Reviewerly/exHarmony huggingface dataset:
Contextual and Possibilistic Reasoning for Coalition Formation

In multi-agent systems, agents often need to cooperate and form coalitions to fulfil their goals, for example by carrying out certain actions together or by sharing their resources. In such situations, some questions that may arise are: Which agent(s) to cooperate with? What are the potential coalitions in which agents can achieve their goals? As the number of possibilities is potentially quite large, how to automate the process? And then, how to select the most appropriate coalition, taking into account the uncertainty in the agents’ abilities to carry out certain tasks? In this article, we address the question of how to identify and evaluate the potential agent coalitions, while taking into consideration the uncertainty around the agents’ actions. Our methodology is the following: We model multi-agent systems as Multi-Context Systems, by representing agents as contexts and the dependencies among agents as bridge rules. Using methods and tools for contextual reasoning, we compute all possible coalitions with which the agents can fulfil their goals. Finally, we evaluate the coalitions using appropriate metrics, each corresponding to a different requirement. To demonstrate our approach, we use an example from robotics.
Symmetrical Collaborations. Jonathan Harvey and his computer music designers

The work of Jonathan Harvey (1939-2012) has been influenced by computer technology since 1969 at Princeton University, and later during a short course on Musys at the private Ems (Electronic Music Studios) of Peter Zinovieff. However, it was at Ircam (Institut de Recherche et Coordination Acoustique/Musique) in Paris that he could finally fully explore his aesthetic vision, a passion for electronic music that lasted for twenty-eight years and resulted in eight pieces realised at the French Institute: among them the renowned tape piece Mortuos Plango, Vivos Voco (1980) and Ritual Melodies (1990), but also mixed pieces like Bhakti (1982), String Quartet No. 4 (2003), or Speakings (2008).This article focuses on Harvey’s collaborations with Ircam computer music designers Stanley Haynes, Jean-Baptiste Barriere, Denis Lorrain, Jan Vandenheede, Cort Lippe, Carl Faia, Gilbert Nouno, and Arshia Cont. This research, grounded on published and unpublished materials, and oral communications, does not focus on technical aspects, but rather on what Harvey and his collaborators did to create and organize smoothly their work, their modes of collaboration, their everyday workflow. Although relatively weak at the beginning, the evidence of collaboration and above all its recognition through available sources grows over time, since it is highly dependent on the awareness computer music designers have built up of their profession along the years. Moreover, the British composer is one of the few who always recognize, in statements, texts, articles or interviews, the important role of the assistants.
A Probability Density-Based Visual Analytics Approach to Forecast Bias Calibration

Biases inevitably occur in numerical weather prediction (NWP) due to an idealized numerical assumption for modeling chaotic atmospheric systems. Therefore, the rapid and accurate identification and calibration of biases is crucial for NWP in weather forecasting. Conventional approaches, such as various analog post-processing forecast methods, have been designed to aid in bias calibration. However, these approaches fail to consider the spatiotemporal correlations of forecast bias, which can considerably affect calibration efficacy. In this article, we propose a novel bias pattern extraction approach based on forecasting-observation probability density by merging historical forecasting and observation datasets. Given a spatiotemporal scope, our approach extracts and fuses bias patterns and automatically divides regions with similar bias patterns. Termed BicaVis, our spatiotemporal bias pattern visual analytics system is proposed to assist experts in drafting calibration curves on the basis of these bias patterns. To verify the effectiveness of our approach, we conduct two case studies with real-world reanalysis datasets. The feedback collected from domain experts confirms the efficacy of our approach.
IPC: Resource and network cost-aware distributed stream scheduling on skewed streams

The performance of distributed stream processing engines is significantly compromised when processing stream data with skewed distribution. Current stream partitioning schemes are not able to meet the rigorous requirements of distributed stream processing. We show that network cost is an essential factor for partitioning data, and this factor should be considered when designing a stream partitioning scheme. Additionally, we should efficiently utilize resources in the data partitioning process. Current stream partitioning schemes either use a shuffle grouping approach that efficiently manages workload but faces scalability issues in terms of memory or uses hash-based key grouping schemes that suffer from load balancing issues. We argue that network cost and resource utilization are two crucial factors for stream partitioning schemes. We propose and implement a distributed stream partitioning scheme call IPC that minimizes the network cost and efficiently utilizes resources by leveraging two techniques: process near source and process at local. It also utilizes key splitting and local load estimation techniques to achieve load balancing. We implement the IPC on top of Apache Storm. Experiment results using large scale real-time datasets show that IPC achieves an up to 4.2x improvement in throughput and reduces processing latency by 97% compared to state-of-the-art designs.
Engineering entanglement, geometric phase, and quantum Fisher information of a three‐level system with energy dissipation

