Miracle-dz/UniFAR-FTUs huggingface dataset:
Segmenting and targeting customers through clusters selection & analysis

This paper investigates the use of machine learning clustering technique to segment and target customers of a wholesale distributor. It describes the selection, analysis, and interpretation of clusters for evaluating customers annual spending on the products. We show how circular statistics can categorize customers by looking at the annual spending on six essential product categories. Several clusters were created using k-means clustering algorithm and an in-depth analysis on these clusters were performed using several techniques to carefully select the best cluster. Automated clustering was able to suggest groups that these customers fall into. The evaluation and interpretation of clusters were able to provide insights into various purchase behaviors and to nominate the best customer group to target.
Fast lane detection with Randomized Hough Transform

Lane detection is an essential component of autonomous mobile robot applications. Any lane detection method has to deal with the varying conditions of the lane and surrounding that the robot would encounter while moving. Lane detection procedure can provide estimates for the position and orientation of the robot within the lane and also can provide a reference system for locating other obstacles in the path of the robot. In this paper we present a method for lane detection in video frames of a camera mounted on top of the mobile robot. Given video input from the camera, the gradient of the current lane in the near field of view are automatically detected. Randomized Hough Transform is used for extracting parametric curves from the images acquired. A priori knowledge of the lane position is assumed for better accuracy of lane detection.
Positive Perturbations of Dual and Integrated Semigroups

Positive perturbations of generators of locally Lipschitz continuous increasing integrated semigroups on an abstract L space are again generators of locally Lipschitz continuous increasing integrated semigroups. Positive perturbations of generators of positive dual semigroups on a dual abstract L space are generators of semigroups that are weakly∗ right continuous. These results are reformulated in terms of cumulative outputs and then applied to age-structured models for population dynamics. ∗ Research partially supported by The Bank of Sweden Tercentenary Foundation, The Swedish Council for Forestry and Agricultural Research, and the Swedish Cancer Foundation ∗∗ Research partially supported by NSF grant DMS-9101979
Low-complexity Decoding is Asymptotically Optimal in the SIMO MAC

A single input multiple output (SIMO) multiple access channel, with a large number of transmitters sending symbols from a constellation to the receiver of a multi-antenna base station, is considered. The fundamental limits of joint decoding of the signals from all the users using a low complexity convex relaxation of the maximum likelihood decoder (ML, constellation search) is investigated. It has been shown that in a rich scattering environment, and in the asymptotic limit of a large number of transmitters, reliable communication is possible even without employing coding at the transmitters. This holds even when the number of receiver antennas per transmitter is arbitrarily small, with scaling behaviour arbitrarily close to what is achievable with coding. Thus, the diversity of a large system not only makes the scaling law for coded systems similar to that of uncoded systems, but, as we show, also allows efficient decoders to realize close to the optimal performance of maximum-likelihood decoding. However, while there is no performance loss relative to the scaling laws of the optimal decoder, our proposed low-complexity decoder exhibits a loss of the exponential or near-exponential rates of decay of error probability relative to the optimal ML decoder.
Emotion Regulation Treatment of Disruptive Behavior: A Preliminary Investigation

Children with oppositional defiant disorder (ODD) can have heterogeneous presentations due to varying combinations of the eight criterion A symptoms. Researchers have identified a subtype of ODD for children with primarily angry/irritable mood symptoms and who are at risk for developing mood and anxiety disorders. Despite the prevalence of anger and mood issues in children with ODD, established treatments for disruptive behavior disorders typically focus primarily on teaching caregivers more effective parenting strategies to address oppositional and defiant behaviors, rather than directly targeting children’s difficulties with emotions. To address the dearth of emotion-focused treatments for ODD, a novel emotion regulation intervention was developed based on a framework offered by Southam-Gerow (2013). The purpose of the current study was to evaluate the initial feasibility of this intervention and to explore its efficacy for reducing ODD and associated emotion regulation problems in middle childhood. Following a non-concurrent multiple baseline design, children ages 8-12 were assessed with semi-structured diagnostic interviews to determine study eligibility, and subsequently enrolled in a 13-week intervention with their caregivers. Treatment feasibility was supported by participant satisfaction ratings as well as treatment fidelity results. Treatment protocol adherence in terms of delivery by the therapist was high, but caregiver symptom reporting was less consistent. Nevertheless, multiple metrics support the efficacy of the intervention in reducing symptoms of ODD as well as some efficacy in improving child emotion regulation abilities. Overall, results support further research into emotion regulation-focused intervention for ODD. Emotion Regulation Treatment of Disruptive Behavior: A Preliminary Investigation
Efficient fault diagnosis using probing

