Reviewerly/exHarmony huggingface dataset:
Quarter-Point Product Quantization for approximate nearest neighbor search

Vector quantization can be used to achieve high accuracy for fast approximate nearest neighbor (ANN) search. Most existing vector quantization methods use codeword expansion to enhance the representative capacity of codebooks learned by clustering methods, which may decrease the quantization distortion and increase the accuracy. In this paper, we propose a novel codeword expansion method named Quarter-point Product Quantization (QPQ) to further minimize quantization distortions, by estimating the distances from the query points to the database points using the quarter points instead of the clustering centers. The distances can be computed more precisely and it will result in a lower distortion using QPQ, which is also a general method could be used to improve all PQ-based methods. Extensive experiments on several approximate nearest neighbor datasets show that our QPQ method can significantly improve the accuracy of PQ-based methods and consistently outperform other state-of-the-art methods.
Intelligent monitoring of indoor surveillance video based on deep learning

With the rapid development of information technology, video surveillance system has become a key part in the security and protection system of modern cities. Especially in prisons, surveillance cameras could be found almost everywhere. However, with the continuous expansion of the surveillance network, surveillance cameras not only bring convenience, but also produce a massive amount of monitoring data, which poses huge challenges to storage, analytics and retrieval. The smart monitoring system equipped with intelligent video analytics technology can monitor as well as pre-alarm abnormal events or behaviours, which is a hot research direction in the field of surveillance. This paper combines deep learning methods, using the state-of-the-art framework for instance segmentation, called Mask R-CNN, to train the fine-tuning network on our datasets, which can efficiently detect objects in a video image while simultaneously generating a high-quality segmentation mask for each instance. The experiment show that our network is simple to train and easy to generalize to other datasets, and the mask average precision is nearly up to 98.5% on our own datasets.
TUTANKHAMEN’S TWO TOMBS: REPLICA CREATION AND THE PRESERVATION OF OUR CULTURAL HERITAGE IN THE DIGITAL AGE

Abstract. There are two tombs of Tutankhamen both located in Luxor, Egypt: one in the Valley of the Kings, excavated into the Theban bedrock and decorated with wall paintings, dating from 1323 BCE; the other, installed 3 km away, opened in April 2014 and is considered to be an ‘exact facsimile’ of the original tomb. Tutankhamen’s tomb is just one example of a cultural heritage site that has been replicated. This list is steadily growing as replicas are finding renewed favour fuelled by technological advancements in three-dimensional recording, capture and fabrication which has enabled the production of highly accurate replicas in both physical and virtual form. Comparisons drawn between the two tombs of Tutankhamen—the original and the replica—aim to highlight the differing approaches of parallel preservation projects and intends to prompt questions surrounding the commissioning and use of replicas in the cultural heritage field and the growing role that 3D digital technology is playing in the preservation/conservation sector. Sites and cultural heritage organization are scrambling to be part of the 3D digital revolution. But, has the transition to 3D and the revival in replicas happened too quickly and at the expense of a site’s other conservation needs? Is technology being used in the service of conservation and preservation or is it the other way around? How can those working with heritage balance the benefit of 3D technology with the overall conservation needs for a site? Using the example of Tutankhamen’s two tombs this paper aims to provoke discussion on these topics.
No Single Metric Captures Productivity

The urge to measure the productivity of developers is not new. Since it is often the case at organizations that more code needs to be written, many attempts have been made to measure productivity based on lines of code (LOC). For example, in early 1982, the engineering management of developers working on software for the Apple Lisa computer decided to start tracking LOC added by each developer. One week, the main user interface designer, Bill Atkinson, optimized Quickdraw’s region calculation machinery and contributed -2000 LOC. The management stopped asking for his LOC. Although measuring engineer productivity by LOC is clearly fraught, anecdotes like this abound on the internet. Organizations have continued to search for better easy ways to measure developer productivity. We argue that there is no metric that adequately captures the full space of developer productivity, and that attempting to find one is counter-productive. Instead, we encourage the design of a set of metrics tailored for answering a specific goal.
Development and Deployment of a Large-Scale Dialog-based Intelligent Tutoring System

There are significant challenges involved in the design and implementation of a dialogbased tutoring system (DBT) ranging from domain engineering to natural language classification and eventually instantiating an adaptive, personalized dialog strategy.These issues are magnified when implementing such a system at scale and across domains.In this paper, we describe and reflect on the design, methods, decisions and assessments that led to the successful deployment of our AI driven DBT currently being used by several hundreds of college level students for practice and selfregulated study in diverse subjects like Sociology, Communications, and American Government.
Improving Post-Filtering of Artificial Speech Using Pre-Trained LSTM Neural Networks

