Reviewerly/exHarmony huggingface dataset:
Explainable Credit Card Fraud Detection with Image Conversion

The increase in the volume and velocity of credit card transactions causes class imbalance and concept deviation problems in data sets where credit card fraud is detected. These problems make it very difficult for traditional approaches to produce robust detection models. In this study, a different perspective has been developed for this problem and a novel approach named Fraud Detection with Image Conversion (FDIC) is proposed. FDIC handles credit card transactions as time series and transforms them into images. These images, which comprise temporal correlations and bilateral relationships of features, are classified by a convolutional neural network architecture as fraudulent or legitimate. When the obtained results are compared with the related studies, FDIC has the best F1-score and recall values, which are 85.49% and 80.35%, respectively. Since the images created during the FDIC process are difficult to interpret, a new explainable artificial intelligence approach is also presented. In this way, feature relationships that have a dominant effect on fraud detection are revealed.
Semantics of Probabilistic Programming: A Gentle Introduction

Reasoning about probabilistic programs is hard because it compounds the difficulty of classic program analysis with sometimes subtle questions of probability theory. Having precise mathematical models, or semantics, describing their behaviour is therefore particularly important. In this chapter, we review two probabilistic semantics. First, an operational semantics which models the local, step-by-step, behaviour of programs, then a denotational semantics describing global behaviour as an operator transforming probability distributions over memory states.
Neuron Activation Frequency Based Test Case Prioritization

Deep neural networks (DNNs) have been increasingly adopted in various applications. Systematic verification and validation is essential to guarantee the quality of such systems. Due to the scalability problem, formal methods can hardly be widely applied in practice. Testing is one of feasible solutions. However, lacking of input space specification for DNNs requires a large set of test cases to be constructed to increase the testing adequacy, which leads to high labeling cost of test cases. In this paper, we put forwards a test case prioritization method for DNN classifiers, which assigns high priorities to those cases that could lead to wrong classifications. The priorities are calculated according to the activation pattern of neurons acquired from the training sets and the activated neurons collected from certain inputs. For a trained model, the method consists of two steps. First, we accumulate neuron activation patterns over the training set, and construct a set of frequently activated neurons based on the frequency (times) of activation for every class. Second, the metrics are computed according to the comparison between the activated neurons of an input and the selected set of frequently activated neurons with its output. The experimentation is carried out over three popular datasets with various neural network structures. The results demonstrate that the test cases with higher priorities are more prone to be mis-classified. And the prioritized test cases over a DNN model within same datasets are also efficient in triggering mis-classification of other DNNs with similar structures.
Text Indexing for Regular Expression Matching

Finding substrings of a text T that match a regular expression p is a fundamental problem. Despite being the subject of extensive research, no solution with a time complexity significantly better than O(|T||p|) has been found. Backurs and Indyk in FOCS 2016 established conditional lower bounds for the algorithmic problem based on the Strong Exponential Time Hypothesis that helps explain this difficulty. A natural question is whether we can improve the time complexity for matching the regular expression by preprocessing the text T? We show that conditioned on the Online Matrix–Vector Multiplication (OMv) conjecture, even with arbitrary polynomial preprocessing time, a regular expression query on a text cannot be answered in strongly sublinear time, i.e., O(|T|1−ε) for any ε&gt;0. Furthermore, if we extend the OMv conjecture to a plausible conjecture regarding Boolean matrix multiplication with polynomial preprocessing time, which we call Online Matrix–Matrix Multiplication (OMM), we can strengthen this hardness result to there being no solution with a query time that is O(|T|3/2−ε). These results hold for alphabet sizes three or greater. We then provide data structures that answer queries in O(|T||p|τ) time where τ∈[1,|T|] is fixed at construction. These include a solution that works for all regular expressions with Expτ·|T| preprocessing time and space. For patterns containing only ‘concatenation’ and ‘or’ operators (the same type used in the hardness result), we provide (1) a deterministic solution which requires Expτ·|T|log2|T| preprocessing time and space, and (2) when |p|≤|T|z for z=2o(log|T|), a randomized solution with amortized query time which answers queries correctly with high probability, requiring Expτ·|T|2Ωlog|T| preprocessing time and space.
An Energy-Efficient Convolution Unit for Depthwise Separable Convolutional Neural Networks