In this paper, we address the problem of efficient diagnosis in real-time systems capable of on-line information gathering, such as sending ”probes” (i.e., test transactions, such as ”traceroute” or ”ping”) in order to identify network faults and evaluate performance of distributed computer systems. We use a Bayesian network to model probabilistic relations between the problems (faults, performance degradation) and symptoms (probe outcomes). Due to intractability of exact probabilistic inference in large systems, we investigated approximation techniques, such as a local-inference scheme called mini-buckets(Dechter & Rish 1997). Our empirical study demonstrates advantages of local approximations for large diagnostic problems: the approximation is very efficient and ”degrades gracefully” with noise; also, the approximation error gets smaller on networks with higher confidence (probability) of the exact diagnosis. Since the accuracy of diagnosis depends on how much information the probes can provide about the system states, the second part of our work is focused on the probe selection task. Small probe sets are desirable in order to minimize the costs imposed by probing, such as additional network load and data management requirements. Our results show that, although finding the optimal collection of probes is expensive for large networks, efficient approximation algorithms can be used to find a nearly-optimal set.
Analysis of the Influence Factors of the Ability of Graduate Employment Based on the Logistic Regression Model

The problem of the grim employment of university graduate is the hot topic of today's college students and even the whole society in China. For now, the situation of university graduate's employment is becoming increasingly serious, the competition of employment is unprecedentedly intense. With the problem of the grim employment of university graduate continuous outstanding, the influence factor of the employment of university graduate has become a research hotspot to solve the problem of employment. This article used multivariate Logistic regression analysis method to analysis with the data obtained from the colleges and universities. It analyzed the factors that affect the employment of university graduate from the angle of quantitative and qualitative through the estimation and test to the model, put forward suggestions on how to promote graduates employment.
Thread detection in dynamic text message streams

Text message stream is a newly emerging type of Web data which is produced in enormous quantities with the popularity of Instant Messaging and Internet Relay Chat. It is beneficial for detecting the threads contained in the text stream for various applications, including information retrieval, expert recognition and even crime prevention. Despite its importance, not much research has been conducted so far on this problem due to the characteristics of the data in which the messages are usually very short and incomplete. In this paper, we present a stringent definition of the thread detection task and our preliminary solution to it. We propose three variations of a single-pass clustering algorithm for exploiting the temporal information in the streams. An algorithm based on linguistic features is also put forward to exploit the discourse structure information. We conducted several experiments to compare our approaches with some existing algorithms on a real dataset. The results show that all three variations of the single-pass algorithm outperform the basic single-pass algorithm. Our proposed algorithm based on linguistic features improves the performance relatively by 69.5% and 9.7% when compared with the basic single-pass algorithm and the best variation algorithm in terms of F1 respectively.
GoPoMoSA: a goal-oriented process modeling and simulation advisor

This paper presents GoPoMoSA, a Goal-oriented Process Modeling and Simulation Advisor that semi-automatically discovers suitable Software Process Modeling and Simulation (SPMS) techniques for (inexperienced) process modelers to achieve their process modeling goals. GoPoMoSA takes the goal-oriented modeling approach that captures the associations among Process Modeling Stakeholder goals and existing SPMS techniques via Relevant Process Elements modeled in the knowledge graphs. We evaluated the accuracy and feasibility of GoPoMoSA with data collected from 212 published SPMS literatures and a real-world process modeling and simulation case on requirements traceability. Our results show that GoPoMoSA (1) was able to find suitable SPMS techniques based on stakeholder goals with an average of 85.38% accuracy; (2) helped novice process modelers effectively and efficiently achieve their goals.
Follow-the-Trial-Fitter: Real-time dressing without undressing

ldquoFollow-the-Trial-Fitterrdquo lets a shopper virtually try on different clothes by deforming clothing images to fit the shopperpsilas motion in real time without changing real clothes on her back. The existing virtual try-on systems enable changes of decoration and color, but they tend to be non-real-time, have limitations on the types of clothes, or use 3D character trial fitters, instead of real shoppers. Many shoppers find trying on clothes to be a time consuming chore that negatively impacts their buying intention. There is no doubt that undressing / dressing is a troublesome task, and this time waster is the biggest adverse aspect of trial fittings. Wepsilave designed a novel real-time dressing system that gets rid of the cumbersome task of putting on-and-off clothes, dealing clothes with any design and color. The shopper can see herself on the display dressed in the clothe shepsilas chosen to ldquotry onrdquo when the shopper stands in front of the systempsilas display, which behaves like a mirror. The virtual garment worn by her ldquoreflectedrdquo image moves in real time smoothly according to her motions such as arm flexes.
Contraction-based stabilisation of nonlinear singularly perturbed systems and application to high gain feedback

