Reviewerly/exHarmony huggingface dataset:
A Disk Failure Prediction Method Based on Active Semi-supervised Learning

Disk failure has always been a major problem for data centers, leading to data loss. Current disk failure prediction approaches are mostly offline and assume that the disk labels required for training learning models are available and accurate. However, these offline methods are no longer suitable for disk failure prediction tasks in large-scale data centers. Behind this explosive amount of data, most methods do not consider whether it is not easy to get the label values during the training or the obtained label values are not completely accurate. These problems further restrict the development of supervised learning and offline modeling in disk failure prediction. In this article, Active Semi-supervised Learning Disk-failure Prediction ( ASLDP ), a novel disk failure prediction method is proposed, which uses active learning and semi-supervised learning. According to the characteristics of data in the disk lifecycle, ASLDP carries out active learning for those clear labeled samples, which selects valuable samples with the most significant probability uncertainty and eliminates redundancy. For those samples that are unclearly labeled or unlabeled, ASLDP uses semi-supervised learning for pre-labeled by calculating the conditional values of the samples and enhances the generalization ability by active learning. Compared with several state-of-the-art offline and online learning approaches, the results on four realistic datasets from Backblaze and Baidu demonstrate that ASLDP achieves stable failure detection rates of 80–85% with low false alarm rates. In addition, we use a dataset from Alibaba to evaluate the generality of ASLDP . Furthermore, ASLDP can overcome the problem of missing sample labels and data redundancy in large data centers, which are not considered and implemented in all offline learning methods for disk failure prediction to the best of our knowledge. Finally, ASLDP can predict the disk failure 4.9 days in advance with lower overhead and latency.
Foundations of cybercriminalistics: From general process models to case-specific concretizations in cybercrime investigations

Despite spectacular stories of successful cyber operations by law enforcement agencies, we continue to be extremely inefficient in fighting cybercrime. The research community has contributed many abstract models to guide digital forensic analyses, but these are usually too abstract to be helpful in concrete cybercrime investigations since they do not give an immediate and straightforward translation of a confronted (digital) crime scene into viable yet promising criminalistic actions. We propose a method to systematically bridge the gap between high-level process models and the demands of actual investigations. The idea is to encode phenomenon-specific knowledge of cybercrime into node-link representations, thereby literally mapping the digital crime scene in well-founded visual representations – so-called cognitive maps. These can be used to derive a prioritized plan of action for targeted acquisition and analysis of case-relevant artifacts. To illustrate our approach, we present a cognitive map for the category of botnet crime and evaluate it with the help of domain experts and by applying it to two real-world cases.
Locally-guided neural denoising

Noise-like artifacts are common in measured or fitted data across various domains, e.g. photography, geometric reconstructions in terms of point clouds or meshes, as well as reflectance measurements and the respective fitting of commonly used reflectance models to them. State-of-the-art denoising approaches focus on specific noise characteristics usually observed in photography. However, these approaches do not perform well if data is corrupted with location-dependent noise. A typical example is the acquisition of heterogeneous materials, which leads to different noise levels due to different behavior of the components either during acquisition or during reconstruction. We address this problem by first automatically determining location-dependent noise levels in the input data and demonstrate that state-of-the-art denoising algorithms can usually benefit from this guidance with only minor modifications to their loss function or employed regularization mechanisms. To generate this information for guidance, we analyze patchwise variances and subsequently derive per-pixel importance values. We demonstrate the benefits of such locally-guided denoising at the examples of the Deep Image Prior method and the Self2Self method.
Digital forensic analysis of mobile automotive maintenance applications

Mobile applications regarding automotive maintenance have gained popularity among many automobile users. These applications may store digital forensics artifacts about the mechanics and movements of the vehicles such as GPS coordinates, vehicle health reports, or speed values which can be valuable for investigating a broad category of cases such as traffic incidents, insurance disputes, or criminal offenses. This study presents the forensics artifacts that can be identified in three automobile maintenance apps and provides guidance about the required technical procedures for the extraction and analysis of these artifacts. On the other hand, although increased utilization of mobile and IoT technologies creates additional opportunities for collecting more artifacts as evidenced in this study, they will cause more burden on the backlogs of forensic labs. This study also proposes a guideline about how to align manual and logical extraction procedures with the digital forensics life-cycle that includes the triage and other follow-up steps.
A rich task-oriented dialogue corpus in Vietnamese

