Miracle-dz/UniFAR-FTUs huggingface dataset:
Pointing in Visual Periphery: Is DF's Dorsal Stream Intact?

Observations of the visual form agnosic patient DF have been highly influential in establishing the hypothesis that separate processing streams deal with vision for perception (ventral stream) and vision for action (dorsal stream). In this context, DF's preserved ability to perform visually-guided actions has been contrasted with the selective impairment of visuomotor performance in optic ataxia patients suffering from damage to dorsal stream areas. However, the recent finding that DF shows a thinning of the grey matter in the dorsal stream regions of both hemispheres in combination with the observation that her right-handed movements are impaired when they are performed in visual periphery has opened up the possibility that patient DF may potentially also be suffering from optic ataxia. If lesions to the posterior parietal cortex (dorsal stream) are bilateral, pointing and reaching deficits should be observed in both visual hemifields and for both hands when targets are viewed in visual periphery. Here, we tested DF's visuomotor performance when pointing with her left and her right hand toward targets presented in the left and the right visual field at three different visual eccentricities. Our results indicate that DF shows large and consistent impairments in all conditions. These findings imply that DF's dorsal stream atrophies are functionally relevant and hence challenge the idea that patient DF's seemingly normal visuomotor behaviour can be attributed to her intact dorsal stream. Instead, DF seems to be a patient who suffers from combined ventral and dorsal stream damage meaning that a new account is needed to explain why she shows such remarkably normal visuomotor behaviour in a number of tasks and conditions.
TOSA: Tolerating Stuck-At-Faults in Edge-based RRAM Inference Accelerators

Applications powered by Deep Neural Networks (DNNs) are shifting from cloud to edge computing due to the promising performance efficiency of Resistive Random Access Memory (RRAM) devices. However, modern RRAM devices have various possible faults, including hard and soft faults. Existing RRAM-based deep learning systems lack a comprehensive fault tolerance approach, leading to safety risks in deploying edge-end DNN inference. In this paper, we propose an effective fault recovery framework called TOSA to safeguard edge-based DNN accelerators from Stuck-At-Faults (SAFs). Two novel ideas are presented to enhance reliability in edge-based RRAM inference accelerators: (1) SAFs-aware priority-based bit-memristor mapping algorithm to match integrated SAF variations and DNN’s defect tolerance, (2) a new retraining method called joint retraining to recover model accuracy from multiple faulty edge devices. We thoroughly evaluate TOSA on multiple DNN models, demonstrating its effectiveness in reducing costs and improving efficiencies. Experimental results show that TOSA achieves a 45.28× speedup when compared to the per-device retraining methods while preserving inference accuracy better than the latest competitor, making it a reliable architecture for accelerating DNN inference on edge devices.
On the Comparative Performance Analysis of GFDMA and SC-FDMA Systems Under Multiple CFO Synchronization Using Null SubCarriers

In this work, the performances of grouped frequency-division multiple-access (GFDMA) and single-carrier frequency division multiple access (SC-FDMA) techniques using null-subcarriers based carrier frequency-offset (CFO) synchr onization are compared with respect to various parameters. GFDMA, is a new emerging technique, which uses multiuser interference cancellation to separate the users sharing the same sub-carrier sets. GFDMA also uses iterative scheme in order to update the estimates and reduce errors iteration by iteration. However, just like SC-FDMA, GFDMA also suffers from carrier frequency offset (CFO), which is formed by multiple access interference (MAI) and inter-carrier interference (ICI). Also, due to the multiple signals, having different CFO values, combining at the receiver, CFO synchronization is much more difficult than that in single-user systems. This paper proposes a null subcarrier-based CFO synchronization method for the uplink of GFDMA and SC-FDMA systems, adopting sub-band based subcarrier assignment scheme. Performances of both technique s are compared under various parameters by simulations.
Diabetes Detection Using Gait Acceleration

Diabetes is a disease affecting various organs and systems as a result of increased blood sugar levels which can cause diabetic neuropathy and diabetic foot ulcer. Traditionally, invasive methods such as pricking the finger and measuring blood glucose are used to detect diabetes. However, invasive methods are inconvenient and can cause discomfort to patients. A different approach for identifying diabetes is through gait analysis where abnormalities in walking patterns can be analysed for diabetes early detection. Therefore, this paper investigates diabetes detection with the acceleration of the hip, knees and ankles collected from wearable sensors using machine learning models. To our knowledge, recent literature on diabetes detection using machine learning and gait data showed questionable results in terms of accuracy and dataset. The model accuracy was not documented properly and non-gait-related data was used. This paper proposes a method using a machine learning model MLP and lower limb acceleration data. The proposed MLP model achieved an accuracy of 85.84%.
FedMP: Robust and Communication-Efficient Federated Multi-Prototype Intrusion Detection Framework in IoT

