Reviewerly/exHarmony huggingface dataset:
The Assessment for Undergraduate Development Using Factor Analysis and K-Means Clustering

Promoting the development of college students is an important research topic for colleges to cultivate talents. The evaluation of the development of undergraduates is an important mean to strengthen the development and construction of colleges as well. Hence, the research based on the development evaluation of undergraduates has important practical significance and theoretical basis. In this paper, we collected the dataset from students who come from several universities in Guangdong Province by doing questionnaires. Then we selected 9 indicators which have positive effect on objects through previous work. In the next step, we constructed 3 factors by using factor analysis, included Learning & Practice Ability, Technical Ability and Innovation & Entrepreneurial Ability. Finally, we carried out K-Means with 3 factors scores and evaluated by the silhouette coefficients. These results show that the ability of most of undergraduates are lower average level of it and percent of these have the better performance in one aspect and around 30%.
A Loop Selection Front-end for Underwater Visual GraphSLAM

This paper presents an algorithm to reject false loops ready to be used as a front-end for GraphSLAM. The proposal operates in two steps. The first one checks each loop independently to reduce the computational cost of the second one, which jointly checks the consistency of several loops. The experiments show the ability of this algorithm to detect wrong loops, favourably comparing to a previously existing approach in terms of final graph quality and time consumption.
LAMNER

Code comment generation is the task of generating a high-level natural language description for a given code method/function. Although researchers have been studying multiple ways to generate code comments automatically, previous work mainly considers representing a code token in its entirety semantics form only (e.g., a language model is used to learn the semantics of a code token), and additional code properties such as the tree structure of a code are included as an auxiliary input to the model. There are two limitations: 1) Learning the code token in its entirety form may not be able to capture information succinctly in source code, and 2) The code token does not contain additional syntactic information, inherently important in programming languages.
Investigating user perceptions of conversational agents for software-related exploratory web search

Conversational agents that respond to user information requests through a natural conversation have the potential to revolutionize how we acquire new information on the Web (i.e., perform exploratory Web searches). Recent advances to conversational search agents use popular Web search engines as a back-end and sophisticated AI algorithms to maintain context, automatically generate search queries, and summarize results into utterances. While showing impressive results on general topics, the potential of this technology for software engineering is unclear.
A Context-Based Multi-Scale Discriminant Model for Natural Image Inpainting

Image inpainting is a classical yet challenging problem in computer vison with many real-world applications. In this paper, we propose a novel method for inpainting based on Convolutional Neural Network (CNN) and Generative Adversarial Networks (GAN). By analogy with the autoencoder, a new context encoder is proposed to generate the contents of a missing image region conditioned on its surroundings. To improve the stability and efficiency, we also modify the architecture of the context encoder by introducing 1*1 convolution, as well as other improvements. In addition, a multi-scale discriminator combining with GAN is presented. During training, the reconstruction and adversarial losses are used for the global image. To generate more details, we further add a local texture loss for the missing part. Qualitative experiments show that our model performs favorably against classical methods in generating visually plausible inpainting results. Moreover, quantitative experiments substantiate the effectiveness of the proposed method in natural image inpainting.
Cross-session Specific Emitter Identification using Adversarial Domain Adaptation with Wasserstein distance

Accurate and robust specific emitter identification (SEI) is very challenging since distribution shift of signals occurs in cross-session scenario. General domain adaptation (DA) is proposed to alleviate the shift by aligning different signal distributions. However, existing general-DA based SEI methods which focus on the shift in the same session cannot be directly applied to cross-session SEI, since the distribution of signals varies more drastically in different sessions due to the continuously changing hardware imperfections. In this paper, we propose a novel method named adversarial domain adaptation with wasserstein distance (ADAW) to tackle the cross-session SEI. Specifically, to alleviate the severer distribution shift of signals in different sessions, a generative model is applied to map the data of previous session to latter session regardless of the degree of radio frequency fingerprints (RFFs) variations. Then, a wasserstein distance guided adversarial unsupervised domain adaptation (UDA) strategy is introduced to learn common feature representations for signals of different sessions, such that the model trained on the signals of previous session can precisely identify the signals of latter session. Experiments on ADS-B signals of same emitters in three distinct time sessions validate the capability of ADAW for SEI under cross-session and noisy conditions.
PENGGUNAAN INSTRUMEN ASESMEN LITERASI BERMUATAN KARAKTER DENGAN MENGGUNAKAN METODE LINK AND MATCH PADA MAHASISWA IKIP SILIWANGI

