Reviewerly/exHarmony huggingface dataset:
Cross-platform rating prediction method based on review topic

Rating prediction is one of the research hotspots in intelligent recommendation. As the number of e-commerce platforms continues to increase, cross-platform user rating prediction has become an important prerequisite for cross-platform recommendations. For the same product, this paper constructs a Dynamic Cross-platform Information Network Model (DCINM) by using reviews. It integrates the information of the same product from multiple platforms. According to the relationship between network nodes, the DCINM can mine the hidden information of user reviews in different platforms. The user preference vector, constructed by combining the original and hidden information of user reviews, can more accurately reflect the relationship between user preferences and ratings. And it can also reduce the predicting ratings error. In the process of rating prediction, the paper proposes a method based on a binary classification algorithm to optimize the GBDT-LR (GABC-LR), which transforms the prediction process of GBDT-LR into the solution of a quadratic equation, such that the new predicted value from the solution is closer to the real values of the test data. The paper optimizes the rating prediction task from the perspectives of data processing and the prediction model, which reduces the error of the rating prediction while achieving cross-platform rating predictions.
Effectively Linking Persons on Cameras and Mobile Devices on Networks

Nowadays, ubiquitous surveillance cameras and wireless networks facilitate to understand online and offline human activities, and considerate attention has been paid to analyze people's activities by associating visual data with network data. It is critical to link mobile devices on networks to their owners in surveillance videos. However, existing works mainly rely on location-related information, while it is invalid to distinguish persons with similar movement behaviors due to the error-prone visual localization of person and wireless localization of devices. To solve this problem, in this paper we propose a framework called PD-Link that uses just one camera and one AP to infer links from mobile devices to their owners. To achieve its goal, PD-Link identifies a feature, person–device interactions (PDI), as the linking cue. We evaluate the performance of PD-Link in realistic scenarios and the experimental results demonstrate the efficacy and effectiveness of our method.
Books in Transit: The Logistics of Library Book Movement

In 2015 RMIT University commenced a two-year building redevelopment project which included completely refurbishing the main library. Approximately 180,000 books were temporarily moved to off-site storage and this posed the challenge of how to manage a working collection so that there would be minimal impact on the university’s students and staff still requiring ‘just in time’ access. In preparation, the library implemented a two-year project to enable it to continue to provide high-level services for the duration of the construction work. This paper considers if logistics processes could be applied to mitigate any access issues caused by off-site storage of library resources.
Template Matching Based on Geometric Invariance in Deep Neural Network

Machine learning models, especially deep neural networks (DNNs), have achieved state of the art in computer vision and speech recognition.However, with wide applications of DNNs, some problems have appeared, such as lack of interpretability and vulnerable to adversarial examples.Whether the judgment of the model is consistent with that of human is a key to the wide application and development of neural networks.In this paper, we propose a novel and interpretable method to enable the model to make the same judgment as humans in the adversarial examples, which is based on the geometric invariance between images of the same category.Template matching is combined with convolution neural network during the training and testing stage.Moreover, we manage to give a theorical proof.The geometric invariance features got from the template matching are fused with the features extracted by the convolutional layers.The experimental results demonstrate the temp_model (network added the template matching) has a higher test accuracy both on benchmark sequences and adversarial examples, and we use a visual method to explain the reason why adding template can make the network perform better.The generality and convergence of the network improve without increasing the model size and training time after adding the template as common sense.
Similarity Measurement of Handwriting by Alignment of Sequences

In court proceedings, the question often arises: how much correspondence is there exactly between disputed writing and comparative writing patterns? According to handwriting examiners, handwriting cannot be measured in numbers. One of the reasons is that the handwriting experts use only a small part of the full feature complex, it is impossible to find and compare all of them manually. The selection is based on the expert’s experience, although contradictory opinions on the same test material can be given by different experts. Thus, the enhancement of expert objectivity in the court processes is highly desirable, my goal is to search for opportunities of application of computer science and approaches. The Soviet-type handwriting expertise methodology that is currently used in Hungary primarily interpreted the writing as a movement. In fact, traditional writing comparison treats handwriting as a moving stream. This observation led to the analysis of handwriting using the sequence matching method. This paper provides an example of how a sequence alignment algorithm for comparing DNA sequences can be used to compare handwriting. The suitability of the algorithm is illustrated by the fitting of fake and genuine signatures, and the expert opinion is based on numerical results on the value of the alignment.
Big Data Analytics for Nabbing Fraudulent Transactions in Taxation System

