bluuebunny/crossref_metadata_2025 huggingface dataset:
Green synthesis of iron oxide nanoparticles usingCeratonia siliqua L.aqueous extract: improvement of colloidal stability by optimizing synthesis parameters, and evaluation of antibacterial activity against Gram-positive and Gram-negative bacteria
Authors: Aksu Demirezen Derya , Yılmaz Şeyda , Demirezen Yılmaz Dilek , Yıldız Yalçın Şevki
Published: September 2022

This study focused on the colloidal stability enhancement of iron oxide nanoparticles synthesized using aqueous extract of theCeratonia siliqua L.(carob pod) by optimizing the synthesis parameters. The synthesis parameters were determined as the concentration of iron ions, the concentration of extract,pHof extract, temperature, stirring rate, and reaction time. The significance of the studied factors in controlling the particle size distribution of nanoparticles was quantitatively evaluated via analysis of variance (ANOVA). Iron oxide nanoparticles were produced with an average zeta potential of +41 ± 0.8 mV, hydrodynamic size of 78 ± 22 nm, and a polydispersity value of 0.42 ± 0.06, respectively. As a result of the stability study by measuring the zeta potential, it was determined that the colloidal stability was maintained for 3 months. Green iron oxide nanoparticles (gIONPs) showed inhibition zones of 24.27 ± 0.12 mm and 20.83 ± 0.11 mm in 250 mg/mL concentration against Gram-negative (Escherichia coli) and Gram-positive (Staphylococcus aureus) bacterial strains, respectively.S. aureuswas susceptible to the gIONPs according to the standard antibiotics of Cefotaxime (≥23 mm), Tetracycline (≥19 mm), Gentamicin (≥15 mm), and Cefoxitin (≥22 mm).E. coliwas susceptible to the gIONPs according to the standard antibiotics of Tetracycline (≥19 mm) and Gentamicin (≥15 mm), but showed resistance to the Cefotaxime (15–22 mm) and Cefoxitin (≤21 mm) standard antibiotics. This study suggests that the green synthesized iron oxide nanoparticles could be used as an antimicrobial agent and a promising candidate for usage in sensor, biomedical, and electronics applications for being in a highly stable structure.
On the general position number of two classes of graphs
Authors: Yao Yan , He Mengya , Ji Shengjin
Published: September 2022

The general position problem is to find the cardinality of the largest vertex subset

S

S

such that no triple of vertices of

S

S

lies on a common geodesic. For a connected graph

G

G

, the cardinality of

S

S

is denoted by

gp

(

G

)

{\rm{gp}}\left(G)

and called the

gp

{\rm{gp}}

-number (or general position number) of

G

G

. In the paper, we obtain an upper bound and a lower bound regarding the

gp

{\rm{gp}}

-number in all cacti with

k

k

cycles and

t

t

pendant edges. Furthermore, the exact value of the

gp

{\rm{gp}}

-number on wheel graphs is determined.
Over represented – under represented: the juxtaposition of Black males in special education programs
Authors: Gibson Lenwood
Published: September 2022

The disproportionate, over-representation of Black boys in special education continues to be a matter of social injustice and inequity in education. Many Black students are placed in special education due to subjective identification processes. This is especially true when it comes to classifications such as Emotional Behavior Disorders (EBD). Black boys classified with EBD are more likely to be educated in exclusionary settings, and once placed, are less likely to return to integrated environments. This often leads to poor educational and life outcomes. The topic of disproportionality in special education has been studied and discussed over the past several decades, yet the problems persist. The purpose of this article is to discuss reasons for the over representation of Black boys who are identified as having EBD. Moreover, this discussion is extended to include the disproportionate, under-representation of Black men as special education professionals, classroom teachers and full-time faculty members in special education teacher preparation programs. The impact of this juxtaposition continues to exacerbate both sides of disproportionality of Black males in special education.
“Life is in the Ears”: what Black learners should hear from general and special educators
Authors: Obiakor Festus E.
Published: September 2022

What Black learners hear from their general and special education professionals affect how they communicate and interact in classrooms and other learning situations and environments. Most students, no matter how young, are very perceptive—they can tell if the teacher or the learning environment is welcoming or unwelcoming. When the environment is not welcoming, Black learners feel psychologically disenfranchised, disadvantaged, and disillusioned. And, when the environment is welcoming, these learners feel motivated and inspired to maximize their fullest potential. Rather than engage Black learners with implicit and explicit messages that are results of negative assumptions, unrealistic expectations, illusory generalizations, and systemic racist statements, general and special educators should send inspiring messages to their learners while also trying to hear from them to allow their silenced voices to be visible in school programs. This is the premise of this article.
Preparation of cuprous oxide-supported silver-modified reduced graphene oxide nanocomposites for non-enzymatic electrochemical sensor
Authors: Qian Yuhong , Wang Yiting , Wang Li
Published: September 2022

