Miracle-dz/UniFAR-FTUs huggingface dataset:
Overparameterization: A Connection Between Software 1.0 and Software 2.0

A new ecosystem of machine-learning driven applications, titled Software 2.0, has arisen that integrates neural networks into a variety of computational tasks. Such applications include image recognition, natural language processing, and other traditional machine learning tasks. However, these techniques have also grown to include other structured domains, such as program analysis and program optimization for which novel, domain-specific insights mate with model design. In this paper, we connect the world of Software 2.0 with that of traditional software - Software 1.0 - through overparameterization: a program may provide more computational capacity and precision than is necessary for the task at hand. In Software 2.0, overparamterization - when a machine learning model has more parameters than datapoints in the dataset - arises as a contemporary understanding of the ability for modern, gradient-based learning methods to learn models over complex datasets with high-accuracy. Specifically, the more parameters a model has, the better it learns. In Software 1.0, the results of the approximate computing community show that traditional software is also overparameterized in that software often simply computes results that are more precise than is required by the user. Approximate computing exploits this overparameterization to improve performance by eliminating unnecessary, excess computation. For example, one - of many techniques - is to reduce the precision of arithmetic in the application. In this paper, we argue that the gap between available precision and that that is required for either Software 1.0 or Software 2.0 is a fundamental aspect of software design that illustrates the balance between software designed for general-purposes and domain-adapted solutions. A general-purpose solution is easier to develop and maintain versus a domain-adapted solution. However, that ease comes at the expense of performance. We show that the approximate computing community and the machine learning community have developed overlapping techniques to improve performance by reducing overparameterization. We also show that because of these shared techniques, questions, concerns, and answers on how to construct software can translate from one software variant to the other.
On benchmarking surrogate-assisted evolutionary algorithms

In this position paper, we discuss the need for systematic benchmarking of surrogate-assisted evolutionary algorithms and give an overview of existing suitable function suites. Based on the findings, we hope to encourage more comparative studies in this field supported by benchmarks and outline how a concerted effort of the community could create better insight into the various previously proposed algorithms and concepts.
Fairness, Accountability and Transparency in Artificial Intelligence: A Case Study of Logical Predictive Models

Machine learning -- the part of artificial intelligence aimed at eliciting knowledge from data and automated decision making without explicit instructions -- is making great strides, with new algorithms being invented every day. These algorithms find myriads of applications, but their ubiquity often comes at the expense of limited interpretability, hidden biases and unexpected vulnerabilities. Whenever one of these factors is a priority, the learning algorithm of choice is often a method considered to be inherently interpretable, e.g. logical models such as decision trees. In my research I challenge this assumption and highlight (quite common) cases when the assumed interpretability fails to deliver. To restore interpretability of logical machine learning models (decision trees and their ensembles in particular) I propose to explain them with class-contrastive counterfactual statements, which are a very common type of explanation in human interactions, well-grounded in social science research. To evaluate transparency of such models I collate explainability desiderata that can be used to systematically assess and compare such methods as an addition to user studies. Given contrastive explanations, I investigate their influence on the model's security, in particular gaming and stealing the model. Finally, I evaluate model fairness, where I am interested in choosing the most fair model among all the models with equal performance.
Multi-scale and Interactive Visual Analysis of Public Bicycle System

Public bicycle system (PBS) is a new emerging and popular mode of public transportation. PBS data can be adopted to analyze human movement patterns. Previous work usually focused on specific scales, and the relationships between different levels of hierarchies are ignored. In this paper, we introduce a multi-scale and interactive visual analytics system to investigate human cycling movement and PBS usage condition. The system supports level-of-detail explorative analysis of spatio-temporal characteristics in PBS. Visual views are designed from global, regional and microcosmic scales. For the regional scale, a bicycle network is constructed to model PBS data, and an flow-based community detection algorithm is applied on the bicycle network to determine station clusters. In contrast to the previous used Louvain algorithm, our method avoids producing super-communities and generates better results. We provide two cases to demonstrate how our system can help analysts explore the overall cycling condition in the city and spatio-temporal aggregation of stations.
A System Prototype for Approximate Query Answering over Incomplete Data