ABSTRACT Recent development of contraction theory-based analysis has opened the door for inspecting differential behaviour of singularly perturbed systems. In this paper, a contraction theory-based framework is proposed for stabilisation of singularly perturbed systems. The primary objective is to design a feedback controller to achieve bounded tracking error for both standard and non-standard singularly perturbed systems. This framework provides relaxation over traditional quadratic Lyapunov-based method as there is no need to satisfy interconnection conditions during controller design algorithm. Moreover, the stability bound does not depend on smallness of singularly perturbed parameter and robust to additive bounded uncertainties. Combined with high gain scaling, the proposed technique is shown to assure contraction of approximate feedback linearisable systems. These findings extend the class of nonlinear systems which can be made contracting.
AI Based Framework for Automatic Test Data Generation

— Software testing is a labor intensive and takes very much effort in software development process. There are many aspects in software testing to assurance quality of software. Gray-box testing is a combination of white-box testing and black-box testing. In gray-box testing, test data is generated from the specification which describes the behavior and data structure of the system. In this paper, a frame work for automatically generating test data from gray-box method is proposed. The proposed framework is pluggable; therefore it can be used in many test generation approaches. Artificial intelligent is contemplated in this research, especially genetic algorithm. UML state diagram is a specification for generating test data because it is similar to control flow graph. Anyway, the proposed framework still needs many further research and investigation.
Altair: The Brightest δ Scuti Star

We present an analysis of observations of the bright star Altair (α Aql) obtained using the star camera on the Wide Field Infrared Explorer (WIRE) satellite. Although Altair lies within the δ Scuti instability strip, previous observations have not revealed the presence of oscillations. However, the WIRE observations show Altair to be a low-amplitude (Δm < 1 parts per thousand [ppt]) δ Scuti star with at least seven modes present.
Mel Cepstrum & Ann Ova: The Difficult Dialog Between MIR and Music Cognition

Mel is a MIR researcher (the audio type) who's always been convinced that his field of research had something to contribute to the study of music cognition. His feeling, however, hasn't been much shared by the reviewers of the many psychology journals he tried submitting his views to. Their critics, rejecting his data as irrelevant, have frustrated him the more he tried to rebut, the more defensive both sides of the debate became. He was close to give up his hopes of interdisciplinary dialog when, in one final and desperate rejection letter, he sensed an unusual touch of interest in the editor's response. She, a cognitive psychologist named Ann, was clearly open to discussion. This was the opportunity that Mel had always hoped for: clarifying what psychologists really think of audio MIR, correcting misconceptions that he himself made about cognition, and maybe, developing a vision of how both fields could work together. The following is the imaginary dialog that ensued. Meet Dr Mel Cepstrum, the MIR researcher, and Prof. Ann Ova, the psychologist.
The Method of Damaged Pavement Image Splicing Based on Image Pyramid and FCM Clustering

There are usually large noise and anomalistic distribution of characteristic points in the damaged pavement images, which bring great difficulty to splice. In view of the characters, this paper forms one kind of splicing method of the pavement images which is based on image Pyramid, the Harris vertex examination and FCM clustering. Firstly, it forms several groups of the structure of the criterion spaces carrying on the transformation of the multi-criteria and multi-resolution Pyramid. Then it takes this stratification structure as the foundation, and carries on the Harris vertex examination to each structure. Thus it makes the characters carry on the FCM clustering and realize splicing of neighboring damaged pavement surface images.
Palmprint recognition based on weighted fusion of DMWT and LBP

To obtain affluent features of the palmprint image, the weighted fusion method of Discrete Multiwavelet Transform (DMWT) features and Local Binary Pattern (LBP) features is proposed. This method fuses the global image features by DMWT and the local image features by LBP, which can overcome the limitation of the single feature extraction method, and synthesize two kinds of the image features. Principal Component Analysis (PCA) is used to solve the dimensional increase problem of this fusion for its powerful decrease ability. The implementations of this method are as follows: firstly, the LBP and DMWT are used to extract the features respectively; secondly, different weighted coefficients are multiplied to these two features; thirdly, the PCA is used to decline the dimension of the fused feature vector; finally, Euclidean distance is calculated to achieve the pattern recognition. Through this method, the best weighted coefficient can be found, and it will be used as the final weighted coefficient. The experimental results demonstrate the effectiveness of this palmprint recognition system.
Social Interaction: Multimodal Conversation with Social Agents

We present a new approach to human-computer interaction, called social interaction. Its main characteristics are summarized by the following three points. First, interactions are realized as multimodal (verbal and nonverbal) conversation using spoken language, facial expressions, and so on. Second, the conversants are a group of humans and social agents that are autonomous and social. Autonomy is an important property that allows agents to decide how to act in an ever-changing environment. Socialness is also an important property that allows agents to behave both cooperatively and collaboratively. Generally, conversation is a joint work and ill-structured. Its participants are required to be social as well as autonomous. Third, conversants often encounter communication mismatches (misunderstanding others' intentions and beliefs) and fail to achieve their joint goals. The social agents, therefore, are always concerned with detecting communication mismatches. We realize a social agent that hears human-to-human conversation and informs what is causing the misunderstanding. It can also interact with humans by voice with facial displays and head (and eye) movement.
Parallel architectures for fuzzy triadic similarity learning

