Reviewerly/exHarmony huggingface dataset:
A Comprehensive Study of the Key Enumeration Problem

In this paper, we will study the key enumeration problem, which is connected to the key recovery problem posed in the cold boot attack setting. In this setting, an attacker with physical access to a computer may obtain noisy data of a cryptographic secret key of a cryptographic scheme from main memory via this data remanence attack. Therefore, the attacker would need a key-recovery algorithm to reconstruct the secret key from its noisy version. We will first describe this attack setting and then pose the problem of key recovery in a general way and establish a connection between the key recovery problem and the key enumeration problem. The latter problem has already been studied in the side-channel attack literature, where, for example, the attacker might procure scoring information for each byte of an Advanced Encryption Standard (AES) key from a side-channel attack and then want to efficiently enumerate and test a large number of complete 16-byte candidates until the correct key is found. After establishing such a connection between the key recovery problem and the key enumeration problem, we will present a comprehensive review of the most outstanding key enumeration algorithms to tackle the latter problem, for example, an optimal key enumeration algorithm (OKEA) and several nonoptimal key enumeration algorithms. Also, we will propose variants to some of them and make a comparison of them, highlighting their strengths and weaknesses.
TFCheck : A TensorFlow Library for Detecting Training Issues in Neural Network Programs

The increasing inclusion of Machine Learning (ML) models in safety-critical systems like autonomous cars have led to the development of multiple model-based ML testing techniques. One common denominator of these testing techniques is their assumption that training programs are adequate and bug-free. These techniques only focus on assessing the performance of the constructed model using manually labeled data or automatically generated data. However, their assumptions about the training program are not always true as training programs can contain inconsistencies and bugs. In this paper, we examine training issues in ML programs and propose a catalog of verification routines that can be used to detect the identified issues, automatically. We implemented the routines in a Tensorflow-based library named TFCheck. Using TFCheck, practitioners can detect the aforementioned issues automatically. To assess the effectiveness of TFCheck, we conducted a case study with real-world, mutants, and synthetic training programs. Results show that TFCheck can successfully detect training issues in ML code implementations.
A study on a low power optimization algorithm for an edge-AI device

Although research on the inference phase of edge artificial intelligence (AI) has made considerable improvement, the required training phase remains an unsolved problem. Neural network (NN) processing has two phases: inference and training. In the training phase, a NN incurs high calculation cost. The number of bits (bitwidth) in the training phase is several orders of magnitude larger than that in the inference phase. Training algorithms, optimized to software, are not appropriate for training hardware-oriented NNs. Therefore, we propose a new training algorithm for edge AI: backpropagation (BP) using a ternarized gradient. This ternarized backpropagation (TBP) provides a balance between calculation cost and performance. Empirical results demonstrate that in a two-class classification task, TBP works well in practice and compares favorably with 16-bit BP (Fixed-BP).
Symbol-triple distance of repeated-root constacyclic codes of prime power lengths

Let [Formula: see text] be an odd prime, [Formula: see text] and [Formula: see text] be positive integers and [Formula: see text] be a nonzero element of [Formula: see text]. The [Formula: see text]-constacyclic codes of length [Formula: see text] over [Formula: see text] are linearly ordered under set theoretic inclusion as ideals of the chain ring [Formula: see text]. Using this structure, the symbol-triple distances of all such [Formula: see text]-constacyclic codes are established in this paper. All maximum distance separable symbol-triple constacyclic codes of length [Formula: see text] are also determined as an application.
A comprehensive assessment of solar and wind energy potential at the University of Lethbridge campus, a medium-sized western Canadian university

Solar photovoltaic (PV) and wind turbine technologies play a significant role in the world energy future. However, lack of awareness of the potential and relative value of renewables is a significant challenge in sustainable energy development. The potential of solar and wind energy sources in producing electricity to meet the electrical demands of the University of Lethbridge has been evaluated. Utilizing Light Detection And Ranging (LiDAR) data and aerial photography to assess the solar photovoltaic electricity potential and wind turbine power curve concept to estimate the wind turbine electricity potential, a feasibility assessment for campus solar PV and wind turbine installations has been conducted. A comparison of the resulting solar PV and wind turbine generation over 5 years with the university electrical demand revealed that wind turbine and solar PV systems together could generate more than 3.6 times of the annual electricity consumption of the university on average. There is 1,015,808 m2 of suitable area available for placing a 46 MW solar PV system on the campus which would produce about 2.8 times of the university annual electricity usage on average. In addition, the proposed wind system could cover about 84% of the university electricity demand annually on average. Employing this system, the annual electricity cost of the university could decrease more than 90%. By installing renewable systems, the university can achieve a sustainable energy future.
PASCAL: Timing SCA Resistant Design and Verification Flow

