Reviewerly/exHarmony huggingface dataset:
Morphological Filter Detector for Image Forensics Applications

Mathematical morphology provides a large set of powerful non-linear image operators, widely used for feature extraction, noise removal or image enhancement. Although morphological filters might be used to remove artifacts produced by image manipulations, both on binary and gray level documents, little effort has been spent towards their forensic identification. In this paper we propose a non-trivial extension of a deterministic approach originally detecting erosion and dilation of binary images. The proposed approach operates on grayscale images and is robust to image compression and other typical attacks. When the image is attacked the method looses its deterministic nature and uses a properly trained SVM classifier, using the original detector as a feature extractor. Extensive tests demonstrate that the proposed method guarantees very high accuracy in filtering detection, providing 100% accuracy in discriminating the presence and the type of morphological filter in raw images of three different datasets. The achieved accuracy is also good after JPEG compression, equal or above 76.8% on all datasets for quality factors above 80. The proposed approach is also able to determine the adopted structuring element for moderate compression factors. Finally, it is robust against noise addition and it can distinguish morphological filter from other filters.
Energy-Aware Cloud Workflow Applications Scheduling With Geo-Distributed Data

Electricity prices differ during different time periods and change from place to place. Cloud workflow applications often require geo-distributed data which is transmitted among heterogeneous servers in intra- and inter- data centers. Such varying electricity prices and data transmission time bring great challenges when optimizing the energy cost for scheduling tasks in workflow applications to heterogeneous servers in cloud data centers. In this article, we minimize the total electricity cost in a deadline constrained energy-aware workflow scheduling problem with data being geographically distributed across data centers. A scheduling algorithm is proposed. Strategies are developed to sequence workflow applications, divide deadlines and sort tasks. An adaptive local search method is presented to improve solutions during the search process which dynamically balances intensification using neighborhood structures of increasing size. Components and parameter values are statistically calibrated over a comprehensive set of random instances. The proposed algorithm is compared to modified classical algorithms for similar problems. Experimental results demonstrate the effectiveness of the proposal for the considered problem.
Vector-light quantum complementarity and the degree of polarization

The dual wave–particle nature of light and the degree of polarization are fundamental concepts in quantum physics and optical science, but their exact relation has not been explored within a full vector-light quantum framework that accounts for interferometric polarization modulation. Here, we consider vector-light quantum complementarity in double-pinhole photon interference and derive a general link between the degree of polarization and wave–particle duality of light. The relation leads to an interpretation for the degree of polarization as a measure describing the complementarity strength between photon path predictability and so-called Stokes visibility , the latter taking into account both intensity and polarization variations in the observation plane. It also unifies results advanced in classical studies by showing that the degree of polarization can be viewed as the ability of a light beam to exhibit intensity and polarization-state fringes. The framework we establish thus provides novel aspects and deeper insights into the role of the degree of polarization in quantum-light complementarity and photon interference.
Energy flow-guided synchronization between chaotic circuits

A variety of nonlinear electric devices are suitable for building chaotic circuits by setting appropriate values for parameters in the linear and nonlinear electronic components. The capacitor and induction coil can contain time-varying physical field energy when the time-varying current passed across these devices. When the outputs end of two chaotic circuits are connected via certain coupling devices, energy flow can be propagated bidirectionally and complete synchronization is reached when the energy pumping from the outputs end is balanced. In this paper, a nonreversing diode is connected into the coupling channel to adjust the energy flow via the coupling components (resistor, capacitor, induction coil), which connects the outputs end of two Chua circuits (in chaotic or periodical states). According to Helmholtz' theorem, the Hamilton energy of the dimensionless chaotic systems under unidirectional coupling is calculated. When the two coupled Chua systems are selected with different initial values, they contain different initial field energies because the Hamilton energy is dependent on the variables of this system completely. When energy flow is propagated and pumped along the coupling channel, synchronization can be realized. Otherwise, synchronization approach is blocked even the coupling intensity is increased greatly. In this work, a single coupling channel connecting a nonreversing diode and resistor (capacitor, induction coil, respectively) in series is built to explore synchronization approach. The results show that the synchronization realization is dependent on the coupling devices and circuit verification is also supplied. It gives important clues to understand the synchronization mechanism in a forward neural network.
The Datafication of Hate: Expectations and Challenges in Automated Hate Speech Monitoring

