Reviewerly/exHarmony huggingface dataset:
Weakly Supervised Fine-grained Image Classification via Correlation-guided Discriminative Learning

Weakly supervised fine-grained image classification (WFGIC) aims at learning to recognize hundreds of subcategories in each basic-level category with only image level labels available. It is extremely challenging and existing methods mainly focus on the discriminative semantic parts or regions localization as the key differences among different subcategories are subtle and local. However, they localize these regions independently while neglecting the fact that regions are mutually correlated and region groups can be more discriminative. Meanwhile, most current work tends to derive features directly from the output of CNN and rarely considers the correlation within the feature vector. To address these issues, we propose an end-to-end Correlation-guided Discriminative Learning (CDL) model to fully mine and exploit the discriminative potentials of correlations for WFGIC globally and locally. From the global perspective, a discriminative region grouping (DRG) sub-network is proposed which first establishes correlation between regions and then enhances each region by weighted aggregating all the correlation from other regions to it. By this means each region's representation encodes the global image-level context and thus is more robust; meanwhile, through learning the correlation between discriminative regions, the network is guided to implicitly discover the discriminative region groups which are more powerful for WFGIC. From the local perspective, a discriminative feature strengthening sub-network (DFS) is proposed to mine and learn the internal spatial correlation among elements of each patch's feature vector, to improve its discriminative power locally by jointly emphasizes informative elements while suppresses the useless ones. Extensive experiments demonstrate the effectiveness of proposed DRG and DFS sub-networks, and show that the CDL model achieves state-of-the-art performance both in accuracy and efficiency.
The ACM Multimedia 2019 Live Video Streaming Grand Challenge

Live video streaming delivery over Dynamic Adaptive Video Streaming (DASH) is challenging as it requires low end-to-end latency, is more prone to stall, and the receiver has to decide online which representation at which bitrate to download and whether to adjust the playback speed to control the latency. To encourage the research community to come together to address this challenge, we organize the Live Video Streaming Grand Challenge at ACM Multimedia 2019. This grand challenge provides a simulation platform onto which the participants can implement their adaptive bitrate (ABR) logic and latency control algorithm, and then benchmark against each other using a common set of video traces and network traces. The ABR algorithms are evaluated using a common Quality-of- Experience (QoE) model that accounts for playback bitrate, latency constraint, frame-skipping penalty, and rebuffering penalty.
DaNet

Reconstructing 3D human shape and pose from a monocular image is challenging despite the promising results achieved by most recent learning based methods. The commonly occurred misalignment comes from the facts that the mapping from image to model space is highly non-linear and the rotation-based pose representation of the body model is prone to result in drift of joint positions. In this work, we present the Decompose-and-aggregate Network (DaNet) to address these issues. DaNet includes three new designs, namely UVI guided learning, decomposition for fine-grained perception, and aggregation for robust prediction. First, we adopt the UVI maps, which densely build a bridge between 2D pixels and 3D vertexes, as an intermediate representation to facilitate the learning of image-to-model mapping. Second, we decompose the prediction task into one global stream and multiple local streams so that the network not only provides global perception for the camera and shape prediction, but also has detailed perception for part pose prediction. Lastly, we aggregate the message from local streams to enhance the robustness of part pose prediction, where a position-aided rotation feature refinement strategy is proposed to exploit the spatial relationship between body parts. Such a refinement strategy is more efficient since the correlations between position features are stronger than that in the original rotation feature space. The effectiveness of our method is validated on the Human3.6M and UP-3D datasets. Experimental results show that the proposed method significantly improves the reconstruction performance in comparison with previous state-of-the-art methods. Our code is publicly available at https://github.com/HongwenZhang/DaNet-3DHumanReconstrution .
LiSRA: Lightweight Security Risk Assessment for decision support in information security

Information security risk assessment frameworks support decision-makers in assessing and understanding the risks their organisation is exposed to. However, there is a lack of lightweight approaches. Most existing frameworks require security-related information that are not available and that are very challenging to gather. So they are not suitable in practice, especially for small and medium-sized enterprises (SMEs) who often lack in data and in security knowledge. On the other hand, other explicit SME approaches have far less informative value than the proposed framework. Moreover, many approaches only provide extensive process descriptions that are challenging for SMEs. In order to overcome this challenge, we propose LiSRA, a lightweight, domain-specific framework to support information security decision-making. It is designed with a two-sided input where domain experts initially provide domain-specific information (e.g. attack scenarios for a specific domain), whereupon users can focus on specifying their security practices and organisational characteristics by entering information that many organisations have already collected. This information is then linked to attack paths and to the corresponding adverse impacts in order to finally assess the total risk. Moreover, LiSRA can be used to get transparent recommendations for future security activities and presents detailed insights on the mitigating effects of each recommendation. The security activities are being evaluated taking into account the security activities already in place, and also considering the dependencies between multiple overlapping activities that can be of complementary, substitutive or dependent nature. Both aspects are ignored by most existing evaluation approaches which can lead to an over-investment in security. A prototype has been implemented, and the applicability of the framework has been evaluated with performance and robustness analyses and with initial qualitative evaluations.
Automatic prediction of the severity of bugs using stack traces and categorical features

