Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Basketball Results Using Cascading Algorithm

Anybody can guess the winners of a basket game. The question is how big the chances are in predicting the real winners. Relying only on the experts' experiences and intuition could not discover all the value and potential of the collected data. Driven by the increasing comprehensive data in sports datasets and data mining technique successfully used in different areas, sports data mining technique emerges and enables us to find hidden knowledge to impact the sports industry. A more scientific approach is needed to use for these data that are collected. Some predictors based only on winning records and some based only on statistical records of both teams. There are also predictors which use both types of data, but the accuracy of applying different individual algorithms is only ranging about 60% - 70%. To achieve better prediction rates and deal with that complexity, a lot of machine learning methods have been implemented over these data. This paper presents an improved technique for predicting basketball game results implementing cascading algorithm. The researchers combined Naive Bayes, Four Factor Analysis, and Fuzzy Logic Algorithms to predict basketball game result in an acceptable level of 69% - 70% accuracy. The researchers tested several times using data sets from NBA game Season 2015-2016, and the cascading algorithm result manages to reach 70% prediction accuracy. The result of this system can be used to assist basketball coaches in making plans for possible team developments. Also, the forecasted results can serve as an aid in building effective gameplay.
Open Innovation Development for Photonic Integration Technology: A Practical Case of RF Interconnects

Photonic integrated circuit (PIC) platforms are in continuous development to offer higher performance and increased functionality. Through an open innovation model for photonics, developments for next technology nodes can be carried out within a collaborative framework. As an example, we present a case in which we develop low loss radio-frequency interconnects for an InP foundry partner. The technology development is expected to enable high modulation data rates using a generic PIC platform.
A Novel Two-Step Method for Stereo Vision Algorithm to Reduce Search Space

Stereo vision is a crucial algorithm in depth detection. By comparing images of a scene from two points, the relative position of objects is extracted. Human's vision system uses this relative shift between the left and right eyes to estimate the depth of information. The main goal of stereo vision is to determine the distance between objects in the scene or, in other words, to obtain depth information. This paper presents a two-step method to reduce the runtime and maintain accuracy of the stereo vision algorithm. Due to the data dependency, its implementation in parallel reduces performance. We have implemented this method for the different values of maximum disparity and window sizes. The simulation result shows that the proposed method is more than 6X faster than the common stereo vision. We have also implemented this method using Compute Unified Device Architecture (CUDA) on a Graphics Processing Unit (GPU), and we have shown that due to data dependency, this method does not work well on the Graphics Processing Unit.
Analysis of Community Detection Algorithms

Community Detection is starting to become an inevitable task for data science community and like-minded analysts to understand the hidden insights and trends in large complex networks. Through this paper we would like to understand and comparatively analyze the prevalent and recent community detection techniques like Minimum Cut Method, Hierarchical Clustering, Girvan Newman Algorithm, Modularity Maximization, Statistical Inference, Clique Percolation & Maximum Permanence Method based on their network relevance, adaptability and robustness to changes and finally their capacity of size and speed. Finally, we will review the realworld application of these algorithms.
Hassium: Hardware Assisted Database Synchronization

We present Hassium, a hardware assisted database concurrency control, which is optimized for contention. We use HTM (Hardware Transaction Memory) although it is known for overreaction to conﬂicts. However, Hassium uses HTM to combine the beneﬁcial parallelism of optimistic concurrency control (OCC) with the strength of traditional pessimistic 2PL (Two-Phase Locking). An evaluation on a 22-core HTM-capable multi-core machine shows that Hassium allows for 2X throughput hike on a range of high contention workloads, and guaranties and maintains satisfactory throughput level under increasing number of threads, where the OCC and 2PL methods fail to do that.
Network Traffic Detection Based on Histogram and Self-similarity Matrix

This paper introduces a new network traffic detection method to detect network malicious traffic in different network architectures. In most network traffic data sets, training data and testing data have the same network architecture and traffic features, like the same IP address distribution, the same IP port utilization, etc. But in real network detection, feature differences between training data sets and target network cannot be ignored. In this paper, we use histogram and self-similarity matrix to express these feature differences and keep the traffic features in use at the same time.This method could learn from anomaly network samples and detect real network traffic with feature drift, zoom, and other variants.
Memory-Oriented Distributed Computing at Rack Scale

