Reviewerly/exHarmony huggingface dataset:
Sequential Hybrid Particle Swarm Optimization and Gravitational Search Algorithm with Dependent Random Coefficients

Particle swarm optimization (PSO) has been proven to show good performance for solving various optimization problems. However, it tends to suffer from premature stagnation and loses exploration ability in the later evolution period when solving complex problems. This paper presents a sequential hybrid particle swarm optimization and gravitational search algorithm with dependent random coefficients called HPSO-GSA, which first incorporates the gravitational search algorithm (GSA) with the PSO by means of a sequential operating mode and then adopts three learning strategies in the hybridization process to overcome the aforementioned problem. Specifically, the particles in the HPSO-GSA enter into the PSO stage and update their velocities by adopting the dependent random coefficients strategy to enhance the exploration ability. Then, the GSA is incorporated into the PSO by using fixed iteration interval cycle or adaptive evolution stagnation cycle strategies when the swarm drops into local optimum and fails to improve their fitness. To evaluate the effectiveness and feasibility of the proposed HPSO-GSA, the simulations were conducted on benchmark test functions. The results reveal that the HPSO-GSA exhibits superior performance in terms of accuracy, reliability, and efficiency compared to PSO, GSA, and other recently developed hybrid variants.
Rulemaking and Inscrutable Automated Decision Tools

Complex machine learning models derived from personal data are increasingly used in making decisions important to peoples’ lives. These automated decision tools are controversial, in part because their operation is difficult for humans to grasp or explain. While scholars and policymakers have begun grappling with these explainability concerns, the debate has focused on explanations to decision subjects. This Essay argues that explainability has equally important normative and practical ramifications for decision-system design. Automated decision tools are particularly attractive when decisionmaking responsibility is delegated and distributed across multiple actors to handle large numbers of cases. Such decision systems depend on explanatory flows among those responsible for setting goals, developing decision criteria, and applying those criteria to particular cases. Inscrutable automated decision tools can disrupt all of these flows.This Essay focuses on explanation’s role in decision-criteria development, which it analogizes to rulemaking. It analyzes whether, and how, decision tool inscrutability undermines the traditional functions of explanation in rulemaking. It concludes that providing information about the many aspects of decision tool design, function, and use that can be explained can perform many of those traditional functions. Nonetheless, the technical inscrutability of machine learning models has significant ramifications for some decision contexts. Decision tool inscrutability makes it harder, for example, to assess whether decision criteria will generalize to unusual cases or new situations and heightens communication and coordination barriers between data scientists and subject matter experts. The Essay concludes with some suggested approaches for facilitating explanatory flows for decision-system design.
Modal features for image texture classification

Feature extraction is a key step in image processing for pattern recognition and machine learning processes. Its purpose lies in reducing the dimensionality of the input data through the computing of features which accurately describe the original information. In this article, a new feature extraction method based on Discrete Modal Decomposition (DMD) is introduced, to extend the group of space and frequency based features. These new features are called modal features. Initially aiming to decompose a signal into a modal basis built from a vibration mechanics problem, the DMD projection is applied to images in order to extract modal features with two approaches. The first one, called full scale DMD, consists in exploiting directly the decomposition resulting coordinates as features. The second one, called filtering DMD, consists in using the DMD modes as filters to obtain features through a local transformation process. Experiments are performed on image texture classification tasks including several widely used data bases, compared to several classic feature extraction methods. We show that the DMD approach achieves good classification performances, comparable to the state of the art techniques, with a lower extraction time.
Design and Implementation of Cost Effective Multi-factor Authentication Framework for ATM Systems

