Miracle-dz/UniFAR-FTUs huggingface dataset:
A tripartite-graph based recommendation framework for price-comparison services

The recommender systems help users who are going through numerous items (e.g., movies or music) presented in online shops by capturing each user? s preferences on items and suggesting a set of personalized items that s/he is likely to prefer [8]. They have been extensively studied in the academic society and widely utilized in many online shops [33]. However, to the best of our knowledge, recommending items to users in price-comparison services has not been studied extensively yet, which could attract a great deal of attention from shoppers these days due to its capability to save users? time who want to purchase items with the lowest price [31]. In this paper, we examine why existing recommendation methods cannot be directly applied to price-comparison services, and propose three recommendation strategies that are tailored to price-comparison services: (1) using click-log data to identify users? preferences, (2) grouping similar items together as a user? s area of interest, and (3) exploiting the category hierarchy and keyword information of items. We implement these strategies into a unified recommendation framework based on a tripartite graph. Through our extensive experiments using real-world data obtained from Naver shopping, one of the largest price-comparison services in Korea, the proposed framework improved recommendation accuracy up to 87% in terms of precision and 129% in terms of recall, compared to the most competitive baseline.
Sequential determination based multipath suppression algorithm for through wall imaging radar

In this paper, we consider the problem of the multiple targets imaging behind the walls with a through wall imaging radar, where a target image might overlap with the multipath ghosts introduced by some other targets. We propose a sequential determination based algorithm to delete the multipath ghosts while keeping the true target images when the overlapping occurred. Specifically, first, we develop a through wall imaging model by addressing on the first-order side wall multipath propagation. Second, we obtain the images of the true targets and possibly the multipath ghosts. Then, we exploit all the locations of these images to form a sequence and judge if they are the multipath ghosts or not in a sequential way. Finally, we delete the marked multipath ghost images. Some simulations are provided to evaluate the performance of the proposed algorithm.
Automatic Recognition Of Retinopathy From Retinal Images

The retinal vessels are the only directly observable vascular networks of our body. The progresssion rate and severity of the pathological changes of retina and blood vessels can be used in the mass screening of systemic diseases such as hypertension and diabetes. We developed some automatic recognition methods to the following characteristics of retinopathy which included the extraction of blood vessels from the retinal background, the separation of arterio-venous crossings such as tapering and humping of veins, the detection of hemorrhages and exudates.
Grid Loading Due to EV Charging Profiles Based on Pseudo-Real Driving Pattern and User Behavior

This paper defines a method for generating individual electric vehicle (EV) charging patterns, and it intends to quantify the realistic loading impact on distribution grid feeders. The inputs are based on the historical driving characteristics of private conventional vehicles from Denmark and home plug-in behavior of EVs from Japan. The first input is used to define properties, such as the daily driven distance and the expected departure and arrival time, which determines the possible home charging window. The second input is used to quantify the probability of performing a domestic charge every day. Because most of the EVs do not need to charge every day, even when considering a 100% EV penetration scenario, the amount of simultaneous charging with domestic single-phase charging power (3.7 kW) determines a coincidence factor lower than 45%. When considering three-phase charging (11 kW), the combined power of the EV population increases only to 50% because of shorter charging sessions. Although the power increase, due to 11-kW charging, is likely to trigger grid components overloading, it is highlighted how uncontrolled distribution of single-phase charging could be responsible for local voltage unbalances.
A novel reservation TDMA based multiple access scheme using adaptive modulation for multimedia wireless communication systems

This paper proposes an adaptive modulation reservation TDMA system (AM-R-TDMA) for flexible and efficient support of multimedia wireless communication services that require different quality of services under dynamically changing traffic and channel conditions. In the proposed system, a channel for voice and long message terminals is assigned by the reservation TDMA mode, and a channel for short message terminals is assigned by a slotted ALOHA protocol using no signal transmitted period of already occupied channels generated by the voice activation effect. To further increase available slots for short message transmission as well as to increase capacity for data traffic, the proposed system employs an adaptive modulation that controls modulation parameters according to the instantaneous channel conditions. Computer simulation confirms that the proposed system is effective in achieving larger capacity of multimedia communication systems.
Locate the Mobile Device by Enhancing the WiFi-Based Indoor Localization Model