A large number of crypto accelerators are being deployed with the widespread adoption of IoT. It is vitally important that these accelerators and other security hardware IPs are provably secure. Security is an extra functional requirement and hence many security verification tools are not mature. We propose an approach/flow - PASCAL - that works on RTL designs and discovers potential Timing Side Channel Attack (SCA) vulnerabilities in them. Based on information flow analysis, this is able to identify Timing Disparate Security Paths that could lead to information leakage. This flow also (automatically) eliminates the information leakage caused by the timing channel. The insertion of a lightweight Compensator Block as balancing or compliance FSM removes the timing channel with minimum modifications to the design with no impact on the clock cycle time or combinational delay of the critical path in the circuit.
An Experimental Study of Building User Profiles for Movie Recommender System

User preferences and interests are usually represented in user profile, which is a crucial part in recommender systems. The more accurate user profile we can build, the better recommendations we can provide for the user. However, challenges in building and representing user profile exist. To overcome these challenges, in this paper, we focus on building user profile and its representation. We compare the different approaches in building user profile and optimization its weights. The study is conducted through experimental work using a real-world dataset, in which top recommendations based on user interests are provided. In addition, we extend the dataset by adding more features with the purposes of comparing its effects on recommendation results. The results show that user profile features and their weights have a great impact on recommendation results. More features we know about an item, more accurate user profile based on user interests we can build. Furthermore, properly optimized weights of the item features can increase the accuracy of recommendations.
A New Approach to Double I/O Performance for Ceph Distributed File System in Cloud Computing

Block storage resources are essential in an Infrastructure-as-a-Service(IaaS) cloud computing system. It is used for storing virtual machines' images. It offers persistent storage service even the virtual machine is off. Distribute storage systems are used to provide block storage services in IaaS, such as Amazon EBS, Cinder, Ceph, Sheepdog. Ceph is widely used as the backend block storage service of OpenStack platform. It converts block devices into objects with the same size and saves them on the local file system. The performance of block devices provided by Ceph is only 30% of hard disks in many cases. One of the key issues that affect the performance of Ceph is the three replicas for fault tolerance. But our research finds that replicas are not the real reason slow down the performance. In this paper, we present a new approach to accelerate the IO operations. The experiment results show that by using our storage engine, Ceph can offer faster IO performance than the hard disk in most cases. Our new storage engine provides more than three times up than the original one.
Using Bidirectional Transformer-CRF for Spoken Language Understanding

Spoken Language Understanding (SLU) is a critical component in spoken dialogue systems. It is typically composed of two tasks: intent detection (ID) and slot filling (SF). Currently, most effective models carry out these two tasks jointly and often result in better performance than separate models. However, these models usually fail to model the interaction between intent and slots and ties these two tasks only by a joint loss function. In this paper, we propose a new model based on bidirectional Transformer and introduce a padding method, enabling intent and slots to interact with each other in an effective way. A CRF layer is further added to achieve global optimization. We conduct our experiments on benchmark ATIS and Snips datasets, and results show that our model achieves state-of-the-art on both tasks.
A Graph-Based Approach for Making Consensus-Based Decisions in Image Search and Person Re-Identification

Image matching and retrieval is the underlying problem in various directions of computer vision research, such as image search, biometrics, and person re-identification. The problem involves searching for the closest match to a query image in a database of images. This work presents a method for generating a consensus amongst multiple algorithms for image matching and retrieval. The proposed algorithm, Shortest Hamiltonian Path Estimation (SHaPE), maps the process of ranking candidates based on a set of scores to a graph-theoretic problem. This mapping is extended to incorporate results from multiple sets of scores obtained from different matching algorithms. The problem of consensus-based decision-making is solved by searching for a suitable path in the graph under specified constraints using a two-step process. First, a greedy algorithm is employed to generate an approximate solution. In the second step, the graph is extended and the problem is solved by applying Ant Colony Optimization. Experiments are performed for image search and person re-identification to illustrate the efficiency of SHaPE in image matching and retrieval. Although SHaPE is presented in the context of image retrieval, it can be applied, in general, to any problem involving the ranking of candidates based on multiple sets of scores.
Towards a Blockchain-Based Healthcare Information System : Invited Paper

