Reviewerly/exHarmony huggingface dataset:
Statement of Removal

Aiming at the problem that uneven illumination easily affects the facial expression recognition rate and reduces the facial expression recognition rate, a facial expression recognition algorithm based on local features and deep belief network (DBN) is proposed. Firstly, the non-uniform illumination invariant feature of LSH in facial expression image is extracted; Secondly, Gauss Laplace operator (log) is used to extract the edge detail features of facial expression; Then, the facial expression features fused by LSH and log are constructed; Finally, the DBN model is used to train the facial expression features fused by LSH and log, and the trained DBN model is used for facial expression recognition. Experiments on Jaffe facial expression database and Uighur facial expression database show that the algorithm has strong robustness and effectiveness.
An Adaptive Scaling Cuckoo Search Algorithm

The cuckoo search algorithm (CSA) is a natureinspired metaheuristic algorithm based on the parasitic behaviour of the cuckoo species, and it has received much attention for solving optimization problems. Nevertheless, the CSA still has some limitations when applied to optimization problems, such as easily falling into local optima and converging slowly. In this paper, we propose a modified local search method with an adaptive boundary scaling mechanism using a chaotic map, which calculates the search radius depending on the position of the current population. Thus, the search ability of the traditional CSA is improved for optimization purposes. To further verify the stability and effectiveness of the proposed adaptive boundary scaling algorithm (ACSA), we compare it with the traditional CSA and nonmodified chaos-embedded CSA (CCSA) on 25 test benchmark functions. The experimental and statistical test results show that the ACSA outperforms its competitors, which means that the proposed algorithm can perform better than these other approaches in terms of their search capabilities for optimization problems.
An improved target detection algorithm based on EfficientNet

Abstract In order to improve the detection accuracy for small-scale targets in complex scenes, an improved target detection algorithm based on EfficientNet is proposed. Firstly, the EfficientNet network is used to optimize the DarkNet53 feature extraction network. Compressing the standard convolution with the depthwise separable convolution, and increasing the depth of the neural network with the residual network that can effectively achieve feature extraction, reduce the number of parameters and improve the detection speed. Secondly, a feature pyramid network is used to design four scales of features for multi-scale feature extraction, which improves the detection of small targets. The experimental results show that the YOLOv3 target detection algorithm improves the detection accuracy by 4.98% compared to the original algorithm on VOC dataset, which improves the detection accuracy and ensures real-time detection for small targets.
FOBA: Flight Operation Behavior Analysis Based on Hierarchical Encoding

Through analyzing flight data we can detect pilot’s improper operation, which effectively improves flight safety. This paper proposes an approach to convert multivariate flight data into symbol series and an auto-regressive semantic understanding model. Our model can predict what kind of pilot operation or aircraft status should appear at the next time step according to data at the current time step. Furthermore, we proposed a prediction model for unsafe event predicting based on our semantic understanding model. The experiment results show that our prediction model outperforms well known classifiers. Finally, experiments show that our model has the application value of correcting pilot operation.
TEXT SUMMARIZATION WITH SENTIMENTAL ANALYSIS

In today's world, Modern organizations deal with terabytes of text, such as email, that often plays a significant role in their day to day operations.The user has to face a task of identifying useful information from these data which is difficult and it requires some amount of time.One possible means is to use text summarization.Text summarization is the process of identifying the most valued/meaningful information in a document and compressing that information into a shorter version preserving its overall meaning.Sentiment analysis is about determining the text given by the user whether it is Positive, Negative or Neutral.We used Gensim Algorithm for generating text summary.This algorithm automatically summarizes the given text, by extracting one or more important sentences from the text.This project is about text summarization which includes sentiment analysis.In UI, a text box will be displayed, which is used to take the input text from the user which need to be summarize.Then it will pre-process the text and show the summarized content.It will be taking input as URL of an article and it is going to provide Title,
THE ORGANIZATIONAL COMMITMENT AS PERCEIVED BY THE STAFF MEMBERS OF THE FACULTY OF EDUCATION, UNIVERSITY OF KHARTOUM