Several researchers have contemplated deep learning-based post-filters to increase the quality of statistical parametric speech synthesis, which perform a mapping of the synthetic speech to the natural speech, considering the different parameters separately and trying to reduce the gap between them. The Long Short-term Memory (LSTM) Neural Networks have been applied successfully in this purpose, but there are still many aspects to improve in the results and in the process itself. In this paper, we introduce a new pre-training approach for the LSTM, with the objective of enhancing the quality of the synthesized speech, particularly in the spectrum, in a more efficient manner. Our approach begins with an auto-associative training of one LSTM network, which is used as an initialization for the post-filters. We show the advantages of this initialization for the enhancing of the Mel-Frequency Cepstral parameters of synthetic speech. Results show that the initialization succeeds in achieving better results in enhancing the statistical parametric speech spectrum in most cases when compared to the common random initialization approach of the networks.
Wavelet-Integrated Deep Networks for Single Image Super-Resolution

We propose a scale-invariant deep neural network model based on wavelets for single image super-resolution (SISR). The wavelet approximation images and their corresponding wavelet sub-bands across all predefined scale factors are combined to form a big training data set. Then, mappings are determined between the wavelet sub-band images and their corresponding approximation images. Finally, the gradient clipping process is used to boost the training speed of the algorithm. Furthermore, stationary wavelet transform (SWT) is used instead of a discrete wavelet transform (DWT), due to its up-scaling property. In this way, we can preserve more information about the images. In the proposed model, the high-resolution image is recovered with detailed features, due to redundancy (across the scale) property of wavelets. Experimental results show that the proposed model outperforms state-of-the algorithms in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).
PSO+K-means Algorithm for Anomaly Detection in Big Data

The use of clustering methods in anomaly detection is considered as an effective approach. The choice of the cluster primary center and the finding of local optimum in the well-known k-means and other classic clustering algorithms are considered as one of the major problems and do not allow to get accurate results in anomaly detection. In this paper to improve the accuracy of anomaly detection based on the combination of PSO (particle swarm optimization) and k-means algorithms, the new weighted clustering method is proposed. The proposed method is tested on Yahoo! S5 dataset and a comparative analysis of the obtained results with the k-means algorithm is performed. The results of experiments show that compared to the k-means algorithm the proposed method is more robust and allows to get more accurate results.
Smart Attendance Marking System using Facial Recognition

Nowadays, face recognition has become one of the key research regions in computer technology. Due to this scientists share a keen interest in this area. In this paper, we proposed an application of facial recognition in attendance marking system. The attendance marking system is a difficult process if it is done manually when there is a huge classroom with more number of students which is time-consuming. By using this facial recognition system we can avoid the issues of fake attendance, proxies and save time. In previous facial recognition attendance systems, there were some drawbacks such as intensity of light, head pose etc. The main research of this paper is to overcome those problems and provide the most accurate attendance marking system. This system which is primarily based face detection and recognition algorithms mechanically detect the student and mark the attendance when he enters the school room. The device architecture and algorithms utilized in each stage are defined on this paper. We used algorithms such as Viola-Jones for face detection, PCA for feature selection and SVM for classification. When as compared to conventional attendance marking system the proposed system saves time and effective way to maintain attendance and records of students.
Deformable Convolutional Matrix Factorization for Document Context-Aware Recommendation in Social Networks

The extreme sparsity of the rating data seriously affects the recommendation quality of the recommendation system. In order to alleviate the problem of data sparsity, some convolutional neural network (CNN)-based models make full use of text data to improve the recommendation accuracy. However, due to the inherent properties of the traditional convolutional network, it can only extract features in a fixed position, and rely on the primitive bounding box based feature extraction, thus ignoring the flexibility of the traditional convolution. In this paper, we adopt a flexible convolutional network called deformable convolutional network (DCN), which extends the convolution transformation model capability by adding an offset layer to the traditional convolution layer, and then propose a novel deformable convolutional network matrix factorization (DCNMF) recommendation model. Specifically, we combine the DCN with word embedding to deeply capture the contextual information of document and build a latent model, which is incorporated into the probabilistic matrix factorization (PMF) model to enhance the recommendation accuracy. We conduct extensive experiments on the real-world datasets, and the experimental results show that the DCNMF outperforms the compared benchmarks.
QoE-Driven Resource Allocation Optimized for Uplink Delivery of Delay-Sensitive VR Video Over Cellular Network

