Reviewerly/exHarmony huggingface dataset:
Decomposing Image Generation into Layout Prediction and Conditional Synthesis

Learning the distribution of multi-object scenes with Generative Adversarial Networks (GAN) is challenging. Guiding the learning using semantic intermediate representations, which are less complex than images, can be a solution. In this article, we investigate splitting the optimisation of generative adversarial networks into two parts, by first generating a semantic segmentation mask from noise and then translating that segmentation mask into an image. We performed experiments using images from the CityScapes dataset and compared our approach to Progressive Growing of GANs (PGGAN), which uses multiscale growing of networks to guide the learning. Using the lens of a segmentation algorithm to examine the structure of generated images, we find that our method achieves higher structural consistency in latent space interpolations and yields generations with better differentiation between distinct objects, while achieving the same image quality as PGGAN as judged by a user study and a standard GAN evaluation metric.
Towards Fine-grained Sampling for Active Learning in Object Detection

We study the problem of using active learning to reduce annotation effort in training object detectors. Existing efforts in this space ignore the fact that image annotation costs are variable, depending on the number of objects present in a single image. In this regard, we examine a fine-grained sampling based approach for active learning in object detection. Over an unlabeled pool of images, our method aims to selectively pick the most informative subset of bounding boxes (as opposed to full images) to query an annotator. We measure annotation efforts in terms of the number of ground truth bounding boxes obtained. We study the effects of our method on the Feature Pyramid Network and RetinaNet models, and show promising savings in labeling effort to obtain good detection performance.
Improved Meet-in-the-Middle Attacks on Reduced-Round Deoxys-BC-256

Abstract In ASIACRYPT 2014, Jean et al. proposed the authentication encryption scheme Deoxys, which is one of the third-round candidates in CAESAR competition. Its internal block cipher is called Deoxys-BC that adopts the tweakey frame. Deoxys-BC has two versions of the tweakey size that are 256 bits and 384 bits, denoted by Deoxys-BC-256 and Deoxys-BC-384, respectively. In this paper, we revaluate the security of Deoxys-BC-256 against the meet-in-the-middle attack to obtain some new results. First, we append one round at the top and two rounds at the bottom of a 6-round distinguisher to form a 9-round truncated differential path with the probability of $2^{-144}$. Based on it, the adversary can attack 9-round Deoxys-BC-256 with $2^{108}$ chosen plaintext-tweaks, $2^{113.6}$ encryptions and $2^{102}$ blocks. Second, we construct a new 6.5-round distinguisher to form 10-round attacking path with the probability of $2^{-152}$. On the basis of it, the adversary could attack 10-round Deoxys-BC-256 with $2^{115}$ chosen plaintext-tweaks, $2^{171}$ encryptions and $2^{152}$ blocks. These two attacks improve the previous cryptanalytic results on reduced-round Deoxys-BC-256 against the meet-in-the-middle attack.
The African wildlife ontology tutorial ontologies

Abstract Background Most tutorial ontologies focus on illustrating one aspect of ontology development, notably language features and automated reasoners, but ignore ontology development factors, such as emergent modelling guidelines and ontological principles. Yet, novices replicate examples from the exercise they carry out. Not providing good examples holistically causes the propagation of sub-optimal ontology development, which may negatively affect the quality of a real domain ontology. Results We identified 22 requirements that a good tutorial ontology should satisfy regarding subject domain, logics and reasoning, and engineering aspects. We developed a set of ontologies about African Wildlife to serve as tutorial ontologies. A majority of the requirements have been met with the set of African Wildlife Ontology tutorial ontologies, which are introduced in this paper. The African Wildlife Ontology is mature and has been used yearly in an ontology engineering course or tutorial since 2010 and is included in a recent ontology engineering textbook with relevant examples and exercises. Conclusion The African Wildlife Ontology provides a wide range of options concerning examples and exercises for ontology engineering well beyond illustrating just language features and automated reasoning. It assists in demonstrating tasks concerning ontology quality, such as alignment to a foundational ontology and satisfying competency questions, versioning, and multilingual ontologies.
Activity-aware Attributes for Zero-Shot Driver Behavior Recognition

In real-world environments, such as the vehicle cabin, we have to deal with novel concepts as they arise. To this end, we introduce ZS-Drive&Act - the first zero-shot activity classification benchmark specifically aimed at recognizing previously unseen driver behaviors. ZS-Drive&Act is unique due to its focus on fine-grained activities and presence of activity-driven attributes, which are automatically derived from a hierarchical annotation scheme. We adopt and evaluate multiple off-the-shelf zero-shot learning methods on our benchmark, showcasing the difficulties of such models when moving to our application-specific task. We further extend the prominent method based on feature generating Wasserstein GANs with a fusion strategy for linking semantic attributes and word vectors representing the behavior labels. Our experiments demonstrate the effectiveness of leveraging both semantic spaces simultaneously, improving the recognition rate by 2.79%.
A domain-specific language for verifying software requirement constraints

