Reviewerly/exHarmony huggingface dataset:
Realtime Optical Flow Estimation on Vein and Artery Ultrasound Sequences Based on Knowledge-Distillation

In this paper, we propose an approach for realtime optical flow estimation in ultrasound sequences of vein and arteries based on knowledge distillation. Knowledge distillation is a technique to train a faster, smaller model by learning from cues of larger models. Mobile devices with limited resources could be key in providing effective point-of-care healthcare and motivate the search of more lightweight solutions in the deep learning based image analysis. For ultrasound video analysis, motion correspondences of image contents (anatomies) have to be computed for temporal context and for real time application, fast solutions are required. We use a PWC-Net's [1] optical flow estimation output to create soft targets to train a PDD-Net [2] as lightweight optical flow estimator. We analyse the students' performance on the challenging task of fast segmentation propagation of vein and arteries in ultrasound images. Experiments show that even though we did not fine-tune the teachers on this task, a model trained with soft targets outperformed a model trained directly with labels and without a teacher.
Understanding Location Privacy of the Point-of-Interest Aggregate Data via Practical Attacks and Defenses

Location-based services have significantly affected mobile users’ everyday life, and location privacy has become essential. Some applications (e.g., location-based recommendation, mobility analytics) do not need the raw location data, and the service providers adopt aggregation to protect users’ location traces. However, some works show that even these aggregation data may disclose users’ location privacy when additional prior knowledge is available to an adversary. We consider the location privacy problem in the presence of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Location Uniqueness</i> , a property by which some geographical locations can be re-identified based on the aggregated point-of-interest information. We first study whether existing protection mechanisms are adequate for defending against this type of attack. Then we present two practical attacks for inferring users’ actual locations based on the POI aggregates. A secure POI aggregate release mechanism is proposed for defending against this type of re-identification attack and achieving differential privacy at the same time. We conduct extensive experiments on real-world datasets. The results show that the existing protection mechanisms cannot provide sufficient protection against location re-identification attacks. The proposed attacks can significantly improve the inference performance, and the proposed protection mechanism achieves satisfactory performance.
Bayesian Estimation of Block Covariance Matrices

We develop estimation methods for Bayesian models where the covariance matrix has a block structure. A block covariance matrix partitions the data into groups or blocks; variances and covariances are equal within blocks while covariances are equal across blocks. We derive the posterior and marginal likelihood for a Gaussian regression model when the number of blocks and the assignment of each series to a block are known. When the block structure is unknown, we build a random partition model which assigns a prior distribution over the space of partitions of the data into blocks. We develop Monte Carlo sampling algorithms that search model space over the different ways the covariance matrix can be partitioned into blocks.
The material consequences of “chipification”: The case of software-embedded cars

Today's modern car is an assemblage of mechanical and digital components, of metal panels that comprise its structure and silicon chips that run its functions. Communication and information studies scholars have interrogated the problematic aspects of the programs that run those functions, revealing serious issues surrounding privacy and security, worker surveillance, and racial, gendered, and class-based bias. This article contributes to that work by taking a step back and asking about the issues inherent not in the software running on these chips, but on the microchips themselves. Using the lens of “chipification”—the process by which a device is rendered capable of reading and processing data through the embedding of microchips—this article explores how the integration of microchips also involves processes that are often borne from, replicate, or create troubling power dynamics of their own. It takes light-duty passenger vehicles as a case study into how chipification is radically reshaping such processes as resource allocation, labor flows, and cultural practices around car manufacture, use, repair, and modification. By naming the process of chipification, this article allows researchers to identify and analyze the ways that integrating data processing capabilities into everyday devices is not a frictionless practice, but rather one that implicates a variety of power dynamics within these massive industries.
A DAG-Based Reputation Mechanism for Preventing Peer Disclosure in SIoV

