Reviewerly/exHarmony huggingface dataset:
Stretch: Balancing QoS and Throughput for Colocated Server Workloads on SMT Cores

In a drive to maximize resource utilization, today's datacenters are moving to colocation of latency-sensitive and batch workloads on the same server. State-of-the-art deployments, such as those at Google, colocate such diverse workloads even on a single SMT core. This form of aggressive colocation is afforded by virtue of the fact that a latency-sensitive service operating below its peak load has significant slack in its response latency with respect to the QoS target. The slack affords a degradation in single-thread performance, which is inevitable under SMT colocation, without compromising QoS targets. This work makes the observation that many batch applications can greatly benefit from a large instruction window to uncover ILP and MLP. Under SMT colocation, conventional wisdom holds that individual hardware threads should be limited in their ability to acquire and hold a disproportionately large share of microarchitectural resources so as not to compromise the performance of a co-running thread. We show that the performance slack inherent in latency-sensitive workloads operating at low to moderate load makes it safe to shift microarchitectural resources to a co-running batch thread without compromising QoS targets. Based on this insight, we introduce Stretch, a simple ROB partitioning scheme that is invoked by system software to provide one hardware thread with a much larger ROB partition at the expense of another thread. When Stretch is enabled for latency-sensitive workloads operating below their peak load on an SMT core, co-running batch applications gain 13% of performance on average (30% max) over a baseline SMT colocation and without compromising QoS constraints.
A Novel Top-N Recommendation Approach Based on Conditional Variational Auto-Encoder

Personalized recommendation has continuously received attention due to its great commercial value in business. Recently variational auto-encoder is employed in top-N recommendation for its effectiveness in deep collaborative filtering. The key challenge of model-based collaborative filtering is to develop effective latent factors representations with user-item interaction records. In this paper, we present a new class of conditional variational auto-encoders (CVAEs) that utilizes the fact of similar users tending to associate with each other on purchasing preference. This type of conditional variational auto-encoder concentrates on learning with label verification signals to ensure an exclusive latent mean factor for users with the same labels. Moreover, to handle complex multi-label combinations, we extend the model with a split-merge framework by learning labels of different conditional attributes separately and then merge the results from multiple prediction pools. Extensive experiments are conducted on two real-life datasets to simulate both user-based and item-based recommendation scenarios. Experimental results are favorable when comparing with the state-of-art methods.
Data objects and documenting scientific processes: An analysis of data events in biodiversity data papers

The data paper, an emerging scholarly genre, describes research data sets and is intended to bridge the gap between the publication of research data and scientific articles . Research examining how data papers report data events, such as data transactions and manipulations, is limited. The research reported on in this article addresses this limitation and investigated how data events are inscribed in data papers. A content analysis was conducted examining the full texts of 82 data papers, drawn from the curated list of data papers connected to the Global Biodiversity Information Facility. Data events recorded for each paper were organized into a set of 17 categories. Many of these categories are described together in the same sentence, which indicates the messiness of data events in the laboratory space. The findings challenge the degrees to which data papers are a distinct genre compared to research articles and they describe data‐centric research processes in a through way. This article also discusses how our results could inform a better data publication ecosystem in the future.
<i>Plasmodium</i> species aware based quantification of malaria parasitemia in light microscopy thin blood smear

Abstract Malaria is a serious worldwide disease, caused by a bite of a female Anopheles mosquito. The parasite transferred into complex life round in which it is grown and reproduces into the human body. The detection and recognition of Plasmodium species are possible and efficient through a process called staining (Giemsa). The staining process slightly colorizes the red blood cells (RBCs) but highlights Plasmodium parasites, white blood cells and artifacts. Giemsa stains nuclei, chromatin in blue tone and RBCs in pink color. It has been reported in numerous studies that manual microscopy is not a trustworthy screening technique when performed by nonexperts. Malaria parasites host in RBCs when it enters the bloodstream. This paper presents segmentation of Plasmodium parasite from the thin blood smear points on region growing and dynamic convolution based filtering algorithm. After segmentation, malaria parasite classified into four Plasmodium species: Plasmodium falciparum , Plasmodium ovale , Plasmodium vivax , and Plasmodium malaria . The random forest and K‐nearest neighbor are used for classification base on local binary pattern and hue saturation value features. The sensitivity for malaria parasitemia (MP) is 96.75% on training and testing of the proposed approach while specificity is 94.59%. Beside these, the comparisons of the two features are added to the proposed work for classification having sensitivity is 83.60% while having specificity is 94.90% through random forest classifier based on local binary pattern feature.
Tap 'n Ghost: A Compilation of Novel Attack Techniques against Smartphone Touchscreens

