Miracle-dz/UniFAR-FTUs huggingface dataset:
How Game Users Consume Virtual Currency: The Relationship Between Consumed Quantity, Inventory, and Elapsed Time Since Last Consumption in the Mobile Game World

In this research, we propose an analytical framework of consumer behavior of mobile game users. We focus on the quantity of virtual currency consumed by users of a mobile game to purchase characters, and clarify the relationship between consumed quantity, inventory held at the time of consumption, and elapsed time since last consumption. For the target game, the basic play is free, and there is a large difference in consumption between the light user and the heavy user. To understand such consumer behavior heterogeneity in the game world, we introduce individual differences and construct a regression model and a hierarchical Bayesian model to consider these individual differences. Also, in Japanese mobile games, an instrument called "Gacha" (Loot Box) promotes consumption; we interpret Loot Box as a promotion and include it in the model analysis. Our goal is to better understand consumer behavior in virtual space by clarifying the relationship between consumed quantity, inventory, and elapsed time by model analysis based on real data. We then suggest marketing strategies to be carried out on the basis of the obtained result.
Robust Joint Hybrid Transceiver Design for Millimeter Wave Full-Duplex MIMO Relay Systems

The joint design of hybrid beamforming matrices is conceived for multiuser mm-wave full-duplex (FD) multiple-input multiple-output (MIMO) relay-aided systems in the presence of realistic channel state information (CSI) errors. Specifically, considering a probabilistic CSI error model, we maximize the system’s worst-case sum rate by jointly optimizing the base station’s (BS’s) analog and digital beamforming matrices, plus the analog receive and transmit beamforming matrices of the relay station (RS) as well as its digital amplify-and-forward beamforming matrix under practical constraints. Explicitly, the transmit power constraints of the BS and RS, the residual self-interference power constraint of the RS, the per-user quality of service constraints, and the unit-modulus constraints on the analog beamforming matrix elements are all taken into account. Since the resultant optimization problem is very challenging due to its highly nonlinear objective function and nonconvex coupling constraints, we first transform it into a more tractable form. We then develop a novel joint optimization algorithm based on the penalty dual decomposition (PDD) technique to solve the resultant problem. The proposed PDD-based algorithm performs double-loop iterations: the inner loop updates the optimization variables in a block coordinate descent fashion, while the outer loop adjusts the Lagrange multipliers and penalty parameter, hence ensuring convergence to the set of stationary solutions of the original problem. Our simulations show that the mm-wave FD hybrid MIMO relay systems relying on our new algorithm significantly outperform both their non-robust FD and conventional half-duplex counterparts.
Short Utterance Speaker Recognition by Reservoir with Self-Organized Mapping

Short utterances cause performance degradation in conventional speaker recognition systems based on i-vector, which relies on the statistics of spectral features. To overcome this difficulty, we propose a novel method that utilizes the dynamics of the spectral features as well as their distribution. Our model integrates echo state network (ESN), a type of reservoir computing architecture, and self-organizing map (SOM), a competitive learning network. The ESN consists of a single-hidden-layer recurrent neural network with randomly fixed weights, which extracts temporal patterns of the spectral features. The input weights of our model are trained using the unsupervised competitive learning algorithm of the SOM, before enrollment, to extract the intrinsic structure of the spectral features, whereas the input weights are fixed randomly in the original ESN. In enrollment, the output weights are trained in a supervised manner to recognize an individual in a group of speakers. Our experiment demonstrates that the proposed method outperforms or is comparable to a baseline i-vector system for text-independent speaker identification on short utterances.
Efficient Intra-SM Slicing through Dynamic Resource Partitioning for GPU Multiprogramming

Conventional translation look-aside buffers(TLBs) are required to complete address translation withshort latencies, as the address translation is on the criticalpath of all memory accesses even for L1 cache hits. Such strictTLB latency restrictions limit the TLB capacity, as the latencyincrease with large TLBs may lower the overall performanceeven with potential TLB miss reductions. Furthermore, TLBsconsume a significant amount of energy as they are accessedfor every instruction fetch and data access. To avoid thelatency restriction and reduce the energy consumption, virtualcaching techniques have been proposed to defer translation toafter L1 cache misses. However, an efficient solution for thesynonym problem has been a critical issue hindering the wideadoption of virtual caching.Based on the virtual caching concept, this study proposes ahybrid virtual memory architecture extending virtual cachingto the entire cache hierarchy, aiming to improve both performanceand energy consumption. The hybrid virtual cachinguses virtual addresses augmented with address space identifiers(ASID) in the cache hierarchy for common non-synonymaddresses. For such non-synonyms, the address translationoccurs only after last-level cache (LLC) misses. For uncommonsynonym addresses, the addresses are translated to physicaladdresses with conventional TLBs before L1 cache accesses. Tosupport such hybrid translation, we propose an efficient synonymdetection mechanism based on Bloom filters which canidentify synonym candidates with few false positives. For largememory applications, delayed translation alone cannot solvethe address translation problem, as fixed-granularity delayedTLBs may not scale with the increasing memory requirements.To mitigate the translation scalability problem, this studyproposes a delayed many segment translation designed for thehybrid virtual caching. The experimental results show that ourapproach effectively lowers accesses to the TLBs, leading tosignificant power savings. In addition, the approach providesperformance improvement with scalable delayed translationwith variable length segments.
Deep Residual Learning for Analyzing Customer Satisfaction using Video Surveillance

