Reviewerly/exHarmony huggingface dataset:
HeatSight: Wearable Low-power Omni Thermal Sensing

Thermal information surrounding a person is a rich source for understanding and identifying personal activities. Different daily activities naturally emit distinct thermal signatures from both the human body and surrounding objects; these signatures exhibit both spatial and temporal components as objects move and thermal energy dissipates, for example, when drinking a cold beverage or smoking a cigarette. We present HeatSight, a wearable system that captures the thermal environment of the wearer and uses machine learning to infer human activity from thermal, spatial, and temporal information in that environment. We achieve this by embedding five low-power thermal sensors in a pentahedron configuration which captures a wide view of the wearer’s body and the objects they interact with. We also design a battery life-saving mechanism that selectively powers only those sensors necessary for detection. With HeatSight, we unlock thermal as an egocentric modality for future interaction research.
Advances in Adaptive Skill Acquisition

Hierarchical Reinforcement Learning (HRL) represents a viable approach to learning complex tasks, especially those with an inner hierarchical structure. The HRL methods decompose the problem into a typically two-layered hierarchy. At the lower level, individual skills are created to solve specific non-trivial subtasks, such as locomotion primitives. The high-level agent can then use these skills as its actions, enabling it to tackle the overall task. The identification of an appropriate skill set, however, is a difficult problem by itself. Most current approaches solve it using a pre-training phase, in which skills are trained and fixed, before launching the training of the high-level agent. Having the skill set fixed prior to main training session can however impose flaws on the HRL system – especially if a useful skill was not successfully identified, and hence is missing from the skill set. Our Adaptive Skill Acquisition framework (ASA) aims specifically for these situations. It can be plugged onto existing HRL architectures and fix the defects within the pre-trained skill set. During the training of the high-level agent, ASA detects a missing skill, trains it, and integrates it into the existing system. In this paper, we present new improvements to the ASA framework, especially a new skill-training reward function, and support for skill-stopping functions enabling better integration. Furthermore, we extend our prior pilot tests into extensive experiments evaluating the functionality of ASA, in comparison to its theoretical boundaries. The source code of ASA is also available online (https://github.com/holasjuraj/asa).
Comparative Study of QoS Parameters for Different Web Services

Web service plays an important role in the new paradigm of distributed computing over the Internet. Web service allows to combine different publicly available services together to create new functionalities. Many public web services can be used at free of cost. Two important elements of web services are Web Services Description Language (WSDL) used for describing and locating web services and Simple Object Access Protocol (SOAP) used for communicating between applications via the Internet. The consumers of web services are always keen to differentiate between QoS-aware and QoS-unaware web services. Measuring QoS is the prerequisite for designing QoS-aware web services. Therefore, a number of metrics need to be identified. Because of the dynamic and unpredictable nature of the web services infrastructure, providing the acceptable QoS is really a challenging task. In this paper we have identified three dominating features–response time, reliability and security–that governs the QoS of a web service. The objective of this work is to carry out a comparative study between publicly available web services by measuring different metrics of QoS for these web services. Hence, using different open-source tools we propose a simplified approach to measure those QoS metrics for publicly available web services. Based on our approach, QoS-aware web services can easily be built from publicly available services.
Hybrid FPGA-CPU pupil tracker

An off-axis monocular pupil tracker designed for eventual integration in ophthalmoscopes for eye movement stabilization is described and demonstrated. The instrument consists of light-emitting diodes, a camera, a field-programmable gate array (FPGA) and a central processing unit (CPU). The raw camera image undergoes background subtraction, field-flattening, 1-dimensional low-pass filtering, thresholding and robust pupil edge detection on an FPGA pixel stream, followed by least-squares fitting of the pupil edge pixel coordinates to an ellipse in the CPU. Experimental data suggest that the proposed algorithms require raw images with a minimum of ∼32 gray levels to achieve sub-pixel pupil center accuracy. Tests with two different cameras operating at 575, 1250 and 5400 frames per second trained on a model pupil achieved 0.5-1.5 μm pupil center estimation precision with 0.6-2.1 ms combined image download, FPGA and CPU processing latency. Pupil tracking data from a fixating human subject show that the tracker operation only requires the adjustment of a single parameter, namely an image intensity threshold. The latency of the proposed pupil tracker is limited by camera download time (latency) and sensitivity (precision).
Contextualise Entities and Relations: An Interaction Method for Knowledge Graph Completion

The incompleteness of Knowledge Graph (KG) stimulates substantial research on knowledge graph completion, however, current state-of-the-art embedding based methods represent entities and relations in a semantic-separated manner, overlooking the interacted semantics between them. In this paper, we introduce a novel entity-relation interaction mechanism, which learns contextualised entity and relation representations with each other. We feature entity interaction embeddings by adopting a translation distance based method which projects entities into a relation-interacted semantic space, and we augment relation embeddings using a bi-linear projection. Built upon our interaction mechanism, we experiment our idea using two decoders, namely a simple Feed-forward based Interaction Model (FIM) and a Convolutional network based Interaction Model (CIM). Through extensive experiments conducted on three benchmark datasets, we demonstrate the advantages of our interaction mechanism, both of them achieving state-of-the-art performance consistently.
Testing the Resilience of Cryptographic Modules Against Fine-Grained Time- and Frequency-Domain EM Side-Channel Analysis Attacks