High performance but computationally expensive Convolutional Neural Networks (CNNs) require both algorithmic and custom hardware improvement to reduce model size and to improve energy efficiency for edge computing applications. Recent CNN architectures employ depthwise separable convolution to reduce the total number of weights and MAC operations. However, depthwise separable convolution workload does not run efficiently in existing CNN accelerators. This paper proposes an energy-efficient CONV unit for pointwise and depthwise operation. The CONV unit utilizes weight stationary to enable high efficiency. The row partial sum reduction is engaged to increase parallelism in pointwise convolution thereby lightening the memory requirements on output partial sums. Our design achieves a maximum efficiency of 3.17 TOPS/W at 0.85V/40nm CMOS which is well-suited for energy constrained edge computing applications.
Towards a Set of Heuristics for Evaluating Chatbots

Chatbots are artificial intelligence tools that interact with people in different contexts. A chatbot can be useful to streamline daily processes, serve customers 24 hours a day, provide information about classes, among other things. The appearance of new development technologies has made creating a chatbot an increasingly fast and straightforward process, bringing this kind of applications to people who had never considered using them before. However, this speed in development can lead to specific problems, many of them caused by the lack of usability evaluations. Heuristic usability evaluations are user interface review processes carried out by experts and are an essential part of any assessment process. To date, there are no heuristics to evaluate the usability of chatbots. Therefore, this work proposes five usability heuristics in chatbots that come from the experience developing this type of applications, as well as from a broad review of state of the art. The set of heuristics was tested using a case study with the help of five experts, who evaluated an education-oriented chatbot. The results revealed that, although the proposed heuristics need refinement, they are an excellent first step in broadening the horizon of usability evaluations in chatbots.
Multimodal Approach for DeepFake Detection

Generative Adversarial Networks (GANs) have become increasingly popular in machine learning because of their ability to mimic any distribution of data. Though GANs can be leveraged for legitimate purposes, they have increasingly been used to create manipulative and misleading synthetic media, known as deepfakes, intended for nefarious purposes. In this submission we discuss a multimodal deepfake detection solution submitted against the Facebook DeepFake Detection Challenge, a state of the art benchmark dataset and competition released at the end of 2019. Our solution incorporates information from single images and series of images, and also incorporates temporal information from audio and video data, and was ultimately ranked among the top 25% of teams.
A fast and effective detection framework for whole-slide histopathology image analysis

Pathologists generally pan, focus, zoom and scan tissue biopsies either under microscopes or on digital images for diagnosis. With the rapid development of whole-slide digital scanners for histopathology, computer-assisted digital pathology image analysis has attracted increasing clinical attention. Thus, the working style of pathologists is also beginning to change. Computer-assisted image analysis systems have been developed to help pathologists perform basic examinations. This paper presents a novel lightweight detection framework for automatic tumor detection in whole-slide histopathology images. We develop the Double Magnification Combination (DMC) classifier, which is a modified DenseNet-40 to make patch-level predictions with only 0.3 million parameters. To improve the detection performance of multiple instances, we propose an improved adaptive sampling method with superpixel segmentation and introduce a new heuristic factor, local sampling density, as the convergence condition of iterations. In postprocessing, we use a CNN model with 4 convolutional layers to regulate the patch-level predictions based on the predictions of adjacent sampling points and use linear interpolation to generate a tumor probability heatmap. The entire framework was trained and validated using the dataset from the Camelyon16 Grand Challenge and Hubei Cancer Hospital. In our experiments, the average AUC was 0.95 in the test set for pixel-level detection.
A Novel Method for Automatic Camouflage Pattern Synthesize

Camouflage plays an indispensable role in modern military. Generally, a camouflage pattern includes a plurality of shapes units, which are split by elemental colors. The fixed camouflage pattern is low adaptability and concealability for changeable battlefields. Moreover, the conventionally camouflage design is time and resource consuming for manual drawing. In this paper, we propose a dynamic camouflage synthesizing method. Firstly, the texture patterns of one class of battlefield images is extracted using convolutional transfer network. We use 3*3 convolution kernels to extract texture features. And the covariance matrix is used as loss function to calculate the loss of different image texture features. Colors construction and their distribution of the specified battlefield images is extracted according to a clustering-based algorithm. Finally, the statistical color units are embedded into the texture patterns. We assess the adaptability and the concealability of synthesized camouflage using Eye Tracking based criteria. Comparing to the People's Liberation Army Type 87 woodland pattern and the digital camouflage pattern synthesized using our previous method, testing results of our synthesized camouflage patterns were better in different saccade indicators. This demonstrates camouflage pattern synthesize proposed got better concealment and the validity of proposed method. Besides, we also compared our method with local binary pattern for extracting texture features. The experiments results indicated the method proposed had better consistency to the scene images.
TRENDS AND PATTERNS OF TEXT CLASSIFICATION TECHNIQUES: A SYSTEMATIC MAPPING STUDY