Many database applications face the problem of querying incomplete data. In such scenarios, certain answers are a principled semantics of query answering. Unfortunately, the computation of certain query answers is a coNP-hard problem. To make query answering feasible in practice, recent research has focused on developing polynomial time algorithms computing a sound (but possibly incomplete) set of certain answers. In this paper we present a system prototype implementing a suite of algorithms to compute sound sets of certain answers. The central tools used by our system are conditional tables and the conditional evaluation of relation algebra. Different evaluation strategies can be applied, with more accurate ones having higher complexity, but returning more certain answers, thereby enabling users to choose the technique that best meets their needs in terms of balance between efficiency and quality of the results.
A memetic algorithm with distance-guided crossover: distributed data-intensive web service composition

Data-intensive Web service composition (DWSC) combines numerous interoperating, distributed and reusable data-intensive services to accomplish a specific task. In this paper, we will propose a Memetic Algorithm (MA) with a novel crossover operator to solve the distributed DWSC problem. In particular, we define two heuristics which enable explicit use of two important sources of information in producing offspring solutions in the MA. 1) location information from the problem to quickly determine the suitability of any solution and 2) useful information embedded in sub-solutions (e.g. common parts of the solutions). Experiments have been performed on benchmark datasets and the results show that our method is more effective than a state-of-the-art baseline method.
Comparison of GAs in black-box scenarios

In black-box optimization scenarios, researchers have no control over the fitness functions, and hence genetic algorithms (GAs) are usually compared by the number of function evaluations. Commonly used statistics include arithmetic mean, median, and standard deviation. However, these statistics can be misleading. For example, when there exist unsolvable instances within limited time, median simply ignores those instances, and arithmetic mean is not applicable at all. In this paper, we propose comparison methods from a practical point of view. Specifically we propose three use cases which cover most of the situations that GA practitioners may encounter. Among these three use cases, two of them are matchups, which requires a pair of GAs to be compared with each other, while the other provides as a standalone performance indicator of GAs.
A Complex Network Approach to Semantic Spaces: How Meaning Organizes Itself

We propose a complex network approach to the emergence of word meaning through the analysis of semantic spaces: NLP techniques able to capture an aspect of meaning based on distributional semantic theories, so that words are linked to each other if they can be substituted in the same linguistic contexts, forming clusters representing semantic fields. This approach can be used to model a mental lexicon of word similarities: a graph G = (N,L) where N are words connected by some type of semantic or associative property L. Networks extracted from a baseline neural language model are analyzed in terms of global properties: they are small world and the probability of degree distribution follows a truncated power law. Moreover, they throw in a strong degree assortativity, a peculiarity that introduces us to the problem of semantic field identification. We support the idea that semantic fields can be identified exploiting the topological information of networks. Several community discovery methods have been tested, identifying from time to time strict semantic fields as crisp communities, linguistic contexts as overlapping communities or meaning conveyed by single words as communities produced starting from a seed-set expansion.
Evolutionary optimization of epidemic control strategies for livestock disease prevention

This paper concerns optimizing counter-epidemic strategies selecting actions which differ with respect to the cost, damage to the animal population and the potential of stopping the disease. Important factors that have to be taken into account are the time it takes for the vaccine to become effective and the dynamic of the epidemic. This paper focuses on optimizing control strategies rather than individual decisions in the case of a particular epidemic. An evolutionary optimizer using simulations for solution evaluation is run in order to gather training data. Based on the training data a regression model is built which can subsequently be used to improve the results obtained when solving new instances of the proposed problem. Experimental results show, that the model can be used to transfer knowledge from previously solved instances to new ones.
A Video Learning Analytics System for Students' Learning Behavior Collection and Visualization

: Nowadays, the flipped classroom or blended-learning models have been widely implemented in school settings. In these teaching models, students are provided with online materials to gain necessary knowledge before or after class. The instructional videos are often the main learning materials which students spend much time on watching. During their learning process, large amount of interaction data of students with their video system are generated. Therefore, this paper presents a video learning analytics system to collect and visualize these behavior data. This system has five main modules, which are login, video, quiz, visualization, and database. The system can collect students' learning behavior data in the background when students watch video in the video module and gather their learning results in the quiz module. Consequently, the visualization module is used to present students' video watching tracks and learning results. Using this system, it is easy for educators and researchers to evaluate the learning effect of students via video. These evaluation results can be used to improve the quality of student learning and teacher instruction.
Social media environments effect on perceived interactivity

