instructor evaluation survey, and the students’ final grades. For this purpose, the Performance Improvement Management (PIM) software Data Envelopment Analysis (PIM-DEA) is applied to evaluate the efficiency of EAP teachers. Later, a sensitivity analysis is applied to prioritize the significant indicators of teacher performance following an interview with the teachers aimed at the betterment of their performance. The result of the study reveals that the degree of student satisfaction in relation to assignments, exams, and grading systems are significant factors related to teachers’ performance. A wider realization of this should be seen, in the researcher’s opinion, vital to the development of the educational sector.
On the effect of selfie beautification filters on face detection and recognition

Beautification and augmented reality filters are very popular in applications that use selfie images captured with smartphones or personal devices. However, they can distort or modify biometric features, severely affecting the capability of recognizing individuals' identity or even detecting the face. Accordingly, we address the effect of such filters on the accuracy of automated face detection and recognition. The social media image filters studied either modify the image contrast or illumination or occlude parts of the face with for example artificial glasses or animal noses. We observe that the effect of some of these filters is harmful both to face detection and identity recognition, specially if they obfuscate the eye or (to a lesser extent) the nose. To counteract such effect, we develop a method to reconstruct the applied manipulation with a modified version of the U-NET segmentation network. This is observed to contribute to a better face detection and recognition accuracy. From a recognition perspective, we employ distance measures and trained machine learning algorithms applied to features extracted using a ResNet-34 network trained to recognize faces. We also evaluate if incorporating filtered images to the training set of machine learning approaches are beneficial for identity recognition. Our results show good recognition when filters do not occlude important landmarks, specially the eyes (identification accuracy >99%, EER<2%). The combined effect of the proposed approaches also allow to mitigate the effect produced by filters that occlude parts of the face, achieving an identification accuracy of >92% with the majority of perturbations evaluated, and an EER <8%. Although there is room for improvement, when neither U-NET reconstruction nor training with filtered images is applied, the accuracy with filters that severely occlude the eye is <72% (identification) and >12% (EER)
A Multi-spectral Dataset for Evaluating Motion Estimation Systems

Visible images have been widely used for motion estimation. Thermal images, in contrast, are more challenging to be used in motion estimation since they typically have lower resolution, less texture, and more noise. In this paper, a novel dataset for evaluating the performance of multi-spectral motion estimation systems is presented. All the sequences are recorded from a handheld multi-spectral device. It consists of a standard visible-light camera, a long-wave infrared camera, an RGB-D camera, and an inertial measurement unit (IMU). The multi-spectral images, including both color and thermal images in full sensor resolution (640 × 480), are obtained from a standard and a long-wave infrared camera at 32Hz with hardware-synchronization. The depth images are captured by a Microsoft Kinect2 and can have benefits for learning cross-modalities stereo matching. For trajectory evaluation, accurate ground-truth camera poses obtained from a motion capture system are provided. In addition to the sequences with bright illumination, the dataset also contains dim, varying, and complex illumination scenes. The full dataset, including raw data and calibration data with detailed data format specifications, is publicly available.
Reflecting Stacked Continuations in a Fine-Grained Direct-Style Reduction Theory

The delimited-control operator shift0 has been formally shown to capture the operational semantics of deep handlers for algebraic effects. Its CPS translation generates λ-terms in which continuation composition is not expressed in terms of nested function calls, as is typical of other delimited-control operators, e.g. shift, but with function applications consuming a sequence of continuations one at a time, as if they formed a stack.
Application of machine learning algorithms for use in material chemistry

Chemical engineering in materials science covers tasks like the synthesis of materials, formulation of raw materials to semifinished products, and the final application of these materials. Research and development in these fields requires knowledge of the property-structure relationship of the chemical compounds and physical parameters of the material properties and processes involved. Typical products that need to be formulated are polymeric materials such as plastics, composites, coatings, adhesives, etc. These materials have to fulfill a big number of features regarding the field of application. The material properties can be related to the chemistry of the raw materials and balanced by the ratio between the ingredients. The task for the chemist in materials science is the detection of these relationships and the optimization of the mixtures with the manufacturing process and properties of the final product. The aim is to develop new materials while their processes mostly rely on empirical experience in dealing with the complex chemistry of all ingredients (resins, polymers, solvents, additives, and inorganic and organic particles). Interactions between those ingredients make the development complicated and sometimes small adjustments can have a huge impact on the characteristics of a product. Material development can also be carried out with machine learning algorithms for modeling data and digital optimization of product properties. The combination of machine learning and high-throughput formulation leads to a unit, which operates a fully automated optimization cycle without interference of the human operator.
A Framework to Understand Attitudes towards Immigration through Twitter

Understanding public opinion towards immigrants is key to prevent acts of violence, discrimination and abuse. Traditional data sources, such as surveys, provide rich insights into the formation of such attitudes; yet, they are costly and offer limited temporal granularity, providing only a partial understanding of the dynamics of attitudes towards immigrants. Leveraging Twitter data and natural language processing, we propose a framework to measure attitudes towards immigration in online discussions. Grounded in theories of social psychology, the proposed framework enables the classification of users’ into profile stances of positive and negative attitudes towards immigrants and characterisation of these profiles quantitatively summarising users’ content and temporal stance trends. We use a Twitter sample composed of 36 K users and 160 K tweets discussing the topic in 2017, when the immigrant population in the country recorded an increase by a factor of four from 2010. We found that the negative attitude group of users is smaller than the positive group, and that both attitudes have different distributions of the volume of content. Both types of attitudes show fluctuations over time that seem to be influenced by news events related to immigration. Accounts with negative attitudes use arguments of labour competition and stricter regulation of immigration. In contrast, accounts with positive attitudes reflect arguments in support of immigrants’ human and civil rights. The framework and its application can inform policy makers about how people feel about immigration, with possible implications for policy communication and the design of interventions to improve negative attitudes.
PLNet: Plane and Line Priors for Unsupervised Indoor Depth Estimation

Unsupervised learning of depth from indoor monocular videos is challenging as the artificial environment contains many textureless regions. Fortunately, the indoor scenes are full of specific structures, such as planes and lines, which should help guide unsupervised depth learning. This paper proposes PLNet that leverages the plane and line priors to enhance the depth estimation. We first represent the scene geometry using local planar coefficients and impose the smoothness constraint on the representation. Moreover, we enforce the planar and linear consistency by randomly selecting some sets of points that are probably coplanar or collinear to construct simple and effective consistency losses. To verify the proposed method’s effectiveness, we further propose to evaluate the flatness and straightness of the predicted point cloud on the reliable planar and linear regions. The regularity of these regions indicates quality indoor reconstruction. Experiments on NYU Depth V2 and ScanNet show that PLNet outperforms existing methods. The code is available at https://github.com/HalleyJiang/PLNet.
A Derived Reasonable Abstract Machine for Strong Call by Value

We present an efficient implementation of the full-reducing call-by-value strategy for the pure λ-calculus in the form of an abstract machine. The presented machine has been systematically derived using Danvy et al.’s functional correspondence that connects higher-order interpreters with abstract-machine models by a well-established transformation technique. It improves on a previously presented machine by Biernacka et al. in terms of efficiency: the new machine simulates β-reduction with the overhead polynomial in the number of β-steps and in the size of the initial term. Thus, the machine makes a “reasonable” (in the sense of Accattoli et al.) implementation of Strong CbV.