Reviewerly/exHarmony huggingface dataset:
MINI-SSD: A Fast Object Detection Framework in Autonomous Driving

In this paper, a python-implemented infrastructure of a CNN-based multi-object detector in autonomous driving using the single shot detector (SSD) is presented. The infrastructure consists of both training and inference for object detection. The main contribution of this paper is the design of the default anchor boxes tiling that reduce the amount of computations by simplifying the software implementation of the SSD object detector. This simplification is done by reducing the data path of the proposed detector. Moreover, a decrease in the inference time of the detector is the result of using tiled defaults boxes and a small number of layers in the VGG CNN. In addition, the CNN model presents an advantage in terms of high confidence boxes prediction. The proposed approach is faster due to the reduced number of layers and computations. The segmentation design of the input image anchor boxes is introduced to explain the software implementation. In addition, both the training and validation loss variations along the period of the training are illustrated.
Non-Rigid Liver Registration in Liver Computed Tomography Images Using Elastic Method with Global and Local Deformations

The study of follow-up liver computed tomography (CT) images is required for the early diagnosis and treatment evaluation of liver cancer. Although this requirement has been manually performed by doctors, the demands on computer-aided diagnosis are dramatically growing according to the increased amount of medical image data by the recent development of CT. However, conventional image segmentation, registration, and skeletonization methods cannot be directly applied to clinical data due to the characteristics of liver CT images varying largely by patients and contrast agents. In this paper, we propose non-rigid liver segmentation using elastic method with global and local deformation for follow-up liver CT images. To manage intensity differences between two scans, we extract the liver vessel and parenchyma in each scan. And our method binarizes the segmented liver parenchyma and vessel, and performs the registration to minimize the intensity difference between these binarized images of follow-up CT images. The global movements between follow-up CT images are corrected by rigid registration based on liver surface. The local deformations between follow-up CT images are modeled by non-rigid registration, which aligns images using non-rigid transformation, based on locally deformable model. Our method can model the global and local deformation between follow-up liver CT scans by considering the deformation of both the liver surface and vessel. In experimental results using twenty clinical datasets, our method matches the liver effectively between follow-up portal phase CT images, enabling the accurate assessment of the volume change of the liver cancer. The proposed registration method can be applied to the follow-up study of various organ diseases, including cardiovascular diseases and lung cancer.
Application of fuzzy combined SVM &amp; graph theory for agriculture productivity prediction

Abstract A fuzzy integrated support vector machine and graph theory concepts are represents the data models for predicting a production. On this account, it has been used in various platforms such as agriculture, medicine, and various engineering applications. Therefore, the development of new computational development for predicting the productivity of events in terms of farming structure is very significant in agriculture. This method used fuzzy integrated support vector machine and graph theory to perform structural tasks suggested by crop influencing factors. Finally, the results obtained illustrate the advantage of predicting the rate of productivity, in addition to the importance of system recommendations that fail to produce the expected output volume at the time of setup or fail to produce the expected output quantum.
Wiktionary Normalization of Translations and Morphological Information

We extend the Yawipa Wiktionary Parser (Wu and Yarowsky, 2020) to extract and normalize translations from etymology glosses, and morphological form-of relations, resulting in 300K unique translations and over 4 million instances of 168 annotated morphological relations.We propose a method to identify typos in translation annotations.Using the extracted morphological data, we develop multilingual neural models for predicting three types of word formationclipping, contraction, and eye dialect-and improve upon a standard attention baseline by using copy attention.
Fast Decryption of Excel Document Encrypted by RC4 Algorithm

In this paper, we give a fast decryption method of Excel document encrypted by RC4 algorithm. Through a detailed analysis of document storage structure and encryption process, we illustrate the inner principle of key generation and data encryption in the block by block manner. We present an efficient way of recovering the intermediate key by using rainbow table attack, which can be directly applied to decrypt the Excel document. The advantage of our method is that the decryption time of one document is no longer affected by the password length and complexity. In our practical test, it has achieved the decryption of Excel encrypted documents in an average of 3 minutes on a common personal computer, which greatly improves decryption efficiency of encrypted documents compared with the current dictionary and brute force attack methods of recovering document password.
Sub-Erroneous Outlier Detection of Cyber Attacks in a Smart Grid State Estimation System