We present a novel attack named "Tap 'n Ghost", which aims to attack the touchscreens of NFC-enabled mobile devices such as smartphones. Tap 'n Ghost consists of two striking attack techniques - "Tag-based Adaptive Ploy (TAP)" and "Ghost Touch Generator." First, using a NFC card emulator embedded in a common object such as table, a TAP system performs tailored attacks on the victim's smartphone by employing device fingerprinting; e.g., popping up a customized dialogue box asking whether or not to connect to an attacker's Bluetooth mouse. Further, Ghost Touch Generator forces the victim to connect to the mouse even if she or he aimed to cancel the dialogue by touching the "cancel" button; i.e., it alters the selection of a button on a screen. After the connection is established, the attacker can remotely take control of the smartphone, with the knowledge about the layout of the screen derived from the device fingerprinting. To evaluate the reality of the attack, we perform an online survey with 300 respondents and a user study involving 16 participants. The results demonstrate that the attack is realistic. We additionally discuss the possible countermeasures against the threats posed by Tap 'n Ghost.
SUR-Net: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning

The Satisfied User Ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the probability distribution of the Just Noticeable Difference (JND) level, the smallest distortion level that can be perceived by a subject. We propose the first deep learning approach to predict such SUR curves. Instead of the direct approach of regressing the SUR curve itself for a given reference image, our model is trained on pairs of images, original and compressed. Relying on a Siamese Convolutional Neural Network (CNN), feature pooling, a fully connected regression-head, and transfer learning, we achieved a good prediction performance. Experiments on the MCL-JCI dataset showed a mean Bhattacharyya distance between the predicted and the original JND distributions of only 0.072.
A Scalable Priority-Aware Approach to Managing Data Center Server Power

Power management is a key component of modern data center design. Power managers must (1) ensure the costand energy-efficient utilization of the data center infrastructure, (2) maintain availability of the services provided by the center, and (3) address environmental concerns associated with the center's power consumption. While several power management techniques have been proposed and deployed in production data centers, there are still many challenges to comprehensive data center power management. This is particularly true in public cloud environments, where different jobs have different priority levels, and where high availability is critical. One example of the challenges facing public cloud data centers involves power capping. As power delivery must be highly reliable and tolerate wide variation in the load drawn by the data center components, the power infrastructure (e.g., power supplies, circuit breakers, UPS) has high redundancy and overprovisioning. During normal operation (i.e., typical server power demands, and no failures in the center), the power infrastructure is significantly underutilized. Power capping is a common solution to reduce this underutilization, by allowing more servers to be added safely (i.e., without power shortfalls) to the existing power infrastructure, and throttling power consumption in the infrequent cases where the demanded power exceeds the provisioned power capacity to avoid shortfalls. However, state-of-the-art power capping solutions are (1) not directly applicable to the redundant power infrastructure used in highly-available data centers; and (2) oblivious to differing workload priorities across the entire center when power consumption needs to be throttled, which can unnecessarily slow down high-priority work. To address this need, we develop CapMaestro, a new power management architecture with three key features for public cloud data centers. First, CapMaestro is designed to work with multiple power feeds (i.e., sources), and exploits server-level power capping to independently cap the load on each feed of a server. Second, CapMaestro uses a scalable, global priority-aware power capping approach, which accounts for power capacity at each level of the power distribution hierarchy. It exploits the underutilization of commonly-employed redundant power infrastructure at each level of the hierarchy to safely accommodate a much greater number of servers. Third, CapMaestro exploits stranded power (i.e., power budgets that are not utilized) in redundant power infrastructure to boost the performance of workloads in the data center. We add CapMaestro to a real cloud data center control plane, and demonstrate the effectiveness of all three key features. Using a large-scale data center simulation, we demonstrate that CapMaestro significantly and safely increases the number of servers for existing infrastructure. We also call out other key technical challenges the industry faces in data center power management.
IoTSim-Stream: Modelling stream graph application in cloud simulation

