Reviewerly/exHarmony huggingface dataset:
An Advanced Abnormal Behavior Detection Engine Embedding Autoencoders for the Investigation of Financial Transactions

Nowadays, (cyber)criminals demonstrate an ever-increasing resolve to exploit new technologies so as to achieve their unlawful purposes. Therefore, Law Enforcement Agencies (LEAs) should keep one step ahead by engaging tools and technology that address existing challenges and enhance policing and crime prevention practices. The framework presented in this paper combines algorithms and tools that are used to correlate different pieces of data leading to the discovery and recording of forensic evidence. The collected data are, then, combined to handle inconsistencies, whereas machine learning techniques are applied to detect trends and outliers. In this light, the authors of this paper present, in detail, an innovative Abnormal Behavior Detection Engine, which also encompasses a knowledge base visualization functionality focusing on financial transactions investigation.
A Noninvasive Approach Using Multi-tier Deep Learning Classifier for the Detection and Classification of Breast Neoplasm Based on the Staging of Tumor Growth

It is estimated on statistics that one out of eight females is affected by breast cancer worldwide. The scope of this work is to formulate a clinical protocol to find early-stage breast cancer and staging non-invasively. This work is to differentiate benign and malignant tumors with a novel ensemble approach. The ensemble utilization of signal processing by a recurrent neural network (RNN) and image processing by deep convolution neural network (DCNN) for the characterization of breast cancer yields the best result in disease prognosis. DCNN based system model named AlexNet is used to effectively classify breast neoplasm with optimum results in the mammogram dataset. In DCNN, the last layer called fully connected (FC) layer is linked to the support vector machine (SVM) classifier. The classifier fusion technique is adopted to combine the result of both imaging and signal processing to obtain the best classification result. Once it classifies with a greater value of true positivity it continues to go with Raman spectroscopy for the identification of spectral features associated with cells and tissues such as DNA, carbohydrate, nucleic acid, lipids, and proteins during the formation of breast neoplasm in the suspected candidate's blood plasma samples. Long Short-Term Memory (LSTM) based RNN is utilized to classify breast neoplasm features from the spectral dataset. After the classification, a combination of principal component analysis with factorial discriminant analysis (PCA-FDA) is used to find the different stages of cancer growth. This method promisingly dealt to have better specificity and sensitivity for all stages.
A Comparative Study of Supervised Machine Learning Techniques for Deceptive Review Identification Using Linguistic Inquiry and Word Count

In e-commerce websites enabling a facility to leave a review/feedback about the product is one of best practice given by developers; which has a significant influence on consumer's buying behavior. Meanwhile, sellers and manufacturers are investigating online reviews for decision making. Therefore, this facility was misused by generating fake reviews. Filtering out of untruthful information becomes essential in this modern era. The goal of this study is to find a robust supervised machine learning approach to identify deceptive reviews through a comparative study for the content-based feature called Linguistic Inquiry and Word Count (LIWC); which have been extracted from one thousand magazine subscription reviews. Principal Component Analysis (PCA) is used as a dimensionality reduction technique. Further, along with five different variances of PCA and without PCA, scenarios were used to compare the performance of seven supervised machine learning techniques. It has been demonstrated that the Ensemble Bagged classifier with 3% PCA variance outperforms other six supervised methods resulting in 88% prediction accuracy.
Locality-Aware Rotated Ship Detection in High-Resolution Remote Sensing Imagery Based on Multiscale Convolutional Network

Ship detection has been an active and vital topic in the field of remote sensing for a decade, but it is still a challenging problem due to the large-scale variations, the high aspect ratios, the intensive and rotated arrangement, and the background clutter disturbance. In this letter, we propose a locality-aware rotated ship detection (LARSD) framework based on a multiscale convolutional neural network (CNN) to tackle these issues. The proposed framework applies a UNet-like multiscale CNN to generate multiscale feature maps with high-level semantic information in high resolution. Then, an anchor-based rotated bounding box regression is applied for directly predicting the probability, the edge distances, and the angle of ships. Finally, a locality-aware score alignment (LASA) is proposed to fix the mismatch between classification results and location results caused by the independence of each subnet. Furthermore, to enlarge the data sets of ship detection, we build a new high-resolution ship detection (HRSD) data set, where 2499 images and 9269 instances were collected from Google Earth with different resolutions. Experiments based on public data set high-resolution ship collection 2016 (HRSC2016) and our HRSD data set demonstrate that our detection method achieves the state-of-the-art performance.
Domain-Aware Dialogue State Tracker for Multi-Domain Dialogue Systems

