Reviewerly/exHarmony huggingface dataset:
Sensing Data in the Home

Human interaction with computers is no longer clearly bounded and so our user expectations no longer fit the pragmatics of design. We are increasingly data subjects within a complex network of lifestyle devices that sense, monitor, and interpret our daily endeavours. When the form that these devices take belies their true nature, a series of social challenges emerge. With the drive to new markets, based solely upon constructing value from human data, we find ourselves in something of a design dilemma. How can we design socially sensitive ‘things’, and what are the implications arising from networking our private spaces? This paper presents an autoethnographic case study, of a smart toilet roll holder, intended to surface some of these issues. Such prototype technologies demonstrate that, without proper consideration, the level of resulting social disruption may stilt progress and stymie the development of emerging data markets.
Post-stitching Depth Adjustment for Stereoscopic Panorama

In general, the viewing experience of stereoscopic panoramas heavily relies on the accuracy of perceived depth information. However, most of existed stereoscopic panorama stitching methods always focus on the improvement, refinement, and correction during the image alignment or panorama stitching. The impairment to the depth information introduced by later steps is rarely under consideration. Thus, we propose one general post-stitching depth correction technique based on sparsely detected control points. The proposed depth correction can be divided into two steps. The global one aims at minimizing the average depth error by adjusting the relative poses of two panoramas. Then, the local step tries to relieve those regions-based depth issues with the morphing technique. For those regions that still suffer from visible depth error after the global correction, we select control points in those depth anomaly patches and utilize the Thin-Plate-Spline technique to warp those features into their target position. To demonstrate the robustness of the proposed depth adjustment technique, various experiments are conducted under different camera arrangements and scenarios. The result indicates that the stereoscopic panoramas after proposed depth adjustment could deliver more reasonable depth information and bring a better viewing experience.
A Framework for Artificially Intelligent Customized Voice Response System Design using Speech Synthesis Markup Language

The idea of integrating a voice response system to control IoT applications is creating a new dimension in human-computer interaction. Google Assistant, Apple's Siri, Microsoft's Cortana, Amazon Alexa are the key players in the market that provide generalized response services. These services are restricted to specific devices and OS platforms, and cannot be modified, as per user requirement. This work presents a low-cost, artificially intelligent voice response system driven by AWS (Amazon Web Services) IoT cloud platform and Raspberry Pi. We used Alexa voice service (AVS) in Raspberry Pi and a microphone for speech reception. AVS is an integral part of AWS IoT that enables our device to act as Alexa voice client. It receives a speech signal, recognizes and interprets the speech signal, and responds to the user, accordingly. This device will serve as Alexa voice client, as well as Eclipse Paho MQTT (Message Queuing Telemetry Transport), simultaneously. This low-cost, voice response system can replace Amazon Echo, Google Home, and Apple HomePod. Moreover, users can customize the response of their query using speech synthesis markup language (SSML) in AVS. This design successfully integrates various Platform as a Service (PAAS) services like, AWS Lambda, Alexa Voice Service (AVS), and AWS IoT.
On the Entropy Loss and Gap of Condensers

Many algorithms are proven to work under the assumption that they have access to a source of random, uniformly distributed bits. However, in practice, sources of randomness are often imperfect, giving n random bits that have only k &lt; n min-entropy. The value n − k is called the entropy gap of the source. Randomness condensers are hash functions that hash any such source to a shorter source with reduced entropy gap g . The goal is to lose as little entropy as possible in this process. Condensers also have an error parameter ε and use a small seed of uniformly distributed bits whose length we desire to minimize as well. In this work, we study the exact dependencies between the different parameters of seeded randomness condensers. We obtain a non-explicit upper bound, showing the existence of condensers with entropy loss log (1+log 1/ε / g ) + O (1) and seed length log ( n − k / ε g ) + O (1). In particular, this implies the existence of condensers with O (log 1 / ε) entropy gap and constant entropy loss. This extends (with slightly improved parameters) the non-explicit upper bound for condensers presented in the work of Dodis et al. (2014), which gives condensers with entropy loss at least log log 1 / ε. We also give a non-explicit upper bound for lossless condensers, which have entropy gap g ≥ log 1 / ε / ε + O (1) and seed length log ( n − k / ε 2 g ) + O (1). Furthermore, we address an open question raised in (Dodis et al. 2014), where Dodis et al. showed an explicit construction of condensers with constant gap and O (log log 1/ ε) loss, using seed length O ( n log 1 / ε). In the same article they improve the seed length to O ( k log k ) and ask whether it can be further improved. In this work, we reduce the seed length of their construction to O (log ( n / ε)log ( k / &amp;epsiv)) by a simple concatenation. In the analysis, we use and prove a tight equivalence between condensers and extractors with multiplicative error. We note that a similar, but non-tight, equivalence was already proven by Dodis et al. (Dodis et al. 2014) using a weaker variant of extractors called unpredictability extractors . We also remark that this equivalence underlies the work of Ben-Aroya et al. (Ben-Aroya et al. 2016) and later work on explicit two-source extractors, and we believe it is interesting in its own right.
Exponential Error Bounds and Decoding Complexity for Block Codes Constructed by Unit Memory Trellis Codes of Branch Length Two