This paper explains an application of big data analytics to detect illegitimate transactions performed by fraudulent communities of people who are engaged in a notorious tax evasion practice called circular trading. We designed and implemented this technique for the commercial taxes department, government of Telangana, India. This problem is solved in two steps. In step one, the problem is formulated as detecting fraudulent communities in a social network, where the vertices correspond to dealers and edges correspond to sales transactions. In step two, specific type of cycles are removed from each fraudulent community, which were identified in step one, to detect the illegitimate transactions. We used RHadoop framework for implementing this technique.
Recognizing Small-Sample Biomedical Named Entity Based on Contextual Domain Relevance

Existing named entity recognition methods are often based on large training samples and cannot effectively recognize fine-grained domain entities with small sample sizes In order to solve this problem, this paper proposes an unsupervised method based on contextual domain relevance for recognizing biomedical named entities with small sample sizes. Based on the distributed semantic model, the statistical and linguistic features of candidate entities in corpora are described by using occurrence frequencies of contexts of candidate entities. Furthermore, the entity-corpus relevance assumption, the log-likelihood ratio and the domain-dependent function are adopted for recognizing objective entities. Experimental results show that, the proposed method can effectively reduce manual interventions and improve the precision rate and recall rate of small-sample biomedical named entity recognition.
Improving RDF Query Performance Using In-memory Virtual Columns in Oracle Database

Many RDF Knowledge Graph Stores use IDs to represent triples to save storage and for ease of maintenance. Oracle is no exception. While this design is good for a small footprint on disk, it incurs overhead in query processing, as it requires joins with the value table to return results or process aggregates/filter/order-by queries. It becomes especially problematic as the result size increases or the number of projected variables increases. Depending on queries, the value table join could take up most of the query processing time. In this paper, we propose to use in-memory virtual columns to avoid value table joins. It has advantages in that it does not increase the footprint on disk, and at the same time it avoids the value table joins by utilizing in-memory virtual columns. The idea is to materialize the values only in memory and utilize the compression and vector processing that come with Oracle Database In-Memory technology. Typically, the value table in RDF is small compared to the triples table. Therefore, its footprint is manageable in memory, especially with compression in columnar format. The same mechanism can be applied to any application where there exists a one-to-one mapping between an ID and its value, such as data warehousing or data marts. The mechanism has been implemented in Oracle 18c. Experimental results using the LUBM1000 benchmark show up to two orders of magnitude query performance improvement.
Estimating Change Effort Using a Combination of Change Impact Analysis Technique with Function Point Analysis

Software effort estimation is one of the methods that can help software project manager in making decision whether to accept or reject requirement changes. Many methods have been developed and Function Point Analysis is one of the methods that is used for software maintenance phase. Looking from software development phase, FPA method faces a challenge on performing estimation when the non-developed software artifacts exists (some of the classes are fully developed; partially developed; and not developed yet). This research aims to develop a method that improves the estimation accuracy through combination of Function Point Analysis method with Change Impact Analysis technique. An evaluation was conducted using two selected case studies where a significant accuracy achievement is achieved.
On Evaluating the Impact of Changes in IoT Data Streams Rate over Query Window Configurations

With the ever increasing number of IoT devices getting connected, an enormous amount of streaming data is being produced with very high velocity. In order to process these large number of data streams, a variety of stream processing platforms and query engines are emerging. In the stream query processing, an infinite data stream is divided into small chunks of finite data using a window operator. Window size and its type play an important role in the performance of any stream query engine. Due to the dynamic nature of IoT, data stream rate fluctuates very often, thus impeding the performance of query engines. In this work, we investigated the impact of any changes in data stream rates over the performance of a distributed query engine (e.g. Flink - https://flink.apache.org/). Our evaluation results indicate a direct impact of any changes in stream rate and window size over the performance of the engines. We propose an adaptive and dynamic query window size and type selector to improve the resilience of query processing engines. We consider several characteristics of input data streams, application workload, and resource constraints and proposes an optimal stream query window size and type for stream query execution.
The Need for Identifying Ways to Monetize Personalization and Recommendation

Research on user modeling and personalization typically only serves the needs of end-users. However, when applied in real-world, commercial contexts, recommendations should also serve the (often monetary) interests of other parties, such as platform providers, sellers and advertisers. This paper provides a brief historical perspective on the research field, contrasts this with the commercial context, and investigates the topics currently addressed at the UMAP and RecSys conferences. The paper concludes with a discussion on the need for the research community to take multi-stakeholder interests into account in the design and evaluation of adaptive systems. This would allow us to foresee unwanted effects, such as online filter bubbles, and to pro-actively find strategies to prevent them.
Learning to remove reflections from windshield images