Organizational commitment is considered an essential concept for ensuring work and staff motivation in a higher education setting. Therefore, this study aimed to assess the staffs’ degree of organizational commitment to the Faculty of Education, University of Khartoum. To investigate research questions, a survey questionnaire was conducted. Among 239 respondents, 106 staff members were surveyed (44.00%). The results indicated that the degree of staff affective, continuance, and normative commitment in the Faculty of Education, University of Khartoum, was high. This study also confirmed that the aggregate degree of organizational commitment among staff members in the Faculty of Education, University of Khartoum, was high. The study revealed no statistically significant difference in the degree of organizational commitment due to gender and academic rank variables. The study also found a statistically significant difference in affective and normative commitment in the degree of organizational commitment related to years of service. The study recommended improving the worldly position of the staff member to maintain a high degree of responsibility at the University of Khartoum.
The Shared-Workspace Framework for Dialogue and Other Cooperative Joint Activities

We present a theory of dialogue as a form of cooperative joint activity. Dialogue is treated as a system involving two interlocutors and a shared workspace that contains their contributions and relevant non-linguistic context. The interlocutors construct shared plans and use them to “post” contributions to the workspace, to comprehend joint contributions, and to distribute control of the dialogue between them. A fundamental part of this process is to simulate their partner’s contributions and to use it to predict the upcoming state of the shared workspace. As a consequence, they align their linguistic representations and their representations of the situation and of the “games” underlying successful communication. The shared workspace is a highly limited resource, and the interlocutors use their aligned representations to say just enough and to speak in good time. We describe how dialogue and cooperative joint activity can be brought together in situated communication, and then sketch some implications of our account for human-like artificial systems.
SecMVX: Analysis on the vulnerability of multi-variant execution

As an active defenses technique, multivariant execution(MVX) can detect attacks by monitoring the consistency of heterogeneous variants with parallel execution. Compared with patch-style passive defense, MVX can defend against known and even unknown vulnerability-based attacks without relying on attack feature information. However, variants generated with software diversity technologies will introduce new vulnerabilities when they execute in parallel. First, we analyze the security of MVX theory from the perspective of formal description. Then we summarize the general forms and techniques for attacks against MVX, and analyze the new vulnerabilities arising from the combination of variant generation technologies. We propose SecMVX, a secure MVX architecture and variant generation technology. Experimental evaluations based on CVEs and SPEC 2006 benchmark show that SecMVX introduces 11.29% of the average time overhead, and avoids vulnerabilities caused by the improper combination of variant generation technologies while keeping the defensive ability of MVX.
Improving Safety in Human-Robot Collaboration via Dynamic Active Constraints Enforcement*

The problem of motion planning in obstacle cluttered environments is an important task in robotics. In the literature several methodologies exist to address the problem. In this work we consider using the feedback-based approach, where the solution comes from designing a controller capable of guaranteeing trajectory tracking with obstacle avoidance. Commonly, all respective studies consider simplified robot dynamics, which is usually insufficient in practical applications. In this work we focus on the collision avoidance problem with respect to a moving spherical object. We assume knowledge of a nominal controller that achieves tracking of a desired trajectory in the absence of obstacles, and we design an auxiliary control scheme to guarantee that the robot’s end-effector will always operate in a safe distance from the moving obstacle’s surface. The controller we develop does not take into account the actual robot dynamics, thus constituting a truly model-free approach. Experimental studies conducted on a KUKA LWR4+ robotic manipulator clarify and verify the proposed control scheme.
Research on Operating System Kernel Security Based on Mandatory Behavior Control Mechanism (MBC)

The current operating system security is mainly achieved by controlling the access to the operating system, which cannot effectively ensure the kernel security and leads to a large amount of data redundancy in the system kernel due to the need to review and other judgments on all access operations in turn. To address these problems, we study the kernel security of operating systems based on the mandatory behavior control mechanism (MBC). Partition management of the kernel is achieved by partitioning the system kernel for isolation and setting inter-partition communication. The kernel functions are divided into three parts: security detection, storage management, and read management, to facilitate modular kernel management. After building the framework of the control The comparative experimental data show that the OS kernel data redundancy ratio is less than 13% and has good security when applying this OS security scheme.
SWOT Analysis of Lending Platform from Blockchain Technology Perspectives

Blockchain Technology become phenomenal issue in the world, emerging with bitcoin and IoT. It had been implemented in many areas in human activity. Advantages of blockchain technology is distributed ledger where resources distributed to all member in network. Loans or credit as part of human activity in their life. When we need cash for a major expense, it might be tempting to borrow from a payday lender or max out a credit card or similar, but you have other options that will not harm your credit or put you in a cycle of debt, even if your credit record is not all that great. Collateral loans could be a way to borrow the money as you need. One of which is the lending platform. Blockchain technology has been implemented in many lending platforms, but there are still any weaknesses that can be refined and optimized. Tool of analysis is SWOT, describes of four analysis, as follow: Strength, Weakness, Opportunity and Threats. This paper purpose to analyzed lending platform measured by look for the weaknesses variable and how to optimize that system performance that can improve for helping people in lending process. In addition, the result of this analysis can use for enhancement recommendation the system based on the weaknesses are found and opportunity for make a lending platform robust
Particle Swarm Optimization Based on Fitness Considering Distance Information for Multiple Local Optimal Solutions Search