We constructed a non-enzymatic H2O2 sensor based on cuprous oxide-supported silver-modified reduced graphene oxide nanocomposites. It was found that the sensor exhibited good performances for sensing H2O2 with a detection limit of 0.34 μM and a wide detection range of 1–310 μM. The combination of graphene with silver and cuprous oxide improved the sensor’s sensitivity for detecting H2O2, with good repeatability, selectivity, and stability. The synthesis method of this nanocomposite provides a new idea for the green preparation of graphene-based nanocomposites and a new method for the construction of a new electrochemical sensor platform.
Towards a new typology of the referential information structure of specificational it-clefts
Authors: Bourgoin Charlotte
Published: September 2022

This paper investigates the referential information structure (IS) of full and reduced specificational it-clefts, which interacts as a distinct layer with their relational IS. Drawing on spontaneous spoken data from the London-Lund Corpus, this study examines how discourse-new and discourse-given information, i.e., referential IS, is distributed over the clefted noun phrase (NP) and cleft-relative clause. To assess the discourse-familiarity of nominal referents and open propositions, I develop an analytical model hinging on the predictability of information in accordance with the prospective dynamic of spoken language. The findings show that the distribution of given and new information is more diverse than described in existing typologies. In particular, it is revealed that the hitherto overlooked pattern in which both value and variable are discourse-given and in which the specification relation may be new or given is in fact the most common one. The findings also show that the choice between full and reduced it-clefts as two basic options is only partly motivated by the discourse-familiarity of the variable. The analysis of the prosodically coded relational IS reveals that multiple prosodic patterns can be mapped onto each category of clefts, thus demonstrating that the conflation of the referential and relational IS of it-clefts is untenable.
Die externe Begutachtung als Mittel zum Zweck – über die Herstellung von Legitimation in Berufungsverfahren an deutschen Universitäten
Authors: Walther Lisa
Published: September 2022

Die externe Begutachtung ist ein wesentliches Element des Berufungsverfahrens, das bei der Beurteilung der Legitimität der von der Berufungskommission erstellten Listenreihung der Bewerber:innen von kommissionsexternen Akteur:innen herangezogen wird. Mittels eines explorativen Forschungsprozesses wird die Frage beantwortet, wie die Kommission diese Legitimation herzustellen versucht. Es wird gezeigt, dass die Kommission unterschiedlichen Maßstäben der Legitimität unterliegt. Die Einhaltung des rechtlichen Rahmens dient der formalen Legitimation der Entscheidung; die inhaltliche Legitimation wird über die Einholung und Berücksichtigung der Fachexpertise hergestellt. Ferner wird gezeigt, wie diese Anforderungen über Praktiken der Kommission im Umgang mit dem Bewertungsprozess erfüllt werden. Die gewonnenen Erkenntnisse werden unter Einbeziehung bewertungssoziologischer Überlegungen diskutiert.
A könyvtárak 2050-ben
Authors: Tóth Máté
Published: September 2022

A könyvtárak több évezred óta léteznek. Az intézményrendszer fennállásának legitimációját minden korszak megtalálta, de jellemzően különböző ideológiák mentén írták le a könyvtárak szükségességét. Az elmúlt évtizedekben az információszolgáltatás és az információs esélyegyenlőség biztosítása volt a könyvtárakat legitimáló legfontosabb tényező. Az elmúlt évek kutatásai és adatai azonban azt sugallják, hogy ez a paradigma nem fogadható el kritikátlanul. A tanulmány célja, hogy az elmúlt évek kutatásai és a szerző személyes tapasztalatai alapján felrajzoljon a könyvtárak, a könyvtári rendszer és a könyvtárosok számára egy szubjektív jövőkép pontjait. 1. A könyvtárak funkciórendszere folyamatosan gazdagodik; 2. A könyvtár továbbra is elsősorban a magasan iskolázott, és magasabb kulturális igényszinttel is rendelkező városi értelmiségi réteg kedvelt intézménye; 3. A könyvtár egyre gazdagabb funkciórendszerében továbbra is egyetlen olyan tevékenység van, amelyben ezek az intézmények monopolhelyzetben vannak, ez pedig a könyvek kölcsönzése; 4. A könyvtár független találkozóhely, közösségi tér, amely a dokumentumok és információk passzív elérhetővé tétele helyett aktívan bemutatja a tartalmakat, ösztönzi az embereket az egymás és az intézményben tárolt tudásvagyon megismerésére. A jövő könyvtára találkozásokat generál, inspirál és véleményalkotásra ösztönöz.
“That’s Why They Call It Window Pain”
Authors: Shelton Stephanie Anne , Melchior Shelly
Published: September 2022