Software requirement analysis can undoubtedly benefit from prevention and early detection of failures, in particular by some kind of automatic analysis. Formal methods offer means to represent and analyze requirements with rigorous tools, avoiding ambiguities, and allowing automatic verification of requirement consistency. However, formalisms often clash in the culture or lack of software analysts' skills, making them challenging to apply. In this article, we propose a Domain-Specific Language (DSL) based on Set Theory for requirement analysts. The Graphical InvaRiant Language (GIRL) can be used to specify software requirement structural invariants, with entities and their relationships. Those invariants can then have their consistency evaluated by the Alloy Analyzer, based on a translational semantics we provide for transforming GIRL models into Alloy specifications with no user intervention. With a prototypical language editor and transformations implemented into an Eclipse plugin, we carried out a qualitative study, with requirement analysts working for a government software company in Brazil, to evaluate usability and effectiveness of the GIRL-based analysis of real software requirements. The participants were able to effectively use the underlying formal analysis since 79 out of 80 assigned invariants were correctly modeled. While participants perceived as low the complexity of learning and using GIRL's simplest, set-based structures and relationships, the most complex logical structures, such as quantification and implication, were challenging. Furthermore, almost all post-study evaluations from the participants were positive, especially as a tool for discovering requirement inconsistencies.
Human Face Recognition using PCA Eigenfaces

This work aims to present face recognition solution using eigenfaces which can be used for various applications like the online attendance system, access control and others. The patterns in human faces have been extracted using the Principal Component Analysis (PCA) and the extracted eigenfaces, which are the eigenvectors of the covariance matrix, represent the extracted features. Euclidean distance based classifier has been utilized which compares the Euclidean distance between the test image and the face images in the training set to ascertain the class to which the test image should belong. Two varying datasets are used in this work with first containing selected facial images from that AT&T or the Olivetti Research Laboratory facial database while second set has been generated locally at DHA Suffa University, Karachi. Seventy percent of the images have been used for training while the remaining thirty percent images have been used for evaluating the classifier. The implemented algorithm is developed in MATLAB and ensures an overall efficiency of around ninety percent for slight variations in facial expressions and postures.
Dissecting Time-Varying Risk Exposures in Cryptocurrency Markets

In this paper, we take a forecasting perspective and compare the information content of a set of market risk factors, cryptocurrency-specific predictors, and sentiment variables for the returns of cryptocurrencies vs traditional asset classes. To this aim, we rely on a flexible dynamic econometric model that not only features time-varying coefficients, but also allows for the entire forecasting model to change over time to capture the time variation in the exposures of major digital currencies to the predictive variables. Besides, we investigate whether the inclusion of cryptocurrencies in an already diversified portfolio leads to additional economic gains. The main empirical results suggest that cryptocurrencies are not systematically predicted by stock market factors, precious metal commodities or supply factors. On the contrary, they display a time-varying but significant exposure to investors' attention. In addition, also because of a lack of predictability compared to traditional asset classes, cryptocurrencies lead to realized expected utility gains for a power utility investor.
AntiDote: Attention-based Dynamic Optimization for Neural Network Runtime Efficiency

Convolutional Neural Networks (CNNs) achieved great cognitive performance at the expense of considerable computation load. To relieve the computation load, many optimization works are developed to reduce the model redundancy by identifying and removing insignificant model components, such as weight sparsity and filter pruning. However, these works only evaluate model components’ static significance with internal parameter information, ignoring their dynamic interaction with external inputs. With per-input feature activation, the model component significance can dynamically change, and thus the static methods can only achieve sub-optimal results. Therefore, we propose a dynamic CNN optimization framework in this work. Based on the neural network attention mechanism, we propose a comprehensive dynamic optimization framework including (1) testing-phase channel and column feature map pruning, as well as (2) training-phase optimization by targeted dropout. Such a dynamic optimization framework has several benefits: (1) First, it can accurately identify and aggressively remove per-input feature redundancy with considering the model-input interaction; (2) Meanwhile, it can maximally remove the feature map redundancy in various dimensions thanks to the multi-dimension flexibility; (3) The training-testing co-optimization favors the dynamic pruning and helps maintain the model accuracy even with very high feature pruning ratio. Extensive experiments show that our method could bring 37.4%∼54.5% FLOPs reduction with negligible accuracy drop on various of test networks.
Analyzing In-Memory NoSQL Landscape