Measuring customer satisfaction based on facial expressions from video surveillance can potentially support real-time analysis. We propose the use of deep residual network (ResNet), which has been a widely used for many image recognition tasks, but not in the context of recognizing facial expressions in video surveillance. A key challenge in collecting video surveillance data in an airport context is to achieve a balanced distribution of all emotions, as most of passengers' faces are either neutral or happy. To solve this issue, there is no existing work that has established the feasibility of using datasets from different domains to train the model. This paper is the first in investigating the benefits of using residual training approach and adopt a pre-trained network from similar tasks to reduce training time. Based on comprehensive experiments, which compare domain-specific, cross-domain and mixed domain training and testing approaches, we confirm the value of augmenting datasets from different domains (CK+, JAFFE, AffectNet) for the surveillance domain.
Improving ASR Error Detection with RNNLM Adaptation

Applications of automatic speech recognition (ASR) such as broadcast transcription and dialog systems, can be helped by the ability to detect errors in the ASR output. The field of ASR error detection has emerged as a way to detect and subsequently correct ASR errors. The most common approach for ASR error detection is features-based, where a set of features are extracted from the ASR output and used to train a classifier to predict correct/incorrect labels.Language models (LMs), either from the ASR decoder or externally trained, can be used to provide features to an ASR error detection system, through scores computed on the ASR output. Recently, recurrent neural network language models (RNNLMs) features were proposed for ASR error detection with improvements to the classification rate, thanks to their ability to model longer-range context.RNNLM adaptation, through the introduction of auxiliary features that encode domain, has been shown to improve ASR performance. This work investigates whether RNNLM adaptation techniques can also improve ASR error detection performance in the context of multi-genre broadcast ASR. The results show that an overall improvement of about 1% in the F-measure can be achieved using adapted RNNLM features.
Sequential Scheduling for Belief-Propagation Demodulation of Rate Compatible Modulation

In this paper, two sequential scheduling (SS) algorithms, group-shuffled BP (GS-BP) and bit-to-symbol residual BP (BS-RBP), are proposed to accelerate the convergence speed of the demodulation of rate compatible modulation (RCM). Unlike the conventional BP demodulation that works in a parallel or flooding manner, the proposed schemes work in serial manner. Since the proposed schemes may make full use of the latest messages available in current iteration, a faster convergence rate and a lower bit error rtae (BER) may be achieved. Simulations show that the proposed schemes can effectively accelerate the convergence speed of RCM demodulation and improve the bit error rate (BER) performance.
Adaptive Uplink OFDMA Random Access Grouping Scheme for Ultra-Dense Networks in IEEE 802.11ax

IEEE 802.11ax, which is the next-generation WLAN standard, aims at providing highly efficient communication in ultra-dense networks. However, due to the high quantity of stations (STAs) in dense deployment scenarios, the potential high collision rate significantly degrades the network efficiency of WLAN. In this paper, we propose an adaptive grouping scheme to overcome this challenge in IEEE 802.11ax using Uplink OFD-MA Random Access (UORA). In order to achieve the optimal utilization efficiency of resource units (RUs), we first analyze the relationship between group size and RU efficiency. Based on this result, an adaptive STA grouping algorithm is proposed to cope with the performance fluctuation of 802.11ax due to remainder stations after grouping. The analysis and simulation results demonstrate that our adaptive grouping algorithm dramatically improves the performance of both the system and each STA in the ultra-dense network.
Learning Node Embeddings for Influence Set Completion

Influence Maximization problem has found numerous applications in the real world and attracted a lot of research in the recent years. However, all previous attempts to provide a solution were based solely on the graph topology. Instead, we show how to employ recent advancement in representation learning and use node embeddings for finding solution as a supervised task. In our experiments, we show that the ranked list of nodes obtained by classifier yields better influence completion set than other baselines.
Context Learning Network for Object Detection

