Reviewerly/exHarmony huggingface dataset:
Co-regulating algorithmic disclosure for digital platforms

ABSTRACT With digital platforms gaining dominant intermediating role and exerting regulatory functions vis-à-vis small and medium-sized enterprises (SMEs) through algorithms, EU institutions have started considering to rely on their analytical capacity to regulate the myriads of market transactions occurring within and through them (so-called platform-to-business, or P2B transactions). Most of the time, the EU suggests recurring to light-tough disclosure duties. Hence, the European model falls short in rebalancing information asymmetry and unequal bargaining power plaguing the SMEs. In practice, the EU model consists either in pure delegation of self-regulatory powers (codes of conduct) or non-enforceable co-regulatory schemes (with technical standards established by the platforms themselves). Other models have been suggested that rely on the regulator's access to the platform's data (so called savvy and data-delegated options). These governance models present several limitations, making the platforms' role as regulatory intermediators little credible. In this scenario, the paper purports that a third option should be considered. In particular, to tackle the multifaceted risks associated with algorithmic decisions by digital platforms, while at the same time avoiding stifling innovation, it makes three suggestions: (1) also information disclosures should be done by an algorithm; (2) that is pre-tested in a co-regulatory process, that involves the regulator and stakeholders; and (3) enforced through legal and other empowerment tools, rather than sole fines.
Evaluation of MoblrN m-learning system: Participants' attitudes and opinions

The advance of pervasive technologies such as mobile technologies and various mobile devices has brought opportunities for educators to design and perform interactive learning activities and has also enabled educationalists to access the virtual learning environment anytime and anywhere without restrictions of time or place and without too much additional effort. The m-learning system, MobLrN, has been developed by the researcher. Different from the m-learning systems developed by other researchers, the MobLrN m-learning system contains all teaching/learning components for m-learning, such as access control, user profile, learning materials, assignments, self-tests, quizzes, performance measurement, announcements, and report generation. Thus, instead of using different systems for different activities, the instructors can use the developed mobile system for all of their teaching activities. The main porpuse of the study was to identify whether the developed m-learning system is acceptable or not as a mobile learning environment. Data were collected through a questionnaire. Descriptive statistical analysis technique and the percentage method were used. The obtained results confirm that the MobLrN m-learning system was deemed to be useful for instructors and students for the future. The system is oriented to anyone who may have an interest in mobile technologies, and all higher education institutes.&#x0D; Keywords: m-learning system; m-learning environment; m-learning; evaluation; distance learning&#x0D; &#x0D; &#x0D;
Semantic Role Labeling Guided Multi-turn Dialogue ReWriter

For multi-turn dialogue rewriting, the capacity of effectively modeling the linguistic knowledge in dialog context and getting rid of the noises is essential to improve its performance.Existing attentive models attend to all words without prior focus, which results in inaccurate concentration on some dispensable words.In this paper, we propose to use semantic role labeling (SRL), which highlights the core semantic information of who did what to whom, to provide additional guidance for the rewriter model.Experiments show that this information significantly improves a RoBERTa-based model that already outperforms previous stateof-the-art systems.
TaxiNLI: Taking a Ride up the NLU Hill

Pre-trained Transformer-based neural architectures have consistently achieved state-of-theart performance in the Natural Language Inference (NLI) task.Since NLI examples encompass a variety of linguistic, logical, and reasoning phenomena, it remains unclear as to which specific concepts are learnt by the trained systems and where they can achieve strong generalization.To investigate this question, we propose a taxonomic hierarchy of categories that are relevant for the NLI task.We introduce TAXINLI, a new dataset, that has 10k examples from the MNLI dataset (Williams et al., 2018) with these taxonomic labels.Through various experiments on TAXINLI, we observe that whereas for certain taxonomic categories SOTA neural models have achieved near perfect accuracies-a large jump over the previous models-some categories still remain difficult.Our work adds to the growing body of literature that shows the gaps in the current NLI systems and datasets through a systematic presentation and analysis of reasoning categories.
A general framework for complex network-based image segmentation

