Reviewerly/exHarmony huggingface dataset:
BiS-KM: Enabling Any-Precision K-Means on FPGAs

K-Means is a popular clustering algorithm widely used and extensively studied in the literature. In this paper we explore the challenges and opportunities in using low precision input in conjunction with a standard K-Means algorithm as a way to improve the memory bandwidth utilization on hardware accelerators. Low precision input through quantization has become a standard technique in machine learning to reduce computational costs and memory traffic. When applied in FPGAs, several issues need to be addressed. First and foremost is the overhead of storing the data at different precision levels since, depending on the training objective, different levels of precision might be needed. Second, the FPGA design needs to accommodate varying precision without requiring reconfiguration. To address these concerns, we propose Bit-Serial K-Means (BiS-KM), a combination of a hybrid memory layout supporting data retrieval at any level of precision, a novel FPGA design based on bit-serial arithmetic, and a modified K-Means algorithm tailored to FPGAs. We have tested BiS-KM with various data sets and compared our design with a state-of-the-art FPGA accelerator. BiS-KM achieves an almost linear speedup as precision decreases, providing a more effective way to perform K-Means on FPGAs.
PAROT: Translating natural language to SPARQL

Abstract This paper provides a dependency based framework for converting natural language to SPARQL. We present a tool known as PAROT (which echos answers from ontologies) which is able to handle user’s queries that contain compound sentences, negation, scalar adjectives and numbered list. PAROT employs a number of dependency based heuristics to convert user’s queries to user’s triples. The user’s triples are then processed by the lexicon into ontology triples. It is these ontology triples that are used to construct SPARQL queries. From the experiments conducted, PAROT provides state of the art results.
Theoretical analysis of skip connections and batch normalization from generalization and optimization perspectives

Abstract Deep neural networks (DNNs) have the same structure as the neocognitron proposed in 1979 but have much better performance, which is because DNNs include many heuristic techniques such as pre-training, dropout, skip connections, batch normalization (BN), and stochastic depth. However, the reason why these techniques improve the performance is not fully understood. Recently, two tools for theoretical analyses have been proposed. One is to evaluate the generalization gap, defined as the difference between the expected loss and empirical loss, by calculating the algorithmic stability, and the other is to evaluate the convergence rate by calculating the eigenvalues of the Fisher information matrix of DNNs. This overview paper briefly introduces the tools and shows their usefulness by showing why the skip connections and BN improve the performance.
Investigating Neutralization Strategies in Digital Piracy: The Role of Content Preferences and Social Norms

Digital piracy is perceived as a victimless crime and therefore still challenges media industries. According to the neutralization theory, criminal behavior is more likely if individuals are able to justify it. As little is known about inhibitors and drivers of these cognitive processes, the current study investigated such preconditions by linking neutralization to social norms and content preferences (movies and series) in order to explain piracy attitudes and behavior. Results of an online survey which adopted a conjoint-task showed that attitudes toward digital piracy mediate neutralization effects on digital piracy behavior. Injunctive norms and the preference for independent movies foster neutralization effects.
A Novel Architecture for Early Detection of Negative Output Features in Deep Neural Network Accelerators

Deep Neural Networks (DNNs) perform billions of computations in applications such as image classification, object detection, and image segmentation. In most well-known DNNs, an activation function follows a convolutional or a fully connected layer. Several popular activation functions involve setting all negative inputs to zero. In this brief, we propose a novel architecture in which the activation function is merged with the prior computational layer. Our proposed dataflow can reduce the computations needed to generate a specific output feature in convolutional and fully connected layers by reordering the computation to achieve early detection of the sign of the output feature. In addition, our computation engine supports zero computation skipping, which further accelerates the layer computation. In this brief, we build on a state-of-the-art DNN accelerator and modify it to perform early detection of negative output features. When compared to the original design, our method achieves a speedup of ×2.19 and reduces energy consumption by ×1.94. The average reduction in the number of multiply-accumulate (MAC) operations is 10.64% and the average reduction in the number of the load operations is 3.86%. These improvements are achieved while maintaining classification accuracy in two popular benchmark networks.
Big Federal Data Centers Implementing FAIR Data Principles: ARM Data Center Example

Atmospheric Radiation Measurement (ARM) is a multi-laboratory/multi-institutional, US Department of Energy Office of Science National User Facility. ARM's data is currently hosted at the ARM Data Center (ADC) in Oak Ridge, Tennessee. The ADC holds more than 12,000 data products, with a total holding of more than 1.8 PB of data that dates back to 1992. This includes data from instruments, value-added products, model outputs, field campaigns, and principle investigator contributed data. In this paper, we discuss how big federal scientific data centers, such as ARM, that use modern and scalable architecture apply findable, accessible, interoperable, and reusable (FAIR) data principles to improve overall efficiency. These principles mainly emphasize machine-to-machine interactions that are directly applicable to ARM because of its data volume.
Stereo Camera and LIDAR Sensor Fusion-Based Collision Warning System for Autonomous Vehicles

