Reviewerly/exHarmony huggingface dataset:
Enhanced Smart Advertising through Federated Learning

Smart advertising is growing in popularity and affecting businesses. Smart advertising is a more friendly, inter-active, personalised, and creative method of promoting a product, and attempts to delight clients. AROUND is a social networking service that emphasizes smart advertising through an effective recommender system. The system considers user profiles, history, social network connections, mood, and IoT-supported positioning to select the most relevant ads using machine learning technology. Although the current deployment of the AROUND system is based on the cloud, an edge-based architecture provides relevant improvement in terms of system response time. In this paper we extend the edge-based strategy to leverage the potential of federated learning on multiple distributed edge servers. We show that federated learning can take advantage of the distributed nature of the system, and leverage the specificities of local features. In fact, in this research, we propose a novel federated learning solution to provide smart advertising as a classification problem which uses ensemble methods and logistic regression as internal (local) models and meta-heuristic algorithms for federated learning aggregation. As part of the experiments, we prove this technology on a real data set with more than one million registers, and show the efficiency in terms of enhanced accuracy and improved training and response speed.
An Educational Chatbot in a Blended Learning Environment

The article discusses the educational potential of chatbots as an interactive learning tool. The didactic principles underlying the developed chatbot are presented. The chatbot interface for facilitating self-paced learning in a course on programming languages for facilitation self-paced learning in a blended learning environment is considered.
Adaptive Risk-Limiting Comparison Audits

Risk-limiting audits (RLAs) are rigorous statistical procedures meant to detect invalid election results. RLAs examine paper ballots cast during the election to statistically assess the possibility of a disagreement between the winner determined by the ballots and the winner reported by tabulation. The design of an RLA must balance risk against efficiency: "risk" refers to a bound on the chance that the audit fails to detect such a disagreement when one occurs; "efficiency" refers to the total effort to conduct the audit.The most efficient approaches—when measured in terms of the number of ballots that must be inspected—proceed by "ballot comparison." However, ballot comparison requires an (untrusted) declaration of the contents of each cast ballot, rather than a simple tabulation of vote totals. This "cast-vote record table" (CVR) is then spot-checked against ballots for consistency. In many practical settings, the cost of generating a suitable CVR dominates the cost of conducting the audit which has prevented widespread adoption of these sample-efficient techniques.We introduce a new RLA procedure: an "adaptive ballot comparison" audit. In this audit, a global CVR is never produced; instead, a three-stage procedure is iterated: 1) a batch is selected, 2) a CVR is produced for that batch, and 3) a ballot within the batch is sampled, inspected by auditors, and compared with the CVR. We prove that such an audit can achieve risk commensurate with standard comparison audits while generating a fraction of the CVR. We present three main contributions: (1) a formal adversarial model for RLAs; (2) definition and analysis of an adaptive audit procedure with rigorous risk limits and an associated correctness analysis accounting for the incidental errors arising in typical audits; and (3) an analysis of efficiency.
Unstable Teams: Management of Remote User Interface Evaluation in Adaptive Software Development

The paper considers problems of management of remote unstable teams in agile software development. The purpose is to discuss the modified model of Adaptive Software Development (ASD) complemented with interface evaluation procedures in the conditions of development by unstable development teams with remote participation and the possibilities of experimental implementation of the model. We propose to add each iteration of the ASD model with autonomous tasks for remote evaluation of the user interface, which are carried out by remotely involved performers. Methods may include behavioral and psychophysiological metrics based on eye-tracking and emotion detection in remote user experience. We justify the effectiveness of experimental verification of the model for remote teams of students undergoing software development training using modified ASD methodology. As a result, the additional problem of ensuring sufficient selection of qualified users for evaluating professional projects interface with behavioral and psychophysiological methods can be solved: the possible way is to involve students undergoing software development training and performing remote autonomous tasks within unstable professional teams.
FLEXNet: Learning Deep Features Using Autoencoders for Floor Plan Image Retrieval

Content-based retrieval of scanned document images is gaining research. A system that automatically analyses and retrieves similar floor plan images depending on user needs might help architects create new ideas and provide client recommendations. Our model, FLEXNet (Floor pLan EXplorer Network), extracts deep features from images using an Autoencoder-based approach and facilitates exploration of a database of floor plan images to retrieve similar images using a K-nearest neighbour classifier. We evaluated FLEXNet on images perturbed with rotation, scaling, and noise in a publicly available dataset. FLEXNet outperforms state-of-the-art methods with an 85% mean average accuracy.
Temporal Consistent Oil Painting Video Stylization

