Miracle-dz/UniFAR-FTUs huggingface dataset:
IMMERSIVE BICYCLE GAME FOR HEALTH VIRTUAL TOUR OF UIN MAULANA MALIK IBRAHIM MALANG

Cancer is the leading cause of death in the world. More than 10 million people worldwide The goal of this research is to promote UIN Maliki Malang using immersive tools bicycle for human health. The system developed using combination of bicycle and computer with various sensors on the wheels, steering and brakes. The device consists of a mountain bike in front of the monitor are useful to display virtual Tour. Children’s reactions to the virtual peer indicate that their virtual peer framework is a promising platform for future behavioral studies of peer influences on children’s bicycle riding behavior. The system results shows immersive bicycle road in UIN Maliki Malang area. This system is useful for refreshing and relaxing human body.
THE INFLUENCE OF BRAND EQUITY TOWARDS REPURCHASING INTENTION OF NOKIA MOBILE PHONES THROUGH THE CONSUMER VALUE

This research is aimed to partially and simultaneously examine the influence of brand equity element towards repurchasing intention of Nokia cell phones in Surabaya through the customer value. The populations of this research are the entire Nokia cell phones customers in Surabaya. The numbers of population taken are 150 respondents. The sample was taken by using purposive sampling method. The data are processed and analyzed by using hierarchical regression analysis. The results of the research show that brand equity elements affect the customer value. Consequently, the customer value affects the repurchasing intention of the customers. In conclusion, the elements of brand equity partially and simultaneously affect the repurchasing intention through the customer value in Surabaya. Based on the results of the research, it is suggested that the producer of Nokia cell phones should creates advertisement in television, which gives the better information about cell phones Nokia specification.
Complexity signatures in the geomagnetic H component recorded by the Tromsø magnetometer (70° N, 19° E) over the last quarter of a century

Abstract. Solar disturbances, depending on the orientation of the interplanetary magnetic field, typically result in perturbations of the geomagnetic field as observed by magnetometers on the ground. Here, the geomagnetic field's horizontal component, as measured by the ground-based observatory-standard magnetometer at Tromso (70° N, 19° E), is examined for signatures of complexity. Twenty-five year-long 10 s resolution data sets are analysed for fluctuations with timescales of less than 1 day. Quantile–quantile plots are employed first, revealing that the fluctuations are better represented by Cauchy rather than Gaussian distributions. Thereafter, both spectral density and detrended fluctuation analysis methods are used to estimate values of the generalized Hurst exponent, α. The results are then compared with independent findings. Inspection and comparison of the spectral and detrended fluctuation analyses reveal that timescales between 1 h and 1 day are characterized by fractional Brownian motion with a generalized Hurst exponent of ~1.4, whereas including timescales as short as 1 min suggests fractional Brownian motion with a generalized Hurst exponent of ~1.6.
The Current Status of Physical Education Curriculum at high schools In Sudan

In this study we investigate the current status of physical education Curriculum in Sudanese High Secondary schools. The main tools for collecting data in this research are analyzing educational polices, Questionnaires and Interviews. Our questionnaire form contains 5 parts for our most important 18 items, and the sample size are 60 forms. The Cranach alpha coefficient for identification equals to 0.8570 which confirms the stability of resolution and suitability for use. The Statistical Software (SPSS) also used for analyzing our questionnaire data. We found that the main problem facing physical education and sport activities in Sudanese High Secondary schools is the absence of the special physical educations curriculum. In addition to that, the shortage in the textbook of physical education, and the scarcity of the physical education teachers at high schools.
La transcripción musical para guitarra flamenca: Análisis e implementación metodológica

La investigacion contiene un estudio historico sobre las grandes figuras de la guitarra flamenca y propone un metodo para transcribir el repertorio de este instrumento al lenguaje musical escrito. Explica como se realiza el proceso de transcripcion de partituras para dicho instrumento (algunas de ellas ya publicadas) y sienta las bases sobre como se debe transcribir, desde perspectivas musicologicas, con criterios flamencos fundamentados. Ademas, detalla los problemas mas frecuentes en la transcripcion y ofrece posibles soluciones. Asimismo, realiza un estudio de las nuevas tecnologias en la transcripcion y expone una experiencia didactica con alumnos de Grado Profesional de Guitarra Flamenca.
Partition Pruning: Parallelization-Aware Pruning for Deep Neural Networks

