Sequential recommendation (SR) plays an important role across various platforms, aiming to predict users’ next items of interest based on their historical interaction sequences. Recent SR studies have employed deep learning techniques, such as Recurrent Neural Networks and Self-Attention (SA) mechanism, demonstrating promising results. Inspired by the emergence of contrastive learning methods, some SR models have utilized contrastive learning to improve the accuracy of recommendations. However, existing SR models employing contrastive learning primarily construct positive and negative sample pairs only from user interaction sequences, i.e., through sequence-level contrastive learning. In our research, we argue that there also exists semantic similarities between items, which can be used to conduct the item-level constructive learning, resulting in better recommendation accuracy. In this paper, we propose MCL4SRec, an SA-based SR model that combines sequence-level and item-level contrastive learning to enhance recommendation accuracy. In our proposed MCL4SRec, the item-level contrastive learning module utilizes items’ category information to construct positive and negative sample pairs, capturing semantic similarities and differences between items. Additionally, in MCL4SRec, we propose to use more side information such as category and brand to further improve the accuracy of recommendations. We conduct extensive experiments on three widely-used datasets to evaluate the proposed MCL4SRec. Experimental results indicate that the average improvements compared with the recent well-known baselines range from ${7. 7 3 \%}$ to ${1 6. 1 8 \%}$ in HR and NDCG, demonstrating the effectiveness of MCL4SRec for SR tasks.

The recommender system is a popular research topic in the past decades, and various models have been proposed. Among them, collaborative filtering (CF) is one of the most effective approaches. The underlying philosophy of CF is to capture and utilize two types of relationships among users/items, that is, the user-item preferences and the similarities among users/items, to make recommendations. In recent years, graph neural networks (GNNs) have gained popularity in many research fields, and in the recommendation field, GNN-based CF models have also been proposed, which are shown to have impressive performance. However, in our research, we observe a crucial drawback of these models, that is, while they can explicitly model and utilize the user-item preferences, the other necessary type of relationship, that is, the similarities among users/items, can only be implied and then utilized, which seems to hinder the performance of these models. Motivated by this, in this article, we first propose a novel dual-message propagation mechanism (DPM). The DPM can explicitly model and utilize both preferences and similarities to make recommendations; thus, it seems to be a better realization of CF's philosophy. Then, a dual-message graph CF (DGCF) model is proposed. Different from the existing models, in the DGCF, each user's/item's embedding is processed by two GNNs, with one handling the preferences and the other handling the similarities. Extensive experiments conducted on three real-world datasets demonstrate that DGCF substantially outperforms state-of-the-art CF models, and the small amount of sacrifice of time efficiency is tolerable considering the substantial improvement of model performance.

User reviews, which contain rich user preferences and item characteristics, can help to better predict user ratings in collaborative filtering (CF). However, one major issue is that reviews are usually posted after users have interacted with items, which limits the ways to use reviews and the ability to improve the performance of existing CF methods. To address this issue, this paper proposes a review construction-based CF method including: 1) a review construction network (RCN), which constructs vector representations of reviews before users interact with items; 2) a rating prediction network (RPN), which shares the intermediate layers with RCN to improve rating prediction performance; 3) several learning tricks, e.g., gradient reversal, to further boost the performance. Moreover, other CF methods, e.g., matrix factorization (MF), can be easily integrated into the proposed method to further boost the performance. Extensive studies on real-world datasets demonstrate that the proposed method integrated with MF significantly outperforms the state-of-the-art CF methods in recommendation accuracy.

In collaborative filtering (CF) algorithms, the optimal models are usually learned by globally minimizing the empirical risks averaged over all the observed data. However, the global models are often obtained via a performance tradeoff among users/items, i.e., not all users/items are perfectly fitted by the global models due to the hard non-convex optimization problems in CF algorithms. Ensemble learning can address this issue by learning multiple diverse models but usually suffer from efficiency issue on large datasets or complex algorithms. In this article, we keep the intermediate models obtained during global model learning as the snapshot models, and then adaptively combine the snapshot models for individual user-item pairs using a memory network-based method. Empirical studies on three real-world datasets show that the proposed method can extensively and significantly improve the accuracy (up to 15.9% relatively) when applied to a variety of existing collaborative filtering methods.

Software systems are widely employed in society. With a limited amount of testing resource available, testing resource allocation among components of a software system becomes an important issue. Most existing research on the testing resource allocation problem takes a single-objective optimization approach, which may not adequately address all the concerns in the decision-making process. In this paper, an architecture-based multi-objective optimization approach to testing resource allocation is proposed. An architecture-based model is used for system reliability assessment, which has the advantage of explicitly considering system architecture over the reliability block diagram (RBD)-based models, and has good flexibility to different architectural alternatives and component changes. A system cost modeling approach which is based on well-developed software cost models is proposed, which would be a more flexible, suitable approach to the cost modeling of software than the approach adopted by others which is based on an empirical cost model. A multi-objective optimization model is developed for the testing resource allocation problem, in which the three major concerns in the testing resource allocation problem, i.e., system reliability, system cost, and the total amount of testing resource consumed, are taken into consideration. A multi-objective evolutionary algorithm (MOEA), called multi-objective differential evolution based on weighted normalized sum (WNS-MODE), is developed. Experimental studies are presented, and the experiments show several results. 1) The proposed architecture-based multi-objective optimization approach can identify the testing resource allocation strategy which has a good trade-off among optimization objectives. 2) The developed WNS-MODE is better than the MOEA developed in recent research, called HaD-MOEA, in terms of both solution quality and computational efficiency. 3) The WNS-MODE seems quite robust from the sensitivity analysis results.

