Miracle-dz/UniFAR-FTUs huggingface dataset:
A Power Load Clustering Method Based on Limited DTW Algorithm

With the rapid development of smart grids, power load analysis has become the main method to divide user groups and understand user consumption behaviors. In this paper, a power load clustering method based on limited dynamic time warping algorithm (LimitDTW) is proposed. On account of the disadvantages of original DTW algorithm, including high time complexity and malignant matching, the LimitDTW algorithm limits the search range to areas near the diagonal of distance matrix. While limiting unreasonable curve scaling, the time complexity is greatly reduced from O(N2) to average O(N) level. Furthermore, the method introduces finite curve translation to optimize time alignment within the search range, especially for users with similar patterns but slight time offsets. And finally, the K-Medoids algorithm is applied for clustering based on user similarity. An experiment is conducted on actual load data of users in the United States during one year. The clustering results divide all 800 users into 9 clusters with different consumption behaviors. Referring to 16 standard types of commercial building defined by the U.S. Department of Energy, the accuracy of clustering reaches 74.125% and performs 9 percent higher than original DTW algorithm. At the same time, the runtime cost of LimitDTW algorithm reaches linear complexity which is far superior to the original one, with both Silhouette Coefficient and Calinski-Harabaz Score better than original DTW algorithm, widely-used fastDTW and LCSS algorithms. At last, we also analyze the load consumption behaviors of each cluster respectively so as to extract typical load patterns and look for the reasons of abnormal fluctuations.
Analyzing and Extending the Salsa Music Dataset

One of the tasks of the Music Information Retrieval area is the characterization of a musical genre. For this purpose, it is necessary to have a dataset of songs with enough information to perform the task. In order to know if there is enough information on the dataset, an analysis of the dataset is required. In this paper we perform an analysis of the Salsa dataset using unsupervised machine learning techniques and then extend it with annotations made by an expert.
Using Testing to Repair Models

Software testing is an important phase in the software development process, aiming at locating faults in artifacts, in order to achieve a degree of confidence that the software behaves according to a specification. While most of the techniques in software testing are applied to debugging, fault-localization, and repair of code, to the best of our knowledge there are fewer works regarding the application of software testing to locating faults in models and to the automated repair of such faults. The goal of this PhD project proposal is to study how testing can be applied to repair models. We describe the research approach and discuss the application cases of combinatorial and feature models. We then discuss future work of applying testing to repair models for other scenarios, such as timed automata.
Influence Maximization in Signed Social Networks With Opinion Formation

Influence maximization (IM) has been widely studied in recent years. Given fixed number of seed users and certain diffusion models, the IM problem aims to select proper seed users in a social networks such that they can achieve the maximal spread of influence. Most previous work assumes that there are only positive relationships between users, and thus users spread influence positively. However, negative relationships also universally exist in various social networks and are complementary to positive relationships in information diffusion. In this paper, the influence maximization problem is addressed in signed social networks that contain both positive and negative relationships. We propose a novel diffusion model called LT-S and two influence spread functions. The proposed LT-S model extends the classical linear threshold model with opinion formation that incorporates both positive and negative opinions and simulates information diffusion in real-world social networks. The influence spread functions under the LT-S model are neither monotone nor submodular which bring challenges to maximization. The RLP algorithm is proposed to tackle the issue, which is improved from R-Greedy algorithm by incorporating two proposed accelerating techniques, the live-edge based and propagation-path based techniques. The results of the extensive experiments on public real signed social network datasets demonstrate that our algorithm outperforms the baseline algorithms in terms of both efficiency and effectiveness.
A Method for Planning Wireless Terminal Access Network Based on Fuzzy Analytic Hierarchy Process

Base station planning is an important and complex problem of wireless telecommunication system, especially when it comes to a heterogeneous network. In the electric power system, the wireless terminal communication network is the key to solving low degree of the power distribution network automation and incomplete coverage of the grid information data collection, two main problems in the process of developing smart grids. There are two standards for power telecommunication in China, namely the 1. 8GHz standard and the 230MHz standard. This paper proposes a method for planning wireless terminal access network based on Fuzzy Analytic Hierarchy Process (FAHP) to choose the proper standard for the base station. The simulation results show that the method proposed can efficiently finish planning the two-standard heterogeneous network and guarantee the communication requirements of the terminal.
Exploring Communities in Large Profiled Graphs (Extended Abstract)

