Reviewerly/exHarmony huggingface dataset:
The Sorry State of TLS Security in Enterprise Interception Appliances

Network traffic inspection, including TLS traffic, in enterprise environments is widely practiced. Reasons for doing so are primarily related to improving enterprise security (e.g., phishing and malicious traffic detection) and meeting legal requirements (e.g., preventing unauthorized data leakage and copyright violations). To analyze TLS-encrypted data, network appliances implement a Man-in-the-Middle (MITM) TLS proxy by acting as the intended web server to a requesting client (e.g., a browser) and acting as the client to the actual/outside web server. As such, the TLS proxy must implement both a TLS client and a server and handle a large amount of traffic, preferably in real-time. However, as protocol and implementation layer vulnerabilities in TLS/HTTPS are quite frequent, these proxies must be at least as secure as a modern, up-to-date web browser and a properly configured web server (e.g., an A+ rating in SSLlabs.com). As opposed to client-end TLS proxies (e.g., as in several anti-virus products), the proxies in network appliances may serve hundreds to thousands of clients, and any vulnerability in their TLS implementations can significantly downgrade enterprise security. To analyze TLS security of network appliances, we develop a comprehensive framework, combining and extending tests from existing work on client-end and network-based interception studies. We analyze 13 representative network appliances over a period of more than a year (including versions before and after notifying affected vendors, a total of 17 versions) and uncover several security issues. For instance, we found that four appliances perform no certificate validation at all, three use pre-generated certificates, and eleven accept certificates signed using MD5, exposing their clients to MITM attacks. Our goal is to highlight the risks introduced by widely used TLS proxies in enterprise and government environments, potentially affecting many systems hosting security, privacy, and financially sensitive data.
RIT-Eyes: realistically rendered eye images for eye-tracking applications

Convolutional neural network-based solutions for video oculography require large quantities of accurately labeled eye images acquired under a wide range of image quality, surrounding environmental reflections, feature occlusion, and varying gaze orientations. Manually annotating such a dataset is challenging, time-consuming, and error-prone. To alleviate these limitations, this work introduces an improved eye image rendering pipeline designed in Blender. RIT-Eyes provides access to realistic eye imagery with error-free annotations in 2D and 3D which can be used for developing gaze estimation algorithms. Furthermore, RIT-Eyes is capable of generating novel temporal sequences with realistic blinks and mimicking eye and head movements derived from publicly available datasets.
Optimization of Information Retrieval Algorithm for Digital Library Based on Semantic Search Engine

The information retrieval of digital library has always been one of the major research topics in the field of library. The ranking of search results directly leads to the efficiency of readers searching for the information that they want to find. Now, there are mainly two general ranking algorithms based on semantic search engines (SSE), that are ranking algorithms for OntoKhoj and AKTiveRank. However, these are not ideal for two sorts of sorting results, which mainly due to the rather one-sided sorting idea; and the coefficients in the formula were difficult to determine. To solve this problem by combining the two algorithms advantages of OntoKhoj and AKTiveRank, a new algorithm, which named Mellom algorithm, was proposed, and its coefficients were optimized by applying genetic algorithm (GA).
Importance Weighted Generative Networks

While deep generative networks can simulate from complex data distributions, their utility can be hindered by limitations on the data available for training. Specifically, the training data distribution may differ from the target sampling distribution due to sample selection bias, or because the training data comes from a different but related distribution. We present methods to accommodate this difference via importance weighting, which allow us to estimate a loss function with respect to a target distribution even if we cannot access that distribution directly. These estimators, which differentially weight the contribution of data to the loss function, offer theoretical guarantees that heuristic approaches lack, while giving impressive empirical performance in a variety of settings.
TEEnder: SGX enclave migration using HSMs

Intel Software Guard Extensions (SGX) is a new method of enhancing application security by creating safe areas of memory (enclaves) where data and code are protected from inspection and tampering. This technology is being applied to cloud computing as well, however, software deployed with SGX enclaves is complex to migrate between machines using traditional methods as SGX uses unique hardware keys for data sealing. This paper proposes a novel method of migrating SGX enclaves between different machines using Hardware Security Modules (HSMs) to encrypt and decrypt data using HSM generated keys. The use of HSMs achieves faster migration for large enclaves or during multiple concurrent migrations. Since the this solution does not depend on the security of remote attestation, and uses the keys stored in the HSM it provides a higher degree of security than current enclave migration solutions.
LSB-based Bit Flipping Methods for Color Image Steganography