Performance of block codes constructed by unit memory (UM) trellis codes is discussed from random coding arguments. There are three methods to obtain block codes from trellis codes, i.e., those of (a) Tail Termination (TT), (b) Direct Truncation (DT), and (c) Tail Biting (TB). In this paper, we derive exponential error bounds and decoding complexity for block codes constructed by the UM trellis codes of branch length two based on the above three methods to uniformly discuss their performance. For the UM trellis codes of branch length two, the error exponent of the tail biting unit memory (TB-UM) trellis codes is shown to be larger than or equal to those of the ordinary block codes, the tail termination unit memory (TT-UM) and the direct truncation unit memory (DT-UM) trellis codes for all rates less than the capacity. Decoding complexity for the TB-UM trellis codes of branch length two exhibits interesting property since their trellis diagrams become simple. Taking into account of the asymptotic decoding complexity, the TB-UM trellis codes are also shown to have a smaller upper bound on the probability of decoding error compared to the ordinary block codes for the same rate with the same decoding complexity.
The Organization of Independent Work of Students in Conditions of Competence-Based Learning

The article reveals the essence of the competence approach to the organization of independent work of students at the University. The implementation of this model leads to an increase in the role and optimization of independent work as a leading component of professional training of the future specialist. The main directions of design and realization of independent work are allocated. The basic forms of the organization of independent activity of students are presented.
The analysis of source code plagiarism in basic programming course

The purpose of this research is to recognize the types of plagiarism that occurred in basic programming courses as a preliminary research in developing an intelligent system to detect source plagiarism in programming class. This research use a hierarchical clustering method which is implemented in term-document matrices. In this research, we propose the use of keywords in the programming language informing the term-document matrix. The result is normalized and then we use Euclidean distance to normalize data for similarity measurement. The result shows that this method is quite effective to classify objects of data indicated by plagiarism. Some types of plagiarism which are mostly occurred are formatting source code, changing comment, renaming identifier, and adding statements or redundant variables.
Unsupervised Person Re-identification Using Reliable and Soft Labels

In this paper, we propose unsupervised person re-identification (ReID) using reliable and soft labels. We provide unsupervised person ReID to consider unknown pedestrian queries. We update ResNet model for person ReID based on reliable and soft labels. First, we perform unsupervised clustering on person images under different cameras, and select samples based similarity between images and cluster centers. Then, we conduct re-clustering for the selected samples and assign labels to them, i.e. reliable labels. We get probability of the unselected samples, i.e. soft labels. Finally, we update ResNet model for person ReID using reliable and soft labels. Experiments on Market-1501 and DukeMTMC-ReID demonstrate that the proposed method outperforms state-of-the-arts for unsupervised person ReID in terms of the cosine distance and accuracy.
The Monitoring of Social Innovation Projects: An Integrated Approach

When the Municipality of Turin first decided to invest in social innovation, a public program and a network of partners were created, and a procedure to support social innovation start-ups was developed, and applied for the first time in 2014. After selection and funding of several young social entrepreneur projects, the Municipality activated a monitoring process. Different methodological approaches, including cognitive mapping, actor network analysis and multicriteria analysis, have been combined to analyse the behaviour of these start-ups and to evaluate whether they would address the social needs of their specific fields, and develop business projects as part of an inclusive and sustainable economy. Each element of this analysis has been proposed and discussed in relation to the monitoring and decision processes. The adopted multi-methodology and its results are here presented as a proposal for new models, metrics and methods for the social economy.
Multiscale Directional Fusion for Depth Map Super Resolution with Denoising

