Reviewerly/exHarmony huggingface dataset:
Research on Multi-target Recognition and Classification Strategy Based on Yolo v5 Framework

With the ever-increasing social labor cost and automation level, the research on the recognition and automatic classification and sorting of multi-target objects has important practical significance. To this end, a set of multi-target object recognition and sorting system based on Yolo v5 is designed, which is composed of three parts: image acquisition part, image target object recognition and positioning part, and sorting execution of different types of targets. First, the sorting system collects the original image of the target object by controlling the movement of the camera. Next, the Yolo v5 target detection algorithm is used to realize the identification of the target object, and at the same time return the position information of the target object to the control host. Finally, the upper host computer controls the movement of the mechanical claws to realize sorting by controlling the decision-making algorithm. In order to improve the recognition accuracy in the target detection process and compress the model size to adapt to the Jetson Nano embedded system, an improved Yolo v5 target detection algorithm is proposed. The experimental results show that the designed multi-target detection system can accurately identify, locate and classify target objects; at the same time, the improved Yolo v5 target detection algorithm proposed has a smaller model and faster recognition speed, which can be realized in Rapid deployment of embedded systems.
Finding Your Feet In Cybersecurity: A Reddit Text Mining Approach

The International Information System Security Certification Consortium, Inc (ISC) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cybersecurity Workforce Study, 2021, underscored the fact that there is a glaring 2.72 million global deficit of cybersecurity talent. Despite the availability of various learning resources, starting or advancing a career in cybersecurity demands extensive time and effort to select the appropriate resources. Individuals need to identify their prior knowledge and interest and map them appropriately to future learning requirements. The task of choosing an appropriate cybersecurity job profile and acquiring the necessary knowledge, ability, and skills across the variety of online information, can be a difficult undertaking. In this work, we explored data from an online community, Reddit, and applied Natural Language Processing techniques to identify the commonly referred skills, roles, and certifications in cybersecurity. This information can then be leveraged to recommend a systematic way on how to navigate a career in cybersecurity.
LENTO: Unpredictable Latency-based continuous authEntication for Network inTensive IoT envirOnments

Started as a hyped technology a few years ago, IoT is now a reality bringing sensing and computing capabilities from SCADA systems to households. At their core, IoT devices connect to the outside world to share sensed or computed data. However, the sensitivity and privacy of shared data has made access management a stringent need also for the IoT. In particular, continuous authentication could solve a few security issues, like session hijacking, via checking device legitimacy for each exchanged message and preventing attackers from pretending their actions came from authenticated devices. To date, device-to-device (D2D) continuous authentication still relies on tokens/certificates or devices’ fingerprints such as battery levels or location. The cited solutions, while being not always implementable on resource constrained devices, are low-entropy based and thus also sporting a non negligible probability of being guessable during impersonation attacks. In this paper, we overcome the above limitations with LENTO: unpredictable Latency-based continuos authEntication for Network inTensive IoT envirOnments. In addition to a thorough analysis, we also offer experimental validation of our proposal. We have deployed LENTO as an additional authentication module of the well-known NextCloud platform, and we have performed an extensive experimental campaign. Collected results confirm our working hypothesis. Network delays can be exploited as random seeds in continuous authentication protocols as they provide as much entropy as standard approaches. To the best of our knowledge, our approach is the first continuous authentication protocol relying purely on the network characteristics, regardless of the underneath computing base trustworthiness. Given the minimal overhead introduced by our solution, it provides continuous authentication even for those devices that cannot afford to run (defacto) standard protocols. As such, LENTO could be retrofitted, offering enhanced security to a plethora of nowadays unsecured devices. • LENTO is a refined/extended version of COLLIDE, further intensifying network interactions in order to better leverage latency. • A new approach to measure the communication latency on the network level to accurately compute latency without involving delays from non-network sources (such as processing delays). • Potential Compatibility with legacy devices (or sensors). • Usage of NIST STS to evaluate the randomness and collection of experimental data. • Experimental results show in comparison with previous approaches.
Preparation of the Hadamard and CNOT gates to realize the maximum entangled states

This paper studies the preparation of quantum gates that enable the realization of maximum entangled states, i.e., Bell states. A series of unitary time-evolution operators are derived to realize the Bell states through the Hadamard and CNOT gates. These operators are decomposed based on the Cartan decomposition method and steered by control laws designed based on the Lyapunov control theorem. The numerical simulations show that by adjusting proper control parameters in the designed Lyapunov-based control laws, the Hadamard and CNOT gates can be faster and better prepared through the decomposed operators, and more stable and accelerated Bell states can be achieved.
Orogen: Advanced relationship predictions for genetic genealogy

