Reviewerly/exHarmony huggingface dataset:
Ensemble feature extraction for multi-container quality-diversity algorithms

Quality-Diversity algorithms search for large collections of diverse and high-performing solutions, rather than just for a single solution like typical optimisation methods. They are specially adapted for multi-modal problems that can be solved in many different ways, such as complex reinforcement learning or robotics tasks. However, these approaches are highly dependent on the choice of feature descriptors (FDs) quantifying the similarity in behaviour of the solutions. While FDs usually needs to be hand-designed, recent studies have proposed ways to define them automatically by using feature extraction techniques, such as PCA or Auto-Encoders, to learn a representation of the problem from previously explored solutions. Here, we extend these approaches to more complex problems which cannot be efficiently explored by relying only on a single representation but require instead a set of diverse and complementary representations. We describe MC-AURORA, a Quality-Diversity approach that optimises simultaneously several collections of solutions, each with a different set of FDs, which are, in turn, defined automatically by an ensemble of modular auto-encoders. We show that this approach produces solutions that are more diverse than those produced by single-representation approaches.
UnitNet: A Sequence-to-Sequence Acoustic Model for Concatenative Speech Synthesis

This paper presents UnitNet, a sequence-to-sequence (Seq2Seq) acoustic model for concatenative speech synthesis. Comparing with the Tacotron2 model for Seq2Seq speech synthesis, UnitNet utilizes the phone boundaries of training data and its decoder contains autoregressive structures at both phone and frame levels. This hierarchical architecture can not only extract embedding vectors for representing phone-sized units in the corpus but also measure the dependency among consecutive units, which makes the UnitNet model capable of guiding the selection of phone-sized units for concatenative speech synthesis. A byproduct of this model is that it can also be applied to statistical parametric speech synthesis (SPSS) and improve the robustness of Seq2Seq acoustic feature prediction since it adopts interpretable transition probability prediction rather than attention mechanism for frame-level alignment. Experimental results show that our UnitNet-based concatenative speech synthesis method not only outperforms the unit selection methods using hidden Markov models and Tacotron-based unit embeddings, but also achieves better naturalness and faster inference speed than the SPSS method using FastSpeech and Parallel WaveGAN. Besides, the UnitNet-based SPSS method makes fewer synthesis errors than Tacotron2 and FastSpeech without naturalness degradation.
Particle Swarm Optimization Algorithm with Multiple Phases for Solving Continuous Optimization Problems

An algorithm with different parameter settings often performs differently on the same problem. The parameter settings are difficult to determine before the optimization process. The variants of particle swarm optimization (PSO) algorithms are studied as exemplars of swarm intelligence algorithms. Based on the concept of building block thesis, a PSO algorithm with multiple phases was proposed to analyze the relation between search strategies and the solved problems. Two variants of the PSO algorithm, which were termed as the PSO with fixed phase (PSOFP) algorithm and PSO with dynamic phase (PSODP) algorithm, were compared with six variants of the standard PSO algorithm in the experimental study. The benchmark functions for single-objective numerical optimization, which includes 12 functions in 50 and 100 dimensions, are used in the experimental study, respectively. The experimental results have verified the generalization ability of the proposed PSO variants.
Investigating bound handling schemes and parameter settings for the interior search algorithm to solve truss problems

Abstract The interior search algorithm (ISA) is an optimization algorithm inspired by esthetic techniques used for interior design and decoration. The algorithm has only one parameter, controlled by θ , and uses an evolutionary boundary constraint handling (BCH) strategy to keep itself within an admissible solution space while approaching the optimum. We apply the ISA to find optimal weight design of truss structures with frequency constraints. Sensitivity of the ISA's performance to the θ parameter and the BCH strategy is investigated by considering different values of θ and BCH techniques. This is the first study in the literature on the sensitivity of truss optimization problems to various BCH approaches. Moreover, we also study the impact of different BCH methods on diversification and intensification. Through extensive numerical simulations, we identified the best BCH methods that provide consistently better results for all truss problems studied, and obtained a range of θ that maximizes the ISA's performance for all problem classes studied. However, results also recommend further fine‐tuning of parameter settings for specific case studies to obtain the best results.
RHAT: Efficient RowHammer-Aware Test for Modern DRAM Modules