Reflection is a common category of contamination in images captured in the wild, particularly, undesired reflections observed on windshield images in surveillance scene bring difficulties to the analysis of surveillance contents. Existing general reflection removal methods do not exploit the specific semantic information in windshield scenes, so they cannot effectively handle the challenges of high noise and low resolution in images taken by surveillance cameras. In this paper, we propose a reflection removal framework specially designed for surveillance windshield images, with the guidance of depth of field features introduced from different levels of blurs, and constraint of specific semantic features for vehicle retrieval. Experimental results on a newly built dataset for this specific task demonstrate that our method outperforms other state-of-the-art reflection removal methods in both visual quality and quantitative metrics.
Soft computing-based non-linear discriminate classifier for multimedia image quality enhancement

Digital image quality measurement is a significant part in image processing systems, especially for efficient transmission. In image compression, the blocking effect is generated due to co-efficient quantification which causes poor quality on the decompressed image. The learning smooth pattern transformation with Greedy method suggests high accuracy in the approximation and classification of data but transformation-invariant coding and classification of digital images were not achieved. Residual component analysis using a Bayesian algorithm estimates the parameters involved to yield a confidence interval but nonlinearity detection was not sensed effectively. In order to overcome the transformation-invariant classification defects and nonlinearity detection, a hybrid approach is introduced in digital images. The hybrid approach is a non-linear discriminate classifier for digital image quality enhancement (NDC-DQE). The NDC-DQE uses class information and finds a set of projection vectors on digital images thereby minimizing the computational complexity. Initially, NDC identifies the degradation type contained in a digital image then measure the quality of that image by using image quality measure for that specific degradation. The NDC-DQE is applied to focus on particular artifacts such as blocking effects and non-blocking effects. The NDC-DQE also uses the statistical framework that covers a large set of common degradations. The NDC-DQE with information fidelity criterion is integrated to derive the mutual information for one subband and multiple subbands. Based on, NDC covers a very large set of possible degradations usually determined in practical digital image used applications. Simulations conducted with NDC-DQE show the performance improvement in image quality in classification accuracy, peak signal to noise ratio, computational complexity, and image quality rate.
Wasserstein Generative Adversarial Network Based De-Blurring Using Perceptual Similarity

The de-blurring of blurred images is one of the most important image processing methods and it can be used for the preprocessing step in many multimedia and computer vision applications. Recently, de-blurring methods have been performed by neural network methods, such as the generative adversarial network (GAN), which is a powerful generative network. Among many different types of GAN, the proposed method is performed using the Wasserstein generative adversarial network with gradient penalty (WGANGP). Since edge information is the most important factor in an image, the style loss function is applied to represent the perceptual information of the edge in order to preserve small edge information and capture its perceptual similarity. As a result, the proposed method improves the similarity between sharp and blurred images by minimizing the Wasserstein distance, and it captures well the perceptual similarity using the style loss function, considering the correlation of features in the convolutional neural network (CNN). To confirm the performance of the proposed method, three experiments are conducted using two datasets: the GOPRO Large and Kohler dataset. The optimal solutions are found by changing the parameter values experimentally. Consequently, the experiments depict that the proposed method achieves 0.98 higher performance in structural similarity (SSIM) and outperforms other de-blurring methods in the case of both datasets.
Development of an Interaction Simulator for the Scenario Analysis of Physical Protection Systems

Scenario analysis is used for the simulation of the physical protection system (PPS) by considering several credible intrusion and defense scenarios. A practical interaction simulator is developed for the scenario analysis of the PPS. The interaction simulator integrates all discrete subsystems of the PPS to form an intermodulation chain of intrusion-detection-response-interruption. Adversary intrusion strategies and defense strategies are regarded as knowledge bases in the interaction simulator. Moreover, agents in the simulator can graphically travel dynamically. Thus, this simulator is used to verify and validate the daily operation flows of each subsystem, applied for long-term management of intrusion consequences, and helped the relevant staffs for training and vulnerability analysis of the PPS.
Feature Engineering of Click-through-rate Prediction for Advertising