Given a graph G and a vertex q ∊ G, the community search (CS) problem aims to efficiently find a subgraph of G whose vertices are closely related to q. Communities are prevalent in social and biological networks, and can be used in product advertisement and social event recommendation. In this paper, we study profiled community search (PCS), where CS is performed on a profiled graph. This is a graph in which each vertex has labels arranged in a hierarchical manner. Compared with existing CS approaches, PCS can sufficiently identify vertices with semantic commonalities and thus find more high-quality diverse communities. As a naive solution for PCS is highly expensive, we have developed a tree index, which facilitates efficient and online solutions for PCS.
Increased Constriction of the Ductus Arteriosus with Combined Administration of Indomethacin and L-NAME in Fetal Rats

We studied age-related changes and the caliber of the ductus arteriosus (DA) after two-pathway inhibition of prostaglandin E2 and nitric oxide (NO) by the combined administration of indomethacin, a cyclooxygenase inhibitor, and NG-nitro-L-arginine methyl ester (L-NAME), an NO synthase inhibitor, in fetal rats. Pregnant rats from day 18 to 21 of gestation were used. They were administered indomethacin orally (3 mg/kg) 3 h before cesarean section, and then L-NAME (50 mg/kg) was injected intraperitoneally 3 h before the rats were killed. Using rapid-freezing and shaving methods, the caliber of the DA in fetal rats was measured. Compared with the indomethacin alone group, indomethacin plus L-NAME further constricted the DA after indomethacin and L-NAME were simultaneously administered 3 h before the rats were sacrificed. The extent of the final DA constriction was almost equal to the addition of each effect of indomethacin and L-NAME. We concluded that the magnitude of DA constriction following indomethacin plus L-NAME was due to the additive effects of these agents, suggesting a possible method to treat patent DA in premature infants.
Automating Root Cause Analysis via Machine Learning in Agile Software Testing Environments

We apply machine learning to automate the root cause analysis in agile software testing environments. In particular, we extract relevant features from raw log data after interviewing testing engineers (human experts). Initial efforts are put into clustering the unlabeled data, and despite obtaining weak correlations between several clusters and failure root causes, the vagueness in the rest of the clusters leads to the consideration of labeling. A new round of interviews with the testing engineers leads to the definition of five ground-truth categories. Using manually labeled data, we train artificial neural networks that either classify the data or pre-process it for clustering. The resulting method achieves an accuracy of 88.9%. The methodology of this paper serves as a prototype or baseline approach for the extraction of expert knowledge and its adaptation to machine learning techniques for root cause analysis in agile environments.
Two-Stream Convolutional Network for Improving Activity Recognition Using Convolutional Long Short-Term Memory Networks

The two-stream convolutional network (ConvNet) plays a vital role in the development of the deep learning network for activity recognition. Recently, there are many studies about activity recognition using the two-stream network as a powerful feature extractor. The combination of two-stream ConvNet and fully connected long short-term memory (FC-LSTM) and the combination of two-stream ConvNet and temporal segment LSTM had achieved the best performance for activity recognition. In this paper, we are motivated to explore the performance’s limit of networks that combine two-stream and recurrent neural network, so we highlight the necessity of maintaining spatial structure throughout the deep learning networks when the sequential data show correlations in space and stress the importance of appropriate fusion method when integrating feature maps and we demonstrate with experiments that these methods work well. Three main contributions can be concluded from our work. First, we propose to combine convolutional LSTM (ConvLSTM) networks with a two-stream ConvNet based on RGB streams and optical streams first. The spatiotemporal features are extracted by a two-stream ConvNet which is pre-trained on the dataset of ImageNet, and then the fused sequential three-dimensional feature maps are classified by the ConvLSTM. Second, we explored the effect of fusing the feature maps of the two-stream network at different layers with different fusing strategy and conclude that appropriate fusing location and fusing method can improve our model to the state-of-art performance. Third, we demonstrated that better overall performance can be achieved, given proper care to the ConvLSTM. Our analysis shows that our proposed network structure can achieve the state-of-art 69.4% accuracy on HMDB51 and 93.9 % accuracy on UCF101 among the methods composed by the ConvNets with the recurrent neural network without pre-training on Kinetics dataset.
Distribution of nuclear pores and chromatin organization in neurons and glial cells of the rat cerebellar cortex