In recent times, the family of rowhammer attacks have posed major security threats. Assessing the rowhammer vulnerability of DRAM modules during post-manufacturing test as well as system development and in-field deployment is a crucial step towards detecting and mitigating it. Detection of rowhammer vulnerable cells is quite challenging due to random physical factors and memory constructions, and very exhaustive leading to time and cost overhead. In this paper, we propose an efficient test framework, called RHAT, to address this concern. RHAT can be employed for both manufacturing test and in-field (deployment) test. It first develops a non-invasive approach to efficiently reverse engineer the address scrambling on DRAM. It then identifies critical vulnerability features by spatial correlation analysis of memory cells. Finally, it offers a fast test algorithm to detect rowhammer-vulnerable cells using smaller representative memory array based on the correlation analysis. We implemented and validated RHAT on DIMM samples from three different DRAM vendors showing significant correlation with satisfactory test coverage and time.
Scientia Potentia Est—On the Role of Knowledge in Computational Argumentation

Abstract Despite extensive research efforts in recent years, computational argumentation (CA) remains one of the most challenging areas of natural language processing. The reason for this is the inherent complexity of the cognitive processes behind human argumentation, which integrate a plethora of different types of knowledge, ranging from topic-specific facts and common sense to rhetorical knowledge. The integration of knowledge from such a wide range in CA requires modeling capabilities far beyond many other natural language understanding tasks. Existing research on mining, assessing, reasoning over, and generating arguments largely acknowledges that much more knowledge is needed to accurately model argumentation computationally. However, a systematic overview of the types of knowledge introduced in existing CA models is missing, hindering targeted progress in the field. Adopting the operational definition of knowledge as any task-relevant normative information not provided as input, the survey paper at hand fills this gap by (1) proposing a taxonomy of types of knowledge required in CA tasks, (2) systematizing the large body of CA work according to the reliance on and exploitation of these knowledge types for the four main research areas in CA, and (3) outlining and discussing directions for future research efforts in CA.
Tree-Chain: A Lightweight Consensus Algorithm for IoT-based Blockchains

In this paper, we propose Tree-chain a fast scalable consensus algorithm that bases the validator selection on an existing function in relatively all existing blockchains: hash function output. Tree-chain is a leader selection consensus algorithm that moves away from a linear blockchain structure, to a tree-structured blockchain where each branch is managed by a particular validator. Each validator is periodically allocated to a random consensus code range which matches specific patterns of the most significant bits of the hash function output. Each validator is then responsible for transactions whose hash falls within the consensus code allocated to the validator. Because transactions are allocated deterministically to validators based on random consensus code allocations, Tree-chain eliminates the significant inefficiency of conventional blockchains for validator selection, such as proof-of work. Tree-chain does not require the validators to follow any additional algorithms before committing blocks which in turn significantly increases its throughput. Implementation results show that Tree-chain can run on small tiny IoT devices with limited resources which in turn highlights its resource efficiency.
3DnST: A Framework Towards Understanding Children’s Interaction with Tinkercad and Enhancing Spatial Thinking Skills

With the proliferation of 3D printing technologies in schools and makerspaces, there is a need for teaching 3D modeling to students. Learning 3D modeling enhances spatial thinking skills, an essential skill for success in STEM. Creating 3D models requires students to have a deep understanding of 3D space, including rotating and scaling. In this study, we propose a framework developed through video-coding from analyzing screen recordings of middle-school students’ usage of a 3D modeling tool - Tinkercad. The proposed framework focuses on identifying challenges students encounter during 3D modeling. These challenges include spatial thinking skills, working with the Tinkercad interface, and mental model formation. We authenticated the framework by collecting and analyzing data from a 3D printing unit in three middle schools. Our results and subsequent analysis can guide educators and researchers on how to use this framework to support students in having productive learning experiences with Computer-Aided Design tools.
Multilingual Ontology Merging Using Cross-lingual Matching

With the growing amount of multilingual data on the Semantic Web, several ontologies (in different natural languages) have been developed to model the same domain. Creating multilingual ontologies by merging such monolingual ones is important to promote semantic interoperability among different ontologies in different natural languages. This is a step towards achieving the multilingual Semantic Web. In this paper, we propose MULON, an approach for merging monolingual ontologies in different natural languages producing a multilingual ontology. MULON approach comprises three modules; Preparation Module, Merging Module, and Assessment Module. We consider both classes and properties in the merging process. We present three real-world use cases describing the usability of the MULON approach in different domains. We assess the quality of the merged ontologies using a set of predefined assessment metrics. MULON has been implemented using Scala and Apache Spark under an open-source license. We have compared our cross-lingual matching results with the results from the Ontology Alignment Evaluation Initiative (OAEI 2019). MULON has achieved relatively high precision, recall, and F-measure in comparison to three state-of-the-art approaches in the matching process and significantly higher coverage without any redundancy in the merging process.
Joint Open Knowledge Base Canonicalization and Linking

