Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Open Data: An Approach for Resolving Data Integrity and Transparency

Smart cities bring together technology, governments, and society to enable a smart economy, smart mobility, a smart environment, smart people, smart living, and smart governance. However, they come with many technological challenges due to the lack of an efficient and secure mechanism for open data. The gap between a government and its citizens of public data prevents society from evolving. Many current proposals face serious problems of integrity and security issues. In this paper, we design and implement a mechanism for secure storing and exchanging open data leveraging Hyperledger Fabric and IPFS (InterPlanetary File System). The proposed solution relies on two of the most interesting properties of blockchain which are immutability and decentralization to bring transparency and integrity to open data while ensuring its availability. The novel idea is to enable users to check the integrity of the data and retrieve all information about data-log without the help of any third party. As a result, it improves the trust among people who need to use data in other applications, also reduces the risk of making wrong decisions due to bad data. Moreover, the implementation results show that our solution is efficient for practical uses.
Convolutional neural network with contextualized word embedding for text classification

Text classification is a fundamental task in natural language processing. This task is widely concerned and applied. However, previous methods mainly use traditional static word embedding, but static word embedding could not deal with the problem of polysemy. For this reason, we propose to utilize contextualized BERT word embedding to effectively encode the input sequence and then use the temporal convolutional module which simply computes a 1-D convolution to extract high-level features, finally, the max-pooling layer retains the most critical features for text classification. We conduct experiments on six commonly used large-scale text categorization datasets, including sentiment analysis, problem classification and topic classification tasks. Due to the limitation of BERT processing long text, we propose an effective truncation method. Experimental results show that our proposed method outperforms previous methods.
Classification of Cancer Microscopic Images via Convolutional Neural Networks

This paper describes our approach for the classification of normal versus malignant cells in B-ALL white blood cancer microscopic images: ISBI 2019—classification of leukemic B-lymphoblast cells from normal B-lymphoid precursors from blood smear microscopic images. We leverage a state of the art convolutional neural network pretrained with the ImageNet dataset and applied several data augmentation and hyperparameters optimization strategies. Our method obtains an F1 score of 0.83 for the final test set in the competition.
Robust 3D Face Alignment with Efficient Fully Convolutional Neural Networks

3D face alignment from monocular images is a crucial process in computer vision with applications to face recognition, animation and other areas. However, most algorithms are designed for faces in small to medium poses (below 45$$^\circ $$), lacking the ability to align faces in large poses up to 90$$^\circ $$. At the same time, many methods are not efficient. The main challenge is that it is time consuming to determine the parameters accurately. In order to address this issue, this paper proposes a novel and efficient end-to-end 3D face alignment framework. We build an efficient and stable network model through Depthwise Separable Convolution and Densely Connected Convolutional, named MobDenseNet. Simultaneously, different loss functions are used to constrain 3D parameters based on 3D Morphable Model (3DMM) and 3D vertices. Experiments on the challenging AFLW, AFLW2000-3D databases show that our algorithm significantly improves the accuracy of 3D face alignment. Model parameters and complexity of the proposed method are also reduced significantly.
Incidental Memory Recall in Virtual Reality: An Empirical Investigation

Modern virtual reality technologies increase the immersive experience for users. Increased immersivity may affect how users perceive, internalize, and thus, recall information about the virtual environment and experience compared to traditional mediums, such as desktop simulations. One critical aspect of recalling information is incidental memory, which refers to the ability for people to recall information they were unaware was relevant for evaluation. This paper investigates the differences of incidental memory recall between (i) immersive virtual reality and (ii) traditional desktop systems. Fifty-two participants completed a series of tasks within identical virtual environments of the two systems and completed an incidental memory recall questionnaire testing their recall of elements within the virtual world. Results found no significant difference in incidental memory recall between immersive virtual reality and traditional desktop conditions. However, when controlling for gaming experience and gender effects significant differences between groups were found.
Zero-shot Domain Adaptation Based on Attribute Information

In this paper, we propose a novel domain adaptation method that can be applied without target data. We consider the situation where domain shift is caused by a prior change of a specific factor and assume that we know how the prior changes between source and target domains. We call this factor an attribute, and reformulate the domain adaptation problem to utilize the attribute prior instead of target data. In our method, the source data are reweighted with the sample-wise weight estimated by the attribute prior and the data themselves so that they are useful in the target domain. We theoretically reveal that our method provides more precise estimation of sample-wise transferability than a straightforward attribute-based reweighting approach. Experimental results with both toy datasets and benchmark datasets show that our method can perform well, though it does not use any target data.
Study of Named Entity Recognition methods in biomedical field

