Miracle-dz/UniFAR-FTUs huggingface dataset:
Automated Vulnerability Discovery System Based on Hybrid Execution

At present, program vulnerabilities occur frequently, which seriously threatens the security of cyberspace. Automated vulnerability discovery technology has attracted more and more attention because of its efficiency and universality. In the current field of vulnerability discovery, it is common to combine fuzzing with symbolic execution technology. Symbolic execution technology is often used to solve the input of complex paths and help fuzzing improve the program coverage, so as to detect vulnerabilities better. However, the symbolic execution has no goals in the process of hybrid execution exploration path, it is easy to cause the execution path to deviate from the expected target points. And generating the test case of the corresponding path requires frequently calling the solver for solution, while the current solver has low efficiency and poor solvability for nonlinear operation. In order to solve the above problems, we propose an automated vulnerability discovery system based on hybrid execution. In the system, we propose the symbolic execution guidance algorithm based on dynamic and static combination to guide the symbolic execution to solve the input reaching the target points, so as to avoid exploring in useless paths and avoid consuming a lot of time and computing resources. In addition we hook some nonlinear functions to optimize the nonlinear function constraint solving, so as to improve the hybrid execution efficiency. We have conducted extensive experiments on the RHG 2019 challenge dataset and the RHG 2021 challenge dataset. The experimental results show the effectiveness and scalability of the system.
Bots of a Feather: Exploring User Perceptions of Group Cohesiveness for Application in Robotic Swarms

Behaviours of robot swarms often take inspiration from biological models, such as ant colonies and bee hives. Yet, understanding how these behaviours are actually perceived by human users has so far received limited attention. In this paper, we use animations to represent different kinds of possible swarm motions intended to communicate specific messages to a human. We explore how these animations relate to the perceived group cohesiveness of the swarm, comprised of five different parameters: synchronising, grouping, following, reacting, and shape forming. We conducted an online user study where 98 participants viewed nine animations of a swarm displaying different behaviours and rated them for perceived group cohesiveness. We found that the parameters of group cohesiveness correlated with the messages the swarm was perceived as communicating. In particular, the message of initiating communication was highly positively correlated with all group parameters, whereas broken communication was negatively correlated. In addition, the importance of specific group parameters differed within each animation. For example, the parameter of grouping was most associated with animations signalling an intervention is needed. These findings are discussed within the context of designing intuitive behaviour for robot swarms.
Exploring Older Adults’ Acceptance, Needs, and Design Requirements towards Applying Social Robots in a Rehabilitation Context

This paper presents a qualitative study that uses video prototypes and interviews to explore older adults’ acceptance, needs, and design requirements towards a social robotic application for physical rehabilitation. Our study identified the benefits of applying social robots (SR) in physical rehabilitation. Further, we discovered participants’ preference for an anthropomorphic social robot design. The data revealed a desire for social interaction could increase motivation for older adults to engage in an active lifestyle and social robot acceptance. However, participants showed low motivation for technology adoption and negatively anthropomorphize the social robot, which lowers acceptance for their application. This work complements the current user-centered explorations with SR in rehabilitation, and provides considerations for SR design for rehabilitative applications.
Adversarial Training for Robust Insider Threat Detection

Insider threat analysis techniques based on machine learning provide convenient and effective automated detection of internally generated cyberattacks. When data are manipulated by adding slight perturbations, the threat intelligence models result in misclassifications of highly skewed class distribution with rare occurrences of events in insider threats. This paper proposes a generative model WGAN-GP conditioned by the class labels, referred to as CWGAN-GP, for insider threat analysis to create synthetic data samples for the rare malicious activities and shows that it generalizes well across different learning algorithms. Further, the robustness of the supervised algorithms to unknown inputs have not been investigated in any other works. This study explores how the synthetically created adversarial samples can increase the robustness of supervised models using adversarial training. We use a target classifier as threat model to generate one-step and iterative adversarial samples and perform a non-targeted test-time attack on the classifiers. We evaluate the robustness of various learning models against synthetic data from other data generation methods and demonstrate that the adversarial training using data generated from CWGAN-GP is less susceptible to adversarial attacks on insider threat classifiers using multiple versions of benchmark CMU CERT data set.
Deep Learning Neural Network Approach for Correlation between Print Parameters and Realized Electrical Performance and Geometry on Ink-Jet Platform

