Reviewerly/exHarmony huggingface dataset:
BlastFunction: an FPGA-as-a-Service system for Accelerated Serverless Computing

Heterogeneous computing platforms are now a valuable solution to continue to meet Service Level Agreements (SLAs) for compute intensive cloud workloads. Field Programmable Gate Arrays (FPGAs) effectively accelerate cloud workloads, however, these workloads have a spiky behavior as well as long periods of underutilization. Sharing the FPGA with multiple tenants then helps to increase the board's time utilization. In this paper we present BlastFunction, a distributed FPGA sharing system for the acceleration of microservices and serverless applications in cloud environments. BlastFunction includes a Remote OpenCL Library to access the shared devices transparently; multiple Device Managers to time-share and monitor the FPGAs and a central Accelerators Registry to allocate the available devices. BlastFunction reaches higher utilization and throughput w.r.t. a native execution thanks to device sharing, with minimal differences in latency given by the concurrent accesses.
A Machine Learning Approach for SNR Prediction in 5G Systems

Channel State Information (CSI) feedback from the User Equipment (UE) on the uplink (UL) channel is an integral part of the 5G-NR standard. It allows next-generation Node-B (gNB) to obtain the information about the channel impairments, and schedule appropriate radio resources to the UE in order to maintain the required Quality of Service (QoS). Errors in the channel estimation by the UE increases erroneous downlink transmission and subsequently inefficient spectral use. There is an inherent tradeoff between CSI feedback periodicity from the UE and the accuracy and relevance of channel estimation within the periodicity by gNB. Since the CSI depends on the received Signal-to-Noise Ratio (SNR) at the UE. Hence, the goal of this work is to design and develop suitable methodologies to estimate a CSI parameter namely, Channel Quality Indicator (CQI) by predicting the SNR using the state-of-the-art Machine Learning (ML) techniques. In this paper, we present an experimental framework to predict CQI using SNR for different environmental scenarios and UE characteristics like delay profile and speed respectively. The experimental results show better performance in terms of CQI to SNR mapping, and error in prediction over current techniques.
Trusted Smart Statistics: How new data will change official statistics

Abstract In this discussion paper, we outline the motivations and the main principles of the Trusted Smart Statistics (TSS) concept that is under development in the European Statistical System. TSS represents the evolution of official statistics in response to the challenges posed by the new datafied society. Taking stock from the availability of new digital data sources, new technologies, and new behaviors, statistical offices are called nowadays to rethink the way they operate in order to reassert their role in modern democratic society. The issue at stake is considerably broader and deeper than merely adapting existing processes to embrace so-called Big Data. In several aspects, such evolution entails a fundamental paradigm shift with respect to the legacy model of official statistics production based on traditional data sources, for example, in the relation between data and computation, between data collection and analysis, between methodological development and statistical production, and of course in the roles of the various stakeholders and their mutual relationships. Such complex evolution must be guided by a comprehensive system-level view based on clearly spelled design principles. In this paper, we aim at providing a general account of the TSS concept reflecting the current state of the discussion within the European Statistical System.
Privacy Vulnerabilities in Public Digital Service Centers in Dhaka, Bangladesh

This paper joins a growing body of work within ICTD and related fields studying the privacy challenges in the Global South. While most of the existing work in this area has focused on uses of technology in personal and home settings, a large part of computing in the Global South centers around public places, such as commercial Digital Service Centers (DSCs). In this paper, we present the findings from a six-month-long ethnography studying 19 Digital Service Centers in Dhaka, Bangladesh. We find that infrastructural limitations, local power politics, lack of knowledge, and insufficient protection mechanisms lead to privacy vulnerabilities for the customers of these centers. We apply the lens of informal markets to analyze these vulnerabilities and connect our findings to the broader concerns of ICTD around development, ethics, and postcolonial computing and discuss potential design and policy implications around these issues.
Local rotation invariance in 3D CNNs

Locally Rotation Invariant (LRI) image analysis was shown to be fundamental in many applications and in particular in medical imaging where local structures of tissues occur at arbitrary rotations. LRI constituted the cornerstone of several breakthroughs in texture analysis, including Local Binary Patterns (LBP), Maximum Response 8 (MR8) and steerable filterbanks. Whereas globally rotation invariant Convolutional Neural Networks (CNN) were recently proposed, LRI was very little investigated in the context of deep learning. LRI designs allow learning filters accounting for all orientations, which enables a drastic reduction of trainable parameters and training data when compared to standard 3D CNNs. In this paper, we propose and compare several methods to obtain LRI CNNs with directional sensitivity. Two methods use orientation channels (responses to rotated kernels), either by explicitly rotating the kernels or using steerable filters. These orientation channels constitute a locally rotation equivariant representation of the data. Local pooling across orientations yields LRI image analysis. Steerable filters are used to achieve a fine and efficient sampling of 3D rotations as well as a reduction of trainable parameters and operations, thanks to a parametric representations involving solid Spherical Harmonics (SH),which are products of SH with associated learned radial profiles. Finally, we investigate a third strategy to obtain LRI based on rotational invariants calculated from responses to a learned set of solid SHs. The proposed methods are evaluated and compared to standard CNNs on 3D datasets including synthetic textured volumes composed of rotated patterns, and pulmonary nodule classification in CT. The results show the importance of LRI image analysis while resulting in a drastic reduction of trainable parameters, outperforming standard 3D CNNs trained with rotational data augmentation.
Integration of IoT and Blockchain to in the Processes of a University Campus