The purpose of this study was to determine the use of character-laden literacy assessments using the link and match method and to determine the relationship between student test results regarding character-loaded literacy assessments and student questionnaire results. The method used in this research is descriptive qualitative method, which is a research method that makes a systematic, accurate, and factual overview of a fact, its nature, and the relationship between the phenomena studied. The data analysis technique in this study, namely the test technique, is to determine the results of character-laden literacy assessments by using the link and match method and the questionnaire technique used to determine the relationship between the results of literacy assessment tests containing character and the results of student questionnaires. The subjects in this study were students of IKIP Siliwangi class B1 class of 2019 Indonesian Language Education Study Program. The results obtained from the student literacy assessment were 72%. And the results of student questionnaires about the average character of 86.5%. Based on the descriptive analysis criteria table, the percentage for literacy assessment is in the high category criteria, while for the questionnaire filled with characters the percentage is in the very good criteria.
Cyber Attacks : Detection and Prevention

Tens of millions of cyber-attacks (Emails, online transactions, live video streaming, online games, and navigation are all examples of fraudulent Internet-based intelligence gathering.) are launched every day against Internet users throughout the world. Various defences have been developed by researchers in response to these attacks. At present, the techniques that cyber attackers use to perpetrate attacks are related to human exploitation. These attacks are more frequent than before, and they are harder to contain. In the area of information management, cybersecurity is essential. In today's world, protecting privacy has been one of the most difficult tasks. "Cyber-crimes" is the first thing that has come to me when I think about cyber security, which are on the rise at an alarming rate. Various governments and corporations are taking various actions to tackle cybercrime. Despite different initiatives, cyber security remains a major issue for many people. Traditional non-confidence counter-measures are unable to prevent violations against individuals. This paper explains the current state of cybersecurity threats, counter-measures and non-confidence tools that are relevant to day-to-day online operations. It offers a valuable cyber-attack taxonomy and classification that aids in the identification of cyber-attacks and cyber-security initiatives.
Graph-based Word Clustering Considering the Distance and the Connectivity of a Co-occurrence

Word clustering is a typical method of natural language processing. Several approaches for word clustering have been developed which consider different factors. The following article presents two factors, including the closest distance and the connectivity of a co-occurrence. The classical clustering algorithms, including k-means and Chinese Whispers, are chosen to compare their cluster quality. The results show that the quality of both proposed clustering algorithms of these two factors is close to k-means clustering.
Personalized Dialogue Generation Method of Chat Robot Based on Topic Perception

Human-Computer interaction system is a significant research direction in the field of human-computer interaction, and the research of open domain chat robot has received extensive attention. There are many problems in the existing chat robot: lack of personalized features, resulting in the process of the same chat, and the conversation has nothing to do with the topic. Therefore, a method of creating personalized conversation based on topic perception is proposed, and a personalized conversation model based on topic perception is designed. Semantic analysis and text similarity calculation are needed to build a conversation model. Based on the dialogue model, the training robot collects the corpus data related to the subject, convolves the corpus data related to the subject, and carries out the topic perception training. Finally, a personalized dialogue mechanism is established to generate personalized dialogue. Through experimental comparison, it is proved that the dialogue generated by this method is more suitable for the chat topic.
A small family service robot system with uncalibrated monocular camera for visual servoing tracking fast moving family targets in short range

Small family service robots are essential applications in service robots, and their accurate tracking of moving targets is the primary condition for robot-human interaction. Compared with long-range target tracking, the limited family space leads to the short distance between the robot and the target, which often leads to the problem of large circumferential relative angular velocity between the robot and the moving target. It dramatically reduces the ability of the robot to track the fast target motion at close range and at different scales. In this paper, a novel family service robot system based on monocular RGB and robot terminal calculation is proposed to track fast-moving targets in close range. Based on the construction of the robot body with two rotation directions, the neural network model is used for visual detection and recognition of the target object. Combined with the historical information of the target, the new relative azimuth information of the target object is calculated to control the changes in the pitch angle and yaw angle of the robot body and realize the servo tracking of the robot vision on the moving target. Experimental results confirm the visual servoing tracking ability of the tiny family service robot system for indoor moving objects, which will positively promote the application of small intelligent service robots.
Dynamical System in Wavelet Chaotic Neuron with Delay and Its Application in Color Image Encryption

