Reviewerly/exHarmony huggingface dataset:
Personal Data as Property

Today, a growing chorus of experts, journalists, and policymakers calls for the creation of property rights in personal data. In theory, property rights emerge when the gains from propertization outweigh the costs of securing those rights. This formula, originally identified by Harold Demsetz, explains the development of property rights in land, intellectual property, and many other assets.Applying Demsetz’s theory, this Article asks whether the time has come to extend property rights in personal data. The answer is yes.The first half of Demsetz’s formula estimates the gains from extending property rights. Under the contract-law-based status quo, the market for personal data suffers from high information and enforcement costs along with inadequate incentives to supply and safeguard data. Propertization promises to mitigate — though not completely resolve — those challenges. The second half of Demsetz’s formula trains on the cost of securing property rights. For property rights to be secure in practice — not just desirable in theory — institutional investments are necessary. The conventional wisdom holds that only state-run institutions, such as courts and regulators, can protect property. But rather than rely on regulators and courts, policymakers should deputize private adjuncts to define and enforce property rights. This approach enlists the most efficient managers of information — data processing firms — in securing property. Compared with a propertization regime that relies on state-run institutions, mobilizing private adjuncts promises to substantially lower the cost of securing property rights.Because the gains from propertization are larger, and the costs smaller, than previously thought, both prongs of Demsetz’s formula favor the creation of property rights in personal data.
Shortest Path Evaluation with Enhanced Linear Graph and Dijkstra Algorithm

Path planning is one of the vital tasks in the intelligent control of autonomous robots. It is of prime importance from industrial as well as commercial point of view. Various autonomous vehicles are gaining popularity to reduce the on-field intervention of humans. The path planning problem is divided into two sub-problems: finding the feasible node pairs and then evaluate the shortest feasible path from the obtained feasible node pairs. Feasible node pair is the part of the path which do not pass through any obstacle. For the known static arena, the most popular method to solve these kinds of problems is the visibility graph (VGraph), but it is computationally intensive. This paper proposes an Enhanced Linear Graph (ELGraph), a method for finding feasible node pairs. The ELGraph has simplified linear structure which exploits the concept of line segment intersection, which may take low computational time. The simulations were carried out for VGraph and ELGraph to get the feasible node pair, and then Dijkstra algorithm evaluates the shortest feasible path. The results obtained shows that ELGraph takes lesser evaluation time and is computationally lesser intensive than VGraph, without compromising on finding the shortest possible feasible path.
SSNE: Effective Node Representation for Link Prediction in Sparse Networks

Graph embedding is gaining popularity for link prediction in complex networks. However, few works focus on the effectiveness of graph embedding models on link prediction in sparse networks. This paper proposes a novel graph embedding model, <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> parse <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> tructural <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> etwork <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> mbedding (SSNE), to obtain node representation for link predication in sparse networks. The SSNE first transforms the adjacency matrix into the <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu {\mathrm{ m}}$ </tex-math></inline-formula> of <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> ormalized <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">H</b> -order <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</b> djacency <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> atrix (SNHAM) and then maps the SNHAM matrix into a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> -dimensional feature matrix for node representation via a neural network model. The mapping operation is proved to be an equivalent variety of singular value decomposition. Finally, we calculate nodal similarities for link prediction based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula> -dimensional feature matrix. The extensive testing experiments based on artificial and real sparse networks suggest that the SSNE shows the effective node representation for link prediction in sparse networks, supported by the better link prediction performance compared to those of structural similarity indexes, matrix optimization, and other graph embedding models.
Analogy-Detail Networks for Object Recognition

The human visual system can recognize object categories accurately and efficiently and is robust to complex textures and noises. To mimic the analogy-detail dual-pathway human visual cognitive mechanism revealed in recent cognitive science studies, in this article, we propose a novel convolutional neural network (CNN) architecture named analogy-detail networks (ADNets) for accurate object recognition. ADNets disentangle the visual information and process them separately using two pathways: the analogy pathway extracts coarse and global features representing the gist (i.e., shape and topology) of the object, while the detail pathway extracts fine and local features representing the details (i.e., texture and edges) for determining object categories. We modularize the architecture and encapsulate the two pathways into the analogy-detail block as the CNN building block to construct ADNets. For implementation, we propose a general principle that transmutes typical CNN structures into the ADNet architecture and applies the transmutation on representative baseline CNNs. Extensive experiments on CIFAR10, CIFAR100, street view house numbers, and ImageNet data sets demonstrate that ADNets significantly reduce the test error rates of the baseline CNNs by up to 5.76% and outperform other state-of-the-art architectures. Comprehensive analysis and visualizations further demonstrate that ADNets are interpretable and have a better shape-texture tradeoff for recognizing the objects with complex textures.
Semantic Matching for Sequence-to-Sequence Learning

