Reviewerly/exHarmony huggingface dataset:
Formalization of Fuzzy Control in Possibility Theory via Rule Extraction

Possibility system has been recently recognized as a potential foundational theory for fuzzy set theory though the concept of possibility originated from the membership function of fuzzy sets.As an application of fuzzy set theory, fuzzy control has been widely used in engineering practices, where control laws are described by fuzzy if-then rules.This paper intends to formulate fuzzy control in the framework of possibility theory by regarding the fuzzification procedure of fuzzy control as extraction process of the fuzzy variable from crisp input, and the fuzzy if-then rules as extracted rules between fuzzy input/output.Typical procedures of fuzzy control, such as fuzzification, implication, aggregation, and defuzzification, are eventually formulated as a series of conditional possibilities operated by ''max-product'' operators.The reformulated fuzzy controller has a more general form, which encompasses the Mamdani fuzzy controller as a special case.Some fundamental concepts of possibility theory, such as randomness/fuzziness, possibility, and conditional possibility, are also discussed, which may be helpful for the correct understanding of possibility theory.Efforts have been made to bridge the two systems of possibility theory and fuzzy sets by the derivation of composition rule of fuzzy relations from conditional possibility.All results are derived for both normalized possibility and non-normalized possibility.This paper strengthened the role of possibility theory as a foundation for fuzzy sets, and as a complementary method to probability theory for handling information with fuzzy uncertainty.
Detecting Entanglement With Deep Quantum Neural Networks

Based on the theoretical basis of quantum computation, entanglement has been further explored as one of the key resources.An outstanding problem in quantum computation is the detecting of entanglement, but no closed-form algorithm exists.According to an advanced matrix rearrangement approach for detecting multipartite quantum states, we give some examples to verify the effectiveness and practicability of this new criterion.Meanwhile, this paper shows how techniques from the quantum neural network can be utilized to detect entanglement.Two models, known as the discrete-variable and the continuous-variable quantum neural network, are applied to solve the separability problem.We demonstrate the utility that such a discrete-variable quantum neural network can be trained to detect the entanglement with great exactness.And the complexity of network and computation is greatly reduced.Besides, the continuousvariable quantum neural network is applied for detecting two-qumode Gaussian state.Compared with the results of the traditional neural network, this deep quantum neural network demonstrates its capability and adaptability as well.
Deep supervised hashing using symmetric relative entropy

By virtue of their simplicity and efficiency, hashing algorithms have achieved significant success on large-scale approximate nearest neighbor search. Recently, many deep neural network based hashing methods have been proposed to improve the search accuracy by simultaneously learning both the feature representation and the binary hash functions. Most deep hashing methods depend on supervised semantic label information for preserving the distance or similarity between local structures, which unfortunately ignores the global distribution of the learned hash codes. We propose a novel deep supervised hashing method that aims to minimize the information loss generated during the embedding process. Specifically, the information loss is measured by the Jensen–Shannon divergence to ensure that compact hash codes have a similar distribution with those from the original images. Experimental results show that our method outperforms current state-of-the-art approaches on two benchmark datasets.
QoE-Aware Resource Allocation for Crowdsourced Live Streaming: A Machine Learning Approach

Driven by the tremendous technological advancement of personal devices and the prevalence of wireless mobile network accesses, the world has witnessed an explosion in crowdsourced live streaming. Ensuring a better viewers quality of experience (QoE) is the key to maximize the audiences number and increase streaming providers' profits. This can be achieved by advocating a geo-distributed cloud infrastructure to allocate the multimedia resources as close as possible to viewers, in order to minimize the access delay and video stalls. Moreover, allocating the exact needed resources beforehand avoids over-provisioning, which may lead to significant costs by the service providers. In the contrary, under-provisioning might cause significant delays to the viewers. In this paper, we introduce a prediction driven resource allocation framework, to maximize the QoE of viewers and minimize the resource allocation cost. First, by exploiting the viewers locations available in our unique dataset, we implement a machine learning model to predict the viewers number near each geo-distributed cloud site. Second, based on the predicted results that showed to be close to the actual values, we formulate an optimization problem to proactively allocate resources at the viewers proximity. Additionally, we will present a trade-off between the video access delay and the cost of resource allocation.
High Securing Cryptography System for Digital Image Transmission

