Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Detection of Articulatory Features in Arabic and English Speech

This study proposes using object detection techniques to recognize sequences of articulatory features (AFs) from speech utterances by treating AFs of phonemes as multi-label objects in speech spectrogram. The proposed system, called AFD-Obj, recognizes sequence of multi-label AFs in speech signal and localizes them. AFD-Obj consists of two main stages: firstly, we formulate the problem of AFs detection as an object detection problem and prepare the data to fulfill requirement of object detectors by generating a spectral three-channel image from the speech signal and creating the corresponding annotation for each utterance. Secondly, we use annotated images to train the proposed system to detect sequences of AFs and their boundaries. We test the system by feeding spectrogram images to the system, which will recognize and localize multi-label AFs. We investigated using these AFs to detect the utterance phonemes. YOLOv3-tiny detector is selected because of its real-time property and its support for multi-label detection. We test our AFD-Obj system on Arabic and English languages using KAPD and TIMIT corpora, respectively. Additionally, we propose using YOLOv3-tiny as an Arabic phoneme detection system (i.e., PD-Obj) to recognize and localize a sequence of Arabic phonemes from whole speech utterances. The proposed AFD-Obj and PD-Obj systems achieve excellent results for Arabic corpus and comparable to the state-of-the-art method for English corpus. Moreover, we showed that using only one-scale detection is suitable for AFs detection or phoneme recognition.
3D Landmark Localization in Point Clouds for the Human Ear

3D landmark localization plays an important role in many aspects of 3D data processing, from morphometric analysis to the initialization of mesh registration algorithms. In this work we address the problem of landmark localization in 3D point clouds by extending leading 2D landmark localization algorithms to the 3D domain. By leveraging the PointNet++ architecture, we can construct an architecture that is invariant to the ordering of the data. Input point clouds are segmented into background and landmark regions, and offset vectors are calculated within the landmark regions to refine predicted landmark locations. We demonstrate a high landmark localization accuracy, even as the number of points in the input point cloud decreases. By making use of a 3D morphable model as a novel means of data augmentation, improved landmark localization accuracy and consistency can be obtained. We present our results for landmark localization on the human ear.
Lockable Signatures for Blockchains: Scriptless Scripts for All Signatures

Payment Channel Networks (PCNs) have given a huge boost to the scalability of blockchain-based cryptocurrencies: Beyond improving the transaction rate, PCNs enabled cheap cross-currency payments and atomic swaps. However, current PCNs proposals either heavily rely on special scripting features of the underlying blockchain (e.g. Hash Time Lock Contracts) or are tailored to a handful of digital signature schemes, such as Schnorr or ECDSA signatures. This leaves us in an unsatisfactory situation where many currencies that are being actively developed and use different signature schemes cannot enjoy the benefits of a PCN.In this work, we investigate whether we can construct PCNs assuming the minimal ability of a blockchain to verify a digital signature, for any signature scheme. In answering this question in the affirmative, we introduce the notion of lockable signatures, which constitutes the cornerstone of our PCN protocols. Our approach is generic and the PCN protocol is compatible with any digital signature scheme, thus inheriting all favorable properties of the underlying scheme that are not offered by Schnorr/ECDSA (e.g. aggregatable signatures or post-quantum security).While the usage of generic cryptographic machinery makes our generic protocol impractical, we view it as an important feasibility result as it may serve as the basis for constructing optimized protocols for specific signature schemes. To substantiate this claim, we design a highly efficient PCN protocol for the special case of Boneh-Lynn-Shacham (BLS) signatures. BLS signatures enjoy many unique features that make it a viable candidate for a blockchain, e.g. short, unique, and aggregatable signatures. Yet, prior to our work, no PCN was known to be compatible with it (without requiring an advanced scripting language). The cost of our PCN is dominated by a handful of calls to the BLS algorithms. Our concrete evaluation of these basic operations shows that users with commodity hardware can process payments with minimal overhead.
MHRD’S INITIATIVE IIC CONTRIBUTION TOWARDS INNOVATION IN HIGHER EDUCATIONAL INSTITUTIONS DURING COVID 19

