Reviewerly/exHarmony huggingface dataset:
Craft Distillation: Layer-wise Convolutional Neural Network Distillation

Convolutional neural networks (CNNs) have achieved tremendous success in solving many challenging computer vision tasks. However, CNNs are extremely demanding for computation capability, memory space, and power capacity. This limits their usage to the cloud and prevents them from being deployed on edge devices with constrained resources and power. To tackle this problem, we propose craft distillation, a novel model compression approach that leverages both depthwise separable convolutions and knowledge distillation to significantly reduce the size of a highly complex model. Craft distillation has three advantages over existing model compression techniques. First, it does not require prior experiences on how to design a good “student model” for effective knowledge distillation. Second, it does not require specialized hardware support (e.g. ASIC or FPGA). Third, it is compatible with existing model compression techniques and can be used with pruning and quantization together to further reduce weight storage and arithmetic operations. Our experimental results show that with proper layer block replacement design and replacement strategy, craft distillation reduces the computational cost of VGG16 by 74.6% compared to the original dense models with negligible influence on accuracy.
Masking of temporal activity for video quality control, measurement and assessment

Every video stream possesses temporal redundancy based on the amount of motion presenting in it. An ample amount of motion in a video sequence may cause distorting artifacts, and in order to avoid them, there is a possibility to mask the motion or temporal activity that is not noticeable to a human eye in real time. The artifacts such as blockiness and blurriness are instigated in the video sequence as soon as it is subjected to the process of compression, and they tend to become more and more intense with the increase in temporal activity. In this paper, an algorithm is proposed to mask the temporal activity using temporal masking coefficient ( q) that is unnoticeable by a human eye to bring down the distortion levels. It is possible to adjust the quality of the video sequence by varying the q parameter and thus controlling its overall quality index. Frames are extracted from the video sequence, and displacement or motion vectors are also calculated from the consecutive frames using a bi-directional block matching algorithm. These motion vectors are used to estimate the quantity of motion present between consecutive frames of the same scene. Video sequences used for this purpose are basically H.264 format. Temporal masking is performed on a video sequence with and without the implementation of motion vector. Structural similarity index and peak signal-to-noise ratio are the quality measurement tools used to assess the performance of the proposed algorithm. A bit rate of 1.2% was saved by implementing proposed algorithm at q = 1 in contrast to the standard H.264/Advanced Video Coding.
Research on Tourism Question Answering System Based on Xi’an Tourism Knowledge Graph

Abstract Question answering (QA) system provides a direct, efficient and accurate way for people to obtain information. At present, open domain QA systems such as Siri and Cortana are widely used in the general field, but they cannot meet the demand of some professional fields. This paper focuses on the background and needs of QA in the tourism field, researching the relevant technologies required for the implementation of QA system, and finally completes the construction of QA system based on the knowledge graph of tourism. The main research contents of this paper are as follows: 1.An algorithm to identify the tourism entities in questions is proposed according to the characteristics of the tourism entities. 2. Referring to the ideas of Liu et al., a convolutional neural network (CNN) model is introduced into attribute linking, but in order to improve the accuracy of attribute linking, we move the similarity calculation of questions and attributes from the outside of the model to the input layer of the model, and also introduce Attention mechanism. Integrate the technology of each module to design and implement a QA system for tourism. We experiment with the system on the constructed Xi’an tourism knowledge graph, and the results prove that the system we designed can answer the natural language questions raised by users about tourism.
Evaluation of Augmented Reality in Industry

Augmented Reality (AR) is seen as a key technology for the development of smart manufacturing. Despite the many possibilities and affordances of this emerging technology, it is a fairly new technology in industry without widespread adoption. Research indicates that there are many affordances of using this technology in industry as well as some challenges. However, there seems to be a lack of research on how to evaluate AR in industry. Based on literature and a case study, we propose guidelines for evaluating AR based on identified dimensions.
On the Impact of Perceptual Compression on Deep Learning

This paper proposes a fundamental answer to a frequently asked question in multimedia evaluation and data set creation: Do artifacts from perceptual compression contribute to error in the machine learning process and if so, how much? Our approach to the problem is an information reinterpretation of the Helmholtz free energy formula to explain the relationship between content and noise when using sensors (such as cameras or microphones) to capture multimedia data. The reinterpretation guides a bit-measurement of the noise contained in images, audio, and video by combining a classifier with perceptual compression, such as JPEG or MP3. Our experiments on CIFAR-10, ImageNet, and CSAIL Places as well as Fraunhofer's IDMT-SMT-Audio-Effects dataset indicate that, at the right quality level, perceptual compression is actually not harmful but contributes to a significant reduction of complexity of the machine learning process. That is, our noise quantification method can be used to speed up the training of deep learning classifiers significantly while maintaining, or sometimes even improving, overall classification accuracy.
A quantum algorithm to estimate the Gowers $$U_2$$ norm and linearity testing of Boolean functions

