Reviewerly/exHarmony huggingface dataset:
ENSEI: Efficient Secure Inference via Frequency-Domain Homomorphic Convolution for Privacy-Preserving Visual Recognition

In this work, we propose ENSEI, a secure inference (SI) framework based on the frequency-domain secure convolution (FDSC) protocol for the efficient execution of image inference in the encrypted domain. Our observation is that, under the combination of homomorphic encryption and secret sharing, homomorphic convolution can be obliviously carried out in the frequency domain, significantly simplifying the related computations. We provide protocol designs and parameter derivations for number-theoretic transform (NTT) based FDSC. In the experiment, we thoroughly study the accuracy-efficiency trade-offs between time- and frequency-domain homomorphic convolution. With ENSEI, compared to the best known works, we achieve 5--11x online time reduction, up to 33x setup time reduction, and up to 10x reduction in the overall inference time. A further 33% of bandwidth reductions can be obtained on binary neural networks with only 3% of accuracy degradation on the CIFAR-10 dataset.
Automatic Text-Based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings (Student Abstract)

Previous works related to automatic personality recognition focus on using traditional classification models with linguistic features. However, attentive neural networks with contextual embeddings, which have achieved huge success in text classification, are rarely explored for this task. In this project, we have two major contributions. First, we create the first dialogue-based personality dataset, FriendsPersona , by annotating 5 personality traits of speakers from Friends TV Show through crowdsourcing. Second, we present a novel approach to automatic personality recognition using pre-trained contextual embeddings (BERT and RoBERTa) and attentive neural networks. Our models largely improve the state-of-art results on the monologue Essays dataset by 2.49%, and establish a solid benchmark on our FriendsPersona. By comparing results in two datasets, we demonstrate the challenges of modeling personality in multi-party dialogue.
Feedback-Based Self-Learning in Large-Scale Conversational AI Agents

Today, most of the large-scale conversational AI agents such as Alexa, Siri, or Google Assistant are built using manually annotated data to train the different components of the system including Automatic Speech Recognition (ASR), Natural Language Understanding (NLU) and Entity Resolution (ER). Typically, the accuracy of the machine learning models in these components are improved by manually transcribing and annotating data. As the scope of these systems increase to cover more scenarios and domains, manual annotation to improve the accuracy of these components becomes prohibitively costly and time consuming. In this paper, we propose a system that leverages customer/system interaction feedback signals to automate learning without any manual annotation. Users of these systems tend to modify a previous query in hopes of fixing an error in the previous turn to get the right results. These reformulations, which are often preceded by defective experiences caused by either errors in ASR, NLU, ER or the application. In some cases, users may not properly formulate their requests (e.g. providing partial title of a song), but gleaning across a wider pool of users and sessions reveals the underlying recurrent patterns. Our proposed self-learning system automatically detects the errors, generate reformulations and deploys fixes to the runtime system to correct different types of errors occurring in different components of the system. In particular, we propose leveraging an absorbing Markov Chain model as a collaborative filtering mechanism in a novel attempt to mine these patterns. We show that our approach is highly scalable, and able to learn reformulations that reduce Alexa-user errors by pooling anonymized data across millions of customers. The proposed self-learning system achieves a win-loss ratio of 11.8 and effectively reduces the defect rate by more than 30% on utterance level reformulations in our production A/B tests. To the best of our knowledge, this is the first self-learning large-scale conversational AI system in production.
Fractional Fuzzy Inference System: The New Generation of Fuzzy Inference Systems

This paper presents a new machinery of compositional rule of inference called fractional fuzzy inference system (FFIS). An FFIS is a fuzzy inference system (FIS) in which consequent parts of a rule base consist of a new type of membership functions called fractional membership functions. Fractional membership functions are characterized using fractional indices. There are two types of fractional indices. Each type can be either constant or dynamic. An FFIS intelligently considers not only the truth degrees of information included in membership functions, but also the volume of the information in the process of making a conclusion. In other words, the volume of information extracted from a membership function depends on the truth degree of information. Concretely, the higher the truth degree, the larger the volume of information that is involved in the process of making a conclusion. It is shown that typical FISs, e.g. Mamdani's or Larsen's FISs, are special cases of FFISs. Specifically, as the fractional indices approach one, the FFIS approaches a typical FIS. In addition, using two theorems proved in this paper, it is demonstrated that, independent of the problem in question, a typical FIS never leads to results which are more satisfactory than those obtained by the FFIS corresponding to the typical FIS provided that a particular set of fractional indices is taken into account. Put another way, it seems sound to expect that applying FFIS always leads to more satisfactory results than applying its corresponding FIS. It is also shown that FFIS grants a special dynamic to FIS which can be also customized according to a new concept called reaction trajectories map (RTM). Particularly, the RTM enables decision makers to select an FFIS more suitable for their purpose. Some more concepts such as the left and right orders of an FFIS and the fracture index are also introduced in this paper.
From Standard Summarization to New Tasks and Beyond: Summarization with Manifold Information