Introduction: Recent advances provide the building blocks for constructing rack-scale architectures with a large pool of disaggregated non-volatile memory (NVM) that can be shared across a high-performance system interconnect by decentralized compute resources. NVDIMMs and new NVM technologies provide byte-addressable persistent storage accessible through loads and stores, rather than the block I/O path used today. High-performance system interconnects, such as Gen-Z, OmniPath, and RDMA over InfiniBand, provide low-latency access from compute nodes to fabricattached memory (e.g., microsecond-scale remote memory latencies are already possible with RDMA). Disaggregated memory architectures1 share several characteristics: 1) a high-capacity pool of memory that can be shared by heterogeneous computing resources at low latency; 2) a partially disaggregated architecture that treats node-local memory as private and disaggregated memory as shared; 3) a heterogeneous memory system containing both volatile DRAM and NVM; 4) unmediated access from a compute node to disaggregated memory provided by one-sided loads/stores or gets/puts and facilitated through atomic operations (e.g., compare-and-swap as in RDMA or Gen-Z); 5) hardwareenforced cache coherence domains limited to a single compute node; and 6) a separation of fault domains between processing and disaggregated memory. MODC: Our goal is to investigate how to program this emerging class of system architectures. We propose memoryoriented distributed computing (MODC), an approach for building system runtimes that exploits disaggregated memory to facilitate work distribution, coordination and fault tolerance. Global state is maintained as shared data structures in disaggregated memory that are visible to all participating processes, rather than being physically partitioned. Because processes on all nodes have direct access to global data structures, data can be efficiently shared, without the need for message overheads. Processes are equally able to analyze and service requests for any part of the dataset, which provides better load balancing and more robust performance for skewed workloads. Shared access to global data also
What is the Validity of Questionnaires Assessing Fruit and Vegetable Consumption in Children when Compared with Blood Biomarkers? A Meta-Analysis

Fruit and vegetable consumption has been associated with improved health outcomes in children. As an extensive number of questionnaires are currently used to assess fruit and vegetable consumption, we performed a systematic review of the criterion validity of questionnaires used to estimate fruit and vegetable consumption in children, considering blood biomarkers as the reference method. Five electronic databases (MEDLINE, CINAHL, Scopus, PsycINFO, Web of Science) were searched from database inception to 23 July 2018. The search strategy used the following sets of descriptors: children; fruits and vegetables; dietary questionnaires; blood biomarkers; and validation coefficient. The search terms were adapted for use with other databases in combination with database-specific filters. Potentially eligible articles were selected independently by two reviewers, separately, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Two articles meeting the inclusion criteria were included. The main reason for study exclusion was the sample age range, which included adolescents. The pooled correlation coefficient was 0.32 (95% confidence interval: 0.24–0.40). This review provided insights into assessment methods of fruit and vegetable consumption in children. Although further studies are required, questionnaires for assessing fruit and vegetable consumption have fair criterion validity in children.
Semantic-based tag recommendation in scientific bookmarking systems

Recently, tagging has become a common way for users to organize and share digital content, and tag recommendation (TR) has become a very important research topic. Most of the recommendation approaches which are based on text embedding have utilized bag-of-words technique. On the other hand, proposed deep learning methods for capturing semantic meanings in the text, have been proved to be effective in various natural language processing (NLP) applications. In this paper, we present a content-based TR method that adopts deep recurrent neural networks to encode titles and abstracts of scientific articles into semantic vectors for enhancing the recommendation task, specifically bidirectional gated recurrent units (bi-GRUs) with attention mechanism. The experimental evaluation is performed on a dataset from CiteULike. The overall findings show that the proposed model is effective in representing scientific articles for tag recommendation.
Translating Navigation Instructions in Natural Language to a High-Level Plan for Behavioral Robot Navigation

We propose an end-to-end deep learning model for translating free-form natural language instructions to a high-level plan for behavioral robot navigation. We use attention models to connect information from both the user instructions and a topological representation of the environment. We evaluate our model’s performance on a new dataset containing 10,050 pairs of navigation instructions. Our model significantly outperforms baseline approaches. Furthermore, our results suggest that it is possible to leverage the environment map as a relevant knowledge base to facilitate the translation of free-form navigational instruction.
Processing and Characterization of Bioactive Borosilicate Glasses and Scaffolds with Persistent Luminescence

In this proceeding, we report our latest results on the development of borosilicate glasses with persistent luminescence (PeL). Those PeL glasses were processed by adding PeL microparticles (MPs) in the glass using the direct doping method. First, we explain the challenges to balance the survival and dispersion of MPs when preparing borosilicate glasses using this method. Then, we show that scaffold can be obtained using the sintering process from these glasses but also by adding PeL microparticles (MPs) in the glass powder prior to sintering. Finally, we discuss the impact of the scaffold fabrication process on the PeL properties of the MPs.
Helpfulness Prediction of Online Product Reviews

The simple question "Was this review helpful to you? " increases an estimated $2.7B revenue to Amazon.com annually 1. In this paper, we propose a solution to the problem of electronic product review accumulation using helpfulness prediction. The popularity of e-commerce and online retailers such as Amazon, eBay, Yelp, and TripAdvisor are largely relying on the presence of product reviews to attract more customers. The major issue for the user submitted reviews is to quantify and evaluate the actual effectiveness by combining all the reviews under a particular product. With the varying size of reviews for each product, it is quite cumbersome for the customers to get hold of the overall helpfulness.Therefore, we propose a feature extraction technique that can quantify and measure helpfulness for each product based on user submitted reviews.
Low-Latency Transaction Execution on Graphics Processors: Dream or Reality?