Currently, universities, as centers of research and innovation, integrate in their processes various technologies that allow improving services and processes for their members. Among the innovative technologies are the Internet of Things that, through a variety of devices, allows obtaining data from the environment and people. This information is processed in cloud computing models and Big Data architectures that obtain knowledge through data analysis. These results lead to improving processes and making better decisions that improve the services available at the university. The integration of technologies allows for the generation of a sustainable environment that seeks the cohesion of the population with the environment, in such a way that economic growth is guaranteed in balance with the environment. However, all technology needs to guarantee the security of processes and data, and for this purpose, a new technology such as blockchain is integrated, which seeks to respond to two needs, the security and agility of processes. Integrating this technology in a university requires the analysis of the blockchain components to generate a new layer that adapts to the architecture of a university campus. This ensures that the data are kept cryptographically private to avoid exposure and that the entire process is verified by multiple blocks.
Effective Data Storage and Dynamic Data Auditing Scheme for Providing Distributed Services in Federated Cloud

Recently, many organizations and industries are using the cloud computing technologies for exchanging the resources and their confidential data. For this purpose, many cloud services are available and also provide the facility to categorize their users as private and public users for accessing their own data from private cloud and public cloud. The combination of these two clouds is called federated cloud which facilitates to allow both kinds of cloud users for accessing their own data on same cloud database. In this scenario, the authorization and authentication process is becoming complex task on cloud. For providing the facility to access their own data only from federated cloud, a new secured data storage and retrieval algorithm called AES and Triple-DES-based Secured Storage and Retrieval Algorithm (ATDSRA) are proposed for storing the private and public cloud user’s data securely on cloud database. Here, the TDES is used for encrypting the input data, data merging and aggregation methods were used for grouping the encrypted input data. Moreover, a new dynamic data auditing scheme called CRT-based Dynamic Data Auditing Algorithm (CRTDDA) is proposed for conducting the cloud data auditing over the federated cloud and also restricting the data access. The proposed new auditing mechanism that is able to protect the stored data from access violence. In addition, the standard Table64 is used for encryption and decryption processes. The experimental results of this work proves the efficiency of the proposed model in terms of security level.
Implementation of Machine Learning to Detect Hate Speech in Bangla Language

Hate speech is a crime in all countries. Hate speech can be for women, religions, countries, cultures. The big problem for hate speech is that it entices the evil people. Moreover, it inspires them to spread hatred in the society. Bangla is one of the topmost spoken languages in the world. But hate speech detection in Bangla language is rare. Our purpose is to detect hate speech in Bangla language. To perform the task, we were in need of the Bangla datasets. But the Bangla dataset is not available. So, we have collected data from Facebook. Collecting data from the social site is very hectic. The data contain mixed languages, grammatical mistakes. So, we made a team to collect the data. Another team was to process the data. And finally, we labeled the data as hate speech or not. The team members had enough knowledge about hate speech. They were neutral towards the data. Our data contain hate speech against women, community, culture, ethnicity, race, sex, disability. Machine Learning approach is ideal for our work. We have used the SVM and Naïve Bayes algorithm for our work and got a maximum accuracy of 72%.
DEL-based epistemic planning: Decidability and complexity

Epistemic planning can be used for decision making in multi-agent systems with distributed knowledge and capabilities. Dynamic Epistemic Logic (DEL) has been shown to provide a very natural and expressive framework for epistemic planning. In this paper, we present a systematic overview of known complexity and decidability results for epistemic planning based on DEL, as well as provide some new results and improved proofs of existing results based on reductions between the problems.
Manipulation Detection in Satellite Images Using Deep Belief Networks

Satellite images are more accessible with the increase of commercial satellites being orbited. These images are used in a wide range of applications including agricultural management, meteorological prediction, damage assessment from natural disasters and cartography. Image manipulation tools including both manual editing tools and automated techniques can be easily used to tamper and modify satellite imagery. One type of manipulation that we examine in this paper is the splice attack where a region from one image (or the same image) is inserted ("spliced") into an image. In this paper, we present a one-class detection method based on deep belief networks (DBN) for splicing detection and localization without using any prior knowledge of the manipulations. We evaluate the performance of our approach and show that it provides good detection and localization accuracies in small forgeries compared to other approaches.
Fiat-Shamir for Repeated Squaring with Applications to PPAD-Hardness and VDFs

