Reviewerly/exHarmony huggingface dataset:
Accelerating SoC Security Verification and Vulnerability Detection Through Symbolic Execution

Model checking is one of the most commonly used technique in formal verification. However, the exponential scale state space renders exhaustive state enumeration inefficient even for a moderate System on Chip (SoC) design. In this paper, we propose a method that leverages symbolic execution to accelerate state space search and pinpoint security vulnerabilities. We automatically convert the hardware design to functionally equivalent C++ code and utilize the KLEE symbolic execution engine to perform state exploration through heuristic search. To reduce the search space, we symbolically represent essential input signals while making non-critical inputs concrete. Experiment results have demonstrated that our method can precisely identify security vulnerabilities at significantly lower computation cost.
Release Planning Patterns to Define the Release Dates of Hardware and Software Increments in the Automotive Domain

Today’s vehicle launches are characterised by innovative features. Handling the vast amount of software and the corresponding hardware are major challenges for original equipment manufacturers (OEMs) that have to be mastered. This contribution introduces release planning patterns to simplify the planning of software and hardware installed in a vehicle. Therefore, we present eight release planning patterns that were conducted at Dr. Ing. h. c. F. Porsche AG. The patterns are applied to plan the scope of hardware and software to be developed for certain releases. Numerous influencing factors such as legal requirements, security aspects and the high networking of the software are also considered in the patterns. The patterns address an automotive context and have been developed from a hardware and software point of view based on two projects from the case company. The gathered knowledge can be used for further application in practice and related domains.
Implementation of Technology to Recognize Segmented and Non-Segmented Text: A Survey

There is a need to preserve the historical handwritten documents for the generations to come. Therefore, handwritten text which is available in various scripts needs to be digitized and later recognized. Various techniques are available for the digitization of handwritten text. Digitization of handwritten text helps to access, manipulate and store the data for the longer time. Various Machine Learning and Deep Learning techniques are being implemented for the recognition of handwritten text. In this paper, a survey has been presented for the recognition of handwritten text using various techniques. This survey will be helpful for the researchers to find the various techniques that can be applied for the recognition of different handwritten scripts to obtain the desired Accuracy.
Data Augmentation Based Anomaly Data Detection for Charging Piles

As electric vehicle (EV) charging facilities continue to grow in size, the proper operation of EV charging posts is of particular importance. However, certain non-human factors can lead to data anomalies in charging posts, thus hindering the normal operation of EV charging posts, as well as the daily operation and profitability of charging stations. Therefore, this paper lectures on the features of generative adversarial networks (GAN) that can retain the original data features and random forests that can detect anomalous data, and performs anomaly detection on the anomalous data detected by the EV charging station management system. Finally, the experimental results show that the GAN used in this paper can generate more anomalous data to augment the original dataset and that the model trained from the data-augmented dataset has higher data anomaly detection capability than the model trained from the dataset with less anomalous data without data augmentation.
Human-computer Interactive Oral Translation Methods and Related Issues

Nowadays, machine translation technology has penetrated into daily life and is a commonly used auxiliary tool for cross-language communication. Therefore, the translation for spoken language is particularly important. In addition to its scientific and engineering significance, oral translation also contains rich economic and social benefits. The purpose of this paper is to study human-machine interactive spoken language translation methods and related issues. Firstly, the concept of interpreting and related work at home and abroad are introduced, and then the machine translation system and interpreting system, which are important components of interpreting, are introduced respectively. The acoustic model and statistical language model based on HMM are established. In the process of creating a statistical language model, in order to solve the problem of sparse data, we adopted a modeling method combining word-based language model and part-of-speech-based language model. Finally, the system is tested by use cases, and the correct rate of oral translation is above 90%.
Eye-Move: An Eye Gaze Typing Application with OpenCV and Dlib Library