<h2>Abstract</h2> Orogen advances the science of relationship prediction by using Ped-sim, which improves upon previous models by incorporating crossover interference and sex-specific genetic maps. The Orogen tool provides accurate relationship predictions for a wide range of relationship types. It properly differentiates between close relatives at 23andMe, which is a newly available functionality for standalone tools. It provides new granularity of close relationships by showing the differences between paternal and maternal sides and in-group relationship types.
Automatic Classification of the Cerebral Vascular Bifurcations Using Dimensionality Reduction and Machine Learning

An intra-cranial aneurysm (ICA) is a bulge in a cerebral artery. It commonly arises onto a weakened area of the blood vessel. Such aneurysms may be particularly devastating when they occur along the Circle of Willis (CoW). Indeed the rupture of an ICA onto this particular area can lead to a permanent disability or even to the patient’s death. Most of those aneurysms arise at the bifurcations of the vascular tree. It is thus crucial to develop an anatomical identification of the bifurcations of the CoW where such aneurysms are preponderant. An automatic classification of the bifurcations could be of great help in detecting the presence of an ICA or predicting the risk of occurence. Here, we present an automatic identification of the CoW bifurcations based on machine learning techniques using various geometrical bifurcations features. Our automatic labeling process involves machine learning as well as dimensionality reduction algorithms used to map the selected bifurcations features to a lower dimensional space. Unlike similar studies in the literature, our main objective here is to free oneself from a classical registration step commonly being applied prior resorting to the classification. In our approach, we aim to collect various geometrical features from the bifurcations of interest, and thanks to dimensionality reduction, to discard the irrelevant ones before using a classifier. In this paper, we present the results of the proposed method on a set of 50 subjects imaged by Magnetic Resonance Angiography and evaluate whether it can replace the registration preprocessing step.
3D mesh transformer: A hierarchical neural network with local shape tokens

Self-attention networks have revolutionized Natural Language Processing (NLP) and are making impressive strides in image analysis tasks such as image classification and object detection. Inspired by this success, we specifically design a novel self-attention mechanism between local shapes and build a shape Transformer. We split the 3D mesh model into shape patches, which we call shape tokens, and provide polynomial fitting representations of these patches as input to the shape Transformer. The shape token encodes local geometric information and resembles the token (word) status in NLP. The simplification of the mesh model provides a hierarchical multiresolution structure, which allows us to realize the feature learning of a multilayer Transformer. We set high-level features formed by the shape Transformer as visual tokens and propose a vector-type self-attention mechanism to construct a 3D visual Transformer. Finally, we realized a hierarchical network structure based on local shape tokens and high-level visual tokens. Experiments show that our fusion network of 3D shape Transformer with explicit local shape context augmentation and 3D visual Transformer with multi-level structural feature learning achieves excellent performance on shape classification and part segmentation tasks.
Education for digital citizenship

The technological revolution of the Media and communication has led to a fundamental change in several fields: political, economic, social, cultural and educational. The whole world is forced to keep pace with this development and benefit from it in order to improve and develop societies. The education of digital citizenship was a modern model for teaching young people the limits that should be adhered to, when using smart devices, electronic computers, and the Internet for the benefit of the country and the citizen, or in other words, the responsible political participation of youth through social media in managing the affairs of their country.
Study and Analysis of Network Steganography Methods

With the advent of new technology, the number of threats and vulnerabilities is increasing at an ever-growing rate. The adversaries are finding new ways to breach the security of the system such that their anonymity is maintained. One such technique used by the adversaries is steganography. It is the practice of hiding information inside a cover media so that the request appears to be legitimate and remains undetected. Network steganography is one such technique which uses existing network protocols to create covert channels so that it can be used as a cover media to hide the information. Though numerous research work has been done on how these covert channels are being established and used, it still remains one of the most evolving topics with the arrival of new convert channels with the ever-growing nature of technology. This paper reviews some of the notable network steganographic methods which are either used for hiding information or used for creation of a secret communication channel.
Color Feature Extraction-Based Near-Duplicate Video Retrieval

Near-duplicate video retrieval (NDVR) is the systematic approach to search near-duplicate videos. Near-duplicate videos are the videos which may differ from each other as per the format, version, editing differences, etc. Using the RGB color features, the vector is generated which is used for retrieval of NDVR based on Euclidean distance. The performance measure precision is used for the verification of the performance of the model. The benchmarked dataset CC_WEB_VIDEO is used.
Improved genetic algorithm for dynamic multi-UAVs target assignment at the end of logistics