Uploading virtual reality (VR) video over cellular networks is expected to boom in the near future, as general consumers could generate the high-quality VR videos with portable 360-degree cameras and are willing to share with others.Consequently, the concerns of uplink bandwidth and delay arose for current popular technology of tile-based VR video streaming, which requires high-quality video to transcode into multiple representations for further adaptive streaming.Motivated by this, we proposed a novel scheme for uplink delivery of tile-based VR video over cellular networks, in which encoding bit rate of each tile is determined by the uplink resource allocation (RA), and the quality of content (QoC) contribution of each tile and channel quality of user equipments (UEs) are jointly considered during RA.Moreover, the RA problem is formulated as a frequency and time dependent non-deterministic polynomial (NP)-hard problem.Furthermore, we propose three algorithms to explore solving the RA problem.The simulation results show that the proposed approximate convex algorithm with low-complexity can achieve higher utility, i.e., higher total quality of experience (QoE) for viewers.
A Semantics-Based Hybrid Approach on Binary Code Similarity Comparison

Binary code similarity comparison is a methodology for identifying similar or identical code fragments in binary programs.It is indispensable in fields of software engineering and security, which has many important applications (e.g., plagiarism detection, bug detection).With the widespread of smart and IoT (Internet of Things) devices, an increasing number of programs are ported to multiple architectures (e.g.ARM, MIPS).It becomes necessary to detect similar binary code across architectures as well.The main challenge of this topic lies in the semantics-equivalent code transformation resulting from different compilation settings, code obfuscation, and varied instruction set architectures.Another challenge is the trade-off between comparison accuracy and coverage.Unfortunately, existing methods still heavily rely on semantics-less code features which are susceptible to the code transformation.Additionally, they perform the comparison merely either in a static or in a dynamic manner, which cannot achieve high accuracy and coverage simultaneously.In this paper, we propose a semantics-based hybrid method to compare binary function similarity.We execute the reference function with test cases, then emulate the execution of every target function with the runtime information migrated from the reference function.Semantic signatures are extracted during the execution as well as the emulation.Lastly, similarity scores are calculated from the signatures to measure the likeness of functions.We have implemented the method in a prototype system designated as BINMATCH which performs binary code similarity comparison across architectures of x86, ARM and MIPS on the Linux platform.We evaluate BINMATCH with nine real-word projects compiled with different compilation settings, on variant architectures, and with commonly-used obfuscation methods, totally performing over 100 million pairs of function comparison.The experimental results show that BINMATCH is resilient to the semantics-equivalent code transformation.Besides, it not only covers all target functions for similarity comparison, but also improves the accuracy comparing to the state-of-the-art solutions.
Optimizing simple deterministically constructed cycle reservoir network with a Redundant Unit Pruning Auto-Encoder algorithm

Echo State Network (ESN) is a specific form of recurrent neural network, which displays very rich dynamics owing to its reservoir based hidden neurons. In the issue, ESN is viewed as a powerful approach to model real-valued time series processes. Nevertheless, ESN has been criticized for its manually experienced or brute-force searching parameters, such as initial input weights and reservoir layer weights, i.e., the conventional randomly generated ESN is unlikely to be optimal because the reservoir layer weights and input layer weights are created randomly. Simple Cycle Reservoir Network (SCRN), which constitutes a type of conclusively constructed input and internal layer weights, can yield performance comparable with conventional ESN. A Redundant Unit Pruning Auto-Encoder (RUP-AE) algorithm is proposed to optimize the input layer weights of SCRN and for resolving the dilemma of ill-conditioned output weights matrix in SCRN, through an unsupervised pre-training process. Initially, the output weights matrix of SCRN is pre-trained by pseudo-inverse algorithm through training data. Then, the pre-trained output weights matrix is pruned by a Redundant Unit Pruning (RUP) algorithm. Finally, the pruned output weights matrix of SCRN is injected to the input weights matrix to ensure the specificity of the auto-encoder. Three tasks, namely nonlinear time series system identification task, real-valued time series benchmark, and standard chaotic time series benchmark, are applied to demonstrate the advantage and superiority of RUP-AE. Extensive experimental results show that our RUP-AE is effective in improving the performance of SCRN. Meanwhile, RUP-AE is able to resolve the dilemma of ill-conditioned output weights matrix in SCRN.
Interpretable Fuzzy Rule-Based Systems for Detecting Financial Statement Fraud

Systems for detecting financial statement frauds have attracted considerable interest in computational intelligence research. Diverse classification methods have been employed to perform automatic detection of fraudulent companies. However, previous research has aimed to develop highly accurate detection systems, while neglecting the interpretability of those systems. Here we propose a novel fuzzy rule-based detection system that integrates a feature selection component and rule extraction to achieve a highly interpretable system in terms of rule complexity and granularity. Specifically, we use a genetic feature selection to remove irrelevant attributes and then we perform a comparative analysis of state-of-the-art fuzzy rule-based systems, including FURIA and evolutionary fuzzy rule-based systems. Here, we show that using such systems leads not only to competitive accuracy but also to desirable interpretability. This finding has important implications for auditors and other users of the detection systems of financial statement fraud.
Assessing the effect of season, montanera length and sampling location on Iberian pig fat by compositional data analysis and standard multivariate statistics

