Reviewerly/exHarmony huggingface dataset:
Distance Adaptive Embedding Learning for Social Network Alignment

Aligning anchor users across multiple social networks has become an emerging task in recent years. Most of the existing methods align anchor users by leveraging network embedding techniques to learn a common embedding space of user nodes. However, nearly all these methods suffer from inappropriate structural proximity measurement and conflicting objectives, incurring inferior alignment results. In this paper, we propose a novel approach named Distance Adaptive Embedding Learning for Network Alignment (DAEL-NA) to tackle the above two issues. For the first issue, we measure structural proximity by Euclidean distance instead of the commonly used cosine similarity for user embedding learning. For the second issue, we propose the notion of Distance Adaptive Embedding Learning (DAEL) to discriminate an anchor user from its neighbors while preserving structural proximity. Experiments conducted over real-world datasets demonstrate the effectiveness of DAEL-NA on social network alignment.
A Context-Aware POI Recommendation

This paper proposes a novel sequential neural network that integrates the collaborative context-learning module to learn sparse and lengthy POI data for top-N recommendation tasks. Our proposed model explores either time and distance irregularities from check-in sequences and incorporates them into the model learning process by carrying a decomposition of the memory cell into short-and long-term interests. The short-term counterpart is adjusted by a proper weight and combined with the long-term counterpart before further processing with the light version of LSTM, where forget and input gates are united into a single decision unit. Also, we further improve our proposed model by allowing multiple contexts collaboratively trained together under the gating mechanism of recurrent neural networks. Extensive experiments over two public datasets show the superiority over existing baselines and state-of-the-art sequence-based models.
Contour Gradient Optimization

Inspired by local cooperation in the real world, a new evolutionary algorithm, Contour Gradient Optimization algorithm (CGO), is proposed for solving optimization problems. CGO is a new type of population-based algorithm that emulates the cooperation among neighbors. Each individual in CGO evolves in its neighborhood environment to find a better region. Each individual moves with a velocity measured by the field of its nearest individuals. The field includes the attractive forces from its better neighbor in the higher contour level and the repulsive force from its worse neighbor in the lower contour level. In this chapter, CGO is compared with six different widely used optimization algorithms, and comparative analysis shows that CGO is better than these algorithms in respect of accuracy and effectiveness.
Information Retrieval

Information retrieval is a set of methods to provide relevant documents based on the input query. Various steps included in Information retrieval are starting from pre-processing, indexing, and then ranking. The explanation using examples are given in the chapter. The applications of Information retrieval are document summarization, question answer system and many more.
Discovering Underground Maps from Fashion

The fashion sense—meaning the clothing styles people wear—in a geographical region can reveal information about that region. For example, it can reflect the kind of activities people do there, or the type of crowds that frequently visit the region (e.g., tourist hot spot, student neighborhood, business center). We propose a method to create underground neighborhood maps of cities by analyzing how people dress. Using publicly available images from across a city, our method automatically segments the map into neighborhoods with a similar fashion sense. Our approach further allows discovering insights about a city, such as detecting distinct neighborhoods (what is the most unique region of NYC?) and answering analogy questions between cities (what is the "Downtown LA" of Bogota?). We also present two new underground map benchmarks derived from non-image data for 37 cities worldwide. Our method shows promising results on both these benchmarks as well as experiments with human judges."The map is not the thing mapped."—Eric Temple Bell
Shared Activity for Digital Fabrication

The goal of this paper is to present a proposal of social interaction for the generation of coparticipative models of food and beverage stalls for the “Festas de Largo” in Salvador, Bahia, using Digital Fabrication as a productive means. This research uses a qualitative approach, from an interpretivist philosophical perspective, and hermeneutic as a mode of analysis. The shared activity is structured to be conducted through action research. This research intends to contribute to the generation of coparticipative numerical models within the context of mass customization, seeking to revert the cultural mischaracterization of the current model of food and beverage stalls in the city. The adoption of a new digital production system for the stalls jointly contemplates popular participation, functional hygienic demands and cultural demands lost over time.
Project Educate: A skill based e-learning for rural areas

In recent times, the use of new technology, tools, and computers has become increasingly important in both learning and teaching. E-learning is widely regarded as the most significant mechanism for knowledge transmission in a variety of fields, particularly education. This E-learning model is based especially on school kids and teachers. The ability to access learning materials at their leisure, as well as space independence, study modes, and feedback possibilities, are all key advantages that encourage students to learn in an e-learning environment. Relatable and comprehensive modules are designed for effective learning and better understanding. Though there are many platforms available in the market most of them are of higher-level cognitive and understanding skills that are not triggered yet. Speaking and learning in a comfortable language is proven to be effective and stays in memory for a long time. This motivates the rural people who are determined to study and help them in different ways.
Algorithmic Lending, Competition, and Strategic Information Disclosure