Due to its excellent performance in privacy protection, federated learning (FL) technology is gradually introduced into the IoT environment to build a distributed intrusion detection framework. However, the previous frameworks have two limitations: 1) high communication overhead caused by the frequent exchange of model parameters is not friendly for resource-constrained IoT devices; 2) single global model hardly handles not independent and identically distributed (Non-IID) intrusion data on different IoT clients. In this paper, we propose a Federated Multi-Prototype intrusion detection framework (FedMP) to address the above limitations. Specifically, FedMP includes a k-means clustering module that extracts low-dimensional local prototypes for IoT clients and a novel aggregation algorithm that fairly aggregates all local prototypes on the central server to generate global prototypes. By exchanging prototypes instead of model parameters between IoT clients and the central server, FedMP aims to train a unique personalized model for each IoT client to adapt to its local intrusion detection tasks. Experimental results on real-world intrusion detection datasets show that FedMP achieves the best detection performance in Non-IID scenarios while significantly reducing communication overhead compared to other state-of-the-art methods.
Affective social interaction with CuDDler robot

This paper introduces an implemented affective social robot, called CuDDler. The goal of this research is to explore and demonstrate the utility of a robot that is capable of recognising and responding to a user's emotional acts (i.e., affective stimuli), thereby improving the social interactions. CuDDler uses two main modalities; a) audio (i.e., linguistics and non-linguistics sounds) and b) visual (i.e., facial expressions) to recognise the user's emotional acts. Similarly, CuDDler has two modalities; a) gesture and b) sound to respond or express its emotional responses. During the TechFest 2012 event, CuDDler successfully demonstrated its capability of recognising the user's emotional acts and responding its expression accordingly. Although, CuDDler is still in its early prototyping stage, the preliminary survey results indicate that the CuDDler has potential to not only aid in human-robot interaction but also contribute towards the long term goal of multi-model emotion recognition and socially interactive robot.
Energy Efficient Routing Protocols and Algorithms for Wireless Sensor Networks – A Survey

Wireless Sensor Networks (WSNs) are an emerging technology for monitoring physical world. The sensor nodes are capable of sensing various types of environmental conditions, have some processing capabilities and ability to communicate the sensed data through wireless communication. Routing algorithms for WSNs are responsible for selecting and maintaining the routes in the network and ensure reliable and effective communication in limited periods. The energy constraint of WSNs make energy saving become the most important objective of various routing algorithms. In this paper, a survey of routing protocols and algorithms used in WSNs is presented with energy efficiency as the main goal.
BEW-YOLO: An Improved Method for PCB Defect Detection Based on YOLOv7

The PCB defect object size is small and the detection process is susceptible to background interference, usually have the problem of missed and false detection. In order to solve the above problems, an improved method based on YOLOv7 is proposed in this paper. Firstly, the bi-level routing attention (BRA) has been added to the header of the original YOLOv7 model to capture global dependencies and ensure the accuracy of small object detection and localization. Secondly, the explicit visual center (EVC) block is introduced before the fusion of mid-level features and high-level features to capture the global remote dependencies of top-level features, extract the features in local corner regions, achieve a comprehensive feature representation. Finally, the loss function is improved by changing the loss function of the original model to Wise-IoU, which allows our model to focus more on ordinary-quality anchor boxes and improve the performance of the detector while decreasing the competitiveness of anchor boxes for high-quality samples and reducing the influence of low-quality samples on the detection results. The experimental results show that the improved model improves 5% in AP and 1.9% in both AR and mAP over the original YOLOv7 model. Meanwhile, comparison experiments are conducted on the data-enhanced PCB dataset, which proves the superiority of our model over other state-of-the-art models.
A Fast Method for Image Mosaicing using Geometric Hashing

The general problem of mosaicing is to create a single seamless image by aligning a series of spatially overlapped images. The result is an image with a field of view greater than that of a single image. Traditionally this research has been aimed at stitching together images taken by aerial or satellite reconnaissance equipment. With the advancement of personal computing equipment, the creation of image mosaic has entered the consumer market. Thus, automation of the process is an important issue. This paper proposes a new method for automatic generation of mosaics using Geometric Hashing. This speeds up the matching process. We show the application of our method on two important cases namely, rigid planar motion and panoramic mosaics. We provide experimental results in support of our proposed method.
MiddleCache: Accelerating TCP based In-memory Key-value Stores using eBPF