In sequence-to-sequence models, classical optimal transport (OT) can be applied to semantically match generated sentences with target sentences.However, in non-parallel settings, target sentences are usually unavailable.To tackle this issue without losing the benefits of classical OT, we present a semantic matching scheme based on the Optimal Partial Transport (OPT).Specifically, our approach partially matches semantically meaningful words between source and partial target sequences.To overcome the difficulty of detecting active regions in OPT (corresponding to the words needed to be matched), we further exploit prior knowledge to perform partial matching.Extensive experiments are conducted to evaluate the proposed approach, showing consistent improvements over sequence-to-sequence tasks.
Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently

Existing hand detection methods usually follow the pipeline of multiple stages with high computation cost, i.e., feature extraction, region proposal, bounding box regression, and additional layers for rotated region detection. In this paper, we propose a new Scale Invariant Fully Convolutional Network (SIFCN) trained in an end-to-end fashion to detect hands efficiently. Specifically, we merge the feature maps from high to low layers in an iterative way, which handles different scales of hands better with less time overhead comparing to concatenating them simply. Moreover, we develop the Complementary Weighted Fusion (CWF) block to make full use of the distinctive features among multiple layers to achieve scale invariance. To deal with rotated hand detection, we present the rotation map to get rid of complex rotation and derotation layers. Besides, we design the multi-scale loss scheme to accelerate the training process significantly by adding supervision to the intermediate layers of the network. Compared with the state-of-the-art methods, our algorithm shows comparable accuracy and runs a 4.23 times faster speed on the VIVA dataset and achieves better average precision on Oxford hand detection dataset at a speed of 62.5 fps.
Protecting Privacy When Sharing and Releasing Data with Multiple Records per Person

This study concerns the risks of privacy disclosure when sharing and releasing a dataset in which each individual may be associated with multiple records. Existing data privacy approaches and policies typically assume that each individual in a shared dataset corresponds to a single record, leading to an underestimation of the disclosure risks in multiple records per person scenarios. We propose two novel measures of privacy disclosure to arrive at a more appropriate assessment of disclosure risks. The first measure assesses individual-record disclosure risk based upon the frequency distribution of individuals’ occurrences. The second measure assesses sensitive-attribute disclosure risk based upon the number of individuals affiliated with a sensitive value. We show that the two proposed disclosure measures generalize the well-known k-anonymity and l-diversity measures, respectively, and work for scenarios with either a single record or multiple records per person. We have developed an efficient computational procedure that integrates the two proposed measures and a data quality measure to anonymize the data with multiple records per person when sharing and releasing the data for research and analytics. The results of the experimental evaluation using real-world data demonstrate the advantage of the proposed approach over existing techniques for protecting privacy while preserving data quality.
The Duality of Subtyping

Subtyping is a concept frequently encountered in many programming languages and calculi. Various forms of subtyping exist for different type system features, including intersection types, union types or bounded quantification. Normally these features are designed independently of each other, without exploiting obvious similarities (or dualities) between features.This paper proposes a novel methodology for designing subtyping relations that exploits duality between features. At the core of our methodology is a generalization of subtyping relations, which we call Duotyping. Duotyping is parameterized by the mode of the relation. One of these modes is the usual subtyping, while another mode is supertyping (the dual of subtyping). Using the mode it is possible to generalize the usual rules of subtyping to account not only for the intended behaviour of one particular language construct, but also of its dual. Duotyping brings multiple benefits, including: shorter specifications and implementations, dual features that come essentially for free, as well as new proof techniques for various properties of subtyping. To evaluate a design based on Duotyping against traditional designs, we formalized various calculi with common OOP features (including union types, intersection types and bounded quantification) in Coq in both styles. Our results show that the metatheory when using Duotyping does not come at a significant cost: the metatheory with Duotyping has similar complexity and size compared to the metatheory for traditional designs. However, we discover new features as duals to well-known features. Furthermore, we also show that Duotyping can significantly simplify transitivity proofs for many of the calculi studied by us.
FORMATION OF IT COMPETENCY WHILE TEACHING SCHOOL STUDENTS