We are both first-generation women PhDs who survived traumatic, abusive childhoods, and found ourselves caretakers of those who once cared for us, in the height of COVID-19 lockdowns. The pandemic complicated our responsibilities in un/comfortable and un/expected ways, as care of parents and academic positions, all differently fragile, required negotiating dis/connections between academia and family, all while the virus and uncertainty hung thick in the air. This article is written as a collaborative travelogue with personal pictures and narratives, to emphasize our traversing back and forth between these worlds, and often pausing on our journeys to rest, to worry, to cry, and to celebrate, in the in-between spaces. Gloria Anzaldúa’s (2000; Anzaldúa & Keating, 2002) discussions of liminalities as uncomfortable and sometimes desirable and sometimes intentional guide our journeys, as does Sara Ahmed’s (2017) emphasis on the power to stretch spaces of discomfort to find pleasure and comfort in traveling through/settling into liminal spaces.
Effect of surfactant type on the magnetic and morphological properties of NiFeO4 / reduced graphene oxide composites
Authors: Boychuk V.M. , Kotsyubynsky V.O. , Rachiy B.I. , Budzulyak I.M. , Zapukhlyak R.I. , Turovska L.V. , Fedorchenko S.V. , Khatsevych O.M.
Published: September 2022

A systematic study of the effect of various types of surfactants (polyethylene glycol, Trinon X-100 and cetyltrimethylammonium bromide) and subsequent annealing on the magnetic ordering and morphology of the nickel ferrite / reduced graphene oxide composite materials has been carried out. Hydrazine hydrate has been used both as an agent to initiate precipitation and as a reducing agent to remove oxygen functionality from the surface of graphene oxide particles. To characterize the material, Mössbauer spectroscopy and nitrogen absorption porosimetry have been used. The obtained result suggests a new way for one-pot synthesis of NiFe2O4/rGO composite materials with controlled magnetic ordering and porous structure.
LAS REGLAS DEL DERECHO PENAL Y UNA APROXIMACIÓN A LOS ELEMENTOS OBJETIVOS DEL PREVARICATO
Authors: Flores Zerpa Allen Martí
Published: September 2022

En el presente trabajo se estudia el delito de prevaricato y su relación con las reglas de competencia. Para ello se analiza el concepto de norma y regla para el Derecho y en especial para el Derecho Penal. En la segunda parte se obtienen consecuencias para el delito de prevaricato a partir de distinguir entre reglas de competencia, reglas de conducta y de consideraciones sobre la justificación del razonamiento jurídico.
Analysis of mixed pulping raw materials of Eucalyptus globulus and Acacia mangium by near infrared spectroscopy technique combined with LASSO algorithm
Authors: Wu Ting , Fang Guigan , Liang Long , Deng Yongjun , Lin Yan , Xiong Zhixin
Published: September 2022

To meet the current demand in China for Eucalyptus globulus and Acacia mangium mixed pulping, a study was conducted to collect the near infrared (NIR) spectra of 150 mixed samples of E. globulus and A. mangium in which the content of E. globulus was manually controlled. After the original spectra were pretreated by first derivative and standard normal variate (SNV), the least absolute shrinkage and selection operator (LASSO) algorithm and cross-validation were used to calculate the optimal adjustment parameters of 14.30, 19.16, 12.10, and 9.74, respectively. The optimal calibration models for the content of E. globulus, holocellulose, pentosan, and acid insoluble lignin were generated. An independent verification of the calibration models showed that the root mean square error of prediction (RMSEP) for these models was 1.59%, 0.54%, 0.66%, and 0.40%, respectively. The absolute deviation (AD) was -2.58% to 2.73%, -0.91% to 0.84%, -1.19% to 1.06%, and -0.61% to 0.64%, respectively. The prediction performance of the four models was sufficient for real-time analysis in the pulping production line. The LASSO algorithm was judged to be efficient for the prediction and analysis of mixed raw materials in pulping industry.
Gamma range auditory-steady state responses as correlates of individual cognitive performance
Authors:   , Parčiauskaitė Vykinta
Published: September 2022

Gama diapazono klausos nuostovieji atsakai, kaip individualių kognityvinių gebėjimų koreliatai
Development and Implementation of a Peer Curbside Consult Service for Pediatric Hospitalists
Authors: O’Neill Laura B. , Bhansali Priti , Rush Margaret , Stokes Stacey , Todd Stephanie , Shah Neha H.
Published: September 2022

OBJECTIVE

To describe the development and implementation of a Peer Curbside Consult Service (PCCS) for a pediatric hospital medicine (PHM) division.

METHODS

We developed a pilot intervention with hospitalists at a freestanding children’s hospital to provide peer consultation services for challenging clinical cases. Postconsultation surveys collected from both the requesting and consulting hospitalists provided feedback about the program. The 12-point Template for Intervention Description and Replication (TIDieR) checklist is used to describe the process for program creation and implementation.

RESULTS

The PCCS has provided 60 consultations in the first 2 years since implementation in April 2020 and supports a large PHM division with >75 members who practice at a tertiary care, freestanding children’s hospital and 7 affiliate sites. Hospitalists request peer consultation for challenging clinical cases. The consultations were typically conducted in person or via telephone. Currently, 11 PHM faculty members within the division volunteer as consultants, with 2 assigned per week. Electronic postconsultation experience surveys were received from 70% of requesting and 89% of consultant hospitalists. We also provide preliminary data from this pilot intervention in the Supplemental Information.