With the recent advances in complex networks theory, graph-based techniques for image segmentation has attracted great attention recently. In order to segment the image into meaningful connected components, this paper proposes an image segmentation general framework using complex networks based community detection algorithms. If we consider regions as communities, using community detection algorithms directly can lead to an over-segmented image. To address this problem, we start by splitting the image into small regions using an initial segmentation. The obtained regions are used for building the complex network. To produce meaningful connected components and detect homogeneous communities, some combinations of color and texture based features are employed in order to quantify the regions similarities. To sum up, the network of regions is constructed adaptively to avoid many small regions in the image, and then, community detection algorithms are applied on the resulting adaptive similarity matrix to obtain the final segmented image. Experiments are conducted on Berkeley Segmentation Dataset and four of the most influential community detection algorithms are tested. Experimental results have shown that the proposed general framework increases the segmentation performances compared to some existing methods.
Integrated measurement server for measurement-device-independent quantum key distribution network

Quantum key distribution (QKD), harnessing quantum physics and optoelectronics, may promise unconditionally secure information exchange in theory.Recently, theoretical and experimental advances in measurement-device-independent (MDI-) QKD have successfully closed the physical backdoor in detection terminals.However, the issues of scalability, stability, cost and loss prevent QKD systems from widespread application in practice.Here, we propose and experimentally demonstrate a solution to build a star-topology quantum access network with an integrated server.By using femtosecond laser direct writing technique, we construct integrated circuits for all the elements of Bell state analyzer together and are able to integrate several such analyzers on single photonic chip.The measured high-visibility Bell state analysis suggests integrated server a promising platform for the practical application of MDI-QKD network.
Perils of embedding for sampling problems

Advances in techniques for thermal sampling in classical and quantum systems would deepen understanding of the underlying physics. Unfortunately, one often has to rely solely on inexact numerical simulation, due to the intractability of computing the partition function in many systems of interest. Emerging hardware, such as quantum annealers, provide novel tools for such investigations, but it is well known that studying general, non-native systems on such devices requires graph minor embedding, at the expense of introducing additional variables. The effect of embedding for sampling is more pronounced than for optimization; for optimization one is just concerned with the ground state physics, whereas for sampling one needs to consider states at all energies. We argue that as the system size or the embedding size grows, the chance of a sample being in the subspace of interest - the logical subspace - can be exponentially suppressed. Though the severity of this scaling can be lessened through favorable parameter choices, certain physical constraints (such as a fixed temperature and range of couplings) provide hard limits on what is currently feasible. Furthermore, we show that up to some practical and reasonable assumptions, any type of post-processing to project samples back into the logical subspace will bias the resulting statistics. We introduce a new such technique, based on resampling, that substantially outperforms majority vote, which is shown to fail quite dramatically at preserving distribution properties.
IMLI

The wide adoption of machine learning in the critical domains such as medical diagnosis, law, education had propelled the need for interpretable techniques due to the need for end users to understand the reasoning behind decisions due to learning systems. The computational intractability of interpretable learning led practitioners to design heuristic techniques, which fail to provide sound handles to tradeoff accuracy and interpretability. Motivated by the success of MaxSAT solvers over the past decade, recently MaxSAT-based approach, called MLIC, was proposed that seeks to reduce the problem of learning interpretable rules expressed in Conjunctive Normal Form (CNF) to a MaxSAT query. While MLIC was shown to achieve accuracy similar to that of other state of the art black-box classifiers while generating small interpretable CNF formulas, the runtime performance of MLIC is significantly lagging and renders approach unusable in practice. In this context, authors raised the question: Is it possible to achieve the best of both worlds, i.e., a sound framework for interpretable learning that can take advantage of MaxSAT solvers while scaling to real-world instances? In this paper, we take a step towards answering the above question in affirmation. We propose IMLI: an incremental approach to MaxSAT based framework that achieves scalable runtime performance via partition-based training methodology. Extensive experiments on benchmarks arising from UCI repository demonstrate that IMLI achieves up to three orders of magnitude runtime improvement without loss of accuracy and interpretability.
Experimental demonstration of quantum advantage for one-way communication complexity surpassing best-known classical protocol

Abstract Demonstrating a quantum advantage with currently available experimental systems is of utmost importance in quantum information science. While this remains elusive for quantum computation, the field of communication complexity offers the possibility to already explore and showcase this advantage for useful tasks. Here, we define such a task, the Sampling Matching problem, which is inspired by the Hidden Matching problem and features an exponential gap between quantum and classical protocols in the one-way communication model. Our problem allows by its conception a photonic implementation based on encoding in the phase of coherent states of light, the use of a fixed size linear optic circuit, and single-photon detection. This enables us to demonstrate in a proof-of-principle experiment an advantage in the transmitted information resource over the best known classical protocol, something impossible to reach for the original Hidden Matching problem. Our demonstration has implications in quantum verification and cryptographic settings.
ConCert: a smart contract certification framework in Coq