In task-oriented dialogue systems the dialogue state tracker component (DST) is responsible for predicting the current state of the dialogue based on the dialogue history and the user utterance. Current DST approaches rely on a predefined domain ontology, a fact that limits their effective usage for large scale conversational agents, where the DST constantly needs to be interfaced with ever-increasing services and APIs. Focused towards overcoming this drawback, we propose a domain-aware dialogue state tracker, that is completely data-driven and it is modeled to predict for dynamic service schemas, including zero-shot domains. Unlike approaches that propose separate models for prediction of intents, requested slots, slot status, categorical slots and non-categorical slots, we propose a single model in an end-to-end architecture. The proposed model utilizes domain and slot information to extract both domain and slot specific representations from a given dialogue, and then uses such representations to predict the values of the corresponding slot in a given domain. Integrating this mechanism with pretrained language models, our approach can effectively learn semantic relations and effectively perform transfer learning between domains or zero-shot tracking for domains not present in training.
An Algorithm for the Automatic Precisiation of the Meaning of Adjectives

Computing with words and perceptions is a technique that has been proven to be incredibly powerful to understand and compute with entities with imprecise nature, such as natural language and human perceptions. To be able to correctly handle these elements, however, a high degree of human-generated data is needed for the precisiation of meaning, the first step of computing with words and perceptions systems. This can be achieved via direct user intervention or with a - not existing yet - data set representing the meaning of words for several people. This aspect reduces the practical usability of computing with words and perceptions to cases where only an extremely reduced set of words needs to be understood (precisi-ated). In the current article, an algorithm able to fully automate the precisiation of the meaning of quantitative and qualitative adjectives using already available data - from a thesaurus - is proposed and analyzed in an exploratory study with six experts as participants. Results indicate an accuracy of the algorithm close to human-level accuracy for the task of precisiating another person's perceptions and help to understand some improvements to be taken into account for future developments of the algorithm. The presented algorithm represents a promising step toward human-like information processing.
Predicting essay quality from search and writing behavior

Abstract Few studies have investigated how search behavior affects complex writing tasks. We analyze a dataset of 150 long essays whose authors searched the ClueWeb09 corpus for source material, while all querying, clicking, and writing activity was meticulously recorded. We model the effect of search and writing behavior on essay quality using path analysis. Since the boil‐down and build‐up writing strategies identified in previous research have been found to affect search behavior, we model each writing strategy separately. Our analysis shows that the search process contributes significantly to essay quality through both direct and mediated effects, while the author's writing strategy moderates this relationship. Our models explain 25–35% of the variation in essay quality through rather simple search and writing process characteristics alone, a fact that has implications on how search engines could personalize result pages for writing tasks. Authors' writing strategies and associated searching patterns differ, producing differences in essay quality. In a nutshell: essay quality improves if search and writing strategies harmonize—build‐up writers benefit from focused, in‐depth querying, while boil‐down writers fare better with a broader and shallower querying strategy.
Non-classicality of two superconducting-qubits interacting independently with a resonator cavity: trace-norm correlation and Bures-distance entanglement

We study the behaviour of the correlation robustness of two spatially isolated charge-qubits started initially with a maximally correlated nonsymmetric Bell-state. Each charged qubit is coupled individually to a superconducting resonator coherent field. The dynamics of the qubit-resonator quantum correlations, based on trace-norm measurement-induced non-locality (MIN) and Bures distance entanglement, are investigated under the effects of the qubit-resonator interactions, intrinsic decoherence, initial coherent intensity cavity as well as of the qubit-resonator detuning. It is found that the trace-norm MIN correlation can be enhanced by the initial coherent field intensity and the detuning. The MIN is more robust, against the qubit-resonator interactions and the decoherence, than the Bures distance entanglement. Furthermore, the correlation robustness analysed for the different cases of the coherent states depends not only on the intrinsic decoherence but also on the coherent states and the qubit-resonator detuning.
Pose Prediction for Mobile Ground Robots in Uneven Terrain Based on Difference of Heightmaps

For traversing uneven terrain in degraded environments, determining the static stability and consequently the tip-over risk of a mobile ground rescue robot is fundamental for planning and evaluation of paths. This paper presents a novel iterative geometric method that reduces the problem of robot pose prediction to two-dimensional image-processing operations by introducing the concept of a robot heightmap. The presented method requires only geometrical and mass information extracted from the widely used unified robot description format (URDF) to compute the robot heightmap, which makes it transferable to a wide range of mobile robot platforms without modification. We demonstrate that the approach accurately predicts the real robot's 6D pose at the input x-y-coordinates. Runtimes allowing the evaluation of poses in the order of ten thousand poses per second show that the method is computationally efficient enough to be used in online path planning.
Improving apparel detection with category grouping and multi-grained branches