In the era of big data, the high velocity of data imposes the demand for processing such data in real-time to gain real-time insights. Various real-time big data platforms/services (i.e. Apache Storm, Amazon Kinesis) allow to develop real-time big data applications to process continuous data to get incremental results. Composing those applications to form a workflow that is designed to accomplish certain goal is the becoming more important nowadays. However, given the current need of composing those applications into data pipelines forming stream workflow applications (aka stream graph applications) to support decision making, a simulation toolkit is required to simulate the behaviour of this graph application in Cloud computing environment. Therefore, in this paper, we propose an IoT Simulator for Stream processing on the big data (named IoTSim-Stream) that offers an environment to model complex stream graph applications in Multicloud environment, where the large-scale simulation-based studies can be conducted to evaluate and analyse these applications. The experimental results show that IoTSim-Stream is effective in modelling and simulating different structures of complex stream graph applications with excellent performance and scalability.
A Systematic Survey on High Utility Itemset Mining

High utility itemset mining considers unit profits and quantities of items in a transaction database to extract more applicable and more useful association rules. Downward closure property, which causes significant pruning in frequent itemset mining, is not established in the utility of itemsets and so the mining problem will require alternative solutions to reduce its search space and to enhance its efficiency. Using an anti-monotonic upper bound of the utility function and exploiting efficient data structures for storing and compacting the dataset to perform efficient pruning strategies are the main solutions to address high utility itemset mining problem. Different mining methods and techniques have attempted to improve performance of extracting high utility itemsets and their several variants, including high-average utility itemsets, top-k high utility itemsets, and high utility itemsets with negative values, using more efficient data structures, more appropriate anti-monotonic upper bounds, and stronger pruning strategies. This paper aims to represent a comprehensive systematic review for high utility itemset mining techniques and to classify them based on their problem-solving approaches.
A hierarchical recurrent approach to predict scene graphs from a visual‐attention‐oriented perspective

Abstract A scene graph provides a powerful intermediate knowledge structure for various visual tasks, including semantic image retrieval, image captioning, and visual question answering. In this paper, the task of predicting a scene graph for an image is formulated as two connected problems, ie, recognizing the relationship triplets, structured as &lt; subject‐predicate‐object &gt;, and constructing the scene graph from the recognized relationship triplets. For relationship triplet recognition, we develop a novel hierarchical recurrent neural network with visual attention mechanism. This model is composed of two attention‐based recurrent neural networks in a hierarchical organization. The first network generates a topic vector for each relationship triplet, whereas the second network predicts each word in that relationship triplet given the topic vector. This approach successfully captures the compositional structure and contextual dependency of an image and the relationship triplets describing its scene. For scene graph construction, an entity localization approach to determine the graph structure is presented with the assistance of available attention information. Then, the procedures for automatically converting the generated relationship triplets into a scene graph are clarified through an algorithm. Extensive experimental results on two widely used data sets verify the feasibility of the proposed approach.
An Attention-aware Bidirectional Multi-residual Recurrent Neural Network (Abmrnn): A Study about Better Short-term Text Classification

Long Short-Term Memory (LSTM) has been proven an efficient way to model sequential data, because of its ability to overcome the gradient diminishing problem during training. However, due to the limited memory capacity in LSTM cells, LSTM is weak in capturing long-time dependency in sequential data. To address this challenge, we propose an Attention-aware Bidirectional Multi-residual Recurrent Neural Network (ABMRNN) to overcome the deficiency. Our model considers both past and future information at every time step with omniscient attention based on LSTM. In addition to that, the multi-residual mechanism has been leveraged in our model which aims to model the relationship between current time step with further distant time steps instead of a just previous time step. The results of experiments show that our model achieves state-of-the-art performance in classification tasks.
Augmented features to detect image splicing on SWT domain

Nowadays, image forgery is a widespread problem in our lives. Image splicing is one method of forging images, and it has become a common tool for malicious users to modify images. Human eyes cannot perceive forgery operations on forged images; therefore, designing an expert system for detecting image authenticity has become a necessity. The splicing method causes some distortions that can be used to detect forgery on a tampered image. In this work, an expert system is designed that uses the statistical and textural properties of an image in a hybrid manner to detect forgery. The method extracts properties (statistical and textural) from high-level sub-bands of stationary wavelet transform (SWT) domain. SWT is used to obtain image's details in a multi scale manner in frequency domain. The method extracts statistical features from three sub-bands via Markov model, which generates transition probability matrices and then obtains Haralic's textural features based on gray level co-occurrence matrices. Features from each sub-bands are concatenated to form final feature vector. This expert system is the first in the literature to be designed using textural and statistical features together. Finally, the designed expert system classifies images as authentic or forged using SVM. The system's performance was evaluated using three publicly available image databases and tested for different attack types. The experimental results show that using statistical and textural features in a hybrid manner improves detection accuracy when compared to similar works in the literature. The main impact of the proposed system is its high accuracy and robust detection of different attack types.
Comparative geochemical evaluation of toxic metals pollution and bacterial communities of industrial effluent tributary and a receiving estuary in Nigeria

