Reviewerly/exHarmony huggingface dataset:
The Role of Human Capital in Science, Technology and Innovation

A s a result of the transformation of the labor market, the constant development of human capital has become crucial.This paper considers the role of human capital in professional development through the prism of 16 semi-structured interviews with both Russian and foreign graduates of a master's program focused on training experts in the field of science, technology, and innovation.Most of the graduates of the program found jobs in the corporate sector and at research centers, but among the interviewees, there were also representatives who chose self-employment or public service.The contribution
CiTIUS-COLE at SemEval-2019 Task 5: Combining Linguistic Features to Identify Hate Speech Against Immigrants and Women on Multilingual Tweets

This article describes the strategy submitted by the CiTIUS-COLE team to SemEval 2019 Task 5, a task which consists of binary classification where the system predicts whether a tweet in English or in Spanish is hateful against women or immigrants or not.The proposed strategy relies on combining linguistic features to improve the classifier's performance.More precisely, the method combines textual and lexical features, embedding words with the bag of words in Term Frequency-Inverse Document Frequency (TF-IDF) representation.The system performance reaches about 81% F1 when it is applied to the training dataset, but its F1 drops to 36% on the official test dataset for the English and 64% for the Spanish language concerning the hate speech class.
The Coq Library as a Theory Graph

Representing proof assistant libraries in a way that allows further processing in other systems is becoming increasingly important. It is a critical missing link for integrating proof assistants both with each other or with peripheral tools such as IDEs or proof checkers. Such representations cannot be generated from library source files because they lack semantic enrichment (inferred types, etc.) and only the original proof assistant is able to process them. But even when using the proof assistant’s internal data structures, the complexities of logic, implementation, and library still make this very difficult. We describe one such representation, namely for the library of Coq, using OMDoc theory graphs as the target format. Coq is arguably the most formidable of all proof assistant libraries to tackle, and our work makes a significant step forward. On the theoretical side, our main contribution is a translation of the Coq module system into theory graphs. This greatly reduces the complexity of the library as the more arcane module system features are eliminated while preserving most of the structure. On the practical side, our main contribution is an implementation of this translation. It takes the entire Coq library, which is split over hundreds of decentralized repositories, and produces easily-reusable OMDoc files as output.
The Effect of Explicit Structure Encoding of Deep Neural Networks for Symbolic Music Generation

With recent breakthroughs in artificial neural networks, deep generative models have become one of the leading techniques for computational creativity. Despite very promising progress on image and short sequence generation, symbolic music generation remains a challenging problem since the structure of compositions are usually complicated. In this study, we attempt to solve the melody generation problem constrained by the given chord progression. In particular, we explore the effect of explicit architectural encoding of musical structure via comparing two sequential generative models: LSTM (a type of RNN) and WaveNet (dilated temporal-CNN). As far as we know, this is the first study of applying WaveNet to symbolic music generation, as well as the first systematic comparison between temporal-CNN and RNN for music generation. We conduct a survey for evaluation in our generations and implemented Variable Markov Oracle in music pattern discovery. Experimental results show that to encode structure more explicitly using a stack of dilated convolution layers improved the performance significantly, and a global encoding of underlying chord progression into the generation procedure gains even more.
Mutual semi-quantum key agreement protocol using Bell states

Quantum key agreement (QKA) can generate a shared secret key which is equally negotiated by all the participants in the protocol. In most of the QKA protocols, all the participants require quantum capabilities. But the quantum devices are too expensive for participants. This paper proposes a mutual semi-quantum key agreement protocol which allows two parties (Alice and Bob) to negotiate a shared secret key equally. In the protocol, Alice can perform any quantum operation, but Bob is a classical participant which can only perform reflection, measurement and reorder operation. Even though Bob has fewer quantum resources, Alice and Bob have an equal contribution to the shared final key, no one can determine the shared key alone. In addition, we demonstrate the security of the proposed protocol. The analysis results show that the proposed protocol not only resists against some common attacks but also assures the fairness property. It is significant for communication participant without enough quantum devices to achieve quantum communication. The proposed protocol can be implemented with present quantum technologies.
An Improved Depth Image Based Virtual View Synthesis Method for Interactive 3D Video