Quantum Fisher information (QFI) and geometric phase have recently performed different tasks in quantum information technology. We investigate the statistical quantities as the QFI and entanglement of a three‐level atom in Λ configuration interacting with a quantized field mode by using linear entropy. We study the dynamical behavior of the geometric phase based on the engineering of a three‐level atomic configuration. We analyze the effect of energy dissipation of the dynamical properties of the geometric phase and the QFI as an entanglement quantifier between the three‐level atom and field. We explore the correlation between the engineering geometric phase and QFI in the absence and presence of energy dissipation effect. We have found that the QFI is very sensitive to the effect of the time‐dependent coupling and energy dissipation.
Permutation polynomials with Carlitz rank 2

Let Fq denote the finite field with q elements. The Carlitz rank of a permutation polynomial is an important measure of complexity of a polynomial. In this paper we find a sharp lower bound for the weight of any permutation polynomial with Carlitz rank 2, improving the bound found by Gómez-Pérez, Ostafe and Topuzoğlu in that case.
Fuzzy c-shells clustering algorithm

Abstract Clustering is a process to classify data into some clusters or classes so then data in same cluster have maximum similarity and data between clusters have minimum similarity. Generally, clustering is separated into two methods, hierarchical and non-hierarchical methods. Non-hierarchical methods are separated as soft clustering and hard clustering. Non-Hierarchical methods that mostly used is K-Means algorithm. K-Means grouping object into certain member and not included in different clusters. Another approaches in clustering technique which is based on fuzzy sets theory is known as fuzzy clustering. Fuzzy clustering is one kind of soft clustering that also widely used because of many advantages than the hard clustering. Several varieties of fuzzy clustering are Fuzzy C-Means (FCM) and Fuzzy C-Shells (FCS). FCM is characterized by centre of cluster and FCS uses radius as additional parameter. This paper concerns FCS clustering algorithm using review of several references. Hence, FCS is characterized by centre of cluster and radius.
When Block Chain and AI Integrates

Block chain and AI are the most well liked technology trends, even if the 2 technologies have extremely completely different developing parties and applications, researchers are discussing and exploring their combinational applications. With integrating these technologies we can enhance blockchain architect along with bosting potential of Ai, and these process knowledge takes the existing system to new higher levels. At a similar time, the combination of machine learning and blockchain can make AI more coherent and understandable by tracing all the datas which is stored in the form of ledger and there by building up the standard in the system. Hence we discuss further more in detail in the paper.
Study on face recognition under unconstrained conditions based on LBP and deep learning

At present, the correct recognition rate of face recognition algorithm is limited under unconstrained conditions. To solve this problem, a face recognition algorithm based on deep learning under unconstrained conditions is proposed in this paper. The algorithm takes LBP texture feature as the input data of deep network, and trains the network layer by layer greedily to obtain optimized parameters of network, and then uses the trained network to predict the test samples. Experimental results on the face database LFW show that the proposed algorithm has higher correct recognition rate than some traditional algorithms under unconstrained conditions. In order to further verify its effectiveness and universality, this algorithm was also tested in YALE and YALE-B, and achieved a high correct recognition rate as well, which indicated that the deep learning method using LBP texture feature as input data is effective and robust to face recognition.
Transparent IDS Offloading for Split-Memory Virtual Machines

To enable virtual machines (VMs) with a large amount of memory to be flexibly migrated, split migration has been proposed. It divides a large-memory VM into small pieces and transfers them to multiple hosts. After the migration, the VM runs across those hosts and exchanges memory data between hosts using remote paging. For such a split-memory VM, however, it becomes difficult to securely run intrusion detection systems (IDS) outside the VM using a technique called IDS offloading. This paper proposes VMemTrans to support transparent IDS offloading for split-memory VMs. In VMemTrans, offloaded IDS can monitor a split-memory VM as if that memory were not distributed. To achieve this, VMemTrans enables IDS running in one host to transparently access VM's remote memory. To consider a trade-off, it provides two methods for obtaining memory data from remote hosts: self paging and proxy paging. We have implemented VMemTrans in KVM and compared the execution performance between the two methods.
A pipeline for detecting and classifying objects in images