Due to the advent and pervasive deployment of wireless local area networks, WiFi-based indoor localization systems have received increasing attention in the last few years. However, their localization accuracy has always been a challenging issue. In addition, because of diverse interference such as multipath effects, the block of signals, an unstable or weak signal in itself, etc., not all the access points (APs) are informative for the localization. Faced with these problems, we propose a WiFi-based localization model by modifying the large localization errors and enhancing the Gaussian process regression (MEGPR). 1) To select the AP subsets that contribute more to the localization and further reduce the computational load, the AP discrimination criterion (APDC) is introduced to quantify the discernibility of the APs detected in the workspace and filter out the APs with low discrimination. 2) Second, to enhance the localization model, the localization residual is fed and learnt by the model. 3) Furthermore, the large localization errors are mitigated by the location modification method (LMM). Experiments were conducted in a real environment with an area of more than 1200 m2 and the results show that compared with other existing localization models, the average localization error of the proposed MEGPR model is minimum, which further verifies the effectiveness of the proposed MEGPR localization model.
'NPL'-A neural programming language

Summary form only given, as follows. To design large neural networks, a notation suitable for standardization has been developed utilizing the C++ programming language. The primary objective of the notation was to develop a concise and simple syntax to represent large neural networking systems that might utilize a variety of paradigms, and to be portable over a wide range of systems. The C++ programming language was selected since it has the promise of becoming the next de facto standard and has been demonstrated to be available on a wide range of hardware platforms. From a functional point of view, the C++ language has proven to be a suitable platform also for such a syntax. With the ability to overload selected operators, it becomes as simple as 'training>>a0>>a1>>a3>>out' to represent a feedforward perceptron. A training cycle is represented as 'a0<<=a1<<=a3<<=training'. The syntax can represent feedback from selected outputs and from selected networks, thus supporting the ability to handle multisource inputs and the fan-out of results, systemwide. <<ETX>>
Applicability of Recurrent Neural Networks to Player Data Analysis in Freemium Video Games

We demonstrate the applicability and practicality of recurrent neural networks (RNNs), a machine learning methodology suited for sequential data, on player data from the mobile video game My Singing Monsters. Since this data comes in as a stream of events, RNNs are a natural solution for analyzing this data with minimal preprocessing. We apply RNNs to monitor and forecast game metrics, predict player conversion, estimate lifetime player value, and cluster player behaviours. In each case, we discuss why the results are interesting, how the trained models can be applied in a business setting, and how the preliminary work can serve as a foundation for future research. Finally, as data on video game players is typically proprietary and confidential and results of research often go unpublished, this thesis serves to contribute to the literature on game user research.
Performance Bounds for Lambda Policy Iteration and Application to the Game of Tetris

We consider the discrete-time infinite-horizon optimal control problem formalized by Markov Decision Processes. We revisit the work of Bertsekas and Ioffe, that introduced $\lambda$ Policy Iteration, a family of algorithms parameterized by $\lambda$ that generalizes the standard algorithms Value Iteration and Policy Iteration, and has some deep connections with the Temporal Differences algorithm TD($\lambda$) described by Sutton and Barto. We deepen the original theory developped by the authors by providing convergence rate bounds which generalize standard bounds for Value Iteration described for instance by Puterman. Then, the main contribution of this paper is to develop the theory of this algorithm when it is used in an approximate form and show that this is sound. Doing so, we extend and unify the separate analyses developped by Munos for Approximate Value Iteration and Approximate Policy Iteration. Eventually, we revisit the use of this algorithm in the training of a Tetris playing controller as originally done by Bertsekas and Ioffe. We provide an original performance bound that can be applied to such an undiscounted control problem. Our empirical results are different from those of Bertsekas and Ioffe (which were originally qualified as "paradoxical" and "intriguing"), and much more conform to what one would expect from a learning experiment. We discuss the possible reason for such a difference.
Generative Adversarial Model-Guided Deep Active Learning for Voltage Dip Labelling

In many real applications, the ground truths of class labels from voltage dip sequences used for training a voltage dip classification system are unknown, and require manual labelling by human experts. This paper proposes a novel deep active learning method for automatic labelling of voltage dip sequences used for the training process. We propose a novel deep active learning method, guided by a generative adversarial network (GAN), where the generator is formed by modelling data with a Gaussian mixture model and provides the estimated probability distribution function (pdf) where the query criterion of the deep active learning method is built upon. Furthermore, the discriminator is formed by a support vector machine (SVM). The proposed method has been tested on a voltage dip dataset (containing 916 dips) measured in a European country. The experiments have resulted in good performance (classification rate 83% and false alarm 3.2%), which have demonstrated the effectiveness of the proposed method.
Spread-spectrum techniques for environmentally-friendly underwater acoustic communications