To tackle three main problems in depth map super resolution (SR) process, which are texture copy artifacts, blurred edge artifacts and jagged edge artifacts, we propose a depth map super resolution with denoising method based on multiscale directional fusion via nonsubsampled contourlet transform (NCST). We first transform low resolution depth maps of multiple views via NSCT. Then NSCT coefficients are denoised by a BayesShrink threshold in nonsubsampled directional filter banks (NSDFB) domain and fused by the max coefficient absolute value (mCAV) rule respectively within each scale and direction. Finally the fused coefficients are synthesized and upscaled to a high resolution depth map utilizing a modified edge-guided joint bilateral filter. Experimental results demonstrate that our method significantly outperforms the state-of-the-art super resolution algorithms quantitatively and visually while mitigating the corrupted noise.
Introducing of New Teaching Methods in Teaching Informatics

Information and communication technologies play an important role in university studies. Researching and measuring the success of the modular system applied in education was carried out in the fall term in the framework of IT education at the Faculty of Education. In this paper we analyse the current stage of the problem, provide a definition of information competence, the standards of information competence are listed, emphasis is put on key competences as the concept of curricula, defining the modular system and subject system in IT education, explaining their advantages and disadvantages. The aim of the paper is to show the importance of the expansion of teacher’s key competences, while also pointing out those other competences which are unavoidable in pedagogical work.
Adversarial Learning-based Data Augmentation for Rotation-robust Human Tracking

This paper analyzes the diversity deficiency of positive training samples used to fine-tune CNN-based tracking networks, especially when confronted with large pose changes and out-of-plane rotation challenges. Therefore, we present a novel adversarial learning-based hard positives generation method and embed it into the multi-domain network (MDNet)-based tracking framework. Instead of adopting the dense sampling strategy to generate monotonous positive samples, we cast it as a cross-domain image transformation problem, which is designed to be able to generate hard positive samples with more diversity and some degree of motion blur and pose direction changes. Experimental results on tracking benchmark demonstrate the effectiveness and robustness of our proposed method compared with the state-of-art trackers.
Pediatric brain extraction from T2‐weighted MR images using 3D dual frame U‐net and human connectome database

Abstract Accurate extraction of brain tissues from magnetic resonance (MR) images is important in neuroradiology. However, brain extraction is more difficult for pediatric brains than for adult brains due to several factors including smaller brain sizes and lower tissue contrasts. In this work, we propose a brain extraction technique that utilizes dual frame (DF) 3D U‐net deep learning architecture and the human connectome project (HCP) database for multislice 2D pediatric T2‐weighted MR images with diseases. To improve segmentation accuracy in small pediatric brains with detailed boundary regions, DF 3D U‐net architecture was used. We pretrained networks with the HCP database to compensate for the limited amount of MR images and manual segmentation masks of pediatric patients. For quantitative analysis, we compared the brain extraction results of brain extraction tool, DF, and conventional 3D U‐net using the dice similarity coefficient (DSC), intersection of union (IoU), and boundary F1 (BF) scores; each deep learning architecture was evaluated with and without pretraining using the HCP. This study included 10 patients with diseases and all images were acquired using a PROPELLER MR sequence. Pretraining using the HCP database enhanced segmentation performance of the network, and the skip connections in the DF 3D U‐net could enhance the contour similarity of segmentation results. Experimental results showed that the proposed method increased the DSC, IoU, and BF scores by 0.8%, 1.6%, and 1.5%, respectively, compared with those of the conventional 3D U‐net without pretraining.
The Design and Implementation of Secure Cloud Desktop System

PCs with Windows XP have been widely used in enterprises. Confidential data is stored on the single hard disk of the PC. There are some disadvantages in this way, such as Poor data reliability, Low data security and so on. So, it is urgent to innovate and change the traditional PC usage model. With mainstream virtualization technology and customized security design to build a secure cloud desktop, it shall meet users’ needs and enhance the reliability and security of the data.
Review on Recent Methods for Segmentation of Liver Using Computed Tomography and Magnetic Resonance Imaging Modalities

The span of modern medical imaging provides new and efficient techniques for segmentation of liver that are used by the clinicians to view in order to diagnose, monitor and treat liver diseases. Liver cancer is one of the most prominent diseases which cause death. Extraction of liver in different modalities is a difficult task because of its varying shape, similarity between organ intensities and variability in liver region intensities. In this review paper, a study has been carried out on liver segmentation in CT and MRI images with different methodologies and datasets. The observation has been made to highlight the merits, demerits and performance metrics of different works published.
Automating the Intentional Encoding of Human-Designable Markers