The sensitive information of vehicles which is closely related to the safety of transportation makes the privacy problems in the vehicular networks a popular concern. The development of artificial intelligence (AI) has led the Internet of Vehicles (IoV) to the next phase of intelligence, the Social IoV (SIoV). For social purposes, vehicles may upload captured images with more sensitive information. As a result, the privacy problem is even more serious in SIoV. The exited studies of privacy-preserving methods in vehicular networks mainly consider the spontaneous privacy disclosure. However, the main privacy leakage in real life comes from peer disclosure rather than spontaneous privacy disclosure, which is usually ignored. Therefore, this article innovatively presents a decentralized scheme for solving the peer disclosure issues in SIoV, which, to the best of our knowledge, is the first research in SIoV peer disclosure discussion. A directed acyclic graph (DAG)-based mutual supervision (Dmsv) algorithm is designed for mutually distrustful vehicles. It is verified that our proposed algorithm reduces at least 72% multidimentional privacy loss of image entropy leakage probability when compared with nonpeer disclosure prevention. The decentralized scheme also achieves a relatively low delay of around 24 s from the transaction generation to a network-wide consensus. This article provides a feasibility of applying DAG in a mobile system and provides guidance on how the transportation situation influences the confirmation delay and how to adjust the incentive mechanism according to the transportation situation to maintain robustness.
Decentralized Edge Learning via Unreliable Device-to-Device Communications

Distributed machine learning has been extensively employed in wireless systems, which can leverage abundant data distributed over massive devices to collaboratively train a high-quality global model. The research efforts of recent works have focused on improving performance (e.g., communication efficiency, energy efficiency, and scalability) of centralized architectures, which include a number of distributed devices and a server. However, centralized architectures may cause congestion at the central node, which is not applicable under some circumstances. To tackle this issue, we introduce a decentralized edge learning framework over wireless networks via unreliable device-to-device (D2D) links and improve its learning performance. The unreliable transmission caused by the channel uncertainty has a negative effect on model convergence. To enhance the performance, we formulate an optimization problem to minimize the overall model deviation under a given latency requirement by jointly optimizing the broadcast data rate and bandwidth allocation. Then, the optimal solution of broadcast data rate is derived and an algorithm for obtaining the optimal bandwidth allocation is developed. Besides, we also propose a decentralized edge learning protocol without a central server and provide the convergence analysis. Finally, extensive simulations are conducted to demonstrate the performance advantages of our proposed algorithm compared against the baseline algorithm.
Development of Voice Control Algorithm for Robotic Wheelchair Using MIN and LSTM Models

In this work, we developed and implemented a voice control algorithm to steer smart robotic wheelchairs (SRW) using the neural network technique. This technique used a network in network (NIN) and long short-term memory (LSTM) structure integrated with a built-in voice recognition algorithm. An Android Smartphone application was designed and configured with the proposed method. A Wi-Fi hotspot was used to connect the software and hardware components of the system in an offline mode. To operate and guide SRW, the design technique proposed employing five voice commands (yes, no, left, right, no, and stop) via the Raspberry Pi and DC motors. Ten native Arabic speakers trained and validated an English speech corpus to determine the method's overall effectiveness. The design method of SRW was evaluated in both indoor and outdoor environments in order to determine its time response and performance. The results showed that the accuracy rate for the system reached 98.2% for the five-voice commends in classifying voices accurately. Another interesting finding from the real-time test was that the root-mean-square deviation (RMSD) for indoor/outdoor maneuvering nodes was 2.2*10–5 (for latitude), while that for longitude coordinates was a whopping 2.4*10–5 (for latitude).
Critical Comparison of Data Imputation Techniques at IoT Edge

The advances within the Internet of Things and sensor systems put the focus on the improvement of the data reliability as close to the edge as possible. This work investigates how well-established techniques can be used for the imputation of contaminated data. We look at the performance of four algorithms for different contamination rates and error bursts of variable length. Furthermore, the algorithms are also evaluated on a constrained environment to showcase the behavior of data imputation methods at the edge of an IoT-based system.
Augmented Semi-Supervised Learning for Salient Object Detection with Edge Computing

Salient object detection (SOD) from raw sensor images in the edge networks can effectively speed up the decisionmaking process in the complex environments, because it simulates the mechanism of human attention to identify salient objects from images.The success of supervised deep learning approaches have been widely proved SOD field.However, the imbalanced and limited training data at each edge device pose a huge challenge for us to deploy deep learning methods in the edge computing environments.In this article, we propose a cloud-edge distributed augmented semi-supervised learning architecture for SOD over the edge networks.The framework consists of two components: the base classification networks are employed in different edge nodes, and the reverse augmented network is employed in cloud.First, the base classification networks are trained with data from edge nodes while the reverse augmented network is trained with the whole data.Then, we concatenate each base classification network with reverse augmented network, thus the latter network can help the training of former network.Finally, we integrate the outputs of all base classification network to generate the pseudo-labels, which are used for semi-supervised learning of the augment network.We demonstrated a convincing performance of our semi-supervised learning framework on four bench-marked data-sets.These results show that our augmented semi-supervised learning framework can outperform other optimization strategies on deep learning for the edge computing.
Sign Language Recognition and Classification Model to Enhance Quality of Disabled People