The automatic rendering of oil painting style video has great artistic and commercial application value. Temporal consistency is the bottleneck of video rendering. However, existing translation methods are either designed for images, or have high training/inference costs on videos due to the estimation of optical flows. This paper explores how to render videos in oil painting styles without video training data. We adopt a motion-based regularization in the training phase and a feature statistics sharing strategy in the inference phase. Experiments show that our model can render vivid and temporally smooth oil painting videos.
Private Eye: On the Limits of Textual Screen Peeking via Eyeglass Reflections in Video Conferencing

Personal video conferencing has become a new norm after COVID-19 caused a seismic shift from in-person meetings and phone calls to video conferencing for daily communications and sensitive business. Video leaks participants’ on-screen information because eyeglasses and other reflective objects unwittingly expose partial screen contents. Using mathematical modeling and human subjects experiments, this research explores the extent to which emerging webcams might leak recognizable textual and graphical information gleaming from eyeglass reflections captured by webcams. The primary goal of our work is to measure, compute, and predict the factors, limits, and thresholds of recognizability as webcam technology evolves in the future. Our work explores and characterizes the viable threat models based on optical attacks using multi-frame super resolution techniques on sequences of video frames. Our models and experimental results in a controlled lab setting show it is possible to reconstruct and recognize with over 75% accuracy on-screen texts that have heights as small as 10 mm with a 720p webcam. We further apply this threat model to web textual contents with varying attacker capabilities to find thresholds at which text becomes recognizable. Our user study with 20 participants suggests present-day 720p webcams are sufficient for adversaries to reconstruct textual content on big-font websites. Our models further show that the evolution towards 4K cameras will tip the threshold of text leakage to reconstruction of most header texts on popular websites. Besides textual targets, a case study on recognizing a closed-world dataset of Alexa top 100 websites with 720p webcams shows a maximum recognition accuracy of 94% with 10 participants even without using machine-learning models. Our research proposes near-term mitigations including a software prototype that users can use to blur the eyeglass areas of their video streams. For possible long-term defenses, we advocate an individual reflection testing procedure to assess threats under various settings, and justify the importance of following the principle of least privilege for privacy-sensitive scenarios.
A Deep Learning Dual-Stream Framework for Fall Detection

There is a need to improve fall detection systems to better discriminate falls from regular daily activities. Multi-sensor systems were proposed for this purpose. However, using wearable sensors may not be practical for elderly people. The use of surveillance cameras and advanced computer vision algorithms is thus an attractive solution. Indeed, raw RGB frames are rich in information including the human body shape and motions during activities. Yet, several disturbing features extracted from the raw frames may prevent distinguishing between normal activities and fall. To address this issue, we here leverage the human skeleton, and the binary history of motion during the activity. The former was analyzed with a Convolution Long Short Memory (ConvLSTM) to include spatial features during sequence learning. The latter was processed with a lightweight network for feature extraction. The outputs from both streams were merged to discriminate between fall and normal activities. Using the UP Fall dataset, our model detected falls with a single camera with a test accuracy of 100%, an improvement of 0.01% over current state-of-the-art methods.
Fast VVC Intra Encoding for Video Coding for Machines

Traditional video coding technologies compress and reconstruct the video frames, which focus on human perception. However, video coding for machines (VCM) uses the feature stream to bridge the correlation between human perception and machine intelligence for vision tasks. We extract the features for the CU with different shapes with part of resnet architecture for VCM. However, the feature-based methods use the model to complete the forward process, which is very time-consuming for its complex architecture and parameter size. The CU architecture for the feature extraction further increases the operation times. A fast algorithm based on the Histogram of oriented gradient (H OG) is proposed for the video coding for machines with VVC intra to overcome the time-consuming problems while maintaining the performance for the vision tasks with codec. The correlation of the mode decision with the VCM performance is discussed to motivate the fast intra coding for V CM. Moreover, the VTM and VVenc are used to verify the universality of the proposed method. The proposed methods can speed up the fast encoding for 35.21 % time saving with 0.26 increment for AP50 for the cityscapes dataset compared with the VTM10.0.
Beyond Phish: Toward Detecting Fraudulent e-Commerce Websites at Scale