People who are unable to type on a computer or a mobile phone due to inadequacies produced by their hands, such as osteoarthritis, carpal tunnel syndrome, trigger finger, Ganglion cysts, and other disorders, can benefit from eye vision technology. There are currently a number of commercial and non-commercial eye-tracking solutions available, including model-based and appearance-based methods; however, some of these solutions are expensive or unreliable in real-world situations, and others require explicit user calibration, which can be time-consuming. As a result, research into deep learning-based eye-tracking systems have switched to improving these systems. Recent eye-tracking research has focused on the development of deep learning-based eye-tracking algorithms that don't require explicit user calibration. Because of the recent emergence of deep learning, gaze estimation models based on convolutional neural networks (CNNs) are becoming more significant and common. In our research, the proposed system will provide a new user -friendly keyboard interface for the user. User can see the keyboard layout and can type the text by the movement of his or her eyes. The user can enhance his typing rate by making use of a word prediction engine. Word prediction is an assistive technology tool that suggests words while typing.
Digital Data Protection using Barcode &amp; Steganographic Approach

In order to secure the digital data, various image steganography methods were proposed. This research work investigates and proposes a novel steganographic approach. This research study considers a hard and fast set of standards to investigate and estimate the advantages and disadvantages of the steganographic methods. As a result, a novel steganography style has been proposed. Steganography provides higher protection than cryptography due to the fact that cryptography covers only the contents of the verbal exchange. Steganographic messages are always first restated with the conventional methods, and then the input image will get changed in some way to include the rephrased conversation. Steganalysis is the discovery of steganography based decrypted program. They are LSB-based Steganography, Steganography with closing full-size bits, and Steganography with photo slant dots. The discussion has been figured out using succession and lopsided cryptography.
Standardization in Interface Design to Increase Higher Ui/Ux in Game Levels Setup

Normally, when most people think of the phrase User Interface (UI), they think of software applications. The user interface (UI) is the part of software that interacts directly with humans. In video games, people identify UI as Heads-up Display (HUD). Players will mostly spend their time seeing the HUD of a game. A good interface always gives out what’s the best information for the player, and minimalize less important information to the user. We analyze 2 similar game that have some quite unique similarities and characteristic. Understanding what can be categorized as a good interface will also help us designing the interface. In this paper, we are going to identify what needs to be done to make a good interface design and implementing it into our new game “Unbreakable Mind”. Unbreakable Mind is game that has been designed by us, with genre of rogue-lite, hack and slash, action-platformer with procedurally generated levels.
Exploring slowness

“We need to go faster!” – There are good reasons for an organization to increase the speed of its product delivery. Among them might be a desire for rapid feedback from customers as well as an ability for a rapid response to such feedback. We reframe the call to action of going faster and propose to go less slowly by addressing waiting times within the value chains of an organization. This article proposes the patterns Visual Value Chain Representation, Wait Time Exposure, and Delivery Speed Measurement to help raise awareness for product delivery speed and to enable an organization to start having conversations with their customers about it.
Estimation of Accuracy Level for Sentiment Analysis using Machine Learning and Deep Learning Models

Due to the digitization of everything, the effect of the growth of social media in nook and corner of the world creates the requisite of gathering and analysing the millions of reviews that people shared about their emotions so as to improve the quality of the corresponding concern. Such thing is defined as sentiment analysis which accommodates online product reviews, movie reviews, personal posts in social media, etc. to categorize them as good and bad. The proposed research contains the performance examination of machine learning and deep learning models for the hotel reviews and IMDB Movie Reviews. The result reveals that the deep learning has higher accuracy. The performance of machine learning models varies depending on the size of the dataset.
College English Score Analysis Based on Data Mining Algorithm

The status of English education has always been very important. In our country, English is one of the compulsory education activities during high school, university and junior college. High school entrance examination, English CET4, CET6, entrance examination, higher education, postgraduate and other examinations, English scores occupy a place, the scale is very important, so it is really very important to learn English well. The main purpose of this paper is to study college English grades under big data through DM algorithms, including the prediction of grades and the countermeasures for subsequent improvement. This paper deals with the theoretical and technical aspects of data mining (DM) and ML, and describes related technologies, including mining processes and related algorithms. Experiments show that more than 70% of students take the initiative to seek opportunities to learn English and speak English occasionally. At the same time, the proportion of girls who actively seek to learn English is higher than that of boys.
Indian Agriculture Supply Chain Management using Blockchain Technology and CNN