Anthropogenic underwater noise has been shown to have a negative impact on marine life. Acoustic data transmissions have also been shown to cause behavioural responses in marine mammals. A promising approach to address these issues is through reducing the power of acoustic data transmissions. Firstly, limiting the maximum acoustic transmit power to a safe limit that causes no injury, and secondly, reducing the radius of the discomfort zone whilst maximising the receivable range. The discomfort zone is dependent on the signal design as well as the signal power. To achieve these aims requires a signal and receiver design capable of synchronisation and data reception at low-received-SNR, down to around −15 dB, with Doppler effects. These requirements lead to very high-ratio spread-spectrum signaling with efficient modulation to maximise data rate, which necessitates effective Doppler correction in the receiver structure. This thesis examines the state-of-the-art in this area and investigates the design, development and implementation of a suitable signal and receiver structure, with experimental validation in a variety of real-world channels. Data signals are designed around m-ary orthogonal signaling based on bandlimited carrierless PN sequences to create an M-ary Orthogonal Code Keying (M-OCK) modulation scheme. Synchronisation signal structures combining the energy of multiple unique PN symbols are shown to outperform single PN sequences of the same bandwidth and duration in channels with low SNR and significant Doppler effects. Signals and receiver structures are shown to be capable of reliable communications with band of 8 kHz to 16 kHz and transmit power limited to less than 170.8 dB re 1 μPa @ 1 m, or 1 W of acoustic power, over ranges of 10 km in sea trials, with low-received-SNR below −10 dB, at data rates of up to 140.69 bit/s. Channel recordings with AWGN demonstrated limits of signal and receiver performance of BER 10−3 at −14 dB for 35.63 bit/s, and −8.5 dB for 106.92 bit/s. Piloted study of multipath exploitation showed this performance could be improved to −10.5 dB for 106.92 bit/s by combining the energy of two arrival paths. Doppler compensation techniques are explored with experimental validation showing synchronisation and data demodulation at velocities over ranges of ±2.7m/s. Non-binary low density parity check (LDPC) error correction coding with M-OCK signals is investigated showing improved performance over Reed-Solomon (RS) coding of equivalent code rate in simulations and experiments in real underwater channels. The receiver structures are implemented on an Android mobile device with experiments showing live real-time synchronisation and data demodulation of signals transmitted through an underwater channel.
CTransE : Confidence-Based Translation Model for Uncertain Knowledge Graph Embedding

Knowledge graphs play an important role in many AI applications such as fact checking. Many studies focused on learning representations of a knowledge graph in a low-dimensional continuous vector space. However, most of the recent studies do not learn embedding representations on uncertain knowledge graphs. Uncertain knowledge graphs, e.g., NELL and Knowledge Vault, are valuable because they can automatically populate themselves with new facts. Nevertheless, the automatic process basically induces uncertainty to knowledge. In this study, we introduced knowledge graph embedding on uncertain knowledge graphs by using adapting confidence-margin-based loss function for translation-based models, namely CTransE, to deal with uncertainty on knowledge graphs. The results show that CTransE can robustly learn representations of uncertain knowledge graphs and outperforms the conventional method on knowledge graph completion task.
A k-nearest neighbor artificial neural network classifier

The authors propose an artificial neural network architecture to implement the k-nearest neighbor (k-NN) classifier. This architecture employs a k-maximum network which has some advantages over the 'winner-take-all' type of networks and other techniques used to select the maximum input. This k-maximum network has fewer interconnections than other networks, and is able to select exactly k maximum inputs as long as its (k-1)/sup th/ and k/sup th/ maximum inputs are distinct. The classification performance of the k-NN classifier is exactly the same as that of the traditional k-NN classifier. However, the parallelism of the network greatly reduces the computational requirement of the traditional k-NN classifier. Unlike the multilayer perceptrons which involve slowly converging back-propagation algorithms, the k-NN artificial neural network classifier does not need any training algorithm after the initial setting of the weights. <<ETX>>
Voxel selection framework based on meta‐heuristic search and mutual information for brain decoding

Visual stimulus decoding is an increasingly important challenge in neuroscience. The goal is to classify the activity patterns from the human brain; during the sighting of visual objects. One of the crucial problems in the brain decoder is the selecting informative voxels. We propose a meta‐heuristic voxel selection framework for brain decoding. It is composed of four phases: preprocessing of fMRI data; filtering insignificant voxels; postprocessing; and meta‐heuristics selection. The main contribution is benefiting a meta‐heuristics search algorithm to guide a wrapper voxel selection. The main criterion to nominate a voxel is based on its mutual information with the provided stimulus label. The results show impressive accuracy rates which are 90.66 ± 3.66 and 91.61 ± 8.24 for DS105 and DS107, respectively. This outperforms the most of existing brain decoders in similar validation conditions. The experimental results are very encouraging which can be successfully used in the brain‐computer interface.
SpSJoin: Parallel Spatial Similarity Joins (Demo Paper)