I. Balandina, T. Lebedeva, A. Milyutina, T. Moskvitina, O. Shefer, G. Shiganova, L. YuzdovaSouth Ural State Humanitarian Pedagogical University (RUSSIAN FEDERATION)
Row and Bounded Polymorphism via Disjoint Polymorphism

Polymorphism and subtyping are important features in mainstream OO languages. The most common way to integrate the two is via 𝖥_{< :} style bounded quantification. A closely related mechanism is row polymorphism, which provides an alternative to subtyping, while still enabling many of the same applications. Yet another approach is to have type systems with intersection types and polymorphism. A recent addition to this design space are calculi with disjoint intersection types and disjoint polymorphism. With all these alternatives it is natural to wonder how they are related.This paper provides an answer to this question. We show that disjoint polymorphism can recover forms of both row polymorphism and bounded polymorphism, while retaining key desirable properties, such as type-safety and decidability. Furthermore, we identify the extra power of disjoint polymorphism which enables additional features that cannot be easily encoded in calculi with row polymorphism or bounded quantification alone. Ultimately we expect that our work is useful to inform language designers about the expressive power of those common features, and to simplify implementations and metatheory of feature-rich languages with polymorphism and subtyping.
ιDOT: a DOT calculus with object initialization

The Dependent Object Types (DOT) calculus serves as a foundation of the Scala programming language, with a machine-verified soundness proof. However, Scala's type system has been shown to be unsound due to null references, which are used as default values of fields of objects before they have been initialized. This paper proposes ιDOT, an extension of DOT for ensuring safe initialization of objects. DOT was previously extended to κDOT with the addition of mutable fields and constructors. To κDOT, ιDOT adds an initialization effect system that statically prevents the possibility of reading a null reference from an uninitialized object. To design ιDOT, we have reformulated the Freedom Before Commitment object initialization scheme in terms of disjoint subheaps to make it easier to formalize in an effect system and prove sound. Soundness of ιDOT depends on the interplay of three systems of rules: a type system close to that of DOT, an effect system to ensure definite assignment of fields in each constructor, and an initialization system that tracks the initialization status of objects in a stack of subheaps. We have proven the overall system sound and verified the soundness proof using the Coq proof assistant.
210 years of transport education: successes and development horizons of secondary vocational education

The system of secondary vocational education (SVE) of the Petersburg State University of Railways of Emperor Alexander I (PGUPS) is considered. Approbation and implementation of a new one is the trend of the modern open source software system. The dynamics of change is forming a new order for a qualitative change in vocational training. Despite many years of cooperation with the main customer — JSC Russian Railways, the branch network of PGUPS does not reduce the requirements for the quality of training of mid-level specialists, since a mutually beneficial partnership requires a prompt response to all changes in the industry, and is ensured by years of proven mutual trust, mutual assistance and mutual responsibility for the professional competence of the industry’s workforce. Today regional, national and world championships are held under the auspices of WorldSkills Russia. The development of training standards that meet international standards and the needs of new high-tech industries is in the sphere of interests of employers and the teaching community. Teachers and masters of industrial training actively participate in competitions and olympiads of professional skill, replicate their experience, conducting open lessons and master classes. The teaching community of VET is taking on new challenges of large-scale reforms in the industry and education. Being in the center of public attention, the branch network of PGUPS is ready to work with conditions as close as possible to high-tech real production.
Linguistic Deviation in Business Advertisements

Advertising can be regarded as a means of persuading consumers to take action with respect to products, ideas or services. It is a complex phenomenon because it involves two interacting processes, namely, communication and persuasion, and both processes are many-faceted. When advertisers intend to make the advertisement language creative and inventive, they use language different from the linguistic norm. Being unconventional, the advertisements which adopt deviation can give the readers unexpected surprise and make a strong impression on their mind. This kind of language phenomenon is technically called linguistic deviation. Then based on some scholars’ classifications, the author classifies different deviations in advertising (deviation of phonology, graphology, lexicon, and grammar) and illustrates each deviation with some typical examples.
Vagueness of Patent Claim Language, Claim Construction and Patent Infringement – What a Mess!