Purpose The purpose of this paper is to treat WeChat moments as social media environments and applies the research model to explore the effect of social media environments on perceived interactivity from the perspective of environmental psychology. Design/methodology/approach This paper proposes social media environments as effective stimuli for future participate in online social interactions. First, two cues of social media environments (user-to-system cues and user-to-user cues) can be important antecedents of users’ perception of interactivity. Second, users’ intention of future participates in online social interactions can be influenced by three dimensions of perceived interactivity (action control, connectedness and responsiveness). Using data from 334 users of WeChat moments, the authors conduct partial least squares analysis to validate the research model. Findings The results indicate that both technological and social environments positively affect three dimensions of perceived interactivity, respectively, including action control, connectedness and responsiveness. Moreover, actual findings also suggest that higher perceived interactivity increases users’ intention of future participate in online social interactions. Originality/value This work contributes to in-depth research on the relationships between social environments and perceived interactivity. Besides, this paper demonstrates that both technological and social cues of social media environments are significant elements in simulating users’ internal experience and behavioral intention. The main conclusions of this study can be valuable to social media developers and managers.
Increasing trust in meta-heuristics by using MAP-elites

Intelligent AI systems using approaches containing emergent elements often encounter acceptance problems. Results do not get sufficiently explained and the procedure itself can not be fully retraced because the flow of control is dependent on stochastic elements. Trust in such algorithms must be established so that users will accept results, without questioning whether the algorithm is sound. In this position paper we present an approach in which the user gets involved in the optimization procedure by letting them chose alternative solutions from a structure-archive which is created by the MAP-Elites algorithm. Analysis of these alternatives along the criteria of multiobjective optimization problems makes solutions comprehensible and hence is a means to build trust. We propose that the solution-focused nature of MAP-Elites allows the history of a solution to be easily shown to the user, explaining why that solution was included in those presented to the user. Here we demonstrate our ideas using a logistics problem previously explored by the authors.
Version Control Is for Your Data Too

Programmers regularly use distributed version control systems (DVCS) such as Git to facilitate collaborative software development. The primary purpose of a DVCS is to maintain integrity of source code in the presence of concurrent, possibly conﬂicting edits from collaborators. In addition to safely merging concurrent non-conﬂicting edits, a DVCS extensively tracks source code provenance to help programmers contextualize and resolve conﬂicts. Provenance also facilitates debugging by letting programmers see diﬀs between versions and quickly ﬁnd those edits that introduced the oﬀending conﬂict (e.g., via git blame ). In this paper, we posit that analogous workﬂows to collaborative software development also arise in distributed software execution; we argue that the characteristics that make a DVCS an ideal ﬁt for the former also make it an ideal ﬁt for the latter. Building on this observation, we propose a distributed programming model, called carmot that views distributed shared state as an entity evolving in time, manifested as a sequence of persistent versions, and relies on an explicitly deﬁned merge semantics to reconcile concurrent conﬂicting versions. We show examples demonstrating how carmot simpliﬁes distributed programming, while also enabling novel workﬂows integral to modern applications such as blockchains. We also describe a prototype implementation of carmot that we use to evaluate its practicality.
A new constrained multi-objective optimization problems algorithm based on group-sorting

Constrained multi-objective optimization problems(CMOPs) have wide applications in many areas. One of the difficulties in solving CMOPs is to handle constraints and optimize objective values simultaneously. In this paper, three improvements are proposed for CMOPs. Firstly, a new strategy of adaptive grouping is proposed to ensure the diversity of the algorithm. Secondly, each sub-population evolves according to the designed crossover operator, which improves the searchability of the algorithm, thus accelerates the convergence process of the algorithm. Finally, a new sorting method based on the information of representative solutions is designed to measure the quality of individuals. The experimental results show that the proposed algorithm performs better in convergence and diversity.
Medical Retrieval using Structured Information Extracted from Knowledge Bases