Wavelet chaotic neuron combines Gauss wavelet function and Sigmoid function as non-monotonic excitation function. A new wavelet chaotic neuron with delay is proposed based on wavelet chaotic neuron. The effect of different parameters on chaotic dynamics of neuron are analyzed by using bifurcation diagram. The sensitivity and randomness of time series generated by wavelet chaotic neuron with delay are analyzed. A color image encryption algorithm based on wavelet chaotic neuron with delay is proposed. The security of the proposed encryption algorithm is researched. From the experimental results, we can see the chaotic sequences generated by wavelet chaotic neuron with delay are feasible and effective in encryption.
Leveraging Differences in Entropy for Password Generating Models

In this paper, we explore the efficacy of password cracking models when trained on passwords belonging to a lower entropy dataset than the entropy of the target set. It compares a Markov Model against a Recurrent Neural Network Model for the percentage of the target set found via a generated set. The generated set is created from a dataset of lower entropy passwords which we expect shows that poorly repeated habits from weaker passwords can be learned and incorporated forward into the generated passwords. The idea behind this approach is that there could be potential speedup in cracking higher entropy passwords which use similar bad habits as low entropy passwords over traditional password cracking methods.
Implementation and Optimization of Graph Computing Algorithms Based on Graph Database

Data mining and knowledge inferring over knowledge graph has gained particular attention and been widely applied in industry over the past years. These works can be generalized to graph computing and graph analysis tasks, such as shortest path search, hop-constrained reachability, PageRank, triangle counting, and closeness centrality computation. However, existing graph database query language (SPARQL, Gremlin, etc.) dose not implement these algorithms. Therefore, CCKS 2022 holds a benchmark of graph computing algorithm based on graph database. We implemented all these five algorithms mentioned above based on graph database and made many optimizations to improve computation efficiency. On the final leaderboard, we took the first place, which shows extremely high practicality and efficiency.
Error Investigation of Pre-trained BERTology Models on Vietnamese Natural Language Inference

Natural Language Inference tasks have emerged in recent years and attracted significant attention from the natural language processing research community. There has been much success in this task with many quality datasets in English and Chinese for research and demonstrating the impressive performance of machine learning models. Pre-trained models play a crucial role, which is reflected in their superior performance compared to other models. However, they are still far from perfect and have many obstacles to the characteristics of the data. Especially in Vietnamese, we have just seen the emergence of the ViNLI benchmark dataset to serve the research community. In this paper, we experiment and analyze how the characteristics in the ViNLI benchmark dataset affect the performance of the pre-trained BETology-based models. In addition, the data parameters of ViNLI are also measured and analyzed on the accuracy of these models to see if it has any impact on the accuracy of the model.
Learning to Cluster Faces with Mixed Face Quality

Face clustering is the task of grouping faces by their underlying identity, and is still a challenging task in practical use due to the common low-quality face images caused by pose, blur, occlusion, illumination etc. To address the issue, this paper proposes a face clustering algorithm, referred as FC-Q, that takes the quality score as extra input. Based on the main observation that two nodes similar in feature subspace but with different identity may have larger score difference, the algorithm first integrates this prior with the modified self-attention mechanism of Transformer to infer reliable linkage likelihood between similar node pairs. Then the algorithm combines the face quality information with the label propagation module to further suppress the abnormal pairings. The effectiveness of the algorithm is evaluated on two public face datasets in good and bad quality. Experimental results validate that our algorithm outperforms the state-of-the-arts under the general circumstance of clustering faces with mixed face quality.
MSIDP: Multi-scale Information Diffusion Prediction with Timestamp Information and Wide Dispersion

Information diffusion prediction is an important task which studies how information items spread among users. Generally, information diffusion prediction is divided into microscopic, macroscopic, and unified multi-scale information prediction, which aims to predict the next influenced user, the total number of influenced users, or the above two within a unified framework, respectively. However, the existing prediction models just adopted the diffusion sequences information of cascades, ignoring two important information for diffusion prediction, i.e. the specific diffusion timestamp and the wide dispersion. The former contains the dynamic changes of users' repost preferences and the diffusion rate, while the latter reflects the user's aggregation of influenced along diffusion paths. In this paper, we propose a novel Multi-Scale Information Diffusion Prediction model (MSIDP) for predicting microscopic and macroscopic information diffusion simultaneously. MSIDP encodes timestamp information and designs a bidirectional graph convolutional network (BGCN) to jointly learn the representations of users from the deep propagation and the wide dispersion, such that more diffusion information can be captured. Extensive experiments on three real datasets demonstrate the effectiveness of our proposed model compared with 11 baseline algorithms in terms of two evaluation metrics.
Face detection system using principal component analysis (PCA) method with eigenface algorithm

