Reviewerly/exHarmony huggingface dataset:
Facilitating Data Inclusion and Empowerment through Arts-Based, Creative and Playful Approaches

Big data is a topic of growing importance in the modern world, these days our lives are shaped by the information we consume. As such data literacy is becoming an increasingly crucial skill to have. However, there are barriers of entry to understanding scientific data for the average person. This workshop will explore ways in which we can overcome those barriers, and attempt to engage people with scientific data through creative, playful, and fun approaches. The goal of this workshop is to promote data inclusivity and literacy, and to demystify the concept of data. This workshop will use art-based approaches as a means of communicating data. It will also involve participants making sense of the data themselves through data storytelling techniques. This workshop will also be a chance to reflect on what barriers to data inclusivity exists, and the role that data plays in building empathy towards others and the world around us.
Analysis of the effect of the lecturer satisfaction with the Naive Bayes Data Mining technique on institutional performance

Abstract The study aimed to analyze the effect on institutional performance of lecturer satisfaction with data extraction techniques. The solution is the technique of Naive Bayes, where data is obtained through interviews and questionnaires conducted in one of the private institutions in the north-sumatra of Medan. The evaluation criteria are readiness, compassion, reliability and accountability. The tests indicate that the level of accuracy is 85.48% with 81.08% precision, and 93.75% recall value. The Naïve Bayes method can also be recommended to predict the degree of satisfaction of the lecturer with institutional performance based on the results of tests using fast miner software.
Analysis of the latent space of pre-trained deep convolutional neural networks in the problem of automatic segmentation of color images

Abstract The paper presents a primary study of the latent space structure of neural networks trained for semantic segmentation. Segmentation was performed in a controlled environment of three classes of colored rectangular shapes. The classic autoencoder and U-net like architectures were chosen as reference architectures. To study the structure of the space, a combination of a perceptron that linearly separates classes and the compression algorithms UMAP and PCA was used. As a result, a tool was obtained for evaluating the quality of a neural network based on the degree of separability of classes in the latent space of the network.
Efficient String Sort with Multi-Character Encoding and Adaptive Sampling

Sorting plays a fundamental role in computer science. It has far reaching applications in database operations and data science tasks. An important class of sorting keys are strings and among all string sorting methods, radix sort is a simple but effective algorithm. Many works have been studied to accelerate radix string sort. One typical approach is to process multiple characters in each sorting pass. However, this approach incurs the crucial issue of the radix being too large. To address the problem, we introduce a novel multi-character encoding based method that can significantly reduce the radix. This new encoding scheme takes advantage of the sparse alphabet space usage as well as the sparsity of distinguishing prefixes of the inputs which are commonly seen in real-world datasets. Combining the effective encoding scheme with an adaptive sampling process to generate the encoding efficiently, our proposed sorting algorithm essentially blends radix sort with sample sort and achieves substantial improvement over other sorting approaches. The results on both real datasets and synthetic datasets show that our method yields an average 4.85× performance improvement over C++ STL sort[21], 1.47× improvement over the state-of-the-art Radix Sort on strings implementation[19] and 2.55× over the multikey quicksort[6]. Preliminary tests in a multi-core environment also show it is competitive or better than the most recent parallel string sorting algorithm pS5[8] which demonstrates the scalability of our method.
Tracing Vulnerable Code Lineage

This paper presents results from the MSR 2021 Hackathon. Our team investigates files/projects that contain known security vulnerabilities and how widespread they are throughout repositories in open source software. These security vulnerabilities can potentially be propagated through code reuse even when the vulnerability is fixed in different versions of the code. We utilize the World of Code [1] infrastructure to discover file-level duplication of code from a nearly complete collection of open source software. This paper describes a method and set of tools to find all open source projects that use known vulnerable files and any previous revisions of those files.
Growth of Micro, Small and Medium Enterprises (MSMEs) in MENA Countries: Constraints and Success Factors

Purpose: This study attempts to contribute to a better understanding of the main constraints/ boosters for MSME growth in selected countries of the MENA region (Tunisia, Egypt, Morocco and Jordan).&#x0D; Design/Methodology/Approach: A multinomial logit model is used to investigate the impact of a three-fold classification of firm growth determinants (company characteristics, manager characteristics and business environment characteristics). The database is provided by the World Bank Enterprise Surveys (MENA ES).&#x0D; Findings: Results give statistically robust evidence that innovative firms are more likely to grow.&#x0D; Implications/Originality/Value: Therefore, the implementation of efficient and well-governed systems of innovation is a priority in the MENA region.&#x0D;
Study Of AI Generated And Real Face Perception

