Miracle-dz/UniFAR-FTUs huggingface dataset:
An embedded method for gene identification in heterogenous data involving unwanted heterogeneity

The various ways of data collection for modern applications such as bioinformatics result in heterogeneous data, which presents challenges for traditional variable selection methods that assume data is independent and identically distributed. Existing statistical models accounting for unwanted variation can be applied for gene identification in heterogeneous genetic data, which however suffer from variable redundancy and also lack of predictability. To cope with that, we propose an embedded variable selection method for gene identification from a sparse learning perspective which is capable of accounting for unwanted heterogeneity blurring the true gene effects. Its performance is investigated by studying two different unsupervised and supervised gene identification problems in which the benchmark data samples are heterogeneous and collected with group structures. The results have demonstrated the superiority of our method over state-of-the art methods by effectively accounting for the unwanted heterogeneity in both cases.
AutoBoT: Resilient and Cost-Effective Scheduling of a Bag of Tasks on Spot VMs

Many data and task parallel applications can be modeled as a Bag of Tasks (BoT), and scheduled on distributed systems such as Grids, Clusters, and Clouds. We propose AutoBoT, a collection of scheduling strategies for BoTs with hard deadlines on Cloud Virtual Machines (VMs), to lower the overall monetary cost – a distinctive factor for Clouds. Besides reliable fixed-price VMs, AutoBoT uniquely reduces costs by including preemptible spot-priced VMs that are much cheaper, but are unreliable and have time-variant pricing. It guarantees timely completion by making active runtime decisions on pricing, number of VMs to acquire/release, and on task placement, checkpointing and migration. Our rigorous simulations of 7 Million BoT runs sampled from the Google cluster workload uses a realistic Cloud model and $6~months$6months of Amazon EC2 pricing data to compare AutoBoT against two baseline algorithms. We analyze the impact of BoT size, data centers, time periods, deadline duration, loss budget and checkpointing strategies. AutoBoT often gives $\approx$≈80% profit and rare but bounded losses, compared to using only fixed-price VMs. Further, its 100 percent completion guarantee is 23-42 percent better than using only spot-priced VMs which offer a similar profit.
Utilizing Flexibility Services from a Large Heat Pump to Postpone Grid Reinforcement

Distribution grid reinforcement problem is considered in this paper. Annual growth of the electric loads may lead to the congestion situations in the distribution network, where current flowing through components exceed their maximum current carrying capacity (MCCC). A conventional solution is to reinforce overloaded components, i.e. to replace it with the one with higher capacity. However, this will lead to the additional investments for the distribution system operator (DSO). An alternative solution is to utilize flexibility services (FS) such as reconfiguration (RE) and demand response (DR) to provide peak reduction from the large controllable loads (e.g. a heat pump) in the system. Several forecasts of the future load growth simulated in the MATPOWER are used to study the proposed solutions and identify their feasibility. The results show that both FS can be used to defer grid reinforcement providing economic benefits for the DSO.
Smart City Issue Management: Extending and Adapting a Software Bug Tracking System

When our team at the university was asked by our city local administration to create a pilot service for managing issues reported by citizens, we thought what we do as programmers when we have issues in our software: we report bugs and daily manage them by providing timely solutions. In this report we describe how we extended and adapted Bugzilla, a famous Open Source software bug tracking system, to be used for managing issues reported by citizens or even sensors deployed in the city. Around Bugzilla we developed our service called Sense. City, together with a mobile application which can be used by any city to manage issues. Sense. City services are provided as open source, so cities can either integrate them with their own services or use for free our web cloud-hosted solution.
Discover the Spatio-temporal Process of Typhoon Disaster Using Micro blog Data

When a disaster occurs, a large number of images and texts attached geographic information often flood the social network in the Internet quickly. All these information provide a new data source for timely awareness of disaster situations. However, due to the regional variation in the number of social media users and characteristics of information propagate in cyberspace, new problems arose in the pattern analysis of spatial point process represented by the check-in data, such as the correlation between check-in points density and disasters events density, the spatial relation between check-in points, the spatial heterogeneity of point pattern and associated influences. In this study, we took the No. 201614 Typhoon as an example and collected Sina Weibo data between September 14 and September 17,2016 using keywords “Typhoon” and “Meranti”. We classified the Weibo texts using Support Vector Machine(SVM) algorithms, and constructed a disaster database containing relevant check-in information. In addition, considering the spatial heterogeneity of Weibo users, we proposed a weighted model based on user activity at the check-in points. Using Moran’s I of the global autocorrelation statistics, we compared the check-in data before and after adding weights and discovered obvious spatial autocorrelation of the check-in data in real geographical locations. We tested our model on Weibo data with keyword “rain” and “power failure”. The results show that series map generated by our model can reflect the typhoon disaster spatio-temporal process trends well.
Towards Biological Sequence Data Service with Insights

