Reviewerly/exHarmony huggingface dataset:
Recognition of Cheating Behavior in Examination Room Based on Deep Learning

In order to ensure the fairness of the examination and solve the problems that the traditional electronic invigilator system cannot automatically analyze the surveillance video and the video censors suffer from high labor intensity, this work presents a method for analyzing cheating behaviors of candidates based on deep learning technology. By analyzing the action of examinee in a single frame of the surveillance video, the traditional object detection algorithm YOLO is combined with the human posture estimation project OpenPose to extract the position information of the candidates, and label the candidates who are suspected of cheating. Due to the particularity of cheating behavior, a dataset for deep learning training is constructed, which marks two kinds of human behavior in the examination scene, including two types of cheating behavior: peeping and passing notes. At the same time, in order to improve the detection speed, the inter-frame difference method is used to extract the key frames, and finally both the accuracy on the test set and the detection speed can reach a better result.
Migrate On-Premises Real-Time Data Analytics Jobs Into the Cloud

Twitter's data platform team is serving a large number of real-time analytics jobs, powering a wide range of data science use cases, from aggregations over time to spam detection. These analytics jobs constitute a crucial step in Twitter's data science infrastructure. As a key part of Twitter's “partly cloudy” strategy, real-time data analytics jobs are being migrated from on-premises into the cloud. We would like to share our migration approach and findings in this paper. The jobs to be migrated vary but follow common patterns, including the “read-modify-write store” and “lambda architecture” patterns. Both patterns can be migrated to the Beam data model in general ways. Besides job patterns, the job IOs are handled by replicating or proxying between on-premises and the cloud. Tests are applied in two phases through monitoring metrics and control tests. A case study demonstrates the business impact of migration. Finally, we discuss lessons learned.
Towards Low-Visibility Enhancement in Maritime Video Surveillance: An Efficient and Effective Multi-Deep Neural Network

Limited by insufficient illumination, the images collected by maritime imaging devices often suffer from low brightness, low contrast, low signal-to-noise ratio, severe information loss, and so on. The above problems restrict the development of maritime- related work such as intelligent supervision, collision warning, accident investigation, etc. To improve the imaging quality of maritime video images, we propose an efficient and effective multi-deep neural network (termed EEMNN) for low-visibility enhancement. In particular, we fuse the multi-scale information extracted from the encoder-decoder module using the dense blocks (DBs) and attention blocks (ABs). It is capable of enhancing the fused information leading to preserving the edges, textures, and other fine details. To prevent the overexposure of enhanced images, we fuse and reconstruct the output features of DBs and ABs with the raw low-light image to get the final enhanced image through two residual blocks (RBs). The mixing of multiple network modules can effectively improve the generalization ability and robustness of our network. Through extensive experiments, EEMNN has higher objective evaluation indicators, more efficient enhancement, more natural maritime scenes, and stronger detail-preservation capabilities compared with other enhancement methods.
In Search of Machine Learning Theory

Machine Learning (ML) and Artificial Intelligence (AI), in general, are based on search algorithms. In this paper, we use paradoxically the same search techniques again to look for the general theory of machine learning itself. In other words, we search for a unifying machine learning theory. For this purpose, we turn out for a help to the general theory of computation, called $-calculus, that is based on meta-search and super-turing/hypercomputational models. We hope that in such a way, we can unify machine learning and this should be useful in the development of new methods, algorithms, embedded devices and computer programs in the future. Firstly, we overview main machine learning areas as our training examples, and by applying our background knowledge we hand pick-up a hypothetical reasonable theory of ML. Next, we justify that this is a good generalization of ML. The open research question remains whether by applying various ML techniques we can induce automatically the optimal ML theory from the hypothesis space of possible theories. Thus, hopefully, the follow-up paper would be titled “In search of the optimal machine learning theory”.
Efficient Learning of Quadratic Variance Function Directed Acyclic Graphs via Topological Layers

Directed acyclic graph (DAG) models are widely used to represent casual relationships among random variables in many application domains. This article studies a special class of non-Gaussian DAG models, where the conditional variance of each node given its parents is a quadratic function of its conditional mean. Such a class of non-Gaussian DAG models are fairly flexible and admit many popular distributions as special cases, including Poisson, Binomial, Geometric, Exponential, and Gamma. To facilitate learning, we introduce a novel concept of topological layers, and develop an efficient DAG learning algorithm. It first reconstructs the topological layers in a hierarchical fashion and then recovers the directed edges between nodes in different layers, which requires much less computational cost than most existing algorithms in literature. Its advantage is also demonstrated in a number of simulated examples, as well as its applications to two real-life datasets, including an NBA player statistics data and a cosmetic sales data collected by Alibaba. Supplementary materials for this article are available online.
Automated Lip-Reading Robotic System Based on Convolutional Neural Network and Long Short-Term Memory