The face is the most informative sign in a people's recognition. The face contains such features as identity, gender, race, mood, attention and emotions. Face recognition is critical for some services, while recent research shows that people recognition can be very different from person to person.In the past few years, a new type of algorithm Generative Adversarial Network (GAN) has appeared that allows you to generate artificial faces that are identical to real faces. This algorithm is currently widely used in the generation of new faces for marketing campaigns, video processing, increasing the resolution of images, as well as in entertainment applications.This study focuses on the effectiveness of recognizing, distinguishing and memorizing real and fake faces. In the introduction, a literature review is presented. It covers issues of decision-making by people, face recognition, and factors affecting the memorization of faces. The second part contains a description of research methodology - data collection, research design, concerns the work (collection, analysis) with data and procedures. Further hypotheses are put forward and the analysis and conclusion are given.
The Application of Blockchain in Sunshine Government Affairs : ——Student Allocation System As an Example

In order to make the teaching resources fair, many cities now implement the allocation mechanism. But in the process of allocation, government staff needs to manually review a lot of work, which increased a lot of pressure to the education management department and wastes a lot of manpower and resources. Moreover, because there are persons take part in the allocation management, the public doesn't have full trust in the allocation. In order to reduce the work pressure of government departments and improve the trust, the government should use blockchain technology to realize management system. By analyzing the business process of student allocation system, the system can store key data with alliance chain technology. Alliance chain breaks through the data barrier of information system among government departments. According to the characteristics of the alliance chain, this paper gave the overall design and business process analysis of the system, and the system experiment finally verified that the blockchain technology has very important significance and feasibility in the sunshine government. The next research work will be to design the application system by using the block chain principle, instead of using the existing block framework.
Help, My Signal has Bad Device!

In response to ongoing discussions about data usage by companies and governments, and its implications for privacy, there is a growing demand for secure communication techniques. While during their advent, most messenger apps focused on features rather than security, this has changed in the recent years: Since then, many have adapted end-to-end encryption as a standard feature. One of the most popular solutions is the Signal messenger, which aims to guarantee forward secrecy (i. e. security of previous communications in case of leakage of long-term secrets) and future secrecy (i. e. security of future communications in case of leakage of short-term secrets). If every user uses exactly one device, it is known that Signal achieves forward secrecy and even post-compromise security (i. e. security of future communications in case of leakage of long-term secrets). But the Signal protocol also allows for the use of multiple devices via the Sesame protocol. This multi-device setting is typically ignored in the security analysis of Signal. In this work, we discuss the security of the Signal messenger in this multi-device setting. We show that the current implementation of the device registration allows an attacker to register an own, malicious device, which gives them unrestricted access to all future communication of their victim, and even allows full impersonation. This directly shows that the current Signal implementation does not guarantee post-compromise security. We discuss several countermeasures, both simple ones aiming to increase detectability of our attack, as well as a broader approach that seeks to solve the root issue, namely the weak device registration flow.
A Feature Extracting and Matching System Based on Magic-Number and AC-Algorithm

With the rapid development of the Internet in recent years, different types of files could be found in the process of analyzing traffic. Sometimes, the transferred files are in a non-public format. To know the type of the transferred file, we need to analyze and extract the features of this file first, and then we can match the file in the traffic through the extracted feature. In the past, when we want to extract the features of a certain type of file for later matching, it is mostly based on manual analysis, which requires a lot of manpower and material resources, and the efficiency is low. In this paper, a system of feature analysis, extraction, and matching based on magic-number and the Aho–Corasick algorithm is designed and implemented. In the feature extraction module, this paper designs an algorithm to extract magic-number from files. Based on the Aho–Corasick algorithm, an algorithm for extracting string fetures is designed. The extracted features are used to identify the file type in the feature matching module. Through the experiments of four common types of files, it is found that the designed method can effectively identify the types of files based on the extracted features, and the recognition accuracy is generally higher than 90%. Through the analysis of the file features proposed in this paper, a lot of repetitive work can be reduced and the efficiency of traffic analysis can be improved.
Using $\text{CRF}+\text{LG}$ for automated classification of named entities in newspaper texts