We propose a quantum algorithm to estimate the Gowers $$U_2$$ norm of a Boolean function, and extend it into a second algorithm to distinguish between linear Boolean functions and Boolean functions that are $$\epsilon $$ -far from the set of linear Boolean functions, which seems to perform better than the classical BLR algorithm. Finally, we outline an algorithm to estimate Gowers $$U_3$$ norms of Boolean functions.
Sensitivity of Multiview 3D Point Cloud Reconstruction to Compression Quality and Image Feature Detectability

In this paper we evaluate the quality of feature detection and 3D reconstruction on a Wide Area Motion Imagery (WAMI) sequence with increasing JPEG compression ratio. Feature detection is critical for computer vision tasks such as 3D reconstruction. For some 3D reconstruction approaches, the quality of a 3D model relies upon consistent detection of the same feature points over consecutive frames in an image sequence. Since the performance of feature detectors is highly sensitive to compression artifacts, we evaluate the influence of image quality on feature detection accuracy. Many datasets (e.g. WAMI) use JPEG compression to decrease the data storage and network bandwidth utilization while attempting to preserve image quality by adaptively adjusting the compression ratio. Consequently, it is important to understand the impact of JPEG compression on the quality of feature detection in 2D space and the subsequent 3D reconstruction results. We design and perform two evaluation procedures on the WAMI sequence. We use structure tensor to detect feature points on an image sequence with increasing JPEG compression ratio (10:1, 15:1, 20:1, 30:1, 40:1, 100:1, and 150:1). Compression ratio of 10:1 is used as the baseline (groundtruth). First we compare the feature points from images of different qualities with the groundtruth features and evaluate them on pixel level in 2D space. After that, a 3D model in the form of point cloud is generated from each set of feature points and compared with the groundtruth point cloud. We provide quantitative and visualized results for the evaluation.
Toward supervised shape-based behavioral authentication on smartphones

Currently, smartphone security has received much more attention as users may use their devices to perform various sensitive tasks. For example, users can utilize mobile banking applications for online shopping, which may store many sensitive data on their devices. Hence there is a need to authenticate users and detect imposters. However, traditional textual passwords are easily compromised and are not convenient for users to remember for a long time due to long-term memory limitation. To complement textual passwords, behavioral authentication is developed by authenticating a user based on the relevant biometric features. In this work, we focus on simple shape-based behavioral authentication that requires users to draw shape(s) for authentication, and investigate how to design such kind of behavioral authentication in practice. We consider two research questions: (1) whether the authentication accuracy varies with different shapes, and (2) how many shapes can be used to achieve good usability. In the evaluation, we perform two user studies with 60 participants and measure some typical supervised learning classifiers. Based on the results, we provide insights on designing a supervised shape-based behavioral authentication system, as compared with similar schemes.
Literature Review on Language Learning Strategy Research in China

This paper was intended to sketch the landscape of the LLS researches conducted by Chinese researchers between 2003 and 2018, most of which took Chinese college student as research subjects. These researches covered eight topics, including the investigation of overall/individual features of LLS, comparison of LLS use between different learner groups, LLS instruction and the like.
Physics Inspired Models in Artificial Intelligence

Ideas originating in physics have informed progress in artificial intelligence and machine learning for many decades. However the pedigree of many such ideas is oft neglected in the Computer Science community. The tutorial focuses on current and past ideas from physics that have helped in furthering AI and machine learning. Recent advances in physics inspired ideas in AI are also explored especially how insights from physics may hold the promise of opening the black box of deep learning. Lastly, current and future trends in this area and outlines of a research agenda on how physics-inspired models can benefit AI machine learning is given.
Scaling exponent analysis and fidelity of the tunable discrete quantum walk in the noisy channel

We studied the behavior of tunable one-dimensional discrete-time quantum walk (DTQW) in the presence of decoherence, modeled as a noisy quantum channel with bit-flip, phase-flip, and general amplitude damping effects. The fidelity of the walker based on the angle parametrized coin operator is examined for different tunable conditions. DTQW with bit-flip and phase-flip or dephasing gave rise to the symmetrical probability density function profile while showing different convergence characteristics on their variance behavior. Meanwhile under decoherence with the general amplitude channel, the walker exhibits the non-symmetric profile of probability density function while retaining faster spread than a normal walk under extreme decoherence which is captured by their scaling exponent. These indicate the existence of the broad class of transport behavior of the walker. We showed that the fidelity of the walker can be optimized by adjusting the parameter of the coin rotation angle θ. This work may be useful for the optimization or better control of the quantum walk under noisy channels.
Data Cache Optimization Model Based on HBase and Redis