Testable prediction outcomes generated by computational models based on available databases are the primary sources helping to design biological experiments. Although numerous databases have been designed by collecting data either only from literature manually or together with prediction outcomes from computational models, there is currently not a comprehensive data service framework delivering better insights for these results. In this paper, we introduce a biological sequence data service towards delivering deeper insights and helping better biological experiments design. The service includes following major components: a comprehensive database for storing biological data, data analytics tools for analysing biological data, and computational models for delivering testable prediction outcomes. Specifically, we present this service in a framework for studies on host-pathogen interactions. The design of this framework aims to improve the understanding of host-pathogen interactions. The relationships of hierarchical databases and their working mechanism, specifically between PPIs and DDIs, are also presented in this framework. Finally, the preliminary and practical experiences of building computational model for prediction is discussed.
A Graph-Based Message Passing Approach for Joint Source-Channel Coding via Information Bottleneck Principle

In this paper, we focus on an extended version of noisy source coding wherein the compressed data shall be transmitted over an imperfect (forward) channel for further processing. As the quantization design framework, we deploy the Information Bottleneck principle and propose a novel treatment by successful exploitation of a quite generic and highly flexible graph-based clustering routine known as Affinity Propagation. We also provide simulation results regarding a typical digital transmission setup to compare the performance of our proposed treatment with a state-of-the-art routine from literature.
Research on Travel Time Prediction Model of Freeway Based on Gradient Boosting Decision Tree

To improve the prediction accuracy of traffic flow, a travel time prediction model based on gradient boosting decision tree (GBDT) is proposed. In order to test the applicability of GBDT, models with different prediction horizons (5 min ahead, 10 min ahead, and 15 min ahead) are established. The 11 variables are viewed as candidates in this paper. Different from other machine learning algorithms as black boxes, GBDT can provide interpretable results through variable importance. In the proposed model, the variable importance shows that for different prediction horizons, the most important influence variable is uniform, which is travel time in the current period. Traffic conditions in the current period have the greatest influence on the predicted travel time. Compared with the back propagation neural network model and the support vector machine model, the proposed GBDT model can produce more accurate prediction results, especially in multi-step prediction, indicating that GBDT is a promising method in travel time prediction.
Consensus in Multi-agent Nonholonomic Systems With Relative Velocity And Orientation

In this paper, a novel distributed consensus control protocol with relative velocity and orientation for multi-agent nonholonomic systems has been proposed. On the basis of the relative measurement of nearby agents, the proposed protocol can ensure leader-follower results and three flocking rules. Besides, by using tools from algebraic graph theory and non-smooth analysis, the range of control gain to preserve the connectedness of the graph and realize consensus is obtained. Finally, some numerical simulations are provided to illustrate the above theoretical results.
Correlation Model Analysis of Nitrogen Addition and Tan Sheep Grazing Effects on Soil Bacterial Community in the Loess Plateau, China

In recent years, the rapid development of high-throughput sequencing technologies and bioinformatics methods has greatly facilitated the monitoring of soil microbial diversity. Monitoring microbial diversity in soil could lead to a number of applications for example a better understanding of the characteristics of spatial and temporal variation of microbial community in soil and the identification of the driving mechanisms of soil microbial composition, diversity, and functional genes in different ecosystems, such as forests, grasslands, wetlands, and farmland. However, there are still no universally adaptable models to explain the relationships between microbial diversity change and ecosystem functions. This study is based on the special geographical environment of the Loess Plateau. Control experiments for grazing and nitrogen addition were set up and, through to analysis soil bacterial community was analyzed through high-throughput sequencing technology. A joint model of soil bacterial community diversity, soil physical and chemical properties, and human disturbance such as grazing has been established. The results show that grazing and nitrogen addition have different effects on soil bacterial community diversity. Within a certain range, nitrogen addition can balance the effects of grazing.
A Clustering Method Based on Neighborhood Chain

One major difficulty in clustering is to discover the underlying structure of the input data space under complex situations without much prior information. This problem consists of two aspects, i.e., to determine the target clusters number based on the input data itself, and to properly assign the data into each cluster. Various methods have attempted to address the problem and achieved success in many applications. However, their performance is not satisfactory when the tasks need to be done under complex situations including (but not limited to) the clusters in non-spherical shapes and different densities. This paper proposes a clustering algorithm based on the neighborhood chain, a closeness measure different from the geometric distances used in most of the existing clustering methods. The proposed algorithm performs effectively for clusters with arbitrary shapes and different densities, and is able to help users determine the proper clusters number in an intuitive way. Experiments and comparisons are implemented and the results have demonstrated the effectiveness of the new clustering method.
Reliably Segmenting Motion Reversals of a Rigid-IMU Cluster Using Screw-Based Invariants

