Reviewerly/exHarmony huggingface dataset:
Evaluating machine comprehension of sketch meaning at different levels of abstraction

Human visual understanding is not unitary, but spans multiple levels of abstraction. For example, people know that real-world objects possess both distinctive features and features shared with other members of the same category. One of the most salient manifestations of this ability is in the domain of sketch comprehension: while one sketch may be evocative of a general category such as “flower” without resembling any particular flower, another sketch may faithfully depict a specific rose. To what degree are current vision algorithms sensitive to such variation in the degree of semantic abstraction expressed in sketches? Here we leveraged a recently collected dataset containing 6,144 sketches of 1,024 real-world objects belonging to 32 categories (Yang & Fan, 2021). Half of these drawings were intended to depict a general category, while the other half were intended to depict a specific object in a color photograph. We measured a sketch’s “genericity” by its ability to selectively evoke its target category but not strongly evoke any exemplar within that category; similarly, we measured a sketch’s “specificity” as its ability to evoke the exemplar it was meant to depict but not other exemplars. Using these metrics and dataset, we fit mixed-effect linear regression models to evaluate two state-of-the-art vision models: VGG-19, a convolutional neural network trained on image categorization tasks; and CLIP-ViT, a transformer-based model trained to distinguish individual image-text pairs. We found that the latent representation in CLIP-ViT was more sensitive overall to these sketches’ genericity (p<.05) and specificity (p<.001) than that learned by VGG-19. Additionally, we found that the degree to which exemplar sketches achieved higher specificity than category sketches was greater for CLIP-ViT than for VGG-19 (p<.001). More broadly, this work provides a general protocol for evaluating how well models emulate key aspects of human sketch comprehension.
Impact of Security Companies’ Response on Security Service Delivery in Nakuru Central Business District, Nakuru City, Kenya.

Insecurity in Nakuru City has increased over the past years, thus demanding urgent attention and mechanisms to control the number of crimes and improve security in the city. The National government, through the National Police Service (NPS), was not able to meet the high demand for security in Nakuru town, thus the rise in the numbers of private security companies as a complement. Therefore, there was a need to investigate the impact which was brought about by the Private security companies responding in order to deliver security services. The study aimed to find out the response strategies that the private security companies used in their service delivery and the impact that was brought about by the response. The study was guided by routine activity theory and deterrence theory. The study area was Nakuru town; the study used a descriptive survey design, and probability and non-probability sampling techniques were used. The sample size was 126 respondents drawn from 12 private security companies. Questionnaires and interviews were used to collect data, and the collected data was analyzed using descriptive statistics. The results of the study showed that there was a positive impact that was brought about by security companies responding to incidences. The responsive strategy that the private security companies had was well organized; the companies were able to respond in time and attend to the clients’ grievances, and this showed a high level of coordination. The findings of the study have informed the National government of the need for private security companies to have workable response plans and strategies for effective response. The findings have also created awareness to the private security companies on the need to have an active response strategy when they are attending to consumers' issues.
Enhancing Text Classification Models with Generative AI-aided Data Augmentation

This study investigated the potential of enhancing the performance of text classification by augmenting the training dataset with external knowledge samples generated by a generative AI, specifically ChatGPT. The study conducted experiments on three models - CNN, HiSAN, and BERT - using the Reuters dataset. First, the study evaluated the effectiveness of incorporating ChatGPT-generated samples and then analyzed the impact of various factors such as sample size, sample variability, and sample length on the models’ performance by varying the number, variety, and length of the generated samples. The models were assessed using macro and micro-averaged scores, and the results revealed that the macro-averaged scores improved significantly across all three models, with the BERT model showing the greatest improvement (from 49.87% to 65.73% in macro F1 score). The study further found that adding 30 distinct samples produced better results than adding 6 duplicates of 5 samples, and samples with 150 and 256 words had similar performance, while those with 50 words performed slightly worse. These findings suggest that incorporating external knowledge samples generated by a generative AI is an effective approach to enhance text classification models’ performance. The study also highlights that the variability of articles generated by ChatGPT positively impacted the models’ accuracy, and longer synthesized texts convey more comprehensive information on the subjects, leading to higher classification accuracy scores. Additionally, we conducted a comparison between our results and those obtained from EDA, a widely used data augmentation generator. The findings clearly demonstrate that our method surpasses EDA and offers additional advantages by reducing computational costs and solving zero-shot problem. Our code is available on GitHub. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
THINGS-drawings: A large-scale dataset containing human sketches of 1,854 object concepts