The computing-based cloud storage model realizes the security and reliable storage of massive image data. Nowadays, how to quickly obtain high-quality image data is the focus of attention in the research field. This paper discusses the cloud storage model, proposing a combined data caching strategy based on HBase and Redis technology. Besides, the model improves the technical defects of Memcached, setting key indexes which is stored in Hbase, having achieved the mapping between index and DataNode. The results of experiment show that the proposed optimization model can improve the ability of rapid acquisition and analysis of image data and achieve a higher retrieval efficiency about image data.
Rule-Based Safety Evidence for Neural Networks

Neural networks have many applications in safety and mission critical systems. As industrial standards in various safety-critical domains require developers of critical systems to provide safety assurance, tools and techniques must be developed that enable effective creation of safety evidence for AI systems. In this position paper, we propose the use of rules extracted from neural networks as artefacts for safety evidence. We discuss the rationale behind the use of rules and illustrate it using the MNIST dataset.
Automated whole breast segmentation for hand-held ultrasound with position information: Application to breast density estimation

Women with higher breast densities have a relatively higher risk to be diagnosed with breast cancer. Hand-held ultrasound (HHUS) can provide precise screening results and detect masses in dense breasts. However, its lack of position information and automatic extraction of breast area hinder the implementation of density estimation. To facilitate reliable breast density evaluation, this study proposed an upgraded version of our whole-breast ultrasound (WBUS) system, which not only can provide precise position information, but also can extract precise breast area automatically based on deep learning method. WBUS images with probe position information were collected from 117 women. For each case, an automatic breast region segmentation by DeepResUnet was conducted, then fibroglandular tissues were extracted from breast region using fuzzy c-mean (FCM) classifier. Finally, the percentage of breast density and breast area of the DeepResUnet predicted region and the breast region of the ground truth were calculated and compared. The average and standard deviation of each breast case for DeepResUnet predicted breast region of 10-fold in Accuracy (ACC) was 0.963±0.054. Sensitivity (SENS) was 0.928±0.11. Specificity (SPEC) was 0.967±0.054. Dice coefficient (Dice) was 0.916±0.98. Region intersection over union (IoU) was 0.856±0.134. Significant and very high correlations of breast density, fibroglandular tissue area and breast area (R = 0.843, R= 0.822 and R = 0.984, all p values < 0.001) were found between the ground truth and the result of the proposed method for ultrasound images. Breast density, fibroglandular tissue, and breast volume evaluated based on the proposed method and WBUS system have significant correlations with ground truth, indicating that the proposed method and WBUS system has the potential to be an alternative modality for breast screening and density estimation in clinical use.
A Financial Fraud Detection Model Based on Organizational Impression Management Strategy

Abstract Financial fraud misleads investors into making wrong decisions based on incorrect information, especially for listed companies’ financial statements fraud. It damages investors’ interests, disturbs the economic order, and creates a crisis of trust, which is extremely harmful. Therefore, it is of great significance to build an effective financial fraud detection model for listed companies. This study uses a sample of 126 Chinese listed companies from 2013 to 2017 to examine the relationship between two organizational impression management strategies (promotion strategy and defense strategy) and financial fraud using the integrated learning methods. This study innovatively analyzes financial fraud using social media data and annual reports’ readability data as non-financial features. The results show that companies implementing the defensive/protective strategy were more likely to commit financial fraud. In addition, the average number of tables in a company’s annual report can significantly help researchers to judge fraud.
The Accuracy of GPS-Enabled Fitbit Activities as Evidence: A Digital Forensics Study

Technology is advancing rapidly and with this advancement, it has become apparent that it is nearly impossible to not leave a digital trace when committing a crime. As evidenced by multiple cases handled by law enforcement, Fitbit data has proved to be useful when determining the validity of alibis and in piecing together the timeline of a crime scene. In our paper, experiments testing the accuracy and reliability of GPS-tracked activities logged by the Fitbit Alta tracker and Ionic smartwatch are conducted. Potential indicators of manipulated or altered GPS-tracked activities are identified to help guide digital forensic investigators when handling such Fitbit data as evidence.
Designing a knowledge base for the development of intelligent models based on the conceptual structure of activity act