Abstract The claim drafting process, which is inherently related to inventions, is not exempt from difficulties. Prominent among these is vagueness, which can often be the chief characteristic of patent claims. This paper argues that such ambiguities may hamper the development and teaching functions of patent documents and can also distort infringement systems. It addresses legal issues related to patent claim construction, including the use of intrinsic or extrinsic evidence to clarify an ambiguous or vague claim. Confusion surrounding the use of expert testimony for determining the meaning of a claim is also highlighted. Largely based on the US law, the paper suggests ways to reduce claim ambiguity, such as the use of claim charts. Emphasis is placed on the clarity of the claims’ wording, because this acts as a safeguard against any infringement risk. In brief, constructing patent claims remains largely an unsettled and uncertain area of patent law. What really matters is the clarity of the claim language because definiteness, preciseness, conciseness and exactness are major guarantees of a patent’s validity.
Data Mining Technique to Determine the Pattern of Fruits Sales &amp; Supplies Using Apriori Algorithm

Abstract Advances in technological developments in the current 4.0 era require retail businesses to increase sales and develop marketing strategies. Determination of which products are widely sold and which products will be propagated in inventory is very important for retail businesses to prevent data accumulation. Data Mining has been widely used to conduct analysis, determine patterns and associations. In this paper, we propose a basic methodology for calculating association analysis with apriori algorithms used to process the most sold products and which products will be propagated in the sales inventory. The results of this study are the calculation model of high-frequency pattern analysis and the formation of association rules and item set combination patterns resulting from the sale of fruit products from retail with the highest support and confidence of the most sold fruit products. Therefore the apriori algorithm can help develop sales strategies.
Bigram feature extraction and conditional random fields model to improve text classification clinical trial document

In the field of health and medicine, there is a very important term known as clinical trials. Clinical trials are a type of activity that studies how the safest way to treat patients is. These clinical trials are usually written in unstructured free text which requires translation from a computer. The aim of this paper is to classify the texts of cancer clinical trial documents consisting of unstructured free texts taken from cancer clinical trial protocols. The proposed algorithm is conditional random Fields and bigram features. A new classification model from the cancer clinical trial document text is proposed to compete with other methods in terms of precision, recall, and f-1 score. The results of this study are better than the previous results, namely 88.07 precision, 88.05 recall and f-1 score 88.06.
Composably secure device-independent encryption with certified deletion

We study the task of encryption with certified deletion (ECD) introduced by Broadbent and Islam (2020), but in a device-independent setting: we show that it is possible to achieve this task even when the honest parties do not trust their quantum devices. Moreover, we define security for the ECD task in a composable manner and show that our ECD protocol satisfies conditions that lead to composable security. Our protocol is based on device-independent quantum key distribution (DIQKD), and in particular the parallel DIQKD protocol based on the magic square non-local game, given by Jain, Miller and Shi (2020). To achieve certified deletion, we use a property of the magic square game observed by Fu and Miller (2018), namely that a two-round variant of the game can be used to certify deletion of a single random bit. In order to achieve certified deletion security for arbitrarily long messages from this property, we prove a parallel repetition theorem for two-round non-local games, which may be of independent interest.
Lost in Learning: Hypertext Navigational Efficiency Measures Are Valid for Predicting Learning in Virtual Reality Educational Games

The lostness measure, an implicit and unobtrusive measure originally designed for assessing the usability of hypertext systems, could be useful in Virtual Reality (VR) games where players need to find information to complete a task. VR locomotion systems with node-based movement mimic actions for exploration and browsing found in hypertext systems. For that reason, hypertext usability measures, such as “lostness” can be used to identify how disoriented a player is when completing tasks in an educational game by examining steps made by the player. An evaluation of two different lostness measures, global and local lostness, based on two different types of tasks, is described in a VR educational game using 13 college students between 14 and 18 years old in a first study and extended using 12 extra participants in a second study. Multiple Linear Regression analyses showed, in both studies, that local lostness, and not global lostness, had a significant effect on a post-game knowledge test. Therefore, we argued that local lostness was able to predict how well-participants would perform on a post-game knowledge test indicating how well they learned from the game. In-game experience aspects (engagement, cognitive interest, and presence) were also evaluated and, interestingly, it was also found that participants learned less when they felt more present in the game. We believe these two measures relate to cognitive overload, which is known to have an adverse effect on learning. Further research should investigate the lostness measure for use in an online adaptive game system and design the game system in such a way that the risk of cognitive overload is minimized when learning, resulting in higher retention of information.