In this letter, a Particle Swarm Optimization with fitness considering distance information is proposed. The proposed Particle Swarm Optimization is expected to obtain more multiple local optimal solutions than Firefly Algorithm which has the ablity to obtain multiple local optimal solutions. The advantage of the proposed Particle Swarm Optimization for multiple local optimal solutions search is demonstrated through some numerical simulations.
Functional Safety of Deep Learning Techniques in Autonomous Driving Systems

This chapter focuses on the existing works in safety risks of deep learning technologies in both general applications and autonomous driving systems. Motivated by Google DeepMind demonstrations on Atari games and Go game, artificial intelligence has attracted tremendous attention on making machines interact with the environment and gain knowledge. To achieve this goal, artificial intelligence has provided a number of powerful techniques as promising solutions to accelerate this process, making vehicles become a rational machine to handle challenging driving tasks automatically. Artificial intelligence is considered a promising solution in the automobile industry which can deliver autonomous driving to the real-world road. In the context of artificial intelligence, technologies such as deep learning have been widely accepted due to their significant capabilities over various tasks. The most datasets mainly represent an ideal environment, which assumes data features can be 100% exposed to the learning process.
MUCIC at CheckThat! 2021: FaDo-fake news detection and domain identification using transformers ensembling

Since the beginning of Covid-19 era in November 2019, the patient growth curve is closely accompanied by the growth of fake news. Therefore, developing tools and models for the detection of fake news from real ones in various domains have become more significant than the earlier days. To address the detection of fake news, in this paper, we, team MUCIC, describe the models submitted to 'Fake News Detection', a shared task organized by CLEF-2021-CheckThat! Lab. This shared task contains two subtasks namely;Fake News Detection of News Articles (Subtask 3A) and Topical Domain Classification of News Articles (Subtask 3B) and both are multi-class text classification tasks. The proposed models have been developed by fine-tuning the three transformer-based language models namely;Roberta, Distilbert, and BERT from HuggingFace using training data and then ensembling them as estimators with majority voting. The proposed models performances evaluated through the evaluation script provided by organizers obtained F1-scores of 0.5309 and 0.8550 for Subtask 3A and Subtask 3B respectively. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Conjectures, Tests and Proofs: An Overview of Theory Exploration

A key component of mathematical reasoning is the ability to formulate interesting conjectures about a problem domain at hand.In this paper, we give a brief overview of a theory exploration system called QuickSpec, which is able to automatically discover interesting conjectures about a given set of functions.QuickSpec works by interleaving term generation with random testing to form candidate conjectures.This is made tractable by starting from small sizes and ensuring that only terms that are irreducible with respect to already discovered conjectures are considered.QuickSpec has been successfully applied to generate lemmas for automated inductive theorem proving as well as to generate specifications of functional programs.We give an overview of typical use-cases of QuickSpec, as well as demonstrating how to easily connect it to a theorem prover of the user's choice.
OPTIMIZING STUDENTS’ READING COMPREHENSION OF DESCRIPTIVE TEXT THROUGH E-LEARNING METHOD WITH WHATSAPP APPLICATION AT STUDENTS OF SMA NEGERI 1 BUKO

This study aims to determine whether the E-learning method can be effective in optimizing students' reading comprehension of descriptive text with WhatsApp Application. This research was conducted in class X MIA 1 SMA Negeri 1 Buko in the academic year of 2020/2021. This study used one group-pretest-postest design of research with the data analysis were the frequency distribution of scores, mean and standard deviation. The questions from the pretest and posttest used the type of multiple choice questions with questions about the descriptive text. From this result, it was found that the mean of posttest Y = 7.8 with standard deviation Sy = 0.79 was higher than mean score at pretest X = 5.33 with standard deviation Sx = 0.89. It means that students' reading comprehension in descriptive text was higher after treatment at posttest than pretest. So the researcher concluded that the application of the E-learning method through WhatsApp application was effective in optimizing students' reading comprehension of descriptive text.
Malware Infections in the U.S. during the COVID-19 Pandemic: An Empirical Study