We present a new way of embedding functional languages into the Coq proof assistant by using meta-programming. This allows us to develop the meta-theory of the language using the deep embedding and provides a convenient way for reasoning about concrete programs using the shallow embedding. We connect the deep and the shallow embeddings by a soundness theorem. As an instance of our approach, we develop an embedding of a core smart contract language into Coq and verify several important properties of a crowdfunding contract based on a previous formalisation of smart contract execution in blockchains.
Digital Transformation and Its Risks in Higher Education: Students' and Teachers' Attitude

The article analyzes the actual problem of the risks of digitalization of higher education.The goal of the study was to determine the level of using of digital tools and technologies by students and teachers in the educational process to identify the main problems and risks of digitalization of higher education.The key problem of digitalization of higher education in Russia is the approval of a technocratic model for its implementation, based on the reduction of this process to "digitization".The real goal of digitalization in universities should give emphasis on improving the creative nature of education.In order to analyze the current situation with the digitalization of higher education, an online survey of students and teachers of the Kazan (Volga) Federal District was used.The research results show that both students and teachers use a limited number of digital educational resources.Students prefer the passive forms of using information and communication technologies (webinars, online courses).Teachers use digital tools primarily for planning and downloading their taught courses.They also use them as an organizing mechanism for their classes, rather than to promote improved learning technologies.The most important conclusion of our study is that the risks of digitalization of education at universities are directly related to the incompetence of teachers in the using of digital educational technologies.Therefore, improving the mechanism of teachers` professional adaptation to digital reality is crucial in improving the quality of education at universities.
Seasonal warranty prediction based on recurrent event data

Warranty return data from repairable systems, such as home appliances, lawn mowers, computers and automobiles, result in recurrent event data.The nonhomogeneous Poisson process (NHPP) model is used widely to describe such data.Seasonality in the repair frequencies and other variabilities, however, complicate the modeling of recurrent event data.Not much work has been done to address the seasonality, and this paper provides a general approach for the application of NHPP models with dynamic covariates to predict seasonal warranty returns.The methods presented here, however, can be applied to other applications that result in seasonal recurrent event data.A hierarchical clustering method is used to stratify the population into groups that are more homogeneous than the overall population.The stratification facilitates modeling the recurrent event data with both time-varying and timeconstant covariates.We demonstrate and validate the models using warranty claims data for two different types of products.The results show that our approach provides important improvements in the predictive power of monthly events compared with models that do not take the seasonality and covariates into account.
SLACC

Successful cross-language clone detection could enable researchers and developers to create robust language migration tools, facilitate learning additional programming languages once one is mastered, and promote reuse of code snippets over a broader codebase. However, identifying cross-language clones presents special challenges to the clone detection problem. A lack of common underlying representation between arbitrary languages means detecting clones requires one of the following solutions: 1) a static analysis framework replicated across each targeted language with annotations matching language features across all languages, or 2) a dynamic analysis framework that detects clones based on runtime behavior.
A verifiable framework of entanglement-free quantum secret sharing with information-theoretical security

Quantum secret sharing (QSS) schemes without entanglement have huge advantages in scalability and are easier to realize as they only require sequential communications of a single quantum system. However, these schemes often come with drawbacks such as exact ($n, n$) structure, security flaws and absences of effective cheating detections. To address these problems, we propose a verifiable framework by utilizing entanglement-free states to construct ($t, n$)-QSS schemes. Our work is the heuristic step towards information-theoretical security in entanglement-free QSS, and it sheds light on how to establish effective verification mechanism against cheating. As a result, the proposed framework has a significant importance in constructing QSS schemes for versatile applications in quantum networks due to its intrinsic scalability, flexibility and information theoretical security.
Vehicle Detection in Overhead Satellite Images Using a One-Stage Object Detection Model