Information production has been growing at an accelerated rate. There is a large amount of information to be processed, which makes tasks related to the collection and explotation of text challenging, requiring effort from the most diverse areas, especially computational linguistics. One of the goals of computational linguistics is to enable the collection and explotation of linguistic datasets, through empirical evidence, extracted with the application of computational resources. This work presents the creation of dataset extracted from a newspaper. Techniques were used for extracting sentences, tokenization, named entities recognition (NER), as well as statistical methods for describing the dataset. Other researchers can directly benefit from the available corpus. This work presents a corpus with 1029 annotated news articles in Portuguese for entities according to HAREM categories. In a sample of 108 pages, our experiments show a 97.0% similarity compared to gold standard texts from the same newspaper. For the NER task and automatic annotation of the extracted dataset, proportions of the datasets of Second Harem and aTribuna100 were used to train a hybrid model CRF+LG. With the trained model, the 1029 articles extracted were automatically annotated. In general, the values of the metrics demonstrate that optimal metrics achievements for the classification model for the 70/30 Proportion, especially for the Person (PER) category, reaching 91.11% and 95.82% for precision and recall, respectively. Overall, the model showed 95.86% accuracy.
Detection and classification of falling in elderly people using customized deep learning algorithm

This research work proposes a fall detection system in elderly people based on customized human body detection using You Only Look Once (YOLO) version-3 algorithm. This scheme provides a high accuracy rate of classification for different cases (stand, sit, and fall). To design such system, IoT based-fall detection is implemented. The Raspberry pi is used to process the tested images captured from the live camera. Then, the YOLO detects the human body and classifies them into three categories. The proposed system has trained for 100 images for each class type based on positive samples. Moreover, in the test phase, static image, and live camera have been used to show the performance of the system in term of the accuracy detection. Finally, the accuracy rate is determined for different distances from the camera in order to improve the validity of the classification. Test results indicate that the proposed system is invariant for the light and environmental conditions and has a good accuracy when the live camera is used. The accuracy rates average of a live camera for different distances are 100%, 95%, and 90% for (stand, sit, and fall) classes respectively.
ColorRL: Reinforced Coloring for End-to-End Instance Segmentation

Instance segmentation, the task of identifying and separating each individual object of interest in the image, is one of the actively studied research topics in computer vision. Although many feed-forward networks produce high-quality binary segmentation on different types of images, their final result heavily relies on the post-processing step, which separates instances from the binary mask. In comparison, the existing iterative methods extract a single object at a time using discriminative knowledge-based properties (e.g., shapes, boundaries, etc.) without relying on post-processing. However, they do not scale well with a large number of objects. To exploit the advantages of conventional sequential segmentation methods without impairing the scalability, we propose a novel iterative deep reinforcement learning agent that learns how to differentiate multiple objects in parallel. By constructing a relational graph between pixels, we design a reward function that encourages separating pixels of different objects and grouping pixels that belong to the same instance. We demonstrate that the proposed method can efficiently perform instance segmentation of many objects without heavy post-processing.
Local Matrix Factorization with Social Network Embedding

In the recommender system, how to construct submatrices for local matrix factorization is an important problem. In this paper, we propose the Local Matrix Factorization with Social Network Embedding (LMFE) method in order to construct more meaningful sub-matrices and improve the performance of the recommender system. Firstly we utilize the user's social information and rating information to construct a heterogeneous information network (HIN). And then extract the node representations of users and items from HIN. We use the representations of the node as the basis for sub-matrix division. Finally, the local matrix factorization is performed on sub-matrix to obtain the prediction results. Experimental results from the real-world dataset Yelp demonstrate that the LMFE can achieve better performance than the comparative method.
Analyzing In-Memory NoSQL Landscape (Extended Abstract)

In-memory key-value stores have quickly become a key enabling technology to build high-performance applications that must cope with massively distributed workloads. In-memory key-value stores (also referred to as NoSQL) primarly aim to offer low-latency and high-throughput data access which motivates the rapid adoption of modern network cards such as Remote Direct Memory Access (RDMA). In this paper, we present the fundamental design principles for exploiting RDMAs in modern NoSQL systems. Moreover, we describe a break-down analysis of the state-of-the-art of the RDMA-based in-memory NoSQL systems. In addition, we compare traditional in-memory NoSQL with their RDMA-enabled counterparts.
Effect of Depth Order on Iterative Nested Named Entity Recognition Models