Machine learning (ML) algorithms used by financial lenders in their screening processes are hidden from the consumers who are affected by their decisions leading many consumers to make sub-optimal decisions when seeking credit. Despite increasing calls for greater transparency, only a few lenders provide personalized approval odds to consumers (e.g. via financial intermediaries like Credit Karma or pre-approval tools). We investigate how competition among algorithmic lenders affects their decisions to provide approval odds to consumers. We show that competitive pressures between lenders can undermine the disclosure incentives. Lenders use asymmetric disclosure of approval odds strategically to soften the competition when their algorithms are fairly accurate. The asymmetric disclosure of approval odds endogenously creates product differentiation and allows lenders to focus on different segments of consumers softening the competition on the interest rates. We find that consumer surplus is highest when both lenders provide approval odds and lowest when neither provides approval odds. However, our analysis also shows that any policy that mandates all lenders to provide personalized approval odds to consumers may not necessarily improve consumer surplus.
Exploring the Campus of a University—An AR-Based Application—“Drishti”

Recently, with the advancement in technology and rapid development of modern electronic devices, the use of mobile-based applications has increased immensely in the past few years. New techniques in mobile devices like—GPS, sensors, built-in camera, compass, accelerometer, and an Internet connection are used by augmented reality applications. In this paper, we introduce an Augmented Reality-based application “Drishti” for exploring a university. It shows the University’s environment, facilities, and provides required important information to the visitors to engage them in new and more intuitive ways. The proposed work was developed using VUFORIA, a toolkit for making an Augmented Reality application that runs on UNITY GAME ENGINE.
Transfer Dynamics in Emergent Evolutionary Curricula

POET-Inspired Neuroevolutionary System for KreativitY (PINSKY) is a system for open-ended learning through neuroevolution in game-based domains. It builds on the Paired Open-Ended Trailblazer (POET) system, which originally explored learning and environment generation for bipedal walkers, and adapts it to games in the General Video Game AI (GVGAI) system. Previous work showed that by coevolving levels and neural network policies, levels could be found for which successful policies could not be created via optimization alone. Studied in the realm of artificial life as a potentially open-ended alternative to gradient-based fitness, minimal criteria (MC)-based selection helps foster diversity in evolutionary populations. The main question addressed by this article is how the open-ended learning actually works, focusing in particular on the role of transfer of policies from one evolutionary branch (“species”) to another. We analyze the dynamics of the system through creating phylogenetic trees, analyzing evolutionary trajectories of policies, and temporally breaking down transfers according to species type. Furthermore, we analyze the impact of the minimal criterion on generated level diversity and interspecies transfer. The most insightful finding is that interspecies transfer, while rare, is crucial to the system's success.
Digital Educational Environment of Professional Training: Modeling and Development

The creation of digital educational environment in the college allows us to qualitatively transform the educational process taking into account the requirements of the information society. The purpose of the information and educational space model development in the work is considered from the standpoint of an adaptation mechanism.This provides the necessary level of knowledge, skills, and awareness in professional fields to enter as an active participant in the spheres of economics, politics, and art. At the same time, the authors note the importance of creating an information and educational space from the position of teaching the search for ways to solve professional problems by graduates of vocational education institutions through the use of ICT. Digital technologies open up new opportunities for teachers and students, ensure the effectiveness and accessibility of the educational process, but they also require the development of a different mentality.
An Entity Relation Extraction Method for Few-Shot Learning on the Food Health and Safety Domain

In recent years, entity relation extraction has been a critical technique to help people analyze complex structured text data. However, there is no advanced research in food health and safety to help people analyze the complex concepts between food and human health and their relationships. This paper proposes an entity relation extraction method FHER for the few-shot learning in the food health and safety domain. For few-shot learning in the food health and safety domain, we propose three methods that effectively improve the performance of entity relationship extraction. The three methods are applied to the self-built data sets FH and MHD. The experimental results show that the method can effectively extract domain-related entities and their relations in a small sample size environment.
Target Tracking for High-Speed Railway Catenary Based on Correlation Filtering Algorithm