CONCLUSIONS

We successfully established a peer consult service that provided just-in-time clinical decision support across the various practice sites. Through transparent reporting using the TIDieR checklist, other divisions may be able to replicate and adapt their own peer consult program.
A Fast Heuristic Search Algorithm for Finding the Longest Common Subsequence of Multiple
Strings
Authors: Wang Qingguo , Pan Mian , Shang Yi , Korkin Dmitry
Published: September 2022

Finding the longest common subsequence (LCS) of multiple strings is an NP-hard problem,
with many applications in the areas of bioinformatics and computational genomics. Although significant efforts
have been made to address the problem and its special cases, the increasing complexity and size of biological
data require more efficient methods applicable to an arbitrary number of strings. In this paper, a novel search
algorithm, MLCS-A*, is presented for the general case of multiple LCS (or MLCS) problems. MLCS-A* is a variant
of the A* algorithm. It maximizes a new heuristic estimate of the LCS in each search step so that the longest
common subsequence can be found. As a natural extension of MLCS-A*, a fast algorithm, MLCS-APP, is also proposed
to deal with large volume of biological data for which finding a LCS within reasonable time is impossible. The
benchmark test shows that MLCS-APP is able to extract common subsequences close to the optimal ones and that
MLCS-APP significantly outperforms existing heuristic approaches. When applied to 8 protein domain families,
MLCS-APP produced more accurate results than existing multiple sequence alignment methods.
Extraction and Visualization of Implicit Social Relations on Social Networking Services
Authors: Song Meesun , Lee Wonkyu , Kim Junghwan
Published: September 2022

Today social network services like blogs, communities, and social networking sites dominate the web. As Web 2.0 has evolved this way, analyzing social networks has become a promising research issue. There have already been several researches on social network analysis based on users' activities in social services. Most of them focus on the links among the users such as citation, trackback, and comment. However, few studies have analyzed the relations within message threads. In general, they considered the one-way relationship from a comment writer to a post writer. Since users communicate with each other primarily by posting comments one after another, the message threads are key to analyzing latent social relationships. In this paper, we propose a novel method to extract the social relations hidden behind message threads. To evaluate our algorithms, we developed an evaluation system and measured the performances. In addition, since the typical node-edge diagram for social network visualization is not intuitive or readable, we also introduce a novel visualization and interaction method suitable for social relation exploration. Further, we expect our work will help enhance social recommendations, advertisements and personalization.
Automated Modelling and Solving in Constraint Programming
Authors: O'Sullivan Barry
Published: September 2022

Constraint programming can be divided very crudely into modeling and solving. Modeling defines the problem, in terms of variables that can take on different values, subject to restrictions (constraints) on which combinations of variables are allowed. Solving finds values for all the variables that simultaneously satisfy all the constraints. However, the impact of constraint programming has been constrained by a lack of "user-friendliness''. Constraint programming has a major "declarative" aspect, in that a problem model can be handed off for solution to a variety of standard solving methods. These methods are embedded in algorithms, libraries, or specialized constraint programming languages. To fully exploit this declarative opportunity however, we must provide more assistance and automation in the modeling process, as well as in the design of application-specific problem solvers. Automated modelling and solving in constraint programming presents a major challenge for the artificial intelligence community. Artificial intelligence, and in particular machine learning, is a natural field in which to explore opportunities for moving more of the burden of constraint programming from the user to the machine. This paper presents technical challenges in the areas of constraint model acquisition, formulation and reformulation, synthesis of filtering algorithms for global constraints, and automated solving. We also present the metrics by which success and progress can be measured.
The Tree Representation of Feasible Solutions for the TSP with Pickup and Delivery and LIFO Loading
Authors: Tu Dejian , Guo Songshan , Qin Hu , Oon Wee-Chong , Lim Andrew
Published: September 2022

The feasible solutions of the traveling salesman problem with pickup and delivery (TSPPD) are represented by vertex lists in existing literature. However, when the TSPPD requires that the loading and unloading operations must be performed in a last-in-first-out (LIFO) manner, we show that its feasible solutions can be represented by trees. Consequently, we develop a variable neighbourhood search (VNS) heuristic for the TSPPD with last-in-first-out loading (TSPPDL) involving several search operators based on the tree data structure. Experiments show that our VNS heuristic is superior to the current best heuristics for TSPPDL in terms of both solution quality and computing time.
Search Space Reduction Using Swamp Hierarchies
Authors: Pochter Nir , Zohar Aviv , Rosenschein Jeffrey , Felner Ariel
Published: September 2022

In various domains, such as computer games, robotics, and transportation networks, shortest paths may need to be found quickly. Search time can be significantly reduced if it is known which parts of the graph include ``swamps''---areas that cannot lie on the only available shortest path, and can thus safely be pruned during search. We introduce an algorithm for detecting hierarchies of swamps, and exploiting them. Experiments support our claims of improved efficiency, showing significant reduction in search time.
Fast Local Search Algorithm for Weighted Feedback Arc Set in Tournaments
Authors: Fomin Fedor , Lokshtanov Daniel , Raman Venkatesh , Saurabh Saket
Published: September 2022