Training an accurate object detector is expensive and time-consuming. One main reason lies in the laborious labeling process, i.e., annotating category and bounding box information for all instances in every image. In this paper, we examine ways to improve performance of deep object detectors without extra labeling. We first explore to group existing categories of high visual and semantic similarities together as one super category (or, a superclass). Then, we study how this knowledge of hierarchical categories can be exploited to better detect objects using multi-grained RCNN branches∗. Experimental results on DeepFashion2 and OpenImagesV4-Clothing reveal that the proposed detection heads with multi-grained branches can boost the overall performance by as high as 2% with no additional time-consuming annotations. In addition, classes that have fewer training samples tend to benefit more from the proposed multi-grained heads with superclass grouping. In particular, we improve the mAP for the last 30% categories (in terms of training sample number) by 2.6% and 4.6% on DeepFashion2 and OpenImagesV4-Clothing, respectively.
IntentKeeper: Intent-oriented Data Usage Control for Federated Data Analytics

Data usage control is of utmost importance for federated data analytics across multiple business domains. However, the existing data usage control approaches are limited due to their complexity and inefficiency. This paper proposes an intent-oriented data usage control system for federated data analytics, called IntentKeeper. The system allows users to specify intents for data usage policies and services easily. Thus, it reduces the data sharing complexity for data providers and consumers. Moreover, IntentKeeper enforces preventive and proactive data usage control for better security and efficiency through joint decisions based on policy enforcement and service orchestration. The use case validations for the automotive industry scenario show that IntentKeeper significantly reduces the complexity of policy specification (up to 75% for moderately complex scenarios) compared to the state-of-the-art flow-based approach. Lastly, the experimental results show that the IntentKeeper system provides sufficiently short response times (less than 40ms) with minimal overhead (less than 10ms).
Gender Classification and Age Prediction using CNN and ResNet in Real-Time

The face is the most dominant part of the human body, we can get a lot of information from facial features such as detecting the face of person, gender classification and even age prediction. In current times, Computer Vision (CV) has been used to train machines to comprehend and envision the real world. In this research, a novel artifact has been presented to detect face, classify genders, and predict age from the human facial images all in real-time using a live stream from a camera source. Convolutional Neural Networks (CNN) have been used for training purposes along with the CV library, Keras. To power this novel study, each model has been trained separately and finely tuned before merging them into the final system. The use of the careful modern architecture of CNN and current regularisation methods have been properly evaluated and implemented. The accuracy of the developed model has been calculated manually achieving an overall accuracy of 85%. All the testing has been performed in real-time. After extensive testing and evaluation, a state of the art novel system has been developed with a combination of simple pre-processing steps. This system can be broadly deployed for security purposes, as in airports and police checkpoints, and also to restrict the access of alcohol from vending machines to under-aged people.
Using the Dark Web

Dark Web as its name implies is usually connected with dark and criminal activities occurring in the web. However, exploring only the criminal part of its community is like exploring only the criminal activities of a town or a city’s community and not reporting its commercial, cultural, social and other valuable activities. This chapter will report both sides of the same coin giving an equal and balanced picture by exploring the various actors of the Dark Web regarding the types of uses, causes and motivations by which they are driven to make use of it.
Row-Streaming Dataflow Using a Chaining Buffer and Systolic Array+ Structure

Convolutional Neural Networks (CNNs) are widely used to solve complex problems in various fields, such as image recognition, image classification, and video analysis. Convolutional (CONV) layers are the most computational part of the CNN inference; various architectures have been proposed to process it efficiently. Among those, a systolic array consists of a 2D array of processing elements, which handle GEneral Matrix Multiplication (GEMM) with high efficiency. However, to process a CONV layer as a GEMM type, image-to-column (im2col) processing, which is also called lowering, is required per layer, necessitating a larger on-chip memory and a considerable amount of repetitive on-chip memory access. In this letter, we propose a systolic array+ (SysAr+) structure augmented with a chaining buffer and a row-streaming dataflow that can maximize data reuse without the im2col pre-process in the CONV layer and the repetitive access from the large on-chip memory. By applying the proposed method to the 3×3 CONV layers, we reduce the energy consumption by up to 19.7 percent in ResNet and 37.4 percent in DenseNet with an area overhead of 1.54 percent in SysAr+, and we improve the performance by up to 32.4 percent in ResNet and 12.1 percent in DenseNet.
Automatic Identification of Keywords in Lecture Video Segments

