Strengthening ties towards a highly-connected world

Online social networks provide a forum where people make new connections, learn more about the world, get exposed to different points of view, and access information that were previously inaccessible. It is natural to assume that content-delivery algorithms in social networks should not only aim to maximize user engagement but also to offer opportunities for increasing connectivity and enabling social networks to achieve their full potential. Our motivation and aim is to develop methods that foster the creation of new connections, and subsequently, improve the flow of information in the network. To achieve our goal, we propose to leverage the strong triadic closure principle, and consider violations to this principle as opportunities for creating more social links. We formalize this idea as an algorithmic problem related to the densest k-subgraph problem. For this new problem, we establish hardness results and propose approximation algorithms. We identify two special cases of the problem that admit a constant-factor approximation. Finally, we experimentally evaluate our proposed algorithm on real-world social networks, and we additionally evaluate some simpler but more scalable algorithms.

Examining the Current Definitions of Computational Thinking

Computational thinking involves understanding human behavior, designing systems and solving problems by applying the mental tools that reflect the computer science and basic concepts. Development of frameworks of computational thinking helps integrate computational thinking into education and daily life. It is important for students to start using the computational methods and tools as well as algorithmic problem solving in their educations from kindergarten level to university level. Importance of training on programming at early age was explained. In addition, the current situation of programming in education in the world was reviewed. Then curricula and projects in different countries were summarized. It is necessary to start studies at an early age to help individuals acquire these skills.

The Complexity of the Ideal Membership Problem for Constrained Problems Over the Boolean Domain

Given an ideal I and a polynomial f the Ideal Membership Problem (IMP) is to test if f ϵ I . This problem is a fundamental algorithmic problem with important applications and notoriously intractable. We study the complexity of the IMP for combinatorial ideals that arise from constrained problems over the Boolean domain. As our main result, we identify the borderline of tractability. By using Gröbner bases techniques, we extend Schaefer’s dichotomy theorem [STOC, 1978] which classifies all Constraint Satisfaction Problems (CSPs) over the Boolean domain to be either in P or NP-hard. Moreover, our result implies necessary and sufficient conditions for the efficient computation of Theta Body Semi-Definite Programming (SDP) relaxations, identifying therefore the borderline of tractability for constraint language problems. This article is motivated by the pursuit of understanding the recently raised issue of bit complexity of Sum-of-Squares (SoS) proofs [O’Donnell, ITCS, 2017]. Raghavendra and Weitz [ICALP, 2017] show how the IMP tractability for combinatorial ideals implies bounded coefficients in SoS proofs.

The reads-from equivalence for the TSO and PSO memory models

The verification of concurrent programs remains an open challenge due to the non-determinism in inter-process communication. One recurring algorithmic problem in this challenge is the consistency verification of concurrent executions. In particular, consistency verification under a reads-from map allows to compute the reads-from (RF) equivalence between concurrent traces, with direct applications to areas such as Stateless Model Checking (SMC). Importantly, the RF equivalence was recently shown to be coarser than the standard Mazurkiewicz equivalence, leading to impressive scalability improvements for SMC under SC (sequential consistency). However, for the relaxed memory models of TSO and PSO (total/partial store order), the algorithmic problem of deciding the RF equivalence, as well as its impact on SMC, has been elusive. In this work we solve the algorithmic problem of consistency verification for the TSO and PSO memory models given a reads-from map, denoted VTSO-rf and VPSO-rf, respectively. For an execution of n events over k threads and d variables, we establish novel bounds that scale as n k +1 for TSO and as n k +1 · min( n k 2 , 2 k · d ) for PSO. Moreover, based on our solution to these problems, we develop an SMC algorithm under TSO and PSO that uses the RF equivalence. The algorithm is exploration-optimal , in the sense that it is guaranteed to explore each class of the RF partitioning exactly once, and spends polynomial time per class when k is bounded. Finally, we implement all our algorithms in the SMC tool Nidhugg, and perform a large number of experiments over benchmarks from existing literature. Our experimental results show that our algorithms for VTSO-rf and VPSO-rf provide significant scalability improvements over standard alternatives. Moreover, when used for SMC, the RF partitioning is often much coarser than the standard Shasha-Snir partitioning for TSO/PSO, which yields a significant speedup in the model checking task.

