Dr Mahakim Newton

The Internet of Things (IoT) has many important applications in multiple domains that include home automation, smart cites, healthcare, agriculture, and environment. IoT comprises a wide range of sensors and actuators that communicate with each other over cloud, fog and edge level networks. Moreover, these devices use various communication protocols and are made by different manufactures. To deal with these diversities, IoT essentially needs interoperable communication interfaces among devices. Unfortunately, existing interoperability solutions are centralised and use fog or cloud level computing resources, making IoT communications latency-prone and poorly scalable. These issues could be handled effectively, if edge level devices could be made interoperable within the edge level and without needing fog or cloud level access. This paper proposes a decentralized interoperability solution that stays fully within the edge level. The solution relies on controller devices that work on the interface boundaries of the edge devices. Unlike existing solutions, the proposed solution adopts a hierarchical interoperability model to handle interoperability at network, syntactical, semantic, and organizational levels. Our solution is non-proprietary, generic over vendors and platforms, and easily extendable to new devices. We compare our proposed solution with existing interoperability solutions for edge devices and show its mobility, efficiency and flexibility.

More training instances could lead to better classification accuracy. However, accuracy could also degrade if more training instances mean further noises and outliers. Additional training instances arguably need additional computational resources in future data mining operations. Instance selection algorithms identify subsets of training instances that could desirably increase accuracy or at least do not decrease accuracy significantly. There exist many instance selection algorithms, but no single algorithm, in general, dominates the others. Moreover, existing instance selection algorithms do not allow direct controlling of the instance selection rate. In this paper, we present a simple and generic cluster-oriented instance selection algorithm for classification problems. Our proposed algorithm runs an unsupervised K Means Clustering algorithm on the training instances and with a given selection rate, selects instances from the centers and the borders of the clusters. On 24 benchmark classification problems, when very similar percentages of instances are selected by various instance selection algorithms, K Nearest Neighbours classifiers achieve more than 2%¿3% better accuracy when using instances selected by our proposed method than when using those selected by other state-of-the-art generic instance selection algorithms.

Predicted inter-residue distances are a key behind recent success in high quality protein structure prediction (PSP). However, prediction of both short and long distance values together is challenging. Consequently, predicted short distances are mostly used by existing PSP methods. In this paper, we use a stacked meta-ensemble method to combine deep learning models trained for different ranges of real-valued distances. On five benchmark sets of proteins, our proposed inter-residue distance prediction method improves mean Local Distance Different Test (LDDT) scores at least by 5% over existing such methods. Moreover, using a real-valued distance based conformational search algorithm, we also show that predicted long distances help obtain significantly better protein conformations than when only predicted short distances are used. Our method is named meta-ensemble for distance prediction (MDP) and its program is available from https://gitlab.com/mahnewton/mdp.

Real-life water quality monitoring applications such as aquaculture domains and water resource management need long range multi-step prediction for disaster control. However, prediction accuracy usually degrades gradually as the prediction target timepoint is further away from the current timepoint. To address this, recent water quality forecasting methods mostly rely on complex deep learning models. In this paper, we propose a simple time-variant iterative ensembling method that strives to significantly improve the performance of a given arbitrary long range multi-step time series predictor for water quality data with minimal increase in computational cost. With the given predictor, our proposed method iteratively uses ensembles of predicted values for preceding steps to improve the prediction accuracy for the succeeding steps. The iterative ensembling operation is performed on the trained model and only at the inference stage, and so does not need any further computing-intensive training for the performance improvement. We experimentally show that the proposed method is effective with 7 predictors and 9 water quality datasets of various types, and it outperforms the state-of-the-art results in those datasets by around 2%¿29% in mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percentage error (MAPE) metrics. Similar improvement has also been found in two other metrics such as normalized Nash¿Sutcliffe model efficiency coefficient (NNSE) metric and Taylor diagram plot. Overall, the proposed iterative ensembling is a promising approach for multi-step long range water quality prediction for high-frequency water quality monitoring systems.