People’s knowledge about objects has traditionally been probed using a combination of feature-listing and rating tasks. However, feature listing fails to capture nuances in what people know about how objects look — their visual knowledge — which cannot easily be described in words. Moreover, rating tasks are limited by the set of attributes that researchers even think to consider. By contrast, freehand sketching provides a way for people to externalize their visual knowledge about objects in an open-ended fashion. As such, sketch behavior provides a versatile substrate for asking a wide range of questions about visual object knowledge that go beyond the scope of a typical study. Here we introduce THINGS-drawings, a new crowdsourced dataset containing multiple freehand sketches of the 1,854 object concepts in the THINGS database (Hebart et al., 2019). THINGS-drawings contains fine-grained information about the stroke-by-stroke dynamics by which participants produced each sketch, as well as a rich set of other metadata, including ratings on various attributes, feature lists, and demographic characteristics of the participants contributing each sketch. As such, THINGS-drawings provides more comprehensive coverage of real-world visual concepts than previous sketch datasets (Eitz et al., 2012; Sangkloy et al., 2016; Jongejan, et al., 2016), which contain less richly annotated sketches of a smaller number of concepts (i.e., ~100-300). This broader scope enables stronger tests of the capabilities of current artificial intelligence systems to understand abstract visual inputs, and thus a benchmark for driving the development of systems that display more human-like image understanding across visual modalities. Moreover, we envision THINGS-drawings as a resource to the vision science community for investigating the richer aspects of many perceptual and cognitive phenomena in a unified manner, including visual imagery, memorability, semantic cognition, and visual communication.
Kinetic particles : from human pose estimation to an immersive and interactive piece of art questionning thought-movement relationships.

Digital tools offer extensive solutions to explore novel interactive-art paradigms, by relying on various sensors to create installations and performances where the human activity can be captured, analysed and used to generate visual and sound universes in real-time. Deep learning approaches, including human detection and human pose estimation, constitute ideal human-art interaction mediums, as they allow automatic human gesture analysis, which can be directly used to produce the interactive piece of art. In this context, this paper presents an interactive work of art that explores the relationship between thought and movement by combining dance, philosophy, numerical arts, and deep learning. We present a novel system that combines a multi-camera setup to capture human movement, state-of-the-art human pose estimation models to automatically analyze this movement, and an immersive 180° projection system that projects a dynamic textual content that intuitively responds to the users’ behaviors. The demonstration being proposed consists of two parts. Firstly, a professional dancer will utilize the proposed setup to deliver a conference-show. Secondly, the audience will be given the opportunity to experiment and discover the potential of the proposed setup, which has been transformed into an interactive installation. This allows multiple spectators to engage simultaneously with clusters of words and letters extracted from the conference text.
Online Candidate Selection System for Elections

This research proposes an innovative online platform for political parties in Sri Lanka to enhance the candidate selection process. The platform incorporates features such as sentiment analysis, background checks, aptitude tests, ranking system, and analysis of candidates' promises and activities. Developed using advanced technologies, it aims to ensure transparency, efficiency, and accessibility for all eligible candidates, ultimately contributing to a more democratic election.
Identifying and Analyzing Reduplication Multiword Expressions in Hindi Text Using Machine Learning

The task of identifying and analyzing Reduplication Multiword Expressions (RMWEs) in Natural Language Processing (NLP) involves extracting repeated words from various text forms and classifying them into Onomatopoeic, non-Onomatopoeic, partial, or semantic types. With the increasing use of low-resource languages in news, opinions, comments, hashtags, reviews, posts, and journals, this study proposes a machine learning-based RMWE identification method for Hindi text. The method employs linguistic patterns and statistical data, along with a proposed threshold boundary detection in statistical filtering. The Jaccard distance of dissimilarity and Sorensen Dice Coefficient of Similarity are used for semantic relation analysis. The proposed approach was evaluated using the publicly available Hindi corpus from IITB, measuring performance between two consecutive thresholds with the lowest error and highest recall. This study proposes an effective method for Indian computational linguistics, with experimental results highlighting its viability and utility, and providing a blueprint for current procedures.
Non-Standard Map Robot Path Planning Approach Based on Ant Colony Algorithms