Current state-of-the-art detectors typically classify candidate proposals using their interior features. However, the valuable contexts are not fully exploited by existing detectors yet, which limits the detection performance. In this paper, we present a context learning network (CLN), which aims to capture pairwise relation between objects and global contexts of each object. The proposed CLN consists of two subnetworks: a multi-layer perceptron (MLP) with three layers and a convolutional neural network (ConvNet) with two layers. The MLP is first designed to capture the pairwise relation context. Pairwise relationship context is then gathered and concatenated to further learn the global contexts by the ConvNet. Finally, we obtain the desired context feature maps with rich contextual information that are useful for accurate object detection. The proposed CLN is lightweight and it is easy to embed in any existing networks for object detection. In this paper, we present a context-aware Faster-RCNN with the proposed CLN and conduct extensive experiments to evaluate its performance. Experimental results demonstrate that the context-aware Faster-RCNN achieves state-of-the-art performance with the 82.1%, 80.7% and 38.4%mAPs on VOC 2007, VOC 2012 and COCO datasets, respectively.
Link Adaptation Schemes Based on Parametric Estimation of SNR Distribution Over Nakagami- $m$  Fading Channels

Link adaptation (LA) schemes maximize average spectral efficiency (ASE) using accurate channel state information (CSI) and knowledge of signal to noise ratio (SNR) distribution. In practical systems, partial CSI is available at the transmitter through feedback of modulation and coding scheme index, often known as channel quality indicator (CQI). However, parameters of SNR distribution (<inline-formula> <tex-math notation="LaTeX">${\Theta }$ </tex-math></inline-formula>) are not known or only assumed to be known. We propose two LA schemes, for execution at the receiver and transmitter, respectively, each without prior knowledge about <inline-formula> <tex-math notation="LaTeX">${\Theta }$ </tex-math></inline-formula>. In the receiver centric schemes, parametric estimation of SNR distribution is performed using unquantized SNR samples. In the transmitter centric scheme, we estimate <inline-formula> <tex-math notation="LaTeX">${\Theta }$ </tex-math></inline-formula> from CQI samples using an iterative joint quantization-estimation algorithm. These parametric estimates form long-term SNR distribution, which is used to compute SNR switching thresholds. Using these thresholds, rate and power adaptation decisions are made based on instantaneous SNR. We have derived the maximum-likelihood estimator and Cramer–Rao lower bound for the proposed estimator using CQI feedback for Nakagami- <inline-formula> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading channels and demonstrate that near optimal ASE can be achieved while using the proposed schemes as in an ideal scenario, where perfect knowledge about CSI and SNR distribution are available <italic>a priori</italic>.
A Domain-Independent Text Segmentation Method for Educational Course Content

In this study, we have proposed a domain-independent text segmentation algorithm which is particularly useful in online educational courses. Text segmentation is proven to be helpful in improving the readability of large corpora of documents, which is essential in education scenarios. While existing domain-dependent text segmentation methods have much better performance than domain-independent methods in most cases, only domain-independent methods are applicable to sparse training content in education scenarios. Our method, unlike other domain-dependent text segmentation methods, doesn't require heavy training on prior documents, but only need to train on the current corpus of documents with topic distributions and word vector representations. Our proposed method develops text boundaries between small text units in three steps. We first calculate input text features via topical distributions (latent Dirichlet allocation) and word embeddings (GloVe). We then calculate similarity values between such textual features and detect distribution changes between the similarities. We finally perform clustering on the similarities and detect sub-topic boundaries via cluster differences. We test our method on two datasets, one from an online education course and one from a popular public dataset - Choi Dataset. The results demonstrate that our method outperforms other state-of-the-art domain-independent text segmentation approaches while achieving performance comparable to a few domain-dependent algorithms.
TSV Induced Stress Model and Its Application in Delay Estimation

Through Silicon Vias (TSVs) used in 3D ICs induce stress in the silicon wafer. This stress causes variations in the mobility and threshold voltage depending upon the value of various stress components, which in turn affects the propagation delay of logic gates. In this paper, a complete methodology is developed for modeling the variation of an inverter and a 2-input NAND gate delay at any point $(r,~\theta )$ around a TSV. Analytical models for stress variations are developed, subsequently, the corresponding variations in mobility and threshold voltage are modeled. These variations are translated into the delay variations using the timing models developed. We show that due to TSV induced stress an Inverter and NAND gate delay varies around 20%. The proposed model predicts this variability with a maximum error of around 9%.
Hardening Encrypted Patient Names Against Cryptographic Attacks Using Cellular Automata