Healthcare information systems are the next big application area for Blockchain technology. However, straightforward extensions of existing digital cryptocurrency systems such as Bitcoin and Ethereum results in systems that are unsuitable for the challenges posed by healthcare systems. In this paper, we propose an architecture for a blockchain-based healthcare information system in which block validation is performed through collective signatures initiated by a designated leader and executed by a pool of witnesses. Furthermore, we describe a smart-contract based approach that allows data owners to explicitly grant or revoke authorizations for other actors to access healthcare data. All accesses, successful or not, are recorded on the blockchain as separate transactions, thus ensuring transparency and privacy protection.
Cloud-Based Sentiment Analysis for Interactive Agents

Emotions play an important role in human-agent interaction. To realise natural interaction it is essential for an agent to be able to analyse the sentiment in users' utterances. Modern agents use a distributed service model in which their functions can be located on any number of computers including cloud-based servers. Outsourcing the speech recognition and sentiment analysis to a cloud service enables even simple agents to adapt their behaviour to the emotional state of their users. In this study we test whether sentiment analysis tools can accurately gauge sentiment in human-chatbot interaction. To that effect, we compare the quality of sentiment analysis obtained from three major suppliers of cloud-based sentiment analysis services (Microsoft, Amazon and Google). In addition, we compare their results with the leading lexicon-based software, as well as with human ratings. The results show that although the sentiment analysis tools agree moderately with each other, they do not correlate well with human ratings. While the cloud-based services would be an extremely useful tool for human-agent interaction, their current quality does not justify their usage in human-agent conversations.
An Evaluation of Discrete and Continuous Mid-Air Loop and Marking Menu Selection in Optical See-Through HMDs

This paper investigates discrete and continuous hand-drawn loops and marks in mid-air as a selection input for gesture-based menu systems on optical see-through head-mounted displays (OST HMDs). We explore two fundamental methods of providing menu selection: the marking menu and the loop menu, and a hybrid method which combines the two. The loop menu design uses a selection mechanism with loops to approximate directional selections in a menu system. We evaluate the merits of loop and marking menu selection in an experiment with two phases and report that 1) the loop-based selection mechanism provides smooth and effective interaction; 2) users prioritize accuracy and comfort over speed for mid-air gestures; 3) users can exploit the flexibility of a final hybrid marking/loop menu design; and, finally, 4) users tend to chunk gestures depending on the selection task and their level of familiarity with the menu layout.
Unbounded Recurrent Fuzzy Min-Max Neural Network for Pattern Classification

A prominent stumbling block in use of the original fuzzy min-max neural network (FMN) and other FMN-based algorithms is their sensitivity to the expansion coefficient or hyperbox expansion parameter. Specifically, these algorithms need to be trained with its different values in the range [0,1], and it is adjusted appropriately to obtain 100 percent accuracy for training data set with a minimum number of hyperboxes. Hence, these algorithms execute multiple passes over the training data for different values of the expansion coefficient. Moreover, usually, its value has to be retuned for the addition of new patterns or classes to the existing classifier. Consequently, as expected these algorithms do not learn in a single pass. This paper proposes an Unbounded Recurrent FMN (URFMN). In the URFMN, several modifications are proposed such as removal of the expansion coefficient parameter, membership functions for different types of hyperboxes, and metamorphosis from feed-forward to the recurrent topology with a novel learning algorithm which works in two phases- offline and online phase. Moreover, during the online phase, it allows the addition of new patterns or classes without the need for retraining. Hence it is an online adaptive algorithm. Its performance is evaluated using the benchmark data sets.
Chat with Smart Conversational Agents

There are more and more smart devices equipped with smart conversational agents, which can engage in chat or free conversation with human. However, the human-machine chat is still in the early stage of development, and there is a lack of effective methods to evaluate chat experience. In this study we proposed an approach to evaluate chat experience with smart conversational agents in smart home. We collected evaluation metrics and applied them in user testing, and then optimized the metrics and constructed an evaluation system. We applied the evaluation system to compare chat experience of five different smart conversational agents.
MVSE: Effort-Aware Heterogeneous Defect Prediction via Multiple-View Spectral Embedding

Cross-Project Defect Prediction (CPDP) predicts defects in a target project using the defect information of the external project. Existing CPDP methods assume that the data of two projects share identical features. When cross-project data contain heterogeneous features, traditional CPDP methods become ineffective. In this paper, we propose a novel approach called Multiple-View Spectral Embedding (MVSE) to address the heterogeneous CPDP issue. MVSE treats the cross-project data as two different views and exploits the spectral embedding method to map the heterogeneous feature sets into a consistent space where the two mapped feature sets have maximal similarity. To evaluate MVSE in the realistic setting, we employ an effort-aware performance indicator that considers the cost of inspection in the context of heterogeneous CPDP scenario. We have conducted extensive experiments to compare MVSE with two state-of-the-art heterogeneous CPDP methods and within-project setting. The experiments on 94 cross project pairs show that MVSE achieves promising results.
Video Summarization using CNN and Bidirectional LSTM by Utilizing Scene Boundary Detection

