Miracle-dz/UniFAR-FTUs huggingface dataset:
DCT: An Scalable Multi-Objective Module Clustering Tool

Maintaining complex software systems is a timeconsuming and challenging task. Practitioners must have a general understanding of the system’s decomposition and how the system’s developers have implemented the software features (probably cutting across different modules). Re-engineering practices are imperative to tackle these challenges. Previous research has shown the benefits of using software module clustering (SMC) to aid developers during re-engineering tasks (e.g., revealing the architecture of the systems, identifying how the concerns are spread among the modules of the systems, recommending refactorings, and so on). Nonetheless, although the literature on software module clustering has substantially evolved in the last 20 years, there are just a few tools publicly available. Still, these available tools do not scale to large scenarios, in particular, when optimizing multi-objectives. In this paper we present the Draco Clustering Tool (DCT), a new software module clustering tool. DCT design decisions make multi-objective software clusterization feasible, even for software systems comprising up to 1,000 modules. We report an empirical study that compares DCT with other available multi-objective tool (HD-NSGA-II), and both DCT and HD-NSGA-II with mono-objective tools (BUNCH and HD-LNS). We evidence that DCT solves the scalability issue when clustering medium size projects in a multi-objective mode. In a more extreme case, DCT was able to cluster Druid (an analytics data store) 221 times faster than HD-NSGA-II.
Catch me if you can: A pursuit-evasion game with intelligent agents in the Unity 3D game environment

In this paper, we present a pursuit-evasion game with intelligent agents in the Unity 3D environment. The pursuit-evasion game consists of one evader and four pursuers which are the red, blue, orange and pink agents. In the game scenario, the goal of the evader, which is controlled by human user, is to collect papers without getting caught by the four pursuer agents. The pursuer agents are constructed such that they can move autonomously in the game field via the deployment of a dynamic waypoint structure and the A* algorithm. The red, blue and orange pursuer agents share the information on location of the evader agent if the evader agent lies in one of their limited line of sight. They try to intercept the evader agent from different directions in collaborative manner. In this context, we designed a PD controller based pursuing system that tries to minimize the distance between the evader and pursuer agent. For the pink agent, we designed a fuzzy logic based pursuing system that is capable to generate reference interception points and force values to catch evader agent. The performance of the pursuing systems is examined by providing experimental results collected from the game-environment. The results show that the performance of the developed pursuing strategies is satisfactory and the fuzzy logic based pursuing system resulted with a better performance when compared to its conventional counterpart.
Optimized Power Allocation Scheme in Optical Satellite Communication Downlinks

Optical satellite networks in the 5G era constitute a major part of the evolved cellular networks since they offer backhaul, relay and gateway capabilities. In this paper, the optical geostationary satellite-to-ground links are studied and the optimal power allocation is investigated. The ergodic capacity is maximized with power constraints referring to the average total transmitting and to the peak transmitting powers. The allocation problem is resolved with convex optimization techniques. It is evaluated considering the impact of both optical attenuation and weak scintillation. The superiority of the new algorithm is shown in terms of bit-rate and spectral-efficiency through comparison simulations.
A Novel Many-Objective Recommendation Algorithm for Multistakeholders

Most traditional recommender systems focus specifically on increasing consumer satisfaction by providing a list of relevant content to consumers. However, the perspectives of other multisided marketplace stakeholders are also equally important, i.e., the exposure for suppliers or providers and profit for the platform. The suppliers want their products to be presented to users, and the objective of the platform is to maximize their profit. Nevertheless, because consumers’ preferences and the objectives of providers as well as the platform may conflict with each other, it degrades the utility of the recommendation methods by only considering users’ views. Therefore, in this work, we use a many-objective optimization method to maintain a tradeoff among five objectives for three stakeholders and obtain multiple Pareto front solutions in a single run. We first combine customer lifetime value and user purchase preference to create a new similarity model (Sim_RFMP) to increase the recommendation accuracy of the recommendation list. Furthermore, we propose a many-objective model (NBHXMAOEA) for multistakeholder recommendation. In NBHXMAOEA, we present a novel N-block heuristic crossover operator (NBHX) that recombines blocks of chromosomes based on heuristics. Through extensive experiments, the results demonstrate that our proposed NBHXMAOEA achieves superior performance in terms of average accuracy, diversity, novelty, provider coverage, and platform profit to its competing methods.
Emerging Roles of Long Noncoding RNAs in the Cytoplasmic Milieu