The work studies the effect of season, montanera length, and sampling location on the Iberian pig fat, using compositional data (CoDa) analysis and standard statistics. CoDa variation array and logratios involving C18:3: (C18:3/C17:1), (C18:3/C20:0), and (C18:3/18:0) as well as the ilr balances (coordinates), based on C18:3, C17:1, and C20:0, showed the highest variances. Discriminant Analysis (DA) led to similar (conventional/coordinates) correct assignations regarding seasons (69/70%), montanera length (71/70%) and sampling location (68/67%). Re-analyzing the subcomposition of only the major fatty acids (FAs), led to slightly poorer results; therefore, the removed FAs might play a role in segregation. Results are in line with those from other authors and could indicate a partial capacity of pigs to control their FA profiles. Overall, CoDa analysis provided useful information on data variability, the effects of factors and, after conversion into coordinates, allowed applying standard statistics while being respectful with the compositional sampling space.
Enhancing quantum transport efficiency by tuning non-Markovian dephasing

We consider the problem of energy transport in a chain of coupled dissipative quantum systems in the presence of non-Markovian dephasing. We use a model of non-Markovianity which is experimentally realizable in the context of controlled quantum systems. We show that non-Markovian dephasing can significantly enhance quantum transport, and we characterize this phenomenon in terms of internal coupling strengths of the chain for some chain lengths. Finally, we show that the phenomenon of dephasing-assisted quantum transport is also enhanced in the non-Markovian scenario when compared to the Markovian case. Our work brings together engineered environments, which are a reality in quantum technologies, and energy transport, which is typically discussed in terms of complex molecular systems. We then expect that it may motivate experimental work and further theoretical investigations on resources which can enhance transport efficiency in a controllable way. This can help in the design of quantum devices with lower dissipation rates, an important concern in any practical application.
Neural Finite-State Transducers: Beyond Rational Relations

Chu-Cheng Lin, Hao Zhu, Matthew R. Gormley, Jason Eisner. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Towards Understanding the Importance of Noise in Training Neural Networks

Numerous empirical evidence has corroborated that the noise plays a crucial rule in effective and efficient training of neural networks. The theory behind, however, is still largely unknown. This paper studies this fundamental problem through training a simple two-layer convolutional neural network model. Although training such a network requires solving a nonconvex optimization problem with a spurious local optimum and a global optimum, we prove that perturbed gradient descent and perturbed mini-batch stochastic gradient algorithms in conjunction with noise annealing is guaranteed to converge to a global optimum in polynomial time with arbitrary initialization. This implies that the noise enables the algorithm to efficiently escape from the spurious local optimum. Numerical experiments are provided to support our theory.
Development the Heat Leak Detection Method for Hidden Thermal Objects by Means the Information-Measuring Computer System

The purpose of this paper is to develop a thermographic method for detecting the places of heat energy leak on the underground thermal pipes using the computer system.Innovativeness of this work would be to develop an analytical model for constructing a heat map with an account of seasonal changes on separate temperature scales.It's proposed to upload the data that was received from the information-measuring computer system into the cloud storage.Due to the heat map laid on Google Maps, the repair equipment could be precisely allocated in place of the heat leakage displayed on the heat map.The implemented process using a Testo 805i infrared thermometer, which data is transmitting to the mobile devices (smartphone, tablet, etc.) with iOS version 10 (or later) and Android 4.3 (or later).The experiments confirm the correctness of the theoretical assumptions and sufficient accuracy for practical usage while elimination the heat pipes leaks.
Braids of partitions for the hierarchical representation and segmentation of multimodal images

Hierarchical data representations are powerful tools to analyze images and have found numerous applications in image processing. When it comes to multimodal images however, the fusion of multiple hierarchies remains an open question. Recently, the concept of braids of partitions has been proposed as a theoretical tool and possible solution to this issue. In this paper, we demonstrate the relevance of the braid structure for the hierarchical representation of multimodal images. We first propose a fully operable procedure to build a braid of partitions from two hierarchical representations. We then derive a framework for multimodal image segmentation, relying on an energetic minimization scheme conducted on the braid structure. The proposed approach is investigated on different multimodal images scenarios, and the obtained results confirm its ability to efficiently handle the multimodal information to produce more accurate segmentation outputs.