Reviewerly/exHarmony huggingface dataset:
FDCNet: Frontend-Backend Fusion Dilated Network Through Channel-Attention Mechanism

Crowd counting has attracted much attention in computer vision owing to its fundamental contribution in public security. But due to occlusions, perspective distortions, scale variations, and background interference it faces a great challenge. In this paper we propose a novel model to count crowds, named FDCNet: frontend-backend fusion dilated network through channel-attention mechanism. It merges the frontend feature map with the backend feature map, achieving a fusion of various scale features without additional branches or extra subtasks. The fusion is fed into the channel-attention block to optimize the procedure and to conduct feature recalibration to use global and spatial information. Furthermore, we utilize dilated layers to obtain a high-quality density map, and the SSIM-based loss function is added to compare the local correlation between the estimated density map and the ground truth. Our FDCNet is verified in four common datasets and gets a brilliant estimation.
Enhancing Graph-Based Keywords Extraction with Node Association

In this paper, we present an enhancing graph-based keywords extraction method with node association (GKENA), which strengths graph-based keywords extraction approaches by applying strong association rule mining and unifying three different node attributes into a single framework. Specifically, we regard one single document as a sequential transaction dataset, and apply an efficient algorithm to exploit closed frequent sets and the strong association rules are generated to represent the correlations among multiple terms for association graph construction. Each graph node represents combinations of two or more terms and three node attributes (i.e. graph structure, node semantics and associations) are unified to transfer extra node information into a Markov chain model to obtain the ranking. Besides, in order to avoid the semantic overlapping among top ranked candidate words, a trustworthy clustering algorithm is employed and the center word in each cluster is selected to construct the keywords sets. Our experiments on both Chinese and English datasets indicate that GKENA can boost the quality of keywords in graph-based keywords extraction techniques.
Two Is Enough – Bisequent Calculus for S5

We present a generalised sequent calculus based on the use of pairs of ordinary sequents called bisequents. It may be treated as the weakest kind of system in the rich family of systems operating on items being some collections of ordinary sequents. This family covers hypersequent and nested sequent calculi introduced for several non-classical logics. It seems that for many such logics, including some many-valued and modal ones, a reasonably modest generalization of standard sequents is sufficient. In this paper we provide a proof theoretic examination of S5 in the framework of bisequent calculus. Two versions of cut-free calculus are provided. The first version is more flexible for proof search but admits only indirect proof of cut elimination. The second version is syntactically more constrained but admits constructive proof of cut elimination. This result is extended to several versions of first-order S5.
A framework for the revision of large-scale image retrieval benchmarks

Content-based Image Retrieval (CBIR) has been studied over decades and starting from conventional local handcrafted methods to CNN-based methods many works have achieved the best performances in retrieval tasks using query expansion, average query expansion, and query fusion techniques. This work presents a novel approach to revisit the large-scale image retrieval benchmarks Oxford building and Paris building using the SIFT and CNN-based approach. In this paper, we have revised two image retrieval methods and combined the approaches for better performance on image retrieval tasks by describing the annotation errors that have not discussed earlier. The new extensive queries were added for each dataset, making it difficult for the retrieval query phase. VGG-16 network used and RootSIFT applied for feature extraction step whereas T-embedding and democratic aggregation applied on the local descriptors. Query expansion which is an extensive technique for retrieval accuracy is used to check the validation of the proposed pipeline, and our framework achieved the state-of-the-art in addressing the retrieval results compared to other CBIR methods.
A method of personal computer operation using Electrooculography signal

In recent years, the human- machine interaction technologies have been well-developed, and the Electrooculography (EOG) signal control is one of the breakthrough applications. In this study, the EOG is used to capture the trajectory of eye movement by measuring the weak voltage changes around the eyes. The movement signals of the eyeballs are obtained, and input to the personal computer for further processing. A simple and low-cost method based on ATmega32U4 chip is provided for people who are amyotrophic lateral sclerosis (ALS) or unable to operate computers physically.
Contourlet Transform Based Seismic Signal Denoising via Multi-scale Information Distillation Network