Despite recent advancements in malicious website detection and phishing mitigation, the security ecosystem has paid little attention to Fraudulent e-Commerce Websites (FCWs), such as fraudulent shopping websites, fake charities, and cryptocurrency scam websites. Even worse, there are no active large-scale mitigation systems or publicly available datasets for FCWs.In this paper, we first propose an efficient and automated approach to gather FCWs through crowdsourcing. We identify eight different types of non-phishing FCWs and derive key defining characteristics. Then, we find that anti-phishing mitigation systems, such as Google Safe Browsing, have a detection rate of just 0.46% on our dataset. We create a classifier, BEYOND PHISH, to identify FCWs using manually defined features based on our analysis. Validating BEYOND PHISH on never-before-seen (untrained and untested data) through a user study indicates that our system has a high detection rate and a low false positive rate of 98.34% and 1.34%, respectively. Lastly, we collaborated with a major Internet security company, Palo Alto Networks, as well as a major financial services provider, to evaluate our classifier on manually labeled real-world data. The model achieves a false positive rate of 2.46% and a 94.88% detection rate, showing potential for real-world defense against FCWs.
New versions Elliptic Curve Group Ring Public Key Cryptosystems

In this work, a group ring (GR), which is defined on an elliptic curve over a prime field, is used to create new versions of public key cryptosystems. First version is elliptic curve group ring ElGamal public key (ECGR-EPKC). On the ECGR- EPKC, a public key, ciphertext and decrypting a plaintext are computing in according to the scalar multiplication that defined on the ECGR over a prime field. Two cases have been considered on the proposed ECGR-EPKC. First case, with an order of ring is less than or equal to the elliptic curve group (ECG). Whereas, on second case, an order of a ring is greater than the order of ECG. Also, the hybrid algorithm of RSA and EPKC based on the ECGR is proposed. The security of the proposed versions, ECGR-EPKE and RSA-EPKC based on ECGR are determined. The proposed RSA-EPKC based on ECGR considers as a more secure for communication schemes in compare with the proposed ECGR-EPKC in two cases and with original algorithms of EPKC and RSA alone.
Efficient FPGA-Based Accelerator of the L-BFGS Algorithm for IoT Applications

The Internet of Things (IoT)-centric applications, such as augmented reality and self-driven cars, require real-time task processing, large bandwidth, and low data transmission latency. FPGA-based edge computing is considered an effective solution to tackle these challenges. As an excellent tool in these applications, nonlinear optimization methods involve computation-intensive and data-dependency operations leading to limited real-time applications. The limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) algorithm ranks among the most efficient algorithms for large-scale optimization problems. In this paper, we propose, for the first time, a high-parallel FPGA-based architecture for the two key parts of the L-BFGS algorithm: the search direction computation and line searching. Compared with the implementation on CPU, the search direction computation and line searching implementation on FPGA achieve <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{39.73}\times$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{5.50}\times$</tex> speedups, respectively. Compared with the straightforward implementation on GPU, the search direction computation on FPGA obtains a speedup of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{31.03}\times$</tex> .
Natural Outlier Rejection with Shepherd's Psychometric Similarity Metric

The human mind does not recognize absolute distances. Instead, it seeks comparisons based on similarity, often called psychometric metrics. While many psychometric metrics have been used in cognitive studies, they are seldom used for machine learning or neural computing studies. In this paper, we present a case of Shepherd's similarity metric that can be effective in naturally removing outliers in natural language classification problems. Natural language processing uses multiple cognitive regions of the human brain, investigations of which can help with developmental studies of the human mind. The proposed similarity metric can help understand the causal links of language processing, giving a sense of human mind functions. A comparison with other similarity metrics indicates that Shepherd's similarity shows unusual tolerance to noise changes and the ability to reject outliers naturally.
zk-BeSC: Confidential Blockchain Enabled Supply Chain Based on Polynomial Zero-Knowledge Proofs

Blockchain technology promises a disruptive enhancement in trust establishment among supply chain actors. In particular, transparency and traceability can significantly get improved by a blockchain based data exchange platform which provides an immutable source of truth driven by a multi-stakeholder consensus. This also helps authenticate the provenance of products which is an added value that can be monetized by the supply chain participants. Nonetheless, due to the transparent nature of the blockchain, privacy and confidentiality are still a major concern for industries. In this paper, we present zk-BeSC, a blockchain enabled supply chain scheme based on zero-knowledge proofs of polynomials. The scheme allows transactions between supply chain participants in a privacy preserving fashion. It also offers tamper-resistance and replication due to the intrinsic nature of the blockchain. ZK-BeSC is fully implemented using an ethereum testnet and a web3 application. The scheme performs efficient proofs and has reduced gas consumption during verification.
Defending Emotional Privacy with Adversarial Machine Learning for Social Good