Human-robot interaction (HRI) is moving towards the human-robot synchronization challenge. In robots like exoskeletons, this challenge translates to the reliable motion segmentation problem using wearable devices. Therefore, our paper explores the possibility of segmenting the motion reversals of a rigid-IMU cluster using screw-based invariants. Moreover, we evaluate the reliability of this framework with regard to the sensor placement, speed and type of motion. Overall, our results show that the screw-based invariants can reliably segment the motion reversals of a rigid-IMU cluster.
K-mer Counting: memory-efficient strategy, parallel computing and field of application for Bioinformatics

Currently, k-mer counting is an important algorithm for bioinformatics research. This review lists the major application fields of k-mer counting in Bioinformatics at the beginning. Next, we introduce the commonly used memory-efficient strategy for k-mer counting tools, because the large amount of memory request is a bottleneck of k-mer counting tools. Next we illustrate the current parallel computing technologies for k-mer counting tool. Finally, we discuss the future study for k-mer counting.
A Method of Joint Angle Estimation Using Only Relative Changes in Muscle Lengths for Tendon-Driven Humanoids with Complex Musculoskeletal Structures

Tendon-driven musculoskeletal humanoids typically have complex structures similar to those of human beings, such as ball joints and the scapula, in which encoders cannot be installed. Therefore, joint angles cannot be directly obtained and need to be estimated using the changes in muscle lengths. In previous studies, methods using table-search and extended kalman filter have been developed. These methods express the joint-muscle mapping, which is the nonlinear relationship between joint angles and muscle lengths, by using a data table, polynomials, or a neural network. However, due to computational complexity, these methods cannot consider the effects of polyarticular muscles. In this study, considering the limitation of the computational cost, we reduce unnecessary degrees of freedom, divide joints and muscles into several groups, and formulate a joint angle estimation method that takes into account polyarticular muscles. Also, we extend the estimation method to propose a joint angle estimation method using only the relative changes in muscle lengths. By this extension, which does not use absolute muscle lengths, we do not need to execute a difficult calibration of muscle lengths for tendon-driven musculoskeletal humanoids. Finally, we conduct experiments in simulation and actual environments, and verify the effectiveness of this study.
CCNFlow:Content-Centric Networking Managed by OpenFlow Controller

Content-Centric-Networking (CCN) and Software Defined Networking (SDN) are considered to be the promising paradigms to build the Future Internet. They are assembling a significant consideration from academic world and manufacturing. The integration of CCN capabilities with OpenFlow operation presents an attractive compromise. However, deployment issues require a gradual approach to allow the development and improvement of the new Internet. The existent solutions integrating CCN with OpenFlow have not been inspected sufficiently.In this paper, we propose CCNFlow, a new approach based– on CCN managed by OpenFlow controller, which provides programmable forwarding policy and caching strategies depending on Popularity of contents. CCNFlow doesn’t necessitate a full network upgrade, it presents an overlay approach.
Automated Counting of Cells in Breast Cytology Images Using Level Set Method

Statistical analysis of cells in breast cytology images is very important for the diagnosis of various diseases in the female population in developed and developing countries. Manual detection and counting of the cancer cell in real time is not only difficult but hugely time-consuming for pathologists. In this paper, we propose an algorithm for automatic analysis of breast cytology using Fine Needle Aspiration Cytology (FNAC) images. The proposed technique uses statistical measures which include perceptual information (like color) and morphological characteristics for the estimation of the initial cell boundary. Similarly, the level set technique is used for efficient and accurate identification of cellular objects which help in the precise counting of individual cancer cell breast cytology images. Experimental results obtained during the demonstration of the proposed approach show high correlations in precision with manual counting by a pathologist. It has been proved that the proposed approach is efficient in processing cells for counting the cancerous cells with high accuracy and avoid discrepancies(like color variations, human error) in manual counting by a pathologist.
Wireless Power Transfer 2.5 kW with Simple Control and High Efficiency

This paper presents design and construction of a 2.5 kW wireless power transfer apparatus, supplied from a single phase mains with good power factor, high efficiency and simple control circuits. The coils are 30 cm in diameter and the power is transferred over a distance of 25 cm. The efficiency is 91 %, power factor is 0.97 and operating frequency is 320 kHz. The receiving-side output voltage is approximately 230 V DC and can be modified with a different receiving LC circuit. The coupled resonant circuits are designed optimally for the best efficiency. The control circuit principle description is included. The practical construction is shown and measurement results are presented.
POSUM: A Portfolio Scheduler for MapReduce Workloads