The use of printed electronics is increasing rapidly and replacing the traditional manufacturing techniques, especially in the consumer electronics sector. In this paper, a closed-loop deep learning approach for correlation of the print parameters with realized electrical performance and geometry estimations on an ink-jet platform is modeled. To print reliable and very fine conductive traces, an estimation of the changes in the print parameters and the realized print dimension is necessary. The inks used for this analysis are both particle and particle-free silver inks, and the comparison of the same is also studied. A closed-loop control algorithm is used to attain the desired electrical and geometrical values by changing the print parameters without any user intervention. This is achieved by an automatic print parameter sensing system using a camera that captures the print to identify the geometry and dimension of the same. Once the realized print parameters are identified, a deep learning neural network regression model based on these parameters is used to predict the desired input print parameters which are used to achieve the desired geometry and dimension of the print. These new parameter values are passed on to the printing software to optimize the print and attain the desired geometry and printing characteristics. This closed-loop system employs a print characteristics sensing system using a camera, a deep learning neural network regression model to predict the new print parameters, and an auto-update system that changes the values in the printing software. These combinations of the system are used to correlate the print parameters with the realized electrical performance and geometry of the print on an ink-jet printing platform.
Constructing Constraint Force Field in Musculoskeletal Robot by Co-optimizing Muscle Arrangements and Constant Activations

Robots with high-precision motion and operation ability are of great application significance. By referring to the biomechanical structure and neural control mechanism of human motion system, the research of musculoskeletal robot system with rigid-flexible coupling characteristics is one of the important ways to improve the operation flexibility and control robustness of robot. Inspired by the equilibrium point hypothesis proposed in neuroscience, this paper proposes a co-optimization algorithm of muscle arrangement and activation to construct constraint force field in the workspace of musculoskeletal robot. When the muscle arrangement is rough due to the insufficient precision of the mechanical structure, the musculoskeletal robot can maintain accurate motion with the help of constraint force field by adopting the optimized constant activation. Experiments are carried out on a musculoskeletal robot model with human-mimetic muscle to demonstrate the effectiveness of the proposed algorithm in movement accuracy, noise robustness and generalization. This work may be of great significance for the further introduction of constraint force field into hardware system of musculoskeletal robot.
Full-field pump-probe thermoreflectance imaging for characterization of thin films and 3D integrated circuits

We evaluate optical pump-probe imaging where laser heating and subsequent non-contact full-field thermoreflectance characterization is used to study thin film materials and 3D integrated circuit chips. The diameter of the heated region can range from one micron up to millimeters or centimeters. The shape of the surface or interface thermoreflectance profile is then captured with 50 ns time-resolution using mega-pixel lock-in camera. By combining the measured thermal surface profile with finite-element modeling, we demonstrate the ability to extract the thermal conductivity of thin films on various substrates. With the proper choice of pump and probe wavelengths, the potential of the technique as a general characterization tool is evaluated. There is a lot of interest in structural integrity and failure-analysis of complex chips during manufacturing. Heat sinking in 3D chips is often a key challenge. The sensitivity of the full-field thermal imaging technique to study the quality of buried (below the surface) layers or contacts in 3D chips is quantified.
Investigation of Transmission Efficiency on Short-range 4x4 MIMO WiCoPT System

This research has investigated the short-range Multi Input Multi Output (MIMO) Wireless Communication and Power Transfer (WiCoPT) system that can simultaneously transmit information and power using the same antenna and frequency band. A 4x4 MIMO system with 2x2 planar patch antennas is used for both transmission and reception. The previous work indicated the proposed WiCoPT system can achieve high-level transmission capacity and power transmission efficiency. In addition, the system significantly improved the positioning-free capability. This paper demonstrates the measured performances and investigates the relationship between the input and output feeding circuits (matching circuits) and the transmission efficiency. The investigation clarifies there is an optimal matching condition to maximize the transmission efficiency under the MIMO control. The maximum efficiency improvement of 35 points is achieved at a transmission distance of 10 mm, with a transmission efficiency of 59%, and the corresponding maximum transmission capacity of 52 bit/s/Hz. These results promise that a variable matching circuit will effectively optimize the propagation condition between the transmitter and the receiver under the MIMO control, resulting in high-level transmission characteristics.
RGB-D SLAM Method Based on Object Detection and K-Means