In this paper, we propose a cryptography system for digital image high securing. Our method is asymmetric utilizing RSA algorithm, which requires a public key for encryption and a private key for decryption. However, the image is encrypted with a combination between the AES-256 CTR mode algorithm and the SHA-2 function. Our algorithm is evaluated by several tools and tests mainly selected from the image cryptography community using many types of standard non compressed images. The experimental and analytical results demonstrate that our encryption scheme provides a high robustness and security. It can resist the most known cryptanalysis attacks. Very good results are obtained, which allows us to confirm the high performance and efficiency of our algorithm for image high protection, which can be used in several domains like military and community privacy.
Collision-free allocation of temporally constrained tasks in multi-robot systems

Multi-robot systems (MRS) are a reference solution for many prominent real-world applications, e.g. management of warehouses or exploration of unknown environments. One of the most fundamental computational problems in MRS is that of planning the assignment of tasks to robots when such tasks have deadlines, i.e. constraints on when the execution must take place. The problem, when multiple objective functions of interest need to be optimized, is both NP-Hard and hard to approximate, and few heuristics are known in the literature to handle it. Unfortunately, none of them guarantees that the trajectories used by the robots when moving between tasks' locations are collision-free at planning time. Rather, they implement a reactive behavior, i.e. they abort the execution of a planned task whenever something goes wrong, e.g. trajectories of robots intersect or a deadline is missed due to some obstacle. This approach induces negative effects on the global performance of the system in the form of waste of energy, due to high distances traveled by the fleet members, or in the form of high convergence time to execute tasks. Therefore, planning the assignments of temporally constrained tasks with the guarantee of avoiding collisions can be a desirable feature for multi-robot systems. In this paper, we present CFAT-D (Collision-Free Allocation of Tasks having Deadlines), a new algorithm that can allocate temporally constrained tasks while guaranteeing that used trajectories are collision-free at planning time. We prove CFAT-D to be correct and showcase its effectiveness through an extensive experimental evaluation. Finally, we provide a roadmap toward the practical implementation of the new strategy in real-world environments.
Evaluating the Potential for Hardware Acceleration of Four NTRU-Based Key Encapsulation Mechanisms Using Software/Hardware Codesign

The speed of NTRU-based Key Encapsulation Mechanisms (KEMs) in software, especially on embedded software platforms, is limited by the long execution time of its primary operation, polynomial multiplication. In this paper, we investigate the potential for speeding up the implementations of four NTRU-based KEMs, using software/hardware codesign, when targeting Xilinx Zynq UltraScale+ multiprocessor system-on-chip (MPSoC). All investigated algorithms compete in Round 1 of the NIST PQC standardization process. They include: ntru-kem from the NTRUEncrypt submission, Streamlined NTRU Prime and NTRU LPRime KEMs of the NTRU Prime candidate, and NTRU-HRSS-KEM from the submission of the same name. The most-time consuming operation, polynomial multiplication, is implemented in the Programmable Logic (PL) of Zynq UltraScale+ (i.e., in hardware) using constant-time hardware architectures most appropriate for a given algorithm. The remaining operations are executed in the Processing System (PS) of Zynq, based on the ARM Cortex-A53 Application Processing Unit. The speed-ups of our software/hardware codesigns vs. purely software implementations, running on the same Zynq platform, are determined experimentally, and analyzed in the paper. Our experiments reveal substantial differences among the investigated candidates in terms of their potential to benefit from hardware accelerators, with the special focus on accelerators aimed at offloading to hardware only the most time-consuming operation of a given cryptosystems. The demonstrated speed-ups vs. functionally equivalent purely software implementations vary between 4.0 and 42.7 for encapsulation, and between 6.4 and 149.7 for decapsulation.
The Effects of Embodiment in Virtual Reality on Implicit Gender Bias

Virtual reality allows users to have a virtual body that is different from their physical body, an idea known as embodiment. In previous research, embodiment in different types of avatars affected implicit attitudes. The purpose of this experiment was to discover how embodiment in different gendered avatars in virtual reality affects implicit gender bias. For embodiment, participants were placed in an office virtual environment with a male or female avatar. First, there was an orientation period where participants grew accustomed to their virtual body while looking at a mirror placed in front of them. Next, virtual humans of different genders walked in and out of the office with the mirror in view. Each participant completed a gender and leadership Implicit Association Test before and after the embodiment experience. The difference between post test scores and preliminary test scores indicates how implicit bias was affected.
Local Compact Binary Count Based Nonparametric Background Modeling for Foreground Detection in Dynamic Scenes