We present a fast local search algorithm that finds an improved solution (if there is any) in the k-exchange neighborhood of the given solutionto an instance of Weighted Feedback Arc Set in Tournaments. More precisely,given an arc weighted tournament T on n vertices and a feedback arc set F (a set of arcs whose deletion from T turns it into a directed acyclic graph), our algorithm decides in time O(2o(k) n log n) if there is a feedback arc set of smaller weight and that differs from F in at most k arcs. To our knowledge this is the first algorithm searching the k-exchange neighborhood of an NP-complete problem that runs in (parameterized) subexponential time. Using this local search algorithm for Weighted Feedback Arc Set in Tournaments, we obtain subexponential time algorithms for a local search variant of Kemeny Ranking — a problem in social choice theory and of One-Sided Cross Minimization — a problem in graph drawing.
Integrating Constraint Satisfaction and Spatial Reasoning
Authors: Kurup Unmesh , Cassimatis Nicholas
Published: September 2022

Many problems in AI, including planning, logical reasoning and probabilistic inference, have been shown to reduce to (weighted) constraint satisfaction. While there are a number of approaches for solving such problems, the recent gains in efficiency of the satisfiability approach have made SAT solvers a popular choice. Modern propositional SAT solvers are efficient for a wide variety of problems. However, particularly in the case of spatial reasoning, conversion to propositional SAT can sometimes result in a large number of variables and/or clauses. Moreover, spatial reasoning problems can often be more efficiently solved if the agent is able to exploit the geometric nature of space to make better choices during search and backtracking. The result of these two drawbacks — larger problem sizes and inefficient search — is that even simple spatial constraint problems are often intractable in the SAT approach. In this paper we propose a spatial reasoning system that provides significant performance improvements in constraint satisfaction problems involving spatial predicates. The key to our approach is to integrate a diagrammatic representation with a DPLL-based backtracking algorithm that is specialized for spatial reasoning. The resulting integrated system can be applied to larger and more complex problems than current approaches and can be adopted to improve performance in a variety of problems ranging from planning to probabilistic inference
Decomposed Utility Functions and Graphical Models for Reasoning about Preferences
Authors: Brafman Ronen , Engel Yagil
Published: September 2022

Recently, Brafman and Engel (2009) proposed new concepts of marginal and conditional utility that obey additive analogues of the chain rule and Bayes rule, which they employed to obtain a directed graphical model of utility functions that resembles Bayes nets. In this paper we carry this analogy a step farther by showing that the notion of utility independence, built on conditional utility, satisfies identical properties to those of probabilistic independence. This allows us to formalize the construction of graphical models for utility functions, directed and undirected, and place them on the firm foundations of Pearl and Paz's axioms of semi-graphoids. With this strong equivalence in place, we show how algorithms used for probabilistic reasoning such as Belief Propagation (Pearl 1988) can be replicated to reasoning about utilities with the same formal guarantees, and open the way to the adaptation of additional algorithms.
Space Efficient Evaluation of ASP Programs with Bounded Predicate Arities
Authors: Eiter Thomas , Faber Wolfgang , Mushthofa Mushthofa
Published: September 2022

Answer Set Programming (ASP) has been deployed in many applications, thanks to the availability of efficient solvers. Most programs encountered in practice have an important property: Their predicate arities are bounded by a constant, and in this case it is known that the relevant computations can be done using polynomial space. However, all competitive ASP systems rely on grounding, due to which they may use exponential space for these programs. We present three evaluation methods that respect the polynomial space bound and a generic framework architecture for realization. Experimental results for a prototype implementation indicate that the methods are effective. They show not only benign space consumption, but interestingly also good runtime compared to some state of the art ASP solvers.
Ordered Completion for First-Order Logic Programs on Finite Structures
Authors: Asuncion Vernon , Lin Fangzhen , Zhang Yan , Zhou Yi
Published: September 2022

In this paper, we propose a translation from normal first-order logic programs under the answer set semantics to first-order theories on finite structures. Specifically, we introduce ordered completions which are modifications of Clark's completions with some extra predicates added to keep track of the derivation order, and show that on finite structures, classical models of the ordered-completion of a normal logic program correspond exactly to the answer sets (stable models) of the logic program.
Situation Calculus as Answer Set Programming
Authors: Lee Joohyung , Palla Ravi
Published: September 2022

We show how the situation calculus can be reformulated in terms of the first-order stable model semantics. A further transformation into answer set programs allows us to use an answer set solver to perform propositional reasoning about the situation calculus. We also provide an ASP style encoding method for Reiter's basic action theories, which tells us how the solution to the frame problem in ASP is related to the solution in the situation calculus.
Trust Models and Con-Man Agents: From Mathematical to Empirical Analysis
Authors: Salehi-Abari Amirali , White Tony
Published: September 2022