With the increased accessibility to neural network frameworks and computation clouds, a wide range of competition websites offer real tasks for the neural network community to join. In this paper, we discuss a problem of object detection and classification. Based on our experience, we describe a general pipeline and necessary steps that may help researchers willing to participate in such competition. We partition the problem into two separate tasks. Firstly, we present state of the art for relevant neural networks concerning accuracy and computation time trade-off. Further, we create a survey of major techniques that leads to accuracy improvement. Namely, we recall image augmentation techniques, demonstrate the impact of various optimizers, and discuss ensemble techniques. The pipeline and techniques reflect our experience with a competition, in which we were able to reach a highly competitive solution and ended in fourth place. The uniqueness of our solution is that we used only free Google Colab computation service and still overperformed many more computation extensive approaches.
Classical term-modal logics

Abstract We introduce classical term-modal logics and argue that they are useful for modelling agent-relative notions of obligation, evidence and abilities, and their interaction with properties of and relations between the agents in question. We spell out the semantics of these logics in terms of neighborhood models, provide sound and strongly complete axiomatizations and establish the decidability of specific (agent-finite) variants.
Teaching-learning of software conceptual design via function-behaviour-structure framework

Conceptual design is one of the initial phases in software design. In this phase the functional requirements are extracted from the problem and transformed into descriptions of solution concepts. Peculiar characteristics of software conceptual design (scd), such as dynamicity and intangibility add to the complexity of this phase. Modeling using Unified Modeling Language (UML) tools and creating various UML representations often characterize industry practices of scd. Modeling and representations are compartmentalized in UML, i.e. the representations correspond to the solution view of function, behaviour or structure. Novices learn about syntax, semantics and processes to create UML representations in their undergraduate courses such as Software Engineering. However, results from our novice studies indicate that they are unable to create scd due to difficulties such as fixation, lack of integration. These difficulties lead to creation of solutions that are neither integrated nor fulfill all functional requirements. Current teaching-learning methods do not explicitly support novices to overcome these difficulties. In this paper we describe the design of a teaching-learning environment, 'think & link'. It is based on the theoretical design framework of Function-behaviour-structure (FBS). Initial studies with learners using 'think & link' indicate conceptual change in novices understanding of scd.
A Novel Tax Evasion Detection Framework via Fused Transaction Network Representation

Tax evasion usually refers to the false declaration of taxpayers to reduce their tax obligations; this type of behavior leads to the loss of taxes and damage to the fair principle of taxation. Tax evasion detection plays a crucial role in reducing tax revenue loss. Currently, efficient auditing methods mainly include traditional data-mining-oriented methods, which cannot be well adapted to the increasingly complicated transaction relationships between taxpayers. Driven by this requirement, recent studies have been conducted by establishing a transaction network and applying the graphical pattern matching algorithm for tax evasion identification. However, such methods rely on expert experience to extract the tax evasion chart pattern, which is time-consuming and labor-intensive. More importantly, taxpayers' basic attributes are not considered and the dual identity of the taxpayer in the transaction network is not well retained. To address this issue, we have proposed a novel tax evasion detection framework via fused transaction network representation (TED-TNR), to detecting tax evasion based on fused transaction network representation, which jointly embeds transaction network topological information and basic taxpayer attributes into low-dimensional vector space, and considers the dual identity of the taxpayer in the transaction network. Finally, we conducted experimental tests on real-world tax data, revealing the superiority of our method, compared with state-of-the-art models.
N2One: Identifying Coreference Object Among User Generated Content with Siamese Network

With the boom of social network, various service is provided based on the user generated content (UGC). Some of these UGCs semantically point to the same entities in the real world. Recognizing the same real-world entity that is referred by various user contents is crucial for providing personalized service, such as recommender systems and user profiling. Recently, most recommender systems recommend content based on keywords detection or structured data. These approaches cannot fully grasp latent semantic of UGC, so it is difficult for them to discover the coreference entities behind UGCs. As a result, when there are less obvious keywords or preset user preference, the experience of such services are poor. In this paper, we formalize this problem as a N to 1 task, which means performing entity resolution among UGCs, and a Siamese Network is presented to deal with it, namely 'N2One'. Extensive experiments are conducted on two commonly used datasets Yelp and IMDB to evaluate the effectiveness of the proposed model, and the results demonstrate its superiority.
Synthetic astronomical image sequence generation