MapReduce ecosystems are (still) widely popular for big data processing in data centers. To address the diverse non-functional requirements arising from many and increasingly more sophisticated users, the community has developed many scheduling policies for MapReduce workloads. Although some individual policies can dynamically optimize for single and stable performance objectives, such as minimizing runtime or cost, or meeting deadlines for realtime-jobs, it seems unlikely that individual policies will remain competitive for increasingly more dynamic workloads and objectives. In contrast, in this work we investigate the ability to dynamically balance performance and cost of a portfolio scheduler for MapReduce workloads. To this end, we design and implement a portfolio scheduling technique, that is, a system capable of adapting to the current workload characteristics and target objectives by periodically evaluating its set of potential policies, and of switching to "the best" policy that targets the current system state. We implement and evaluate our system with real-world experiments on a workload containing a mixture of real-time and batch jobs, with the purpose of minimizing deadline violations, while keeping batch job slowdown in check. Our results show that POSUM is a promising alternative: it can out-perform the individual policies of its portfolio for the combined optimization goal, even without precise predictions.
Expert-in-the-loop Learning for Sleep EEG Data

This work addresses the area of a computer-assisted sleep staging using a standard scalp EEG recordings and AASM 2012 scoring rules. We focused on real clinical EEG data containing a large amount of artifacts and/or missing electrodes. The sleep-related features were extracted for 30-seconds segments. Power-in-band features were estimated by a method using Continuous Wavelet Transform (CWT). In addition, entropy, spectral entropy, fractal dimensions and statistical features were used as the input of classifiers. Inter-personal differences and the characteristics of extracted features were evaluated for individual sleep classes. Two expert-in-the-loop strategies and three different classifiers were used to classify data into sleep stages. The results were compared with a fully automated classification and with gold standard expert sleep staging. Due to the proposed improvements the final mean classification sensitivity of expertin-the-loop approach was increased up to 18.4%. The implemented solution allows to classify sleep recordings contaminated by a large amount of the naturally occurring artifacts that are impossible to process by traditional automated classification methods.
A Benchmarking Framework for Systematic Evaluation of Compliant Under-Actuated Soft End Effectors in an Industrial Context

This paper presents an approach for systematic evaluation of robotic end effectors for an industrial use case that handles delicate, deformable, non-regular objects such as fruits and vegetables. To handle these objects, soft under-actuated hands are the most promising technology so far. However, the approach directions suitable for grasping objects are not usually easy to establish due to the under-actuation effect; therefore, we propose an experimental protocol to serve as framework for data collection, which aims to assess the best directions for grasping using a particular end effector. This protocol, focused on reproducibility and comparability, allows for a better understanding of how a particular hand embodiment influences grasping success for individual products. The protocol can also be used as an effective tool for redesign, and it was applied to evaluate two well-known soft hands (RBO Hand and Pisa/IIT SoftHand) for handling groceries.
Technical Analysis on Financial Time Series Data Based on Map-Reduce Programming Model: A Case Study

Technical analysis is a widely used method for forecasting the price direction on the financial time series data. This method requires the use of different number and types of analysis algorithms (technical indicators) together. Although these algorithms show successful performance on small-scalefinancial time series data, significant performance decreases are detected when the size of data increased. On the large-scale financial time series data, it is necessary to implement these algorithms based on the Map-Reduce programming model and examine the performance of the algorithms which are implemented based on this model comparatively. For this purpose, seven different indicators are studied within the scope of this study, new versions of these indicators are implemented using Map-Reduce parallel data processing model and performance comparisons are made with these algorithms. As a result of these comparisons on single-node and multi-node, significant performance gains have been obtained using Map-Reduce programming model.
Using multi-anchors to identify patients suffering from multimorbidities

Chronic diseases, and in particular the co- occurrence of more than one chronic disease in an patient, which is known as multimorbidity, represent an increasing problem in modern society. For the individual patients the consequences are potentially serious, especially if not diagnosed and/or treated. In order to allow for an efficient allocation of health resources, and slow down the progression of these diseases, methods for predicting the health status of chronic patients have been developed. In this work, we propose a data-driven approach to identify the health status of chronically ill patients using data extracted from their electronic health records. For this purpose we take advantage of recent advances in machine learning for healthcare and use anchor learning that exploits vast amounts of unlabeled data. Moreover, in order to identify patients suffering from multimorbidities, we adapt the anchor method to a multi-anchor learning framework. The experiments show that using multi- anchor learning one can accurately identify patients who suffer from one or more chronic conditions. In fact, the performance is almost comparable to a completely supervised baseline.