Recent work has demonstrated that several trust and reputation models can be exploited by malicious agents with cyclical behaviour. In each cycle, the malicious agent with cyclical behaviour first regains a high trust value after a number of cooperations and then abuses its gained trust by engaging in a bad transaction. Using a game theoretic formulation, Salehi-Abari and White have proposed the AER model that is resistant to exploitation by cyclical behaviour. Their simulation results imply that FIRE, Regret, and a model due to Yu and Singh, can always be exploited with an appropriate value for the period of cyclical behaviour. Furthermore, their results demonstrate that this is not so for the proposed adaptive scheme. This paper provides a mathematical analysis of the properties of five trust models when faced with cyclical behaviour of malicious agents. Three main results are proven. First, malicious agents can always select a cycle period that allows them to exploit the four models of FIRE, Regret, Probabilistic models, and Yu and Singh indefinitely. Second, malicious agents cannot select a single, finite cycle period that allows them to exploit the AER model forever. Finally, the number of cooperations required to achieve a given trust value increases monotonically with each cycle. In addition to the mathematical analysis, this paper empirically shows how malicious agents can use the theorems proven in this paper to mount efficient attacks on trust models.
Approximation Algorithms and Mechanism Design for Minimax Approval Voting
Authors: Caragiannis Ioannis , Kalaitzis Dimitris , Markakis Evangelos
Published: September 2022

We consider approval voting elections in which each voter votes for a (possibly empty) set of candidates and the outcome consists of a set of k candidates for some parameter k, e.g., committee elections. We are interested in the minimax approval voting rule in which the outcome represents a compromise among the voters, in the sense that the maximum distance between the preference of any voter and the outcome is as small as possible. This voting rule has two main drawbacks. First, computing an outcome that minimizes the maximum distance is computationally hard. Furthermore, any algorithm that always returns such an outcome provides incentives to voters to misreport their true preferences. In order to circumvent these drawbacks, we consider approximation algorithms, i.e., algorithms that produce an outcome that approximates the minimax distance for any given instance. Such algorithms can be considered as alternative voting rules. We present a polynomial-time 2-approximation algorithm that uses a natural linear programming relaxation for the underlying optimization problem and deterministically rounds the fractional solution in order to compute the outcome; this result improves upon the previously best known algorithm that has an approximation ratio of 3. We are furthermore interested in approximation algorithms that are resistant to manipulation by (coalitions of) voters, i.e., algorithms that do not motivate voters to misreport their true preferences in order to improve their distance from the outcome. We complement previous results in the literature with new upper and lower bounds on strategyproof and group-strategyproof algorithms.
Coalitional Structure Generation in Skill Games
Authors: Bachrach Yoram , Meir Reshef , Jung Kyomin , Kohli Pushmeet
Published: September 2022

We consider optimizing the coalition structure in Coalitional Skill Games (CSGs), a succinct representation of coalitional games. In CSGs, the value of a coalition depends on the tasks its members can achieve. The tasks require various skills to complete them, and agents may have different skill sets. The optimal coalition structure is a partition of the agents to coalitions, that maximizes the sum of utilities obtained by the coalitions. We show that CSGs can represent any characteristic function, and consider optimal coalition structure generation in this representation. We provide hardness results, showing that in general CSGs, as well as in very restricted versions of them, computing the optimal coalition structure is hard. On the positive side, we show that the problem can be reformulated as constraint satisfaction on a hyper graph, and present an algorithm that finds the optimal coalition structure in polynomial time for instances with bounded tree-width and number of tasks.
Complexity of Computing Optimal Stackelberg Strategies in Security Resource Allocation Games
Authors: Korzhyk Dmytro , Conitzer Vincent , Parr Ronald
Published: September 2022

Recently, algorithms for computing game-theoretic solutions have been deployed in real-world security applications, such as the placement of checkpoints and canine units at Los Angeles International Airport. These algorithms assume that the defender (security personnel) can commit to a mixed strategy, a so-called Stackelberg model. As pointed out by Kiekintveld et al. (2009), in these applications, generally, multiple resources need to be assigned to multiple targets, resulting in an exponential number of pure strategies for the defender. In this paper, we study how to compute optimal Stackelberg strategies in such games, showing that this can be done in polynomial time in some cases, and is NP-hard in others.
Stability and Incentive Compatibility in a Kernel-Based Combinatorial Auction
Authors: Lahaie Sebastien
Published: September 2022

We present the design and analysis of an approximately incentive-compatible combinatorial auction. In just a single run, the auction is able to extract enough value information from bidders to compute approximate truth-inducing payments. This stands in contrast to current auction designs that need to repeat the allocation computation as many times as there are bidders to achieve incentive compatibility. The auction is formulated as a kernel method, which allows for flexibility in choosing the price structure via a kernel function. Our main result characterizes the extent to which our auction is incentive-compatible in terms of the complexity of the chosen kernel function. Our analysis of the auction's properties is based on novel insights connecting the notion of stability in statistical learning theory to that of universal competitive equilibrium in the auction literature.
Integrating Sample-Based Planning and Model-Based Reinforcement Learning
Authors: Walsh Thomas , Goschin Sergiu , Littman Michael
Published: September 2022

Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, despite their exponentially large state spaces. Unfortunately, these algorithms all require access to a planner that computes a near optimal policy, and while many traditional MDP algorithms make this guarantee, their computation time grows with the number of states. We show how to replace these over-matched planners with a class of sample-based planners — whose computation time is independent of the number of states — without sacrificing the sample-efficiency guarantees of the overall learning algorithms. To do so, we define sufficient criteria for a sample-based planner to be used in such a learning system and analyze two popular sample-based approaches from the literature. We also introduce our own sample-based planner, which combines the strategies from these algorithms and still meets the criteria for integration into our learning system. In doing so, we define the first complete RL solution for compactly represented (exponentially sized) state spaces with efficiently learnable dynamics that is both sample efficient and whose computation time does not grow rapidly with the number of states.
Enhancing ASP by Functions: Decidable Classes and Implementation Techniques
Authors: Calimeri Francesco , Cozza Susanna , Ianni Giovambattista , Leone Nicola
Published: September 2022

This paper summarizes our line of research about the introduction of function symbols (functions) in Answer Set Programming (ASP) – a powerful language for knowledge representation and reasoning. The undecidability of reasoning on ASP with functions, implied that functions were subject to severe restrictions or disallowed at all, drastically limiting ASP applicability. We overcame most of the technical difficulties preventing this introduction, and we singled out a highly expressive class of programs with functions (FG-programs), allowing the (possibly recursive) use of function terms in the full ASP language with disjunction and negation. Reasoning on FG-programs is decidable, and they can express any computable function (causing membership in this class to be semi-decidable). We singled out also FD-programs, a subset of FG-programs which are effectively recognizable, while keeping the computability of reasoning. We implemented all results into the DLV system, thus obtaining an ASP system allowing to encode any computable function in a rich and fully declarative KRR language, ensuring termination on every FG program. Finally, we singled out the class of DFRP programs, where decidability of reasoning is guaranteed and Prolog-like functions are allowed.
Automatic Derivation of Finite-State Machines for Behavior Control
Authors: Bonet Blai , Palacios Hector , Geffner Hector
Published: September 2022

Finite-state controllers represent an effective action selection mechanisms widely used in domains such as video-games and mobile robotics. In contrast to the policies obtained from MDPs and POMDPs, finite-state controllers have two advantages: they are often extremely compact, and they are general, applying to many problems and not just one. A limitation of finite-state controllers, on the other hand, is that they are written by hand. In this paper, we address this limitation, presenting a method for deriving controllers automatically from models. The models represent a class of contingent problems where actions are deterministic and some fluents are observable. The problem of deriving a controller is converted into a conformant problem that is solved using classical planners, taking advantage of a complete translation into classical planning introduced recently. The controllers derived are ‘general’ in the sense that they do not solve the original problem only, but many variations as well, including changes in the size of the problem or in the uncertainty of the initial situation and action effects. Several experiments illustrating the automatic derivation of controllers are presented.
Constraint Programming for Data Mining and Machine Learning
Authors: De Raedt Luc , Guns Tias , Nijssen Siegfried
Published: September 2022

Machine learning and data mining have become aware that using constraints when learning patterns and rules can be very useful. To this end, a large number of special purpose systems and techniques have been developed for solving such constraint-based mining and learning problems. These techniques have, so far, been developed independently of the general purpose tools and principles of constraint programming known within the field of artificial intelligence. This paper shows that off-the-shelf constraint programming techniques can be applied to various pattern mining and rule learning problems (cf. also (De Raedt, Guns, and Nijssen 2008; Nijssen, Guns, and De Raedt 2009)). This does not only lead to methodologies that are more general and flexible, but also provides new insights into the underlying mining problems that allow us to improve the state-of-the-art in data mining. Such a combination of constraint programming and data mining raises a number of interesting new questions and challenges.
Activity and Gait Recognition with Time-Delay Embeddings
Authors: Frank Jordan , Mannor Shie , Precup Doina
Published: September 2022

Activity recognition based on data from mobile wearable devices is becoming an important application area for machine learning. We propose a novel approach based on a combination of feature extraction using time-delay embedding and supervised learning. The computational requirements are considerably lower than existing approaches, so the processing can be done in real time on a low-powered portable device such as a mobile phone. We evaluate the performance of our algorithm on a large, noisy data set comprising over 50 hours of data from six different subjects, including activities such as running and walking up or down stairs. We also demonstrate the ability of the system to accurately classify an individual from a set of 25 people, based only on the characteristics of their walking gait. The system requires very little parameter tuning, and can be trained with small amounts of data.
The Boosting Effect of Exploratory Behaviors
Authors: Sinapov Jivko , Stoytchev Alexander
Published: September 2022