In this paper we take a close look into the role of GPUs for executing OLTP workloads, with a focus on CRUD operator-based processing, as opposed to more complex OLTP trans-actions. To this end we develop a prototype system supporting GPU and CPU variants of DSM and NSM processing, with a delegation-based approach that uses a single-thread scheduler to manage concurrency control, enabling reads with guaranteed bounded staleness. We evaluate our prototype using workloads from the Yahoo! cloud serving benchmark. We report the impact of layout choices, batching conﬁguration and concurrency control designs. Through our study we are able to pinpoint that the contradicting needs in GPU processing for small batches to reduce waiting time, but large batches to reduce execution time, is the essential challenge for OLTP on these processors, aﬀecting all design choices we study. Hence, we propose two preconditions for supporting OLTP with GPUs, aiming to guide researchers in ﬁnding scenarios for extending the applicability of GPUs in supporting data management tasks.
Multi-Objective Chance Constrained Programming of Spare Parts Based on Uncertainty Theory

The optimization of spare parts inventory is very important in the modern aerospace engineering system, especially in the environment with low management effectiveness and a wide variety of spare parts. At present, there are many optimization models for spare parts inventory, and the single-objective optimization inventory is mostly used. But the single-objective optimization model has some limitations. First, in the applications of practical engineering, a single-goal decision problem is generally rare, and most of the decisions we have experienced involve many complicated goals. Second, it is difficult to truly present the actual situation when the mathematical programming model is used to discuss the optimization problem in practical engineering application. The solution to solve the model is a hybrid intelligent algorithm by combining the genetic algorithm with the inverse uncertainty distribution function. Finally, an example is given to illustrate the feasibility of the optimization model.
Safety and efficacy of remote ischemic conditioning in pediatric moyamoya disease patients treated with revascularization therapy

BACKGROUND: Revascularization surgery has been the standard treatment to prevent ischemic stroke in pediatric Moyamoya disease (MMD) patients with ischemic symptoms. However, perioperative complications, such as hyperperfusion syndrome, new infarct on imaging, or ischemic stroke, are inevitable. Remote ischemic conditioning (RIC) is a noninvasive and easy-to-use neuroprotective strategy, and it has potential effects on preventing hyperperfusion syndrome and ischemic infarction. AIMS: The aim of this study is to investigate the safety and efficacy of RIC in pediatric MMD patients undergoing revascularization surgery. METHOD: A total of 60 pediatric MMD patients with one or more ischemic symptoms will be recruited and allocated in 1:1 ratio to the RIC group and sham group, respectively. Both RIC and sham RIC will be performed twice daily for 7 consecutive days before revascularization surgery with different cuff pressures during the ischemia period (50 mmHg over-systolic blood pressure and 30 mmHg). Single photon emission computed tomography will be performed within 7 days preoperatively and 3 months postoperatively, respectively, to evaluate the cerebral perfusion status. Other outcomes, including safety, plasma biomarker, functional outcome, and the incidence of infarction and its size, will also be evaluated. CONCLUSION: This study will provide insights into the preliminary proof of principle, safety, and efficacy of RIC in pediatric MMD patients undergoing revascularization surgery therapy, and this data will provide parameters for future larger scale clinical trials if efficacious.
Social-Aware Incentive Mechanism for AP Based Mobile Data Offloading

Without properly addressed, the dramatic increase in mobile data traffic may lead to severe traffic congestion problems in cellular networks. Mobile data offloading is a promising method to reduce the traffic congestion by redirecting the traffic of a cellular network to other types of access networks. In addition, with the popularity of online social platforms (e.g., Wechat and Facebook), the social relationship of mobile users (MUs) has become an important factor to impact the decision of data offloading. In this paper, we take the social relationship of MUs into the consideration of incentive mechanism design and design two social-aware incentive mechanisms to achieve efficient mobile data offloading, in which a PageRank-based algorithm is used to model the social relationship of MUs. The proposed incentive mechanisms satisfy the properties of individual rationality and truthfulness. Extensive simulation results demonstrate the nice performance of the proposed mechanisms by comparing with other counterparts.
Dependence of the Effective Length of a Receiving Antenna on the Space Charge Distribution in a Model Source of Quasi-Electrostatic Chorus Emissions