In-memory key-value stores have quickly become a key enabling technology to build high-performance applications that must cope with massively distributed workloads. In-memory key-value stores (also referred to as NoSQL) primarly aim to offer low-latency and high-throughput data access which motivates the rapid adoption of modern network cards such as Remote Direct Memory Access (RDMA). In this paper, we present the fundamental design principles for exploiting RDMAs in modern NoSQL systems. Moreover, we describe a break-down analysis of the state-of-the-art of the RDMA-based in-memory NoSQL systems regarding the indexing, data consistency, and the communication protocol. In addition, we compare traditional in-memory NoSQL with their RDMA-enabled counterparts. Finally, we present a comprehensive analysis and evaluation of the existing systems based on a wide range of configurations such as the number of clients, real-world request distributions, and workload read-write ratios.
Convolutional Neural Network based Automated Attendance System by using Facial Recognition Domain

This project aims to recognize faces in an image, video, or via live camera using a deep learning-based Convolutional Neural Network model that is fast as well as accurate. Face recognition is a process of identifying faces in an image and has practical applications in a variety of domains, including information security, biometrics, access control, law enforcement, smart cards, and surveillance system. Deep Learning uses numerous layers to discover interpretations of data at different extraction levels. It has improved the landscape for performing research in facial recognition. The state-of-the-art implementation has been bettered by the introduction of deep learning in face recognition and has stimulated success in practical applications. Convolutional neural networks, a kind of deep neural network model has been proven to achieve success in the face recognition domain. For real-time systems, sampling must be done before using CNNs. On the other hand, complete images (all the pixel values) are passed as the input to Convolutional Neural Networks. The following steps: feature selection, feature extracti on, and training are performed in each step. This might lead to the assumption, where convolutional neural network implementation has a chance to get complicated and time-consuming.
Collaborative Accelerators for Streamlining MapReduce on Scale-up Machines With Incremental Data Aggregation

The MapReduce programming paradigm has been increasingly adopted to implement data-intensive applications processing both small and large scale datasets. As most jobs in data centers have a data footprint in the order of gigabytes, emerging high-end scale-up machines are capable of running most data center processing tasks, thus significantly improving power and server density. However, this approach provides limited performance and energy efficiency because of inefficient utilization of the memory subsystem and serial execution within the MapReduce programming model. Recent work has proposed a distributed hardware acceleration architecture, called CASM, which augments each core in a scale-up machine with a lightweight compute engine. The CASM's network of accelerators operates concurrently with the cores in executing MapReduce stages and reduces significantly traffic to/from storage. In this article, we study the benefits and applicability of CASM, by offering an extensive analysis of design parameters and of its scalable performance on a wide range of applications, and exploring its applicability to incremental data aggregation tasks. Our experimental evaluation indicates that CASM reduces off-chip traffic by four times on average over a chip multiprocessor solution, while scaling well with the number of cores in the system, and it is highly effective in providing incremental results that approximate final outcomes.
An Agent-Based Cloud Service Negotiation in Hybrid Cloud Computing

With the advent of evolution of cloud computing, large organizations have been scaling the on-premise IT infrastructure to the cloud. Although this being a popular practice, it lacks comprehensive efforts to study the aspects of automated negotiation of resources among cloud customers and providers. This paper proposes a full-fledged framework for the multi-party, multi-issue negotiation system for cloud resources. It introduces a robust cloud marketplace system to buy and sell cloud resources. The belief–desire–intention (BDI) model-based cloud customer and provider agents concurrently negotiate on multiple issues, pursuing a hybrid tactic of time- and resource-based dynamic deadline algorithms to generate offers and counter-offers. The cloud marketplace-based system is further augmented with the assignment of behavior norm score and Reputation Index to the agents to establish trust among them.
Automated Depth Video Monitoring For Fall Reduction : A Case Study

Patient falls are a common, costly, and serious safety problem in hospitals and healthcare facilities. We have created a system that reduces falls by using computer vision to monitor fall risk patients and alert staff of unsafe behavior before a fall happens. This paper is a companion and followup to "Modeling bed exit likelihood in a camera-based automated video monitoring application," in which we describe the Ocuvera system. [1] Here additional details are provided on that system and its processes. We report clinical results, detail practices used to iterate rapidly and effectively on a massive video database, discuss details of our people tracking algorithms, and discuss the engineering effort required to support the new Azure Kinect depth camera.
Background Subtraction Based on Modified Pulse Coupled Neural Network in Compressive Domain