Active object exploration is one of the hallmarks of human and animal intelligence. Research in psychology has shown that the use of multiple exploratory behaviors is crucial for learning about objects. Inspired by such research, recent work in robotics has demonstrated that by performing multiple exploratory behaviors a robot can dramatically improve its object recognition rate. But what is the cause of this improvement? To answer this question, this paper examines the conditions under which combining information from multiple behaviors and sensory modalities leads to better object recognition results. Two different problems are considered: interactive object recognition using auditory and proprioceptive feedback, and surface texture recognition using tactile and proprioceptive feedback. Analysis of the results shows that metrics designed to estimate classifier model diversity can explain the improvement in recognition accuracy. This finding establishes, for the first time, an important link between empirical studies of exploratory behaviors in robotics and theoretical results on boosting in machine learning.
Biped Walk Learning Through Playback and Corrective Demonstration
Authors: Mericli Cetin , Veloso Manuela
Published: September 2022

Developing a robust, flexible, closed-loop walking algorithm for a humanoid robot is a challenging task due to the complex dynamics of the general biped walk. Common analytical approaches to biped walk use simplified models of the physical reality. Such approaches are partially successful as they lead to failures of the robot walk in terms of unavoidable falls. Instead of further refining the analytical models, in this work we investigate the use of human corrective demonstrations, as we realize that a human can visually detect when the robot may be falling. We contribute a two-phase biped walk learning approach, which we experiment on the Aldebaran NAO humanoid robot. In the first phase, the robot walks following an analytical simplified walk algorithm, which is used as a black box, and we identify and save a walk cycle as joint motion commands. We then show how the robot can repeatedly and successfully play back the recorded motion cycle, even if in open-loop. In the second phase, we create a closed-loop walk by modifying the recorded walk cycle to respond to sensory data. The algorithm learns joint movement corrections to the open-loop walk based on the corrective feedback provided by a human, and on the sensory data, while walking autonomously. In our experimental results, we show that the learned closed-loop walking policy outperforms a hand-tuned closed-loop policy and the open-loop playback walk, in terms of the distance traveled by the robot without falling.
Recognizing Multi-Agent Activities from GPS Data
Authors: Sadilek Adam , Kautz Henry
Published: September 2022

Recent research has shown that surprisingly rich models of human behavior can be learned from GPS (positional) data. However, most research to date has concentrated on modeling single individuals or aggregate statistical properties of groups of people. Given noisy real-world GPS data, we---in contrast---consider the problem of modeling and recognizing activities that involve multiple related individuals playing a variety of roles. Our test domain is the game of capture the flag---an outdoor game that involves many distinct cooperative and competitive joint activities. We model the domain using Markov logic, a statistical relational language, and learn a theory that jointly denoises the data and infers occurrences of high-level activities, such as capturing a player. Our model combines constraints imposed by the geometry of the game area, the motion model of the players, and by the rules and dynamics of the game in a probabilistically and logically sound fashion. We show that while it may be impossible to directly detect a multi-agent activity due to sensor noise or malfunction, the occurrence of the activity can still be inferred by considering both its impact on the future behaviors of the people involved as well as the events that could have preceded it. We compare our unified approach with three alternatives (both probabilistic and nonprobabilistic) where either the denoising of the GPS data and the detection of the high-level activities are strictly separated, or the states of the players are not considered, or both. We show that the unified approach with the time window spanning the entire game, although more computationally costly, is significantly more accurate.
Informed Lifting for Message-Passing
Authors: Kersting Kristian , El Massaoudi Youssef , Hadiji Fabian , Ahmadi Babak
Published: September 2022

Lifted inference, handling whole sets of indistinguishable objects together, is critical to the effective application of probabilistic relational models to realistic real world tasks. Recently, lifted belief propagation (LBP) has been proposed as an efficient approximate solution of this inference problem. It runs a modified BP on a lifted network where nodes have been grouped together if they have — roughly speaking — identical computation trees, the tree-structured “unrolling” of the underlying graph rooted at the nodes. In many situations, this purely syntactic criterion is too pessimistic: message errors decay along paths. Intuitively, for a long chain graph with weak edge potentials, distant nodes will send and receive identical messages yet their computation trees are quite different. To overcome this, we propose iLBP, a novel, easy-to-implement, informed LBP approach that interleaves lifting and modified BP iterations. In turn, we can efficiently monitor the true BP messages sent and received in each iteration and group nodes accordingly. As our experiments show, iLBP can yield significantly faster more lifted network while not degrading performance. Above all, we show that iLBP is faster than BP when solving the problem of distributing data to a large network, an important real-world application where BP is faster than uninformed LBP.
Representation Discovery in Sequential Decision Making
Authors: Mahadevan Sridhar
Published: September 2022

Automatically constructing novel representations of tasks from analysis of state spaces is a longstanding fundamental challenge in AI. I review recent progress on this problem for sequential decision making tasks modeled as Markov decision processes. Specifically, I discuss three classes of representation discovery problems: finding functional, state, and temporal abstractions. I describe solution techniques varying along several dimensions: diagonalization or dilation methods using approximate or exact transition models; reward-specific vs reward-invariant methods; global vs. local representation construction methods; multiscale vs. flat discovery methods; and finally, orthogonal vs. redundant representa- tion discovery methods. I conclude by describing a number of open problems for future work.