Abstract Imperceptibility is one of the most important aspects of the steganography method. Specifically, for steganography in images, embedded messages must not be felt by the human visual system. Humans have a higher sensitivity in color images compared to grayscale images, therefore the method that is still being tested on grayscale images needs to be retested on color images. Previous research has shown that the bit flipping method can increase imperceptibility to around 9dB in grayscale images. In this research the bit flipping method is tested on color images in the RGB format, the message capacity embedded in the image is 1 bit per pixel. The results of the imperceptibility test turned out to result in a more varied increase due to the greater number of layers in the color image. However, the bit flipping method is also proven to work well on color images, and even a maximum PSNR increase of more than 13dB. At the extraction stage, the message image can also be extracted perfectly, namely with the value NC = 1.
eQual: informing early design decisions

When designing a software system, architects make a series of design decisions that directly impact the system's quality. The number of available design alternatives grows rapidly with system size, creating an enormous space of intertwined design concerns that renders manual exploration impractical. We present eQual, a model-driven technique for simulation-based assessment of architectural designs. While it is not possible to guarantee optimal decisions so early in the design process, eQual improves decision quality. eQual is effective in practice because it (1) limits the amount of information the architects have to provide and (2) adapts optimization algorithms to effectively explore massive spaces of design alternatives. We empirically demonstrate that eQual yields designs whose quality is comparable to a set of systems' known optimal designs. A user study shows that, compared to the state-of-the-art, engineers using eQual produce statistically significantly higher-quality designs with a large effect size, are statistically significantly more confident in their designs, and find eQual easier to use.
Fault prediction for distribution network based on CNN and LightGBM algorithm

Fault prediction plays a significant role in enhancing the safety, reliability, and stability of distribution network. However, the problem of enormous time-series data and discrete data makes the prediction great challenge. The imbalance between normal and fault samples will reduce the accuracy of the model. In addition, the influence of time-series variables on distribution network is direct and continuous, so the time-series feature extraction is the key technique for fault prediction. In this work, we propose a fault prediction method for distribution network based on CNN and LightGBM algorithm. This method deeply learns feature of time-series data by utilizing CNN, and improves the adaptability for imbalanced dataset by training LightGBM submodels. Experimental results based on fault dataset of a district in Chongqing from 2017 to 2018 show that fault prediction performance can be ameliorated by utilizing this method.
ARphy

ARphy is a tangible interface that extends current ways of organizing photo collections by enabling people to interact with digital photos using physical objects in Augmented Reality. ARphy contextually connects photos with real objects and utilizes physical affordances so that people can add more meanings to their collections and interact with them naturally. We also created an ARphy Interaction Design Toolkit, which can add ARphy-compatible interactions to any object, so that people can register their own things for organizing collections. We developed a prototype using seven everyday objects and evaluated ARphy through a qualitative user study. Our findings indicate that ARphy is intuitive, immersive, and enjoyable and has the potential for selectively managing collections using photos and objects that have personal meanings.
Tabloidization versus Credibility: Short Term Gain for Long Term Pain

Print news agencies have been under pressure from falling sales and advertising revenue and increased competition. As the Internet became the dominant medium, news agencies invested heavily in their websites and apps, providing their news for free, rather than selling a print edition. Reducing the cost of production and removing access barriers such as geographic location had the potential to increase readership and advertising, covering costs and maintaining profits. Unfortunately, this business model has for the most part failed. Many higher quality news agencies are now implementing paywalls on their news websites to once again monetize their product. Others have begun to emulate the look and feel of tabloid news websites to increase readership and stickiness and advertising revenue. This study shows the negative impact of such visual tabloidization on initial impressions of credibility, which may have long term detrimental effects on the news agency.
The Art of Efficient In-memory Query Processing on NUMA Systems: a Systematic Approach

Data analytics systems commonly utilize in-memory query processing techniques to achieve better throughput and lower latency. Modern computers increasingly rely on Non-Uniform Memory Access (NUMA) architectures to achieve scalability. A key drawback of NUMA architectures is that many existing software solutions are not aware of the underlying NUMA topology and thus do not take full advantage of the hardware. Modern operating systems are designed to provide basic support for NUMA systems. However, default system configurations are typically sub-optimal for large data analytics applications. Additionally, rewriting the application from the ground up is not always feasible.In this work, we evaluate a variety of strategies that aim to accelerate memory-intensive data analytics workloads on NUMA systems. Our findings indicate that the operating system default configurations can be detrimental to query performance. We analyze the impact of different memory allocators, memory placement strategies, thread placement, and kernel-level load balancing and memory management mechanisms. With extensive experimental evaluation, we demonstrate that the methodical application of these techniques can be used to obtain significant speedups in four commonplace in-memory query processing tasks, on three different hardware architectures. Furthermore, we show that these strategies can improve the performance of five popular database systems running a TPC-H workload. Lastly, we summarize our findings in a decision flowchart for practitioners.
A Hierarchical Blockchain-based Data Service Platform in MEC Environments