Examining Some of Students’ Views on the Nature of Science (NOS) in Traditional Lecture Format Teaching Environment

The preparation of a scientifically literate society is the main goal of science education throughout the world and this has resulted in the emphasis of nature of science in the curriculum. The purpose of this research project is to examine the aforementioned students’ views on NOS tenets, its relationship to their academic achievements and background, and how it changes through their study of science. The study took place at the City College of New York, an urban, commuter, public college, and minority serving institute. The research data was collected through the administration of a survey that contained three of the NOS questions and academic and background information about the students. The data suggest that students possess inadequate understanding of the nature of science when they begin their academic fields of science study. This inadequate understanding is resistant to change in traditional science teaching settings. The data provide evidence that the inadequate understanding of nature of science does not change as the result of exposure to science courses, the field of science studied, and the students’ academic achievement as measured by grade point average. Our data show that traditional instruction in college science courses does not address nature of science and does not cause a conceptual change in the students’ understanding of NOS. The lack of correlation between students’ understanding of nature of science and credits completed or grade point average could be attributed to students relying on rote-learning and algorithmic problem-solving to achieve high grades and succeed in science, which hinders their meaningful learning of science and the development of conceptual understanding. Thus, science teaching and instruction should address naïve conception on the NOS and changes the instruction methods to consider NOS naïve conceptions and learning challenges. Science teaching and learning curriculum and instruction should immerse students in science learning activities that nurtures their understanding of the nature of science through participating in novel science research and inquiry-based learning activities.

Bag Query Containment and Information Theory

The query containment problem is a fundamental algorithmic problem in data management. While this problem is well understood under set semantics, it is by far less understood under bag semantics. In particular, it is a long-standing open question whether or not the conjunctive query containment problem under bag semantics is decidable. We unveil tight connections between information theory and the conjunctive query containment under bag semantics. These connections are established using information inequalities, which are considered to be the laws of information theory. Our first main result asserts that deciding the validity of a generalization of information inequalities is many-one equivalent to the restricted case of conjunctive query containment in which the containing query is acyclic; thus, either both these problems are decidable or both are undecidable. Our second main result identifies a new decidable case of the conjunctive query containment problem under bag semantics. Specifically, we give an exponential-time algorithm for conjunctive query containment under bag semantics, provided the containing query is chordal and admits a simple junction tree.

MP-RW-LSH

Approximate Nearest Neighbor Search (ANNS) is a fundamental algorithmic problem, with numerous applications in many areas of computer science. Locality-Sensitive Hashing (LSH) is one of the most popular solution approaches for ANNS. A common shortcoming of many LSH schemes is that since they probe only a single bucket in a hash table, they need to use a large number of hash tables to achieve a high query accuracy. For ANNS- L 2 , a multi-probe scheme was proposed to overcome this drawback by strategically probing multiple buckets in a hash table. In this work, we propose MP-RW-LSH, the first and so far only multi-probe LSH solution to ANNS in L 1 distance, and show that it achieves a better tradeoff between scalability and query efficiency than all existing LSH-based solutions. We also explain why a state-of-the-art ANNS -L 1 solution called Cauchy projection LSH (CP-LSH) is fundamentally not suitable for multi-probe extension. Finally, as a use case, we construct, using MP-RW-LSH as the underlying "ANNS- L 1 engine", a new ANNS-E (E for edit distance) solution that beats the state of the art.

Reviews of Two Programming Books

In this article I review two recent competitive programming books, published in 2020, which have not yet been presented in the Olympiads in Informatics journal. The books are Algorithmic Thinking by Daniel Zingaro and Competitive Programming in Python by Christoph Dürr and Jill-Jênn Vie. Both the books are introductory books but their approaches are different. Algorithmic Thinking focuses on the process of learning algorithmic problem solving and uses competitive programming problems as motivating challenges. Competitive Programming in Python develops skills for programming contests and job interviews, and shows how the Python language can be used in competitive programming.