Parameters of recent neural networks require a huge amount of memory. These parameters are used by neural networks to perform machine learning tasks when processing inputs. To speed up inference, we develop Partition Pruning, an innovative scheme to reduce the parameters used while taking into consideration parallelization. We evaluated the performance and energy consumption of parallel inference of partitioned models, which showed a 7.72x speed up of performance and a 2.73x reduction in the energy used for computing pruned layers of TinyVGG16 in comparison to running the unpruned model on a single accelerator. In addition, our method showed a limited reduction some numbers in accuracy while partitioning fully connected layers.
Caching or No Caching in Dense HetNets?

Caching the content closer to the user equipments (UEs) in heterogenous cellular networks (HetNets) improves user-perceived Quality-of-Service (QoS) while lowering the operators backhaul usage/costs. Nevertheless, under the current networking strategy that promotes aggressive densification, it is unclear whether cache-enabled HetNets preserve the claimed cost-effectiveness and the potential benefits. This is due to 1) the collective cost of caching which may inevitably exceed the expensive cost of backhaul in a dense HetNet, and 2) the excessive interference which affects the signal reception irrespective of content placement. We analyze these significant, yet overlooked, issues, showing that while densification reduces backhaul load and increases spectral efficiency in cache-enabled dense networks, it simultaneously reduces cache-hit probability and increases the network cost. We then introduce a caching efficiency metric, area spectral efficiency per unit spent cost, and find it enough to cache only about 3% of the content library size in the cache of small-cell base stations. We further show that range expansion, known to be of substantial value in wireless networks, is almost impotent to curb the caching inefficiency. Surprisingly, unlike the conventional wisdom recommending traffic offloading from macro cells to small cells, in cache-enabled HetNets, it is more beneficial to exclude offloading altogether or to do the opposite.
GeNet: Deep Representations for Metagenomics

We introduce GeNet, a method for shotgun metagenomic classification from raw DNA sequences that exploits the known hierarchical structure between labels for training. We provide a comparison with state-of-the-art methods Kraken and Centrifuge on datasets obtained from several sequencing technologies, in which dataset shift occurs. We show that GeNet obtains competitive precision and good recall, with orders of magnitude less memory requirements. Moreover, we show that a linear model trained on top of representations learned by GeNet achieves recall comparable to state-of-the-art methods on the aforementioned datasets, and achieves over 90% accuracy in a challenging pathogen detection problem. This provides evidence of the usefulness of the representations learned by GeNet for downstream biological tasks.
The SuperM-Tree: Indexing metric spaces with sized objects

A common approach to implementing similarity search applications is the usage of distance functions, where small distances indicate high similarity. In the case of metric distance functions, metric index structures can be used to accelerate nearest neighbor queries. On the other hand, many applications ask for approximate subsequences or subsets, e.g. searching for a similar partial sequence of a gene, for a similar scene in a movie, or for a similar object in a picture which is represented by a set of multidimensional features. Metric index structures such as the M-Tree cannot be utilized for these tasks because of the symmetry of the metric distance functions. In this work, we propose the SuperM-Tree as an extension of the M-Tree where approximate subsequence and subset queries become nearest neighbor queries. In order to do this, we introduce metric subset spaces as a generalized concept of metric spaces. Various metric distance functions can be extended to metric subset distance functions, e.g. the Euclidean distance (on windows), the Hausdorff distance (on subsets), the Edit distance and the Dog-Keeper distance (on subsequences). We show that these examples subsume the applications mentioned above.
Design and Evaluation of Smart-Contract-based System Operations for Permissioned Blockchain-based Systems