With the increase in railway operating mileage, the pressure on railway inspections has also increased significantly. The realization of automated inspections along the railway can not only improve work efficiency, but also save manpower and material resources. The catenary is an important component of the train power system, and it is necessary to study the automatic inspection technology of the catenary. This paper proposes to use a tracking algorithm based on correlation filtering to track the catenary. Considering to meet real-time requirements, the ECO-HC algorithm based on manual features is adopted. The experimental results show that the ECO-HC algorithm basically realizes the tracking of the catenary and the algorithm speed reaches 57 fps, which meets the real-time requirements; the comparison between the ECO-HC algorithm and the classic tracking algorithm based on correlation filtering proves the superiority of the ECO-HC algorithm.
Monitoring System of Suspended Pole-holding Group Tower Based on IOTIPS Architecture

Preventing construction safety risks in the construction of power transmission towers is the primary task. However, as the current tower construction mostly relies on the experience of the constructors, the hidden dangers of the construction are extremely huge. In order to solve the current problems of intelligence, refinement, and low degree of modeling in the field of tower construction monitoring, combined with the Internet of Power (IOTIPS) architecture, a suspended pole-holding tower monitoring system based on the IOTIPS architecture is proposed, and the system It is divided into intelligent perception layer, wireless transmission layer, information integration layer and application management layer. In this paper, the system monitoring points are determined by means of mechanical model simulation calculation, tower construction accident analysis, and expert opinions collected, so as to clarify the software and hardware module design of each level of the system; according to the characteristics of field construction, ZigBee communication technology is used to build a local area network. Interconnected with 4G WAN, complete data on-site interaction and long-distance transmission, realizing the force of pulling wire, inclination angle of pole, force of hoisting rope, inclination angle of hoisting rope, hoisting height, site wind speed and other state quantities during the whole process of tower construction On-site and remote real-time monitoring and early warning. The actual engineering verification shows that the system monitoring error is controlled within 10%, the application is good, and it has high engineering value.
An Ontology-Based Automation System

This paper presents an ontology-based approach to benefit automatic fertilization management for citrus orchards located in mountainous region. The core of the fertilization approach is the citrus fertilization ontology, which covers knowledge about citrus fertilizers and fertilization application. Specially, our approach can provide not only the yearly fertilization quantities of required pure nitrogen, phosphorus, and potassium according to their disease symptoms, but also the suitable fertilizing recommendations for the citrus orchards with different soil properties. The current version of the ontology (ver. 2.9.10) contains 103 classes, 34 properties, 800 instances, which are defined by 3056 RDF triples and is evaluated by using 90 competency questions. Furthermore, we run experiments with our proposal targeting at four citrus orchards in Chongqing, and compare its outputs with the reference values advised by the agri-professionals of citrus planting.
Data Quotation Period and the Quality of ANN Learning in the Polish Power Exchange Model

The work contains the results of research on the impact of the length of data quotations on the Day-Ahead Market and thus the number of teaching and testing pairs on the learning quality of the Artificial Neural Network as a model of the Polish Power Exchange.
Biography: Arthur Lawrence Norberg (1938–2021)

The history of the computing community lost one of its early leaders in September 2021 with the passing of Arthur Norberg [1]. He was born in 1938 into a working-class Irish family in Providence, RI. He attended on scholarship a private religious academy as a high school student, where he did well in the topics that interested him, in particular English literature and history. From age twelve, and for the rest of his life until his retirement, he worked—often working full time while he was in college and graduate school. During college, for example, he worked summers in his father's jewelry salvage business.
Explanation

Any form of artificial superintelligence has to be transparent, in other words, how it works must be comprehensible to humans at all times. Not just a few selected experts but to any human who requests information. This includes children and individuals with an intellectual disability. An advanced artificial intelligence must explain in human comprehensible form why certain actions were performed and how certain outcomes were achieved. These explanations must take the form of videos, multimedia content or demonstrations so that anybody can understand them. If humans want to have any trust in an artificial superintelligence then explanation is a core requirement.
MASSIVE DATA BALANCE SCHEDULING IN CLOUD COMPUTING ENVIRONMENT

At present, in the cloud computing environment, the massive data has different attribute characteristics, which cannot be made balance scheduling.There are often long scheduling time, and the balance of CPU, memory and bandwidth is poor.Based on the improved clonal selection algorithm, the balance scheduling method for massive data in cloud computing environment is proposed.Firstly, the massive data balance loading and scheduling, as well as the minimized task execution time are taken as the target, to construct the data scheduling model in the cloud computing environment.According to the principle of clonal selection, the attributes of the massive data in the cloud computing environment are defined as antigens, and the antibody encoding mode of the massive data balance scheduling is designed.Meanwhile, the individual data with the higher affinity to the antigens are selected from the antibody for mutation treatment, and the balance of massive data scheduling is quantified in cloud computing environment.After obtaining the quantization function, the control parameters of massive data scheduling are analyzed and studied, so as to build a balance scheduling model for massive data in cloud computing environment.The simulation results show that the proposed algorithm can make effectively balance scheduling for the massive data in the cloud computing environment, its scheduling time is short, the balance of CPU, memory and bandwidth is higher and the reliability is strong.
Monitoring Login Shell Alert Notification Application