Due to the mass availability of textual data on Web, text classification (TC), classifying texts into predetermined sets becomes a spotlight for researchers. A number of TC applications have been proposed yet very few studies reported an overview of TC research area in a proper and systematic manner. This paper aims to provide an overview of TC research trends and gaps by structuring and analyzing research patterns, encountered problems and problem-solving methods in TC. In other words, this study highlights problem types, data sources, choice of language of text and types of applied techniques in TC. An intensive systematic study is conducted by applying guidelines proposed by Petersen and colleagues in 2007. In this paper, ninety-six literatures from five electronic databases from 2006 to 2017 were systematically reviewed and followed each and every step properly in accordance with systematic mapping study. Nine main problems in TC research area were identified and significant findings which highlighted the evolution of TC research within the past 12 years were investigated. Different from other review articles, this paper highlighted issues and technical gaps of TC area in a useful and effective manner.
Quantum Receivers for Entanglement Assisted Classical Optical Communications

Entanglement assisted (EA) communication represents an interesting alternative to classical communication, in particular in low-brightness and highly noisy regime when it outperforms significantly corresponding classical counterpart in terms of the channel capacity. Even though that the EA capacity is known for decades the optimum quantum receiver has not been determined yet. In this paper, we propose several low-complexity quantum receivers outperforming recently proposed receivers employing the optical parametric amplifier as a basic building block. We demonstrate by simulations that the capacity of the proposed EA schemes, employing Gaussian modulation and the proposed low-complexity joint receivers, can significantly outperform both the Holevo capacity and classical homodyne and heterodyne channel capacities.
XRTB: A Cross Reality Teleconference Bridge to incorporate 3D interactivity to 2D Teleconferencing

We present XRTeleBridge (XRTB), an application that integrates a Mixed Reality (MR) interface into existing teleconferencing solutions like Zoom. Unlike conventional webcam, XRTB provides a window into the virtual world to demonstrate and visualize content. Participants can join via webcam or via head mounted display (HMD) in a Virtual Reality (VR) environment. It enables users to embody 3D avatars with natural gestures and eye gaze. A camera in the virtual environment operates as a video feed to the teleconferencing software. An interface resembling a tablet mirrors the teleconferencing window inside the virtual environment, thus enabling the participant in the VR environment to see the webcam participants in real-time. This allows the presenter to view and interact with other participants seamlessly. To demonstrate the system’s functionalities, we created a virtual chemistry lab environment and presented an example lesson using the virtual space and virtual objects and effects.
Comparing Freeman Chain Code 4 Adjacency Algorithm and LZMA Algorithm in Binary Image Compression

Image is one product that has experienced a significant increase in quality. Insufficient internet capacity makes it difficult to access large images. Image compression is one way to solve this problem, but image compression can make image quality degrade. This research uses lossless compression algorithms, namely Chain Code Algorithm and LZMA Algorithm, to reduce the quality of the image and can be adjusted the tolerance value manually. In the application that will be created, the user can select the image to be compressed, then compress it according to the tolerance value that has been set and with the compression result of a text file, where the text file can be extracted and reconstructed into an image. There will be a preview of the results of the image compression and a description of the size and quality reduction levels. The conclusion of this research is that Chain Code Algorithm and LZMA Algorithm are able to compress image file sizes / images with extensions .jpg, .png. .bmp with a success rate of 90% from 50 tests.
Pengembangan Sistem E-Procurement Pada SMK Yadika Baturaja Dengan Pendekatan Extreme Programming

At SMK Yadika Baturaja, the procurement process is still using a conventional system, this causes a complicated administrative process so that it takes a long time to procure goods. The e-Procurement system offers problem solving solutions in procurement of goods and can make it easier to connect with suppliers via the internet. To develop the e-Procurement system, the extreme programming (XP) system development method is used, which is a branch of agile development. XP is able to do software development with requirements still changing or requirements that change very quickly. From the research results show that the XP method helps in software development, this can be seen in the stages offered make it easier to determine user needs. This is confirmed by the results of the usability test showing a value of 89.25%, which indicates that the system is in a good category.
Conceptual Technological Framework for Smart Cities to Move towards Decentralized and User-Centric Architectures Using DLT

Nowadays, many urban areas are developing projects that are included within the area of smart cities. These systems tend to be highly heterogeneous and involve a large number of different technologies and participants. In general, cities deploy systems to integrate data and to provide protocols to ease interconnectivity between different subsystems. However, this is not enough to build a completely interoperable smart city, where control fully belongs to city administrators and citizens. Currently, in most cases, subsystems tend to be deployed and operated by providers creating silos. Furthermore, citizens, who should be the center of these systems, are often relegated to being just another participant. In this article, we study how smart cities can move towards decentralized and user-centric systems relying on distributed ledger technologies (DLT). For this, we define a conceptual framework that describes the interaction between smart city components, their participants, and the DLT ecosystem. We analyze the trust models that are created between the participants in the most relevant use cases, and we study the suitability of the different DLT types.
Hierarchical semantic interaction-based deep hashing network for cross-modal retrieval