Sign language recognition can be considered as an effective solution for disabled people to communicate with others. It helps them in conveying the intended information using sign languages without any challenges. Recent advancements in computer vision and image processing techniques can be leveraged to detect and classify the signs used by disabled people in an effective manner. Metaheuristic optimization algorithms can be designed in a manner such that it fine tunes the hyper parameters, used in Deep Learning (DL) models as the latter considerably impacts the classification results. With this motivation, the current study designs the Optimal Deep Transfer Learning Driven Sign Language Recognition and Classification (ODTL-SLRC) model for disabled people. The aim of the proposed ODTL-SLRC technique is to recognize and classify sign languages used by disabled people. The proposed ODTL-SLRC technique derives EfficientNet model to generate a collection of useful feature vectors. In addition, the hyper parameters involved in EfficientNet model are fine-tuned with the help of HGSO algorithm. Moreover, Bidirectional Long Short Term Memory (BiLSTM) technique is employed for sign language classification. The proposed ODTL-SLRC technique was experimentally validated using benchmark dataset and the results were inspected under several measures. The comparative analysis results established the superior performance of the proposed ODTL-SLRC technique over recent approaches in terms of efficiency.
Communication-Efficient and Byzantine-Robust Differentially Private Federated Learning

Federated learning, as a novel paradigm of machine learning, is facing a series of challenges such as efficiency, privacy and robustness. The recently proposed EF-DP- SIGNSGD provides theoretical privacy protection for SIGNSGD with majority vote but weakens the capability to resist Byzantine attacks to some extent. To overcome this shortcoming and further greatly improve the communication efficiency, a new method called PCS-DP- SIGNSGD is proposed via using parallel compressed sensing. Simulation and analysis demonstrate that compared with EF-DP- SIGNSGD, PCS-DP- SIGNSGD can match or even improve the accuracy and enjoy stronger Byzantine robustness with 50% to 80% improvement in the uplink communication efficiency.
Improved Differential-Linear Attack with Application to Round-Reduced Speck32/64

Since the differential-linear cryptanalysis was introduced by Langford and Hellman in 1994, there have been many works inheriting and developing this technique. It has been used to attack numerous ciphers, and in particular, sets the record for Serpent, ICEPOLE, Chaskey, 8-round AES, and so on. In CRYPTO 2020, Beierle et al. showed that the data complexity of differential-linear attack can be significantly reduced by generating enough right pairs artificially. In this paper, we manage to find the property in the differential propagation of modular addition. Based on this, we can select special bits to flip to produce right pairs in a certain differential-linear attack. For application, we focus on the differential-linear attack of the ARX cipher Speck32/64. With the differential-linear trail we concatenate, we construct 9-round and 10-round distinguishers with the correlation of $$2^{11.58}$$ and $$2^{14.58}$$ , respectively. Then we use enough flipped bits to reduce the complexity of the key recovery attack. As a result, we can use only $$2^{25}$$ chosen plaintexts to attack 14-round Speck32/64 with the time complexity of about $$2^{62}$$ , which has a slight improvement than before. To our best knowledge, this is the first differential-linear attack of the Speck family.
Research on the Detection of Causality for Textual Emotion-Cause Pair Based on BERT

The detection of textual Emotion-Cause Pair causality is very helpful for improving the accuracy of emotion-cause extraction and understanding the causes behind specific events. To solve the polysemy problem of static word vector representation in word2vec, an Emotion-Cause Pair causality discrimination model based on Bidirectional Encoder Representation from Transformers (BERT) is proposed. Firstly, each independent clause in the document is transformed into a word vector sequence by pretraining BERT, and the semantic representation of each independent clause is obtained by pooling. Secondly, the generated independent clause vector is used as the input of Bidirectional Long Short-Term Memory (BiLSTM) or SelfAttention, and then the deep semantic representation of the relevant independent clause context is obtained. Finally, the feature vectors extracted in the previous two stages are subjected to multi-target weighted fusion correction and input to the fully connected layer, and the maximum probability label sequence is calculated by the Softmax function to achieve causality detection. The experimental results show that compared with the baseline model, the Recall value of the proposed method is increased by 9.01%, and the F1 value is increased by 2.71%.
A 16-nm SoC for Noise-Robust Speech and NLP Edge AI Inference With Bayesian Sound Source Separation and Attention-Based DNNs