In Iranian Sign Language (ISL), alongside the movement of fingers/arms, the dynamic movement of lips is also essential to perform/recognize a sign completely and correctly. In a follow up of our previous studies in empowering the RASA social robot to interact with individuals with hearing problems via sign language, we have proposed two automated lip-reading systems based on DNN architectures, a CNN-LSTM and a 3D-CNN, on the robotic system to recognize OuluVS2 database words. In the first network, CNN was used to extract static features, and LSTM was used to model temporal dynamics. In the second one, a 3D-CNN network was used to extract appropriate visual and temporal features from the videos. The accuracy rate of 89.44% and 86.39% were obtained for the presented CNN-LSTM and 3D-CNN networks, respectively; which were fairly promising for our automated lip-reading robotic system. Although the proposed non-complex networks did not provide the highest accuracy for this database (based on the literature), 1) they were able to provide better results than some of the more complex and even pre-trained networks in the literature, 2) they are trained very fast, and 3) they are quite appropriate and acceptable for the robotic system during Human-Robot Interactions (HRI) via sign language.
GAN-LSTM Joint Network Applied to Seismic Array Noise Signal Recognition

The purpose of seismic data processing in nuclear explosion monitoring is to accurately and reliably detect seismic or explosion events from complex ambient noises. Accurate detection and identification of seismic phases are of great significance to the detection and parameter estimation of seismic events. In seismic phase identification, discriminating between noise signals and real seismic signals is essential. Accurate identification of noise signals helps reduce false detections, improves the accuracy of automatic bulletins, and relieves the workload of analysts. At the same time, in seismic exploration, the prime objective in data processing is also to enhance the signal and suppress the noises. In this study, we combined a generative adversarial network (GAN) with a long short-term memory network (LSTM) to discriminate between noise and phases in seismic waveforms recorded by the International Monitoring System (IMS) array MKAR. First, using the beamforming data of the array as the input, we obtained the signal features of seismic phases through the learning of the GAN discriminator network. Then, we input these features and trained the joint network on mixed seismic phase and noise data, and successfully classified seismic phases and noise signals with a recall of 95.28% and 97.64%, respectively. Based on this model, we established a real-time data processing method, then validated the effectiveness of this method with real 2019 data of MKAR. We also verified whether improved noise signal identification improves the quality of phase association and event detection.
Internalization the Concept of Local Wisdom for Students in the Listening Class

The purpose of this study is to find out the principle of composing listening materials that contain local wisdom based on the dimensions of roles and parts. The problem formulation of this study is how the principle of composing listening materials that contain the value of local wisdom based on the dimensions of roles and parts. The findings of this study can be used as a reference in making learning models, especially listening materials which can be used as a means to foster studentsâ€™ characters related to their culture and appreciation toward local wisdom. The research design used in this study is research and development (R & D) design. This research produces roles with local wisdom to be an emotionally better person for a harmonious life, especially when the students are engaged in group discussion activities. To teach local wisdom related to environment care can be done through learning activities. It can be seen how the students are actively participating in the listening activities. Then, cognitive domain can be carried out in the learning process in the form of memorizing or remembering. Meanwhile, the affective domain can be performed through performance, discussion, and group work. It is expected that the finding can be an alternative reference to develop listening activities by internalizing local wisdom as a national identity. Also, the listening activities prepare young generations to preserve local wisdom from a nation.
Balanced allocation on hypergraphs

We consider a variation of balls-into-bins which randomly allocates m balls into n bins. Following Godfrey's model (SODA, 2008), we assume that each ball t, 1⩽t⩽m, comes with a hypergraph H(t)={B1,B2,…,Bst}, and each edge B∈H(t) contains at least a logarithmic number of bins. Given d⩾2, our d-choice algorithm chooses an edge B∈H(t), uniformly at random, and then chooses a set D of d random bins from the selected edge B. The ball is allocated to a least-loaded bin from D. We prove that if the hypergraphs H(1),…,H(m) satisfy a balancedness condition and have low pair visibility, then after allocating m=Θ(n) balls, the maximum load of any bin is at most logd⁡log⁡n+O(1), with high probability. Moreover, we establish a lower bound for the maximum load attained by the balanced allocation for a sequence of hypergraphs in terms of pair visibility.
LiteratureQA: A Qestion Answering Corpus with Graph Knowledge on Academic Literature