Innovation is the word which means new idea, creative thought, and practical implementation of invention. The MHRD initiative IIC is helpful for the students to learn entrepreneurship skills in their initial years of graduation and nurture them till completion of their qualification. The innovation cell of higher educational institutions will enhance the skills, aptitude of the students and prepare them for future endeavors. The main motive of MIC Innovation Cell is to inspire, persuade and cherish young and talented students to work with innovative thoughts and convert their ideas into prototypes and cherish their work. The e-sessions by the IIC-Institutes will overcome the need for physical innovation cell activities which is impossible in current situation due to COVID-19 pandemic. The quarter 3 and 4 have been merged into one period because of ongoing pandemic i.e., one complete semester and all IIC institutes have to report accordingly for the scores and star ratings. The e-sessions by IIC institutes have been successful and gathered appreciation from students and faculty members. There is a growing awareness that entrepreneurial outlook, which combines inventiveness, a sense of initiative, predicament solving, vagueness tolerance and financial and technological knowledge in formal education for benefit of individuals and society. The MHRD initiative of IIC will enable the foundation of robust modernization and Start up ecosystem across all HEIs.
Machine Learning Approach for the Detection of Hate Speech in Sinhala Unicode Text

Hate speech published online platforms has become a critical issue in Sri Lanka since this has caused conflicts between different ethnic groups. One of the main barriers to stop this crime is the lack of resources to detect online hate content in Sinhala automatically. Due to the vast amount of content published on online platforms every minute, an automatic method must be implemented in order to solve this issue. As a solution, we suggest a deep learning mechanism that utilizes two convolution neural networks (CNNs) which will first classify a given text corpus as hateful or not. Then, if the text corpus contains hate content text, it will again be classified according to its hate level which can be used by authorities to make decisions. In order to convert the text data into numerical vectors, we have used FastText word embedding in this study.Results indicate an accuracy of 83% and 60% for hate speech classification and hate level classifications, respectively.
A Constant Time Full Hardware Implementation of Streamlined NTRU Prime

This paper presents a constant time hardware implementation of the NIST round 2 post-quantum cryptographic algorithm Streamlined NTRU Prime. We implement the entire KEM algorithm, including all steps for key generation, encapsulation and decapsulation, and all en- and decoding. We focus on optimizing the resources used, as well as applying optimization and parallelism available due to the hardware design. We show the core en- and decapsulation requires only a fraction of the total FPGA fabric resource cost, which is dominated by that of the hash function, and the en- and decoding algorithm. For the NIST Security Level 3, our implementation uses a total of 1841 slices on a Xilinx Zynq Ultrascale+ FPGA, together with 14 BRAMs and 19 DSPs. The maximum achieved frequency is 271 MHz, at which the key generation, encapsulation and decapsulation take 4808 $$\upmu $$ s, 524 $$\upmu $$ s and 958 $$\upmu $$ s respectively. To our knowledge, this work is the first full hardware implementation where the entire algorithm is implemented.
Use of Blockchain Technology in Wireless Body Area Networks

With the limited service available to traditional people, millions of the people have been impacted by growing population of older people and patients with chronic conditions. EHealthcare networks starting to deliver medical services using rapid increases in the number of biomedical sensors, the Internet of Things and advanced communication and network technologies, particularly for remote physical supervision, with the goal of increasing the quality and effectiveness of conventional medical system. The aim is to deliver a healthy and powerful approach to health care using wireless internet network as a transmission and storage mechanism for connecting the patient's devices and the technology in the blockchain. Customers today have various problems with the Wireless Sensor Network (WSNs). Cost performance, low energy consumption, efficient communication of data between nodes and protection are major challenges in WSN. Blockchain is able to solve a variety of WSN problems, including secure data transfers and confidence, etc. In this paper we have done the survey of use of blockchain technology in wireless body area network.
Design of Smart Car Control System for Gesture Recognition Based on Arduino

A smart car control system, based on Bluetooth control for gesture recognition is designed, which uses the MPU6050 six-axis attitude sensor to collect attitude information as well as through multi-angle fusion and direction closed-loop control to create the corresponding digital gestures for different instructions. After the digital recognition, the relevant instructions are transmitted to the Arduino smart car via radio frequency, which realizes the remote control of the intelligent vehicle through human gestures to complete the corresponding command actions. The experimental results show that this design realizes the function of controlling the direction of the smart car through gesture changes. The movement trajectory of the smart car can better feedback the movement of the hand; at the same time, it also shows that the system operates stably and has a strong anti-interference ability.
Self-supervised Deformation Modeling for Facial Expression Editing

Deep generative models have recently demonstrated impressive results in photo-realistic facial image synthesis and editing. Existing neural network-based approaches usually only rely on texture generation to edit expressions and largely neglect the motion information. However, facial expressions are inherently the result of muscle movement. In this work, we propose a novel end-to-end network that disentangles the task of facial editing into two steps: a “motionediting” step and a “texture-editing” step. In the “motion-editing” step, we explicitly model facial movement through an image deformation, warping the image into the desired expression. In the “texture-editing” step, we generate the necessary textures, such as teeth and shading effects, for a photorealistic result. Our physically-based task-disentanglement system design allows each step to learn a focused task, and thus need not generate texture to hallucinate motion. Our system is trained in a self-supervised manner, requiring no ground truth deformation annotation. Using Action Units [8] as the representation for facial expression, our method improves the state-of-the-art facial expression editing performance in both qualitative and quantitative evaluations.
Process Debt: a First Exploration