Depth image-based rendering (DIBR) is one of the main techniques in interactive 3D video systems. But a critical problem of DIBR is that how to eliminate the holes appearing in virtual view images. In this paper, an improved depth image based virtual view synthesis algorithm is proposed. In depth map pre-processing, only local areas of the depth map with sudden changes are pre-processed by the asymmetric filter. After this, we combine the forward and inverse 3D-warping to synthesize the virtual view. Firstly, it warps the left image to the virtual view and then only reversely warps the region of the holes in the virtual view to the right view to get the lost information of holes. Compared with the traditional method which have to warp the two whole images, computation cost can be reduced by 30% efficiently. Finally, we employ an improved image inpainting based on depth information to fill hole regions. We have improved the method in two portions: 1) depth information is added to the priority computation and searching the best matching patch and 2) as the filling proceeds, the confidence term of the newly inpainted pixels is updated with the mean square error (MSE) of pixels in the target matching patch and the source matching patch. Experimental results show significant objective and subjective gains of the proposed method in comparison to the reference methods.
Turn it, Pour it, Twist it

Interaction designers for mobile phones mainly focus on displays but have only little knowledge about sensor characteristics. Beside multitouch input, mobile devices provide versatile possibilities to interact in a physical, device-based manner due to their built-in hardware. Even though such interactions may provide many advantages in everyday life, they have limited visibility in interaction design. Interaction designers are seldom experts in gesture and pattern recognition and even prototypical implementations of simple mobile-based interactions need advanced technical knowledge. Hence, the potential for designing mobile device-based interactions is often not fully exploited. To contribute to a common knowledge of mobile device-based interactions, this paper proposes Mobile Spaces. This model aims at supporting designers of mobile applications to broaden their view on interaction possibilities with one or more collocated devices which go beyond the screen. We discuss the applicability of Mobile Spaces by means of several examples from research.
A Compelling Virtual Tour of the Dunhuang Cave With an Immersive Head-Mounted Display

The Dunhuang Caves are the home to the largest Buddhist art sites in the world and are listed as a UNESCO World Heritage Site. Over time, the murals have been damaged by both humans and nature. In this article, we present an immersive virtual reality system for exploring spatial cultural heritage, which utilizes the digitized data from the Dunhuang Research Academy to represent the virtual environment of the cave. In this system, the interaction techniques that allow users to flexibly experience any of the artifacts or displays contribute to their understanding of the cultural heritage. Additionally, we evaluated the system by conducting a user study to examine the extent of user acquaintance after the entire experience. Our result has shown what participants learn from the spatial context and augmented information in the VR. This can be used as design considerations for developing other spatial heritages.
A Cascade Model for Proposition Extraction in Argumentation

We present a model to tackle a fundamental but understudied problem in computational argumentation: proposition extraction.Propositions are the basic units of an argument and the primary building blocks of most argument mining systems.However, they are usually substituted by argumentative discourse units obtained via surface-level text segmentation, which may yield text segments that lack semantic information necessary for subsequent argument mining processes.In contrast, our cascade model aims to extract complete propositions by handling anaphora resolution, text segmentation, reported speech, questions, imperatives, missing subjects, and revision.We formulate each task as a computational problem and test various models using a corpus of the 2016 U.S. presidential debates.We show promising performance for some tasks and discuss main challenges in proposition extraction.
Generating a fluent API with syntax checking from an LR grammar

This paper proposes a fluent API generator for Scala, Haskell, and C++. It receives a grammar definition and generates a code skeleton of the library in the host programming language. The generated library is accessed through a chain of method calls; this style of API is called a fluent API. The library uses the host-language type checker to detect an invalid chain of method calls. Each method call is regarded as a lexical token in the embedded domain specific language implemented by that library. A sequence of the lexical tokens is checked and, if the sequence is not acceptable by the grammar, a type error is reported during compilation time. A contribution of this paper is to present an algorithm for generating the code-skeleton for a fluent API that reports a type error when a chain of method calls to the library does not match the given LR grammar. Our algorithm works in Scala, Haskell, and C++. To encode LR parsing, it uses the method/function overloading available in those languages. It does not need an advanced type system, or exponential compilation time or memory consumption. This paper also presents our implementation of the proposed generator.
Will Artificial Intelligence Translate Big Data Into Improved Medical Care or Be a Source of Confusing Intrusion? A Discussion Between a (Cautious) Physician Informatician and an (Optimistic) Medical Informatics Researcher