In unmanned logistics end distribution, task distribution is a problem that must be solved. In this paper, we calculate the distribution range that can be covered by multiple UAVs, and make distributed interconnection communication and calculation between multiple intelligent bodies, i.e., between multiple UAVs and ground platforms. In the process of executing the task, the distribution result is adjusted by real-time replanning, so that the total distance in the distribution result is minimized. After the UAV takes off and starts to execute the distribution task, the objective function model is established based on the real-time location of the UAV, the location of the end point of the movement, the length of the path of the task execution, and the time collaboration of the multi-UAV execution task as constraints, and solved using the Improved Genetic Algorithm. Through the results of simulation experiments, we can see that the task allocation strategy used in this paper can perform resource calculation more efficiently and get optimized allocation results.
Co-MLHAN: contrastive learning for multilayer heterogeneous attributed networks

Abstract Graph representation learning has become a topic of great interest and many works focus on the generation of high-level, task-independent node embeddings for complex networks. However, the existing methods consider only few aspects of networks at a time. In this paper, we propose a novel framework, named , to learn node embeddings for networks that are simultaneously multilayer , heterogeneous and attributed . We leverage contrastive learning as a self-supervised and task-independent machine learning paradigm and define a cross-view mechanism between two views of the original graph which collaboratively supervise each other. We evaluate our framework on the entity classification task. Experimental results demonstrate the effectiveness of and its variant , showing their capability of exploiting across-layer information in addition to other types of knowledge.
A semantic‐based method for analysing unknown malicious behaviours via hyper‐spherical variational auto‐encoders

Abstract In the User and Entity Behaviour Analytics (UEBA), unknown malicious behaviours are often difficult to be automatically detected due to the lack of labelled data. Most of the existing methods also fail to take full advantage of the threat intelligence and incorporate the impact of the behaviour patterns of the benign users. To address this issue, this paper proposes a Generalised Zero‐Shot Learning (GZSL) method based on hyper‐spherical Variational Auto‐Encoders (VAEs). Compared to the VAEs, the authors’ proposed method is more robust and suitable for capturing data with richer and more nuanced structures. The authors’ method analyses the unknown malicious behaviours by projecting them and their semantic attributes to shared space. These are then matched by the cosine similarity. The authors further use a Graph Convolutional Network (GCN) to reduce the impact of different user behaviour patterns before projection. The experimental results indicate that the proposed method is efficient in the analysis of unknown malicious behaviours.
NOUN PHRASE CONSTRUCTION IN DESCRIPTIVE WRITING BY SENIOR HIGH SCHOOL STUDENTS

This study analyzed the types of noun phrase construction. There were two problems in this study, namely the types of noun phrase construction found in writing by the first year students of senior high school and the dominant type of noun phrase constructions found in writing by the first year students of senior high school. The data in this study were taken from the students’ samples of writing particularly descriptive writing. There were two parallel classes involved which consisted of 50 students as total population and sample. This study was designed as a qualitative descriptive. The results of the analysis found that the students mostly used three types of noun phrase construction; they were Modifier + Head (M + H), Head + Modifier (H + M), and Modifier + Head + Modifier (M + H + M). As the most dominant type of noun phrase construction made by the students was the types of Modifier + Head (M + H) particularly in the arrangement of Determiner + Noun (68.03%).
Text-to-Image Person Search Based on SSAN Model and Re-rank Post-Processing

Text-based person search (TBPS) is a task aiming to retrieve a target person image that most meet the given text description from a large image gallery. However, due to the huge modality gap, intra-class variance and limited data, it is very challenging to solve this fine-grained cross-modal retrieval task. Therefore, this paper proposes a method based on semantically self-aligned network (SSAN) and re-rank post-processing to solve above challenges. Firstly, in the training process, this paper adopts a more effective data augmentation strategy to solve the problem of limited data. Secondly, different from most TBPS tasks, this paper uses ResNet101 as the feature extractor of image branch to extract the global and fine-grained features of image more effectively. Thirdly, this paper adds a re-rank post-processing to improve the performance of person search. Extensive experiments demonstrate that the method proposed in this paper achieves a competitive performance in CUHK-PEDES dataset, and has been significantly improved over the previous models in Rank-1, Rank-5, Rank-10 and mAP.
An Electric Vehicle Charging Transaction Model Based on Blockchain