Banks and financial institutions all over the world have adopted and continue to adopt Automated Teller Machine (ATM) systems into their transactions to extend banking hours, and also provide convenience for their customers. ATM systems are networked computerized systems, and as the case is in these systems, their security must be given the highest priority. Among the many strategies for ensuring secured networked systems, authentication is very important. Authentication is the process of verifying the identity of a user or a process that attempts to access information resources from a system. Good authentication methods and schemes are one of the best standard ways of implementing security on computerized systems. The importance of selecting an environment appropriate authentication method is perhaps the most crucial decision in designing secure systems. Authentication protocols are capable of simply authenticating the connecting party or authenticating the connecting party as well as authenticating itself to the connecting party. The verification process is usually based on authentication factors like facts, characteristics, behaviors, or knowledge known only to both the claimant and the verifier. Based on these authentication factors, authentication is classified into knowledge-based (KBA), token-based (TBA) and biometrics-based (BBA) authentications. In this paper, we designed and implemented a hybrid and secure cost-effective authentication framework for ATM systems based on the strengths of the three main authentication classifications.
Faster Montgomery and double-add ladders for short Weierstrass curves

The Montgomery ladder and Joye ladder are well-known algorithms for elliptic curve scalar multiplication with a regular structure. The Montgomery ladder is best known for its implementation on Montgomery curves, which requires 5M+4S+1m+8A per scalar bit, and 6 field registers. Here (M, S,m,A) represent respectively field Multiplications, Squarings, multiplications by a curve constant, and Additions or subtractions. This ladder is also complete, meaning that it works on all input points and all scalars. Many protocols do not use Montgomery curves, but instead use prime-order curves in short Weierstrass form. These have historically been much slower, with ladders costing at least 14 multiplications or squarings per bit: 8M + 6S + 27A for the Montgomery ladder and 8M+ 6S + 30A for the Joye ladder. In 2017, Kim et al. improved the Montgomery ladder to 8M+ 4S + 12A + 1H per bit using 9 registers, where the H represents a halving. Hamburg simplified Kim et al.’s formulas to 8M+ 4S + 8A + 1H per bit using 6 registers. Here we present improved formulas which compute the Montgomery ladder on short Weierstrass curves using 8M+ 3S + 7A per bit, and requiring 6 registers. We also give formulas for the Joye ladder that use 9M+3S+7A per bit, requiring 5 registers. One of our new formulas supports very efficient 4-way vectorization. We also discuss curve invariants, exceptional points, side-channel protection and how to set up and finish these ladder operations. Finally, we show a novel technique to make these ladders complete when the curve order is not divisible by 2 or 3, at a modest increase in cost. A sample implementation of these techniques is given in the supplementary material, also posted at https://github.com/bitwiseshiftleft/ladder_formulas
Randomized Decoding of Gabidulin Codes Beyond the Unique Decoding Radius

We address the problem of decoding Gabidulin codes beyond their unique error-correction radius. The complexity of this problem is of importance to assess the security of some rank-metric code-based cryptosystems. We propose an approach that introduces row or column erasures to decrease the rank of the error in order to use any proper polynomial-time Gabidulin code error-erasure decoding algorithm. The expected work factor of this new randomized decoding approach is a polynomial term times $$q^{m(n-k)-w(n+m)+w^2+\min \{2\xi (\frac{n+k}{2}-\xi ),wk\} }$$, where n is the code length, q the size of the base field, m the extension degree of the field, k the code dimension, w the number of errors, and $$\xi := w-\tfrac{n-k}{2}$$. It improves upon generic rank-metric decoders by an exponential factor.
Improving the generalization performance of deep networks by dual pattern learning with adversarial adaptation

In this paper, we present a dual pattern learning network architecture with adversarial adaptation (DPLAANet). Unlike conventional networks, the proposed network has two input branches and two loss functions. This architecture forces the network to learn robust features by analysing dual inputs. The dual input structure allows the network to have a considerably large number of image pairs, which can help address the overfitting issue due to limited training data. In addition, we propose to associate the two input branches with two random interest values during training. As a stochastic regularization technique, this method can improve the generalization performance. Moreover, we introduce to use the adversarial training approach to reduce the domain difference between fused image features and single image features. Extensive experiments on CIFAR-10, CIFAR-100, FI-8, the Google commands dataset, and MNIST demonstrate that our DPLAANets exhibit better performance than the baseline networks. The experimental results on subsets of CIFAR-10, CIFAR-100, and MNIST demonstrate that DPLAANets have a good generalization performance on small datasets. The proposed architecture can be easily extended to have more than two input branches. The experimental results on subsets of MNIST show that the architecture with three branches outperforms two branches when the training set is extremely small.
Human Action Recognition Using Laban Movement Analysis and Dynamic Time Warping