Aiming at the problem that the traditional visual simultaneous localization and mapping (SLAM) algorithm is easily affected by moving targets in dynamic environment, which leads to the degradation of system localization accuracy, a visual SLAM algorithm based on object detection and K-Means is proposed for application in dynamic environment. It incorporates the YOLOv5n object detection network with the addition of a leak detection judgment and repair algorithm and the K-means clustering algorithm, which effectively rejects dynamic objects in images and maximizes the retention of static information. Experiments on publicly available datasets show that the error of this paper's method is smaller than that of other SLAM algorithms applied in dynamic environments, and it can guarantee real-time operation.
Towards An Integrated Framework for Neural Temporal Point Process

Temporal point process (TPP) is a powerful tool to model the generation of irregular event sequences. With the rise of deep learning, neural TPPs have been proposed and made a great success. In neural TPPs, a state is extracted to characterize the influence of past events and updated accordingly when a new event is observed. Existing works can be categorized into two frameworks; each has pros and cons. Thus, we integrate these frameworks to overcome their shortages. To propose a model under the integrated framework, we first propose a novel and theoretically well-founded continuous-time layer normalized gated recurrent unit (CLNGRU) model, which is simpler, more robust, and more expressive than a pioneering work. Then we propose an enhanced version of CLNGRU (CLNGRU-E) under the integrated framework. We conduct experiments on synthetic and real datasets. Experimental results show the superiority of our models against the state-of-the-art methods on both generative and predictive tasks. Finally, we show the advantages of our models in recovering the underlying generative processes from sampled sequences.
Multi-Scale Cascaded Generator for Music-driven Dance Synthesis

Dance is a creative performance art and must keep coherent with the rhythm and style of music. To address these issues, most of the existing music-driven dance synthesis methods utilize deep generative models and capture the dynamic characteristics of dance motions. However, we observe that dance motions contain big-scale body part movements and small-scale joint movements that are mutually coordinated and related, and the generated dance motions, particularly affected by $L_{1}$ loss, are too restrictive and conservative. In this paper, we propose a multi-scale cascaded music-driven dance synthesis network (MC-MDSN) that first generates big-scale body motions conditioned on music and then further refines local small-scale joint motions. Furthermore, we design a multi-scale feature loss to capture the dynamic characteristics of each scale motions and the relations between different scale motion joints. Experimental results show that our method generates better dance motions than the baselines.
The Robot Screener Will See You Now: A Socially Assistive Robot for COVID-19 Screening in Long-Term Care Homes

The rapid spread of COVID-19 around the globe has increased the need to adopt autonomous social robots within our healthcare systems. In particular, socially assistive robots can help to improve the day-to-day functioning of our healthcare facilities including long-term care, while keeping residents and staff safe by performing repetitive tasks such as health screening. In this paper, we present the first human-robot interaction study with an autonomous multi-task socially assistive robot used for non-contact screening in long-term care homes. The robot monitors temperature, checks for face masks, and asks screening questions to minimize human-to-human contact. We investigated staff perceptions of 7 attributes: screening experience without and with the robot, efficiency, cognitive attitude, freeing up staff, safety, affective attitude, and intent to use the robot. Furthermore, we investigated the influence of demographics on these attributes. Study results show that, overall, staff rated these attributes high for the screening robot, with a statistically significant increase in cognitive attitude and safety after interacting with the robot. Differences between gender and occupation were also determined. Our study highlights the potential application of an autonomous screening robot for long-term care homes.
LockBoost: Detecting Malware Binaries by Locking False Alarms

High-stake applications, such as malware detection, demand a high true-positive rate (TPR) while meeting a firm (usually very low) false-positive rate (FPR, i.e., false alarms). This goal is hard to achieve using classic cost-sensitive learning, which requires a cost ratio that is difficult to specify in practice. Also, selecting a threshold point on the receiver operating characteristic (ROC) curve to meet a specified low FPR often leads to a poor TPR, as TPR and FPR adversely affect one another. To address the above requirement, we propose a novel approach called “LockBoost” that locks FPR at a specified level while iteratively boosting TPR during the training process. LockBoost produces a sequence of classifiers where each next classifier “picks up” the true positives missed due to the enforcement of the low FPR by the previous classifier. We adapt LockBoost to detect malware binaries in four datasets, including two recently released public benchmarks, SOREL and BODMAS. With FPR locked at the target level of 0.1% or lower, we achieve ≈9% and ≈7% TPR improvement over state-of-the-art methods on sequential and tabular features, respectively.
Smartphone Proximity Detection Using WiFi and BLE Fingerprinting