Text summarization is the research area aiming at creating a short and condensed version of the original document, which conveys the main idea of the document in a few words. This research topic has started to attract the attention of a large community of researchers, and it is nowadays counted as one of the most promising research areas. In general, text summarization algorithms aim at using a plain text document as input and then output a summary. However, in real-world applications, most of the data is not in a plain text format. Instead, there is much manifold information to be summarized, such as the summary for a web page based on a query in the search engine, extreme long document (e.g. academic paper), dialog history and so on. In this paper, we focus on the survey of these new summarization tasks and approaches in the real-world application.
Definitions, characteristics and measures of IT project complexity - a systematic literature review

As the world of Information Technology (IT) engineering becomes more complex every day, the formal study of project complexity becomes more and more important for managing projects effectively, to avoid poor performance and failure. Complexity is not yet clearly understood nor sufficiently defined and the terminology itself is being overloaded and over-used. This paper is a systematic literature review that attempts to identify and classify proposed definitions and measures of IT project complexity. The results include a map of the identified approaches and definitions, a list of classifications of project complexity, a set of proposed measurement tools and complexity measures available to practitioners. The paper contributes to establishing a common language when discussing complexity, as well as to a better understanding of project complexity and its implications to practical IT engineering projects.
Depth in convolutional neural networks solves scene segmentation

Feed-forward deep convolutional neural networks (DCNNs) are, under specific conditions, matching and even surpassing human performance in object recognition in natural scenes. This performance suggests that the analysis of a loose collection of image features could support the recognition of natural object categories, without dedicated systems to solve specific visual subtasks. Research in humans however suggests that while feedforward activity may suffice for sparse scenes with isolated objects, additional visual operations ('routines') that aid the recognition process (e.g. segmentation or grouping) are needed for more complex scenes. Linking human visual processing to performance of DCNNs with increasing depth, we here explored if, how, and when object information is differentiated from the backgrounds they appear on. To this end, we controlled the information in both objects and backgrounds, as well as the relationship between them by adding noise, manipulating background congruence and systematically occluding parts of the image. Results indicate that with an increase in network depth, there is an increase in the distinction between object- and background information. For more shallow networks, results indicated a benefit of training on segmented objects. Overall, these results indicate that, de facto, scene segmentation can be performed by a network of sufficient depth. We conclude that the human brain could perform scene segmentation in the context of object identification without an explicit mechanism, by selecting or “binding” features that belong to the object and ignoring other features, in a manner similar to a very deep convolutional neural network.
DataV: Data Visualization on large high-resolution displays

In recent years, the technology and applications of visualizations on large high-resolution displays (LHDs) have received widespread attention because of its perceptual benefits and improved productivity. However, existing work on LHD visualization lacks both comprehensive guidance for design requirements and tools developed for its specific usage scenarios. In this paper, we present the scenarios, design, and implementation of DataV, a Software-as-a-Service (SaaS) visual deployment tool that enables rapid construction and cross-platform publishing of interactive visualization on LHDs. Our framework can support rich components for the high-performance rendering of multi-source heterogeneous data. DataV provides a full-fledged toolchain to help the user efficiently specify layout and interactions. We present its accessibility and impressive visual effects with examples and comparison with Tableau, Power BI, VisComposer, and iVisDesigner. We also report the performance of using DataV for 3D map rendering by comparing it with deck.gl.
A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data

Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most settings, spatial context is often expressed in terms of ZIP code or region coordinates such as latitude and longitude. However, traditional anomaly detection techniques cannot handle more than one contextual attribute in a unified way. In this paper, a new hybrid approach based on deep learning is proposed to solve the anomaly detection problem in multivariate spatio-temporal dataset. It works under the assumption that no prior knowledge about the dataset and anomalies are available. The architecture of the proposed hybrid framework is based on an autoencoder scheme, and it is more efficient in extracting features from the spatio-temporal multivariate datasets compared to the traditional spatio-temporal anomaly detection techniques. We conducted extensive experiments using buoy data of 2005 from National Data Buoy Center and Hurricane Katrina as ground truth. Experiments demonstrate that the proposed model achieves more than 10% improvement in accuracy over the methods used in the comparison where our model jointly processes the spatial and temporal dimensions of the contextual data to extract features for anomaly detection.
JackHammer: Efficient Rowhammer on Heterogeneous FPGA-CPU Platforms