Recent work established that it is possible for human artists to encode information into hand-drawn markers, but it is difficult to do when simultaneously maintaining aesthetic quality. We present two methods for relieving the mental burden associated with encoding, while allowing an artist to draw as freely as possible. A 'Helper Overlay' guides the artist with real-time feedback indicating where visual features should be added or removed, and an 'Autocomplete Tool' directly adds necessary features to the drawing for the artist to touch up. Both methods are enabled by a two-part algorithm that uses a tree-search for finding 'major' changes and a dynamic programming method for finding the minimum number of 'minor' changes. A 24-person study demonstrates that a majority of participants prefer both tools over previous methods of manual encoding, with the Helper Overlay being the more popular of the two.
Retweeting Prediction Using Matrix Factorization with Binomial Distribution and Contextual Information

Retweeting provides an efficient way to expand information diffusion in social networks, and many methods have been proposed to model user's retweeting behaviors. However, most of existing works focus on devising an effective prediction method based on social network data, and few research studies explore the data characteristic of retweeting behaviors which is typical binary discrete distribution and sparse data. To this end, we propose two novel retweeting prediction models, named Binomial Retweet Matrix Factorization (BRMF) and Context-aware Binomial Retweet Matrix Factorization (CBRMF). The two proposed models assume that retweetings are from binomial distributions instead of normal distributions given the factor vectors of users and messages, and then predicts the unobserved retweetings under matrix factorization. To alleviate data sparsity and reduce noisy information, CBRMF first learns user community by using community detection method and message clustering by using short texts clustering algorithm from social contextual information on the basis of homophily assumption, respectively. Then CBRMF incorporates the impacts of homophily characteristics on users and messages as two regularization terms into BRMF to improve the prediction performance. We evaluate the proposed methods on two real-world social network datasets. The experimental results show BRMF achieves better the prediction accuracy than normal distributions based matrix factorization model, and CBRMF outperforms existing state-of-the-art comparison methods.
Filtering Email Addresses, Credit Card Numbers and Searching for Bitcoin Artifacts with the Autopsy Digital Forensics Software

Email addresses and credit card numbers found on digital forensic images are frequently an important asset in a forensic casework. However, the automatic harvesting of these data often yields many false positives. This paper presents the Forensic Enhanced Analysis (FEA) module for the Autopsy digital forensic software. FEA aims to eliminate false positives of email addresses and credit card numbers harvested by Autopsy, thus reducing the workload of the forensic examiner. FEA also harvests potential Bitcoin public addresses and private keys and validates them by looking into Bitcoin’s blockchain for the transactions linked to public addresses. FEA explores the report functionality of Autopsy and allows exports in CSV, HTML and XLS formats. Experimental results over four digital forensic images show that FEA eliminates as many as $$40\%$$ of email addresses and $$55\%$$ of credit card numbers.
The code for securing web applications

Web applications are becoming more and more prominent in our daily lives. A challenge that follows is protecting these applications from unauthorised users. This paper introduces a scheme for generating and verifying authentication codes, where these codes can be implemented to restrict the access to only the intended users of the web application. The scheme incorporates a changing key acquired from a secret key and a random number, a symmetrical block cipher and the Message Authentication Code system to produce multiple different authentication codes that are linked to each user’s personal identification. By using cryptography, this design embeds mathematical structure within the codes, making it superior to current database systems.
Robust Adaptive SOMs Challenges in a Varied Datasets Analytics

The advancement of available technology in use cause the production of huge amounts of data which need to be categorised within an acceptable time for end users and decision makers to be able to make use of the data contents. Present unsupervised algorithms are not capable to process huge amounts of generated data in a short time. This increases the challenges posed by storing, analyzing, recognizing patterns, reducing the dimensionality and processing Data. Self-Organizing Map (SOM) is a specialized clustering technique that has been used in a wide range of applications to solve different problems. Unfortunately, it suffers from slow convergence and high steady-state error. The work presented in this paper is based on the recently proposed modified SOM technique introducing a Robust Adaptive learning approach to the SOM (RA-SOM). RA-SOM helps to overcome many of the current drawbacks of the conventional SOM and is able to efficiently outperform the SOM in obtaining the winner neuron in a lower learning process time. To verify the improved performance of the RA-SOM, it was compared against the performance of other versions of the SOM algorithm, namely GF-SOM, PLSOM, and PLSOM2. The test results proved that the RA-SOM algorithm outperformed the conventional SOM and the other algorithms in terms of the convergence rate, Quantization Error (QE), Topology Error (TE) preserving map using datasets of different sizes. The results also showed that RA-SOM maintained an efficient performance on all the different types of datasets used, while the other algorithms a more inconsistent performance, which means that their performance could be data type-related.