This paper introduces a new Vietnamese multi-domain task-oriented dialogue corpus which is fully labeled with rich information on dialogue structure and contextual information. The corpus contains 1910 dialogues, with a total of more than 18,000 turns in four domains (i.e., ProductInfo, OrderInfo, Shipping and Chatchit). To the best of our knowledge, this is the first dialogue corpus towards building automated conversations in e-commerce. We describe the rigorous annotation process of labelling rich information about dialogue segmentation, dialogue acts (DAs, a.k.a communicative functions), dependency relations, rhetorical relations and slot-values on both user and system sides. This corpus will alleviate the shortage of dialogue datasets in low-resource languages, namely Vietnamese. It can be exploited in diverse contexts to facilitate research toward building complete dialogue systems. The large size and rich annotation of the corpus make it suitable to investigate a variety of different tasks in conversational systems. In this paper, we perform extensive experiments and report preliminary results for future studies in this interesting yet unexplored field. Specifically, we illustrate the usage of the corpus in developing key modules such as natural language understanding, belief tracking, dialogue policy management and natural language generation.
Differential Effects of Cultural Orientation on the Changes in Group Creativity

Recently, Asian countries, including Korea and China, are rapidly changing from a traditional collectivist culture to an individualistic culture. Therefore, it is necessary to investigate how individualism as an individual traits affects group creativity, which essentially requires social interaction. Under the background, we divided the cultural orientations of Chinese university students into the four types of Triandis (1995): vertical individualism, horizontal individualism, vertical collectivism, and horizontal collectivism. And then we constructed 4 homogeneous groups according to each types, and their group creativity was tested three times over an 8-week period. The results were first, in the last measurement, the groups with the highest group creativity were the horizontal collectivist team and the vertical individualistic team. Although the horizontal collectivist team had the lowest score among the four teams at the beginning, it continued to show a steep rise over time, showing the highest score in the last. The vertical individualist team started with a high score from the beginning and maintained a consistently high level. The vertical collectivist team seemed to maintain a high level of group creativity from the beginning to the middle, but showed a sharp decrease at the end. The horizontal individualism team showed low group creativity from the beginning and tended to keep it at a low level. This tendency was also supported by peer ratings and expert ratings. In the subsequent discussion, the causes of this tendency were analyzed, implications and limitations, and suggestions for future research were discussed.
On Objective and Subjective Quality of 6DoF Synthesized Live Immersive Videos

We address the problem of quantifying the perceived quality in 6DoF (Degree-of-Freedom) live immersive video in two steps. First, we develop a set of tools to generate (or collect) datasets in a photorealistic simulator, AirSim. Using these tools, we get to change diverse settings of live immersive videos, such as scenes, trajectories, camera placements, and encoding parameters. Second, we develop objective and subjective evaluation procedures, and carry out evaluations on a sample immersive video codec, MPEG MIV, using our own dataset. Several insights were found through our experiments: (1) the two synthesizers in TMIV produce comparable target view quality, but RVS runs 2 times faster; (2) Quantization Parameter (QP) is a good control knob to exercise target view quality and bitrate, but camera placements (or trajectories) also impose significant impacts; and (3) overall subjective quality has strong linear/rank correlation with subjective similarity, sharpness, and color. These findings shed some light on the future research problems for the development of emerging applications relying on immersive interactions.
Narrative Dataset: Towards Goal-Driven Narrative Generation

In this paper, we propose a new dataset called the Narrative dataset, which is a work in progress, towards generating video and text narratives of complex daily events from long videos, captured from multiple cameras. As most of the existing datasets are collected from publicly available videos such as YouTube videos, there are no datasets targeted towards the task of narrative summarization of complex videos which contains multiple narratives. Hence, we create story plots and conduct video shooting with hired actors to create complex video sets where 3 to 4 narratives happen in each video. In the story plot, a narrative composes of multiple events corresponding to video clips of key human activities. On top of the shot video sets and the story plot, the narrative dataset contains dense annotation of actors, objects, and their relationships for each frame as the facts of narratives. Therefore, narrative dataset richly contains holistic and hierarchical structure of facts, events, and narratives. Moreover, Narrative Graph, a collection of scene graphs of narrative events with their causal relationships, is introduced for bridging the gap between the collection of facts and generation of the summary sentences of a narrative. Beyond related subtasks such as scene graph generation, narrative dataset potentially provide challenges of subtasks for bridging human event clips to narratives.
STVGFormer