Nuclear pores were assessed on freeze‐fracture replicas from different neuronal and glial cell types of the rat cerebellar cortex. Nuclear diameter and perimeter were measured on semithin sections, and nuclear surface area and volume were calculated from these data. The proportion of inner nuclear membrane in apposition to condensed chromatin was measured on thin sections. The values of nuclear pore numerical density (number/μm2) were as follows (mean ± S.D.): Purkinje cells, 22 ± 3; Golgi cells 17 ± 3; granule cells, 6 ± 4; stellate and basket cells, 6 ± 1; protoplasmic astrocytes, 11 ± 1; Bergmann glia, 10 ± 1; oligodendrocytes, 6 ± 1. The total number of nuclear pores per nucleus varied from 18,451 ± 2,336 (Purkinje cells) to 621 ± 394 (granule cells) among neurons, and from 1,782 ± 162 (protoplasmic astrocytes) to 402 ± 67 (oligodendrocytes) among glial cells. The number of nuclear pores per unit nuclear volume (number/μm3), a parameter related to nucleocytoplasmic transport capacity, varied from 15 ± 2 in Purkinje cells to 6 ± 4 in granule cells. The proportion of nuclear membrane free of condensed chromatin was significantly (P < 0.01) correlated to pore numerical density and total number of pores per nucleus. Some nuclear pores were associated in clusters of two or more pores. The amount of pore clustering was measured by counting the proportion of pores associated in clusters. This proportion varied among the different cell types from 82% in Purkinje cells to 44% in stellate and basket cells. The amount of pore clustering showed a positive linear correlation to pore numerical density and pore number per nucleus. However, the proportion of pores in clusters was not significantly correlated with the amount of condensed chromatin applied against the inner nuclear membrane.
Taster: Self-Tuning, Elastic and Online Approximate Query Processing

Current Approximate Query Processing (AQP) engines are far from silver-bullet solutions, as they adopt several static design decisions that target specific workloads and deployment scenarios. Offline AQP engines target deployments with large storage budget, and offer substantial performance improvement for predictable workloads, but fail when new query types appear, i.e., due to shifting user interests. To the other extreme, online AQP engines assume that query workloads are unpredictable, and therefore build all samples at query time, without reusing samples (or parts of them) across queries. Clearly, both extremes miss out on different opportunities for optimizing performance and cost. In this paper, we present Taster, a self-tuning, elastic, online AQP engine that synergistically combines the benefits of online and offline AQP. Taster performs online approximation by injecting synopses (samples and sketches) into the query plan, while at the same time it strategically materializes and reuses synopses across queries, and continuously adapts them to changes in the workload and to the available storage resources. Our experimental evaluation shows that Taster adapts to shifting workload and to varying storage budgets, and always matches or significantly outperforms the state-of-the-art performing AQP approaches (online or offline).
Clinical review 141: lipids and atherosclerosis: lessons learned from randomized controlled trials of lipid lowering and other relevant studies.

Despite evidence from population studies and clinical trials dating to 1984 (1), The Lipid Hypothesis, the idea that a reduction in blood cholesterol reduced coronary heart disease (CHD) risk was viewed with skepticism. Subsequently, angiographic studies using a range of drug and lifestyle regimens demonstrated that progression of coronary atherosclerosis could be reduced and clinical cardiac events prevented. A new and expanded opportunity for the treatment of hypercholesterolemia and prevention of CHD emerged with the use of statins. The landmark Scandinavian Simvastatin Survival Study (4S) was the first randomized, controlled trial to convincingly demonstrate that coronary events and total mortality were decreased by a reduction in low-density lipoprotein cholesterol (LDL-C) (2). Within the past decade, major clinical endpoint trials (2–7), enrolling more than 30,000 patients, have emphasized the value of LDL-C reduction. This reduction in CHD is particularly noteworthy because total and LDL-C reduction were accomplished with a variety of drugs (statins, fibrates, and bile acidbinding resins), dietary changes, lifestyle modifications, and surgery (ileal bypass), indicating that reduction in cholesterol and LDL-C was more important than how it was achieved. CHD events, cardiovascular morbidity and mortality, and all-cause mortality can be prevented with statins or fibrates in patients with average or elevated LDL-C who have preexisting CHD
Hide-and-Disclose: On-Site Information Sharing for Privacy-Aware Mobile IoT Communications

In this demonstration, we present a privacy-aware device-to-device communication framework that enables both obfuscation of information source and fine-grade information disclosure control (which we call the Hide-and-Disclose). Instead of using persistently assigned identifiers, introducing randomly generated short-lived identifiers (also known as pseudo-IDs) becomes a popular approach for reducing leakage of sender's location privacy information. To achieve reliable information sharing among mobile IoT devices using pseudo-IDs, issuing pseudo-IDs and information provisioning must be supported by the reliable network service. However, data access by using pseudo-IDs significantly increases database management cost due to the increase in query index size, and frequent invalidation and replication of pseudo-IDs and data associated by the pseudo-IDs. To overcome this problem, Hide-and-Disclosure allows to integrate database operations based on grouping of pseudo-ID and associated data pairs owned by the same IoT device. To further provide fine-grade access control, this framework accepts IoT devices to add rules for data access (i.e., access permission and lifetime) so that IoT devices can disclose own information to the target device. To evaluate the framework, we have implemented the prototype in Linux system.
Toward a Better IPA Experience for a Connected Vehicle by Means of Usage Prediction