The severity of a bug is often used as an indicator of how a bug negatively affects system functionality. It is used by developers to prioritize bugs which need to be fixed. The problem is that, for various reasons, bug submitters often enter the incorrect severity level, delaying the bug resolution process. Techniques that can automatically predict the severity of a bug can significantly reduce the bug triaging overhead. In our previous work, we showed that the accuracy of description-based severity prediction techniques could be significantly improved by using stack traces as a source of information. In this study, we expand our previous work by exploring the effect of using categorical features, in addition to stack traces, to predict the severity of bugs. These categorical features include faulty product, faulty component, and operating system. We experimented with other features and observed that they do not improve the severity prediction accuracy. A Software system is composed of many products; each has a set of components. Components interact with each to provide the functionality of the product. The operating system field refers to the operating system on which the software was running on during the crash. The proposed approach uses a linear combination of stack trace and categorical features similarity to predict the severity. We adopted a cost sensitive K Nearest Neighbor approach to overcome the unbalance label distribution problem and improve the classifier accuracy. Our experiments on bug reports of Eclipse submitted between 2001 and 2015 and Gnome submitted between 1999 and 2015 show that the accuracy of our severity prediction approach can be improved from 5% to 20% by considering categorical features, in addition to stack traces. The accuracy of predicting the severity of bugs is higher when combining stack traces and three categorical features, product, component, and operating system.
Solar irradiance time series derived from high-quality measurements, satellite-based models, and reanalyses at a near-equatorial site in Brazil

This study analyzes five years of 1-min solar global horizontal irradiance (GHI) and direct normal irradiance (DNI) observations obtained at Petrolina (northeast Brazil). Quality-assured hourly and daily averages are obtained after applying filters and methodologies based on a Baseline Solar Radiation Network (BSRN) quality-control procedure. To calculate correct hourly averages, a minimum fraction of 20% of valid GHI or DNI minutely data is needed, as well as at least 60% of valid days to calculate correct daily-mean monthly values. An asymmetric diurnal pattern is found in GHI and DNI during all months, attributed to consistently higher cloudiness in the morning. The quality-assured hourly and monthly-mean GHI and DNI time series are compared to estimates from 11 solar databases regularly used in solar resource assessment studies: CAMS, CERES, ERA5, INPE, MERRA-2, Meteonorm, NASA-POWER, NSRDB, SARAH, SWERA-BR, and SWERA-US. For hourly GHI values, a range of RMS differences is found between the best (CAMS, 17.3%) and the worst (MERRA-2, 38.9%) results. The latter database is also affected by a larger bias (18.7%) than CAMS (4%). Larger RMS differences are found with hourly DNI, in a range extending from 37% (CAMS) to 63.4% (ERA5). Biases are all above 12%, except for CERES (−1%). For long-term mean-monthly GHI results, low biases of less than 1% are obtained with CAMS, CERES and NASA-POWER, whereas MERRA-2 overestimates (13%). Larger biases are found for mean-monthly DNI, spanning between CAMS (3%) and Meteonorm (−18.4%). Overall, CAMS appears the most consistent solar database for long-term irradiance time series at Petrolina. The significant differences found here between modeled databases are larger than expected, and underline the importance of regional validation studies like this one to decrease the incidence of uncertainties in solar resource assessments on the design and performance of solar energy projects.
Finding help with programming errors: An exploratory study of novice software engineers’ focus in stack overflow posts

Monthly, 50 million users visit Stack Overflow, a popular Q&A forum used by software developers, to share and gather knowledge and help with coding problems. Although Q&A forums serve as a good resource for seeking help from developers beyond the local team, the abundance of information can cause developers, especially novice software engineers, to spend considerable time in identifying relevant answers and suitable suggested fixes. This exploratory study aims to understand how novice software engineers direct their efforts and what kinds of information they focus on within a post selected from the results returned in response to a search query on Stack Overflow. The results can be leveraged to improve the Q&A forum interface, guide tools for mining forums, and potentially improve granularity of traceability mappings involving forum posts. We qualitatively analyze the novice software engineers’ perceptions from a survey as well as their annotations of a set of Stack Overflow posts. Our results indicate that novice software engineers pay attention to only 27% of code and 15–21% of text in a Stack Overflow post to understand and determine how to apply the relevant information to their context. Our results also discern the kinds of information prominent in that focus.
DeepGRU: Deep Gesture Recognition Utility