In this technical report, we introduce our solution to human-centric spatio-temporal video grounding task. We propose a concise and effective framework named STVGFormer, which models spatio-temporal visual-linguistic dependencies with a static branch and a dynamic branch. The static branch performs cross-modal understanding in a single frame and learns to localize the target object spatially according to intra-frame visual cues like object appearances. The dynamic branch performs cross-modal understanding across multiple frames. It learns to predict the starting and ending time of the target moment according to dynamic visual cues like motions. Both the static and dynamic branches are designed as cross-modal transformers. We further design a novel static-dynamic interaction block to enable the static and dynamic branches to transfer useful and complementary information from each other, which is shown to be effective to improve the prediction on hard cases. Our proposed method achieved 39.6% vIoU and won the first place in the HC-STVG track of the 4th Person in Context Challenge.
Time flies by: Analyzing the Impact of Face Ageing on the Recognition Performance with Synthetic Data

The vast progress in synthetic image synthesis enables the generation of facial images in high resolution and photorealism. In biometric applications, the main motivation for using synthetic data is to solve the shortage of publicly-available biometric data while reducing privacy risks when processing such sensitive information. These advantages are exploited in this work by simulating human face ageing with recent face age modification algorithms to generate mated samples, thereby studying the impact of ageing on the performance of an open-source biometric recognition system. Further, a real dataset is used to evaluate the effects of short-term ageing, comparing the biometric performance to the synthetic domain. The main findings indicate that short-term ageing in the range of 1-5 years has only minor effects on the general recognition performance. However, the correct verification of mated faces with long-term age differences beyond 20 years poses still a significant challenge and requires further investigation.
Sub-byte quantization of Mobile Face Recognition Convolutional Neural Networks

Converting convolutional neural networks such as MobileNets to a full integer representation is already quite a popular method to reduce the size and computational footprint of classification networks but its effect on face recognition networks is relatively unexplored. This work presents a method to reduce the size of MobileFaceNet using sub-byte quantization of the weights and activations. It was found that 8-bit and 4-bit versions of MobileFaceNet can be obtained with 98.68% and 98.63% accuracy on the LFW dataset which reduces the footprint to 25% and 12.5% of the original weights respectively. Using mixed-precision, an accuracy of 98.17% can be achieved whilst requiring only 10% of the original weight footprint. It is expected that with a larger training dataset, higher accuracies can be achieved.
Model-Free Template Reconstruction Attack with Feature Converter

State-of-the-art template reconstruction attacks assume that an adversary has access to a part or whole of the functionality of a target model. However, in a practical scenario, rigid protection of the target system prevents them from gaining knowledge of the target model. In this paper, we propose a novel template reconstruction attack method utilizing a feature converter. The feature converter enables an adversary to reconstruct an image from a corresponding compromised template without knowledge about the target model. The proposed method was evaluated with qualitative and quantitative measures. We achieved the Successful Attack Rate(SAR) of 0.90 on Labeled Faces in the Wild Dataset(LFW) with compromised templates of only 1280 identities.
Detection of the Optimal Reflection Symmetry Axis with the Jaccard Index and the Radon Transform

This study determines the optimal reflection symmetry axis of an object in a binary image using the Jaccard index. We propose to find the global optimum on the grid by estimating the upper bounds of the Jaccard index with the Radon transform. Several approaches to improving the Jaccard index computation for a given line are considered. It is shown that for the shear symmetry the optimal skew for a candidate symmetry axis can be found analytically. Some strategies for enumerating possible symmetry axes and selecting the initial approximation are proposed. The experiments show that the Jaccard index for the optimal axis found by the proposed method is not inferior to the exhaustive brute-force of the axes passing through the points of the object contour considering the rasterization error. In terms of speed, the proposed method significantly exceeds the previously developed methods for limiting the exhaustive search.
Taxonomy and Bot Threats in Social Networks

As the impact of social services is continuously growing, there increase in various security threats that can use social bots. At the same time, existing social threat taxonomies leverage suspicious actions of account or behavioral patterns. As the result, known threat classifications are almost always unrelated to bot classifications. In this paper, we propose a taxonomy of bots, which differs from the existing ones as it considers not only the functional bot features but also scenarios for bot creation and management. We also propose the classification of bot threats, which was validated with a survey method. For that, we made a survey with the participation of experts who study social bots. Based on their assessments, we created an integral score for each type of threat. The presented taxonomy can be used in bot detection models and risk analysis to assess the risk of threats.
Comparison of Metrics for Colorized Image Quality Evaluation