India is a key player in the global food production industry. Indians have been cultivating land since the Indus Valley Civilization. Although historically significant, this country’s farmers are starving to death while their counterparts in other countries receive the benefits of their labour to the tune of billions. Lack of proper supply chain management is a contributing factor. It’s common knowledge that poorly cared-for farming machinery quickly deteriorates. However, current methods of supply chain management do not enable farmers to track the whereabouts of their commodities, even though the government supplies them with storage facilities. Since no system is in place, they have no idea where their packages are at any one time during the shipping process. Therefore, there is a need for more stringent supply chain management, including checks at each level and double-checking by farmers and government officials to ensure the integrity of goods. This study suggests implementing Blockchain technology, which allows for more transparent information on the status of commodities, to enhance the relationship between producers and consumers. By recording this information on the blockchain, all of the farmers and government employees involved in the shipping process can stay apprised of the operation’s finer points. Records can be kept forever in an immutable format.
A Novel Object Detection Framework using Convolutional Neural Networks (CNN) and RetinaNet

Object detection is a computer vision technique that received high significant attention in recent decades. Object detection algorithms typically employ machine learning or deep learning to produce valid results. People can quickly recognize and locate objects of interest in the provided input images or videos. Object detection aims to use a computer to mimic this intelligence. Deep learning techniques have significantly improved the cure for object detection. This research aims to incorporate radical object detection techniques to achieve high accuracy. This research study covers a variety of factors and algorithms used in object detection methods using deep learning techniques detection algorithms, datasets and software hardware requirements used in the detection of objects. Benchmark datasets for object detection were discussed. The discussed method uses CNN with RetinaNet (Residual Networks and Feature Pyramid Networks) was implemented with benchmark dataset COCO. RetinaNet provides low loss and high accuracy (96%) Object Detection has a variety of applications like Autonomous driving, Traffic monitoring and Maintenance, People Counting and Video safety applications.
Towards a new POI Recommendation Approach based on Implicit Trust between users

Nowadays, Recommender Systems (RSs) have become the indispensable solution to the problem of information overload in many different fields (e-commerce, e-tourism, ...) because they offer their customers with more adapted and increasingly personalized services. In this context, collaborative filtering (CF) techniques are used by many RSs since they make it easier to provide recommendations of acceptable quality by leveraging the preferences of similar user communities. However, these types of techniques suffer from the problem of the sparsity of user evaluations, especially during the cold start phase. Indeed, the process of searching for similar neighbors may not be successful due to insufficient data in the matrix of user-item ratings (case of a new user or new item). To solve this kind of problem, we can find in the literature several solutions which allow to overcome the insufficiency of the data thanks to the social relations between the users. These solutions can provide good quality recommendations even when data is sparse because they permit for an estimation of the level of trust between users. This type of metric is often used in tourism domain to support the computation of similarity measures between users by producing valuable POI (point of interest) recommendations through a better trust-based neighborhood. However, the difficulty of obtaining explicit trust data from the social relationships between tourists leads researchers to infer this data implicitly from the user-item relationships (implicit trust). In this paper, we make a state of the art on CF techniques that can be utilized to reduce the data sparsity problem during the RSs cold start phase. Second, we propose a method that essentially relies on user trustworthiness inferred using scores computed from users’ ratings of items. Finally, we explain how these relationships deduced from existing social links between tourists might be employed as additional sources of information to minimize cold start problem.
Improved A* Method for High Efficiency Forklift Path Planning

As industrial transport vehicles, forklifts are widely used in factories and warehouses, responsible for the loading, unloading, and handling of goods. The forklift has orientation requirements when working, but its volume is too large to complete in-place turning. What is more, traditional path planning algorithms can not plan the turning track of a forklift. In this paper, we propose an improved <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A^{\ast}$</tex> method for high efficiency forklift path planning. First, we convert the forklift map into a raster map according to specific rules and then use the defined T structure combined with improved <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A^{\ast}$</tex> algorithm for path planning. Experiments show that the proposed method can plan the travel path of the forklift between the starting point and the terminal point, meeting the requirement of forklift orientation at the end in the meantime.
Patterns of Agile Coach roles

Agile software development, and other applications of agile working, continue to grow both in terms of usage and in terms of knowledge and lore. As a result the Agile Coach role has become an established role; yet the role is mired in confusion. This paper aims to reduce this confusion by differentiating the agile coach role.
Toward Organizational Pattern Ontology