Due to the high efficiency of hashing technology and the high abstraction of deep networks, deep hashing has achieved appealing effectiveness and efficiency for large-scale cross-modal retrieval. However, how to efficiently measure the similarity of fine-grained multi-labels for multi-modal data and thoroughly explore the intermediate layers specific information of networks are still two challenges for high-performance cross-modal hashing retrieval. Thus, in this paper, we propose a novel Hierarchical Semantic Interaction-based Deep Hashing Network (HSIDHN) for large-scale cross-modal retrieval. In the proposed HSIDHN, the multi-scale and fusion operations are first applied to each layer of the network. A Bidirectional Bi-linear Interaction (BBI) policy is then designed to achieve the hierarchical semantic interaction among different layers, such that the capability of hash representations can be enhanced. Moreover, a dual-similarity measurement (“hard” similarity and “soft” similarity) is designed to calculate the semantic similarity of different modality data, aiming to better preserve the semantic correlation of multi-labels. Extensive experiment results on two large-scale public datasets have shown that the performance of our HSIDHN is competitive to state-of-the-art deep cross-modal hashing methods.
A Cascaded Classification-Segmentation Reversible System for Computer-Aided Detection and Cells Counting in Microscopic Peripheral Blood Smear Basophils and Eosinophils Images

Computer-aided image analysis has a pivotal role in automated counting and classification of white blood cells (WBCs) in peripheral blood images. Due to their different characteristics, our proposed approach is based on investigating the variations between the basophils and eosinophils in terms of their color histogram, size, and shape before performing the segmentation process. Accordingly, we proposed a cascaded system using a classification-based segmentation process, called classification-segmentation reversible system (CSRS). Prior to applying the CSRS system, a Histogram-based Object to Background Disparity (HOBD) metric was deduced to determine the most appropriate color plane for performing the initial WBC detection (first segmentation). Investigating the local histogram features of both classes resulted in a 92.4% initial classification accuracy using the third-degree polynomial support vector machine (SVM) method. Subsequently, in the proposed CSRS approach, transformation-based segmentation algorithms were developed to fit the specific requirements of each of the two predicted classes. The proposed CSRS system is used, where the images from an initial classification process are fed into a second segmentation process for each class separately. The segmentation results demonstrated a similarity index of 94.9% for basophils, and 94.1% for eosinophils. Moreover, an average counting accuracy of 97.4% for both classes was achieved. In addition, a second classification was carried out after applying the CSRS, achieving a 5.2% increase in accuracy compared to the initial classification process.
Analysis of Revenue Incentive Dynamic Mechanism of Financial Supply Chain from the Perspective of the Internet of Things

With the rapid development of society, all walks of life need the support of the Internet of Things, and the financial industry is no exception. This article integrates blockchain technology with supply chain finance and builds a supply chain financial alliance architecture based on blockchain technology and an underlying model of the Ethereum blockchain system suitable for supply chain finance. We innovated new supply chain finance models and operating mechanisms and proposed business scenarios for supply chain finance from the perspective of blockchain. Taking into account the actual operation of the blockchain supply chain financial platform, the principal-agent model and the incentive theory are applied, and the supply chain financial accounts receivable model is taken as an example in the case of complete information and incomplete information. The incentive mechanism between the service provider of the chain supply chain financial platform and the core enterprise promotes the better implementation of blockchain technology and supply chain finance. Based on the existing theoretical research, this paper identifies the key influencing factors of the supply chain’s cross-enterprise incentive mechanism. These influencing factors system includes two dimensions: transaction factors and relationship factors. Transaction factors include resource dependence, uncertainty, and cooperation experience; relationship factors include corporate reputation, trust level, and relationship commitment. Based on the nature of the incentive mechanism, information sharing and revenue sharing are extracted as the measurement dimensions of the supply chain’s cross-enterprise incentive mechanism. On this basis, this article draws on the existing enterprise life cycle division method and constructs a hypothetical model of the influencing factors of the incentive mechanism in the incubation period, the growth period, and the maturity period. Relevant data was collected through questionnaires, and SPSS and AMOS software were used to perform statistical analysis, reliability analysis, exploratory factor analysis, confirmatory factor analysis, and structural equation hypothesis testing on the data. The performance of each influencing factor in different stages of the enterprise’s life cycle and the importance of each influencing factor in the same life cycle stage are obtained.