Hate speech has been identified as a pressing problem in society and several automated approaches have been designed to detect and prevent it. This paper reports and reflects upon an action research setting consisting of multi-organizational collaboration conducted during Finnish municipal elections in 2017, wherein a technical infrastructure was designed to automatically monitor candidates’ social media updates for hate speech. The setting allowed us to engage in a twofold investigation. First, the collaboration offered a unique view for exploring how hate speech emerges as a technical problem. The project developed an adequately well-working algorithmic solution using supervised machine learning. We tested the performance of various feature extraction and machine learning methods and ended up using a combination of Bag-of-Words feature extraction with Support-Vector Machines. However, an automated approach required heavy simplification, such as using rudimentary scales for classifying hate speech and a reliance on word-based approaches, while in reality hate speech is a linguistic and social phenomenon with various tones and forms. Second, the action-research-oriented setting allowed us to observe affective responses, such as the hopes, dreams, and fears related to machine learning technology. Based on participatory observations, project artefacts and documents, interviews with project participants, and online reactions to the detection project, we identified participants’ aspirations for effective automation as well as the level of neutrality and objectivity introduced by an algorithmic system. However, the participants expressed more critical views towards the system after the monitoring process. Our findings highlight how the powerful expectations related to technology can easily end up dominating a project dealing with a contested, topical social issue. We conclude by discussing the problematic aspects of datafying hate and suggesting some practical implications for hate speech recognition.
A survey of FPGA design for AI era

Abstract FPGA is an appealing platform to accelerate DNN. We survey a range of FPGA chip designs for AI. For DSP module, one type of design is to support low-precision operation, such as 9-bit or 4-bit multiplication. The other type of design of DSP is to support floating point multiply-accumulates (MACs), which guarantee high-accuracy of DNN. For ALM (adaptive logic module) module, one type of design is to support low-precision MACs, three modifications of ALM includes extra carry chain, or 4-bit adder, or shadow multipliers which increase the density of on-chip MAC operation. The other enhancement of ALM or CLB (configurable logic block) is to support BNN (binarized neural network) which is ultra-reduced precision version of DNN. For memory modules which can store weights and activations of DNN, three types of memory are proposed which are embedded memory, in-package HBM (high bandwidth memory) and off-chip memory interfaces, such as DDR4/5. Other designs are new architecture and specialized AI engine. Xilinx ACAP in 7 nm is the first industry adaptive compute acceleration platform. Its AI engine can provide up to 8X silicon compute density. Intel AgileX in 10 nm works coherently with Intel own CPU, which increase computation performance, reduced overhead and latency.
A quadrilateral scene text detector with two-stage network architecture

Many of the state-of-the-art methods can only localize scene texts with rotated rectangle boundaries, which may result in incorrect rectification of the detected scene texts and erroneous elimination of proposals or detections during non-maximum suppression (NMS). A few existing methods that can detect scene texts with quadrilateral boundaries, are just based on one-stage architectures or sliding windows scanning and thus have sub-optimal performance. To address these problems, we propose an end-to-end two-stage network architecture for scene text detection, which can accurately localize scene texts with quadrilateral boundaries. At the first stage, we propose a quadrilateral region proposal network (QRPN) for generating quadrilateral proposals, based on a newly proposed quadrilateral regression algorithm. At the second stage, we introduce a novel weighted RoI pooling module with learned weight masks to pool the features, and then classify the proposals and refine their shapes with the proposed quadrilateral regression algorithm again. Specially, during training, we adopt a dual-branch structure of detection heads, that is, jointly train the quadrilateral detection head and an additional rotated rectangle detection head. Furthermore, we develop an accelerated NMS algorithm with O(nlogn) complexity, for redundant quadrilateral text proposals and detections eliminating during the first and the second stage, respectively. Experiments on several challenging benchmarks demonstrate the superior performance of the proposed method, which achieves state-of-the-art results on widely used benchmarks ICDAR 2017 MLT, RCTW, and ICDAR 2015 Incidental Scene Text benchmark.
Autonomous Drone Cinematographer: Using Artistic Principles to Create Smooth, Safe, Occlusion-Free Trajectories for Aerial Filming

Autonomous aerial cinematography has the potential to enable automatic capture of aesthetically pleasing videos without requiring human intervention, empowering individuals with the capability of high-end film studios. Current approaches either only handle off-line trajectory generation, or offer strategies that reason over short time horizons and simplistic representations for obstacles, which result in jerky movement and low real-life applicability. In this work we develop a method for aerial filming that is able to trade off shot smoothness, occlusion, and cinematography guidelines in a principled manner, even under noisy actor predictions. We present a novel algorithm for real-time covariant gradient descent that we use to efficiently find the desired trajectories by optimizing a set of cost functions. Experimental results show that our approach creates attractive shots, avoiding obstacles and occlusion 65 times over 1.25 h of flight time, re-planning at 5 Hz with a 10 s time horizon. We robustly film human actors, cars and bicycles performing different motion among obstacles, using various shot types.
Computer-aided tumor detection in automated breast ultrasound using a 3-D convolutional neural network