Natural Language Processing (NLP) is very important in modern data processing taking into consideration different sources, forms and purpose of data as well as information in different areas our industry, administration, public and private life. Our studies concern Natural Language Processing techniques in biomedical field. The increasing volume of information stored in medical health record databases both in natural language and in structured forms is creating increasing challenges for information retrieval (IR) technologies. The paper presents the comparison study of chosen Named Entity Recognition techniques for biomedical field.
A block-based image encryption scheme using cellular automata with authentication capability

Cellular automata (CA) are discrete dynamical system formed by a finite array of identical objects called cells. Each cell is endowed with a state which changes at every time-step depending on the states of its adjacent cells at previous time-steps. This feature provides confusion and therefore makes CA attractive in cryptography. This paper presents an authenticated image encryption algorithm based on CA. To accelerate the process, we divide the image into blocks and use a permutation algorithm to apply chaos on the blocks and then use CA-based algorithm to change the pixels. The authentication mechanism of our scheme, adjustable to the desired level, can detect slight tampering in the cipher image before full decryption. Existing image encryption schemes usually cannot provide parallel processing capability and high sensitivity to changes simultaneously. This study tries to overcome this drawback as well. We show that our proposal fulfills desired security properties including large key space and robustness against statistical, differential and chosen-plaintext attacks.
B-Human 2019 – Complex Team Play Under Natural Lighting Conditions

In the RoboCup Standard Platform League 2019, the team B-Human won the main competition and, together with Berlin United - Nao-Team Humboldt, the Mixed Team Competition. For being successful in such a competitive environment, many sophisticated solutions for a variety of robotics tasks need to be found and integrated in a reliable and efficient manner. In this paper, we focus on three aspects that we consider as crucial for this year’s success and that have not been published before: a system of neural networks for ball classification and position estimation, a new framework for behavior specification along with its application to passes and set plays, and a set of approaches for maintaining the reliability of our robot team throughout a game.
Literasi Informasi Melawan Hoaks Bidang Kesehatan di Komunitas Online

This study aims to describe the development of information literacy in online communities to fight hoaxes in the health sector. The research method used is qualitative content analysis. The results show the seven pillars of information literacy, which are identification, coverage, planning, information gathering, evaluation, management and presentation of information are built into the Indonesian Hoaxes group. Anyone can participate by asking questions, discussing, and clarifying hoaxes in online media. Administrator and community members have the same role to give each other information literacy so that they get the same understanding of the truth information.
On the Study of Automated Identification of Firearms Through Associated Striations

This paper presents possible application of image processing for automatic segmentation of striations and application of machine learning technique for pattern recognition and interpretation of those bullet striations. For identifying the bullet striations an automated technique is proposed. Experimentation was performed on four real test bullets of 5.56 mm calibre fired from two different firearms and two real bullets of 9 mm calibre fired from the same firearm. Experimental results show that it is possible to detect striations from microscopic images of bullets using image processing techniques. Shape features of these striations are computed whose patterns are analysed. Using K-nearest neighbour machine learning technique, we were able to classify firearms correctly with an average accuracy rate of around 84.67%.
Machine Unlearning

Once users have shared their data online, it is generally difficult for themto revoke access and ask for the data to be deleted. Machine learning (ML)exacerbates this problem because any model trained with said data may havememorized it, putting users at risk of a successful privacy attack exposingtheir information. Yet, having models unlearn is notoriously difficult. Weintroduce SISA training, a framework that expedites the unlearning process bystrategically limiting the influence of a data point in the training procedure.While our framework is applicable to any learning algorithm, it is designed toachieve the largest improvements for stateful algorithms like stochasticgradient descent for deep neural networks. SISA training reduces thecomputational overhead associated with unlearning, even in the worst-casesetting where unlearning requests are made uniformly across the training set.In some cases, the service provider may have a prior on the distribution ofunlearning requests that will be issued by users. We may take this prior intoaccount to partition and order data accordingly, and further decrease overheadfrom unlearning. Our evaluation spans several datasets from different domains,with corresponding motivations for unlearning. Under no distributionalassumptions, for simple learning tasks, we observe that SISA training improvestime to unlearn points from the Purchase dataset by 4.63x, and 2.45x for theSVHN dataset, over retraining from scratch. SISA training also provides aspeed-up of 1.36x in retraining for complex learning tasks such as ImageNetclassification; aided by transfer learning, this results in a small degradationin accuracy. Our work contributes to practical data governance in machineunlearning.
What Can Learned Intrinsic Rewards Capture