Open Information Extraction (OIE) methods extract a large number of OIE triples (noun phrase, relation phrase, noun phrase) from text, which compose large Open Knowledge Bases (OKBs). However, noun phrases (NPs) and relation phrases (RPs) in OKBs are not canonicalized and often appear in different paraphrased textual variants, which leads to redundant and ambiguous facts. To address this problem, there are two related tasks: OKB canonicalization (i.e., convert NPs and RPs to canonicalized form) and OKB linking (i.e., link NPs and RPs with their corresponding entities and relations in a curated Knowledge Base (e.g., DBPedia). These two tasks are tightly coupled, and one task can benefit significantly from the other. However, they have been studied in isolation so far. In this paper, we explore the task of joint OKB canonicalization and linking for the first time, and propose a novel framework JOCL based on factor graph model to make them reinforce each other. JOCL is flexible enough to combine different signals from both tasks, and able to extend to fit any new signals. A thorough experimental study over two large scale OIE triple data sets shows that our framework outperforms all the baseline methods for the task of OKB canonicalization (OKB linking) in terms of average F1 (accuracy).
Application of Distributed Seagull Optimization Improved Algorithm in Sentiment Tendency Prediction

Emotion analysis is of great practical significance in the aspects of network control, public opinion monitoring and public sentiment guidance. In order to obtain better accuracy of emotion classification and analyze users’ emotion tendency more accurately, a distributed model of emotion classification with improved Seagull optimization algorithm (SOA) is proposed. The improvement of Cauchy variation and uniform distribution of SOA (CC-SOA) is to solve the problems of slow convergence speed, easy to fall into local optimal and poor accuracy of SOA. The uniform population distribution strategy can increase the diversity of the population and enhance the search ability of the local optimal solution of the algorithm. Cauchy variation is helpful to jump out of the local optimal solution and finally reach the global optimal solution. Due to the large amount of data to be processed and the long training time, single machine mode processing could not meet the actual requirements. Combining logistic regression with CC-SOA, a new model LG-SOA is proposed. Finally, LG-CCSOA is distributed processing on Spark platform, Distributed computing is carried out on different nodes, and the final running time is greatly reduced. After testing with benchmark function, the simulation results show that the CC-SOA has higher convergence accuracy and faster convergence speed. It has higher prediction accuracy for both small and large data sets, and the Spark platform improves the running efficiency of the algorithm.
Time Saving Students’ Formative Assessment: Algorithm to Balance Number of Tasks and Result Reliability

Feedback is a crucial component of effective, personalized learning, and is usually provided through formative assessment. Introducing formative assessment into a classroom can be challenging because of test creation complexity and the need to provide time for assessment. The newly proposed formative assessment algorithm uses multivariate Elo rating and multi-armed bandit approaches to solve these challenges. In the case study involving 106 students of the Cloud Computing course, the algorithm shows double learning path recommendation precision compared to classical test theory based assessment methods. The algorithm usage approaches item response theory benchmark precision with greatly reduced quiz length without the need for item difficulty calibration.
Table Redundancy Method for Protecting Against Fault Attacks

Fault attacks (FA) intentionally inject some fault into the encryption process for analyzing a secret key based on faulty intermediate values or faulty ciphertexts. One of the easy ways for software-based countermeasures is to use time redundancy. However, existing methods can be broken by skipping comparison operations or by using non-uniform distributions of faulty intermediate values. In this paper, we propose a secure software-based redundancy, aptly named table redundancy, applying different linear and nonlinear transformations to redundant computations of table-based block cipher structures. To reduce the table size and the number of lookups, some outer tables that are not subjected to FA are shared, while the inner tables are protected by table redundancy. The basic idea is that different transformations protecting redundant computations are correctly decoded if the redundant outcomes are combined without faulty values. In addition, this recombination provides infective computations because a faulty byte is likely to propagate its error to adjacent bytes due to the use of 32-bit linear transformations. Our method also presents a stateful feature in the connection with detected faults and subsequent plaintexts for preventing iterative fault injection. We demonstrate the protection of AES-128 against FA and show a negligible advantage of FA.
Blockchained Adaptive Federated Auto MetaLearning BigData and DevOps CyberSecurity Architecture in Industry 4.0

Maximizing the production process in modern industry, as proposed by Industry 4.0, requires extensive use of Cyber-Physical Systems (CbPS). Artificial intelligence technologies, through CbPS, allow monitoring of natural processes, making autonomous, decentralized and optimal decisions. Collection of information that optimizes the effectiveness of decisions, implies the need for big data management and analysis. This data is usually coming from heterogeneous sources and it might be non-interoperable. Big data management is further complicated by the need to protect information, to ensure business confidentiality and privacy, according to the recent General Data Protection Regulation - GDPR. This paper introduces an innovative holistic Blockchained Adaptive Federated Auto Meta Learning Big Data and DevOps Cyber Security Architecture in Industry 4.0. The aim is to fill the gap found in the ways of handling and securing industrial data. This architecture, combines the most modern software development technologies under an optimal and efficient framework. It successfully achieves the prediction and assessment of threat-related conditions in an industrial ecosystem, while ensuring privacy and secrecy.
Human-Machine Interface for Controlling a Light Robotic Arm by Persons with Special Needs