Automated breast ultrasound (ABUS) is a widely used screening modality for breast cancer detection and diagnosis. In this study, an effective and fast computer-aided detection (CADe) system based on a 3-D convolutional neural network (CNN) is proposed as the second reader for the physician in order to decrease the reviewing time and misdetection rate. Our CADe system uses the sliding window method, a CNN-based determining model, and a candidate aggregation algorithm. First, the sliding window method is performed to split the ABUS volume into volumes of interest (VOIs). Afterward, VOIs are selected as tumor candidates by our determining model. To achieve higher performance, focal loss and ensemble learning are used to solve data imbalance and reduce false positive (FP) and false negative (FN) rates. Because several selected candidates may be part of the same tumor and they may overlap each other, a candidate aggregation method is applied to merge the overlapping candidates into the final detection result. In the experiments, 165 and 81 cases are utilized for training the system and evaluating system performance, respectively. On evaluation with the 81 cases, our system achieves sensitivities of 100% (81/81), 95.3% (77/81), and 90.9% (74/81) with FPs per pass (per case) of 21.6 (126.2), 6.0 (34.8), and 4.6 (27.1) respectively. According to the results, the number of FPs per pass (per case) can be diminished by 56.8% (57.1%) at a sensitivity of 95.3% based on our tumor detection model. In conclusion, our CADe system using 3-D CNN with the focal loss and ensemble learning may have the capability of being a tumor detection system in ABUS image.
A PMU-Based Data-Driven Approach for Classifying Power System Events Considering Cyberattacks

Transient event identification is essential for power system operation and situational awareness. The increased penetration of the high sampling frequency phasor measurement units (PMUs) enables using PMU data to analyze power system events, and thus enhances power system visualization, monitoring and control. At the same time, the risks associated with cyberattacks on power systems increase. A malicious cyberattack on PMUs, aiming at generating fake transient data, may lead to incorrect actions that jeopardize system reliability. Therefore, it is critical to distinguish between fake data and real data when analyzing transient events. Utilizing PMU measurements, this article develops a data-driven approach, based on text-mining methodologies, for classifying transient events and identifying fake events caused by false data attacks. The developed methodology provides credible information regarding the cause of various events, and facilitates postevent decision-making to prevent potential cascading failures. Case studies, performed on the IEEE 30-bus and IEEE 118-bus systems, show that the developed approach is efficient in classifying false data and identifying different transient events regardless of the system topology, loading conditions, or the placement of PMUs.
Adversarial Machine Learning in Recommender Systems (AML-RecSys)

Recommender systems (RS) are an integral part of many online services aiming to provide an enhanced user-oriented experience. Machine learning (ML) models are nowadays broadly adopted in modern state-of-the-art approaches to recommendation, which are typically trained to maximize a user-centred utility (e.g., user satisfaction) or a business-oriented one (e.g., profitability or sales increase). They work under the main assumption that users' historical feedback can serve as proper ground-truth for model training and evaluation. However, driven by the success in the ML community, recent advances show that state-of-the-art recommendation approaches such as matrix factorization (MF) models or the ones based on deep neural networks can be vulnerable to adversarial perturbations applied on the input data. These adversarial samples can impede the ability for training high-quality MF models and can put the driven success of these approaches at high risk.
Evolving and Diversifying Selling Practices on Drug Cryptomarkets: An Exploration of Off-Platform “Direct Dealing”

This is the first study to explore how cryptomarket actors are increasingly adopting encrypted messaging applications to “ direct deal” beyond the provided platforms, to obviate the protocols of cryptomarkets, and to diversify the communication experience of drug buying via the dark net. Drawing on 965 forum posts discussing encrypted messaging applications, results showed that direct dealing may be more likely to occur in the context of preestablished trust between vendors and buyers, during instances of law enforcement crackdowns, and when buyers are enticed by discounts or promotions. Our findings also suggested a general hesitancy toward direct dealing, as it was often associated with greater exposure to scams, and perceptions that direct dealing increases the risks concerning personal security and detection from law enforcement. These findings provide insight into the interconnection of online drug markets, and how actors make decisions to drift between multichannel supply points mediated by perceptions of trust and risk.
Fast Method of ID Documents Location and Type Identification for Mobile and Server Application