The proliferation of personal artificial intelligence (AI) -assistant technologies with speech-based conversational AI interfaces is driving the exponential growth in the consumer Internet of Things (IoT) market. As these technologies are being applied to keyword spotting (KWS), automatic speech recognition (ASR), natural language processing (NLP), and text-to-speech (TTS) applications, it is of paramount importance that they provide uncompromising performance for context learning in long sequences, which is a key benefit of the attention mechanism, and that they work seamlessly in polyphonic environments. In this work, we present a 25-mm2 system-on-chip (SoC) in 16-nm FinFET technology, codenamed SM6, which executes end-to-end speech-enhancing attention-based ASR and NLP workloads. The SoC includes: 1) FlexASR, a highly reconfigurable NLP inference processor optimized for whole-model acceleration of bidirectional attention-based sequence-to-sequence (seq2seq) deep neural networks (DNNs); 2) a Markov random field source separation engine (MSSE), a probabilistic graphical model accelerator for unsupervised inference via Gibbs sampling, used for sound source separation; 3) a dual-core Arm Cortex A53 CPU cluster, which provides on-demand single Instruction/multiple data (SIMD) fast fourier transform (FFT) processing and performs various application logic (e.g., expectation–maximization (EM) algorithm and 8-bit floating-point (FP8) quantization); and 4) an always-ON M0 subsystem for audio detection and power management. Measurement results demonstrate the efficiency ranges of 2.6–7.8 TFLOPs/W and 4.33–17.6 Gsamples/s/W for FlexASR and MSSE, respectively; MSSE denoising performance allowing 6 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> smaller ASR model to be stored on-chip with negligible accuracy loss; and 2.24-mJ energy consumption while achieving real-time throughput, end-to-end, and per-frame ASR latencies of 18 ms.
Affinity-Based Algorithmic Pricing: A Dilemma for EU Data Protection Law

The emergence of big data and machine learning has allowed sellers and online platforms to tailor pricing for customers in real-time, but as many legal scholars have pointed out, personalised pricing poses a threat to the fundamental values of privacy and non-discrimination, raising legal and ethical concerns. However, most of those studies neglect affinity-based algorithmic pricing, which may bypass the General Data Protection Regulation (GDPR). This paper evaluates current data protection law in Europe against online algorithmic pricing. The first contribution of the paper is to introduce and clarify the term “online algorithmic pricing” in the context of data protection legal studies, as well as a new taxonomy of online algorithmic pricing by processing the data types. In doing so, the paper finds that the legal nature of affinity data is hard to classify as personal data. Therefore, affinity-based algorithmic pricing is highly likely to circumvent the GDPR. The second contribution of the paper is that it points out that even though some types of online algorithmic pricing can be covered by the GDPR, the data rights provided by the GDPR struggle to provide substantial help. The key finding of this paper is that the GDPR fails to apply to affinity-based algorithmic pricing, but the latter still can lead to privacy invasion. Therefore, four potential resolutions are raised, relating to group privacy, the remit of data protection law, the ex-ante measures in data protection, and a more comprehensive regulatory approach.
A Standard Content for University Websites Using Heuristic Evaluation

With the rapid development of the internet, a growing number of educational websites, and the high demanding usage of the Word Wide Web (WWW) as an information-seeking platform, the importance of e-learning has also increased. Additionally, as more complex information is included in the university website, it also has become difficult to look for needed information. The need to look into university website usability and standard content are crucial to fully ensure the website function. This study aims to identify a standard university website content using heuristic evaluation. The strategies start from a review of the studies on standard university website contents and benchmarking against other universities. Then, a prototype of the university website will develop using the standard content obtained and finally evaluated on the usability. The results show that the About Us, Admission, and Contact Us section was identified as a standard and best practice for university website content. This study will help in designing standard website content for the University.
Virtual Machine Allocation Strategy Based on CloudSim

Cloud computing is a rapid developing computation methodology, which plays an important role in both research and industrial applications. One of the core problems for cloud computation is sharing resource by networks thus the allocation of resource is a significant issue for the Infrastructure as a Service (one of the layers of cloud stack). A highly efficient algorithm is demanded to maximize the utilization of computing and storage resources. The proposed project analyzes the simulation process of cloud computation tasks and the resource allocation strategies in CloudSim environment, in order to propose an optimized resource allocation strategy such that the resource allocation of hosts becomes more efficient.