While the important functions of long noncoding RNAs (lncRNAs) in nuclear organization are well documented, their orchestrating and architectural roles in the cytoplasmic environment have long been underestimated. However, recently developed fractionation and proximity labelling approaches have shown that a considerable proportion of cellular lncRNAs is exported into the cytoplasm and associates nonrandomly with proteins in the cytosol and organelles. The functions of these lncRNAs range from the control of translation and mitochondrial metabolism to the anchoring of cellular components on the cytoskeleton and regulation of protein degradation at the proteasome. In the present review, we provide an overview of the functions of lncRNAs in cytoplasmic structures and machineries und discuss their emerging roles in the coordination of the dense intracellular milieu. It is becoming apparent that further research into the functions of these lncRNAs will lead to an improved understanding of the spatiotemporal organization of cytoplasmic processes during homeostasis and disease.
An Optimal Bus Dispatching Model Based on African Buffalo Algorithm

Bus is an indispensable product of a city, and its operation reflects the management level of a city. In bus dispatching, most schemes only meet the demand of passenger flow, without considering the interests of the bus company and the satisfaction of passengers. In actual conditions, the two are often contradictory. How to balance the two interest has become a topic worth discussing. In this paper, we propose a bus scheduling optimization model that takes the interests of the bus company and the satisfaction of passengers as the dual goals. The bus departure interval is used as the decision variable, and the African buffalo optimization (ABO) algorithm is used to solve the problem. The results show that the bus departure interval varies with passenger flow. In practical applications, the future passenger flow can be predicted based on historical data, and then the model mentioned in the paper can be used to solve the optimal bus dispatch plan.
ALDB: Debugging Alloy Models of Behavioural Requirements

Declarative modelling languages, such as Alloy, are becoming popular for describing behavioural requirements very early in system development because automated analysis of these models provides valuable feedback. Typically, these languages are supported by constraint solvers (SAT, SMT) for providing instances or model checking properties. However, a user can quickly find simple bugs and gain confidence in their model by concretely simulating steps of the transition system. We present ALDB: a debugger for models of transition systems written in the Alloy language. It provides a familiar debugging interface to walk around in the behaviour of the model, enabling users to quickly explore scenarios, find errors via concrete simulation, and incrementally build up to bounded model checking.
Driver Characteristics Oriented Autonomous Longitudinal Driving System in Car-Following Situation

Advanced driver assistance system such as adaptive cruise control, traffic jam assistance, and collision warning has been developed to reduce the driving burden and increase driving comfort in the car-following situation. These systems provide automated longitudinal driving to ensure safety and driving performance to satisfy unspecified individuals. However, drivers can feel a sense of heterogeneity when autonomous longitudinal control is performed by a general speed planning algorithm. In order to solve heterogeneity, a speed planning algorithm that reflects individual driving behavior is required to guarantee harmony with the intention of the driver. In this paper, we proposed a personalized longitudinal driving system in a car-following situation, which mimics personal driving behavior. The system is structured by a multi-layer framework composed of a speed planner and driver parameter manager. The speed planner generates an optimal speed profile by parametric cost function and constraints that imply driver characteristics. Furthermore, driver parameters are determined by the driver parameter manager according to individual driving behavior based on real driving data. The proposed algorithm was validated through driving simulation. The results show that the proposed algorithm mimics the driving style of an actual driver while maintaining safety against collisions with the preceding vehicle.
Single-Cell RNA Sequencing Unravels Heterogeneity of the Stromal Niche in Cutaneous Melanoma Heterogeneous Spheroids