The objective of a reinforcement learning agent is to behave so as to maximise the sum of a suitable scalar function of state: the reward. These rewards are typically given and immutable. In this paper, we instead consider the proposition that the reward function itself can be a good locus of learned knowledge. To investigate this, we propose a scalable meta-gradient framework for learning useful intrinsic reward functions across multiple lifetimes of experience. Through several proof-of-concept experiments, we show that it is feasible to learn and capture knowledge about long-term exploration and exploitation into a reward function. Furthermore, we show that unlike policy transfer methods that capture how the agent should behave, the learned reward functions can generalise to other kinds of agents and to changes in the dynamics of the environment by capturing what the agent should strive to do.
Plan2Vec: Unsupervised Representation Learning by Latent Plans

In this paper we introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning. Plan2vec constructs a weighted graph on an image dataset using near-neighbor distances, and then extrapolates this local metric to a global embedding by distilling path-integral over planned path. When applied to control, plan2vec offers a way to learn goal-conditioned value estimates that are accurate over long horizons that is both compute and sample efficient. We demonstrate the effectiveness of plan2vec on one simulated and two challenging real-world image datasets. Experimental results show that plan2vec successfully amortizes the planning cost, enabling reactive planning that is linear in memory and computation complexity rather than exhaustive over the entire state space.
End to End Trainable Active Contours via Differentiable Rendering

We present an image segmentation method that iteratively evolves a polygon. At each iteration, the vertices of the polygon are displaced based on the local value of a 2D shift map that is inferred from the input image via an encoder-decoder architecture. The main training loss that is used is the difference between the polygon shape and the ground truth segmentation mask. The network employs a neural renderer to create the polygon from its vertices, making the process fully differentiable. We demonstrate that our method outperforms the state of the art segmentation networks and deep active contour solutions in a variety of benchmarks, including medical imaging and aerial images.
Securing APIs with Transport Layer Security (TLS)

Securing APIs with Transport Layer Security (TLS) is the most common form of protection we see in any API deployment. If you are new to TLS, please check Appendix C first, which explains TLS in detail and how it works. In securing APIs, we use TLS to secure or encrypt the communication—or protect the data in transit—and also we use TLS mutual authentication to make sure only the legitimate clients can access the APIs.
Chameleon: Adaptive Code Optimization For Expedited Deep Neural Network Compilation

Achieving faster execution with shorter compilation time can foster further diversity and innovation in neural networks. However, the current paradigm of executing neural networks either relies on hand-optimized libraries, traditional compilation heuristics, or very recently genetic algorithms and other stochastic methods. These methods suffer from frequent costly hardware measurements rendering them not only too time consuming but also suboptimal. As such, we devise a solution that can learn to quickly adapt to a previously unseen design space for code optimization, both accelerating the search and improving the output performance. This solution dubbed CHAMELEON leverages reinforcement learning whose solution takes fewer steps to converge, and develops an adaptive sampling algorithm that not only focuses on the costly samples (real hardware measurements) on representative points but also uses a domain knowledge inspired logic to improve the samples itself. Experimentation with real hardware shows that CHAMELEON provides 4.45×speed up in optimization time over AutoTVM, while also improving inference time of the modern deep networks by 5.6%.
Extreme Classification via Adversarial Softmax Approximation

Training a classifier over a large number of classes, known as 'extreme classification', has become a topic of major interest with applications in technology, science, and e-commerce. Traditional softmax regression induces a gradient cost proportional to the number of classes C, which often is prohibitively expensive. A popular scalable softmax approximation relies on uniform negative sampling, which suffers from slow convergence due a poor signal-to-noise ratio. In this paper, we propose a simple training method for drastically enhancing the gradient signal by drawing negative samples from an adversarial model that mimics the data distribution. Our contributions are three-fold: (i) an adversarial sampling mechanism that produces negative samples at a cost only logarithmic in C, thus still resulting in cheap gradient updates; (ii) a mathematical proof that this adversarial sampling minimizes the gradient variance while any bias due to non-uniform sampling can be removed; (iii) experimental results on large scale data sets that show a reduction of the training time by an order of magnitude relative to several competitive baselines.
DRMF: A Distributed Resource Management Framework for Industry 4.0 Environments