In order to improve the traffic in large cities and to avoid congestion, advanced methods of detecting and predicting vehicle behaviour are needed. Such methods require complex information regarding the number of vehicles on the roads, their positions, directions, etc. One way to obtain this information is by analyzing overhead images collected by satellites or drones, and extracting information from them through intelligent machine learning models. Thus, in this paper we propose and present a one-stage object detection model for finding vehicles in satellite images using the RetinaNet architecture and the Cars Overhead With Context dataset. By analyzing the results obtained by the proposed model, we show that it has a very good vehicle detection accuracy and a very low detection time, which shows that it can be employed to successfully extract data from real-time satellite or drone data.
Learning the Structure of Auto-Encoding Recommenders

Autoencoder recommenders have recently shown state-of-the-art performance in the recommendation task due to their ability to model non-linear item relationships effectively. However, existing autoencoder recommenders use fully-connected neural network layers and do not employ structure learning. This can lead to inefficient training, especially when the data is sparse as commonly found in collaborative filtering. The aforementioned results in lower generalization ability and reduced performance. In this paper, we introduce structure learning for autoencoder recommenders by taking advantage of the inherent item groups present in the collaborative filtering domain. Due to the nature of items in general, we know that certain items are more related to each other than to other items. Based on this, we propose a method that first learns groups of related items and then uses this information to determine the connectivity structure of an auto-encoding neural network. This results in a network that is sparsely connected. This sparse structure can be viewed as a prior that guides the network training. Empirically we demonstrate that the proposed structure learning enables the autoencoder to converge to a local optimum with a much smaller spectral norm and generalization error bound than the fully-connected network. The resultant sparse network considerably outperforms the state-of-the-art methods like Mult-vae/Mult-dae on multiple benchmarked datasets even when the same number of parameters and flops are used. It also has a better cold-start performance.
The Use of Mobile Learning Technologies for Corporate Training and Development: A Contextual Framework

This chapter presents a thorough review on the mobile learning concept.It also explores how businesses are using mobile learning (m-learning) technologies for the training and development of their human resources.The research involved semi-structured interviews and an online survey.The research participants were expected to share their opinions about the costs and benefits of using m-learning applications (apps).The findings reported that the younger course participants were more likely to embrace the m-learning technologies than their older counterparts.They were using different mobile devices, including laptops, hybrids as well as smartphones and tablets to engage with m-learning applications at work, at home and when they are out and about.This contribution has identified the contextual factors like the usefulness and the ease of use of m-learning applications (apps), individual learning styles and their motivations, time, spatial issues, integration with other learning approaches, as well as the cost and accessibility of the m-learning technology.In conclusion, this contribution identifies future research avenues relating to the use of m-learning technologies among businesses and training organisations.
P$$^{2}$$Net: Patch-Match and Plane-Regularization for Unsupervised Indoor Depth Estimation

This paper tackles the unsupervised depth estimation task in indoor environments. The task is extremely challenging because of the vast areas of non-texture regions in these scenes. These areas could overwhelm the optimization process in the commonly used unsupervised depth estimation framework proposed for outdoor environments. However, even when those regions are masked out, the performance is still unsatisfactory. In this paper, we argue that the poor performance suffers from the non-discriminative point-based matching. To this end, we propose P $$^2$$ Net. We first extract points with large local gradients and adopt patches centered at each point as its representation. Multiview consistency loss is then defined over patches. This operation significantly improves the robustness of the network training. Furthermore, because those textureless regions in indoor scenes (e.g., wall, floor, roof, etc.) usually correspond to planar regions, we propose to leverage superpixels as a plane prior. We enforce the predicted depth to be well fitted by a plane within each superpixel. Extensive experiments on NYUv2 and ScanNet show that our P $$^2$$ Net outperforms existing approaches by a large margin.
Associative3D: Volumetric Reconstruction from Sparse Views

This paper studies the problem of 3D volumetric reconstruction from two views of a scene with an unknown camera. While seemingly easy for humans, this problem poses many challenges for computers since it requires simultaneously reconstructing objects in the two views while also figuring out their relationship. We propose a new approach that estimates reconstructions, distributions over the camera/object and camera/camera transformations, as well as an inter-view object affinity matrix. This information is then jointly reasoned over to produce the most likely explanation of the scene. We train and test our approach on a dataset of indoor scenes, and rigorously evaluate the merits of our joint reasoning approach. Our experiments show that it is able to recover reasonable scenes from sparse views, while the problem is still challenging. Project site: https://jasonqsy.github.io/Associative3D .