Reviewerly/exHarmony huggingface dataset:
OPTIMUS: A Security-Centric Dynamic Hardware Partitioning Scheme for Processors that Prevent Microarchitecture State Attacks

Hardware virtualization allows multiple security-critical and ordinary (insecure) processes to co-execute on a processor. These processes temporally share hardware resources and endure numerous security threats on the microarchitecture state. State-of-the-art secure processor architectures, such as MI6 and IRONHIDE enable capabilities to execute security-critical processes in hardware isolated enclaves utilizing the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">strong isolation</i> security primitive. The MI6 processor purges small state resources on each enclave entry/exit and statically partitions the last-level cache and DRAM regions to ensure strong isolation. IRONHIDE takes a spatial approach and creates two isolated clusters of cores in a multicore processor to ensure strong isolation for processes executing in the enclave cluster. Both architectures observe performance degradation due to static partitioning of shared hardware resources. OPTIMUS proposes a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">security-centric</i> dynamic hardware resource partitioning scheme that operates entirely at runtime and ensures strong isolation. It enables <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">deterministic</i> resource allocations at the application level granularity, and limits the number of hardware reconfigurations to ensure <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bounded</i> information leakage via the timing and termination channels. The dynamic hardware resource partitioning capability of OPTIMUS is shown to co-optimize performance and security for the MI6 and IRONHIDE architectures.
Machine Learning Approach for Answer Detection in Discussion Forums: An Application of Big Data Analytics

Nowadays, data are flooding into online web forums, and it is highly desirable to turn gigantic amount of data into actionable knowledge. Online web forums have become an integral part of the web and are main sources of knowledge. People use this platform to post their questions and get answers from other forum members. Usually, an initial post (question) gets more than one reply posts (answers) that make it difficult for a user to scan all of them for most relevant and quality answer. Thus, how to automatically extract the most relevant answer for a question within a thread is an important issue. In this research, we treat the task of answer extraction as classification problem. A reply post can be classified as relevant, partially relevant, or irrelevant to the initial post. To find the relevancy/similarity of a reply to the question, both lexical and nonlexical features are used. We proposed to use LinearSVC, a variant of support vector machine (SVM), for answer classification. Two selection techniques such as chi-square and univariate are employed to reduce the feature space size. The experimental results showed that LinearSVC classifier outperformed the other state-of-the-art classifiers in the context of classification accuracy for both Ubuntu and TripAdvisor (NYC) discussion forum datasets.
A new design of a low-power reversible Vedic multiplier

In recent years, there has been an increasing tendency towards designing circuits based on reversible logic, and has received much attention because of preventing internal power dissipation. In digital computing systems, multiplier circuits are one of the most fundamental and practical circuits used in the development of a wide range of hardware such as arithmetic circuits and Arithmetic Logic Unit (ALU). Vedic multiplier, which is based on Urdhva Tiryakbhayam (UT) algorithm, has many applications in circuit designing because of its high speed in performing multiplication compared to other multipliers. In Vedic multipliers, partial products are obtained through vertical and cross multiplication. In this paper, we propose four [Formula: see text] reversible Vedic multiplier blocks and use each one of them in its right place. Then, we propose a [Formula: see text] reversible Vedic multiplier using the four aforementioned multipliers. We prove that our design leads to better results in terms of quantum cost, number of constant inputs and number of garbage outputs, compared to the previous ones. We also expand our proposed design to [Formula: see text] multipliers which enable us to develop our proposed design in every dimension. Moreover, we propose a formula in order to calculate the quantum cost of our proposed [Formula: see text] reversible Vedic multiplier, which allows us to calculate the quantum cost even before designing the multiplier.
Research data management at a public university in Malawi: the role of “three hands”