The use of PINs and passwords still have a low level of accuracy and are inadequate, so it is necessary to develop a security system using a face detection system.The purpose of this study is to detect faces using the PCA method with the eigenface algorithm and find out its accuracy. The data used is image data in the form of human face images with a system display using the Python GUI application. This method is used to simplify the observed variables by shrinking the number of pixels from a set of drawing spaces so that the new coordinate system can better draw distinctive features.
Insulator Defect Detection Based on Feature Fusion and Attention Mechanism

The performance of insulator defect detection model is not satisfactory due to the small object size, imbalanced and insufficient data. In this paper, based on YOLOv5 model, we propose an insulator defect detection method incorporating feature fusion and attention mechanism. Firstly, multi-scale feature fusion is introduced to strengthen the ability to extract minute features from images. Secondly, an attention mechanism based on SE-C module is proposed to improve the detection of defective objects. In addition, K-means++ is used to customize anchor boxes to meet the actual requirements and avoid mismatches. The experimental results show that the proposed model achieves 92.4% precision on the public insulator dataset, which demonstrates the applicability of the auto-detection system for insulator defects significantly.
Catch Me if You Can: A Novel Task for Detection of Covert Geo-Locations (CGL)

Most of the visual scene understanding tasks in the field of computer vision involve the identification of the objects present in the scene. Image regions like hideouts, turns, and other obscured regions of the scene also contain crucial information, for specific surveillance tasks. In this work, we propose an intelligent visual aid for the identification of such locations in an image, which has either the potential to create an imminent threat from an adversary or appear as the target zones needing further investigation to identify concealed objects. Covert places (CGL) for hiding behind an occluding object are concealed 3D locations, not usually detectable from the viewpoint (camera). Hence, this involves delineating specific image regions around the outer boundary of the projections of the occluding objects, as places to be accessed around the potential hideouts. CGL detection finds applications in military counter-insurgency operations and surveillance with path planning for an exploratory robot. Given an RGB image, the goal is to identify all CGLs in the 2D scene. Identification of such regions would require knowledge about the 3D boundaries of obscuring items (pillars, furniture), their spatial location with respect to the background, and other neighboring regions of the scene. We propose this as a novel task, termed Covert Geo-Location (CGL) Detection. Classification of any region of an image as a CGL (as boundary sub-segments of an occluding object, concealing the hideout) requires examining the relation with its surroundings. CGL detection would thus require an understanding of the 3D spatial relationships between boundaries of occluding objects and their neighborhoods. Our method successfully extracts relevant depth features from a single RGB image and quantitatively yields significant improvement over existing object detection and segmentation models adapted and trained for CGL detection. We also introduce a novel hand-annotated CGL detection dataset containing 1.5K real-world images for experimentation.ACK: IMPRINT (MHRD/DRDO) GoI, for support
Evolving Role of PKI in Facilitating Trust

A digital certificate is by far the most widely used artifact to establish secure electronic communication over the Internet. It certifies to its user that the public key encapsulated in it is associated with the subject of the certificate. A Public Key Infrastructure (PKI) is responsible to create, store, distribute, and revoke digital certificates. To establish a secure communication channel two unfamiliar entities rely on a common certificate issuer (a part of PKI) that vouches for both entities' certificates - thus authenticating each other via public keys listed in each other's certificates. Therefore, PKIs act as a trusted third party for two previously unfamiliar entities. Certificates are static data structures, their revocation status must be checked before usage; this step inadvertently involves a PKI for every secure channel establishment - leading to privacy violations of relying parties. As PKIs act as trust anchors for their subjects, any inadvertent event or malfeasance in PKI setup breaches the trust relationship leading to identity theft. Alternative PKI trust models, like PGP and SPKI, have been proposed but with limited deployment. With several retrofitting amendments to the prevalent X.509 standard, the standard has been serving its core objective of entity authentication but with modern requirements of contextual authentication, it is falling short to accommodate the evolving requirements. With the advent of blockchain as a trust management protocol, the time has come to rethink flexible alternatives to PKI core functionality; keeping in mind the modern-day requirements of contextual authentication-cum-authorization, weighted trust anchors, privacy-preservation, usability, and cost-efficient key management. In this paper, we assess this technology's complementary role in modern-day evolving security requirements. We discuss the feasibility of re-engineering PKIs with the help of blockchains, and identity networks.