Astronomical image reconstruction from multiple observations requires detailed knowledge of the imaging system. The reconstruction accuracy is determined by the imaging model correctness. Numerous parameters determine the model, and their precise estimation is a precondition for successful reconstruction. The best way to measure the model sensitivity on the parameter accuracy is a detailed simulation of the whole imaging process that takes into account all of the model parameters. The simulation enables an artificial manipulation of these parameters to reach the required accuracy. This paper describes a novel approach to generate a synthetic astronomical image sequence from the chosen star catalogs. Synthesis assumes point sources extracted from the catalogs and described through their angular position and magnitude in different spectral bands. The model parameters shall be tuned to the actual raw image captured by the SLR camera and chosen lens. The sequence of synthetic images of approximately the same part of the sky, but slightly shifted, is generated. The small shifts result that the light field integrates into different spatially displaced sampling grids, thus facilitating high-resolution image reconstruction from spatially subsampled projections.
A High-Performance Index for Real-Time Matrix Retrieval

With the embedding techniques, many real-world objects can be represented using matrices. For example, a document can be represented by a matrix, where each row of the matrix represents a word. On the other hand, we have witnessed that many applications continuously generate new data represented by matrices and require real-time query answering on the data. These continuously generated matrices need to be well managed for efficient retrieval. In this paper, we propose an index for real-time matrix retrieval. Besides fast query response, the index also supports real-time insertion by exploiting the LSM-tree. Since the index is built for matrices, it consumes much more memory and requires much more time to search than the traditional index for information retrieval. To tackle the challenges, we power our proposed index with precise and fuzzy inverted lists, and propose a series of novel techniques to improve the memory consumption and the search efficiency of the index. The proposed techniques include vector signature, vector residual sorting, hashing based lookup, and dictionary initialization to guarantee the index quality. Comprehensive experimental results show that our proposed index can support real-time search on matrices and is more efficient than the state-of-the-art method.
Boosting the Robustness of Capsule Networks with Diverse Ensemble

The recent proposed Capsule networks encounter performance degeneration for the distorted images, which challenges the robustness of the models. To address this issue, we propose to enhance the robustness of capsule by constructing ensembles of the individual networks. By exploring the interaction among each individual network, we further propose to improve the robustness for the ensemble capsules by promoting the diversity during the training process. Extensive results on the various noises demonstrate that our algorithm can improve the robustness for the vanilla capsule networks in terms of the Gaussian as well as the adversarial perturbations.
Selective encryption in the CCSDS standard for lossless and near-lossless multispectral and hyperspectral image compression

In this paper, we investigate low-complexity encryption solutions to be embedded in the recently proposed CCSDS standard for lossless and near-lossless multispectral and hyperspectral image compression. The proposed approach is based on the randomization of selected components in the image compression pipeline, namely the sign of prediction residual and the fixed part of Rice-Golomb codes, inspired by similar solutions adopted in video coding. Thanks to the adaptive nature of the CCSDS algorithm, even simple randomization of the sign of prediction residuals can provide a sufficient scrambling of the decoded image when the encryption key is not available. Results on the standard CCSDS test set show that the proposed technique uses on average only about 20% of the keystream compared to a conventional stream cipher, with a negligible increase of the rate of the encoder.
Issue-Slot Based Predication Encoding Technique for VLIW Processors

Predication is a well-known alternative to conditional branching. However, the implementation of predication is costly in terms of extending the instruction set of the processor architecture. In this paper, a predication encoding technique for VLIW processors is proposed. Instead of using additional bits in the instruction encoding, the assigned issue-slot of a conditionally executed instruction encodes the associated predicate register. The number of addressable predicate registers scales with the number of issue-slots. All predicate registers have only one read and write port and can be accessed in parallel. Compared to the related work, no additional instruction encoding bits for selecting a predicate register are required and the processor core area increases only by about 1% per predicate register set. With the proposed predication technique, the processing performance increases by up to 4.5% when using two instead of one predicate register for a digital filter case study with floating-point emulation operations. A second case study shows, that conditional execution with two predicate register in combination with loop unrolling and operation merging almost doubles the achieved parallel instructions per cycle for a bit-reversal permutation algorithm.
Inventory Optimization Models of Equipment Spare Parts Based on Computer Simulation

Through making an analysis of the randomicity of the spare parts demand, probability theory and mathematical statistics are applied to establish the support expense minimum model based on equipment availability, confidence values of the equipment readiness rate and support probability of spare parts. The arithmetic of spare parts joint support storage based on expense is presented. Then inventory optimization method of spare parts joint support based on computer simulation is determined. Applicability of the method is given by way of a numerical example. The inventory optimization method based on computer simulation provides a theoretical basis for solving the problems of inventory and expense in other relative areas.