Artificial intelligence (AI), the computerized capability of doing tasks, which until recently was thought to be the exclusive domain of human intelligence, has demonstrated great strides in the past decade. The abilities to play games, provide piloting for an automobile, and respond to spoken language are remarkable successes. How are the challenges and opportunities of medicine different from these challenges and how can we best apply these data-driven techniques to patient care and outcomes? A New England Journal of Medicine paper published in 1980 suggested that more well-defined "specialized" tasks of medical care were more amenable to computer assistance, while the breadth of approach required for defining a problem and narrowing down the problem space was less so, and perhaps, unachievable. On the other hand, one can argue that the modern version of AI, which uses data-driven approaches, will be the most useful in tackling tasks such as outcome prediction that are often difficult for clinicians and patients. The ability today to collect large volumes of data about a single individual (eg, through a wearable device) and the accumulation of large datasets about multiple persons receiving medical care has the potential to apply to the care of individuals. As these techniques of analysis, enumeration, aggregation, and presentation are brought to bear in medicine, the question arises as to their utility and applicability in that domain. Early efforts in decision support were found to be helpful; as the systems proliferated, later experiences have shown difficulties such as alert fatigue and physician burnout becoming more prevalent. Will something similar arise from data-driven predictions? Will empowering patients by equipping them with information gained from data analysis help? Patients, providers, technology, and policymakers each have a role to play in the development and utilization of AI in medicine. Some of the challenges, opportunities, and tradeoffs implicit here are presented as a dialog between a clinician (SJN) and an informatician (QZT).
On Concept-Based Explanations in Deep Neural Networks

Human explanations of high-level decisions are often expressed in terms of key concepts the decisions are based on. In this paper, we study such concept-based explainability for Deep Neural Networks (DNNs). First, we define the notion of completeness, which quantifies how sufficient a particular set of concepts is in explaining a model's prediction behavior based on the assumption that complete concept scores are sufficient statistics of the model prediction. Next, we propose a concept discovery method that aims to infer a complete set of concepts that are additionally encouraged to be interpretable, which addresses the limitations of existing methods on concept explanations. To define an importance score for each discovered concept, we adapt game-theoretic notions to aggregate over sets and propose ConceptSHAP. Via proposed metrics and user studies, on a synthetic dataset with apriori-known concept explanations, as well as on real-world image and language datasets, we validate the effectiveness of our method in finding concepts that are both complete in explaining the decisions and interpretable. (The code is released at this https URL)
The matrix of tasks, resources and competences for research libraries

The lag of Russia in the development of the fifth technological order has had a dramatic impact on research and technological libraries. Until the early 1990s, they were rightfully considered unique access points to relevant scientific knowledge, and now they are experiencing an acute crisis, looking for their place in the radically changed landscape of scholarly communications The article identifies the main challenges that led to the crisis. The aim of the study is to find answers to these challenges, namely: new tasks that libraries can take on and that will be in demand by the scientific community. However, their solution is possible only after mastering new competencies and providing necessary resources. To systematize this picture, a matrix of tasks, resources and competencies is constructed.
Understanding Family Collaboration Around Lightweight Modification of Everyday Objects in the Home

The internet-of-things (IoT) carries substantial costs by urging households to replace their possessions with new, internet connected versions of everyday objects. Beyond financial, these costs include waste, work to arrange and orchestrate objects to suit households, and that of acquiring new skills. Upcycling domestic objects could offer households greater discretion and control over these costs by supporting the ability to tailor IoT to the home. To understand how households might do this, we conducted a home study with 10 diverse American households over 7 days to surface the approaches families are likely to use when tailoring IoT to their existing possessions. We asked family members to enact their process using endowed sticker props---IoT Stickers---to modify objects in their home. We develop a framework of how families make light weight modifications of domestic possessions, summarize trends of their object modifications, and describe the burdens such a system could impose.
A Retinex-based total variation approach for image segmentation and bias correction

Image segmentation methods usually suffer from intensity inhomogeneity problem caused by many factors such as spatial variations in illumination (or bias fields of imaging devices). In order to address this problem, this paper proposes a Retinex-based variational model for image segmentation and bias correction. According to Retinex theory, the input inhomogeneous image can be decoupled into illumination bias and reflectance parts. The main contribution of this paper is to consider piecewise constant of the reflectance, and thereby introduce the total variation term in the proposed model for correcting and segmenting the input image. This is different from the existing model in which the spatial smoothness of the illumination bias is employed only. The existence of the minimizers to the variational model is established. Furthermore, we develop an efficient algorithm to solve the model numerically by using the alternating minimization method. Our experimental results are reported to demonstrate the effectiveness of the proposed method, and its performance is competitive with that of the other testing methods.
CONTINUOUS PIGGERY WASTEWATER TREATMENT WITH ANAEROBIC BAFFLED REACTOR (ABR) BY BIO-ACTIVATOR EFFECTIVE MICROORGANISMS (EM4)