We present the problem of click-through-rate (CTR) for search advertising in ALiMaMa, which displays user information, item information, shop information and trade results. Traditionally, people use logistic regression (LR) to predict it. However, because of the lack of learning ability and the sparse feature matrix, the prediction results are always not so satisfying. In this paper, we mainly propose some feature engineering methods based on gradient boosting decision tree (GBDT) and Bayesian smoothing to obtain a wonderful feature, which has more useful information and is not so sparse. Also, we use xgboost (XGB) instead of LR as our prediction model. The proposed methods are evaluated using offline experiments and the experiment results prove that the log loss drop near $$5\%$$ after using these feature engineering methods and XGB. Obviously, it is an excellent performance.
Social Network Forensics through Smartphones and Shared Images

The fast growth of Social Networks (SNs), amplified by the ever-increasing use of smartphones, has intensified online cybercrimes. This trend has accelerated digital investigations through SNs. In particular, camera Sensor Pattern Noise (SPN) uniquely characterizing each smartphone has attracted a lot of attention. In this paper, we propose a clustering and classification approach to achieve Smartphone Identification (SI) and User Profiles Linking (UPL) across SNs to provide investigators with significant findings in SN forensics. We test the proposed methods on a dataset of 2,000 images shared on Google+, Facebook, WhatsApp, and Telegram taken by 10 smartphones. The results show the effectiveness of our approach in distinguishing between the same models of the same smartphone brands despite the loss of image detail through the compression process on SNs. The average of sensitivity and specificity values are, respectively, 98.5% and 99.5% for SI and UPL across the SNs.
In-Situ Close-Range Imaging with Plenoptic Cameras

This paper discusses the concept of plenoptic hand lens imagers for in-situ close-range imaging during planetary exploration missions. Hand lens imagers, such as the Mars Hand Lens Imager on-board the Mars rover Curiosity, are important cameras for in-situ investigations, e.g. of rock layer, minerals or dust. They are also important for the preparation and documentation of other instrument operations and for rover health assessment. Due to the small working distance between object and the camera's main lens, significant physical limitations affect the imaging performance. Most evident is the limited depth of field of a few millimeters for working distances of a few centimeters. This requires a highly accurate positioning of the camera and also limits the in-focus content of an image significantly. Hence, in order to have an extended object completely in focus, a sequence of images, each being focused to a different distance, is required. A single, passive camera is insufficient to compute depth from a single shot; only the combination of multiple images, either taken from different vantage points or at different focal settings, allows this. To overcome those limitations, we propose the use of plenoptic cameras as hand lens imagers. From a single exposure, they allow to create an extended depth of field image and at the same time a metric depth map while maintaining a more open aperture. These and other advantages might make it possible to omit space grade focus mechanisms in the future. A plenoptic camera is achieved by adding an additional matrix of lenslets shortly in front of the image sensor of a conventional camera. Hence, available space camera hardware can be used to form a new type of sensor. Due to its recording concept, a plenoptic camera maintains the depth of the scene as it is projected into the camera by the main lens. Thanks to the parallax between the lenslets, it is possible to compute depth via triangulation for each image point as well as a high resolution 2-D extended depth of field image. This paper provides an overview of the state of the art of hand lens imaging from which we derive a set of common requirements for future devices. We briefly introduce the plenoptic camera technology and provide first experimental results on the imaging performance based on samples of test targets and rocks. The results show that our preliminary plenoptic camera setup can comply with the requirements for in-situ hand lens imaging in terms of image quality, depth estimation and the usability for planetology.
Preliminary Investigation of Predicting Next-Use Mobile Apps Using App Semantic Representations

This study presents our preliminary investigation of predicting the next-use mobile Apps based on the App usage history of a target user that can facilitate the user to select an App from an entire list of Apps. The proposed method is designed to train a next-use App prediction model using the usage history of other users (source users) to cope with the cold-start problem of a system in which the training data from a target user is considered to be insufficient when the user begins to use the prediction system. We predict the usage of cold-start users and Apps by leveraging the semantic similarities between the Apps that are installed on the smartphones of the source users and the Apps that are installed on the smartphones of the target user.
The Assumptions of Direction Dependence Analysis

Direction dependence analysis attempts to discern the direction of a causal effect, using statistical features of the data, such as skew and kurtosis of variables, and their residuals in regression models. Wiedermann and Sebastian discuss the use of this analysis in the context of mediation, and introduce methods to distinguish three different causal structures. In this commentary, I highlight some connections to literature in computer science, review the assumptions of the proposed analysis critically, and provide an example in which I argue that the analysis of Wiedermann and Sebastian can yield incorrect conclusions.