In this paper, we introduce LiteratureQA, a large question answering (QA) corpus consisting of publicly available academic papers. Different from other QA corpus, LiteratureQA has its unique challenges such as how to leverage the structured knowledge of citation networks. We further examine some popular QA method and present a benchmark approach of answering academic questions by combining both semantic text and graph knowledge to improve the prevalent pre-training model. We hope this resource could help research and development of tasks for machine reading over academic text.
Topology-aware space distortion for structured visualization spaces

We propose topology-aware space distortion (TASD), a family of interactive layout techniques for non-linearly distorting geometric space based on user attention and on the structure of the visual representation. TASD seamlessly adapts the visual substrate of any visualization to give more screen real estate to important regions of the representation at the expense of less important regions. In this paper, we present a concrete TASD technique that we call ZoomHalo for interactively distorting a two-dimensional space based on a degree-of-interest (DOI) function defined for the space. Using this DOI function, ZoomHalo derives several areas of interest, computes the available space around each area in relation to other areas and the current viewport extents, and then dynamically expands (or shrinks) each area given user input. We use our prototype to evaluate the technique in two user studies, as well as showcase examples of TASD for node-link diagrams, word clouds, and geographical maps.
A 12-nm Autonomous Driving Processor With 60.4 TOPS, 13.8 TOPS/W CNN Executed by Task-Separated ASIL D Control

This article proposes an automotive safety integrity level (ASIL) D, which is the highest automotive integrity level specified in ISO 26262, and a target application processor for driving automation systems with a high convolutional neural network (CNN) processing performance of 60.4 trillion operations per second (TOPS) with high power efficiency of 13.8 TOPS/W. To achieve both performance and power efficiency, a stream processing architecture specialized for CNN hardware module is adopted to implement many low-power arithmetic units. In addition, 6 MB of local memories are implemented to reduce data transfers between dynamic random access memory (DRAM) and the CNN hardware, thereby improving the execution efficiency of the arithmetic units and saving power consumption caused by the data transfer. In order to support high safety integrity level with power efficiency, we provide safety mechanisms with high failure detection rates only for the hardware used in applications that require ASIL D support. The configuration of two CNN modules can switch between lockstep and single operation depending on the use case, thus achieving a tradeoff between performance and ASIL while maintaining power efficiency. In addition, the freedom from interference (FFI) concept defined in ISO 26262 is achieved by providing mechanisms to prevent low safety level tasks from interfering with high safety level tasks running concurrently inside the processor.
Efficient Attribute-Based Signature for Monotone Predicates

By using attribute based signature (ABS), users can sign messages and prove that their attributes conform to the ones published by the designated attribute authority through certain verification policies. In the verification process, the identity and attributes of the signer will not be disclosed, which makes ABS scheme a convenient tool for applications requiring privacy preserving authentication and other role-based security applications.However, previous ABSs suffer from efficiency issues related to the monotone or more expressive predicates. In this work, we propose a general construction to reduce the size of the signature, and give an instantiation based on SDH assumption in random oracle model. To achieve it, we apply a new approach to implement the monotonic access structure control so that we can reduce the cost of the signatures to be close to the number of attributes used by the signer instead of the number of attributes involved in the signature.
Low-Resource Language Discrimination toward Chinese Dialects with Transfer Learning and Data Augmentation

Chinese dialects discrimination is a challenging natural language processing task due to scarce annotation resource. In this article, we develop a novel Chinese dialects discrimination framework with transfer learning and data augmentation (CDDTLDA) in order to overcome the shortage of resources. To be more specific, we first use a relatively larger Chinese dialects corpus to train a source-side automatic speech recognition (ASR) model. Then, we adopt a simple but effective data augmentation method (i.e., speed, pitch, and noise disturbance) to augment the target-side low-resource Chinese dialects, and fine-tune another target ASR model based on the previous source-side ASR model. Meanwhile, the potential common semantic features between source-side and target-side ASR models can be captured by using self-attention mechanism. Finally, we extract the hidden semantic representation in the target ASR model to conduct Chinese dialects discrimination. Our extensive experimental results demonstrate that our model significantly outperforms state-of-the-art methods on two benchmark Chinese dialects corpora.
Comparison of Deep Architectures for Indoor RF Signal Classification