After years of development, FPGAs are finally making an appearance on multi-tenant cloud servers. Heterogeneous FPGA-CPU microarchitectures require reassessment of common assumptions about isolation and security boundaries, as they introduce new attack vectors and vulnerabilities. In this work, we analyze the memory and cache subsystem and study Rowhammer and cache attacks enabled by two proposed heterogeneous FPGA-CPU platforms from Intel: the Arria 10 GX with an integrated FPGA-CPU platform, and the Arria 10 GX PAC expansion card which connects the FPGA to the CPU via the PCIe interface. We demonstrate JackHammer, a novel, efficient, and stealthy Rowhammer from the FPGA to the host’s main memory. Our results indicate that a malicious FPGA can perform twice as fast as a typical Rowhammer from the CPU on the same system and causes around four times as many bit flips as the CPU attack. We demonstrate the efficacy of JackHammer from the FPGA through a realistic fault attack on the WolfSSL RSA signing implementation that reliably causes a fault after an average of fifty-eight RSA signatures, 25% faster than a CPU Rowhammer. In some scenarios our JackHammer attack produces faulty signatures more than three times more often and almost three times faster than a conventional CPU Rowhammer. Finally, we systematically analyze new cache attacks in these environments following demonstration of a cache covert channel across FPGA and CPU.
Cyber–Physical Systems Forensics: Today and Tomorrow

Cyber–Physical Systems (CPS) connect the physical world (systems, environments, and humans) with the cyber world (software, data, etc.) to intelligently enhance the operational environment they serve. CPS are distributed software and hardware components embedded in the physical world and possibly attached to humans. They offer smart features, such as enhancing and optimizing the reliability, quality, safety, health, security, efficiency, operational costs, sustainability, and maintainability of physical systems. CPS are also very vulnerable to security attacks and criminal activities. In addition, they are very complex and have a direct impact on their environment. Therefore, it is hard to detect and investigate security attacks, while such attacks may have a catastrophic impact on the physical world. As a result, CPS must incorporate security measures in addition to suitable and effective forensics capabilities. When the security measures fail and an attack occurs, it becomes imperative to perform thorough forensics analysis. Adding effective forensics tools and capabilities will support the investigations of incidents. This paper defines the field of CPS forensics and its dimensions: Technical, Organizational, and Legal. Then, it reviews examples of current research efforts in the field and the types of tools and methods they propose for CPS forensics. In addition, it discusses the issues and challenges in the field that need to be addressed by researchers and developers of CPS. The paper then uses the review outcomes to discuss future research directions to address challenges and create a more effective, efficient, and safe forensics tools and for CPS. This discussion aims to create a starting point for researchers where they can identify the gaps and challenges and create suitable solutions through their research in CPS forensics.
TOWARDS DATA PRIVACY AND SECURITY FRAMEWORK IN BIG DATA GOVERNANCE

Data privacy and security are among the most important aspects to be considered in implementing a data-driven system.A well generated big data contains a wealth of information that can reveal personal information that should be kept in private.Although various studies are widely documented, the methods and policies to ensure the privacy and security of data in big data governance remain unclear.Big data governance also seeks to protect the affected person to disclose new and critical personal information that is not intended for disclosure.Thus, this study focuses on data privacy and security issues in big data governance.The objective of this study aims to propose of big data governance framework that complements data privacy and security factors.This study uses a qualitative approach in the development of the research framework based on a systematic literature review and evaluated by experts to validate the framework.This study is expected to benefits the public and private sectors where the proposed framework could be applied as a guide to preventing any data leakage or misuse of big data.However, the study could be expanded in the future to include technological and legal perspective.
A Permissioned Distributed Ledger for the US Beef Cattle Supply Chain

Distributed ledgers using blockchain have gained traction in the supply chain industry due to their unique features of immutability and transparency. They have given people the abilities to solve business problems which were impossible using traditional systems. The US beef cattle industry lacks adequate traceability as most of the farm owners consider such data confidential; possibly harming their businesses if exposed. This article attempts to solve this problem by proposing a smart contract-based supply chain framework using a permissioned blockchain network. This system supports anonymity for the users to protect identities and lets every user store their data locally, while ensuring that the changes are recorded in the chain with cryptographic proofs (hashes). The proposed framework also has methods for the users to perform business transactions and transfer animal-related data to new owners as required. In addition to that, smart contracts have been added to conduct anonymous surveys for data aggregation. The technical contribution of this article is in the system design on how users, data, and communications are handled to maintain data ownership and user privacy while ensuring immutability and confidentiality at different levels of data aggregation. This article also contains an evaluation of the system using integration tests where the outcomes meet the expected design requirements. The framework can be applied to the US beef cattle industry as well as other supply chains with minimal modifications.
What affects digitalization process in developing economies? An evidence from SMEs sector in Oman