Conventional electric wheelchairs are usually controlled by joysticks or by hand gesture technologies, which cannot meet the needs of persons with acute special needs - persons who have limb movement limitations and can barely move or turn their head. For them, adapted machine user interfaces where created. But other challenges remained - as the possibility to interact with the surroundings, not only to navigate thru them. Recent studies reveal that the technological developments offered a real opportunity for the creation of different support devices, which to compensate for the loss of mobility. In the past, the only alternative to exceed these limitations was to ask for human help. One of the most common solutions of those support devices are the robotic light arms, especially the ones specially designed to be mounted on wheelchairs to help users perform handling tasks. This paper describes a smart power wheelchair equipped with artificial vision sensors and a robotic arm designed to be used in applications for people with physical disabilities. We present a human-machine interface that allows the control of the robotic arm movements using the eye tracking sensor.
A hybrid ant colony optimization algorithm for the knapsack problem with a single continuous variable

Knapsack problem with a single continuous variable (KPC) is a natural extension of the standard 0-1 knapsack problem. In the KPC, the capacity of the knapsack is not fixed, so it becomes more difficult to solve. Although several metaheuristics have been proposed to solve the KPC, their performances are not stable enough. This paper proposes a hybrid ant colony optimization (HACO) algorithm to solve the KPC. In the HACO algorithm, a simplified ACO algorithm is designed to search the optimal subset of items, and the mutation operator of differential evolution algorithm is used to search the optimal value of continuous variable. The parameter of the mutation operator is adaptively configured via an ACO algorithm. In the simplified ACO algorithm, an independent selection strategy and a pheromone-based selection strategy are designed to speed up the algorithm and simplify the tuning of parameters respectively. Personal best solutions constructed by each ant are used to deposit pheromone. Furthermore, a value-based greedy optimization strategy is introduced to enhance the solutions. The HACO algorithm is experimentally analyzed on 40 KPC instances. The experimental results show that the HACO algorithm is better than other competitors in terms of obtaining the best solution and the mean solution.
Integration of Professional Certifications with Information Systems Business Analytics Track Curriculum

In this study, we showcase a design of an undergraduate Business Analytics track that integrates professional certifications from Amazon Web Services, Google, SAS, and Salesforce with core Business Analytics courses in an Information Systems undergraduate degree program. Certifications provide an excellent way for students to attain practical, experiential, and demonstrable skills which increasingly more employers look for in job candidates' portfolios. In close collaboration with industry partners, curriculum designers and faculty in institutions of higher learning can leverage high quality hands-on training materials provided by the certification vendors and align it with the core academic course content. Excellent teaching by the faculty combined with industry-relevant practical certification training is the recipe for a successful career building for Information Systems students.
A (non‐central) chi‐squared mixture of non‐central chi‐squareds is (non‐central) chi‐squared and related results, corollaries and applications

Our main, novel, result is that a certain non‐central chi‐squared mixture of non‐central chi‐squared distributions is itself a scaled non‐central chi‐squared distribution. From this and a link to a known result on a mixture representation for a scaled central chi‐squared distribution, numerous further mixture results, both old and new, ensue. These include mixture results for central F , non‐central F and Libby–Novick distributions. The main result involves distributions all with the same degrees of freedom; it is also extended to the case where the mixing non‐central chi‐squared distribution has degrees of freedom an even number larger than that of the conditional non‐central chi‐squared distribution, with further consequences pursued.
Decoding Confusing Code: Social Representations among Developers

Context. Confusing code is any code element in which developers have considerable difficulty reaching its comprehension. These difficulties may result from a plethora of characteristics of the source code and external issues. In this way, there is still a lack of knowledge on the core issues of confusing code. This knowledge is important for optimizing efforts in promoting program comprehension. Goal. In this paper, we investigate the social representations of confusing code among two distinct communities of software developers from industry. Method. We conducted free association tasks with the developers to characterize what is in their minds about confusing code. Then, we compiled and classified the associations composing the social representations of confusing code by each community. Finally, we compared the social representations from both communities. Results. We found that developers of both communities strongly associate confusing code with a common set of undesirable characteristics of the source code, such as different types of code smells and the badly naming of code elements. Besides, we found that the incidence of confusing code is a potential source of discouragement and conflict. In this way, we discuss alternatives for development teams overcoming these issues. Conclusion. The findings of our study reveal an initial set of core issues of confusing code. These issues can drive future investigations on technologies for promoting code comprehension.