Reviewerly/exHarmony huggingface dataset:
CMPFFNet: Cross-Modal and Progressive Feature Fusion Network for RGB-D Indoor Scene Semantic Segmentation

Depth information can contribute to the semantic segmentation of scenes from red–green–blue (RGB) images. Therefore, the amount of information that can be obtained from RGB and RGB-depth (RGB-D) images is significantly greater for this task. However, RGB and RGB-D modalities are different in terms of object representation. Features that are extracted from these modalities and fused effectively are key to scene semantic segmentation. In addition, complete segmentation requires the fusion of multiscale features to unify global information. However, existing approaches primarily use multiscale features for sequential integration. This study introduces a cross-modal and progressive feature fusion network (CMPFFNet) for semantic segmentation of indoor scenes in RGB-D images. First, a multimodal adaptive alignment fusion (MAAF) module based on an attention mechanism is introduced. This module aligns the two modal channels by additive attention and then computes the spatial similarity between the two modalities based on the dot product to incorporate the complementary information of the depth modality into the RGB modality. In addition, a reverse attention augmentation (RAA) module is introduced to augment the more abstract high-level features for two adjacent multilevel features using the concrete semantic information of the lower-level features in them. After augmenting the extracted multilevel features, a multilevel feature progressive fusion (MFPF) module is deployed; this module sequentially fuses the neighboring two features progressively with emphasis on the spatial semantics. The network uses the Segformer network with high performance as a backbone in multiple computer vision tasks to enhance the segmentation capability. Experimental results obtained from two publicly available datasets of indoor scenes reveal that the proposed CMPFFNet outperforms existing models in semantic segmentation of indoor scenes of RGB-D images. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This study introduces a cross-modal and progressive feature fusion network (CMPFFNet) for indoor scene semantic segmentation in RGB-D images. The complementary information of the depth modality is incorporated into the RGB modality in both channel and spatial forms to form a discriminative representation for easy segmentation. A multilevel feature aggregation decoder is proposed to predict the results of semantic segmentation of scenes. The network uses the Segformer network with high performance as a backbone in multiple computer vision tasks to enhance the segmentation capability.
High-Threshold Quantum Computing by Fusing One-Dimensional Cluster States

We propose a measurement-based model for fault-tolerant quantum computation that can be realized with one-dimensional cluster states and fusion measurements only; basic resources that are readily available with scalable photonic hardware. Our simulations demonstrate high thresholds compared with other measurement-based models realized with basic entangled resources and 2-qubit fusion measurements. Its high tolerance to noise indicates that our practical construction offers a promising route to scalable quantum computing with quantum emitters and linear-optical elements.
Integrating cybersecurity risk management into strategic management: a comprehensive literature review

Purpose- This literature review aims to delve into the nexus between cybersecurity risk management and strategic management, comprehensively exploring how organizations weave risk management strategies into their broader strategies to safeguard digital assets and infrastructure against the backdrop of ever-evolving cyber threats. Methodology- The review employs a qualitative methodology, synthesizing insights from a diverse selection of scholarly works encompassing cybersecurity, risk management, and strategic management. These insights are analyzed to unveil patterns and trends that highlight the integration of cybersecurity risk management within strategic organizational frameworks. Findings- The review uncovers a critical interdependence between cybersecurity risk management and strategic management, showcasing how organizations formulate proactive measures to mitigate cyber risks while aligning them with overarching strategic goals. It also underscores the role of organizational culture, leadership commitment, and technological advancements in shaping effective cybersecurity risk management strategies. Conclusion- The synthesis of scholarly findings accentuates the pivotal role of cybersecurity risk management in modern organizations. The review underscores the importance of fostering a strategic mindset towards cybersecurity, with a proactive approach that integrates risk management efforts within the broader organizational strategy. This not only shields digital assets but also promotes resilience, enabling organizations to thrive despite an increasingly dynamic and hostile digital landscape. Keywords: Strategic management, cyber security, cyber risks, risk management, organizational strategy. JEL Codes: M00, M10, M15
Teaching Machines to Learn by Metaphors