Cyber-physical systems (CPS) are designed to provide observation and control of processes, designed for high operation, reliability, and safety performance levels. CPS were historically developed void of contact with the Internet or other cyber-attack threat vectors. The integration of the Internet, Internet of Things, and Industrial Internet of Things exposed the cybersecurity resource limited programmable logic controllers, intelligent electronic devices, sensors, and actuators to an expanded range of threats. The need for increased cyber security in this environment requires improvement in controls against vulnerabilities in CPS. Many CPS operate with the incorporation of a state estimation system. State estimation systems provide insight into the typically unobservable system state given observable measurements and relations of those measurements to the system state. Measurements outside of 3-sigma are discarded as bad measurements. Current methods establish the accepted boundary of measurements. The potential inclusion of cyber-attack data within accepted boundary define a set of sub-erroneous outliers which are within normal operations yet below current outliers. Outlier algorithms may be implemented to detect sub-erroneous outliers. A cyber-aware state estimator can accommodate for the attack and provide a correct state estimate given the implementation of a Cyber Attack Matrix constructed from sub-erroneous outlier detection. The sub-erroneous outlier detections evaluated found that a Grubb's test or Mahalanobis distance provided good performance.
3D Cameras and Algorithms for Multi-Angle Gripping and Control of Robotic Arm

This research develops an automated multi-angle identification and gripping path planning method of a robotic arm. First, we integrate a 3D camera to obtain the image, position, and distance of the workpiece and then send the image to the remote host via a network connection to identify the workpiece and calculation path with a deep learning algorithm. Through the process, the best path and the angle of arm movement are found. The experimental results show that the system continuously reads real-time images from the 3D camera and performs the calculations to correct the moving path when the arm moves. The overall operation is very smooth, and the workpiece can be accurately clamped.
Sampling Workloads with Dynamic Time Scale to Promote the Energy Efficiency of Datacenters

In modern datacenters (DCs), service resources are allowed to be partially supplied, thus maintaining a good energy efficiency. To match the supplied service resources with the resource demand of workload, the demand is estimated according to the status of workload. Then, in accordance with the estimations, the service resources supplied can be adjusted to match the demand. Generally, some workload factors, which are sampled from workloads, are used to reflect the workload status. But the values of a factor can be very different, when using different Time Scales (TSs) to sample the factor. As a result, the TS used can greatly affect the accuracy of estimation results. In this paper, we propose a Dynamic TS Sampling (DTSS) method to sample workload factors with dynamic TSs. DTSS uses fine-grained TSs for workloads with high variability, and coarse-grained TSs for workloads with moderate variability. Thereby, DTSS can obtain the representative values of factors to reflect workloads' status. Accordingly, based on these values, a good estimation can be made on the resource demand of workloads. Finally, by providing suitable service resources for workloads, DTSS significantly promotes the energy efficiency of DCs.
An Error-Correcting Output Code Framework for Lifelong Learning without a Teacher

An intelligent system should learn new concepts continuously and autonomously. The system should recognize that a concept is new and learn the concept without any guidance. In this paper, a novel error correcting output code (ECOC)-based framework, motivated by the complementary learning systems (CLS) theory, is proposed to perform (i) rapid detection of a new concept and (ii) learning and storing of the new concept via reinforced encoding. Experimental results on six datasets show the feasibility and competitive performance of the proposed ECOC-based framework for life-long learning using four different base classifiers against two baseline approaches. Moreover, we demonstrate the performance of our proposed ECOC-based framework on a continual learning scenario without any label feedback using a realistic but stringent cumulative performance measure, which combines detection error and classification error.
Constrained Clustering using Gaussian Processes

Constrained clustering is an important machine learning, signal processing and data mining tool, for discovering clusters in data, in the presence of additional domain information. The present work introduces a probabilistic scheme for constrained clustering based on the popular Gaussian Process framework. The proposed scheme accommodates pairwise, must-and cannot-link constraints between data, does not require hyperparameter tuning, and enables assessment of the reliability of obtained results. Preliminary results on real data showcase the potential of the proposed approach.
SEM: Adaptive Staged Experience Access Mechanism for Reinforcement Learning

Experience memory, which stores and replays experience from the past for off-policy reinforcement learning (RL) agents, has become an essential component of cutting-edge algorithms in recent years. In most previous work, experience memory emphasizes recently acquired experiences while neglects long-ago ones, which causes high variance and catastrophic forgetting in continual learning. To solve the problem, we introduce the Staged Experience Mechanism(SEM) - a novel management mechanism of experience memory. SEM adaptively regulates the proportion of experiences based on the current learning stages, enabling agents not only to learn from new experiences but also from very old ones given limited memory capacity. In the experiment, SEM is coupled with two state-of-the-art off-policy algorithms compared with classic experience replay mechanisms on the suite of MuJoCo tasks. The result shows that RL with SEM is more stable and less variable than its counterparts in most tested continuous control environments, which reduces the training variance by 5% ∼ 104% while ensuring optimal scoring performance.
An Empirical Study of Emotion Recognition from Thermal Video Based on Deep Neural Networks