Purpose Considering that research data is increasingly hailed as an important raw material for current and future science discoveries, many research stakeholders have joined forces to create mechanisms for preserving it. However, regardless of generating rich research data, Africa lags behind in research data management thereby potentially losing most of this valuable data. Therefore, this study was undertaken to investigate the research data management practices at a Malawian public university with the aim to recommend appropriate data management strategies. Design/methodology/approach The study is inspired by the pragmatic school of thought thereby adopting quantitative and qualitative research approaches. Quantitative data was collected using a questionnaire from 150 researchers and 25 librarians while qualitative data was collected by conducting an interview with the Director of Research. Findings Researchers are actively involved in research activities thereby generating large quantities of research data. Although researchers are willing to share their data, only a handful follow through. Data preservation is poor because the university uses high risk data storage facilities, namely personal computers, flash disks, emails and external hard drives. Researchers and librarians lacked core research data-management competencies because of the lack of formal and information training opportunities. Challenges that frustrate research data-management efforts are many but the key ones include absence of research data management policies, lack of incentives, lack of skills and unavailability of data infrastructure. Research limitations/implications The study's findings are based on one out of four public universities in the country; hence, the findings may not adequately address the status of research data management practices in the other universities. Practical implications Considering that the university under study and its counterparts in Malawi and Africa in general operate somewhat in a similar economic and technological environment, these findings could be used as a reference point for other universities intending to introduce research data management initiatives. Originality/value With seemingly limited studies about research data management in Africa and particularly in Malawi, the study sets the tone for research data management debates and initiatives in the country and other African countries.
Improvement Proposal of Automatic GPU Offloading Technology

Recently, utilization of hardware other than CPU (Central Processing Unit) such as GPU (Graphics Processing Unit) or FPGA (Field-Programmable Gate Array) is increasing including education field. However, when using heterogeneous hardware other than CPUs, barriers of technical skills such as CUDA (Compute Unified Device Architecture) and HDL (Hardware Description Language) are high. Based on that, I have proposed environment adaptive software that enables automatic conversion, configuration, and high-performance operation of once written code, according to the hardware to be placed. Partly of the offloading to the GPU and FPGA was automated previously. In this paper, I improve and propose a previous automatic GPU offloading method to expand applicable software and enhance performances more. I evaluate the effectiveness of the proposed method in multiple applications.
Data-efficient Co-Adaptation of Morphology and Behaviour with Deep Reinforcement Learning

Humans and animals are capable of quickly learning new behaviours to solve new tasks. Yet, we often forget that they also rely on a highly specialized morphology that co-adapted with motor control throughout thousands of years. Although compelling, the idea of co-adapting morphology and behaviours in robots is often unfeasible because of the long manufacturing times, and the need to re-design an appropriate controller for each morphology. In this paper, we propose a novel approach to automatically and efficiently co-adapt a robot morphology and its controller. Our approach is based on recent advances in deep reinforcement learning, and specifically the soft actor critic algorithm. Key to our approach is the possibility of leveraging previously tested morphologies and behaviors to estimate the performance of new candidate morphologies. As such, we can make full use of the information available for making more informed decisions, with the ultimate goal of achieving a more data-efficient co-adaptation (i.e., reducing the number of morphologies and behaviors tested). Simulated experiments show that our approach requires drastically less design prototypes to find good morphology-behaviour combinations, making this method particularly suitable for future co-adaptation of robot designs in the real world.
Implementation of four kriging models for depth inpainting

Image inpainting is a process of reconstructing the missing pixels by inferencing information from the known part of an image. This paper aims to increase the precision of depth inpainting by proposing four models based on kriging techniques. Our four kriging models are based on two different semivariance models (exponential model and spherical model) and two different color-similarity functions. The inpainting algorithm is designed to extract both color and depth information of RGB-D images. For efficiency assessment, we look at Root Mean Square Error (RMSE), Structural Similarity Index Measure (SSIM), and Peak Signal to Noise Ratio (PSNR) of the reconstructed images. We then make a performance comparison with the previous six conventional methods. Finally, we show that our implemented models outperform the conventional ones. The accuracy of our four kriging models is competitive, and the PSNR values lie between 30.81 to 45.46 dB.
Controlled quantum key agreement based on maximally three-qubit entangled states

In this paper, we propose a two-party and a three-party controlled quantum key agreement (QKA) protocols with three-qubit GHZ states and Bell measurements. Compared with previous protocols, the significant change of our schemes is that a supervisor is introduced for controlling the agreement process to improve the controllability. Moreover, our protocols ensure each communication participant contribute equally to the agreement keys, and all participants can negotiate the shared keys without exchanging classical bits between them. The performance analysis shows that our protocols can be immune to outsider and participant attack.
A Recommender for Resource Allocation in Compute Clouds Using Genetic Algorithms and SVR