Bilateral interaction between humans and robots is one of the areas that has attracted much attention in recent years. Automation of human behavior recognition is one of the main steps in achieving this goal. In this regard, in this paper we have designed a process for automatic identification of human gestures. The process consists of two main parts. In the first part, by using the Laban Movement Analysis method, we define a robust descriptor, and in the second part, we determine the robustness of the descriptor using the Dynamic time Warping algorithm. The method proposed in this paper has been tested on four public data-sets namely MSR Action 3D, Florence 3D actions, UTKinect-Action3D and SYSU 3D HUMAN-OBJECT INTERACTION data-sets. Given the results obtained from previous work, the efficiency of the proposed method can be more accurately understood. The results obtained confirm the effectiveness and the performance of our model which outperforms results presented in similar works on action recognition.
Corrigendum to ‘machine learning for clinical decision support in infectious diseases: a narrative review of current applications’ clinical microbiology and infection (2020) 584–595

The authors regret that the at publication the fifth authors name had been inverted thus was incorrect. The above is the correct authorship list which has been updated online. The authors would like to apologise for any inconvenience caused. Machine learning for clinical decision support in infectious diseases: a narrative review of current applicationsClinical Microbiology and InfectionVol. 26Issue 5PreviewMachine learning (ML) is a growing field in medicine. This narrative review describes the current body of literature on ML for clinical decision support in infectious diseases (ID). Full-Text PDF Open Archive
Bridging the Gap Between Probabilistic Model Checking and Probabilistic Planning: Survey, Compilations, and Empirical Comparison

Markov decision processes are of major interest in the planning community as well as in the model checking community. But in spite of the similarity in the considered formal models, the development of new techniques and methods happened largely independently in both communities. This work is intended as a beginning to unite the two research branches. We consider goal-reachability analysis as a common basis between both communities. The core of this paper is the translation from Jani, an overarching input language for quantitative model checkers, into the probabilistic planning domain definition language (PPDDL), and vice versa from PPDDL into Jani. These translations allow the creation of an overarching benchmark collection, including existing case studies from the model checking community, as well as benchmarks from the international probabilistic planning competitions (IPPC). We use this benchmark set as a basis for an extensive empirical comparison of various approaches from the model checking community, variants of value iteration, and MDP heuristic search algorithms developed by the AI planning community. On a per benchmark domain basis, techniques from one community can achieve state-ofthe-art performance in benchmarks of the other community. Across all benchmark domains of one community, the performance comparison is however in favor of the solvers and algorithms of that particular community. Reasons are the design of the benchmarks, as well as tool-related limitations. Our translation methods and benchmark collection foster crossfertilization between both communities, pointing out specific opportunities for widening the scope of solvers to different kinds of models, as well as for exchanging and adopting algorithms across communities.
Compositional Data Analysis Approach in the Measurement of Social-Spatial Segregation: Towards a Sustainable and Inclusive City

The location and context in which people live influences and conditions their opportunities in life. This becomes relevant in a world subject to rapid urban and demographic growth, in which different economic, social, and political forces generate and accentuate disparities in cities. The foregoing generates an unequal distribution of the different social groups in the territory known as socio-spatial segregation. The study of this phenomenon incorporates a large number of variables belonging to different dimensions. Nonetheless, few studies have addressed socio-spatial segregation with a multivariate analysis approach. In addition, the existing studies may have obtained misleading outcomes by not acknowledging the inherent compositional nature of their variables. The objective of the present study is twofold: (i) To assess whether the phenomenon of socio-spatial segregation in Guadalajara, Mexico exists; and (ii) to introduce and stress the use of compositional techniques for the study of socio-spatial segregation. The study applied principal component analysis and cluster analysis considering the compositional nature of census variables, particularly from economic and educative indicators. In addition, the study used geographical information tools to depict and interpret the results. The results are intended to serve in the fulfillment of the Sustainable Development Goals towards inclusive and sustainable cities.
An efficient and secure data auditing scheme based on fog-to-cloud computing for Internet of things scenarios