Recently it was shown on the basis of general theory of antennas in plasmas that the effective length of a receiving dipole antenna can be much greater than its geometric length in the case of reception of quasi-electrostatic chorus emissions propagating close to the resonance cone. For such calculations one needs to specify a model wave source, since the actual source—receiver geometry is not well known in the case of chorus emissions generated in a distributed region by a complicated nonlinear process. In this paper, we analyze how the effective length of a spacecraft-borne receiving antenna depends on the parameters of a model source of quasi-electrostatic chorus emissions. In particular, we study the dependence of the effective length on characteristic scales of the space charge distribution in the source and the distance between the source and the receiving antenna.
Health related quality of life in patients having schizophrenia negative symptoms – a systematic review

ABSTRACT Background: Schizophrenia negative symptoms (SNS) contribute substantially to poor functional outcomes, loss in productivity and poor quality of life. It is unclear which instruments may be used for assessing quality of life in patients with SNS. Objective: The objective of this review was to identify instruments assessing health-related quality of life (HRQoL) validated in patients with SNS and to assess their level of validation. Data sources: We conducted a systematic literature review in Medline and the ISPOR database in March 2016 to identify studies on the quality of life in patients with SNS published by March 2016. Data extraction: Psychometric properties and validation steps. Data synthesis: After applying inclusion/exclusion criteria, 49 studies were selected for the analysis of HRQoL instruments; however, none of these instruments only addressed patients with SNS. Of these, 19 HRQoL instruments used in patients with schizophrenia or including patients with SNS among others, in the context of instrument validation, were identified (4 generic, 10 non-specific mental health, 5 schizophrenia-specific). Conclusion: No HRQoL instrument has been validated in patients with SNS only; for the remaining instruments identified, it remains unclear whether they were intended to capture HRQoL in patients with SNS.
AdaShift: Decorrelation and Convergence of Adaptive Learning Rate Methods

Adam is shown not being able to converge to the optimal solution in certain cases. Researchers recently propose several algorithms to avoid the issue of non-convergence of Adam, but their efficiency turns out to be unsatisfactory in practice. In this paper, we provide new insight into the non-convergence issue of Adam as well as other adaptive learning rate methods. We argue that there exists an inappropriate correlation between gradient $g_t$ and the second-moment term $v_t$ in Adam ($t$ is the timestep), which results in that a large gradient is likely to have small step size while a small gradient may have a large step size. We demonstrate that such biased step sizes are the fundamental cause of non-convergence of Adam, and we further prove that decorrelating $v_t$ and $g_t$ will lead to unbiased step size for each gradient, thus solving the non-convergence problem of Adam. Finally, we propose AdaShift, a novel adaptive learning rate method that decorrelates $v_t$ and $g_t$ by temporal shifting, i.e., using temporally shifted gradient $g_{t-n}$ to calculate $v_t$. The experiment results demonstrate that AdaShift is able to address the non-convergence issue of Adam, while still maintaining a competitive performance with Adam in terms of both training speed and generalization.
Multi-Pseudo Regularized Label for Generated Data in Person Re-Identification

Sufficient training data normally is required to train deeply learned models. However, due to the expensive manual process for a labeling large number of images (i.e., annotation), the amount of available training data (i.e., real data) is always limited. To produce more data for training a deep network, generative adversarial network can be used to generate artificial sample data (i.e., generated data). However, the generated data usually does not have annotation labels. To solve this problem, in this paper, we propose a virtual label called Multi-pseudo Regularized Label (MpRL) and assign it to the generated data. With MpRL, the generated data will be used as the supplementary of real training data to train a deep neural network in a semi-supervised learning fashion. To build the corresponding relationship between the real data and generated data, MpRL assigns each generated data a proper virtual label which reflects the likelihood of the affiliation of the generated data to pre-defined training classes in the real data domain. Unlike the traditional label which usually is a single integral number, the virtual label proposed in this paper is a set of weight-based values each individual of which is a number in (0,1] called multi-pseudo label and reflects the degree of relation between each generated data to every pre-defined class of real data. A comprehensive evaluation is carried out by adopting two state-of-the-art convolutional neural networks (CNNs) in our experiments to verify the effectiveness of MpRL. Experiments demonstrate that by assigning MpRL to generated data, we can further improve the person re-ID performance on five re-ID datasets, i.e., Market-1501, DukeMTMC-reID, CUHK03, VIPeR, and CUHK01. The proposed method obtains +6.29%, +6.30%, +5.58%, +5.84%, and +3.48% improvements in rank-1 accuracy over a strong CNN baseline on the five datasets, respectively, and outperforms state-of-the-art methods.