The Fiat-Shamir transform is a methodology for compiling a (public-coin) interactive proof system for a language L into a non-interactive argument system for L. Proving security of the Fiat-Shamir transform in the standard model, especially in the context of succinct arguments, is largely an unsolved problem. The work of Canetti et al. (STOC 2019) proved the security of the Fiat-Shamir transform applied to the Goldwasser-Kalai-Rothblum (STOC 2008) succinct interactive proof system under a very strong "optimal learning with errors" assumption. Achieving a similar result under standard assumptions remains an important open question. In this work, we consider the problem of compiling a different succinct interactive proof system: Pietrzak's proof system (ITCS 2019) for the iterated squaring problem. We construct a hash function family (with evaluation time roughly $$2^{\lambda ^{\epsilon }}$$ ) that guarantees the soundness of Fiat-Shamir for this protocol assuming the sub-exponential ( $$2^{-n^{1-\epsilon }}$$ )-hardness of the n-dimensional learning with errors problem. (The latter follows from the worst-case $$2^{n^{1-\epsilon }}$$ hardness of lattice problems.) More generally, we extend the "bad-challenge function" methodology of Canetti et al. for proving the soundness of Fiat-Shamir to a class of protocols whose bad-challenge functions are not efficiently computable. As a corollary (following Choudhuri et al., ePrint 2019 and Ephraim et al., EUROCRYPT 2020), we construct hard-on-average problems in the complexity class $$\mathbf {CLS}\subset \mathbf {PPAD}$$ under the $$2^{\lambda ^\epsilon }$$ -hardness of the repeated squaring problem and the $$2^{-n^{1-\epsilon }}$$ -hardness of the learning with errors problem. Under the additional assumption that the repeated squaring problem is "inherently sequential", we also obtain a Verifiable Delay Function (Boneh et al., EUROCRYPT 2018) in the standard model. Finally, we give additional PPAD-hardness and VDF instantiations demonstrating a broader tradeoff between the strength of the repeated squaring assumption and the strength of the lattice assumption.
Quantum algorithm for simulating molecular vibrational excitations

The excitation of vibrational modes in molecules affects the outcome of chemical reactions, for example by providing molecules with sufficient energy to overcome activation barriers. In this work, we introduce a quantum algorithm for simulating molecular vibrational excitations during vibronic transitions. We discuss how a special-purpose quantum computer can be programmed with molecular data to optimize a vibronic process such that desired modes get excited during the transition. We investigate the effect of such excitations on selective bond dissociation in pyrrole and butane during photochemical and mechanochemical vibronic transitions. The results are discussed with respect to experimental observations and classical simulations. We also introduce quantum-inspired classical algorithms for simulating molecular vibrational excitations in special cases where only a limited number of modes are of interest.
Exploiting Earth Observation Data to Impute Groundwater Level Measurements with an Extreme Learning Machine

Groundwater resources are expensive to develop and use; they are difficult to monitor and data collected from monitoring wells are often sporadic, often only available at irregular, infrequent, or brief intervals. Groundwater managers require an accurate understanding of historic groundwater storage trends to effectively manage groundwater resources, however, most if not all well records contain periods of missing data. To understand long-term trends, these missing data need to be imputed before trend analysis. We present a method to impute missing data at single wells, by exploiting data generated from Earth observations that are available globally. We use two soil moisture models, the Global Land Data Assimilation System (GLDAS) model and National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) soil moisture model to impute the missing data. Our imputation method uses a machine learning technique called Extreme Learning Machine (ELM). Our implementation uses 11 input data-streams, all based on Earth observation data. We train and apply the model one well at a time. We selected ELM because it is a single hidden layer feedforward model that can be trained quickly on minimal data. We tested the ELM method using data from monitoring wells in the Cedar Valley and Beryl-Enterprise areas in southwest Utah, USA. We compute error estimates for the imputed data and show that ELM-computed estimates were more accurate than Kriging estimates. This ELM-based data imputation method can be used to impute missing data at wells. These complete time series can be used improve the accuracy of aquifer groundwater elevation maps in areas where in-situ well measurements are sparse, resulting in more accurate spatial estimates of the groundwater surface. The data we use are available globally from 1950 to the present, so this method can be used anywhere in the world.
A Novel Fuzzy-Power Zeroing Neural Network Model for Time-Variant Matrix Moore–Penrose Inversion With Guaranteed Performance