Deterministic design optimization (DO) may lead to unreliable designs due to not taking into consideration uncertainties. In recent years, DO under uncertainty has attracted more and more attention. In DO practice, both aleatory and epistemic uncertainties may exist, furthermore, the two types of uncertainties may affect both the objective function and the constrains of the optimization problem. In this paper, we develop a DO model which could cater for the above-mentioned situation. We also propose a new algorithm, sequential optimization and reliability and possibility assessment (SORPA), to solve the developed DO model. Numerical example is given, and the results obtained demonstrate the feasibility of the developed DO model and the efficiency of the proposed algorithm.

For a software development project, management often faces the dilemma of when to stop testing the software and release it for operation, which requires careful decision making as it has great impact on both software reliability and project cost. In most existing research on the optimal software release problem, the cost considered was the Expected Cost (EC) of the project. However, what concerns management is the Actual Cost (AC) of the project rather than the EC. Treatment (such as minimization) of the EC may not ensure the desired low level of the AC due to the uncertainty (variability) involved in the AC. In this paper, we study the uncertainty in software cost and its impact on optimal software release time in detail. The uncertainty is quantified by the variance of the AC and several risk functions. A risk-control approach to the optimal software release problem is proposed. New formulations of the problem which are extensions of current formulations are developed and solution procedures are established. Several examples are presented. Results reveal that it seems crucial to take into account the uncertainty in software cost in the optimal software release problem; otherwise, unsafe decisions may be reached which could be a false dawn to management.

China is one of the countries with vast coal resources in the world. However, Many coal mines in China are seriously endangered by coal fires. Investigations showed that there were 104 coal fires in Northern China. About 100-200 million tons of coal is being lost because of coal fires each year, accounting for one-fifth of national annual coal production. More seriously, coal fire also endangered human beings and property security, induced disastrous ecological damage and environmental pollution. Though the problem of coal fires is long standing and not only limited to China, little has been done around the world for regular monitoring of these fires based on remotely sensed data. Some researchers used daytime thermal infrared (TIR) images from Landsat TM or ETM+ band 6 to monitor coal fires. Nevertheless, the combined impacts such as solar radiation, topographic shadowing, cover of vegetation, emissivity and different thermal inertia of ground objects result in the different background temperatures in different coal fires, and also lead to the thermal anomalies extracted out from multi-temporal thermal IR images lack of comparability. The intensity and extending range of thermal anomalies change frequently. It is a big puzzle for us to process continuous monitoring for coal fires. As a result, this research took different surface environmental parameters into account, and extracted out thermal anomalies based on the normalization of multi-temporal thermal IR images.

A non-Manhattan Hexagon/Triangle Placement (HTP for short) paradigm is proposed in the present paper. Main feature of this paradigm lies in adapting to the Y- architecture which is one of the promising non-Manhattan VLSI circuit layout architectures. Aim of the HTP is to place a set of equilateral triangles with given size onto a hexagonal chip with maximal chip area usage. Based on the O-tree representation, some adaptive packing rules are adopted to develop an effective placement algorithm for solving the HTP problem in BBL mode. Two examples with benchmark data transformed from the Manhattan BBL mode placement (ami33/49) are presented to justify the feasibility and effectiveness of our algorithms. Experiment results demonstrate that the chip area usage of 94% is achieved through simulated annealing optimization.

The PLP (power-law process) or the Duane model is a simple model that can be used for both reliability growth and reliability deterioration. GOF (goodness-of-fit) tests for the PLP have attracted much attention. However, the practical use of the PLP model is its graphical analysis or the Duane plot, which is a log-log plot of the cumulative number of failures versus time. This has been commonly used for model validation and parameter estimation. When a plot is made, and the coefficient of determination, R/sup 2/, of the regression line is computed, the model can be tested based on this value. This paper introduces a statistical test, based on this simple procedure. The distribution of R/sup 2/ under the PLP hypothesis is shown not to depend on the true model parameters. Hence, it is possible to build a statistical GOF test for the PLP. The critical values of the test depend only on the sample size. Simulations show that this test is reasonably powerful compared with the usual PLP GOF tests. It is sometimes more powerful, especially for deteriorating systems. Implementing this test needs only the computation of a coefficient of determination. It is much easier than, for example, computing an Anderson-Darling statistic. Further study is needed to compare more precisely this new test with the existing ones. But the R/sup 2/ test provides a very simple and useful objective approach for decision making with regard to model validation.

It is widely recognized that the debugging processes are usually imperfect. Software faults are not completely removed because of the difficulty in locating them or because new faults might be introduced. Hence, it is of great importance to investigate the effect of the imperfect debugging on software development cost, which, in turn, might affect the optimal software release time or operational budget. In this paper, a commonly used cost model is extended to the case of imperfect debugging. Based on this, the effect of imperfect debugging is studied. As the probability of perfect debugging, termed testing level here, is expensive to be increased, but manageable to a certain extent with additional resources, a model incorporating this situation is presented. Moreover, the problem of determining the optimal testing level is considered. This is useful when the decisions regarding the test team composition, testing strategy, etc., are to be made for more effective testing.

For many safety critical systems, redundancy is the only acceptable method to achieve high operational reliability as individual modules can hardly be certified to have reached that level. When limited resources are available in the testing of a redundant software system, it is important to allocate the testing-time efficiently so that the maximum reliability of the complete system is achieved. In this paper, this problem is investigated in detail. A general formulation is presented and a specific case is used to illustrate the procedure. The case where individual module reliability requirements are given is also considered.