Recently, convolutional neural network (CNN) based models have achieved great success in many multimedia applications. Seismic signals as one kind of important multimedia resources are often interfered by noise in practice, which bring difficulties for utilizing these resources effectively. To this end, this paper presents a novel CNN architecture in the contourlet transform (CT) domain for seismic signal denoising. First of all, we propose multi-scale information distillation (MSID) module to fully exploit features from seismic signals. And then, a series of MSIDs are cascaded in a coarse-to-fine manner to restore the noisy seismic signals, which is named as deep multi-scale information distillation network (D-MSIDN). In addition, we propose to formulate the denoising problem as the prediction of CT coefficients, which is able to make D-MSIDN further remove noise and preserve richer structure details than that in spatial domain. We use synthetic seismic signals and public Society of Exploration Geophysicists (SEG) and European Association of Geoscientists & Engineers (EAGE) salt and overthrust seismic data to demonstrate the superior performance of the proposed method over other state-of-the-art methods. From some qualitative and quantitative results, we find that our method is capable of obtaining data with higher quality assessment and preserving much more useful signals than other methods. In particular, our denoising performances are more considerable for seismic data with lower signal-to-noise ratio.
Correlations between airborne and terrestrial gamma-ray spectrometric data in the bitumen deposit area, Ogun State, Nigeria

Radioelements deposit (uranium, thorium and potassium) were found to be influenced by geological bedrock compositions and their response varies from place to place. In this study, two radiometric surveys (ground and airborne) were carried out to investigate the possible relationship in the response of radioelements deposit to the geological composition of a bitumen rich area in Ogun State, Nigeria. Regression analysis was adopted to establish the empirical mathematical relation that exists between the two surveys. With this, the correlation between the two surveys was deduced and interpreted in conjunction with the geological map of the study area. Coefficient of variation was also utilized to assess the level of homogeneity of the surveys carried out. A linear empirical mathematical model was established for the two surveys. The results revealed the coefficient of correlation (r) of 0.66407, 0.70398 and 0.68792 for thorium, uranium and potassium respectively. The p-values obtained at 95% confidence level for the three radioelements also indicated that there exists a significant relationship (p-value < 0.05) in the two surveys carried out. The results obtained for uranium and potassium for the two surveys were found to be comparable while slight disparity exists in that of thorium. The disparity was attributed to environmental factors such as temperature, precipitation and soil moisture. Contour plot was used to depict the radioelements distribution across the study area. Uranium and thorium were found to be highly enhanced and formed the major dominance in the study area. The study shows that the responses of radioelement from the two surveys are statistically correlated and comparable irrespective of the geological composition and the time at which they were carried out.
The interface between homicide and the Internet. A classification

It has been argued that the Internet presents numerous new opportunities for crime, including homicide. So far, empirical scholarly research in this domain is rather limited. In order to discover how perpetrators have used the Internet in the homicides they have committed, we conducted an international newspaper search (2006–2017). Based on this analysis, we identify five distinct types of Internet use in homicides, namely Internet as an encyclopedia, Internet as a platform, Internet as a trigger, Internet as a market place, and Internet as a meeting place. Our findings indicate that these types do not significantly differ from key characteristics of homicides in general to the degree that they constitute unique forms of homicide. Rather, Internet-related homicides constitute a replication, or facilitation of already existing forms of homicide assisted through new technological means.
A Challenging Voice Dataset for Robotic Applications in Noisy Environments

Artificial Intelligence plays a fundamental role in the speech-based interaction between humans and machines in cognitive robotic systems. This is particularly true when dealing with very crowded environments, such as museums or fairs, where cognitive systems could be profitably used. The existing datasets “in the wild” are not sufficiently representative for this purposes, thus there is a growing need to make publicly available a more complex dataset for speaker recognition in extremely noisy conditions. In this paper, we propose the Speaker Recognition dataset in the Wild (SpReW), a novel and more challenging Italian audio database for speaker recognition tasks. Moreover, we report a quantitative evaluation of a novel CNN architecture for Speaker Identification tasks called SincNet, on the proposed dataset. SincNet has been chosen as a baseline architecture since it has obtained impressive results on widely used controlled datasets. Experimental results demonstrate the difficulties when dealing with very noisy test sets and few clearly acquired samples for training.
Technological approach to development of adaptive e-learning system

Adaptation of learning process for students with regard to their individual characteristics has currently become topical. The article proposes a technological approach to development of adaptive e-learning system, principles of its elaboration and description its structure and components. The scientific novelty of the study consists in structuring the educational content as a hierarchy of terms, original methods and algorithms of adapting learning material, and the learning outcomes assessment model which provides for the assessment of the level of subject competence development. The structure of the system which includes the educational content model, the user model, the adaptation model, and the learning outcomes assessment model is presented in the article. The proposed approach to development the educational content model is based on the integration and development of the methods of logical-andgnoseological concept analysis with graph theory methods. The adaptive elearning course of a mathematical discipline is developed and implemented into educational process. System of web-based teaching includes a set of organizational and pedagogical conditions contributing for student to acquire knowledge and experience, to develop the skills which result in developing competence required for his or her professional activities. Materials of this study are of interest for both teachers who develop electronic educational courses and managers of educational institutions interested in implementing such courses into educational process
Predictive Systems: The Game Rock-Paper-Scissors as an Example