We investigate how semantic relations between concepts extracted from medical documents, and linked to a reference knowledge base, can be employed to improve the retrieval of medical literature. Semantic relations explicitly represent relatedness between concepts and carry high informative power that can be leveraged to improve the eﬀec-tiveness of the retrieval. We present preliminary results and show how relations are able to provide a sizable increase of the precision for several topics, albeit having no impact on others. We then discuss some future directions to minimize the impact of negative results while maximizing the impact of good results.
Secure and Efficient Storage of Video Data in a CCTV Environment

Closed-circuit television (CCTV) technology continuously captures and stores video streams. Users are typically required by policy to store all the captured video for a certain period. Accordingly, increasing the number of CCTV operation cycles and photographing positions expands the amount of data to be stored. However, expanding the available storage space for video data incurs increased costs. In recent years, this problem has been addressed with cloud storage solutions, which enable multiple users and devices to access and store data simultaneously. However, because of the large amount of data to be stored, a vast storage space is required. Consequently, cloud storage administrators need a way to store data more efficiently. To save storage space, deduplication technology has been proposed to prevent duplicate storage of the same data. However, because cloud storage is hosted on remote servers, data encryption technology must be applied to address data exposure issues. Although deduplication techniques for encrypted data have been studied, there have been various security vulnerabilities. We attempted to solve this problem by addressing various issues such as poison attacks, property forgery, and ownership management while removing the redundant data and handling the data more securely.
Solving legends of the three kingdoms based on hierarchical macro strategy model

Legends of the Three Kingdoms is a Chinese card game based on the novel Romance of the Three Kingdoms. As a turn-based strategy game, Legends of the Three Kingdoms is an imperfect information game where agents interact with one another to deduce the unknown information. The most famous solution for an imperfect information game is to find a Nash equilibrium where no agent can benefit from changing strategies. Tencent AI Lab has recently developed a novel learning-based Hierarchical Macro Strategy model for AI to master MOBA games, a sub-genre of RTS games. In this paper, we adopt this method on our AI to solve Legends of the Three Kingdoms in a shrunken size of 4 players.
Automatic Identification of Bond Information Based on OCR and NLP

: With the rapid development of the financial industry, the realization of automatic recognition of bond information is a hot issue in the current financial science and technology research. This paper realizes the automatic recognition of bond information through the combination of OCR and NLP technology. The text information of bond is extracted from the picture by OCR technology. In view of various kinds of interference in the image, the difference subtraction operation based on HSV model of color image is adopted to get better image information for image-based sequence model. The image-based sequence model is composed of convolution neural network and recursive neural network. The bond information can be well recognized by the model. Finally, the text information is processed by the algorithm based on Levenshtein distance, and the key-value is extracted, which has a certain practical value.
Evolutionary refinement of the 3D structure of multi-domain protein complexes from small angle X-ray scattering data

DADIMODO is an Evolutionary Algorithm based software program for solving an inverse problem arising from biological Small Angle X-ray Scattering (SAXS) data analysis. The problem consists in refining the three-dimensional model of a multi-domain protein complex against SAXS experimental data. We use an "all-atoms" structure representation allowing for an energy control for every newly generated model so as to prevent steric clashes and converge to a physically feasible structure. Sophisticated variation operators had to be specifically tailored for this application and constitute our optimization algorithm's particularity. Atomic structure manipulations being computationally expensive, we report the critical implementation on a computer cluster for parallelized feasibility check and evaluation stage. DADIMODO has been successfully used in many biostructural data analysis cases and was recently made available to the corresponding research community as a web service.
Towards a Domain-Specific AOP language for Ubiquitous Computing

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Theoretical Window Size for Classification in the Presence of Sudden Concept Dr ift

In classifying sequential data, a new classifier is needed af ter a sudden concept change. However, the old classifier may b e better than a new classifier trained on a small window of new data. We d erive a general formula for the size of this window, with a closed-form expression for two equiprobable Gaussian classes. Numerical experiments demonstrate that swapping the classifiers after the window has been acquired is better than using the new classifier right after the change or not modifying the class ifier at all.