Background subtraction is the most popular technique for foreground detection to automatically separate foreground objects from background in video sequences.Much research effort has been directed into exploiting different features for background modeling to improve robustness of background models.However, most features used for background modeling are local features (such as intensities, colors and textures) which may have limitations when dealing with environments with complex and diverse changes in backgrounds, since changes may not emerge only within local regions.In this paper, inspired by mature human visual system (HVS), we propose a background subtraction method based on modified pulse coupled neural network (PCNN) in compressive domain, extracting global information for background modeling by making use of global coupling characteristics of the PCNN.Enlightened by theory of compressive sensing, we first employ compressive projection matrices to reduce data dimensionality.Then, we modify the PCNN to receive multi-channel color information as external stimuli and adjust linking strength and linking range for each neuron to enhance the ability of the network in global coupling.The modified PCNN is utilized to generate output responses containing global information of scenes for background modeling.Experimental results have demonstrated that our method has excellent performance and outperforms several advanced techniques in terms of visual and quantitative evaluations.
Initiating android phone technology using QR codes to make innovative functional clothes

Purpose Android mobile phones are usually used in everyday life. QR (quick response) codes are one of the smart and interesting technologies to convey information fast and accurately through smartphones. So printing QR codes on different textiles by inkjet method concerns specific people, such as with Autism, and Down syndrome, deaf and dumb children and also people having Alzheimer’s disease, who do not have the ability to communicate to others properly; these people cannot know, express or remember information about their selves. So printing QR codes in their clothes can help improve their lives. Design/methodology/approach In this study, different factors affecting QR codes scanability on fabrics were examined to clarify optimum conditions of recognition and readability. This paper examines different factors as QR Code different resolutions, sizes and lighting conditions. Different weave structures were also used. A questionnaire was done in a deaf and dumb school, for clarifying how QR codes can improve communication to some extent with others, especially those who do not realize sign language. In this study, different factors affecting QR codes’ scanability on fabrics were examined to clarify the optimum conditions of recognition and readability. This paper examines different factors as QR Code different resolutions, sizes and lighting conditions. Different weave structures were also used. A questionnaire was done in deaf and dumb school, to clarifying how QR codes can improve communication to some extent with others who especially do not realize sign language. Findings The finding of this study can provide basic data for future researches on QR codes printing on textiles. The effect of weave structure on QR code reading is obvious, as code reading time consumed in the plain fabric is more than that required in a twill structure. This economical method can enhance wearer life, especially for some cases as deaf and dumb cases. Originality/value These creative functions will surprise users and motivate them to scan the Codes. Increasing utility of QR codes in the textile and clothing field, give sustainability to the wearer clothes and accessories.
CIASM-Net: A Novel Convolutional Neural Network for Dehazing Image

When light propagates in the medium such as haze, the image information collected by the imaging sensor is seriously degraded due to the scattering of particles, which greatly limits the application value of the image. In this paper, a novel convolutional neural network model called CIASM-Net is proposed to implement image dehazing. CIASM-Net includes color feature extraction convolutional networks and deep defogging convolutional networks. The color feature extraction convolution network is used to extract the color features of foggy images; the deep dehazing convolution network improves the inverse atmospheric scattering model convolution network, and uses a multi-scale convolution layer instead of the original convolution layer to estimate the transmittance value. Moreover, we add a pyramid pooling layer to the network to extract global features. To obtain the optimized network model, we use the classic RESIDE training set to train the network model. We have performed extensive experiments on the synthetic hazy dataset and the real-world hazy dataset in the RESIDE test set. The experimental results have proved that the model has satisfactory results.
Combined Metrics for Quality Assessment of 3D Printed Surfaces for Aesthetic Purposes: Towards Higher Accordance with Subjective Evaluations

Objective quality assessment for 3D printing purposes may be considered as one of the most useful applications of machine vision in smart monitoring related to the development of the Industry 4.0 solutions. During recent years several approaches have been proposed, assuming observing the side surfaces, mainly based on the analysis of the regularity of visible patterns, which represent the consecutive printed layers. These methods, based on the use of general purpose image quality assessment (IQA) metrics, Hough transform, entropy and texture analysis, make it possible to classify the printed samples, independently of the filament’s colour, into low and high quality classes, with the use of photos or 3D scans of the side surfaces. The next step of research, investigated in this paper, is the combination of various proposed approaches to develop a combined metric, possibly highly correlated with subjective opinions. Since the correlation of single metrics developed mainly for classification is relatively low, their combination makes it possible to achieve much better results, verified using an original, newly developed database containing 107 captured images and 3D scans of the 3D printed surfaces with various colours and local distortions caused by external factors, together with Mean Opinion Scores (MOS) gathered from independent observers. Obtained results are promising and may be a starting point for further research towards the optimisation of the newly developed metrics for the automatic assessment of the 3D printed surfaces, mainly for aesthetic purposes.