Recently, data service companies analyzing IoT data to provide valuable knowledge emerge as a promising business model. To sustain such business model, the establishment of a data service platform for IoT data sharing and data service delivery becomes a necessity. Today, blockchain-based platforms are believed to be reliable but have poor system performance, considerable storage overheads and high energy consumption. MEC technology is then applied to help IoT devices participate in the blockchain network. However, the considerable loading of blockchain operations turns MEC hosts into a performance bottleneck, instead. Therefore, this paper exploits the hierarchical blockchain architecture in the proposed IoT data service platform, called HB-IoDS, to significantly reduce the storage overheads and service providing time. Furthermore, to alleviate the heavy burdens on MEC hosts, HB-IoDS provides the adaptive Proof-of-Work (APoW) consensus algorithm that derives an appropriate difficulty to achieve PoW condition according to device capability. By this mean, resource-constrained IoT devices can also perform blockchain operations which are usually regarded as energy-consuming. Finally, simulation results show that HB-IoDS outperforms existing blockchain-based platforms in terms of system performance, energy consumption, and storage costs. As a result, the proposed HB-IoDS is the most practical IoT data service platform.
Blockchain Analysis Tool of a Cryptocurrency

In recent years, cryptocurrencies have become more and more popular and the growing adoption has led to an increasing number of financial transactions being stored on the blockchain. Although cryptocurrencies have built a reputation as an anonymous means of payment, they are usually rather pseudonymous, transparent and everlasting logbooks about financial transactions, which are publicly available. Thus, analyzing a crypto address can reveal payment partners, money flows, behavior patterns and more. In this work, a program is presented, which provides an analysis of this kind and displays the results in a simple format. The technical properties of the blockchain that this analysis is based on are explained. This paper explains the possible impact of total transparency on the blockchain and our tool on our society.
Transfer Metric Learning for Unseen Domains

Abstract We propose a transfer metric learning method to infer domain-specific data embeddings for unseen domains, from which no data are given in the training phase, by using knowledge transferred from related domains. When training and test distributions are different, the standard metric learning cannot infer appropriate data embeddings. The proposed method can infer appropriate data embeddings for the unseen domains by using latent domain vectors, which are latent representations of domains and control the property of data embeddings for each domain. This latent domain vector is inferred by using a neural network that takes the set of feature vectors in the domain as an input. The neural network is trained without the unseen domains. The proposed method can instantly infer data embeddings for the unseen domains without (re)-training once the sets of feature vectors in the domains are given. To accumulate knowledge in advance, the proposed method uses labeled and unlabeled data in multiple source domains. Labeled data, i.e., data with label information such as class labels or pair (similar/dissimilar) constraints, are used for learning data embeddings in such a way that similar data points are close and dissimilar data points are separated in the embedding space. Although unlabeled data do not have labels, they have geometric information that characterizes domains. The proposed method incorporates this information in a natural way on the basis of a probabilistic framework. The conditional distributions of the latent domain vectors, the embedded data, and the observed data are parameterized by neural networks and are optimized by maximizing the variational lower bound using stochastic gradient descent. The effectiveness of the proposed method was demonstrated through experiments using three clustering tasks.
DeepUAge: Improving Underage Age Estimation Accuracy to Aid CSEM Investigation

Age is a soft biometric trait that can aid law enforcement in the identification of victims of Child Sexual Exploitation Material (CSEM) creation/distribution. Accurate age estimation of subjects can classify explicit content possession as illegal during an investigation. Automation of this age classification has the potential to expedite content discovery and focus the investigation of digital evidence through the prioritisation of evidence containing CSEM. In recent years, artificial intelligence based approaches for automated age estimation have been created, and many public cloud service providers offer this service on their platforms. The accuracy of these algorithms have been improving over recent years. These existing approaches perform satisfactorily for adult subjects, but perform wholly inadequately for underage subjects. To this end, the largest underage facial age dataset, VisAGe, has been used in this work to train a ResNet50 based deep learning model, DeepUAge, that achieved state-of-the-art beating performance for age estimation of minors. This paper describes the design and implementation of this model. An evaluation, validation and comparison of the proposed model is performed against existing facial age classifiers resulting in the best overall performance for underage subjects.
Critical loci in computer vision and matrices dropping rank in codimension one