Emotion recognition is a crucial problem in affective computing. Most of previous works utilized facial expression from visible spectrum data to solve emotion recognition task. Thermal videos provide temperature measurement of human body over time, which can be used to recognize affective states by learning its temporal pattern. In this paper, we conduct comparative experiments to study the effectiveness of the existing deep neural networks when applied to emotion recognition task from thermal video. We analyze the effect of various approaches for frame sampling in video, temporal aggregation between frames, and different convolutional neural network architectures. To the best of our knowledge, we are the first w ork t o c onduct s tudy on emotion recognition from thermal video based on deep neural networks. Our work can provide preliminary study to design new methods for emotion recognition in thermal domain.
Fine-grained Autoscaling with In-VM Containers and VM Introspection

Clouds often provides a mechanism called autoscaling to deal with load increases of services running in virtual machines (VMs). When a VM is overloaded, scale-out is performed and automatically increases the number of VMs. However, when multiple services run in one VM, the entire VM is always scaled out even if only one service is over-utilized. In this case, only an over-utilized service should be scaled out, but it is not easy for clouds to accurately monitor the resource usage of services inside VMs. This paper proposes Ciel, which runs each service in a container created inside a VM for separation of services and enables fine-grained autoscaling of VMs. Using VM introspection, Ciel accurately monitors the resource usage of each in-VM container from the outside of a VM in a non-intrusive manner. If it detects an overloaded in-VM container, it creates a new VM of minimum size and boots only the container that needs to be scaled out in the VM. This can minimize both the cost of the VM and the time taken for scale-out. We have implemented Ciel using Xen and Docker and showed the effectiveness.
Decision Network: a New Network-Based Classifier

In recent year, the combination of machine learning and complex networks is gaining more and more attention. Some network-based machine learning methods which transform the vector-based instances into a network has shown a lot of potential. Some researchers believe that the network can show more information than vector-based datasets. In this paper, we proposed a network-based classifier named decision network(DN). DN abstracts the corresponding relationships between attribute values and class labels into a weighted bipartite network. The weight of the edge between an attribute value node and a label node represents the tendency to assign the instance with this attribute value to the corresponding class. Compared with the existing classifier, DN is more comprehensible and easier to implement. We evaluated the performance of DN on 7 real-world datasets by using 10-fold cross validation. It performs better than 9 other methods.
From Hertzsprung’s problem to pattern-rewriting systems

Drawing on a problem posed by Hertzsprung in 1887, we say that a given permutation π∈𝒮 n contains the Hertzsprung pattern σ∈𝒮 k if there is a factor π(d+1)π(d+2)⋯π(d+k) of π such that π(d+1)-σ(1)=⋯=π(d+k)-σ(k). Using a combination of the Goulden–Jackson cluster method and the transfer-matrix method we determine the joint distribution of occurrences of any set of (incomparable) Hertzsprung patterns, thus substantially generalizing earlier results by Jackson et al. on the distribution of ascending and descending runs in permutations. We apply our results to the problem of counting permutations up to pattern-replacement equivalences, and using pattern-rewriting systems – a new formalism similar to the much studied string-rewriting systems – we solve a couple of open problems raised by Linton et al. in 2012.
Emotional Communication Assist Interface App for People with Dysarthria

Text to speech (TTS) helps people who have speech disorders communicate with other people. The current TTS can express emotion, but this requires extra time and effort. We developed a TTS application by which users can render messages with emotion with ease by changing the pitch angle of a smartphone. We designed two types of TTS: free input and phrase selection. Also, we calculated the TTS editing parameters for each degree of emotion on the basis of the Online Game Voice Chat Corpus (OGVC).
Everyone has the right to understand: Teaching legal rights to ESL students