This paper proposes a forward collision warning system for an autonomous vehicle based on a novel point to pixel multi-sensor data fusion algorithm which combines both the LIDAR 2D data and the image pixel data from the stereo camera to detect, classify and track the obstacles in front of the vehicle in real time. The LIDAR and stereo camera sensors were synchronized and calibrated, then obtained distance measurements from both the sensors were combined using Kalman filter algorithm performing multi-sensor data fusion in real time on an embedded platform. The region of interest (ROI) was selected from the camera image, and then the fused distance data was overlaid on top of the contour. Distance and angle of the target are obtained from the LIDAR, and target classification was performed by applying MobileNet SSD deep learning algorithm to camera data. The root mean squared error (RMSE) and mean absolute error (MAE) of the proposed fusion algorithm are 93.802 mm and 83.453 mm lower than the individual distance measurements from Stereo Camera and 2D LIDAR Sensor. Along with that uncertainty and variance of the fused measurements were also decreased.
Enhancing the security of AES through small scale confusion operations for data communication

With the advent of computers, networks, distributed systems and wireless data transfer technologies, data communication is subjected to potential threats and hence needs to be protected. Advanced Encryption Standard (AES) is an efficient encryption algorithm used for secure wireless communication, but its security is under threat due to different forms of theoretical and practical attacks such as linear cryptanalysis, differential cryptanalysis, boomerang attack, truncated differentials, hybrid attacks, related-key distinguishing attack, and side channel attacks. This work is focused on reducing the attacks by increasing the complexity of cryptanalysis through small scale variations in the shift row transformation and key expansion unit of the AES algorithm. The proposed small scale variations in the linear layer of the cipher unit and in the word column of key expansion unit enhances confusion during encryption. The inherent diffusion property of the algorithm along with the proposed confusion characteristics allows us to increase the overall security of the algorithm. The confusion and diffusion characteristics of the modified algorithm are found to be higher in terms of balance, Strict Avalanche Criterion and bit independence criteria. The AES variants are also tested using NIST-800-22 statistical test suite for random number generators, the small scale variations made in the architecture provides a higher degree of randomness in the ciphertext thus increasing cryptanalysis complexity. This paper also proposes an obfuscation control unit that allows on-demand selection of the three AES variants resulting in a variable encrypting pattern for a plaintext-key pair thus adding more bottlenecks to the adversaries.
Streaming Machine Learning Algorithms with Big Data Systems

Designing low latency applications that can process large volumes data with higher efficiency is a challenging problem. With the limited time to process data, usage of online algorithms are becoming important in the big-data applications. Stream processing is a well-known area that has been studied for a long time. In this research, our objective is to use state of the art big-data analytic engines to implement online algorithms and compare the strengths and weaknesses in each system. We use a streaming version of Support Vector Machines (SVM) and KMeans to do the analysis. Apache Flink, Apache Storm and Twister2 streaming frameworks are used to implement these algorithms. Our study focuses on the efficiency of online training of these algorithms and the results show higher performance in Twister2 framework for these algorithms.
Dynamic image reconstruction and synthesis framework using deep learning algorithm

This research study shows an effective deformable complex 3D image reconstruction and image synthesis technique by consolidating needed high-level features from a deep convolutional neural network (CNN) system. By recognising the inherent deep features in image patches lead to information discovery in medicinal imaging. Utilising the ADNI and LONI imaging datasets, the performance of the proposed deep learning algorithm image reconstruction and synthesis performance was verified. For validation, various performance indices obtained with the proposed deep learning algorithm were compared with two conventional algorithms namely support vector machine and CNN. Likewise, to reveal the adaptability of the proposed image reconstruction and synthesis system, synthesis and reconstruction experiments were directed on the 7 T cerebrum magnetic resonance image. As presented in the study outcomes, the proposed method can accomplish predominant performance compared with other cutting-edge techniques with either low- or high-level features in terms of the synthesis and reconstruction rate. The proposed algorithm has a training time of 5 s with a structural similarity index of 0.97. In all investigations, the outcome shows that the proposed image reconstruction framework reliably exhibited progressively precise outcomes when contrasted with best in class. Hence, it can be used for possible precise image reconstruction and synthesis related applications.
Putting women back in their place. Reflections on slut-shaming, the case Asia Argento and Twitter in Italy