Toxic metals/metalloid contaminations of estuarine sediments due to compromised tributaries arouse significant interest in studying bacterial community that triggers natural attenuation processes. Geo-accumulation index (Igeo), contamination factor (CF), pollution load index (PLI), and Hakanson potential ecological risk index (RI) as a sum of risk factors (Er) were used to quantify toxic metal/metalloid-pollution status of Lagos Lagoon (2W) and 'Iya-Alaro' tributary (4W) sediments in comparison with pristine 'Lekki Conservation Centre' sediment (L1-B). Bacteriology of the ecosystems was based on culture-independent analyses using pyrosequencing. 2W and 4W were extremely contaminated with mercury (Igeo > 7), whereas, cadmium contamination was only observed in 4W. The two ecosystems were polluted with toxic metal based on PLI, where mercury (Er = 2900 and 1900 for 4W and 2W, respectively) posed very high ecological risks. Molecular fingerprinting revealed that Proteobacteria, Firmicutes, and Acidobacteria predominately contributed the 20 most abundant genera in the two ecosystems. The 240 and 310 species present in 2W and 4W, respectively, but absent in L1-B, thrive under the metal concentrations in the polluted hydrosphere. Whereas, the 58,000 species missing in 2W and 4W but found in L1-B would serve as indicators for systems impacted with metal eco-toxicity. Despite toxic metal pollution of the ecosystems understudied, bacterial communities play vital roles in self-recovery processes occurring in the hydrosphere.
Hidden Markov Model-Based Smart Annotation for Benchmark Cyclic Activity Recognition Database Using Wearables

Activity monitoring using wearables is becoming ubiquitous, although accurate cycle level analysis, such as step-counting and gait analysis, are limited by a lack of realistic and labeled datasets. The effort required to obtain and annotate such datasets is massive, therefore we propose a smart annotation pipeline which reduces the number of events needing manual adjustment to 14%. For scenarios dominated by walking, this annotation effort is as low as 8%. The pipeline consists of three smart annotation approaches, namely edge detection of the pressure data, local cyclicity estimation, and iteratively trained hierarchical hidden Markov models. Using this pipeline, we have collected and labeled a dataset with over 150,000 labeled cycles, each with 2 phases, from 80 subjects, which we have made publicly available. The dataset consists of 12 different task-driven activities, 10 of which are cyclic. These activities include not only straight and steady-state motions, but also transitions, different ranges of bouts, and changing directions. Each participant wore 5 synchronized inertial measurement units (IMUs) on the wrists, shoes, and in a pocket, as well as pressure insoles and video. We believe that this dataset and smart annotation pipeline are a good basis for creating a benchmark dataset for validation of other semi- and unsupervised algorithms.
Dual-layer security of image steganography based on IDEA and LSBG algorithm in the cloud environment

The architecture development of cloud computing technology is growing tremendously in recent times, which leads to improvement of scalability, accessibility and cost reduction measures in the IT sectors of all enterprises. In this service, the data storage without reviewing security policies and procedures is a challenging task and probabilities of extracting secret information by an unauthorized intervention are more. However, to prevent the breaches of security in the cloud service, the steganography art plays an essential role in the data communication medium to improve the security measures, and it is an indispensable technique for hiding the secret information into a cover object. This paper describes the implementation of new steganography method with International Data Encryption Standard Algorithm (IDEA) and Least Significant Bit Grouping (LSBG) algorithm for embedding the secret information into an original image and extracting the same. The result shows the improvement of data embedding capacity and reduces the issues related to data security by effective utilization of this new approach, which reveals the remarkable achievement of the combinational execution of steganography and cryptography technique. The IDEA and LSBG have some vital qualities such as data confidentiality, integrity verification, capacity and robustness, which are crucial factors to achieve successful implementation of steganography process in data security system. The effectiveness and properties of the stego image can be evaluated by some specific measures like mean squared error, root mean squared error, peak signal to noise ratio and structural similarity index matrix to analyse the image quality. The results show that the proposed technique outperforms the existing methodologies and resolves the data security problem in data transmission and storage system of cloud computing services.