Abstract In recent years, English as a second language (ESL) educators have developed a variety of online resources for addressing legal issues. The problem is that the authors’ understanding of laws and police procedures varies greatly, and so does their familiarity with relevant research, the breadth of topical coverage, and the quality of police‐themed units, discussion questions, and lesson plans. The aim of this article is to offer a research‐based approach to the integration of legal topics in ESL curricula. This discussion draws on the authors’ collective experience with public advocacy (Communication of Rights Group, 2015), research on the Miranda rights (Pavlenko, 2008; Pavlenko, Hepford, &amp; Jarvis, 2019), and teaching the Miranda rights and other legal topics to diverse ESL populations, ranging from international college students to middle school pupils.
Basic Knowledge Construction Technique to Reduce The Volume of Low-Dimensional Big Data

Big-data has the characteristics of high volume, velocity, and variety (3v) and continues to grow exponentially following the development of the use of world information and communication technology. The main problem in the use of big data is data deluge. The need for technology and big-data storage and processing methods to offset the exponential data growth rate is potentially unlimited, giving rise to the problem of increasing exponential technology requirements as well. In this paper, we propose a new approach in the realm of big-data analysis, through separating the basic-knowledge construction process from the original data into knowledge with much smaller velocity and volume. There are three problems to be solved, such as formulating basic-knowledge, developing a method for constructing basic-knowledge from initial data, and developing a technique for analyzing basic-knowledge into final knowledge. In this study, the technique used to build basic-knowledge is clustering-based. Analysis of basic-knowledge into final-knowledge is limited to the clustering-based analysis process. The main contributions in this paper are basic-knowledge formulation, new big-data analytic architecture, basic-knowledge construction algorithms (DSC4BKC), and analysis algorithms from basic-knowledge (BDAfBK) to final-knowledge. To test our proposed method, we use the BIRCH clustering algorithm with O(n) complexity as the baseline. We also used the artificial test-data generated from WEKA, and the IRIS4D and Diabetes data from the UCI Machine Learning Data Set for validation. Our test shows that the proposed method much more efficient in using data storage (84.69% up to 99.80%), faster in processing (20.84% up to 86.91%, and produces final-knowledge that is similar to the baseline.
Dashboard Visualization Techniques in Information Security

The usage of data dashboards is essential for ensuring fast and accurate decision making. A prompt understanding of the required parameters and their visualization are important components of information security management. This article describes a set of dashboard visualization techniques based on an advanced understanding of the tasks assigned to standardized roles in the information security field. A deep understanding of these roles is an integral component of the process of the creation of a dashboard visualization tool. The detailed visualization increases the speed of response to an information security incident, while an adaptation to a specific role enables more ergonomic representation of data.
An improved symbol reduction technique based Huffman coder for efficient entropy coding in the transform coders

Entropy coding is the essential block of transform coders that losslessly converts the quantized transform coefficients into the bit-stream suitable for transmission or storage. Usually, the entropy coders exhibit less compression capability than the lossy coding techniques. Hence, in the past decade, several efforts have been made to improve the compression capability of the entropy coding technique. Recently, a symbol reduction technique (SRT) based Huffman coder is developed to achieve higher compression than the existing entropy coders at similar complexity of the regular Huffman coder. However, the SRT-based Huffman coding is not popular for the real-time applications due to the improper negative symbol handling and the additional indexing issues, which restrict its compression gain at most 10–20% over the regular Huffman coder. Hence, in this paper, an improved SRT (ISRT) based Huffman coder is proposed to properly alleviate the deficiencies of the recent SRT-based Huffman coder and to achieve higher compression gains. The proposed entropy coder is extensively evaluated on the ground of compression gain and the time complexity. The results show that the proposed ISRT-based Huffman coder provides significant compression gain against the existing entropy coders with lower time consumptions.
Score Prediction Network and Graph-based Selection for Semantic Line Detection

In this paper, we propose a novel semantic line detection algorithm. For an input image, we first detect semantic lines using a semantic line detector by classifying candidate lines. Then, we predict scores indicating whether they are harmonized or not between the detected lines. To this end, we develop a score prediction network (SPNet). Finally, we construct a graph consisting of the detected lines and the predicted scores between them and iteratively select the reliable semantic lines. Experimental results demonstrate that the proposed algorithm detects semantic lines accurately.
Kernel Based Cache Effect on Blockchain Node Synchronization

The blockchain network of any cryptocurrency is composed of multiple nodes working together in a unit. Moreover each node needs to completely synchronize the blockchain. This paper proposes a kernel based cache on blockchain node to reduce the latency of blockchain synchronization. Implementation details are mentioned along with some evaluation results. This research work can be helpful for futuristic low latency time smart contract application services.