Process Debt, like Technical Debt, can be the source of short-term benefits but often is harmful in the long term for a software organization. Nonetheless, information about Process Debt is scarce in current literature. We conducted an exploratory study of Process Debt in four international organizations by interviewing 16 practitioners. The findings show that Process Debt can be a harmful phenomenon that needs attention and new practices, as it is different from Technical Debt. We provide an initial framework, composed by a definition and a conceptual model for Process Debt, showing types, causes, consequences, and debt accumulation patterns.
Assessment and Adjustment of Approximate Inference Algorithms Using the Law of Total Variance

A common method for assessing validity of Bayesian sampling or approximate inference methods makes use of simulated data replicates for parameters drawn from the prior. Under continuity assumptions, quantiles of functions of the simulated parameter values for corresponding posterior distributions are uniformly distributed. Checking for uniformity when a posterior density is approximated numerically provides a diagnostic for algorithm validity. Furthermore, adjustments to achieve uniformity can improve the quality of approximate inference methods. The present article develops a moment-based alternative to the conventional checking and adjustment methods using quantiles. The new approach relates prior and posterior expectations and covariances through the tower property of conditional expectation and the law of total variance. For adjustment, approximate inferences are modified so that the correct prior to posterior relationships hold. We illustrate the method in three examples. The first uses an auxiliary model in a likelihood-free inference problem. The second considers corrections for variational Bayes approximations in a deep neural network generalized linear mixed model. Our final application considers a deep neural network surrogate for approximating Gaussian process regression predictive inference. Supplementary files for this article are available online.
Hierarchical Attention-Based Multiple Instance Learning Network for Patient-Level Lung Cancer Diagnosis

Lung cancer is the leading cause of cancer-related deaths worldwide, while the risk factors for lung cancer mortality can be significantly reduced if the accurate early diagnoses for small malignant lung nodules are possible. In this paper, we propose a hierarchical attention-based multiple instance learning (HA-MIL) framework for patient-level lung cancer diagnosis by introducing two-level cascaded attention mechanisms, one at nodule level and the other at attribute level. The proposed HA-MIL framework is constructed by aggregating important attribute representation into nodule representation and then aggregating important nodule representation into lung cancer representation. The experiments on the public Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset showed that the HA-MIL model performed significantly better than the previous approaches such as the higher-order transfer learning, instance-space MIL and embedding-space MIL, which demonstrated the effectiveness of hierarchical multiple instance learning based on two-level attentions. The results analysis suggested that the HA-MIL model also found the key nodules and attributes by higher attention weights, which were more interpretable for the model decision making.
Scene-Graph-Guided message passing network for dense captioning

Dense captioning task aims to both localize and describe salient regions in images with natural languages. It can benefit from the rich visual concepts, including objects, pair-wise relationships and so on. However, due to the challenging combinatorial complexity of formulating <subject-predicate-object> triplets, very little work has been done to integrate them into the dense captioning task. Inspired by the recent success in scene graph generation for object and relationship detections, we propose a scene-graph-guided message passing network for dense caption generation. We first exploit message passing between objects and their relationships with a feature refining structure. Moreover, we formulate the message passing as the inter-connected visual concept generation problem while the objective function of scene graph generation is used to guide the region feature learning. Scene graph guide can propagate the structured knowledge of graph through the concept-region message passing mechanism (CR-MPM), which can improve the regional feature representation. Finally, the refined regional features are encoded by a LSTM-based decoder to generate dense captions. Our model can achieve competing performances on Visual Genome comparing against existing methods. Qualitative experiments also confirm the effect of our model in the dense captioning task.
Machine Learning Techniques for Vehicle Matching with Non-Overlapping Visual Features

Emerging Vehicle-to-Everything (V2X) technologies promise to improve the perception of streets by enabling data sharing like camera views between multiple vehicles. However, to ensure accuracy of such enhanced perception, the problem of vehicle matching becomes important; the goal of a vehicle matching system is to identify if images of vehicles seen by different cameras correspond to the same vehicle. Such a system is necessary to avoid duplicate detections for a vehicle seen by multiple cameras and to avoid detections being discarded due to a false match being made. One of the most challenging scenarios in vehicle matching is when the camera positions have very large viewpoint differences, as will commonly be the case when the cameras are in geographically separate locations like in vehicles and street infrastructure. In these scenarios, traditional handcrafted features will not be sufficient to create these correspondences due to the lack of common visual features. In this paper we will examine the performance of random forests and neural networks as classifiers for both learned features and high level visual features when used for this vehicles matching problem. Additionally, a novel dataset of vehicles from cameras with very large viewpoint differences was recorded to validate our method; our preliminary results achieve high classification accuracy with low inference time which shows the feasibility of a real time vehicle matching system.
DebateVis: Visualizing Political Debates for Non-Expert Users