Compared to racist and ethnicist discourses, literature on sexist discourses – both off and on-line – as hate speech is relatively underinvestigated. This is partly due to the tendency to minimise accusations of sexism and to reframe misogyny as ‘acceptable’ by constructing it as a form of humour. We decided to focus on slut-shaming, one of the most virulent forms of hate speech, which has always existed but was boosted by social media, becoming a stable low-cost ingredient of today’s rape culture. We propose to consider online slut-shaming as a form of ‘technology-facilitated sexual violence’, where digital technologies are used to facilitate both virtual and face-to-face sexually based harms. According to feminist analysis of sexual violence, this would be a matter of power rather than sex: sex would be the weapon, not the motive. We have tested this research hypothesis by focusing on the Italian reception of the MeToo campaign, triggered by Asia Argento’s denunciation. More specifically, two Different Twitter corpora produced within the same five months period were examined by means of a quantitative and a qualitative methodology.
Fast and Accurate Traffic Sign Recognition for Self Driving Cars using RetinaNet based Detector

Increase in the number of vehicles on road necessitates the use of automated systems for driver assistance. These systems form important components of self-driving vehicles also. Traffic Sign Recognition system is such an automated system which provides contextual awareness for the self-driving vehicle. CNN based methods like Faster R-CNN for object detection provide human level accuracy and real time performance and are proven successful in Traffic Sign Recognition systems [1]. Single stage detection systems such as YOLO [2] and SSD [3], despite offering state-of-the-art realtime detection speed, are not preferred for traffic sign detection problem due to its reduced accuracy and small object detection issues. RetinaNet has shown promising results with respect to accuracy and speed required for object detection problems. It uses Focal Loss [4] and Feature Pyramid Network (FPN) [5] for tackling the low accuracy and small object detection problems. In this paper, an approach for traffic sign recognition system for self-driving cars based on RetinaNet is presented with comparative analysis of its performance with Faster R-CNN based sign detector [1] and YOLOv3 [9] based detector. RetinaNet forms the traffic sign detection network and a CNN-based classifier forms the traffic sign class recognizer. The network training and evaluation are done using the German Traffic Sign Detection Benchmark (GTSDB) [6] dataset and the classifier performance is verified using German Traffic Sign Recognition Benchmark (GTSRB) [7] dataset.
Cognitive architecture for natural language comprehension

Human interactions with computers in natural language have always been a challenging task. Numerous computational systems are being designed to bring this interaction as close to the natural language commands as possible. Cognitive architectures are worthy solutions to solve this problem. In the present work, the authors have developed a cognitive architecture, which is capable of learning using natural language input. It can learn with minimal input data and has been designed using neural networks. The system is inspired by conventional cognitive models for working memory. They have proposed the system with a central executive unit controlling the short-term memory and long-term memory to comprehend the text input given as a natural language command and generate the output in natural language. The system comprises a feedback system as well to improve the learning process. The model has been trained using 1000 English sentences taught to primary school students. The system is capable of comprehending the relations between words, verbs, nouns, pronouns, adjectives, and generates logical reasoning-based answers.
Factoring RSA Keys in the IoT Era

RSA keys are at risk of compromise when using improper random number generation. Many weak keys can efficiently be discovered and subsequently compromised by finding reused prime factors in a large data set. We collect and analyze 75 million RSA certificates from the Internet, and find that 1 in 172 keys share a factor with another. In contrast, only 5 of 100 million certificates found in a sample from Certificate Transparency logs are compromised by the same technique. The discrepancy in rates of compromise is overwhelmingly due to IoT devices exposed to the Internet, which may be subject to design constraints and limited entropy. The widespread susceptibility of these IoT devices poses a potential risk to the public due to their presence in sensitive settings. We conclude that device manufacturers must ensure their devices have access to sufficient entropy and adhere to best practices in cryptography to protect consumers.
Recent Advances and Applications of Keystroke Dynamics

The increasing number of security risks has led to development of biometric security frameworks especially behavioral biometrics. Behavioral biometrics is used for surveillance purposes as well. Keystroke Dynamics is a type of behavioral biometrics which identifies operators by their typing patterns. Presently keystroke dynamics can be easily implemented as it does not require special hardware. It is an evolving research field and is also used in soft biometrics and emotion recognition. The core purpose of this paper is to present the state of art of keystroke dynamics to fellow researchers and to guide them towards future research direction in this area.
PYNQ- Torch: a framework to develop PyTorch accelerators on the PYNQ platform