In simple-decision-making scenarios such as in repeated two-person games human behavior is to some extend predictable. To investigate this research question, we focused on developing a system for the Rock-Paper-Scissor (RPS) game. Our approach included three steps: (i) To generate a large data-base of experimental data, (ii) to analyze the data to detect systematic patterns and deviations from rational behavior within the test persons, and (iii) to employ methods from machine learning to identify patterns and predict the next throw of the opponent. We identified as the best current approach a Gated-Reccurent-Unit using User Statistics, which is able to predict the next throw and hence win in about 50% of the cases, beating state-of-the-art approaches. Potentials and limitations of our approach are discussed.
Artificial Intelligence: Issues, Challenges, Opportunities and Threats

The world is experiencing a period of instability in a range of pillar institutions in the international system. These instabilities and unsustainable systems may have serious implications for humanity. Catastrophic physical phenomena are on the rise, lately and many say that this is due to human disrespect to the environment. Urgently valuable and sustainable solutions are needed. One scientific approach to address these challenging questions is Artificial Intelligence (AI). Theories of AI are reviewed. Machine learning (ML), Neural Networks (NN) and Deep Learning (DL) are briefly presented. Certain criticisms of AI and DL are carefully analyzed. A number of challenges and opportunities of AI are identified. The future of AI and potential threats of it are discussed. Artificial Intelligence (AI) and Deep Learning (DL) are relying mainly on data analysis without taking into consideration the human nature. Theories of Fuzzy Cognitive Maps (FCM) seem to provide a useful tool in developing new AI theories answering this problem.
Adversarial Training for Unknown Word Problems in Neural Machine Translation

Nearly all of the work in neural machine translation (NMT) is limited to a quite restricted vocabulary, crudely treating all other words the same as an &lt; unk &gt; symbol. For the translation of language with abundant morphology, unknown (UNK) words also come from the misunderstanding of the translation model to the morphological changes. In this study, we explore two ways to alleviate the UNK problem in NMT: a new generative adversarial network (added value constraints and semantic enhancement) and a preprocessing technique that mixes morphological noise. The training process is like a win-win game in which the players are three adversarial sub models (generator, filter, and discriminator). In this game, the filter is to emphasize the discriminator’s attention to the negative generations that contain noise and improve the training efficiency. Finally, the discriminator cannot easily discriminate the negative samples generated by the generator with filter and human translations. The experimental results show that the proposed method significantly improves over several strong baseline models across various language pairs and the newly emerged Mongolian-Chinese task is state-of-the-art.
Performance assessment of photovoltaic modules using improved threshold-based methods

Threshold-based methods are extensively applied for performance assessment of photovoltaic (PV) modules. When applying the conventional methods with fixed normal thresholds, the main problem is the low detection rate of relatively small power losses. As a result, taking the uncertainties in the PV data due to the dynamic environment into account when designing the normal thresholds is a real challenge. To address this issue, improved threshold-based methods for PV performance assessment based on probabilistic models are proposed in this paper. This is motivated by the availability of probabilistic models which allow to quantify the likely uncertainty in the deterministic point estimation. Two probabilistic models are developed using the quantile regression forests (QRF) method and the Bayesian regression (BR) method. The developed models provide confidence intervals for PV output efficiency which act as the normal thresholds for PV performance assessment. The proposed methods are tested on a grid-connected PV plant located in East China, and compared against the traditional methods based on deterministic models. They are also compared with an adaptive threshold-based method which updates the adaptive thresholds by a moving window. Results indicate that the proposed methods are able to identify outliers with 10% power losses almost twice as many as the ones detected by the other methods.
An RGB-D based Framework for Ambient Assistive Living Applications

The ageing population is putting stress on both public and private structures that provide services to this community. In order to ease this problem, assistive living systems can be deployed to support the elderly within their homes, providing them with a better quality of life while keeping healthcare costs low. In this paper we present a framework to monitor elderly activities using an RGB-D system. The solution uses depth information to determine the location of the elderly and report it to the cloud for later analysis. In turn relatives and carers can access the data to monitor the activity of the associated individual. Moreover, the system provides real-time alerts both to the elderly and the carers if abnormal behavior, such as a fall, is detected or no movement is registered for a long time span.
Application of 3D electronic speckle pattern interferometry for the analysis of thermal response in printed circuit boards