Simple Summary Cutaneous malignant melanoma is one of the most dangerous forms of skin cancer affecting humans. Frequently, it is linked to DNA damage due to ultraviolet radiation. Photoageing along with chronological ageing are therefore critically important factors in melanoma biology. The tissue microenvironment is also heavily affected by induced senescence. There is growing evidence that senescent dermal fibroblasts can consequently promote tumour progression. We focused on the analysis of microenvironmental factors represented in melanoma heterogeneous spheroids by either photodamaged or normal dermal fibroblasts. Our effort was primarily focused on the determination of the functional diversity of fibroblasts in heterogeneous spheroids. Therefore, we analysed these 3D models by single-cell RNA sequencing and advanced bioinformatic analysis. We aimed to map the fibroblast diversity resulting from previously acquired damage caused by exposure to extrinsic and intrinsic stimuli. Using this robust methodology, we highlighted molecules that could become important for the control of melanoma cell–cancer-associated fibroblast interaction as an essential part of the tumour microenvironment. Abstract Heterogeneous spheroids have recently acquired a prominent position in melanoma research because they incorporate microenvironmental cues relevant for melanoma. In this study, we focused on the analysis of microenvironmental factors introduced in melanoma heterogeneous spheroids by different dermal fibroblasts. We aimed to map the fibroblast diversity resulting from previously acquired damage caused by exposure to extrinsic and intrinsic stimuli. To construct heterogeneous melanoma spheroids, we used normal dermal fibroblasts from the sun-protected skin of a juvenile donor. We compared them to the fibroblasts from the sun-exposed photodamaged skin of an adult donor. Further, we analysed the spheroids by single-cell RNA sequencing. To validate transcriptional data, we also compared the immunohistochemical analysis of heterogeneous spheroids to melanoma biopsies. We have distinguished three functional clusters in primary human fibroblasts from melanoma spheroids. These clusters differed in the expression of (a) extracellular matrix-related genes, (b) pro-inflammatory factors, and (c) TGFβ signalling superfamily. We observed a broader deregulation of gene transcription in previously photodamaged cells. We have confirmed that pro-inflammatory cytokine IL-6 significantly enhances melanoma invasion to the extracellular matrix in our model. This supports the opinion that the aspects of ageing are essential for reliable melanoma 3D modelling in vitro.
Multi-Objective Residential Electricity Scheduling Based on Forecasting Generation and Demand via LSTM

This paper proposes a combined day-ahead forecasting and scheduling energy management system (EMS) with multiple complimentary objectives on household level. Our comparative case study, analyses, economical, ecological and grid independence aspects of our model include the utilization of renewable energy source (RES) with a controllable diesel generator and an energy storage system (ESS). The latter includes both stationary and electric vehicle batteries. The forecasting of RES and uncontrollable load of a single household is based on an LSTM approach, which is then used for day-ahead electricity scheduling. In order to be consistent with the forecasting horizon, day-ahead wholesale electricity market prices are used.
Super-resolution Reconstruction of Rock Thin-Section Image based on SinGAN

The rock thin section images are of great significance to the study of petroleum geological characteristics and oil and gas exploration. Due to the limitations of various factors, the obtained images of rock thin-sections often have low resolution, which limits the researchers’ grasp of their detailed information to some extent. The traditional super-resolution algorithm of neural network will need a large amount of data as a training set, in order to improve rock thin-section image super-resolution reconstruction algorithm texture detail information reduction ability, the paper using single image generative adversarial network for rock thin-section image super-resolution reconstruction, does not need to input a large number of data sets, of single image super-resolution reconstruction image. Rock cast thin section images from an oil field area in Ordos basin were used for training, and peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) evaluation indexes were used for model evaluation. The experimental results show that the super-resolution image processing based on this method has good visual effects and evaluation indexes.
An ACS-fed Super Wideband Active Antenna

In this paper, an asymmetric coplanar strip (ACS) fed microstrip antenna is proposed, which is matched by an active circuit to increase the bandwidth. The proposed antenna is composed of feeding line, asymmetric ground plane, rectangle radiation patch. The antenna operates at 384 MHz and 645 MHz with a small fractional bandwidth of 19.2% and 18%, respectively, when the active circuit is not incorporated into the antenna matching network. By properly designing and optimizing the active circuit, the bandwidth has been greatly enhanced and a −10 dB fractional bandwidth of 197.7% from 5 MHz to 877 MHz has been achieved for the proposed antenna system.
Multidimensional intervention in individuals with mild cognitive impairment: a pilot nonrandomized study.

BACKGROUND There is an increasing need for non-pharmacological treatments that can enhance the cognitive function of individuals with mild cognitive impairment. We firstly performed multidimensional intervention based on the concept of the International Classification of Functioning, Disability and Health, and aimed to explore its short-term effect on the improvement of cognitive function in individuals with mild cognitive impairment. METHODS Twenty-four individuals with mild cognitive impairment in this pilot study were recruited from the memory clinic and neurology ward in Shanghai Tenth People's Hospital from August 2018 to August 2019. According to participants' personal wishes, 13 and 11 participants were enrolled into an intervention group and a control group, respectively. Based on the International Classification of Functioning, Disability and Health, we performed baseline assessments to all participants. According to the assessment results and the wishes and hobbies of the patients, then doctors, therapists, nurses, patients and their families together chose the appropriate multidimensional interventions to the intervention group in seven 1-hour sessions and health education to the both groups. After one week, all participants underwent reevaluation of cognitive function. RESULTS There were significant differences between the two groups on the improvement of cognitive function. The intervention group (mean ± SD, 3.460±1.613) scored higher than the control group (1.360±0.924) on the change score of the total score in the Montreal Cognitive Assessment (t =3.808, P<0.001, 95% CI: 0.955-3.240), though not on the change score of any cognitive domain score. Regression results showed that the change score of the total score was negatively correlated with the baseline score of Abstraction score (aR2 =0.583, β =-0.506, P=0.031) and the modified Barthel index score (β =-0.464, P=0.045) in the intervention group. CONCLUSIONS The pilot study demonstrated that the short-term multidimensional intervention may produce cognitive benefits in individuals with mild cognitive impairment.
A Survey of Key Issues in UAV Data Collection in the Internet of Things