Protein contact maps capture coevolutionary interactions between amino acid residue pairs that are spatially within certain proximity threshold. Predicted contact maps are used in many protein related problems that include drug design, protein design, protein function prediction, and protein structure prediction. Contact map prediction has achieved significant progress lately but still further challenges remain with prediction of contacts between residues that are separated in the amino acid residue sequence by large numbers of other residues. In this paper, with experimental results on 5 standard benchmark datasets that include membrane proteins, we show that contact map prediction could be significantly enhanced by using ensembles of various state-of-the-art short distance predictors and then by converting predicted distances into contact probabilities. Our program along with its data is available from https://gitlab.com/mahnewton/ecp.

Toxicity prediction using quantitative structure-activity relationship has achieved significant progress in recent years. However, most existing machine learning methods in toxicity prediction utilize only one type of feature representation and one type of neural network, which essentially restricts their performance. Moreover, methods that use more than one type of feature representation struggle with the aggregation of information captured within the features since they use predetermined aggregation formulas. In this paper, we propose a deep learning framework for quantitative toxicity prediction using five individual base deep learning models and their own base feature representations. We then propose to adopt a meta ensemble approach using another separate deep learning model to perform aggregation of the outputs of the individual base deep learning models. We train our deep learning models in a weighted multitask fashion combining four quantitative toxicity data sets of LD50, IGC50, LC50, and LC50-DM and minimizing the root-mean-square errors. Compared to the current state-of-the-art toxicity prediction method TopTox on LD50, IGC50, and LC50-DM, that is, three out of four data sets, our method, respectively, obtains 5.46, 16.67, and 6.34% better root-mean-square errors, 6.41, 11.80, and 12.16% better mean absolute errors, and 5.21, 7.36, and 2.54% better coefficients of determination. We named our method QuantitativeTox, and our implementation is available from the GitHub repository https://github.com/Abdulk084/QuantitativeTox.

Mixed Blocking Permutation Flowshop Scheduling Problem (MBPFSP) with the objective of makespan minimisation is NP-Hard. It has important industrial applications that include the cider production industry. MBPFSP has made some progress in recent years. However, within practical time limits, existing incomplete algorithms still either find low quality solutions or struggle with large problems. One key reason behind this is the typical way of using generic heuristics or metaheuristics that usually lack problem specific structural knowledge. In MBPFSP, a machine could be blocked with the currently finished job until the subsequent machine is available to process the same job. These blocking constraints affect the makespan. So MBPFSP search should naturally take explicit steps to take the blocking constraints into account. Unfortunately, existing research on MBPFSP just uses only the makespan to compare generated solutions, but the search otherwise is not aware of the blocking constraints. Moreover, existing such methods use either an exhaustive or a random neighbourhood generation strategy. In this work, we aim to advance MBPFSP search by better exploiting the problem specific structural knowledge. We use the constraint and the objective functions to obtain such problem specific knowledge and we exploit such knowledge both in a constructive search method and in a local search method. In this paper, we also present an acceleration method to efficiently evaluate insertion-based neighbourhoods of MBPFSP. Our experimental results on three standard testbeds demonstrate that our proposed algorithms significantly improve over existing best-performing algorithms.

Aircraft sequencing problem (ASP) is an NP-Hard problem. It involves allocation of aircraft to runways for landing and takeoff, minimising total tardiness. ASP has made significant progress in recent years. However, within practical time limits, existing incomplete algorithms still either find low quality solutions or struggle with large problems. One key reason behind this is the typical way of using generic heuristics or metaheuristics that usually lack problem specific structural knowledge. As a result, existing such methods use either an exhaustive or a random neighbourhood generation strategy. So their search guidance comes only from the evaluation function that is used mainly after the neighbourhood generation. In this work, we aim to advance ASP search by better exploiting the problem specific structural knowledge. We use the constraint and the objective functions to obtain such problem specific knowledge and we exploit such knowledge both in a constructive search method and in a local search method. Our motivation comes from the constraint optimisation paradigm in artificial intelligence, where instead of random decisions, constraint-guided more informed optimisation decisions are of particular interest. We run our experiments on a range of standard benchmark problem instances that include instances from real airports and instances crafted using real airport parameters, and contain scenarios involving multiple runways and both landing and takeoff operations. We show that our proposed algorithms significantly outperform existing state-of-the-art aircraft sequencing algorithms.