Background subtraction is one of the most fundamental and challenging tasks in computer vision. Many background subtraction algorithms work well under the assumption that the backgrounds are static over short time periods but degrade dramatically in dynamic scenes, such as swaying trees, rippling water, and waving curtains. In this paper, we propose an effective background subtraction method to address these difficulties by combining color features with texture features in the ViBe framework. Specifically, we present a novel local compact binary count (LCBC) feature that can capture local binary gray-scale difference information and totally discard the local binary structural information. The effective fusion of color and LCBC information significantly improves the performance of the ViBe model, making it very robust to background variations while still highlighting the moving objects. We further embed the total variation (TV) norm regularization technique into the proposed method, which can enhance the spatial smoothness of foreground objects, thereby further improving the accuracy of the method. We evaluate the proposed method against ten sequences containing dynamic backgrounds and show that our method outperforms many state-of-the-art methods in reducing the false positives without compromising the reasonable foreground definitions. The experimental results on challenging well-known data sets demonstrate that the proposed method works effectively on a wide range of dynamic background scenes.
High-Dimensional Filtering Using Nested Sequential Monte Carlo

Sequential Monte Carlo (SMC) methods comprise one of the most successful approaches to approximate Bayesian filtering.However, SMC without a good proposal distribution can perform poorly, in particular in high dimensions.We propose nested sequential Monte Carlo (NSMC), a methodology that generalizes the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm.This way we can compute an "exact approximation" of, e.g., the locally optimal proposal, and extend the class of models for which we can perform efficient inference using SMC.We show improved accuracy over other state-of-the-art methods on several spatiotemporal state space models.
Application of Classification Method C4.5 on Selection of Exemplary Teachers

Abstract The study aims to analyze the selection of exemplary teachers using C4.5 algorithm, which is one of the existing decision tree methods in data mining theory. Teacher data was obtained from the school in SMA Negeri 2 Pematangsiantar (2010-2016). The data used contains information about teacher history and teacher assessment data. This research uses interview technique and questionnaire in obtaining data. There are 11 attributes used in the assessment process: NUPTK , name, age, education, status, Appointment Letter, competence, award, work, teaching load, personality, Position, and label. The system has been tested using the Rapidminer application with 16 data samples. Where the rules are obtained as many as 24 rules. The level of accuracy of the system states that four of the ten attributes have a very significant correlation to the model of relationship rules in determining the proposed model teacher, such as ( Position, competence, education, and personality ). The four attributes (Position, competence, education, and personality) contribute 82.8% to the model of the rule of connectedness rules in determining the best teacher.
SA-IGA: a multiagent reinforcement learning method towards socially optimal outcomes

In multiagent environments, the capability of learning is important for an agent to behave appropriately in face of unknown opponents and dynamic environment. From the system designer’s perspective, it is desirable if the agents can learn to coordinate towards socially optimal outcomes, while also avoiding being exploited by selfish opponents. To this end, we propose a novel gradient ascent based algorithm (SA-IGA) which augments the basic gradient-ascent algorithm by incorporating social awareness into the policy update process. We theoretically analyze the learning dynamics of SA-IGA using dynamical system theory and SA-IGA is shown to have linear dynamics for a wide range of games including symmetric games. The learning dynamics of two representative games (the prisoner’s dilemma game and the coordination game) are analyzed in detail. Based on the idea of SA-IGA, we further propose a practical multiagent learning algorithm, called SA-PGA, based on Q-learning update rule. Simulation results show that SA-PGA agent can achieve higher social welfare than previous social-optimality oriented Conditional Joint Action Learner (CJAL) and also is robust against individually rational opponents by reaching Nash equilibrium solutions.
Survey on hardware implementations of visual object trackers

Visual object tracking is an active topic in the computer vision domain with applications extending over numerous fields. The main sub-tasks required to build an object tracker (e.g. object detection, feature extraction and object tracking) are computationally intensive. Also, the real-time operation of the tracker is indispensable for almost all of its applications. Therefore, complete hardware or hardware/software co-design approaches are pursued for better tracker implementations. This study presents a literature survey of the hardware implementations of object trackers over the last two decades. Although several tracking surveys exist in the literature, a survey addressing the hardware implementations of the different trackers is missing. The authors believe this survey would fill the gap and complete the picture with the existing surveys of how to design an efficient tracker and point out the future directions researchers can follow in this field. They highlight the lack of hardware implementations for state-of-the-art tracking algorithms as well as for enhanced classical algorithms. They also stress the need for measuring the tracking performance of the hardware-based trackers. Additionally, enough details of the hardware-based trackers need to be provided to allow a reasonable comparison between the different implementations.
Key Lessons From Tailoring Agile Methods for Large-Scale Software Development