Recently, enterprises have paid attention to permissioned blockchain (BC), where business transactions among inter-authorized organizations (forming a consortium) can automatically be executed on the basis of a distributed consensus protocol, and applications of BC have expanded as permissioned BC has adopted the features of the smart contract (SC), which is programmable user-defined business logic deployed in BC and executed with the consensus protocol. A single BC-based system will be built across multiple management domains (e.g., the data centers of each organization) having different operational policies (e.g., operational procedures, timing, etc.); although establishing system management and operations over BC-based systems (e.g., SC installation for updates) will be important for production uses, such multi-domain formation will trigger a problem in that executing system operations over BC-based systems will become time-consuming and costly due to the difficulty in unifying and/or adjusting operational policies. Toward solving the problem, we propose an operations execution method for BC-based systems; the primary idea is to define operations as a smart contract so that unified and synchronized cross-organizational operations can be executed effectively by using BC-native features. For the recent BC architecture in which participating nodes have different types of roles, we designed the proposed method as a hybrid architecture characterized with in-BC consensus establishment and execution status management and out-BC operations execution for all types of nodes operated by agents that listen to triggered events including operational instructions defined in SCs. We implemented a prototype with Hyperledger Fabric v1.2. 0. A cost estimation with the prototype shows that the total yearly cost of SC installation operations could be reduced by 74 percent compared with a manual method.
Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search

A fundamental question lies in almost every application of deep neural networks: what is the optimal neural architecture given a specific data set? Recently, several Neural Architecture Search (NAS) frameworks have been developed that use reinforcement learning and evolutionary algorithm to search for the solution. How-ever, most of them take a long time to find the optimal architecture due to the huge search space and the lengthy training process needed to evaluate each candidate. In addition, most of them aim at accuracy only and do not take into consideration the hardware that will be used to implement the architecture. This will potentially lead to excessive latencies beyond specifications, rendering the resulting architectures useless. To address both issues, in this paper we use Field Programmable Gate Arrays (FPGAs) as a vehicle to present a novel hardware-aware NAS framework, namely FNAS, which will provide an optimal neural architecture with latency guaranteed to meet the specification. In addition, with a performance abstraction model to analyze the latency of neural architectures without training, our framework can quickly prune architectures that do not satisfy the specification, leading to higher efficiency. Experimental results on common data set such as ImageNet show that in the cases where the state-of-the-art generates architectures with latencies $ 7.81\times$ longer than the specification, those from FNAS can meet the specs with less than 1% accuracy loss. Moreover, FNAS also achieves up to $ 11.13\times$ speedup for the search process. To the best of the authors’ knowledge, this is the very first hardware aware NAS.Acm Reference FormatWeiwen Jiang, Xinyi Zhang, Edwin H. -M. Sha, Lei Yang, Qingfeng Zhuge, Yiyu Shi, JingtongHu. 2019.Accuracy vs. Effiency:Achieving Both through FPGA-Implementation Aware Neural Architecture Search. In The 56th Annual Design Automation Conference 2019 (DAC ’19), June 2–6, 2019, Las Vegas, NV, USA. ACM, 6 pages. https://doi.org/10.1145/3316781.3317757
Comparing Task Simplifications to Learn Closed-Loop Object Picking Using Deep Reinforcement Learning

Enabling autonomous robots to interact in unstructured environments with dynamic objects requires manipulation capabilities that can deal with clutter, changes, and objects’ variability. This letter presents a comparison of different reinforcement learning-based approaches for object picking with a robotic manipulator. We learn closed-loop policies mapping depth camera inputs to motion commands and compare different approaches to keep the problem tractable, including reward shaping, curriculum learning, and using a policy pre-trained on a task with a reduced action set to warm-start the full problem. For efficient and more flexible data collection, we train in simulation and transfer the policies to a real robot. We show that using curriculum learning, policies learned with a sparse reward formulation can be trained at similar rates as with a shaped reward. These policies result in success rates comparable to the policy initialized on the simplified task. We could successfully transfer these policies to the real robot with only minor modifications of the depth image filtering. We found that using a heuristic to warm-start the training was useful to enforce desired behavior, while the policies trained from scratch using a curriculum learned better to cope with unseen scenarios where objects are removed.
ÜCRETSİZ VERİ MADENCİLİĞİ ARAÇLARI VE TÜRKİYE’DE BİLİNİRLİKLERİ ÜZERİNE BİR ARAŞTIRMA