This paper proposes the summarization technique for the multimedia data present in the form of video, over the internet to provide a quick overview of the content present in it. This is very challenging task because finding the significant and useful portion of the video, needs to understand the content present in it. Moreover, the categories of the videos over the wide web are very diverse, like home videos, documentaries and sports videos etc. So, it makes video summarization more tough because of the unavailability of the prior knowledge. Currently, traditional hand crafted features have been utilized for video summarization, which fails to capture the information and content from all the scenes. To tackle this problem, we first find the scene boundaries using motion features. Then we pass the data to our proposed CNN architecture that gives us the frame level importance against each frame present in specific scene. The redundancy of the frames has been removed using the bidirectional LSTM. Experiments have been performed using the publically available dataset TVSUM50. Obtained results show that our proposed methodology outperforms the traditional feature based approaches in terms of relative F measure score.
HMM-based Speech Synthesis System incorporated with Language Identification for Low-resourced Languages

Text-to-speech (TTS) synthesis systems are of benefit towards learning new or foreign languages. These systems are currently available for various major languages but not available for low-resourced languages. Scarcity of these systems may lead to challenges in learning new languages specifically low-resourced languages. Development of language-specific systems like TTS and Language identification (LID) have an important task to address in mitigating the historical linguistic effects of discrimination and domination imposed onto low-resourced indigenous languages. This paper presents the development of a multi-language LID+TTS synthesis system that generate audio of input text using the predicted language in four South African languages, namely: Tshivenda, Sepedi, Xitsonga and IsiNdebele. On the front-end, is the LID module that detects language of the input text before the TTS synthesis module produces output audio. The LID module is trained on a 4 million words dataset resulted with 99% accuracy outperforming the state-of-the-art systems. A robust method for building TTS voices called hidden Markov model method is used to build new voices in the selected languages. The quality of the voices is measured using the mean opinion score and word error rate metrics that resulted with positive results on the understandability, naturalness, pleasantness, intelligibility and overall impression of the system of the newly created TTS voices. The system is available as a website service.
Entropy-Based Gradient Compression for Distributed Deep Learning

Nowadays, with the increasing amount of data and scale of network models, distributed deep learning, i.e., training a deep neural network using multiple workers distributed across different computing nodes, is becoming more and more popular. One of the major challenges of distributed deep learning lies in the frequent communication of gradients among workers, because it may cause severe bottlenecks, in terms of time latency and bandwidth. In this paper, we propose a novel approach named Entropy-based Gradient Compression (EGC) to reduce communication overhead. The major components of EGC are two algorithms: the entropy-based threshold selection algorithm and the automatic learning rate correction algorithm. To improve the accuracy, EGC also includes two commonly used algorithms: gradient residual and momentum correction. To evaluate the performance of EGC, we conduct experiments based on applications of image classification and language modeling using public datasets including Cifar10, Tiny ImageNet and Penn Treebank. The experiment results show that, compared with existing works, EGC can achieve a gradient compression ratio about 1000× while keeping the accuracy similar or even higher.
Becoming Cognitive Science

Cognitive science continues to make a compelling case for having a coherent, unique, and fundamental subject of inquiry: What is the nature of minds, where do they come from, and how do they work? Central to this inquiry is the notion of agents that have goals, one of which is their own persistence, who use dynamically constructed knowledge to act in the world to achieve those goals. An agentive perspective explains why a special class of systems have a cluster of co-occurring capacities that enable them to exhibit adaptive behavior in a complex environment: perception, attention, memory, representation, planning, and communication. As an intellectual endeavor, cognitive science may not have achieved a hard core of uncontested assumptions that Lakatos (1978) identifies as emblematic of a successful research program, but there are alternative conceptions according to which cognitive science has been successful. First, challenges of the early, core tenet of "Mind as Computation" have helped put cognitive science on a stronger foundation-one that incorporates relations between minds and their environments. Second, even if a full cross-disciplinary theoretic consensus is elusive, cognitive science can inspire distant, deep, and transformative connections between pairs of fields. To be intellectually vital, cognitive science need not resemble a traditional discipline with its associated insularity and unchallenged assumptions. Instead, there is strength and resilience in the diverse perspectives and methods that cognitive science assembles together. This interdisciplinary enterprise is fragile and perhaps inherently unstable, as the looming absorption of cognitive science into psychology shows. Still, for many researchers, the excitement and benefits of triangulating on the nature of minds by integrating diverse cases cannot be secured by a stable discipline with an uncontested core of assumptions.