In-memory key-value stores are widely used in modern web services to support large-scale user requests by caching popular data. Their performance is critical, and BMC, the state-of-the-art work, builds an in-kernel cache and processes requests before the stack using eBPF to reduce the overhead of the kernel network stack. However, BMC fails to support stateful protocol TCP because pre-stack processing creates TCP state bias between the client and server.TCP is widely used by in-memory key-value stores, is even the only choice for some applications (e.g., Redis), and also suffers from performance issues. In this work, we present MiddleCache, a TCP-enabled in-memory key-value store acceleration design. Our key observation is that the TCP state bias of the client and server can be inferred and eliminated with packet length. The design of MiddleCache has two key parts: (i) A compact TCP state maintenance mechanism that accumulates packet lengths and applies corrections to the packet header, which realize TCP support within the constrains of eBPF. (ii) Lock-free accumulation counters that support high-performance concurrent access by utilizing Receive Side Scaling (RSS). Our experiments show that, compared with Memcached, MiddleCache reduces 56% processing latency on cache hit and achieves a 3.8× throughput improvement on Facebook-like small-size requests workload.
Money Laundering Detection on Ethereum: Applying Traditional Approaches to New Scene

With the continuous evolution of blockchain technology, cryptocurrency platforms such as Ethereum have emerged as centers for digital asset transactions and smart contracts deployment. However, this nascent financial ecosystem also introduces potential money laundering risks. The traditional financial industry has accumulated significant antimoney laundering (AML) experience and technical means for monitoring and detecting money laundering activities. Yet, the adaptability of these established AML algorithms in the context of blockchain remains unclear. This paper aims to investigate the practical adaptability of traditional AML algorithms on Ethereum data through empirical experiments. We gather eight real-world money laundering case datasets collected from Ethereum and conduct experiments using three traditional AML algorithms on these datasets. We evaluate the performance of these algorithms from various angles, including precision, recall, and the distribution of detected accounts' labels in comparison to the original datasets. It turns out algorithms demonstrate distinct performance in diverse money laundering cases, indicating that the adaptability of traditional AML algorithms on Ethereum data presents certain adaptability and limitations. Holoscope's accuracy demonstrates the value of dense subgraph properties in Ethereum money laundering detection, and further research can be conducted based on this model framework combined with the money laundering characteristics of Ethereum. Our study provides valuable insights for strengthening AML mechanisms on blockchain platforms and offers guidance for further research on detecting money laundering accounts in blockchain environments.
GNIDP: Gaussian-Noise-based Information Diffusion Prediction Model

Due to the significant Influence of social networks, information diffusion prediction, which aims to study the spread of messages among users, has become a crucial objective in various scenarios. Existing works have mostly attempted to integrate multiple source features such as social relationships and user preferences for prediction. Although it can improve the accuracy of predictions to some extent, there are still issues with complex models, low computational efficiency, and limited prediction performance. To address these challenges, this paper proposes a Gaussian-Noise-based Information Diffusion Prediction Model (GNIDP). As we know, we are the first to model the information diffusion in the social network as Gaussian noise diffusion, specifically, GNIDP utilizes Gaussian noise to simulate information diffusion patterns during information propagation, while capturing the evolving diffusion patterns over time across different time slots. Consequently, our model does not rely on social topology to achieve prediction, while improving the efficiency and accuracy of predictions. Experiments on two real social network datasets demonstrates that GNIDP achieves 13.54% relative gains over the best baseline on average. Therefore, the proposed model can be applied in scenarios where social topology is missing or difficult to obtain particularly in large-scale datasets requiring efficient diffusion prediction.
Matching Skill and Tasks: Cyclical Fluctuations in the Overqualification of New Hires

This paper demonstrates that downturns can affect job match quality by influencing job tasks. Cognitive and manual task shares and education-based overqualification measures are generated from Canadaâ€™s Labour Force Survey and the O*NET database. Manual tasks are shown to be countercyclical among newly formed jobs. Task measures also displace the predictive power of labor market conditions for the probability of overqualification. The paper develops and calibrates a search model with two-sided heterogeneity that can account for these empirical findings. Predictions differ from prior models because production processes and vacancy posting costs differ. A single percentage point increase in unemployment is accompanied by an increase in the share of manual task vacancies by 6 percentage points, leading to an increase in overqualification by 3.5 percentage points. A policy experiment shows that increased unemployment benefits may not reduce overqualification.
Locomotion pattern generation and mechanisms of a new biped walking machine

This paper describes the mechanism of a 16 d.f. biped walking machine, Waseda biped humanoid robot-2 lower limb (WABIAN-2LL), which has two 7 d.f. legs and a 2 d.f. waist actuated by DC servo motors with reduction gears. WABIAN-2LL is designed with large movable angle ranges like those of a human. Its height and weight are 1200 mm and 40 kg, respectively. It is able to walk with its knees stretched using the redundancy of the legs and to move around an object using a hip-bending motion without touching the object. A knee-stretched locomotion pattern generation is also proposed in this paper, which separately creates joint angles in a supporting and a swinging phase. During knee-stretched walking, the joint rate of the knee will approach infinitely when the knee is stretched. This singularity problem is solved by using the motion of the waist, not the posture of the trunk. The effectiveness of the mechanisms and pattern generations of WABIAN-2LL is verified through dynamic walking experiments.
An Australian geography of unhealthy housing