Humans have an uncanny ability to learn new concepts with very few examples. Cognitive theories have suggested that this is done by utilizing prior experience of related tasks. We propose to emulate this process in machines, by transforming new problems into old ones. These transformations are called metaphors. Obviously, the learner is not given a metaphor, but must acquire one through a learning process. We show that learning metaphors yield better results than existing transfer learning methods. Moreover, we argue that metaphors give a qualitative assessment of task relatedness.
Prosody prediction for arabic via the open-source boundary-annotated qur’an corpus

humans or machines. To develop phrase break classifiers, we need a boundary-annotated and part-ofspeech tagged corpus. Boundary annotations in English speech corpora are descriptive, delimiting intonation units perceived by the listener; manual annotation must be done by an expert linguist. For Arabic, there are no existing suitable resources. We take a novel approach to phrase break prediction for Arabic, deriving our prosodic annotation scheme from Tajwid (recitation) mark-up in the Qur’an which we then interpret as additional text-based data for computational analysis. This mark-up is prescriptive, and signifies a widely-used recitation style, and one of seven original styles of transmission. Here we report on version 1.0 of our Boundary-Annotated Qur’an dataset of 77430 words and 8230 sentences, where each word is tagged with prosodic and syntactic information at two coarse-grained levels. We then use this dataset to train, test, and compare two probabilistic taggers (trigram and HMM) for Arabic phrase break prediction, where the task is to predict boundary locations in an unseen test set stripped of boundary annotations by classifying words as breaks or non-breaks. The preponderance of non-breaks in the training data sets a challenging baseline success rate: 85.56%. However, we achieve significant gains in accuracy with a trigram tagger, and significant gains in performance recognition of minority class instances with both taggers via the Balanced Classification Rate metric. This is initial work on a longterm research project to produce annotation schemes, language resources, algorithms, and applications for Classical and Modern Standard Arabic.
Exploiting User Interest on Social Media for Aggregating Diverse Data and Predicting Interest

More and more users have been taking various actions to diverse resources referred to by URLs such as news, web pages, images, products, movies as a result of the growth of social media. They are annotating, tweeting in Twitter, reblogging in Tumblr, and Liking in Facebook, etc. Analyses about these diverse actions will be useful for aggregating or integrating diverse resources. In this paper, we view users’ actions to resources as expressing their some interests, and by investigating how their interests are expressed in social media, we get suggestions for aggregations. Our results show that a certain kind of action (such as tagging on Delicious) can be used to make predictions on a different kind of action (such as favorite on Twitter). These analyses will be useful for aggregating or integrating diverse contents on multiple sources. In addition to some experimental analyses, we propose a novel method to predict users’ interests in social media, using time-evolving, multinomial relational data. Our experimental results show that the proposed method significantly outperforms standard tensor analysis and an existing state-of-the-art method (LDA) in prediction tasks.
Intelligent Trading Agents for Massively Multi-Player Game Economies

As massively multi-player gaming environments become more detailed, developing agents to populate these virtual worlds as capable non-player characters poses an increasingly complex problem. Human players in many games must achieve their objectives through financial skills such as trading and supply chain management as well as through combat and diplomacy. In this paper, we examine the problem of creating intelligent trading agents for virtual markets. Using historical data from EVE Online, a science-fiction based MMORPG, we evaluate several strategies for buying, selling, and supply chain management. We demonstrate that using reinforcement learning to determine policies based on the market microstructure gives trading agents a competitive advantage in amassing wealth. Imbuing agents with the ability to adapt their trading policies can make them more resistant to exploitation by other traders and capable of participating in virtual economies on an equal footing with humans.
FLP Semantics Without Circular Justifications for General Logic Programs

The FLP semantics presented by (Faber, Leone, and Pfeifer 2004) has been widely used to define answer sets, called FLP answer sets, for different types of logic programs such as logic programs with aggregates, description logic programs (dl-programs), Hex programs, and logic programs with first-order formulas (general logic programs). However, it was recently observed that the FLP semantics may produce unintuitive answer sets with circular justifications caused by self-supporting loops. In this paper, we address the circular justification problem for general logic programs by enhancing the FLP semantics with a level mapping formalism. In particular, we extend the Gelfond-Lifschitz three step definition of the standard answer set semantics from normal logic programs to general logic programs and define for general logic programs the first FLP semantics that is free of circular justifications. We call this FLP semantics the well-justified FLP semantics. This method naturally extends to general logic programs with additional constraints like aggregates, thus providing a unifying framework for defining the well-justified FLP semantics for various types of logic programs. When this method is applied to normal logic programs with aggregates, the well-justified FLP semantics agrees with the conditional satisfaction based semantics defined by (Son, Pontelli, and Tu 2007); and when applied to dl-programs, the semantics agrees with the strongly well-supported semantics defined by (Shen 2011).
Trap Avoidance in Local Search Using Pseudo-Conflict Learning