We propose DeepGRU, a novel end-to-end deep network model informed by recent developments in deep learning for gesture and action recognition, that is streamlined and device-agnostic. DeepGRU, which uses only raw skeleton, pose or vector data is quickly understood, implemented, and trained, and yet achieves state-of-the-art results on challenging datasets. At the heart of our method lies a set of stacked gated recurrent units (GRU), two fully-connected layers and a novel global attention model. We evaluate our method on seven publicly available datasets, containing various number of samples and spanning over a broad range of interactions (full-body, multi-actor, hand gestures, etc.). In all but one case we outperform the state-of-the-art pose-based methods. For instance, we achieve a recognition accuracy of 84.9% and 92.3% on cross-subject and cross-view tests of the NTU RGB+D dataset respectively, and also 100% recognition accuracy on the UT-Kinect dataset. We show that even in the absence of powerful hardware, or a large amount of training data, and with as little as four samples per class, DeepGRU can be trained in under 10 min while beating traditional methods specifically designed for small training sets, making it an enticing choice for rapid application prototyping and development.
HyScale: Hybrid and Network Scaling of Dockerized Microservices in Cloud Data Centres

When designing modern software, care must be taken to allow for applications to scale based on the demands of its users while still accommodating flexibility in development. Recently, microservices architectures have garnered the attention of many organizations-providing higher levels of scalability, availability, and fault isolation. Many organizations choose to host their microservices architectures in cloud data centres to offset costs. Incidentally, data centres become over-encumbered during peak usage hours and underutilized during off-peak hours. Traditional microservice scaling methods perform either horizontal or vertical scaling exclusively. When used in combination, however, these methods offer complementary benefits and compensate for each other's deficiencies. To leverage the high availability of horizontal scaling and the fine-grained resource control of vertical scaling, we developed two novel hybrid autoscaling algorithms and a dedicated network scaling algorithm and benchmarked them against Google's popular Kubernetes horizontal autoscaling algorithm. Results indicated up to 1.49x speedups in response times for our hybrid algorithms, and 1.69x speedups for our network algorithm under high-burst network loads.
Classifying Phishing Email Using Machine Learning and Deep Learning

In this work, we applied deep semantic analysis, and machine learning and deep learning techniques, to capture inherent characteristics of email text, and classify emails as phishing or non -phishing.
B-SIDH: Supersingular Isogeny Diffie-Hellman Using Twisted Torsion

This paper explores a new way of instantiating isogeny-based cryptography in which parties can work in both the $$(p+1)$$ -torsion of a set of supersingular curves and in the $$(p-1)$$ -torsion corresponding to the set of their quadratic twists. Although the isomorphism between a given supersingular curve and its quadratic twist is not defined over $$\mathbb {F}_{p^2}$$ in general, restricting operations to the x-lines of both sets of twists allows all arithmetic to be carried out over $$\mathbb {F}_{p^2}$$ as usual. Furthermore, since supersingular twists always have the same $$\mathbb {F}_{p^2}$$ -rational j-invariant, the SIDH protocol remains unchanged when Alice and Bob are free to work in both sets of twists. This framework lifts the restrictions on the shapes of the underlying prime fields originally imposed by Jao and De Feo, and allows a range of new options for instantiating isogeny-based public key cryptography. These include alternatives that exploit Mersenne and Montgomery-friendly primes, as well as the possibility of significantly reducing the size of the primes in the Jao-De Feo construction at no known loss of asymptotic security. For a given target security level, the resulting public keys are smaller than the public keys of all of the key encapsulation schemes currently under consideration in the NIST post-quantum standardisation effort. The best known attacks against the instantiations proposed in this paper are the classical path finding algorithm due to Delfs and Galbraith and its quantum adapation due to Biasse, Jao and Sankar; these run in respective time $$O(p^{1/2})$$ and $$O(p^{1/4})$$ , and are essentially memory-free. The upshot is that removing the big-O’s and obtaining concrete security estimates is a matter of costing the circuits needed to implement the corresponding isogeny. In contrast to other post-quantum proposals, this makes the security analysis of B-SIDH rather straightforward. Searches for friendly parameters are used to find several primes that range from 237 to 256 bits, which all offer a conjectured security comparable to the 434-bit prime used to target NIST level 1 security in the SIKE proposal. One noteworthy example is a 247-bit prime for which Alice’s secret isogeny is 7901-smooth and Bob’s secret isogeny is 7621-smooth.
Advancing Speech Recognition With No Speech Or With Noisy Speech