While smart factories are becoming widely recognized as a fundamental concept of Industry 4.0, their implementation has posed several challenges insofar that they generate, process, and exchange vast amounts of security critical and privacy sensitive data, which makes them attractive targets of attacks and unauthorized access. Security requirements in such scenario include integrity, confidentiality, traceability and notarization of exchanged data in the one hand plus access control, privacy and trust in the other one. In this context, we design a distributed resource management framework using the emerging smart contracts technology for Industry 4.0 applications and more specifically for smart factories environments. This last, named DRMF, utilizes three Ethereum smart contracts specifically a Governance Contract (GC), an Access Contract (AC) and a Lookup Contract (LC) that are respectively responsible for the registration of new joining entities as well as those requesting consensus partaking permissions, second the dynamic access authorization and third the mapping between the required services and contracts ensuring their management. Using the blockchain technology, this framework is expected to achieve distributed, flexible, verifiable and trustworthy access control in addition to a transparent, traceable and notarized resource usage and sharing. Results from a real implementation plus performance evaluation prove the proposed concept and demonstrate its feasibility.
Discerning the Threats in Cloud Computing Security

One of the Internet-based computing services is Cloud computing has attaining tremendous experience in this competitive world. It is broadly applied in most of the organisations in recent days for the reason is that it modifies the way of how the Information Technology (IT) of a firm is managed and organized. It gives various advantages to the businesses include continuous availability, unlimited storage, lower costs, least maintenance, service quality, scalability, easy utilization, simplicity, reliability, flexibility and elasticity etc. With the higher utilization of services of cloud computing create issues of security in cloud computing. Therefore this paper firstly describe the types and overview of cloud computing, later reviews the three common and important security threats of utilizing cloud and the papers shared the solution for this was observed. Security is one of the major critical aspects in cloud computing because of the sensitivity of user’s data.
Cryptosystem for Secure Data Transmission using Advance Encryption Standard (AES) and Steganography

Recently, the rate of data transfer over the internet globally has increased and this called for more data security as security of data is of great concern for individuals as well as business owners. Cryptography and steganography are two major key players for data security technique. Cryptography is use to perform encryption on the secrete message while steganography hides the secrete message in digital media, image in this regards. This paper employed these two techniques using Advanced Encryption Standard (AES) for the cryptography and Least Significant Bit (LSB) for the steganography. Combining the two algorithms ensured data integrity, data security, and flexibility. The changes in the secrete message carrier (Stego) is insignificant and is often not noticeable by the nicked eyes, thus this make the interception of the message often difficult by intruder.
Creation of a Nigerian Voice Corpus for Indigenous Speaker Recognition

Abstract One of the goals of Word Bank’s Identification for Development (ID4D) is the realization of robust digital identification systems as a means of sustainable development priority. ID4D’s most recent report shows about 1.1 billion of the world’s population are yet to be identified for development. Africa represents about half of that number while Nigeria represents about a quarter of Africa’s share. Biometrics is the state-of-the-art approach for identification using human behavioral and/or physiological digitally calibrated traits and one such trait is the voice. The backbone of biometric research is the database employed in the design of biometric systems. Although many voice databases are publicly available such as the THCHS-30 for Chinese and Microsoft Indian language Speech Corpus for Indians, none is currently publicly available or free for Nigerians. The creation of such an indigenous database (or corpus) can open doors to Nigerian automatic speaker recognition as well as for indigenous language, ethnicity, gender, age group and emotion classification amongst others. This work is a first step in the direction of creating a Nigerian Voice Corpus (NVC) to aid indigenous voice biometric research. A voice corpus of popular Nigerians was created by curation of audio samples of 14 women and 23 men from YouTube. The corpus contains 10 different samples of 5 seconds duration for each individual resulting in a total of 370 samples. The created corpus was used to carry out speaker recognition experiment by dividing the audio samples into 25ms non-overlapping frame durations. Silent frames were excluded using short-term spectral energy threshold for Voice Activity Detection (VAD). This was followed by extraction of Mel Frequency Cepstral Coefficient (MFCC) as descriptors to discriminate different speakers using Support Vector Machine (SVM) with median Gaussian function. An overall recognition accuracy of 93.24% was achieved demonstrating the feasibility and research potential in this direction.