Remote monitoring and maintaining logs of each user activities is a cumbersome task for an administrator. Most of organization needs tools for monitoring user login activities and today PC enthusiasts and amateur efficiency experts explain how to use these tools for their own purposes. Client and Servers running confidential services must be monitored for unauthorized access and modification of data. An availability monitoring tool is practically a necessity for organizations to perform routine tasks. The proposed application helps to monitor and notify user of authorized and unauthorized login session and can prevent from any classes of intruders. A masquerade can take control of a system or misfeasor can misuse the privileges or clandestine user can do both. The application is written inside a Linux configuration file such that whenever an administration tries to open any non GUI terminal out of six terminals, an image is captured with date and time and captured details is sent to administrator’s mail id as an alert message. The application is embedded in a Linux configuration file. When authorized user logins to bash shell, the application also captures video and saved in local home directory and captured image is sent to user’s mail with alert message in a subject field with date and time of access details. The application is written using a shell script and tested on Ubuntu which is an open source operating system.
Approach to the problem of information protection in the organization

Information is a strategic resource of modern society, hence the importance that their protection has for the functioning of organizations, as well as for their very survival. Scientific theory today agrees that the issue of security, ie protection of information and their resources goes beyond the area of necessary knowledge that is exclusively technical in nature and without which it is not possible to establish an information system, but that non-technical aspects are crucial for the information protection process. culture, size of the organization, amount of budget for security needs and more. The process of arranging information protection in an organization largely depends on understanding the relationship between the concepts of information security (IS) and IT security / security in the IT field, where in practice there is a harmful practice that IT security is superior. The term information protection (IS). This practice leads to neglect of non-technical aspects of information protection as part of the organization's security system, and especially in the area of developing employees' awareness of safe behavior within the organization, which ultimately gives the organization greater exposure to security challenges, risks and threats.
On the complexity of fair coin flipping

A two-party coin-flipping protocol is ε-fair if no efficient adversary can bias the output of the honest party (who always outputs a bit, even if the other party aborts) by more than ε. Cleve [STOC '86] showed that r-round o(1/r)-fair coin-flipping protocols do not exist. Awerbuch, Blum, Chor, Goldwasser, and Micali [Manuscript '85] constructed a Θ(1/r)-fair coin-flipping protocol, assuming the existence of one-way functions. Moran, Naor, and Segev [Journal of Cryptology '16] constructed an r-round coin-flipping protocol that is Θ(1/r)-fair (thus matching the aforementioned lower bound of Cleve [STOC '86]), assuming the existence of oblivious transfer. The above gives rise to the intriguing question of whether oblivious transfer, or more generally “public-key primitives,” is required for an o(1/r)-fair coin-flipping protocol. Towards answering this intriguing question, Maji and Wang [Crypto '18] have recently showed that in the random oracle model (ROM), any coin-flipping protocol can be biased by Ω(1/r). This implies that o(1/r)-fair coin-flipping protocol cannot be constructed from one-way function, or from a family of collision-resistant hash functions, in a black-box way. This result does not rule out, however, non black-box constructions, and black-box constructions based on primitives that cannot be realized in the ROM. We make a different progress towards answering above question by showing that, for any constant r∈N, the existence of an 1/(c⋅r)-fair, r-round coin-flipping protocol implies the existence of an infinitely-often key-agreement protocol, where c denotes some universal constant (independent of r). Our reduction is non black-box and makes a novel use of the recent dichotomy for two-party protocols of Haitner, Nissim, Omri, Shaltiel, and Silbak [SICOMP '20] to facilitate a two-party variant of the recent attack of Beimel, Haitner, Makriyannis, and Omri [FOCS '18] on multi-party coin-flipping protocols.
Authentication Enhancement Against SQL Injection Attacks (SQLIAs)

The Databases are subject to many potential attacks, but the most common is the SQLIA injection attack. Several types of SQLIA attack are existed, however, the most severe and rife is bypass authentication, since the authentication is the headmost and vital defensive wall. Thus, this paper proposes an approach that enhances authentication against SQL injection attack (SQLIA) namely bypass authentication.