In a context of document co-clustering, we define a new similarity measure which iteratively computes similarity while combining fuzzy sets in a three-partite graph. The fuzzy triadic similarity (FT-Sim) model can deal with uncertainty offers by the fuzzy sets. Moreover, with the development of the Web and the high availability of storage spaces, more and more documents become accessible. Documents can be provided from multiple sites and make similarity computation an expensive processing. This problem motivated us to use parallel computing. In this paper, we introduce parallel architectures which are able to treat large and multi-source data sets by a sequential, a merging or a splitting-based process. Then, we proceed to a local and a central (or global) computing using the basic FT-Sim measure. The idea behind these architectures is to reduce both time and space complexities thanks to parallel computation.
An optimized load balance based on data popularity on HBASE

Load balancing is an important technique for improving distributed system performance. It results in a better utilization of server resources and quick response time for user requests. In distributed database, it is crucial to take data popularity into account. It is known that servers storing popular data usually become hotspots, so using forecasting methods to predict the popular data in advance will have great impact on performance. For HBase, several methods are proposed for balancing the load but still some improvement in terms of efficiency is required. In this paper, we present an efficient load balancing algorithm which alleviates hotspots by considering data popularity using secondary exponential smoothing method. In the algorithm we construct a region move evaluation function which takes data locality into consideration for further improvement of the throughput in HBase. The experimental result illustrates that our optimized algorithm runs faster and better, and gains about 3% throughput improvement as the cluster is much more balanced.
Title Acupuncture and clomiphene citrate for live birth in polycysticovary syndrome : study design of a randomized controlled trial

Acupuncture is an alternative therapy to induce ovulation in women with polycystic ovary syndrome (PCOS), but there is no study reporting the live birth rate following ovulation induction by acupuncture or its potential as an adjuvant treatment to clomiphene citrate (CC). We assess the efficacy of acupuncture with or without CC in achieving live births among 1000 PCOS women in Mainland China. This paper reports the methodology of an ongoing multicenter randomized controlled trial. The randomization scheme is coordinated through the central mechanism and stratified by the participating sites. Participants will be randomized into one of the four treatment arms: (A) true acupuncture and CC, (B) control acupuncture and CC, (C) true acupuncture and placebo CC, and (D) control acupuncture and placebo CC. To ensure the quality and integrity of the trial we have developed a unique multinational team of investigators and Data and Safety Monitoring Board. Up to the end of April 2013, 326 subjects were recruited. In conclusion, the success of this trial will allow us to evaluate the additional benefit of acupuncture beyond the first line medicine for fertility treatment in PCOS women in an unbiased manner.
Demystifying Cloud Benchmarking Paradigm - An in Depth View

Industry has been boomeranging with "Cloud" in every forte and IaaS (Infrastructure as a Service) layer has always been the backbone of any cloud computing service. The infrastructure layer of any cloud service consists of an array of physical processors along with storage and network components with a virtualization layer on the top that can expose the combined processing power and storage of the array as a set of virtual instances. To ensure that a particular software system has the right virtual infrastructure attributes, it becomes imperative that the cloud infrastructure in question be audited or measured by the software designer before deploying the system on a particular cloud. Whether you are a cloud company that provides this virtual infrastructure or you are a software architect in charge of choosing the right infrastructure provider, you need to have the right set of tools for measuring the performance of the infrastructure. In other words, you need to benchmark the cloud service in specific areas to find out how the service is performing. This is called Cloud Benchmarking. At a conceptual level, cloud benchmarking is not any different from regular computing system benchmarking. However, there is a vast difference between the two when it comes to implementation of the benchmarking methodology and measurement of the critical QOS parameters. This research paper discusses the relevant aspects of cloud benchmarking such as the cloud characteristics to benchmark, the parameters to measure, the role of automation in cloud benchmarking and finally the performance-cost normalization of the benchmarking results so that the cloud provider offering the best value for money can be determined.
Base Station Controlled and Energy Efficient Centralized Hierarchical Routing Protocol (BSEECH)

The basic purpose of wireless sensor network is to optimize residual energy of nodes and extend the life-time of network. This paper provides an improved algorithm for cluster head selection by reducing network overhead on clusters and transmits the data from source to sink through expanding nodes' life-time. This paper provides method for transmitting data by dividing nodes into different levels in hierarchical format.