Recently, a connected vehicle comes into wide use. A connected vehicle enables seamless access from outside of the vehicle. A user can easily control the vehicle remotely as if it is a home appliance device. IPA (Intelligent Personal Assistants) or smart speaker adoption is growing rapidly these years. We have already released an IPA application for remote control of a vehicle to enhance accessibility of our connected vehicle. Since the launch, we found significant drop of users from using application, which is reportedly caused by social or mental obstacles to use IPA in some researches. To mitigate these obstacles, we propose proactive suggestion by IPA. We apply a vehicle usage prediction model on our connected vehicle data. The prediction enable spontaneous suggestion by IPA, therefore the user doesn't need to speak wake- up word. In our model, firstly LDA automatically discover usage patterns and clustering method on the latent space. The result suggests some user segments which represent habitual vehicle usage. The prediction is performed by dynamic Bayesian networks for each user segment. An evaluation with actual service data confirms applicability of the prediction model and potential of IPA log itself can also assist vehicle usage prediction.
Intent-Preserving Test Repair

Repairing broken tests in evolving software systems is an expensive and challenging task. One of the main challenges for test repair, in particular, is preserving the intent of the original tests in the repaired ones. To address this challenge, we propose a technique for test repair that models and considers the intent of a test when repairing it. Our technique first uses a search-based approach to generate repair candidates for the broken test. It then computes, for each candidate, its likelihood of preserving the original test intent. To do so, the technique characterizes such intent using the path conditions generated during a dynamic symbolic execution of the tests. Finally, the technique reports the best candidates to the developer as repair recommendations. We implemented and evaluated our technique on a benchmark of 91 broken tests in 4 open-source programs. Our results are promising, in that the technique was able to generate intentpreserving repair candidates for over 79% of those broken tests and rank the intent-preserving candidates as the first choice of repair recommendations for almost 70% of the broken tests.
A Collaborative Filtering based Recommender System for Suggesting New Trends in Any Domain of Research

Recommender system, an information filtering technology used in many items is presented in web sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media in general. In today’s world, time has more value and the researchers have no much time to spend on searching for the right articles according to their research domain. More than 250 research paper recommender systems were published and the quantity of research papers published every day is increasing rapidly. Thus it needs an efficient searching and filtering mechanism to choose the quality research papers, so that the effort and time of researchers can be saved. The recommender system proposed here uses three major factors used for building this system which includes datasets, prediction rating based on users and cosine similarity. The ratings are made by user which will be determined by the number of accurate ratings they provide. The results are then sorted by using cosine similarity. We propose a research-paper recommender system using collaborative filtering approach to recommend a user with best research papers in their domain according to their queries and based on the similarities found from other users on the basis of their queries, which will help in avoiding time consuming searches for the user.
Research on AECMS Energy Management Strategy of PHEV

In this paper, the Adaptive Equivalent Consumption Minimization Strategy (AECMS) of PHEV is designed based on the Equivalent Consumption Minimization Strategy (ECMS), which the equivalent of the oil and electricity equivalence factor changes with the initial SOC of the battery and mileage, is created based on genetic algorithm. The system simulation model was established by Simulink and Cruise, and the results were compared and analyzed. The simulation results show that PHEV Adaptive Equivalent Consumption Minimization Strategy can adapt to driving cycle characteristics, and the SOC of the battery can be reasonably planned, then the economic performance of PHEV is improved.
Analysis of the Effectiveness Path Planning Methods and Algorithm for the Anthropomorphic Robot Manipulator

The article deals with the high-energy consumption of excessive anthropomorphic robots manipulators problem. Among the existing ways of solving this problem, solving the problems of energy-efficient traffic control methods of are chosen. Earlier on this topic, the authors developed mathematical methods for energy-efficient planning of the path of the anthropomorphic robot manipulator in the working area with a stationary obstacle. Earlier on this topic, the authors developed mathematical methods for energy-efficient motion path planning of the anthropomorphic robot manipulator in the working area with a stationary obstacle. The proposed methods have reduced computational complexity for real-time operation mode. The principle of the presented mathematical apparatus operation is to construct a virtual rectilinear energy-efficient path for moving the manipulator effector to a given position and then iteratively pushing the manipulator links out of the obstacle area to build the necessary movement route. The developed methods can be modified according to the necessary criteria of motion manipulator quality. This article presents the results of the new algorithm energy efficiency analysis that combines the previously described methods. A comparison with the selected analogue, the principle of which is based on the representation of objects in the form of oriented connected containers, is accomplished. The results of an experiments series on the simulation model implemented in MatLab show a reduction in the energy consumption of manipulators when performing targeted operations in the working area with or without an obstacle. A promising area of further research is the modification of the proposed mathematical apparatus for working in a zone with multiple stationary and dynamic obstacles, as well as the elimination of collisions when working with a human.