In recent years, electric vehicles have achieved rapid development, but the problems of transaction trust and data security have also followed. Therefore, this paper proposes a blockchain-based electric vehicle charging transaction model, and designs the network and transaction process of alliance blockchain supporting multi charging operators. The Hyperledger Fabric blockchain network solves the interconnection problem of different operators' devices, ensures the security and transparency of the transaction. Besides, this paper designs a Service Billing Model based on the user's reputation, and implements it with smart contract. From the results, the model well simulates the impact of user reputation on the service fee. It ensures the profitability and sustainable operation of the platform, and encourages users to improve their reputation, which is helpful for the efficient and stable operation of the transaction platform.
On Data Complexities of Reciprocal Impossible Differential Attacks

In this work, we examine the question of how much data is required in an impossible differential (ID) attack on an arbitrary SPN (Substitution Permutation Network) cipher at minimum. Unlike most common attacks, the wrong keys are sieved one by one in an ID attack which generally entails a lot of data. Particularly, the ID attacks on AES use around $2^{100}$ $CP$ (Chosen Plaintexts) when there are at least one initial and one final rounds enclosing a 4-round ID characteristic; the minimum data achieved so far is $2^{71}$ $CP$ , to the best of our knowledge. We examine if indeed high data complexity is a necessary condition for an arbitrary ID attack on an SPN cipher. We call an attack a reciprocal attack if it does not matter whether it is mounted on the encryption or decryption function. We show that reciprocal ID attacks on SPN ciphers entail a lot of data. A reciprocal ID attack exploiting at least one structure in encryption and decryption directions require at least $2^{(n+1)/3}$ data where $n$ is the block length of the SPN cipher. In particular, we prove that the minimum data required by a reciprocal ID attack on AES is $2^{64}$ $CP$. To show that this result is not true for nonreciprocal ID attacks, we construct a counter example. That is, we mount a nonreciprocal ID attack on 6-round AES to outperform the data complexity. Our ID attack requires $2^{30}$ $CP$, which is much lower than the bound, $2^{64}$ $CP$. Our attack is definitely not the best attack. Our motivation is to show that our results for reciprocal attacks may not be true in general.
New wavefront phase sensor used for 3D shape measurements of silicon wafers

Wave Front Phase Imaging (WFPI) is a new wafer shape measurement technique that acquires millions of data points in just seconds or less, on a full 300mm silicon wafer. This provides lateral resolution well below 100μm with the possibility of reaching the lens’ optical resolution limitation between 3-4μm. The system has high repeatability with root-mean-square (RMS) standard deviation (σ<sub>RMS</sub>) in the single digit nm for the global wafer shape geometry and for nanotopography it reaches in the sub ångström (Å = 10<sup>-10</sup> m) range. WFPI can collect data on the entire wafer to within a single pixel away from the wafer edge roll off<sup>1</sup>. The flatness of the silicon wafers used to manufacture integrated circuits (IC) is controlled to tight tolerances to help ensure that the full wafer is sufficiently flat for lithographic processing. Advanced lithographic patterning processes require a detailed map of the wafer shape to avoid overlay errors caused by depth-of-focus issues<sup>2</sup>. In this paper we go deep into the theoretical explanation as to how the wave front phase sensor works.
Construction and Implementation of a Data-Oriented Platform for Rural Ecological Entrepreneurship based on Rural Remote Sensing Image Change Detection Algorithm

Construction and implementation of a data-oriented platform for rural ecological entrepreneurship based on rural remote sensing image change detection algorithm is studied in the paper. The image fusion method is used to detect the change information, and only the multispectral data of one phase and the high geometric resolution data of the other phase are used, which saves the cost of dynamic monitoring and information processing. In addition, the advantages of multi-source remote sensing information technology that can obtain thematic data efficiently, accurately, in a large area and in real time are used. With these advantages, this paper designs the novel data-oriented platform for rural ecological entrepreneurship. The system is implemented, and the data algorithm is tested.
SocialNLP’22: 10th International Workshop on Natural Language Processing for Social Media

SocialNLP is a new inter-disciplinary area of natural language processing (NLP) and social computing. We consider three plausible directions of SocialNLP: (1) addressing issues in social computing using NLP techniques; (2) solving NLP problems using information from social networks or social media; and (3) handling new problems related to both social computing and natural language processing. The 10th SocialNLP workshop is held at TheWebConf 2022 and NAACL 2022. For SocialNLP @ TheWebConf 2022, eventually we accepted six papers, including five research papers and one data paper. Each submission is reviewed by at least two reviewers. The acceptance ratio is 60%. We will also have a special event to celebrate the success of SocialNLP over the past ten years. Last, we sincerely thank to all authors, program committee members, and workshop chairs, for their great contributions and help in this edition of SocialNLP workshop.