Artificial intelligence based on deep learning has gained popularity in a broad range of applications. Software libraries and frameworks for deep learning provide developers with tools for fast deployment, hiding the algorithmic complexity for training and inference of large neural networks. These frameworks allow mitigating the computational complexity of such algorithms by interfacing parallel computing libraries for specific graphic processing units, which are not available on all platforms, especially if embedded. The framework we propose in this paper enables fast prototyping of custom hardware accelerators for deep learning. In particular we describe how to design, evaluate and deploy accelerators for PyTorch applications written in Python and running on PYNQ compatible platforms, which are based on Xilinx Zynq Systems on Chips. This approach does not require traditional ASIC-style design tools, but rather it simplifies the interfacing between hardware and software components of the neural network, which includes support for deployment on embedded platforms. As an example, we use the framework to design hardware accelerators for a computationally demanding sound synthesis algorithm based on a recurrent neural network.
Transcoding Resources Forecasting and Reservation for Crowdsourced Live Streaming

During the last decade, empowered by the technological advances of mobile devices and the revolution of wireless mobile network access, crowdsourced live streaming has become more popular. Ensuring a stable high-quality playback experience is necessary to maximize the number of viewers and profits for content providers. Additionally, because of the instability of network conditions and the heterogeneity of the end-users capabilities, transcoding the original video into multiple bitrates is required. Video transcoding is a computationally exhaustive process, where generally a single cloud instance needs to be reserved to produce one single video bitrate representation. On-demand renting of resources or inadequate resources pre-renting may cause delay of the video playback or serving the viewers with a lower quality. On the other hand, if resources provisioning is much higher than required, the extra resources will be wasted. In this paper, we introduce our resources reservation framework for geo-distributed cloud sites, to maximize the Quality of Experience (QoE) of viewers and minimize the cost to the content providers. First, we formulate an offline optimization problem to allocate transcoding resources at the viewers' proximity, while creating a trade off between the network cost and viewers QoE. Second, based on the optimizer resource allocation decisions on historical live videos, we create our time series datasets containing historical records of the optimal resources needed at each geo-distributed cloud site. Finally, we adopt machine learning to build our distributed time series forecasting models to proactively forecast the exact needed transcoding resources ahead of time at each geo-distributed cloud site.
Horizontal Attacks Against ECC: From Simulations to ASIC

In this paper we analyse the impact of different compile options on the success rate of side channel analysis attacks. We run horizontal differential side channel attacks against simulated power traces for the same kP design synthesized using two different compile options after synthesis and after layout. As we are interested in the effect on the produced ASIC we also run the same attack against measured power traces after manufacturing the ASIC. We found that the compile_ultra option reduces the success rate significantly from 5 key candidates with a correctness of between 75 and 90% down to 3 key candidates with a maximum success rate of 72% compared to the simple compile option. Also the success rate after layout shows a very high correlation with the one obtained attacking the measured power and electromagnetic traces, i.e. the simulations are a good indicator of the resistance of the ASIC.
Density-based outlier scoring on Kepler data

ABSTRACT In the present era of large-scale surveys, big data present new challenges to the discovery process for anomalous data. Such data can be indicative of systematic errors, extreme (or rare) forms of known phenomena, or most interestingly, truly novel phenomena that exhibit as-of-yet unobserved behaviours. In this work, we present an outlier scoring methodology to identify and characterize the most promising unusual sources to facilitate discoveries of such anomalous data. We have developed a data mining method based on k-nearest neighbour distance in feature space to efficiently identify the most anomalous light curves. We test variations of this method including using principal components of the feature space, removing select features, the effect of the choice of k, and scoring to subset samples. We evaluate the performance of our scoring on known object classes and find that our scoring consistently scores rare (&amp;lt;1000) object classes higher than common classes. We have applied scoring to all long cadence light curves of Quarters 1–17 of Kepler’s prime mission and present outlier scores for all 2.8 million light curves for the roughly 200k objects.
Multilevel symmetric pattern design and optimization for high-speed and high-accuracy 3D shape measurement

The binary defocusing technique has enabled speed breakthroughs for 3D shape measurement, yet simultaneously achieving high accuracy and high speed remains difficult. To overcome this limitation, we propose to utilize multilevel symmetric pattern for high-speed and high-accuracy 3D shape measurement. Compared to conventional binary patterns, multilevel pattern could bring more flexibility for eliminating undesired high-frequency harmonics, thus has the potential to greatly enhance the phase quality and measurement accuracy. In this paper, the symmetric pattern design principle and related optimization procedure are presented in order to find the “best” multilevel fringe patterns. Both simulation and experiments verify that with respect to conventional methods, the proposed method could consistently generate better fringe patterns for a wide range of fringe periods. Furthermore, we developed an absolute 3D shape measurement system with the speed of 667 Hz, verifying that the proposed method is applicable for high-speed, high-accuracy applications.