Linking information across different databases enables new research in the medical sciences. Recent EU privacy regulations recommend encrypting personal identifiers used for linking. In this contribution, a new method for hardening such a privacy-preserving record linkage technique (PPRL) against attacks is presented. The new hardening method prevents re-identifications and cryptographic attacks while still delivering acceptable linkage quality. Using real-world mortality data, we compare clear-text and several current PPRL methods with our newly proposed method. While all PPRL methods will have to balance security and quality, the use of a cellular automata transformation to protect against attacks will decrease the linkage quality only slightly, while preventing all currently known methods of decrypting Bloom filter-based private linkage keys.
Deep Learning Algorithms for Detecting Fake News in Online Text

Spreading of fake news is a social phenomenon that is pervasive at the social level between individuals, and also through social media such as Facebook and Twitter. Fake news that we are interested in is one of many kinds of deception in social media, but it’s more important one as it is created with dishonest intention to mislead people. We are concerned about this issue because we have noticed that this phenomenon has recently caused through the means of social communication to change the course of society and peoples and also their views, for example, during revolutions in some Arab countries have emerged some false news that led to the absence of truth and stirs up public opinion and also fake of news is one of the factors Trump successes in the presidential election. So we decided to face and reduce this phenomenon, which is still the main factor to choose most of our decisions. Techniques of fake news detection varied, ingenious, and often exciting. In this paper our objective is to build a classifier that can predict whether a piece of news is fake or not based only its content, thereby approaching the problem from a purely deep learning perspective by RNN technique models (vanilla, GRU) and LSTMs. We will show the difference and analysis of results by applying them to the dataset that we used called LAIR. We found that the results are close, but the GRU is the best of our results that reached (0.217) followed by LSTM (0.2166) and finally comes vanilla (0.215). Due to these results, we will seek to increase accuracy by applying a hybrid model between the GRU and CNN techniques on the same data set.
MBSS:A General Paradigm for Static Schedule for Nested Loops with Dynamic Loop Boundary on CGRAs

Coarse-grained Reconfigurable Architectures (CGRAs) have been identified as a desirable platform for repetitive, datapath-oriented and computationally intensive applications. However, because the performance improvements are mostly achieved by exploiting the parallelism of the innermost loop, CGRAs are still poor at handling complicated applications, like loops with uncertain/unfixed boundary and imperfect nests. Existing compilation schemes for loops with dynamic loop boundary (DBLs) are inefficient and cannot support all types of control flow with no performance degradation under existing CGRAs architecture. This paper addresses the problems and makes the following contributions: i) To solve the issue of a loop with dynamic boundary, we present an improved architecture in hardware. The hybrid interconnects network is provided judiciously to support low latency control signal transformation and provide flexible power for complicated applications. ii) A precise problem formulation for DBL mapping onto CGRA. We abstract the problem with the generation of control and data flow graph (CDFG) and the mapping from CDFG to CGRA. iii) Based on the formulated problem, we provide Dynamic Boundary Static Schedule (DBSS) and Mixed Boundary Static Schedule (MBSS) methods to efficiently map the single level DBL and nested loop mixed with static and dynamic loop boundary separately. An evaluation is drafted finally under the enhanced hardware which is accessible to process the data flow with its high quality as well as the handcraft. MBSS takes performance improvement of 38%, 2.2× speed up on average and less energy consumption.
TrackNet - A Deep Learning Based Fault Detection for Railway Track Inspection

Reliable and economical inspection of rail tracks is paramount to ensure the safe and timely operation of the railway network. Automated vision based track inspection utilizing computer vision and pattern recognition techniques have been regarded recently as the most attractive technique for track surface defect detection due to its low-cost, high-speed, and appealing performance. However, the different modes of failures along with the immense range of image variations that can potentially trigger false alarms makes the vision based track inspection a very challenging task. In this paper, a multiphase deep learning based technique which initially performs segmentation, followed by cropping of the segmented image on the region of interest which is then fed to a binary image classifier to identify the true and false alarms is proposed. It is shown that the proposed approach results in improved detection performance by mitigating the false alarm rate.
Three new species of the spider genus Luzonacera Li & Li, 2017 from Philippines (Araneae, Psilodercidae)

Abstract Three new species of Luzonacera Li & Li, 2017 are described: L.francescoballarini Li & Li, sp. n. (♂♀), L.lattuensis Li & Li, sp. n. (♂♀) and L.peterjaegeri Li & Li, sp. n. (♂♀). Prior to this study, the genus was known by two species, both from Luzon Island, Philippines. So far, the genus and all five species are endemic to Luzon Island and can be found in dry or humid caves in a dark environment.