Veri madenciligi, istatistik temelli ve bilgisayar destekli teknikler kullanilarak veriden bilgiye ulasma sureci olarak tanimlanabilir. Veri analizleri icin siklikla kullanilan veri madenciligi araclarinin sayisi gunden gune artis gostermektedir. Bu calismanin amaci ucretsiz veri madenciligi araclarini derlemek ve tanitmaktir. Bu amaca uygun olarak, akademik ve ticari arastirmalarda kullanilan ucretsiz veri madenciligi araclarinin onemli bir kismi belirlenmistir. Bu yazilimlar hakkinda tarihce bilgisi, kullanim alani vb. bazi genel bilgiler verilmistir. Ayrica, Turkiye’de veri madenciligi konusunda arastirma, proje vb. calismalarda bulunan kisilere kartopu orneklem metodu ile ulasilarak bu veri madenciligi yazilimlarinin Turkiye’deki bilinirligi ve kullanim yayginliklari belirlenmeye calisilmistir. Arastirmacilarin, calismaya konu olan 38 adet yazilimdan yalnizca 5 tanesi uzerinde yogunlastiklari geri kalan yazilimlarin bircogunu kullanmadiklari gibi bu yazilimlardan haberdar da olmadiklari gorulmustur. Calismanin Turkiye’deki arastirmacilar icin veri madenciligi araclari icin veri seti olusturma ve farkli veri madenciligi araclarini arastirmacilara tanitma amacina ulasacagi dusunulmektedir. Veri madenciligi surecinde arastirmacinin girdi sayisi, girdi tipi, kullanilacak veri madenciligi yetenegi gibi vermesi gereken cok sayida karar vardir. Kullanilacak veri madenciligi araci da bu kararlardan biridir. Calismanin, arastirmacilarin calismalarinda kullandiklari veri madenciligi araclari konusunda tercih sanslarini arttirarak calisma surecinin ve sonuclarinin kalitesine katki yapacagi dusunulmektedir.
Delay Analysis of Spatially Coded MIMO-ZFBF With Retransmissions in Random Networks

For a low-mobile Poisson bipolar network and under line-of-sight/non-line-of-sight (LOS/NLOS) path-loss model, we study repetitive retransmissions (RR) and blocked incremental redundancy (B-IR). We consider spatially-coded multiple-input multiple-output (MIMO) zero-forcing beamforming (ZFBF) multiplexing system, whereby the packet success reception is determined based on the aggregate data rate across spatial dimensions of the MIMO system. Characterization of retransmission performance in this low-mobile configuration is practically important, but inherently complex due to a substantial rate correlation across retransmissions and intractability of evaluating the probability density function of aggregate data rate. Adopting tools of stochastic geometry, we first characterize the rate correlation coefficient (RCC) for both schemes. Our results show that, compared to the RR scheme, the B-IR scheme has higher RCC while its coverage probability is substantially larger. We demonstrate that the spotted contention between coverage probability and RCC causes the mean transmission delay (MTD) of B-IR to become either smaller or larger than the MTD of the RR scheme. Finally, we develop a numerical approximation of MTD, and evaluate the effective spatial throughput (EST), which is reciprocal to MTD, of the RR and B-IR schemes. Our numerical results highlight fundamental tradeoffs between densification, multiplexing gain, block length, and activity factor of nodes. We further observe that for dense networks: first, LOS component is considerably instrumental to enhance EST; second, EST of the B-IR scheme can be much higher than that of the RR scheme; third, when Doppler spread exists, it can improve MTD of B-IR, whereas it does not cast any meaningful effect on MTD of RR.
Automatic Local Rewriting for Combinatorial Optimization