A spatial similarity join of two geospatial datasets finds pairs of records that are simultaneously similar on spatial and tex- tual attributes. Such join is useful for a variety of applica- tions, like data cleansing, record linkage, duplications detec- tion and geocoding enhancement. Efficient techniques exist for the individual joins on either spatial or textual attributes. However, the combined problem has received much less re- search attention. This paper presents the SpSJoin (Spatial Similarity join) system to fill in this need. SpSJoin is a plat- form that merges geospatial and text processing techniques for efficiently performing spatial similarity joins. The plat- form leverages parallel computing with MapReduce to tackle scalability issues in joining large datasets. The efficiency of the proposed techniques are experimentally validated with a join case for improving the geolocation of entities in a real geospatial dataset with referential entities of another dataset.
Object Re-Identification Based on Deep Learning

With the explosive growth of video data and the rapid development of computer vision technology, more and more relevant technologies are applied in our real life, one of which is object re-identification (Re-ID) technology. Object Re-ID is cur-rently concentrated in the field of person Re-ID and vehicle Re-ID, which is mainly used to realize the cross-vision tracking of person/vehicle and trajectory prediction. This chapter combines theory and practice to explain why the deep network can re-identify the object. To introduce the main technical route of object Re-ID, the examples of person/vehicle Re-ID are given, and the improvement points of existing object Re-ID research are described separately.
Workplace Democracy and the Problem of Equality

• Professional communicators should be paying more attention to nonhierarchical workplaces, in particular, those with democratic aims. • Democratic workplaces demand the development of certain ethical dispositions/characteristics in professional communicators: mêtis and equality as an ongoing practice. • While the goals of democratic organizations are often noble, inequalities can still emerge, thus the need to recognize the equality of oneself and others as an ongoing ethical practice rather than something solved solely through institutional mechanisms. • A heuristic, provided in the closing of this article, prompts professional communicators to examine their practices in terms of equality. Practitioner’s Takeaway: Purpose: Professional communicators are becoming more invested in unique configurations of power in organizations, including non-hierarchical and democratic workplaces. While organizations dedicated to democratic processes may enact power differently than conventional organizations, they may fall short of practicing equality. This article explains the differences in non-hierarchical workplaces, considers businesses where democracy is a goal, and argues for considering equality as a habitual practice, particularly when writing regulatory documents.
Improved TF-IDF Keyword Extraction Algorithm *

nd , 2012; revised: Dec. 16 th , 2012; accepted: Dec. 25 th , 2012 Abstract: According to the TF-IDF extract algorithm, this paper proposes a new extraction algorithm based on the words frequency statistics. Combining with sections mark technology, this algorithm assigns corresponding position weight to the words located in different position and calculates the words similarities with the same parts of speech which have a high counter in the result of the word segmentation, then merge the words with a higher similarity, finally we get the keyword sorted by the weight via the TF-IWF algorithm. This method optimized the traditional Chinese keyword extract algorithm, which take little notice of the higher similarity words, and lead to low-accuracy. The results show the new approach has better algorithm performance compared with the previous TF-IDF algorithm and the key- words set extracted can generally express the content of the article.
Nonparametric algorithm of electronic components test data pattern recognition

The paper discusses the quality diagnostics of electrical radio components based on the results of non-destructive testing. A proposed clustering algorithm does not require preliminary information on the number of classes and the training sample. The algorithm allows to automatically determine the number of classes. The division into classes is due to the different characteristics of the measured variables, which correspond to different product quality ranges.
Give Me More Feedback II: Annotating Thesis Strength and Related Attributes in Student Essays

While the vast majority of existing work on automated essay scoring has focused on holistic scoring, researchers have recently begun work on scoring specific dimensions of essay quality. Nevertheless, progress on dimension-specific essay scoring is limited in part by the lack of annotated corpora. To facilitate advances in this area, we design a scoring rubric for scoring a core, yet unexplored dimension of persuasive essay quality, thesis strength, and annotate a corpus of essays with thesis strength scores. We additionally identify the attributes that could impact thesis strength and annotate the essays with the values of these attributes, which, when predicted by computational models, could provide further feedback to students on why her essay receives a particular thesis strength score.
Video Feature Extraction Based on Modified LLE Using Adaptive Nearest Neighbor Approach

Locally linear embedding (LLE) is an unsupervised learning algorithm which computes the low dimensional, neighborhood preserving embeddings of high dimensional data. LLE attempts to discover non-linear structure in high dimensional data by exploiting the local symmetries of linear reconstructions. In this paper, video feature extraction is done using modified LLE alongwith adaptive nearest neighbor approach to find the nearest neighbor and the connected components. The proposed feature extraction method is applied to a video. The video feature description gives a new tool for analysis of video.