In light of events such as the recent pandemic and many potential applications in fields such as the social sciences, healthcare, and architecture, the detection of interactions or proximity between people has become increasingly important. In this context, this paper investigates the limitations of a machine learning-based approach that detects the proximity of two devices based on the WiFi and BLE fingerprints of their radio environments. More specifically, (i) we compare the use of a rudimentary set of two features and an extended, more complex set of features, (ii) we investigate the use of separate classifiers that treat WiFi and BLE features separately, and (iii) we investigate whether using only one of the two communication technologies for detection could provide better results. In addition, we also try to use techniques such as undersampling and oversampling or their combination to deal with the highly imbalanced set of examples. Our results show that the use of a more complex set of features that can be subjected to further feature selection procedures can provide a performance benefit of about 4.6 percentage points. In terms of the communication technologies used, our results also show that using BLE alone always gives significantly worse results than using WiFi alone or WiFi and BLE together. On the other hand, there is no clear winner between using WiFi alone or combining WiFi and BLE, as both provide comparable results. Finally, our results also show that using under/oversampling helps in scenarios where the classification task is somewhat more complex, but not in those where the diversity between instances is low; thus, the classification problem is simpler.
Learning Musical Instrument with the Help of Social Robots: Attitudes and Expectations of Teachers and Parents

In music education, staying motivated has always been a crucial goal, especially for children. Earlier research has demonstrated how social robots could help improve motivation and increase the performance of children practicing music pieces. As important stakeholders, the attitudes and expec- tations of parents and music teachers are important factors that affect children’s and their own acceptance of applying social robots in music education. In this study, a survey was created and used to assess parents’ and music teachers’ attitudes, expectations, and intentions related to using social robots in music education. The survey results suggested that parents and music teachers expect robots to act as assistive and motivating agents, give positive feedback, and schedule and structure practice sessions. Moreover, parents expect a social robot to evaluate student performance, while music teachers focus more on positive encouragement. The biggest concerns of these stakeholders are whether the robots can convey the emotional aspects of music and demonstrate how to play the instrument. The intention of using robots is positively correlated with positive attitudes towards robots in general and the positive attitude towards robots specifically in the context of music education. The current study contributes insights into parents’ and music teachers’ attitudes and expectations as determinants of their acceptance of of social robots in music education, and provides a method for measuring these attitudes and expectations.
Multi-Label Learning with Missing Labels via Common and Label-Specific Features

In multi-label learning with missing labels, previous works usually only consider all features, or only consider label-specific features. It is obviously inappropriate to select a subset of features only considering which have a great discriminability for all labels or for a label. Besides, they are built on an assumption that if two labels are correlated, their regression coefficient vectors should be similar. However, it is hard to hold this assumption in real-world applications. Therefore, we propose a novel multi-label classification approach for missing labels via considering common and label-specific features with correlation information. More specifically, we firstly utilize $l_{1}$- norm regularizer over label correlation matrix, and a new sup-plementary label matrix is augmented from the incomplete label matrix by learning label correlations. Then, for a better label recovery, we introduce $l_{2,1}$ -norm and $l_{1}$ -norm regularizers to learn common and label-specific features simultaneously. Thirdly, to make more rational use of label correlation to solve missing labels, we use a regularizer to constrain label correlations on outputs of labels instead of regression coefficient matrix. Finally, an objective function is designed in terms of the above processing. Extensive experiments conducted on eight data sets demonstrate the effectiveness of the proposed approach.
Multi-modal False Information Detection Based on Adversarial Learning

Nowadays, with the development of multimedia technology, rumor spreaders tend to produce false information with multi-modal content to attract the attention of the news readers. However, it is challenging to capture implicit clues among multi-modal data to produce effective representations of false information detection. Moreover, as they tend to evade the detector, it is necessary to develop a robust detection modal that can resist multi-modal adversarial attacks, which is less studied in existing works. To address these issues, in this paper, we propose a novel multi-modal false information detection framework with adversarial training (MFAT). By adopting a pretrained multi-modal model and a cross-modal attention mechanism, MFAT is able to capture fine-grained element-level relationships and coarse-grained modal-level relationships simultaneously, and thus can better capture various multi-modal clues. Additionally, MFAT is also enhanced in robustness and generalization by defending against the adversarial attacks on multi-modal features. Experiments on two real-world datasets demonstrate that MFAT can significantly outperform state-of-the-art baselines. We also show the impacts of three types of multi-modal attacks, and verify that the invulnerability of the model is improved. Codes will be released upon acceptance.