With the widespread use of fog-to-cloud computing–based Internet of things devices, how to ensure the integrity of the data uploaded to the cloud has become one of the most important security issues. This article proposes an efficient and secure data auditing scheme based on fog-to-cloud computing for Internet of things scenarios, which can better meet performance and security requirements. The proposed scheme realizes data sharing under the condition of protecting privacy by encrypting sensitive information. Using the private key separation method, the private key is divided into two parts using identity information generation and random selection which are, respectively, held by the user and the fog center. Then, using the two-time signature method, the Internet of things and fog computing center use two parts of the private key to generate the original signature and final signature in two separate times. Since the fog computing center only has a part of the private key generated using the identity information, the security of the system will not be damaged due to the leakage of part of the private key held by the fog center, and the fog center significantly participates in the signature generation process, which significantly reduces the computation and communication overhead of the Internet of things device. Security analysis and performance evaluation show that the proposed scheme is safe and efficient.
Deep Echo State Networks in Industrial Applications

This paper analyzes the impact of reservoir computing, and, in particular, of Deep Echo State Networks, to the modeling of highly non-linear dynamical systems that can be commonly found in the industry. Several applications are presented focusing on forecasting models related to energy content of steelwork byproduct gasses. Deep Echo State Network models are trained, validated and tested by exploiting datasets coming from a real industrial context, with good results in terms of accuracy of the predictions.
Image to Modern Chinese Poetry Creation via a Constrained Topic-aware Model

Artificial creativity has attracted increasing research attention in the field of multimedia and artificial intelligence. Despite the promising work on poetry/painting/music generation, creating modern Chinese poetry from images, which can significantly enrich the functionality of photo-sharing platforms, has rarely been explored. Moreover, existing generation models cannot tackle three challenges in this task: (1) Maintaining semantic consistency between images and poems; (2) preventing topic drift in the generation; (3) avoidance of certain words appearing frequently. These three points are even common challenges in other sequence generation tasks. In this article, we propose a Constrained Topic-aware Model (CTAM) to create modern Chinese poetries from images regarding the challenges above. Without image-poetry paired dataset, we construct a visual semantic vector to embed visual contents via image captions. For the topic-drift problem, we propose a topic-aware poetry generation model. Additionally, we design an Anti-frequency Decoding (AFD) scheme to constrain high-frequency characters in the generation. Experimental results show that our model achieves promising performance and is effective in poetry’s readability and semantic consistency.
Examination of the eye-hand coordination related to computer mouse movement

Eye-hand coordination means the ability to combine seeing and hand movement.Eye-hand coordination is a complex process consisting of a series of conscious actions.The fine motor skills of the hand were not born with us but learned.The development of eye-hand coordination has begun in infancy through various ball games, construction games and puzzle games.Co-ordinated work of eye and hand movement is the basis for many activities.The proper functioning of eye-hand coordination is necessary for many everyday activities such as writing, reading or driving.The joint work of the eyes and hands is vital for certain forms of movement (ball-catching, kicking).The eye plays an essential role in regulating fine movements.In this paper a general eye-hand coordination task is examined in relation to mouse cursor movement on computer screen.An eye-hand tracking system was used to observe the gaze and hand path during the mouse cursor movement and the acquired data were analyzed by statistical t-test.
Efficient Team Formation in Social Networks based on Constrained Pattern Graph

Finding a team that is both competent in performing the task and compatible in working together has been extensively studied. However, most methods for team formation tend to rely on a set of skills only. In order to solve this problem, we present an efficient team formation method based on Constrained Pattern Graph (called CPG). Unlike traditional methods, our method takes into account both structure constraints and communication constraints on team members, which can better meet the requirements of users. First, a CPG preprocessing method is proposed to normalize a CPG and represent it as a CoreCPG in order to establish the basis for efficient matching. Second, a Communication Cost Index (called CCI) is constructed to speed up the matching between a CPG and its corresponding social network. Third, a CCI-based node matching algorithm is proposed to minimize the total number of intermediate results. Moreover, a set of incremental maintenance strategies for the changes of social networks are proposed. We conduct experimental studies based on two real-world social networks. The experiments demonstrate the effectiveness and the efficiency of our proposed method in comparison with traditional methods.