Housing problems, such as affordability, poor quality of condition, or damp are key determinants of health and wellbeing. Importantly though, a growing body of research has shown that unhealthy housing is the combined result of multiple housing problems acting together. Although the spatial distribution of discrete housing problems is well established, little is known of Australia’s geography of unhealthy housing. We have previously defined and validated an Australian Index of Housing Insults, which captures the multiple ways in which housing adversely influences individual health—including, but not limited to, people’s tenure security, affordability, quality, and neighbourhood characteristics. Using the Household Income and Labour Dynamics in Australia (HILDA) dataset, a nationally representative longitudinal survey of Australian households, this paper describes Australia’s geography of unhealthy housing. The analysis examines the prevalence, characteristics, and distribution of the population who are vulnerable to unhealthy housing. Our findings reveal both a worsening landscape of households at risk because of their accommodation, and a changing pattern of unhealthy housing in Australia over time. The paper considers how these findings may impact future policy settings and the potential to improve the health of Australia’s population through targeted housing interventions.
Defeasible argumentation for multi-agent planning in ambient intelligence applications

This contribution presents a practical extension of a theoretical model for multi-agent planning based upon DeLP, an argumentation-based defeasible logic. Our framework, named DeLP-MAPOP, is implemented on a platform for open multi-agent systems and has been experimentally tested, among others, in applications of ambient intelligence in the field of health-care. DeLP-MAPOP is based on a multi-agent partial order planning paradigm in which agents have diverse abilities, use an argumentation-based defeasible reasoning to support their own beliefs and refute the beliefs of the others according to their knowledge during the plan search process. The requirements of Ambient Intelligence (AmI) environments featured by the imperfect nature of the context information and heterogeneity of the involved agents make defeasible argumentation be an ideal approach to resolve potential conflicts caused by the contradictory information coming from the ambient agents. Moreover, the ability of AmI systems to build a course of action to achieve the user's needs is also a claiming capability in such systems. DeLP-MAPOP shows to be an adequate approach to tackle AmI problems as it gathers together in a single framework the ability of planning while it allows agents to put forward arguments that support or argue upon the accuracy, unambiguity and reliability of the context-aware information.
The Influence of Principal Leadership on the Quality of Education in SMA

This research aims to determine the influence of the principal's leadership on the quality of education at State Senior High School (SMA) 12 Jambi City. The method used in this research is a quantitative research method using a survey approach. Furthermore, the population in the study were teachers at SMA Negeri 12 Jambi City. Meanwhile, the sample used in this research was the entire population, namely 48 teachers
Decision Trees for Helpdesk Advisor Graphs

We use decision trees to build a helpdesk agent reference network to facilitate the on-the-job advising of junior or less experienced staff on how to better address telecommunication customer fault reports. Such reports generate field measurements and remote measurements which, when coupled with location data and client attributes, and fused with organization-level statistics, can produce models of how support should be provided. Beyond decision support, these models can help identify staff who can act as advisors, based on the quality, consistency and predictability of dealing with complex troubleshooting reports. Advisor staff models are then used to guide less experienced staff in their decision making; thus, we advocate the deployment of a simple mechanism which exploits the availability of staff with a sound track record at the helpdesk to act as dormant tutors.
Seasonal and Interannual Mixed‐Layer Heat Budget Variability in the Western Tropical Atlantic From Argo Floats (2007–2012)

Oceanic and atmospheric processes were investigated in order to explore the causes of seasonal and interannual variability of sea surface temperatures (SST) in the western tropical Atlantic (WTA; 20 8 S–20 8 N, 15 8 W–60 8 W). A mixed-layer (ML) heat budget was performed by using Argo proﬁles and supplementary data sets based on satellite and atmospheric products during the period 2007–2012. The WTA is divided into four boxes which represent the main temporal and spatial heterogeneities of this region. An analysis of error of each term pointed out that the mean net surface heat ﬂuxes are systematically underestimated by 20 W m 2 2 . A correction of this term provides realistic estimates of the vertical mixing which was obtained as residual term. In agreement with previous studies, the results show that surface ﬂux is the most important process that governs the seasonal cycle of the heat content. Changes in shortwave radiation and latent heat ﬂuxes dictate the oceanic response to the meridional migration of the ITCZ. Along the equator, surface ﬂuxes modulate the annual cycle of ML temperature, but are strongly balanced by horizontal advection. The entrainment term proves a small contribution to the cooling of the ML. On an interannual time scale, the strong positive (negative) SST anomalies