This paper studies the effect of the order of depth of mention on nested named entity recognition (NER) models. NER is an essential task in the extraction of biomedical information, and nested entities are common since medical concepts can assemble to form larger entities. Conventional NER systems only predict disjointed entities. Thus, iterative models for nested NER use multiple predictions to enumerate all entities, imposing a predefined order from largest to smallest or smallest to largest. We design an order-agnostic iterative model and a procedure to choose a custom order during training and prediction. We propose a modification of the Transformer architecture to take into account the entities predicted in the previous steps. We provide a set of experiments to study the model’s capabilities and the effects of the order on performance. Finally, we show that the smallest to largest order gives the best results.
Unsupervised Horizontal Pyramid Similarity Learning for Cross-Domain Adaptive Person Re-Identification

Although person re-identification has made great progress, unsupervised cross-domain adaptive person re-identification is still a challenging problem. With no labeled data in target domain, the performance may have a significant drop. In this paper, we propose an unsupervised cross-domain adaptive person re-identification framework based on horizontal pyramid similarity learning (UHPS). Firstly, horizontal pyramid features are extracted by dividing the deep feature maps into different number of partial feature bins. These feature bins with diverse scales can incorporate not only the global information but also local information in different spatial scales, making the framework more robust in complex environment. Then, horizontal pyramid similarity learning is proposed with the mechanism of fusing together the internal similarity of the target domain and the similarity between the source domain and target domain. Finally, the unsupervised clustering algorithm DBSCAN embeded with the horizontal pyramid similarity is employed to select training data in the target domain and estimate the pseudo labels in each training iteration, for the purpose of adapting the framework to the target domain. The results on Market1501 and DukeMTMC-reID confirm that the proposed framework can adapt to the target domain effectively and outperforms the state-of-the-art unsupervised cross domain person re-identification approaches.
Design and Implementation of Real Time Monitoring System of Textile Equipment Based on MQTT

Abstract At present, with the progress of network information technology, the idea of intelligent factory and intelligent manufacturing is imperative. It is imperative to realize intellectualization and informatization in traditional manufacturing plants. According to the intelligentized demand of textile equipment, the real-time monitoring of the spindle status of the textile machine is designed and realized, and the functions of the real-time statistics of the operation efficiency, the number of broken ends, the spindle status of the textile machine, the statistical report and so on are made in real time. It has achieved remote and efficient production management and fault detection. It effectively reduces the cost of manual operation and maintenance, and improves the intelligence level of textile mills.
Expanding the Design Space for Technology-Mediated Theatre Experiences

Work combining live performance and technology often involves incorporating technology directly into the performance as it occurs onstage, including interactive costumes, or performer-controlled sets, lighting or sound. We invert this common approach, developing technology-mediated experiences outside the temporal and spatial confines of a live theatre production. We describe the 4-month co-design process with expert theatre practitioners, and detail how the process 1) shaped our design guidelines and 2) expands the discussion around existing best practices for cross-disciplinary collaboration. In the style of research through design, we present three annotated prototypes: the Augmented Playbill, the Prayer Wheel, and Tarot Cards as well as accompanying AR applications to convey the decisions we made and the philosophy we iteratively developed throughout the project. These artifacts also embody our six design guidelines: resonant affordances, extended narrative, reflective interaction, selective reveal, personalized experience, and privileged access.
Special Features of TLA + Temporal Logic of Actions for Verifying Access Control Policies

The paper considers special features of applying Lamport's temporal logic of actions when verifying access control policies for arbitrary data protection tools. It justifies the necessity of implementing verification in the process of development and certification of various software tools and algorithms, in particular, policies for controlling subjects' access to objects. It contains a general structure of notation or specification of the system being studied in a formal language suitable for verification, and its particular version in the TLA + language. The paper considers special features of using Lamport's temporal logic of actions, and gives recommendations regarding dos and don'ts when initializing the modeled system, when forming and using invariants or temporal properties, history and auxiliary variables, safety and liveness properties, as well as when accounting for the termination of the verification process. Such features and recommendations are formulated in a quite universal way and do not depend on the applied verification approach and on the system being studied. The paper lists typical errors that may be done during verification, which make its results useless, while artificially creating a feeling of confidence in the system's “rightness”, “correctness” or “security / safety”. The conclusion presents key features that can have a significant impact on verification results, as well as on feasibility of its implementation. It proposes one of the possible directions for further research on the development of a general approach to substantiating conformity of the verified system specification in some formal language with its practical implementation.