Political debates provide an important opportunity for voters to observe candidate behavior, learn about issues, and make voting decisions. However, debates are generally broadcast late at night and last more than ninety minutes, so watching debates live can be inconvenient, if not impossible, for many potential viewers. Even voters who do watch debates may find themselves overwhelmed by a deluge of information in a substantive, issue-driven debate. Media outlets produce short summaries of debates, but these are not always effective as a method of deeply comprehending the policies candidates propose or the debate techniques they employ. In this paper we contribute reflections and results of an 18-month design study through an interdisciplinary collaboration with journalism and political science researchers. We characterize task and data abstractions for visualizing political debate transcripts for the casual user, and present a novel tool (DebateVis) to help non-expert users explore and analyze debate transcripts.
Supervised Anomaly Detection with Highly Imbalanced Datasets Using Capsule Networks

Detecting anomalous patterns in data is a relevant task in many practical applications, such as defective items detection in industrial inspection systems, cancer identification in medical images, or attacker detection in network intrusion detection systems. This paper focuses on detection of anomalous images, this is images that visually deviate from a reference set of regular data. While anomaly detection has been widely studied in the context of classical machine learning, the application of modern deep learning techniques in this field is still limited. We here propose a capsule-based network for anomaly detection in an extremely imbalanced fully supervised context: we assume that anomaly samples are available, but their amount is limited if compared to regular data. By using a variant of the standard CapsNet architecture, we achieved state-of-the-art results on the MNIST, F-MNIST and K-MNIST datasets.
Distributed Algorithm for Truss Maintenance in Dynamic Graphs

Cohesive subgraphs are applied in various fields. Mining cohesive components such as k-truss have attracted a lot of effort to improve time efficiency in large-scale graphs. The k-truss is a subgraph where each edge is contained in at least \(k-2\) triangles and the problem of truss decomposition is computing the k-trusses of a graph for all k. However, most graphs in real scenarios are usually changing over time. The previous studies take the static graphs as input, and the truss maintenance in dynamic graphs receives little attention. This paper focuses on distributed algorithms for truss maintenance. We present a distributed model underlying the real distributed processing model Pregel. Based on the model, we propose truss decomposition and truss maintenance algorithms. To confirm the effectiveness and efficiency of the proposed algorithms, we conduct extensive experiments over both real-world and synthetic graphs.
A new face detection method based on Faster RCNN

Abstract The Non-Maximum Suppression (NMS) method based on Faster RCNN uses hard threshold to identify candidate face frames which to be detected. In complex scenes with partial occlusion of the face and uneven lighting, the phenomenon of missing and false detection of the face is prone to occur. Aiming at this problem, this paper proposes a new face detection method to extract face features through CNN, and generates a large number of face candidate frames which to be detected by the Region Proposal Network (RPN); the hard threshold of NMS is improved by linear weighting method, and the face candidate frame is screened by the linearly weighted NMS. Comparison experiment results show that on the FDDB dataset, the new face detection method in this paper can effectively avoid the false detection and missed detection of multiple faces under partial occlusion and uneven illumination, also has high detection accuracy and robustness.
Referential Form and Memory for the Discourse History

Abstract The way we refer to things in the world is shaped by the immediate physical context as well as the discourse history. But what part of the discourse history is relevant to language use in the present? In four experiments, we combine the study of task‐based conversation with measures of recognition memory to examine the role of physical contextual cues that shape what speakers perceive to be a part of the relevant discourse history. Our studies leverage the differentiation effect , a phenomenon in which speakers are more likely to use a modified expression to refer to an object (e.g., dotted sock) if they had previously described a similar object (e.g., striped sock) than when they had not described a similar object. Two physical cues—the background that framed the to‐be‐described pictures and the position of the pictures in the display—were manipulated to alter perceptions about the relevant discourse context. We measured the rate with which speakers modify referring expressions to differentiate current from past referents. Recognition memory measures following the conversation probed what was and was not remembered about past discourse referents and contexts. Analysis of modification rates indicated that these contextual grouping cues shaped perceptions about the relevant discourse context. The contextual cues did not affect memory for the referents, but the memory for past referents was better for speakers than for listeners. Our findings show that perceptions about the relevant discourse history are a key determinant of how language is used in the moment but also that conversational partners form asymmetric representations of the discourse history.