In this paper we demonstrate end to end continuous speech recognition (CSR) using electroencephalography (EEG) signals with no speech signal as input. An attention model based automatic speech recognition (ASR) and connectionist temporal classification (CTC) based ASR systems were implemented for performing recognition. We further demonstrate CSR for noisy speech by fusing with EEG features.
Learning Motion in Feature Space: Locally-Consistent Deformable Convolution Networks for Fine-Grained Action Detection

Fine-grained action detection is an important task with numerous applications in robotics and human-computer interaction. Existing methods typically utilize a two-stage approach including extraction of local spatio-temporal features followed by temporal modeling to capture long-term dependencies. While most recent papers have focused on the latter (long-temporal modeling), here, we focus on producing features capable of modeling fine-grained motion more efficiently. We propose a novel locally-consistent deformable convolution, which utilizes the change in receptive fields and enforces a local coherency constraint to capture motion information effectively. Our model jointly learns spatio-temporal features (instead of using independent spatial and temporal streams). The temporal component is learned from the feature space instead of pixel space, e.g. optical flow. The produced features can be flexibly used in conjunction with other long-temporal modeling networks, e.g. ST-CNN, DilatedTCN, and ED-TCN. Overall, our proposed approach robustly outperforms the original long-temporal models on two fine-grained action datasets: 50 Salads and GTEA, achieving F1 scores of 80.22% and 75.39% respectively.
Determination of the effect of red blood cell parameters in the discrimination of iron deficiency anemia and beta thalassemia via Neighborhood Component Analysis Feature Selection-Based machine learning

Differential diagnosis of iron deficiency anemia (IDA) and β-thalassemia is a time-taking and costly procedure. Complete blood count (CBC) is a quick, inexpensive, and easily accessible test which is used as the primary test for the diagnosis of anemia. However, as CBC cannot successfully discriminate between IDA and β-thalassemia, advanced techniques are needed. To date, numerous red blood cell (RBC) indices have been investigated and various parameters have been proposed for each index. In the present study, a differential diagnosis of IDA and β-thalassemia was performed by using RBC indices and machine learning techniques including Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). The RBC indices were used as input parameters for the classifier and the performances of SVM and KNN were evaluated separately, in order to determine the effectivity of both techniques. Fewer parameters were given as an inputs to machine learning algorithms, and higher performance was achieved. On the other hand, a feature selection technique, the Neighborhood Component Analysis Feature Selection (NCA) algorithm, was used for selecting features from the datasets, and the parameters selected via NCA provided high performance (97% Area Under the ROC curve [AUC]). Taken together, the results indicated that the RBC indices used in the study showed higher performance compared to those reported in the literature. By using these indices, not only the individual effect of each index parameter on the machine learning model was investigated but also a different subset of features from those employed in the literature was established. In addition, as distinct from the literature, the study revealed that different CBC parameters were efficient in distinguishing between IDA and β-thalassemia in male and female patients. Accordingly, the RBC indices employed in the study can be easily and inexpensively used in clinical and daily practice for the discrimination of IDA and β-thalassemia.
Joint Learning in the Spatio-Temporal and Frequency Domains for Skeleton-Based Action Recognition

Benefiting from its succinctness and robustness, skeleton-based action recognition has recently attracted much attention. Most existing methods utilize local networks (e.g. recurrent network, convolutional network, and graph convolutional network) to extract spatio-temporal dynamics hierarchically. As a consequence, the local and non-local dependencies, which contain more details and semantics respectively, are asynchronously captured in different level of layers. Moreover, existing methods are limited to the spatio-temporal domain and ignore information in the frequency domain. To better extract synchronous detailed and semantic information from multi-domains, we propose a residual frequency attention (rFA) block to focus on discriminative patterns in the frequency domain, and a synchronous local and non-local (SLnL) block to simultaneously capture the details and semantics in the spatio-temporal domain. In addition, to optimize the whole learning processes of the multi-branch network, we put it under a pseudo multi-task learning paradigm. During training, 1) a soft-margin focal loss (SMFL) is proposed to optimize the intra-branch separated learning process, which can automatically conduct data selection and encourage intrinsic margins in classifiers; 2) A mutual learning policy is also proposed to further facilitate the inter-branch collaborative learning process. Eventually, our approach achieves the state-of-the-art performance on several large-scale datasets for skeleton-based action recognition.