Colorization is a process of converting grayscale images into visually acceptable colorized images. It is a complex problem because no unique solution exists, i.e., the objects within the image cannot be associated with one color only. Colorized image quality assessment is also a complex problem because in real life the reference image for comparison with colorized results does not exist. The main purpose of colorization is convincing the observer in the credibility of the colorized image, disregarding color accuracy. For that reason, both subjective and objective evaluation of colorization results show imperfections. In this paper, the objective quality evaluation of colorized images has been conducted with five existing metrics and one new metric. The database of colorized images has been obtained by deep learning colorization methods. Based on the conducted subjective testing, a correlation between objective metrics and subjective grades has been determined. The results indicate that the improvement of objective metrics for the evaluation of colorized images is needed.
Detecting Functional Safety Violations in Online AI Accelerators

With the ubiquitous deployment of Deep Neural Networks (DNNs) in low latency mission critical applications, there has been an extensive proliferation of custom-built AI inference accelerators at the edge. Drastic technology scaling in recent years has made these circuits highly vulnerable to faults due to various reasons like aging, latent defects, single event upsets, etc. Such faults are highly detrimental to the classification accuracy of the AI accelerator, leading to the critical Functional Safety (FuSa) violation, when used in mission-critical applications. In order to detect such violations in mission mode, we analyze the efficiency of a software-based self test scheme that employs functional test patterns, akin to instances in the application dataset. Such patterns are either selected from the dataset of the DNN, or generated from scratch utilizing the concept of Generative Adversarial Networks (GANs). When evaluated on state-of-the-art DNNs on multivariate exhaustive datasets, the GAN generated test patterns significantly improve FuSa violation detection coverage by up to 130.28%, compared to the selected test patterns, thereby accomplishing efficient testing of the AI accelerator, online, in mission mode.
Quantum Noise in the Flow of Time: A Temporal Study of the Noise in Quantum Computers

Over the last couple of years, Quantum Computing (QC) has captured the interest of computer scientists due to the fact of quantum speedup, the possibility of solving NPhard problems, and achieving higher compute power. However, mitigating the impact of the noise inside each quantum device presents an immediate challenge. These changes open up new opportunities to investigate the effect of calibration parameters for individual characteristics of each qubit in a manner of time. In this paper, we investigate the temporal behavior of noisy intermediate-scale quantum (NISQ) computers based on calibration data and the characteristics of individual devices. In particular, we collect calibration data of IBM-Q machines over the last two years and compare the quantum error robustness against the processor types, quantum topology, and quantum volumes of the IBM-Q machines.
Finite Mixtures of Multivariate Wrapped Normal Distributions for Model Based Clustering of <i>p</i> -Torus Data

We consider a finite mixture model of multivariate Wrapped Normal distributions to handle non homogeneous circular data on a p-dimensional torus ( p≥2). The Wrapped Normal distribution is a valid alternative to model multivariate circular or directional data on a p-torus. Parameter estimation is carried out through a nested (classification) EM algorithm, by exploiting the ideas of unwrapping circular data. The source of incompleteness in the outer E-step is represented by unobserved group memberships, whereas the source of incompleteness in the inner E-step is given by the unobserved vectors of wrapping coefficients. The finite sample behavior of the proposed method has been investigated by Monte Carlo numerical studies and real data examples. Supplemental materials for the article, including data and R codes for implementing methods, running simulations and replicate data analyses, are available online.
A Fuzzy Evaluation Method Based on Industrial Cluster Investment Risk Assessment Method Based on IoT Applications

Hazard evaluation and the board are logical subject that goes back 30-40 years. For how to conceptualize, investigate, and oversee hazard, standards and strategies were set up. These ideas that move toward still comprise a major piece of the field’s premise today, despite the fact that there have been numerous progressions in both the hypothetical stage and functional models and cycles. The nonlinear properties of the appraisal cycle give the premise to fluffy assessment. It measures and incorporates the nonlinear assessment area utilizing the fluffy activity rule in fluffy math, bringing about a quantitative appraisal strategy that is practically identical. Modern bunch estimation and evaluation are vital parts of modern group hypothesis research; in any case, they do not stand out enough to be noticed. Modern bunch hazard factors are extremely indistinct, and common assessment and estimating approaches have restrictions. Thus, this work, which depends on fluffy mathematic hypothesis, utilizes an updated fluffy far reaching strategy to exhaustively research and assess modern group hazard and foster a danger appraisal model.