Toxicity prediction of chemical compounds is a grand challenge. Lately, it achieved significant progress in accuracy but using a huge set of features, implementing a complex blackbox technique such as a deep neural network, and exploiting enormous computational resources. In this paper, we strongly argue for the models and methods that are simple in machine learning characteristics, efficient in computing resource usage, and powerful to achieve very high accuracy levels. To demonstrate this, we develop a single task-based chemical toxicity prediction framework using only 2D features that are less compute intensive. We effectively use a decision tree to obtain an optimum number of features from a collection of thousands of them. We use a shallow neural network and jointly optimize it with decision tree taking both network parameters and input features into account. Our model needs only a minute on a single CPU for its training while existing methods using deep neural networks need about 10 min on NVidia Tesla K40 GPU. However, we obtain similar or better performance on several toxicity benchmark tasks. We also develop a cumulative feature ranking method which enables us to identify features that can help chemists perform prescreening of toxic compounds effectively.

Empty or limited storage capacities between machines introduce various types of blocking constraint in the industries with flowshop environment. While large applications demand flowshop scheduling with a mix of different types of blocking, research in this area mainly focuses on using only one kind of blocking in a given problem instance. In this paper, using makespan as a criterion, we study permutation flowshops with zero capacity buffers operating under mixed blocking conditions. We present a very effective scatter search (SS) algorithm for this. At the initialisation phase of SS, we use a modified version of the well-known Nawaz, Enscore and Ham (NEH) heuristic. For the improvement method in SS, we use an Iterated Local Search (ILS) algorithm that adopts a greedy job selection and a powerful NEH-based perturbation procedure. Moreover, in the reference set update phase of SS, with small probabilities, we accept worse solutions so as to increase the search diversity. On standard benchmark problems of varying sizes, our algorithm very significantly outperforms well-known existing algorithms in terms of both the solution quality and the computing time. Moreover, our algorithm has found new upper bounds for 314 out of 360 benchmark problem instances.

All-interval series is a standard benchmark problem for constraint satisfaction search. An all-interval series of size n is a permutation of integers [0, n) such that the differences between adjacent integers are a permutation of [1, n). Generating each such all-interval series of size n is an interesting challenge for constraint community. The problem is very difficult in terms of the size of the search space. Different approaches have been used to date to generate all the solutions of AIS but the search space that must be explored still remains huge. In this paper, we present a constraint-directed backtracking-based tree search algorithm that performs efficient lazy checking rather than immediate constraint propagation. Moreover, we prove several key properties of all-interval series that help prune the search space significantly. The reduced search space essentially results into fewer backtracking. We also present scalable parallel versions of our algorithm that can exploit the advantage of having multi-core processors and even multiple computer systems. Our new algorithm generates all the solutions of size up to 27 while a satisfiability-based state-of-the-art approach generates all solutions up to size 24.

We describe a constraint-based automated planner named Transition Constraints for Parallel Planning (TCPP). TCPP constructs its constraint model from a redefined version of the domain transition graphs (DTG) of a given planning problem. TCPP encodes state transitions in the redefined DTGs by using table constraints with cells containing don't cares or wild cards. TCPP uses Minion the constraint solver to solve the constraint model and returns a parallel plan. We empirically compare TCPP with the other state-of-theart constraint-based parallel planner PaP2. PaP2 encodes action successions in the finite state automata (FSA) as table constraints with cells containing sets of values. PaP2 uses SICStus Prolog as its constraint solver. We also improve PaP2 by using don't cares and mutex constraints. Our experiments on a number of standard classical planning benchmark domains demonstrate TCPP's efficiency over the original PaP2 running on SICStus Prolog and our reconstructed and enhanced versions of PaP2 running on Minion.