Lecture video is an increasingly important learning resource. However, the challenge of quickly finding the content of interest in a long lecture video is a critical limitation of this format. This paper introduces automatic discovery of keywords (or tags) for lecture video segments to improve navigation. A lecture video is divided into topical segments based on the frame-to-frame similarity of content. A user navigates the lecture video assisted by visual summaries and keywords for the segments. Keywords provide an overview of the content discussed in the segment to improve navigation. The input to the keyword identification algorithm is the text from the video frames extracted by OCR. Automatically discovering keywords is challenging as the suitability of an N-gram to be a keyword depends on a variety of factors including frequency in a segment and relative frequency in reference to the full video, font size, time on screen, and the existence in domain and language dictionaries. This paper explores how these factors are quantified and combined to identify good keywords. The key scientific contribution of this paper is the design, implementation, and evaluation of a keyword selection algorithm for lecture video segments. Evaluation is performed by comparing the keywords generated by the algorithm with the tags chosen by experts on 121 segments of 11 videos from STEM courses.
PriSE: Slenderized Privacy-Preserving Surveillance as an Edge Service

With a myriad of edge cameras deployed in urban areas, many people are seriously concerned about the invasion of their privacy. The edge computing paradigm allows enforcing privacy-preserving measures at the point where the video frames are created. However, the resource constraints at the network edge make existing compute-intensive privacy-preserving solutions unaffordable. In this paper, we propose slenderized and efficient methods for Privacy-preserving Surveillance as an Edge service (PriSE) after investigating a spectrum of image-processing, image scrambling, and deep learning (DL) based mechanisms. At the edge cameras, the PriSE introduces an efficient and lightweight Reversible Chaotic Masking (ReCAM) scheme preceded by a simple foreground object detector. The scrambling scheme prevents an interception attack by ensuring end-to-end privacy. The simplified motion detector helps save bandwidth, processing time, and storage by discarding those frames that contain no foreground objects. On a fog/cloud server, the scrambling scheme is coupled with a robust window-detector to prevent peeping via windows and a multi-tasked convolutional neural network (MTCNN) based face-detector for the purpose of de-identification. The extensive experimental studies and comparative analysis show that the PriSE is able to efficiently detect foreground objects and scramble frames at the edge cameras, and detect and denature window and face objects at a fog/cloud server to ensure end-to-end communication privacy and anonymity, respectively. This is done just before the frames are sent to the viewing stations.
Continuous compliance

Vendors who wish to provide software or services to large corporations and governments must often obtain numerous certificates of compliance. Each certificate asserts that the software satisfies a compliance regime, like SOC or the PCI DSS, to protect the privacy and security of sensitive data. The industry standard for obtaining a compliance certificate is an auditor manually auditing source code. This approach is expensive, error-prone, partial, and prone to regressions.
Optoelectronic convolutional neural networks based on time-stretch method

In this paper, a new architecture of optoelectronic convolutional neural networks (CNNs) based on time-stretch method is proposed. In this loop-shaped structure mainly composed of fiber optical and electronic devices, computations of data from each layer of CNN which are carried by light pulses with high repetition rate can be accomplished in a serial way. Therefore, a 5-layer CNN with two convolution layers, two mean pooling layers and one fully-connected layer are implemented. Under the test of handwriting digit recognition, its accuracy can reach up to 95% under ideal circumstances. Tests under different relative noise levels have been conducted and analyzed as well.
Polynomial Multiplication Systolic Array for Homomorphic Encryption in Secure Network Communications

Homomorphic encryption is a type of encryption that allows computation to be done directly on encrypted data, without the need to perform any decryption in the process. Brakerski/Fan-Vercauteren (BFV) is a homomorphic encryption scheme that uses Ring Learning with Error (RLWE) problem and encrypts data in ring polynomial form. The encryption and decryption in this scheme involve high degree polynomial multiplication. In this paper, we propose a systolic array design to accelerate polynomial multiplication using convolution method. In implementation result, we show result area for 2x2 and 4x4 polynomial multiplication systolic array, as a proof of concept we show a Verilog simulation for 15 degrees polynomial multiplication, and design space exploration with variation in bit-size and systolic array matrix size. From our exploration we found optimized design size that can be implemented in PYNQ-ZI board. The homomorphic encryption is useful for secure network communications and cloud analytics.
Whack-a-Meltdown: Microarchitectural Security Games [Systems Attacks and Defenses]

In early 2018, the disclosure of Spectre <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> and Meltdown <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> exposed the security risks inherent in speculative and out-of-order execution, which were hitherto considered harmless and valuable performance optimizations. In a nutshell, these attacks demonstrated that transient execution, where the computer executes code speculatively before reverting execution, leaves side effects on the microarchitecture, allowing adversaries to retrieve data across software- and hardware-enforced security boundaries.