Robot path planning is an important component of ensuring the robots complete work tasks effectively. Nowadays, most maps used for robot path planning obtain relevant coordinate information through sensor measurement, establish a map model based on coordinate information, and then carry out path planning for the robot, which is time-consuming and labor-intensive. To solve this problem, a method of robot path planning based on ant colony algorithms after the standardized design of non-standard map grids such as photos was studied. This method combines the robot grid map modeling with image processing, bringing in calibration objects. By converting non-standard actual environment maps into standard grid maps, this method was made suitable for robot motion path planning on non-standard maps of different types and sizes. After obtaining the planned path and pose, the robot motion path planning map under the non-standard map was obtained by combining the planned path and pose with the non-standard real environment map. The experimental results showed that this method has a high adaptability to robot non-standard map motion planning, can realize robot path planning under non-standard real environment maps, and can make the obtained robot motion path display more intuitive and convenient.
Modeling of Human Motion Perception Mechanism: A Simulation based on Deep Neural Network and Attention Transformer

The motion perception system has been widely explored as an essential function for creatures to perceive and interact with the world. Nevertheless, the current motion models based on the V1-MT structure struggle to derive informative motion flows from local motion energy in complex natural scenes. This study takes advantage of the flexibility of deep neural networks (DNNs) to simulate the motion perception process. To tackle this challenging problem while trading off neurophysiological plausibility, we developed a two-stage process that merged the classical motion energy constraint with a well-developed transformer structure. The first stage consists of a group of cells with trainable spatiotemporal frequencies tunning to capture different preferences of local motion energy. The subsequent stage implemented the attention mechanism from deep learning. It enables recurrent integration of the local motion signals via pairwise correlation across each spatial location to resolve the aperture problem. The supervised training was applied to fit the motion ground truth in digital videos, which consist of a sizeable multi-frame training set, including the self-designed non-texture dataset, MPI-Sintel dataset, natural image sequences with pseudo-labels, etc. Based on drifting gratings and plaids, we used virtual neurophysiology to measure model's activation and found some similar representations between humans' V1-MT and the model's two processing stages. The model generalizes well from non-texture stimuli used in psychophysics to complex natural scenes, demonstrating similarity to human interpretation. In more complex scenarios, the model demonstrates a high-level function of integrating local energy to infer global motion flow. In summary, the proposed model could finely replicate the performance from simple drifting Gabor to the complex natural scene and introduce some characteristics similar to the known human visual system's properties.
Visual representations shift from a retinal to a real-world reference frame during visual working memory

Visual information in our everyday environment is anchored to a real-world reference frame – a tall building remains upright, even when you tilt your head, which changes the projection of the building on your retina from a vertical to a diagonal orientation. Presumably, retinotopic cortex represents perceptual inputs in a retinal reference frame. But what about visual information held in working memory? Here, we investigate which reference frame the brain uses during visual working memory by dissociating retinal and real-world reference frames via head tilt, combined with 64-channel electroencephalography. Nineteen participants completed between 2592–3168 trials during which they briefly viewed (150ms) and then remembered (1500ms) a randomly oriented grating for recall via method-of-adjustment. In interleaved blocks of trials, the participant's head was either kept upright, or tilted by 45º using a custom rotating chinrest. The remembered orientation could be decoded throughout the delay by training and testing within head-upright blocks, and within head-tilted blocks. Importantly, the question of reference frames can be addressed by decoder cross-generalization: If remembered orientations are represented in a retinal reference frame, a decoder trained on head-upright trials would predict a 45º offset in decoded orientation when tested on head-tilted trials (after all, a vertical building becomes diagonal on the retina after head tilt). Conversely, if mnemonic representations are anchored to the real world, no such offset should be observed. Our analysis reveals an early orientation representation in an approximately retinal coordinate frame, while later during the delay the representation approaches a real-world coordinate frame. These results suggest that visual representations can dynamically undergo a reference frame transformation that likely occurs during the transition from perception to working memory, shifting from retinal to real-world anchoring.
Towards Trust Driven On-Demand Client Deployment in Federated Learning

Federated Learning (FL) systems choose a certain number of clients from each round to take part in the learning. The ability to have more available clients in the learning areas is achieved using containerization technology. However, reliability concerns raise doubts about the trustworthiness of these devices as Docker containers are deployed on them to be able to serve as clients in FL scenarios. Moreover, the default random selection does not take into consideration trust values when selecting clients. Clients who are malicious may poison the learning process or the entire model if they are selected. In order to overcome these issues, we propose in this work that a trust factor must be considered while selecting these clients and deploying models in our architecture. We build a trust framework between the server and its available clients. The trust factor is continuously monitored and updated by checking clients that are not serving successfully the deployed jobs. In addition, it concludes relevant information about the local accuracy changes of each client by applying a two-step verification method to detect any label flipping and random updated weights. The simulations utilize the mobile data challenge dataset. In each round, clients with high trustworthiness are selected. The simulations are compared with the default random selection method, and the centralized model. By assigning trust values to each client, updating this factor, and crediting malicious clients with a low trust factor, the proposed architecture is able to detect these malicious clients while minimizing the number of normal and discarded rounds.
ResMap: Exploiting Sparse Residual Feature Map for Accelerating Cross-Edge Video Analytics