Resource allocation in Cloud Computing has been done reactively, hindering service guarantees and generating unnecessary charging of idle resources. In order to mitigate these problems, this work proposes and evaluates a predictive resource allocation approach, implemented as a Configuration Recommender, based on Support Vector Regression (SVR) and Genetic Algorithms (GA). This combination is used to estimate application runtime and recommends a viable and valid configuration of resources in the cloud, regarding execution time and monetary costs. As a case study, machine learning applications based on the Weka tool are chosen. The results show that predicted times were very close to actual ones, achieving an efficient estimation of time and cost and their consequent reduction.
AI-QMIX: Attention and Imagination for Dynamic Multi-Agent Reinforcement Learning

Real world multi-agent tasks often involve varying types and quantities of agents and non-agent entities. Agents frequently do not know a priori how many other agents and non-agent entities they will need to interact with in order to complete a given task, requiring agents to generalize across a combinatorial number of task configurations with each potentially requiring different strategies. In this work, we tackle the problem of multi-agent reinforcement learning (MARL) in such dynamic scenarios. We hypothesize that, while the optimal behaviors in these scenarios with varying quantities and types of agents/entities are diverse, they may share common patterns within sub-teams of agents that are combined to form team behavior. As such, we propose a method that can learn these sub-group relationships and how they can be combined, ultimately improving knowledge sharing and generalization across scenarios. This method, Attentive-Imaginative QMIX, extends QMIX for dynamic MARL in two ways: 1) an attention mechanism that enables model sharing across variable sized scenarios and 2) a training objective that improves learning across scenarios with varying combinations of agent/entity types by factoring the value function into imagined sub-scenarios. We validate our approach on both a novel grid-world task as well as a version of the StarCraft Multi-Agent Challenge minimally modified for the dynamic scenario setting. The results in these domains validate the effectiveness of the two new components in generalizing across dynamic configurations of agents and entities.
Self-PU: Self Boosted and Calibrated Positive-Unlabeled Training

Many real-world applications have to tackle the Positive-Unlabeled (PU) learning problem, i.e., learning binary classifiers from a large amount of unlabeled data and a few labeled positive examples. While current state-of-the-art methods employ importance reweighting to design various risk estimators, they ignored the learning capability of the model itself, which could have provided reliable supervision. This motivates us to propose a novel Self-PU learning framework, which seamlessly integrates PU learning and self-training. Self-PU highlights three self-oriented building blocks: a self-paced training algorithm that adaptively discovers and augments confident positive/negative examples as the training proceeds; a self-calibrated instance-aware loss; and a self-distillation scheme that introduces teacher-students learning as an effective regularization for PU learning. We demonstrate the state-of-the-art performance of Self-PU on common PU learning benchmarks (MNIST and CIFAR-10), which compare favorably against the latest competitors. Moreover, we study a real-world application of PU learning, i.e., classifying brain images of Alzheimer's Disease. Self-PU obtains significantly improved results on the renowned Alzheimer's Disease Neuroimaging Initiative (ADNI) database over existing methods. The code is publicly available at: this https URL.
Understanding (Mis)Behavior on the EOSIO Blockchain

EOSIO has become one of the most popular blockchain platforms since its mainnet launch in June 2018. In contrast to the traditional PoW-based systems (e.g., Bitcoin and Ethereum), which are limited by low throughput, EOSIO is the first high throughput Delegated Proof of Stake system that has been widely adopted by many decentralized applications. Although EOSIO has millions of accounts and billions of transactions, little is known about its ecosystem, especially related to security and fraud. In this paper, we perform a large-scale measurement study of the EOSIO blockchain and its associated DApps. We gather a large-scale dataset of EOSIO and characterize activities including money transfers, account creation and contract invocation. Using our insights, we then develop techniques to automatically detect bots and fraudulent activity. We discover thousands of bot accounts (over 30% of the accounts in the platform) and a number of real-world attacks (301 attack accounts). By the time of our study, 80 attack accounts we identified have been confirmed by DApp teams, causing 828,824 EOS tokens losses (roughly \$2.6 million) in total.
Appearance variation adaptation tracker using adversarial network