To solve combinatorial problems, traditional search-based optimization uses heuristics to guide the search. Deciding in which specific conditions and situations a certain heuristic can be applied is time-consuming and might cost decades to tune. In this paper, we learn a policy to pick heuristics and rewrite the local components of the current solution to iteratively improve it until convergence. The policy factorizes into a regionpicking policy and a rule-picking policy, and it employs a neural network trained with reinforcement learning. We evaluate our approach in three domains: expression simplification, online job scheduling and SAT. Our approach outperforms Z3 (De Moura & Bjørner, 2008), a state-of-the-art theorem prover, in expression simplification, outperforms DeepRM (Mao et al., 2016) and Google OR-tools (Google, 2019) in online job scheduling, and outperforms NeuroSAT (Selsam et al., 2019) and DG-DAGRNN (Amizadeh et al., 2019) in SAT with a small number of variables.
Which Factorization Machine Modeling is Better: A Theoretical Answer with Optimal Guarantee

Factorization machine (FM) is a popular machine learning model to capture the second order feature interactions. The optimal learning guarantee of FM and its generalized version is not yet developed. For a rank k generalized FM of d dimensional input, the previous best known sampling complexity is O[k3d · polylog(kd)] under Gaussian distribution. This bound is sub-optimal comparing to the information theoretical lower bound O(kd). In this work, we aim to tighten this bound towards optimal and generalize the analysis to sub-gaussian distribution. We prove that when the input data satisfies the so-called τ-Moment Invertible Property, the sampling complexity of generalized FM can be improved to O[k2d · polylog(kd)/τ2]. When the second order self-interaction terms are excluded in the generalized FM, the bound can be improved to the optimal O[kd · polylog(kd)] up to the logarithmic factors. Our analysis also suggests that the positive semi-definite constraint in the conventional FM is redundant as it does not improve the sampling complexity while making the model difficult to optimize. We evaluate our improved FM model in real-time high precision GPS signal calibration task to validate its superiority.
Apprentissage fédératif pour la prédiction du churn : une évaluation

Résumé. Les smartphones sont omniprésents dans notre quotidien. Ils constituent une ressource informatique à portée de la main avec un accès direct à une quantité considérable d’informations personnelles. Ils représentent une source de données très précieuse pour les opérateurs de télécommunication, mais la nature très décentralisée de ces données et les attentes évidentes des clients en matière de respect de la vie privée requièrent de nouvelles approches en apprentissage statistique. L’apprentissage ubiquitaire (ou ubiquitous datamining) qui intègre des terminaux avec la capacité de traiter localement leurs propres données, est une alternative intéressante à la centralisation de masse en traitement de données. L’apprentissage fédératif, (ou federated learning) est une réalisation de l’ubiquitous datamining, qui permet de déployer certains modèles d’apprentissage automatique sur des terminaux “autonomes” tels les smartphones. Cet article propose une évaluation détaillée de ce type d’apprentissage distribué dans le cadre de la prédiction de l’attrition (ou churn) sur des données issues d’un opérateur télécom.
Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry

This paper proposes a novel classification framework and a novel data reduction method to distinguish multiclass motor imagery (MI) electroencephalography (EEG) for brain computer interface (BCI) based on the manifold of covariance matrices in a Riemannian perspective. For method 1, a subject-specific decision tree (SSDT) framework with filter geodesic minimum distance to Riemannian mean (FGMDRM) is designed to identify MI tasks and reduce the classification error in the nonseparable region of FGMDRM. Method 2 includes a feature extraction algorithm and a classification algorithm. The feature extraction algorithm combines semisupervised joint mutual information (semi-JMI) with general discriminate analysis (GDA), namely, SJGDA, to reduce the dimension of vectors in the Riemannian tangent plane. And the classification algorithm replaces the FGMDRM in method 1 with k-nearest neighbor (KNN), named SSDT-KNN. By applying method 2 on BCI competition IV dataset 2a, the kappa value has been improved from 0.57 to 0.607 compared to the winner of dataset 2a. And method 2 also obtains high recognition rate on the other two datasets.
Regularity in Weighted Graphs a Symmetric Function Approach

This work describes how the class of k-regular multigraphs with edge multiplicities from a finite set can be expressed using symmetric species results of Mendez. Consequently, the generating functions can be computed systematically using the scalar product of symmetric functions. This gives conditions on when the classes are D-finite using criteria of Gessel, and a potential route to asymptotic enumeration formulas.