A key challenge in developing efficient local search solvers is to effectively minimise search stagnation (i.e. avoiding traps or local minima). A majority of the state-of-the-art local search solvers perform random and/or Novelty-based walks to overcome search stagnation. Although such strategies are effective in diversifying a search from its current local minimum, they do not actively prevent the search from visiting previously encountered local minima. In this paper, we propose a new preventative strategy to effectively minimise search stagnation using pseudo-conflict learning. We define a pseudo-conflict as a derived path from the search trajectory that leads to a local minimum. We then introduce a new variable selection scheme that penalises variables causing those pseudo-conflicts. Our experimental results show that the new preventative approach significantly improves the performance of local search solvers on a wide range of structured and random benchmarks.
Estimating Search Tree Size with Duplicate Detection

In this paper we introduce Stratified Sampling with Duplicate Detection (SSDD), an algorithm for estimating the number of state expansions performed by heuristic search algorithms seeking solutions in state spaces represented by undirected graphs. SSDD is general and can be applied to estimate other state-space properties. We test SSDD on two tasks: (i) prediction of the number of A* expansions in a given f-layer when using a consistent heuristic function, and (ii) prediction of the state-space radius. SSDD has the asymptotic guarantee of producing perfect estimates in both tasks. Our empirical results show that in task (i) SSDD produces good estimates in all four domains tested, being in most cases orders of magnitude more accurate than a competing scheme, and in task (ii) SSDD quickly produces accurate estimates of the radii of the 4x4 Sliding-Tile Puzzle and the 3x3x3 Rubik's Cube.
Bounded Suboptimal Heuristic Search in Linear Space

It is commonly appreciated that solving search problems optimally can overrun time and memory constraints. Bounded suboptimal search algorithms trade increased solution cost for reduced solving time and memory consumption. However, even suboptimal search can overrun memory on large problems. The conventional approach to this problem is to combine a weighted admissible heuristic with an optimal linear space algorithm, resulting in algorithms such as Weighted IDA* (wIDA*). However, wIDA* does not exploit distance-to-go estimates or inadmissible heuristics, which have recently been shown to be helpful for suboptimal search. In this paper, we present a linear space analogue of Explicit Estimation Search (EES), a recent algorithm specifically designed for bounded suboptimal search. We call our method Iterative Deepening EES (IDEES). In an empirical evaluation, we show that IDEES dramatically outperforms wIDA* on domains with non-uniform edge costs and can scale to problems that are out of reach for the original EES.
Reaching the Goal in Real-Time Heuristic Search: Scrubbing Behavior is Unavoidable

Real-time agent-centered heuristic search is a well-studied problem where an agent that can only reason locally about the world must travel to a goal location using bounded computation and memory at each step. Many algorithms have been proposed for this problem, and theoretical results have also been derived for the worst-case performance. Assuming sufficiently poor tie-breaking, among other conditions, we derive theoretical best-case bounds for reaching the goal using LRTA*, a canonical example of a real-time agent-centered heuristic search algorithm. We show that the number of steps required to reach the goal can grow asymptotically faster than the state space, resulting in a "scrubbing" when the agent repeatedly visits the same state. This theoretical result, supported by experimental data, encourages recent work in the field that uses novel tie-breaking schemas and/or perform different types of learning.
An AI System for Large Open Virtual World

In recent years, computer games have reached unprecedented level of graphical fidelity to the real world. As the non-player characters (NPCs) in the game world look more and more realistic, players expect them to manifest believable behavior as well. This is accented especially in games that feature large open worlds, which players may explore freely and it is thus not possible to explicitly account for all possible player interactions. In this paper we focus mainly on ambient AI - the logic behind day to day behaviors of NPCs as they sleep, work and entertain themselves in the virtual world. In this context, it is of great importance to build a system that handles many NPCs (up to several hundreds) quickly. In this paper we report on an implementation of a particular AI system that was approved for deployment in an upcoming high-budget game. The system features a hierarchy of control similar to the subsumption architecture and a visual agent-based language inspired by behavior trees. We describe the challenges involved in building such a system and specific design decisions we have made that let us achieve a level of behavioral fidelity unmatched by existing games. Finally we evaluate the performance of the system in a realistic setting.
Implementation Walkthrough of a Homegrown “Abstract State Machine” Style System in a Commercial Sports Game

When I began working at Sony Computer Entertainment of America in 2002, the AI system they were using was very dated. Over the next few years, I designed and developed an almost completely data driven system that has proven to be very powerful, extremely extensible, and designer friendly. This system uses a homegrown data structure, the use of which in many ways resembles the software method of using Abstract State Machines for decomposing complex logical constructs iteratively. This paper will provide an overview of the construction and usage of the system, as well as the pros and cons of this type of game AI engine.
Like Partying? Your Face Says It All. Predicting the Ambiance of Places with Profile Pictures