Deploying deep convolutional neural network (CNN) to perform video analytics at edge poses a substantial system challenge, as running CNN inference incurs a prohibitive cost in computational resources. Model partitioning, as a promising approach, splits CNNs and distributes them to multiple edge devices in closer proximity to each other for serial inferences, however, it causes considerable cross-edge delay for transmitting intermediate feature maps. To overcome this challenge, we present ResMap, a new edge video analytics framework that significantly improves the cross-edge transmission and flexibly partitions the CNNs. Briefly, by exploiting the sparsity of the intermediate raw or residual feature map, ResMap effectively removes the redundant transmission, thereby decreasing the cross-edge transmission delay. In addition, ResMap incorporates an Online Data-Aware Scheduler to regularly update the CNN partitioning scheme so as to adapt to the time-varying edge runtime and video content. We have implemented ResMap fully based on COTS hardware, and the experimental results show that ResMap reduces the intermediate feature map volume by 14.93-46.12% and improves the average processing time by 17.43-30.6% compared to other alternative designs.
Keamanan Informasi Menggunakan iso27002 Pada Data Organisasi Paskibra Kopral15 di SMPN 15 Medan

The type of audit used in this study is an internal audit using the Iso 27002 information security standard. KOPRAL or Garuda Lima Belas Soldier Command is an Extracurricular Paskibra in SMP N 15 Medan, this extracurricular is quite popular with many existing students, the initial purpose of its formation This Paskibra is to raise the red and white flag on national days, such as independence, youth oath, and teacher's day, currently KOPRAL has 29 batches.
Analisis Keamanan Fisik Data Prodi Sistem Informasi UIN Sumatera Utara Medan Menggunakan Standar ISO 27001

Physical security is the first step in computer security. If physical storage is not properly secured, data, even the computer applications themselves, cannot be guaranteed to be secure. Analysis of Physical Data Security of the Information Systems Study Program UIN North Sumatra Medan using point A.9 of the ISO 27001 Standard. Analysis of the physical security of the information system study program data was carried out through literature review, use of ISO 27001 points, interviews with related parties regarding the physical security of the current study program data. This study aims to find out the details and problems faced by the Medan North Sumatra UIN Information Systems Study Program in using its physical security system.
Penerapan Framework Cobit 5 Untuk Audit Tata Kelola Keamanan Informasi Pada PT Larasati Gianis

In the era of the modern revolution 4.0, almost all human activities are done with technology without technology. Even in organizations, agencies and companies really need technology. PT. Larasati Gianis is engaged in construction services in which the construction of highways, buildings, bridges, etc. The large value of information technology (IT) storage capacity, especially for information systems (IS) at PT Larasati Gianis, has made many parties start to wonder how to manage data archives. In the Larasati Giants company, file archiving has a very important position. Because it can make it easier for companies to store data and archive files and data that is stored more securely against the risks that might occur. PT. Larasati Gianis in the field of data archiving is still not optimally utilizing existing technology. Doing the process between IT in the company today and the expected IT process has not been achieved. With data storage using a G-drive, PT Larasati Gianis can store data easily, quickly and safely.
Data base or Data repository? &amp;#8211; A brief and very selective history of data management in chemistry.

Way back in the late 1980s or so, research groups in chemistry started to replace the filing of their paper-based research data by storing it in an easily retrievable digital form. This required a computer database and initially these were accessible only on specific dedicated computers in the laboratory.
Pedagogically-Informed Implementation of Reinforcement Learning on Knowledge Graphs for Context-Aware Learning Recommendations

Context-aware recommender systems are important tools to address the learning context in technology enhanced learning. However, the contextual factors of learning, as well as the mechanisms of integrating them into the recommendation, are mostly defined from a technical perspective, rather than a pedagogical one. In this paper, we introduce a new approach for generating pedagogically informed, context-aware, learning recommendations. We build on the context definition in situated and subject-oriented learning theories. Then, we utilize a knowledge graph structure to build the environment for a path exploration and ranking algorithm, which is influenced by agent exploration in reinforcement learning (RL), for creating sequential learning-path recommendations. Our design of the agent’s reward function integrates learning-context factors in the recommender system. We evaluate the proposed solution qualitatively with domain experts, and quantitatively using semantic-similarity measures to compare our recommended paths to expert-curated learning content. Our evaluation shows an enriched recommendation based on the learners’ context, as well as a better discovery of relevant educational content.