In this paper we discuss the problem of simultaneous document type recognition and projective distortion parameters estimation for the images of ID documents. There are two considered cases. In the first case a video stream captured using mobile devices is processed on the device. The second case considers photos or scanned images which are processed on a server. For each case the requirements are defined for the input data and processing speed. The universal approach is proposed, which allows solving the problem in both cases. The approach is based on representing the image as a constellation of feature points and descriptors, but in order to perform more accurate distortion parameters estimation straight lines and quadrangles are extracted from the input image and used as additional features. Techniques are described which allow to combine matched feature points, lines, and quadrangles to geometric verification using RANSAC. Best alternative selection criteria are proposed along with methods of solution accuracy estimation. The differences between methods of preliminary analysis of the input image and geometric primitives location are discussed in relation to the considered problems. For quality estimation an open dataset MIDV-500 is used, together with its extension for server-side problem version, created in scope of this work. Results show that using lines and quadrangles increase the location accuracy, and the proposed algorithm surpasses previously published works in classification precision and computational performance.
Technical assessment of captive solar power plant: A case study of Senai airport, Malaysia

Solar PV system in the airport environment is a relatively new application. Unlike land-based solar systems, the site selection for the airport-based PV power plant is a complicated process and lacks proper methodology. The objective of this work was to develop a general sitting procedure for an airport-based solar PV system and identify ideal sites for solar farms in Senai International airport, Malaysia. Feasible sites were selected with due consideration to airport and aviation compatibility constraints. Next, suitability of such selected sites is assessed based on environmental impact and proximity to electrical infrastructure. Using glare prediction software, the adherence to FAA’s solar interim policy is assessed. Eleven (11) sites which lie within the airport are chosen for the study. The duration of glare from sites 2, 3, 4, 6 were 1125, 4724, 3805, 1125 min receptively. As a result, design parameters are changed for these sites. The results of the study showed that the solar PV potential and theoretical energy generation from the selected sites of the airport were 12.50 MW and 16,745 MWh respectively. The knowledge on the suitability of sites and prior glare assessment increases the level of confidence to airport stakeholders and project developers.
Guiding Cross-lingual Entity Alignment via Adversarial Knowledge Embedding

Cross-lingual Entity Alignment (CEA) aims at identifying entities with their counterparts in different language knowledge graphs. Knowledge embedding alignment plays an important role in CEA due to its advantages of easy implementation and run-time robustness. However, existing embedding alignment methods haven't considered the problem of embedding distribution alignment which refers to the alignment of spatial shapes of embedding spaces. To this end, we present a new Adversarial Knowledge Embedding framework (AKE for short) that jointly learns the representation, mapping and adversarial modules in an end-to-end manner. By reducing the discrepancy of embedding distributions, AKE can approximately preserve an isomorphism between source and target embeddings. In addition, we introduce two new orthogonality constraints into mapping to obtain the self-consistency and numerical stability of transformation. Experiments on real-world datasets demonstrate that our method significantly outperforms state-of-the-art baselines.
Personalized Knowledge Graph Summarization: From the Cloud to Your Pocket

The increasing scale of encyclopedic knowledge graphs (KGs) calls for summarization as a way to help users efficiently access and distill world knowledge. Motivated by the disparity between individuals' limited information needs and the massive scale of KGs, in this paper we propose a new problem called personalized knowledge graph summarization. The goal is to construct compact "personal summaries" of KGs containing only the facts most relevant to individuals' interests. Such summaries can be stored and utilized on-device, allowing individuals private, anytime access to the information that interests them most. We formalize the problem as one of constructing a sparse graph, or summary, that maximizes a user's inferred "utility" over a given KG, subject to a user-and device-specific constraint on the summary's size. To solve it, we propose GLIMPSE, a summarization framework that provides theoretical guarantees on the summary's utility and is linear in the number of edges in the KG. In an evaluation with real user queries to open-source, encyclopedic KGs of up to one billion triples, we show that GLIMPSE efficiently creates summaries that outperform strong baselines by up to 19% in query answering F1 score.
A Hierarchical Clustering Approach to Fuzzy Semantic Representation of Rare Words in Neural Machine Translation

Rare words are usually replaced with a single <;unk> token in the current encoder-decoder style of neural machine translation, challenging the translation modeling by an obscured context. In this article, we propose to build a fuzzy semantic representation (FSR) method for rare words through a hierarchical clustering method to group rare words together, and integrate it into the encoder-decoder framework. This hierarchical structure can compensate for the semantic information in both source and target sides, and providing fuzzy context information to capture the semantic of rare words. The introduced FSR can also alleviate the data sparseness, which is the bottleneck in dealing with rare words in neural machine translation. In particular, our method is easily extended to the transformer-based neural machine translation model and learns the FSRs of all in-vocabulary words to enhance the sentence representations in addition to rare words. Our experiments on Chinese-to-English translation tasks confirm a significant improvement in the translation quality brought by the proposed method.