To choose restaurants and coffee shops, people are increasingly relying on social-networking sites. In a popular site such as Foursquare or Yelp, a place comes with descriptions and reviews, and with profile pictures of people who frequent them. Descriptions and reviews have been widely explored in the research area of data mining. By contrast, profile pictures have received little attention. Previous work showed that people are able to partly guess a place's ambiance, clientele, and activities not only by observing the place itself but also by observing the profile pictures of its visitors. Here we further that work by determining which visual cues people may have relied upon to make their guesses; showing that a state-of-the-art algorithm could make predictions more accurately than humans at times; and demonstrating that the visual cues people relied upon partly differ from those of the algorithm.
Explicit Knowledge Programming for Computer Games

The main aim of this paper is to raise awareness of higher-order knowledge (knowledge about someone else's knowledge) as an issue for computer game AI. We argue that a number of existing game genres, especially those involving social interaction, are natural fields of application for an approach we call explicit knowledge programming. We motivate the use of this approach, and describe a simple implementation based upon it. A survey of recent literature and computer games illustrates its novelty.
Silver-Modified Mexican Clinoptilolite-Rich Tuffs With Various Particle Sizes as Antimicrobial Agents Against Escherichia coli

Water disinfection was performed using Mexican clinoptilolite-rich tuffs from Oaxaca and Sonora with different particle sizes exchanged with silver ions. Samples of Escherichia coli (ATCC 8739) suspended in distilled water (wastewater-like) and municipal wastewater containing coliforms (E. coli as a representative microorganisms from the coliform consortium), were treated in this investigation. E. coli is considered an indicator of microbiological contamination of water, so levels of this microorganism after zeolite treatment indicate how well the disinfection process worked. It was found that particle size, the amount of silver in the modified zeolitic rocks, the origin of the zeolitic material and the type of aqueous media containing E. coli all play important roles in the wastewater disinfection process.&#x0D;
Representational Requirements for a Plan Based Approach to Automated Camera Control

Automated camera control has been an active area of research for a number of years. The problem has been addressed in the Graphics, AI and Game communities from different perspectives. The main focus of the research in the Graphics community has been frame composition and coherence. The AI community has focused on intelligent shot selection, and the Games community strives for real- time cinematic camera control. While the proposed solutions in each of these fields are promising, there has not been much effort spent on listing out the requirements of an intelligent camera control system and how these can be satisfied through a combination of approaches taken from these different fields. This paper attempts to list out the representational requirements with a view of finding a unifying representation for combining these disparate approaches. We show how a plan based approach can capture some of these requirements and it can be connected to a geometric constraint solver for camera placement.
Towards Dynamically Crafted Affective Experiences Through Emotional Response Player Modelling

Designing adaptive games for individual emotional experiences is an elaborate task due to the necessary dedicated hardware, complex signal processing and unique emotional bonds each player may forge with the game. The research work I propose focuses on developing a novel affective interaction scheme where players’ emotional reactions are learned by a computational model that leverages this information to influence their future emotional states. To this end I aim at interpreting the causality relations between physiologically measured emotional response variations and their eliciting events. I then plan using these reactions to build each player’s individual emotional reaction model, which will guide the game engine’s logic on how to plan future game events, in order to elicit a target affective experience. Ultimately, I expect this technique to allow game designers to focus on defining high-level rules for generating their envisioned affective gameplay experience, rather than in manually tuning it in a repetitive and time-consuming fashion.
Improved Depth Perception of Single-view Images

In many situations, we perceive 3-D depth feeling from 2-D images. Among various depth cues, monocular depth cues make us perceive depth from single-view images. In this paper, we propose several methods to enhance monocular depth cues and improve depth perception of single-view images. The proposed methods deal with four depth cues: linear perspective, aerial perspective, focus, and shadow effects. Once we have a 2-D colour image and its corresponding depth information, we can modify its depth cues and enhance 3-D depth perception in the 2-D image. Experimental results show that the proposed methods can enhance depth perception of single-view images without any considerable visual distortions.
Estimation of the Proportion of Sterile Couples Using the Negative Binomial Distribution

A Sterile family is a couple who has no children by their deliberate choice or because they are biologically infertile.Couples who are childless by chance are not considered to be sterile.The object is to estimate the proportion of sterile couples in Jordan indirectly based on the 1994 population census, by separating the two types of childless couples into sterile and fertile couples.Three methods of fitting a negative binomial distribution to the completed family size data obtained from 1994-population census are investigated.It appeared that the third method gives the best fit.Based on the fitted distribution, the proportion of sterile couples is estimated at 6.1% of all couples.This estimate is much lower than the corresponding estimate of sterile couples in the USA, which was estimated at 11%.The difference between the two can be due to some socio-cultural factors influencing the deliberate choice of couples to have no children.The method of estimation can be applied on other populations.