In this paper, we study the performance of three different Deep Learning (DL) network architectures in Radio Frequency (RF) signal classification tasks considering an indoor environment. We compare the classification accuracy of 7 modulation types (BPSK, QPSK, GMSK, 16-QAM, 64-QAM, GFSK, and CPFSK) with Convolutional Neural Network (CNN), Long Short Term Memory (LSTM) network, and Residual Network (ResNet) architectures by varying receiver positions in a building layout along with two different transmitter positions. It is seen that, in the considered scenario, for a given transmitter position, CNN and LSTM architectures provide better classification accuracy depending on the receiver positions. It is also seen that in certain receiver positions, some of the modulation types perform better than others.
Company Ranking Prediction Based on Network Big Data

With the advent of the era of big data, it has become one of the important tasks of data mining to utilize the massive info on the internet to realize the forecast of relevant aspects. In recent years, some scholars have studied how to use company’s information for the revenue or stock forecasts, but there is very little research on inter-company ranking predictions. So from the perspective, this paper brings in the Learning-to-Rank (LtR) method into the corporation ranking prognostication, and proposes the Company Ranking Prediction Model (CRPM) grounded on LtR. First, obtain the information on the Fortune 500 official website and other correlative sites. Through data analysis, data cleaning, ranking processing, label division and data formatting, a brand-new corporation data-set containing four major categories of features (10 characteristics in total) is constructed. Then bring in four commonly utilized LtR approaches, employing the previously created data-set to train different models. Finally, each model is evaluated by the two information retrieval metrics, normalized discounted cumulative gain and mean average precision. The experimental results show that the assessment best-performing model CRPM-LambdaMART and CRPM-RandomForests can tellingly forecast the ranking of Fortune 500 companies in the next year, which has certain practical value for financiers to reasonably planning their investments and corporation managements to legitimately programming their strategic deployments.
The Effect of Random Projection on Local Intrinsic Dimensionality

Much attention has been given in the research literature to the study of distance-preserving random projections of discrete data sets, the limitations of which are established by the classical Johnson-Lindenstrauss existence lemma. In this theoretical paper, we analyze the effect of random projection on a natural measure of the local intrinsic dimensionality (LID) of smooth distance distributions in the Euclidean setting. The main contribution of the paper consists of upper and lower bounds on the LID in the vicinity of a reference point after random projection. The bounds depend only on the LID in the original data domain and the target dimension of the projection; as the difference between the target and intrinsic dimensionalities grows, these bounds converge to the LID of the original domain. The paper concludes with a brief discussion of the implications for applications in databases, machine learning and data mining.
A Three-Step Machine Learning Pipeline for Detecting and Explaining Anomalies in the Time Series of Industrial Process Plants

Anomaly detection offers an important type of machine learning analysis for industrial automation systems because it is one of the highest goals in the industrial domain to avoid production disturbances and to stay productive. If devices are not operating anymore within normal bounds, or if the produced product quality is no longer within normal bounds, then the plant operator wants to be informed as early as possible. Anomaly detection can provide this type of information for the operator. This paper presents a solution pipeline for the detection and explanation of anomalies in the multivariate time series of industrial plant processes, and a possible implementation of this pipeline that is fully based on the use of neural network architectures. The pipeline consists of three consecutive steps, that build on each other, to successively explore in depth the anomalies and their underlying root-causes. The three steps of the pipeline are: 1) the detection of the anomaly itself, 2) pointing to the location where the anomaly comes from, and 3) determining the type of anomaly at this location. The evaluation of the pipeline is performed to detect a set of 16 simulated plant equipment failures that have been obtained from a real-world industrial process that is typically found in oil production fields, called the separator process.
Indexed Polygon Matching Under Similarities

Polygons appear as constructors in many applications and deciding if two polygons match under similarity transformations and noise is a fundamental problem. Solutions in the literature consider only matching pairs of polygons, implying a sequential comparison when we have a collection. In this paper, we present the first algorithm allowing indexed retrieval of polygons under similarities. We reduce the problem to searching points in the plane, exact searching in the absence of noise, and approximate searching for similar noisy polygons. The above gives a \(O(n+\log (m))\) time algorithm to find the matching polygons under noise and O(1) time for exact similar polygons. We tested our heuristic for indexed polygons in an extensive collection of convex, star-shaped, simple, and self-intersecting polygons. For small amounts of noise, we achieve perfect recall for all polygons. For large amounts of noise, the lowest recall is for convex polygons, while attaining the highest recall is for general (self-intersecting) polygons. The above is not a significant limitation. To recover convex polygons efficiently before indexing, we define a random permutation of the vertices, converting all input polygons to a general polygon and achieving the same successful recovery rates, which is a perfect recall for high noise levels.