The authors provide advice from one of the largest development programs in Norway, where 12 scrum teams combined agile practices with traditional project management. The Perform program delivered 12 releases over a four-year period, finishing on budget and on time. The authors summarize 10 key lessons on five crucial topics that are relevant to other large development projects seeking to combine scrum with traditional project management.
QANet: Tensor Decomposition Approach for Query-Based Anomaly Detection in Heterogeneous Information Networks

Complex networks have now become integral parts of modern information infrastructures. This paper proposes a user-centric method for detecting anomalies in heterogeneous information networks, in which nodes and/or edges might be from different types. In the proposed anomaly detection method, users interact directly with the system and anomalous entities can be detected through queries. Our approach is based on tensor decomposition and clustering methods. We also propose a network generation model to construct synthetic heterogeneous information network to test the performance of the proposed method. The proposed anomaly detection method is compared with state-of-the-art methods in both synthetic and real-world networks. Experimental results show that the proposed tensor-based method considerably outperforms the existing anomaly detection methods.
Studying the difference between natural and programming language corpora

Code corpora, as observed in large software systems, are now known to be far more repetitive and predictable than natural language corpora. But why? Does the difference simply arise from the syntactic limitations of programming languages? Or does it arise from the differences in authoring decisions made by the writers of these natural and programming language texts? We conjecture that the differences are not entirely due to syntax, but also from the fact that reading and writing code is un-natural for humans, and requires substantial mental effort; so, people prefer to write code in ways that are familiar to both reader and writer. To support this argument, we present results from two sets of studies: 1) a first set aimed at attenuating the effects of syntax, and 2) a second, aimed at measuring repetitiveness of text written in other settings (e.g. second language, technical/specialized jargon), which are also effortful to write. We find that this repetition in source code is not entirely the result of grammar constraints, and thus some repetition must result from human choice. While the evidence we find of similar repetitive behavior in technical and learner corpora does not conclusively show that such language is used by humans to mitigate difficulty, it is consistent with that theory. This discovery of “non-syntactic” repetitive behaviour is actionable, and can be leveraged for statistically significant improvements on the code suggestion task. We discuss this finding, and other future implications on practice, and for research.
Recover and RELAX: Concern-Oriented Software Architecture Recovery for Systems Development and Maintenance

The stakeholders of a system are legitimately interested in whether and how its architecture reflects their respective concerns at each point of its development and maintenance processes. Having such knowledge available at all times would enable them to continually adjust their systems structure at each juncture and reduce the buildup of technical debt that can be hard to reduce once it has persisted over many iterations. Unfortunately, software systems often lack reliable and current documentation about their architecture. In order to remedy this situation, researchers have conceived a number of architectural recovery methods, some of them concern-oriented. However, the design choices forming the bases of most existing recovery methods make it so none of them have a complete set of desirable qualities for the purpose stated above. Tailoring a recovery to a system is either not possible or only through iterative experiments with numeric parameters. Furthermore, limitations in the scalability of the employed recovery algorithms make it prohibitive to apply the existing techniques to large systems. Finally, since several current recovery methods employ nondeterministic sampling, their inconsistent results do not lend themselves well to tracking a systems course over several versions, as needed by its stakeholders. RELAX (RELiable Architecture EXtraction), a new concern based recovery method that uses text classification, addresses these issues efficiently (1) by assembling the overall recovery result from smaller, independent parts, (2) basing it on an algorithm with linear time complexity and (3) being tailorable to the recovery of a single system or a sequence thereof through the selection of meaningfully named, semantic topics. An intuitive and informative architectural visualization rounds out RELAX's contributions. RELAX is illustrated on a number of existing open-source systems and compared to other recovery methods. Index Terms-software architecture, architectural change, software evolution, open source software, architecture recovery, software development management, software maintenance.
Automated Bahasa Indonesia essay evaluation with latent semantic analysis

Abstract Essay Examination is one of many ways to evaluate the learning process of students. The Essay test can measure a student’s capability of memory and to develop an idea. With the vast development of Information Technology, an essay test is done in a more sophisticated way with a platform called e-learning. This research implemented an e-learning interface to conduct an essay test, starting with giving out questions, answering it and marking it. The system will generate an automatic mark, using the Latent Semantic Analysis (LSA) method by measuring the relevance of the key answer and the student’s answer. Before entering the LSA phase, the answer will go through pre-processing step which is cleansing, case folding, tokenization, stop word, convert negation and stemming. The result of the accuracy of this system compared to teacher’s manual assessment is 83.3%. Expectedly, this research will help teachers in making their assessment process more efficient.