Critical loci for projective reconstruction from three views in four dimensional projective space are defined by an ideal generated by maximal minors of suitable 4×3 matrices, N, of linear forms. Such loci are classified in this paper, in the case in which N drops rank in codimension one, giving rise to reducible varieties. This rests on the complete classification of matrices of size (n+1)×n for n≤3, which drop rank in codimension one. Instability of reconstruction near non-linear components of critical loci is explored experimentally. The classification of special matrices as above is also leveraged to study degenerate critical loci for suitable projections from P3.
Account Management in Proof of Stake Ledgers

Blockchain protocols based on Proof-of-Stake (PoS) depend—by nature—on the active participation of stakeholders. If users are offline and abstain from the PoS consensus mechanism, the system’s security is at risk, so it is imperative to explore ways to both maximize the level of participation and minimize the effects of non-participation. One such option is stake representation, such that users can delegate their participation rights and, in the process, form “stake pools”. The core idea is that stake pool operators always participate on behalf of regular users, while the users retain the ownership of their assets. Our work provides a formal PoS wallet construction that enables delegation and stake pool formation. While investigating the construction of addresses in this setting, we distil and explore address malleability, a security property that captures the ability of an attacker to manipulate the delegation information associated with an address. Our analysis consists of identifying multiple levels of malleability, which are taken into account in our paper’s core result. We then introduce the first ideal functionality of a PoS wallet’s core which captures the PoS wallet’s capabilities and is realized as a secure protocol based on standard cryptographic primitives. Finally, consider the wallet core in conjunction with a PoS ledger and investigate how delegation and stake pools affect a PoS system’s security.
Understanding Cybersecurity Practices in Emergency Departments

Emergency departments (EDs) have unique operational requirements within hospitals. They have strong availability demands, are staffed by rotating personnel, and must provide services as quickly as possible. Modern EDs are also heavily computerized, and as such cybersecurity practices play a key role in meeting the expected operational standards. To better understand the cybersecurity challenges in EDs, we conducted a survey asking 347 ED personnel across Canada about their cybersecurity practices. The survey collected information relating to authentication and password management, use of personal devices for handling patient data, Internet connectivity on personal and hospital systems, and institutional security policies. Our results show that across multiple hospitals, deployed computer security systems fail to integrate with the requirements of staff and patients, leading to interruptions and inefficiencies.
Tiramisu: Black-Box Simulation Extractable NIZKs in the Updatable CRS Model

Zk-SNARKs, as the most efficient NIZK arguments in terms of proof size and verification, are ubiquitously deployed in practice. In applications like Hawk [S&P'16], Gyges [CCS'16], Ouroboros Crypsinous [S&P'19], the underlying zk-SNARK is lifted to achieve Black-Box Simulation Extractability (BB-SE) under a trusted setup phase. To mitigate the trust in such systems, we propose Tiramisu (In Italian, Tiramisu literally means "lift me up"), as a construction to build NIZK arguments that can achieve updatable BB-SE, which we define as a new variant of BB-SE. This new variant allows updating the public parameters, therefore eliminating the need for a trusted third party, while unavoidably relies on a non-black-box extraction algorithm in the setup phase. In the cost of one-time individual CRS update by the parties, this gets around a known impossibility result by Bellare et al. from ASIACRYPT'16, which shows that BB extractability cannot be achieved with subversion ZK (ZK without trusting a third party). Tiramisu uses an efficient public-key encryption with updatable keys which may be of independent interest. We instantiate Tiramisu, implement the overhead and present efficient BB-SE zk-SNARKs with updatable parameters that can be used in various applications while allowing the end-users to update the parameters and eliminate the needed trust.
Detecting negative emotions during social media use on smartphones

Emotions are integral to the social media user experience; we express our feelings, react to posted content and communicate with emoji. This may lead to emotional contagion and undesirable behaviors such as cyberbullying and flaming. Nearly real-time negative emotion detection during the use of social media could mitigate these behaviors, but existing techniques rely on corpora of aggregated user-generated data - posted comments or social graph structure. This paper explores how live data extracted from smartphone sensors can predict binary affect, valence and arousal during the typical social media tasks of browsing content and chatting. Results show that momentary emotion can be predicted, using features from screen touches and device motions, with peak F1-scores of 0.86, 0.86, 0.88 for affect, valence and arousal.