The present work reports the development of a method for measurement of sub-micrometer displacements due to the thermal response of printed circuit boards. The developed system employs Electronic Speckle Pattern Interferometry in three dimensions, obtaining three orthogonal displacements of the specimen’s surface from the resulting phase maps. Afterwards, the displacements are corrected, removing the rigid rotation of the physical support and the relative rotation between the two regions of interest is calculated. This method provides a very high-resolution displacement measurement tool, which is very important, for example, in the design phase of outdoor surveillance cameras to optimize image quality.
‘What Drives Commuter Behaviour?’: A Bayesian Clustering Approach for Understanding Opposing Behaviours in Social Surveys

Summary The city of Exeter, UK, is experiencing unprecedented growth, putting pressure on traffic infrastructure. As well as traffic network management, understanding and influencing commuter behaviour is important for reducing congestion. Information about current commuter behaviour has been gathered through a large on-line survey, and similar individuals have been grouped to explore distinct behaviour profiles to inform intervention design to reduce commuter congestion. Statistical analysis within societal applications benefit from incorporating available social scientist expert knowledge. Current clustering approaches for the analysis of social surveys assume that the number of groups and the within-group narratives are unknown a priori. Here, however, informed by valuable expert knowledge, we develop a novel Bayesian approach for creating a clear opposing transport mode group narrative within survey respondents, simplifying communication with project partners and the general public. Our methodology establishes groups characterizing opposing behaviours based on a key multinomial survey question by constraining parts of our prior judgement within a Bayesian finite mixture model. Drivers of group membership and within-group behavioural differences are modelled hierarchically by using further information from the survey. In applying the methodology we demonstrate how it can be used to understand the key drivers of opposing behaviours in any wider application.
ETHICS AND CYBERSECURITY ARE NOT MUTUALLY EXCLUSIVE

Ethics is probably the last topic that anybody thinks about when planning a cybersecurity response. Still, the organization’s honest commitment to the confidentiality, integrity and availability of its data depends entirely on how it views its ethical responsibilities. So, in many respects ethics underlies everything. To this point there has been no commonly accepted basis for establishing an ethical stance regarding cybersecurity. The CSEC2017 model provides that basis now. This article will outline the ethical factors related to cybersecurity as recommended by the model and discuss how they apply in the creation of a practical response.
Manhattan Distance Based Voronoi Partitioning for Efficient Multi-robot Coverage

In this paper we address the problem of area coverage using multiple cooperating robots. One of the main concerns of using multiple robots is of avoiding repetitive coverage apart from complete coverage of the given area. Partitioning the area to be covered into cells and allotting one each cell to each of the robots for coverage is a simple and elegant solution for this problem. However, the spacial partitioning may lead to additional problems leading to either incomplete coverage or coverage overlap near the partition boundary. We propose a manhattan distance based Voronoi partitioning scheme of $$2D\times 2D$$ gridded region, where D is the size of the robot footprint. We show that the proposed partitioning scheme completely eliminates coverage gaps and coverage overlap using illustrative results.
On t-revealing codes in binary Hamming spaces

In this paper, we introduce t-revealing codes in the binary Hamming space Fn. Let C⊆Fn be a code and denote by It(C;x) the set of elements of C which are within (Hamming) distance t from a word x∈Fn. A code C is t-revealing if the majority voting on the coordinates of the words in It(C;x) gives unambiguously x. These codes have applications, for instance, to the list decoding problem of the Levenshtein's channel model, where the decoder provides a list based on several different outputs of the channel with the same input, and to the information retrieval problem of the Yaakobi-Bruck model of associative memories. We give t-revealing codes which improve some of the key parameters for these applications compared to earlier code constructions.
Reactive behavioural adaptation of service compositions

Abstract We are assisting to a paradigmatic shift in developing Web applications since their components are often distributed and deployed as services among different organizations. Their logic is based on a set of actions that can be linked together by exploiting higher level languages more suitable to address the scale of the Web. On this multi‐organization scale, applications can be influenced by different context events generated by the environment where they run. Handling these events requires run‐time adaptations of the application's behaviour to react, properly and quickly, to changes. The paper addresses these needs by proposing a programming paradigm based on “autonomic service compositions,” ie, compositions that are able to self‐change their structure, according to a specific life cycle, to allow for the continuation of execution, even if unexpected events arise. The approach exploits autonomic computing and reasoning for taking decision on information collected during processes execution. Autonomic actions on composition structures are performed using Event Condition Action rules and a set of meta‐operations. The approach is detailed, analysed, and discussed with reference to some examples derived from a real‐world application.