Visual trackers using deep neural networks have demonstrated favorable performance in object tracking. However, training a deep classification network using overlapped initial target regions may lead an overfitted model. To increase the model generalization, we propose an appearance variation adaptation (AVA) tracker that aligns the feature distributions of target regions over time by learning an adaptation mask in an adversarial network. The proposed adversarial network consists of a generator and a discriminator network that compete with each other over optimizing a discriminator loss in a mini-max optimization problem. Specifically, the discriminator network aims to distinguish recent target regions from earlier ones by minimizing the discriminator loss, while the generator network aims to produce an adaptation mask to maximize the discriminator loss. We incorporate a gradient reverse layer in the adversarial network to solve the aforementioned mini-max optimization in an end-to-end manner. We compare the performance of the proposed AVA tracker with the most recent state-of-the-art trackers by doing extensive experiments on OTB50, OTB100, and VOT2016 tracking benchmarks. Among the compared methods, AVA yields the highest area under curve (AUC) score of 0.712 and the highest average precision score of 0.951 on the OTB50 tracking benchmark. It achieves the second best AUC score of 0.688 and the best precision score of 0.924 on the OTB100 tracking benchmark. AVA also achieves the second best expected average overlap (EAO) score of 0.366, the best failure rate of 0.68, and the second best accuracy of 0.53 on the VOT2016 tracking benchmark.
Automatic segmentation of nuclei from pap smear cell images: A step toward cervical cancer screening

Abstract Cervical cancer is identified as the fourth most recurrent cancer among women across the globe. The cancer is treatable, if identified at the early stage. Pap smear test is the most common and the best tool for initial screening of cancer. Pap smear cell level image analysis is an open issue. The limitation of the analysis is due to the complexity of the cell structure. The smear cell image is composed of cytoplasm and nucleus. The shape and structure of the nucleus determines the cancer prevalence. Segmentation of nucleus is an important step in cancer detection. There are various methods developed for nucleus segmentation. The article proposes multithresholding algorithm to segment cytoplasm and nucleus region from the background. Morphological operations are used for correcting the segmented output. Support vector machine classifier is used for classifying the smear cell as normal or abnormal based on the extracted features of the segmented output. The obtained accuracy of the classifier, sensitivity and specificity for single smear cell are 99.66%, 99.85% and 99.17%.
The impact of semantic annotation techniques on content-based video lecture recommendation

Increasing videos available in educational content repositories makes searching difficult, and recommendation systems have been used to help students and teachers receive a content of interest. Speech is an important carrier of information in video lectures and is used by content-based video recommendation systems. Although automatic speech recognition (ASR) transcripts have been used in modern video recommendation systems, it is not clear how annotation techniques work with noisy text. This article presents an analysis on a set of semantic annotation techniques when applied to text extracted from video lecture speech and their impact on two tasks: annotation and similarity analysis. Experiments show that topic models have good results in this scenario. Besides, a new benchmark for this task has been created and researchers can use it to evaluate new techniques.
Effective Ways to Build and Evaluate Individual Survival Distributions

An accurate model of a patient's survival can help determine the appropriate treatment for terminal patients. Unfortunately, risk scores (e.g., from Cox Proportional Hazard models) do not provide survival probabilities, single-time probability models (e.g., the Gail model, predicting 5 year probability) only provide for a single time point, and standard Kaplan-Meier survival curves provide only population averages for a large class of patients meaning they are not specific to patients. This motivates an alternative class of tools that can learn a model which provides an survival which gives survival probabilities across all times - such as extensions to the Cox model, Accelerated Failure Time, an extension to Random Survival Forests, and Multi-Task Logistic Regression. This paper first motivates such individual survival distribution (ISD) models, and explains how they differ from standard models. It then discusses ways to evaluate such models - namely Concordance, 1-Calibration, Brier score, and various versions of L1-loss - and then motivates and defines a novel approach D-Calibration, which determines whether a model's probability estimates are meaningful. We also discuss how these measures differ, and use them to evaluate several ISD prediction tools, over a range of survival datasets.
Deepfakes for the Good: A Beneficial Application of Contentious Artificial Intelligence Technology

Deepfake algorithms are one of the most recent albeit controversial developments in Artificial Intelligence, because they use Machine Learning to generate fake yet realistic content (e.g., images, videos, audio, and text) based on an input dataset. For instance, they can accurately superimpose the face of an individual over the body an actor in a destination video (i.e., face swap), or exactly reproduce the voice of a person and speak a given text. As a result, many are concerned with the potential risks in terms of cybersecurity. Although most focused on the malicious applications of this technology, in this paper we propose a system for using deepfakes for beneficial purposes. We describe the potential use and benefits of our proposal and we discuss its implications in terms of human factors, security risks, and ethical aspects.