Data collection, as the most basic task of Internet of Things (IoT), is an important research topic. Considering the large-scale wireless sensor networks (WSN) deployed in complex geographical environments, Unmanned Aerial Vehicle (UAV)-aided data collection, where UAVs are dispatched to collect data. Although UAV-aided data collection overcomes the limitations of data collection in complex geographic environments, there are still some key issues that need to be addressed. In this paper, we will introduce the key technologies of UAV-aided data collection in IoT in detail and propose our ideas.
An Indoor Positioning and Navigation System Using Named Data Networking

Named data networking (NDN) is a new and promising Internet architecture, which aims to replace the current transmission control protocol/Internet protocol (TCP/IP)-based Internet. The NDN internet architecture has introduced several benefits in numerous applications. An NDN-based indoor positioning and navigation system can further optimize existing localization technologies with reduced the server load and faster response time. In this study, an NDN-based approach for the existing Wi-Fi fingerprinting-based indoor positioning and navigation was investigated. Among the many features of NDN, the network-level caching can reduce the computational load and response time of the localization and navigation server. The theoretical analysis of the runtime complexity shows that the NDN’s network-level caching performance is better than those of the conventional algorithms. In this paper, naming methods for different services and a server-side algorithm for handling the NDN requests are proposed. The real-world implantation and testing results show a better overall performance than that of TCP/IP. This network-level optimization for indoor positioning and navigation opens new opportunities because it can be combined with other application-level optimization techniques for more efficient indoor positioning and navigation in the future.
Four-dimensional Modulation Superposition NOMA Scheme with Non-ideal Channel Estimation

With the increasing demand for higher communication capacity, the scarcity of spectrum resources consequently led to research on the improvement of spectrum efficiency. In this paper, thus, a non-orthogonal multiple access (NOMA) scheme with four-dimensional modulation superposition and non-ideal channel estimation is proposed. The four-dimensional modulation is based on the spherical code which is different from the traditional QAM modulation with two dimensions. We analyze the upper bound of the two users's word error probabilities in a NOMA group, with non-ideal channel estimation. Then, the power allocation factor optimizing the transmit power with a given word error probability is derived. Simulation results show that, our proposed power allocation factor can work well with different channel estimation errors.
Indomethacin markedly blunts cerebral perfusion and reactivity, with little cognitive consequence in healthy young and older adults

Cognitive function depends on adequate cerebrovascular perfusion and control. However, it is unknown whether acutely‐reduced cerebral blood flow (CBF) impairs cognition in healthy adults. In the present study, we used a placebo‐controlled, single‐blinded, randomized cross‐over design to test the hypothesis that acutely‐reduced CBF (using a pharmacological aid; indomethacin) would impair cognition in young and older healthy adults. At baseline, older adults had lower cognitive performance and CBF, but similar cerebrovascular reactivity to CO2 and dynamic cerebral autoregulation compared to young adults. In both young and older adults, cognitive performance on a mental switching task was slightly (7%) reduced after indomethacin, but not significantly associated with reductions in CBF (∼31%). These results indicate that cognitive performance is broadly resilient against a ∼31% reduction in CBF per se in healthy young and older adults.
Symbolic Observer-Based Controller for Uncertain Nonlinear Systems

Symbolic control is an approach to the control of continuous or hybrid systems with specifications expressed in a logic form. This approach is based on the use of symbolic models describing the dynamical system behavior with a finite description of the transition relation between its states. In the literature, many results using this approach assume the availability of full and exact information about the system states to compute the control actions. In this letter, we consider a more realistic scenario where only partial information about the plant states is available. This letter proposes an abstraction that makes it possible to synthesize output-feedback controllers. The presence of disturbances and output noise is also considered. A direct path between observer designs in the classical theory and control synthesis in formal methods is established and a numerical example is provided to illustrate the results.