On the strength of the abundant development of zeroing neural network (ZNN) and the wide application of fuzzy logic system (FLS), this article presents a fuzzy-power ZNN (FPZNN) model for addressing the time-variant matrix Moore-Penrose inversion problem. Different from the original constant or time-variant parameters, a fuzzy power parameter is generated from the FLS, and is first embedded into the FPZNN model to adjust the convergence rate. For the purpose of highlighting the superior performance of the FPZNN model, the other three classical neural network models are developed for comparison purposes. The convergence and noise-tolerance of the FPZNN model are analyzed to guarantee its excellent performance, where the model-implementation and differential errors are taken into account in a noisy environment. Besides, simulative experiments including two kinds of examples are provided to display the advantages of the FPZNN model under three commonly used activation functions. Both the presented theorems and the simulative experiments verify the superiority of the FPZNN model.
Detecting SQL Injection Attacks in Cloud SaaS using Machine Learning

Software as a Service (SaaS) has been adopted in a fast pace for applications and services to run on software cloud platform. However, the success of SaaS in cloud computing cannot obscure the security challenges faced by the web applications deployed on cloud SaaS. Like other web-based systems, cloud applications are prone to most of the common web attacks. The SQL injection attack is one of the most potential threat to a SaaS application. This may result in loss of sensitive and important data (e.g., financial, personal). Through this kind of attacks, the attacker can steal critical and confidential information to a business or an organization leading to high impact on tangible (e.g., data) and intangible (e.g., reputation) assets. The purpose of this research is to investigate the potential of using machine learning techniques for SQL injection detection on the application level. The algorithms to be tested are classifiers trained on different malicious and benign payloads. They take a payload as input and decide whether the input contains a malicious code or not. The results show that these algorithms can distinguish normal payloads from malicious payloads with a detection rate higher than 98%. The paper also compares the performance of different machine learning models in detecting SQL injection attacks.
Always Have a Backup Plan: Fully Secure Synchronous MPC with Asynchronous Fallback

Protocols for secure Multi-Party Computation (MPC) can be classified according to the underlying communication model. Two prominent communication models considered in the literature are the synchronous and asynchronous models, which considerably differ in terms of the achievable security guarantees. Synchronous MPC protocols can achieve the optimal corruption threshold n/2 and allow every party to give input, but become completely insecure when synchrony assumptions are violated. On the other hand, asynchronous MPC protocols remain secure under arbitrary network conditions, but can tolerate only n/3 corruptions and parties with slow connections unavoidably cannot give input. A natural question is whether there exists a protocol for MPC that can tolerate up to $$t_s < n/2$$ corruptions under a synchronous network and $$t_a < n/3$$ corruptions even when the network is asynchronous. We answer this question by showing tight feasibility and impossibility results. More specifically, we show that such a protocol exists if and only if $$t_a + 2t_s < n$$ and the number of inputs taken into account under an asynchronous network is at most $$n-t_s$$ .
Saliency detection via multiple-morphological and superpixel based fast fuzzy C-mean clustering network

To model a human perception-based saliency detection algorithm in cluttered and noisy background images is a challenging problem in computer vision. Recently, many saliency detection algorithms have been proposed, which exploit the background information, or boundary priors of an image, to detect the salient object similar to the human attention system. These algorithms may not provide satisfying detection results for color images due to the assimilation of local spatial information. In this paper, we propose an unsupervised saliency detection technique, which uses multi-color space-based morphological gradient images. These gradient images contain different edge features, which are useful to obtain an accurate counter-based superpixel image containing both foreground and background clusters. A robust background measuring technique is implemented to remove background clusters, which describes the spatial information of an image cluster to image boundaries. This geometric clarification method effectively removes multiple low-level clues to produce a precise and uniform saliency map. These initially obtained saliency maps are fused using a multi-map fusion technique, and a compact saliency map prevails. The proposed algorithm is evaluated by executing different experiments on nine data sets. The results show that the proposed algorithm performs well for the detection of both single and multiple objects. The proposed algorithm is computationally efficient and provides better saliency detection results than state-of-the-art algorithms.
Definition and evaluation of topography measurement noise in optical instruments

The pursuit of low noise in optical instruments for areal surface topography measurement is relevant to many surface types, ranging from super-polished optical surfaces to weakly reflecting or scattering textures that require enhanced signal sensitivity. We clarify the definition and experimental methods for quantifying random noise in areal surface topography measurements. We also propose a parameter, the topographical noise density, that concisely summarizes the effects of measurement bandwidth. To illustrate these ideas, we present results from a commercial phase-shifting interference microscope showing an RMS measurement noise of 0.03 nm for a 1-s data acquisition of 1 million surface topography image points, after application of a 3 × 3-pixel convolution filter. The results follow the expected inverse square root dependence on the data acquisition time for fast averaging of topography maps, resulting in a measurement noise of <0.01 nm for a 10-s data acquisition.