The main objective of this paper is to investigate the SMEs’ leader perspective about the basic factors influencing the transformation into digitalization by SMEs they lead, using technological, organizational, and environmental (TOE) Model. The data were collected from 61 SMEs leaders in Oman, to achieve the study objective TOE model has been adopted. Internal consistency and data normality, and factor analysis were implemented. Structural equation modeling (SEM) used to test the proposed hypotheses. The outcomes of SEM indicate that TOE factors are significantly affects the ability of SMEs to digitalize their business process. The study findings come in the context of Omani definition of SMEs. More, no control was made for industry type to which SMEs participants are belong. Leaders of SMEs should frame strategies to simplify the digital transformation of their enterprises and attempt to provide organizational and technological facilities that will smooth their digitalization which will improve SMEs capabilities, as well as, increasing the international competitiveness of the SMEs. To the best of the authors' knowledge, this study is one of the first that investigated the digital transformation among SMEs from the leaders’ perspective in Oman.
Arabic Handwritten Character Recognition based on Convolution Neural Networks and Support Vector Machine

Recognition of Arabic characters is essential for natural language processing and computer vision fields.The need to recognize and classify the handwritten Arabic letters and characters are essentially required.In this paper, we present an algorithm for recognizing Arabic letters and characters based on using deep convolution neural networks (DCNN) and support vector machine (SVM).This paper addresses the problem of recognizing the Arabic handwritten characters by determining the similarity between the input templates and the pre-stored templates using both fully connected DCNN and dropout SVM.Furthermore, this paper determines the correct classification rate (CRR) depends on the accuracy of the corrected classified templates, of the recognized handwritten Arabic characters.Moreover, we determine the error classification rate (ECR).The experimental results of this work indicate the ability of the proposed algorithm to recognize, identify, and verify the input handwritten Arabic characters.Furthermore, the proposed system determines similar Arabic characters using a clustering algorithm based on the K-means clustering approach to handle the problem of multi-stroke in Arabic characters.The comparative evaluation is stated and the system accuracy reached 95.07%CRR with 4.93% ECR compared with the state of the art.
Multiple model efficient particle filter based track-before-detect for maneuvering weak targets

It is a tough problem to jointly detect and track a weak target, and it becomes even more challenging when the target is maneuvering. The above problem is formulated by using the Bayesian theory and a multiple model (MM) based filter is proposed. The filter presented uses the MM method to accommodate the multiple motions that a maneuvering target may travel under by adding a random variable representing the motion model to the target state. To strengthen the efficiency performance of the filter, the target existence variable is separated from the target state and the existence probability is calculated in a more efficient way. To examine the performance of the MM based approach, a typical track-before-detect (TBD) scenario with a maneuvering target is used for simulations. The simulation results indicate that the MM based filter proposed has a good performance in joint detecting and tracking of a weak and maneuvering target, and it is more efficient than the general MM method.
FAPS: A fair, autonomous and privacy-preserving scheme for big data exchange based on oblivious transfer, Ether cheque and smart contracts

Decision-making plays an increasing important role in our digitalized society, and hence the need for trustworthy data. For example, in a traditional trading process, a trusted middle person generally helps to arbitrate any dispute between two or more parties involved in the trading. However, having such a trusted middle person can be labor-intensive, increases transaction costs, and lacks fairness during arbitration due to the subjectivity of the middle person. While we can avoid the need for a middle person using smart contract-based approaches, there are other limitations (e.g., fairness and privacy preservation) that need to be addressed. In this paper, we present a fair scheme for big data exchanging that allows buyers and sellers to autonomously and fairly complete transactions, without involving any third-party middle person. Specifically, our scheme uses smart contracts and oblivious transfer protocol, in combination with our proposed Ether cheque system. Smart contracts are used to achieve transaction fairness, autonomy and trading timing control. The oblivious transfer protocol helps us to preserve the privacy of transactions. We use Ether cheque to improve the fairness of the transaction and make the transaction more convenient. Also, our proposed approach can facilitate the identification of a cheating party.