Protecting the privacy of personal information, including emotions, is essential, and organizations must comply with relevant regulations to ensure privacy. Unfortunately, some organizations do not respect these regulations, or they lack transparency, leaving human privacy at risk. These privacy violations often occur when unauthorized organizations misuse machine learning (ML) technology, such as facial expression recognition (FER) systems. Therefore, researchers and practitioners must take action and use ML technology for social good to protect human privacy. One emerging research area that can help address privacy violations is the use of adversarial ML for social good. Evasion attacks, which are used to fool ML systems, can be repurposed to prevent misused ML technology, such as ML-based FER, from recognizing true emotions. By leveraging adversarial ML for social good, we can prevent organizations from violating human privacy by misusing ML technology, particularly FER systems, and protect individuals' personal and emotional privacy. In this work, we propose an approach called Chaining of Adversarial ML Attacks (CAA) to create a robust attack that fools misused technology and prevents it from detecting true emotions. To validate our proposed approach, we conduct extensive experiments using various evaluation metrics and baselines. Our results show that CAA significantly contributes to emotional privacy preservation, with the fool rate of emotions increasing proportionally to the chaining length. In our experiments, the fool rate increases by 48% in each subsequent chaining stage of the chaining targeted attacks (CTA) while keeping the perturbations imperceptible ($\epsilon = 0.0001$).
FPGA Implementation of High Speed 64-Bit Data Width True Random Number Generator using Clock Managers With Metastability

Currently, cybersecurity plays a crucial role in various fields to ensure secure data communication. True Random Number Generators (TRNGs) are crucial components for many critical security applications. While analog-based entropy sources are often used in digital-based solutions, there is a high demand for digital-based solutions, especially for Field Programmable Gate Array (FPGA)-based digital systems. A unique technique has been developed to simplify the design of TRNGs on FPGA devices. This technique utilizes the runtime capabilities of the hardware primitives provided by the Digital Clock Manager (DCM) to adjust the phase shift between two clock signals. An auto-tuning approach automatically adjusts the phase difference between the clock signals to force one or more flip-flops (FFs) to enter the metastability zone, which is used as a source of unpredictability in the system. Additionally, the fast carry-chain hardware primitive is uniquely used to further enhance the randomness of the generated bits. Lastly, a powerful on-chip post-processing strategy is employed to prevent any interference with the TRNG output. This work was implemented in verilog HDL, with 32 and 64 data width, and synthesized in Xilinx Zynq FPGA. The characteristics of the TRNG design were evaluated based on area, delay, and power consumption.
A side-channel resistant implementation of AES combining clock randomization with duplication

Deep learning transformed side-channel analysis and made many conventional countermeasures obsolete. This brings the need for more effective, deep learning-resistant defense mechanisms. We propose a method for protecting hardware implementations of cryptographic algorithms that combines clock randomization with duplication. The presented method ensures that the duplicated block generates algorithmic noise that is dependent on the input of the primary block and has a similar power profile. In addition, the duplicated block does not create any secret key-related leakage. We evaluate the presented method on the example of the Advanced Encryption Standard (AES) algorithm implemented in FPGA. Our experimental results show that the protected AES implementation is resistant to deep learning-based power analysis.
A Decentralized Data Sharing Model using Blockchain with Fine Grained Access Control

In recent years, the use of cloud services for data sharing poses a severe threat to the security and privacy of the data being shared over a public communication channel. The owners not only lose complete ownership of the data being uploaded to the cloud but the security and privacy of the data are also at stake. The interplanetary file system, IPFS has introduced a decentralized way to store data on a large scale, thereby overcoming the issue of a single point of failure and unavailability of data at all times. For added security, the use of attribute-based encryption before uploading the data on the IPFS provides an extra layer of security and confidentiality. ABE enables data owners to encrypt their data under a list of attributes with only the user possessing those attributes can decrypt the said file. The use of ABE provides fine-grained access control on the data being shared and provides the facility for easy updations of access rights. In this paper, we analyzed various data-sharing schemes based on Ethereum and IPFS and presented a scheme incorporating ciphertext policy, attribute-based encryption, CP-ABE along with Ethereum and smart contracts for enabling data storage and data sharing in the Internet of Medical Things, IoMT. The use of partial decryption also reduces the load on the resource-constrained IoT devices of a user. To further make the framework efficient and always available, the ability to access the file using a keyword search is also added to the framework. Experimental results prove that the proposed scheme uses less storage overhead as compared to some of the existing schemes thus reducing the computation time and cost too.