Abstract This article discusses the development of intelligent models based on the conceptual understanding of a dynamically complex environment. The method proposed in this work allows solving these problems based on a synthesis of the activity approach and situational analysis. As a result, the conceptual structure of the act of activity is realized. The combination of these structures leads to the construction of a decision matrix, which represents a knowledge base about a dynamically complex environment. An analysis of these interrelated aspects leads to the construction of four conceptual plans: the functional structure, processes, context and regularity. Conceptual plans make it possible to combine model ideas about the subject area, thereby simplifying the choice of intelligent modules for decision support systems. Recognition of the task of constructing conceptual structures as non-trivial, complex and time-consuming led to the idea of implementing the software system “Designer + Solver + Interpreter”. The software toolkit allows not only visualization of conceptual structures and implementation of knowledge bases for intelligent models, but also the studies for completeness and adequacy.
iHDT++: improving HDT for SPARQL triple pattern resolution

RDF self-indexes compress the RDF collection and provide efficient access to the data without a previous decompression (via the so-called SPARQL triple patterns). HDT is one of the reference solutions in this scenario, with several applications to lower the barrier of both publication and consumption of Big Semantic Data. However, the simple design of HDT takes a compromise position between compression effectiveness and retrieval speed. In particular, it supports scan and subject-based queries, but it requires additional indexes to resolve predicate and object-based SPARQL triple patterns. A recent variant, HDT++, improves HDT compression ratios, but it does not retain the original HDT retrieval capabilities. In this article, we extend HDT++ with additional indexes to support full SPARQL triple pattern resolution with a lower memory footprint than the original indexed HDT (called HDT-FoQ). Our evaluation shows that the resultant structure, iHDT++ , requires 70 - 85% of the original HDT-FoQ space (and up to 48 - 72% for an HDT Community variant). In addition, iHDT++ shows significant performance improvements (up to one level of magnitude) for most triple pattern queries, being competitive with state-of-the-art RDF self-indexes.
Automatic Keyphrase Extraction Using SVM

The Internet has a plethora of text articles, and it has become a necessity to extract only the relevant information from all the sources. Automatic keyphrase extraction is an essential part of the process of information extraction as it is impossible to manually identify all the keyphrases in textual sources. Keyphrase extraction has thus become an indispensable component of contemporary world of Internet. Researchers have treated keyword extraction as a classification problem where the input candidate words are classified as keywords or non-keywords. The paper tries to address two major issues in keyphrase extraction process, namely candidate selection and extraction of relevant features. Noun phrases extracted using specified regular expressions are considered as candidate words. A supervised machine learning method based on statistical and linguistic features is proposed for keyword extraction using SVM. The experimental results compared with well-known methods, namely SingleRank, ExpandRank, baseline TF-IDF, and the latest work show considerable improvement over the previously achieved results.
Efficient Algorithm for the b-Matching Graph

The b-matching graph is a useful approach to computing a graph from high-dimensional data. Unlike the k-NN graph that greedily connects each data point to its k nearest neighbors and typically has more than k edges, each data point in the b-matching graph uniformly has b edges; the idea is reduce edges between cross-clusters that have different semantics. In addition, edge weights are obtained from regression results of each data pointand restricted to be non-negative to improve the robustness for data noise. The b-matching graph can more effectively model high-dimensional data than the traditional k-NN graph. However, the construction cost of the b-matching graph is impractical for large-scale data sets. This is because, to determine edges in the graph, it needs to iteratively update messages between all pairs of data points until convergence, and it computes non-negative edge weights of each data point by applying a solver intended for quadratic programming problems. Our proposal, b-dash, can efficiently construct a b-matching graph because of its two key techniques: (1) it prunes unnecessary update messages in determining edges and (2) it incrementally computes edge weights by exploiting the Sherman-Morrison formula. Experiments show that our approach is up to 58.6 times faster than the previous approaches while guaranteeing result optimality.
Network covert channels detection using data mining and hierarchical organisation of frequent sets

Currently, malware developers are increasingly turning their attention towards various types of information hiding techniques to conceal their malicious actions on the compromised machine or the network. One group of such mechanisms are network covert channels (CCs) which utilize subtle modifications to the legitimate network traffic to carry secret data. Unfortunately, nowadays no general detection approach exists that is able to fight covert communication in an efficient and scalable manner. On the contrary, typically for a given information hiding technique a dedicated detection solution is devised. That is why, in this paper we investigate possibility to utilize data mining approach to detect network covert channels: both distributed and undistributed. Specifically, we propose to rely on the hierarchical organisation of frequent sets discovered by the data mining algorithm and use it together with an outlier detection-based traffic classifier. Initial performance results reveal that the proposed solution has potential but it needs to be further evaluated in more realistic scenarios.