Aim: The purpose of this study was to determine the efficiency and characteristics of pig wastewater treatment. This was to be achieved using ABR with the addition of effective microorganism4 (EM4) as a bio-activator during the startup process (R1) and without EM4 (R2). Methodology and Results: Piggery wastewater is poured into ABR with 12 hours HRT (hydraulic retention time), though it is reduced to 6 hours after the concentration is stable. The COD removal efficiency at 12 hours HRT was 60% (R1) and 51% (R2). However, the results did not change significantly, since the 6 hours HRT COD efficiency was 57.8% (R1) and 51.3% (R2). The biomass growth rate at R1 is faster than R2 with Food to Microorganism Ratio (F/M) 0.4-0.89 (R1) and 0.68-1.38 (R2) while the yield of methane gas formation was 0.25-0.28 L-CH4/g-COD. Conclusion, significance, and impact study: COD effluent is the total organic material present in the piggery wastewater effluent, which is discharged into water bodies without meeting the set quality standards. This damages the quality of water bodies. The wastewater treatment needs to be prioritized to meet quality standards of COD effluent. Also, the addition of EM4 to the activated sludge reactor improves the COD removal efficiency and biomass growth, though advanced treatment is still needed for piggery wastewater.
Weakly Supervised Attentional Model for Low Resource Ad-hoc Cross-lingual Information Retrieval

We propose a weakly supervised neural model for Ad-hoc Cross-lingual Information Retrieval (CLIR) from low-resource languages. Low resource languages often lack relevance annotations for CLIR, and when available the training data usually has limited coverage for possible queries. In this paper, we design a model which does not require relevance annotations, instead it is trained on samples extracted from translation corpora as weak supervision. This model relies on an attention mechanism to learn spans in the foreign sentence that are relevant to the query. We report experiments on two low resource languages: Swahili and Tagalog, trained on less that 100k parallel sentences each. The proposed model achieves 19 MAP points improvement compared to using CNNs for feature extraction, 12 points improvement from machine translation-based CLIR, and up to 6 points improvement compared to probabilistic CLIR models.
Joint High Dynamic Range Imaging and Super-Resolution From a Single Image

This paper presents a new framework for jointly enhancing the resolution and the dynamic range of an image, i.e., simultaneous super-resolution (SR) and high dynamic range imaging (HDRI), based on a convolutional neural network (CNN). From the common trends of both tasks, we train a CNN for the joint HDRI and SR by focusing on the reconstruction of high-frequency details. Specifically, the high-frequency component in our work is the reflectance component according to the Retinex-based image decomposition, and only the reflectance component is manipulated by the CNN while another component (illumination) is processed in a conventional way. In training the CNN, we devise an appropriate loss function that contributes to the naturalness quality of resulting images. Experiments show that our algorithm outperforms the cascade implementation of CNN-based SR and HDRI.
Complexity and information in invariant inference

This paper addresses the complexity of SAT-based invariant inference, a prominent approach to safety verification. We consider the problem of inferring an inductive invariant of polynomial length given a transition system and a safety property. We analyze the complexity of this problem in a black-box model, called the Hoare-query model, which is general enough to capture algorithms such as IC3/PDR and its variants. An algorithm in this model learns about the system's reachable states by querying the validity of Hoare triples. We show that in general an algorithm in the Hoare-query model requires an exponential number of queries. Our lower bound is information-theoretic and applies even to computationally unrestricted algorithms, showing that no choice of generalization from the partial information obtained in a polynomial number of Hoare queries can lead to an efficient invariant inference procedure in this class. We then show, for the first time, that by utilizing rich Hoare queries, as done in PDR, inference can be exponentially more efficient than approaches such as ICE learning, which only utilize inductiveness checks of candidates. We do so by constructing a class of transition systems for which a simple version of PDR with a single frame infers invariants in a polynomial number of queries, whereas every algorithm using only inductiveness checks and counterexamples requires an exponential number of queries. Our results also shed light on connections and differences with the classical theory of exact concept learning with queries, and imply that learning from counterexamples to induction is harder than classical exact learning from labeled examples. This demonstrates that the convergence rate of Counterexample-Guided Inductive Synthesis depends on the form of counterexamples.
Homologous Audio Copy-move Tampering Detection Method Based on Pitch

In order to verify the authenticity and reliability of the audio content, a detection method based on the pitch feature of homologous audio copy-move forgery is proposed. The principle of this method is that even if the same person speaks the same word twice, the pitch sequences of the two syllables are different. Firstly, the homologous audio should be non-linearly preprocessed. And then the pitch sequence is extracted as the feature of the audio. Because homologous audio focuses more on the characteristics of speech itself, and the sequence of pitches extracted from different syllables is usually completely different. Next, the difference between the individual syllables is calculated. Finally, it is determined whether the corresponding syllable has a copy-move forgery by comparing the difference between the syllables and the set threshold. The experimental results show that the method is robust to various conventional attacks. And the proposed algorithm can accurately locate the syllables which is copy-move forgery, and has better efficiency.