The COVID-19 pandemic has changed the world in many ways, especially in the landscape of cyber threats. The pandemic has pro-vided cybercriminals with more opportunities to commit crimes due to more people engaging in online activities, along with the increased use of computers for school, work, and social events. The current study seeks to explore cybercrime trends, in particular malware infections, during the COVID-19 pandemic. Thus, this study examines the relationship between the number of malware in-fections, COVID-19 positive cases, closed non-essential businesses, and closed K-12 public schools in the United States. Data utilized in this study derives from (1) Kaspersky Cyberthreat Real-Time Map, (2) Centers for Disease Control and Prevention (CDC), and (3) COVID-19 US State Policy Database over the course of six months from January of 2020 to June of 2020. The findings of this study reveal that there are associations between the number of malware infections, COVID-19 positive cases, and closed non-essential busi-nesses. However, interestingly, there is no link between the number of malware infections and closed K-12 public schools. Policy impli-cations and the limitations of this study are also discussed.
JomIoT Medic: Saving Lives

Nonadherence medication refers to the misuse or forgetting to take medication on time, it Is a fatal issue that causes a huge number of health issues and complex failure in the body and unfortunately. This paper proposes an advanced innovative medication reminder device consist of a mobile application and it is connected wirelessly using IoT to the hardware part as a special medication container in a shape of a smartphone cover or chain. To evaluate the device a survey was distributed to different population samples of the different age groups from both local and private sectors followed by an intense interview from the same sampling population. This medication device is the perfect and most convenient method to stick to medication time and the number of pills to help in fast recovery at all ages without complications and most importantly to save money. Managerial and practical implications are discussed.
Weakly supervised semantic segmentation of tomographic images in the diagnosis of stroke

This paper presents an automatic algorithm for the segmentation of areas affected by an acute stroke on the non-contrast computed tomography brain images. The proposed algorithm is designed for learning in a weakly supervised scenario when some images are labeled accurately, and some images are labeled inaccurately. Wrong labels appear as a result of inaccuracy made by a radiologist in the process of manual annotation of computed tomography images. We propose methods for solving the segmentation problem in the case of inaccurately labeled training data. We use the U-Net neural network architecture with several modifications. Experiments on real computed tomography scans show that the proposed methods increase the segmentation accuracy.
Zero-Shot Joint Modeling of Multiple Spoken-Text-Style Conversion Tasks Using Switching Tokens

In this paper, we propose a novel spoken-text-style conversion method that can simultaneously execute multiple style conversion modules such as punctuation restoration and disfluency deletion without preparing matched datasets. In practice, transcriptions generated by automatic speech recognition systems are not highly readable because they often include many disfluencies and do not include punctuation marks. To improve their readability, multiple spoken-text-style conversion modules that individually model a single conversion task are cascaded because matched datasets that simultaneously handle multiple conversion tasks are often unavailable. However, the cascading is unstable against the order of tasks because of the chain of conversion errors. Besides, the computation cost of the cascading must be higher than the single conversion. To execute multiple conversion tasks simultaneously without preparing matched datasets, our key idea is to distinguish individual conversion tasks using the on-off switch. In our proposed zero-shot joint modeling, we switch the individual tasks using multiple switching tokens, enabling us to utilize a zero-shot learning approach to executing simultaneous conversions. Our experiments on joint modeling of disfluency deletion and punctuation restoration demonstrate the effectiveness of our method.
SpeakerStew: Scaling to Many Languages with a Triaged Multilingual Text-Dependent and Text-Independent Speaker Verification System

In this paper, we describe SpeakerStew - a hybrid system to perform speaker verification on 46 languages. Two core ideas were explored in this system: (1) Pooling training data of different languages together for multilingual generalization and reducing development cycles; (2) A novel triage mechanism between text-dependent and text-independent models to reduce runtime cost and expected latency. To the best of our knowledge, this is the first study of speaker verification systems at the scale of 46 languages. The problem is framed from the perspective of using a smart speaker device with interactions consisting of a wake-up keyword (text-dependent) followed by a speech query (text-independent). Experimental evidence suggests that training on multiple languages can generalize to unseen varieties while maintaining performance on seen varieties. We also found that it can reduce computational requirements for training models by an order of magnitude. Furthermore, during model inference on English data, we observe that leveraging a triage framework can reduce the number of calls to the more computationally expensive text-independent system by 73% (and reduce latency by 59%) while maintaining an EER no worse than the text-independent setup.