Organizational patterns of agile software development are proven practices for dealing organizational principles. Finding and selecting the right pattern is difficult. One way to select a pattern is to follow the sequence and compositions given in a pattern language. However, in general, pattern languages do not always reveal every reliable connection. Patterns are described in informal and unstructured text, making it difficult to understand their connections. This work attempts to provide a conceptual ontology model for organizational patterns – describing the collection of concepts (terms) and their relations. This contribution can be an attempt to express, expose, and share semantic knowledge between patterns using ontology. The resulting ontology can be used on top of the organizational patterns repositories to help retrieve patterns based on their logical connections.
Modular Design of English Pronunciation Proficiency Evaluation System Based on Speech Recognition Technology

With the vigorous development of computer technology, speech recognition technology (SRT) is gradually applied to various fields. Especially in foreign language teaching, SRT assists speech learning software, which can provide learners with learning resources such as multiple sounds, images, and videos. It can also help learner’s correct pronunciation and improve the correct level of English pronunciation. Therefore, SRT has strong practicability in the English pronunciation proficiency evaluation system. The research theme of this paper is the modular design of English pronunciation proficiency evaluation system based on SRT. This article first elaborates the related technologies of the English pronunciation proficiency evaluation system, and carries out modular design of the system from four aspects: intonation, speaking speed, intonation, and rhythm. Finally, quantitative audio-visual dual-modal speech recognition and cross-modal speech recognition tests were carried out on the system. According to the experiment, in the English pronunciation proficiency evaluation system the accuracy of intonation, speech speed, rhythm and intonation is as high as 88.39%, the adjacent agreement is as high as 99.76%. This means that the system is in the evaluation of English pronunciation level is feasible and effective.
Gradient Information from Google GBoard NWP LSTM is Sufficient to Reconstruct Words Typed

Federated Learning is now widely deployed by Google on Android handsets for distributed training of neural networks. While Federated Learning aims to avoid sharing sensitive user data with Google, in this paper we show that when used for GBoard next word prediction Federated Learning provides little privacy to users. Namely, we demonstrate that the words typed by a user can be quickly and accurately reconstructed from the gradients of the GBoard LSTM used for next word prediction. Use of mini-batches does not protect against reconstruction.
Simultaneous Extraction of Entities and Relations Based on Pre-trained Language Model and Pre-defined Language Templates

With the advent of the era of big data, the need to obtain core information from text data is getting stronger and stronger. And the knowledge graph can visually represent the core information of the text. Entity relation extraction is a challenge and key part of knowledge graph construction. In this paper, we propose a model that can effectively perform entity relation extraction. The model adopts a joint training approach based on parameter sharing to solve the error propagation problem in pipelined extraction. At the same time, the model uses a pre-trained language model as the basis to solve the problem of lack of semantic knowledge in traditional models. Further, it uses a linguistic template-based approach to bridge the discrepancies in training and fine-tuning of pre-trained language models. To validate the proposed approach, we conduct comparative experiments on the SemEval2010 dataset and conll04 dataset. The validation results demonstrate that our model can improve the accuracy, recall, and F1 score of joint entity relationship extraction compared to the baseline models.
Element Similarity Calculator in XML Schema Matching

XML is one of the standard ways for representing and exchanging information on the Web. However, XML schemas that represent the same/similar information are usually heterogeneous. To reconcile the heterogeneity of XML schemas, there are many approaches in the literature that match the elements of XML schemas to each other. The calculation step that is in common among all the matching approaches is the similarity calculation among XML elements. We extract a pattern that abstracts the similarity metrics that have been applied by the existing XML matching approaches. The pattern provides to data/software engineers a skeleton of abstract classes that the engineers should extend for implementing the automated calculation of element similarity.
CASTrans unsupervised timing anomaly detection networks

Unsupervised time series anomaly detection has always been a challenging topic. Compared with supervised time series anomaly detection, it can find the abnormal occurrence point more quickly, efficiently and accurately. In this paper we propose a strengthened feature attention module, Channel-Attention Block(CA Block) On the basis of Block, we construct the feature extraction module CABlock-Relu-CABlock (CRC), and design a new self-attention module Channel-Attention Series combined with self-attention Self-Attention(CASSA), and finally built Channel-Attention Series Transformer(CASTrans) with the above two basic modules CRC and CASSA. We have conducted full experiments on the time series anomaly detection datasets PSM, MSL, SMAP and SMD.The experimental results show that, compared with the advanced methods in recent years, most of the indexes of Recall, Precision and F1Score of CASTrans are higher than other SOTA methods.