The ability of various countermeasures to secure cryptographic modules implementing the Advanced Encryption Standard (AES) algorithm is experimentally evaluated using fine-grained time- and frequency-domain electromagnetic side-channel analysis (EM SCA) attacks. Because an infeasibly large number of measurements of on-chip EM emanations should be required to break the cryptographic protection of a secure cryptosystem, a novel approach is used to keep acquisition costs low while testing the system resilience to EM SCA attacks. An adaptive scan protocol is used first to rapidly isolate optimal measurement configurations on an unsecured implementation. Then, the effectiveness of these optimal configurations are evaluated in the presence of countermeasures. The methodology is used to disclose the key from unsecured and secured FPGA implementations of AES-128. The most secure countermeasures are found to be >25× more resilient against the time-domain EM SCA attack and >20× more resilient against the frequency-domain EM SCA attack.
IA-CNN: A generalised interpretable convolutional neural network with attention mechanism

In recent years, convolutional neural network (CNN) has been widely used in security, autonomous driving, and healthcare. Even though CNN has achieved a great performance, the results produced by CNN are difficult to explain and sometimes irresponsible. The black-box nature of CNN makes it lack trust. In this paper, we propose an attention based CNN structure, named IA -CNN, which highly improves the interpretability of the CNN models. Each feature map of the last conv-layer only has one response (one key point) of the target object, which is directly connected to the output. We also combine the attention mechanism to weakly supervise the last conv-layer. In this way, our model can clearly show that which features the model extracted are the keys to the output prediction. Meanwhile, our IA-CNN structure can be used in various classical models with higher performance in the fine-grained classification and comparative performance in the ordinary classification task. Note that our IA-CNN structure is an end-to-end model, the last conv-layer of which can extract key points from images automatically and is connected to the output prediction linearly.
Libraries of Tajikistan: current state and development prospects

In the article, the author reveals the systems of libraries in the Republic: the number of libraries, the total amount of library collections in traditional and electronic media. Training and professional retraining of personnel, perfecting the librarians social image, scientific-research and scientific methodical work effectiveness’ growing are of great importance. The dominant role of the National Library of Tajikistan as a center for scientific research of the dynamics and prospects for the development of library and information technologies, advanced training and retraining of library workers in the Republic is observed. The article touches upon priority trends of the National Library development, gives characteristics of some long-term projects, such as “Creation of an electronic collection of book monuments of Tajik-Persian literature”, “Creation of an electronic collection of book monuments on the culture and art of the Tajik people”, aimed at forming the national segment of the electronic collection of book monuments of the Tajik people. Another important direction of its work on preserving and popularizing the historical and cultural heritage is the creation of historical and local history resources for the state public libraries of the Republic.
Mirroring Public Key Infrastructures to Blockchains for On-Chain Authentication

In blockchain systems, the lack of established identity management processes pose a problem for applications requiring smart contract owners to be authenticated. One issue that previously proposed solutions face is the accumulation of a critical mass of trusted data that makes the system usable. In this work, we propose an identity assertion and verification framework for Ethereum that overcomes this bootstrapping problem. It achieves this by leveraging TLS certificates, which are part of the established infrastructure that is commonly used for authenticating internet connections. We design and implement an TLS certificate-based authentication framework whose key features are the smart contract-based validation and storage of certificates and address-identity bindings. Looking at the current TLS ecosystem, we find that a large share of all domain certificates is issued by a small number of intermediate and root certificates. Therefore, we decide to store and maintain certificates in one smart contract to minimize processing costs. The evaluation of our prototype implementation shows that the associated cost of our system is within a feasible operating range, with the costs of submitting a new certificate currently averaging around $1.81 and the cost of creating an address-identity binding averaging around $1.32. Our system is a pragmatic and, most importantly, quickly bootstrapped method for an identity assertion and verification framework for Ethereum.
Conceptual Modeling Interacts with Machine Learning – A Systematic Literature Review

Due to the advancement in the digital world, society’s expectation towards Machine Learning is very high, especially in Conceptual Modeling. However, the relationship between Machine Learning and Conceptual models are very interesting. Literature in this field has identified the relationship and interaction between Machine Learning and Conceptual Models. However, to the best of our knowledge, there is not a Systematic Literature Review devoted to studying in deep the interaction of these two fields. In this paper, the authors conduct a Systematic Literature Review to get to know how Machine Learning is used in Conceptual Modeling. Results show the deep connection of Machine Learning with Conceptual Models in a solid way, as well as providing challenges and opportunities for future research.
Towards Searching Efficient and Accurate Neural Network Architectures in Binary Classification Problems

In recent years, deep neural networks have had great success in machine learning and pattern recognition. Architecture size for a neural network contributes significantly to the success of neural networks. In this study, we optimize the selection process by investigating different search algorithms to find a neural network architecture size that yields the highest accuracy. We apply binary search on a very well-defined binary classification network search space and compare the results to those of linear search. We also propose how to relax some of the assumptions regarding the data set so that our solution can be generalized to any binary classification problem. We report a 100-fold running time improvement over the naive linear search when we apply the binary search method to our data sets in order to find the best architecture candidate. By finding the optimal architecture for any binary classification problem quickly, we hope that our research contributes to discovering intelligent algorithms for optimizing architecture selection in machine learning.
Regional Aircraft Interiors Evaluation in a Real Time Ray-Traced Immersive Virtual Environment

This paper describes the Immersive VR Interiors Simulator developed by the VR Lab @ CIRA, the Italian Aerospace Research Centre, through which, leveraging the immersive approach and the first-person interaction, it was possible to carry out reachability, habitability and comfort tests of a new regional aircraft prototype.More than one seats configuration was loaded in the app preparation phase in order to setup a switch logic among them. In the same way, by simply pressing the buttons on his/her handheld controller, users could change internal and external lights conditions.The experimental activity was focused to the seating zone of the passengers’ cabin. Overall cabin assessment, with the user standing in the cabin (including navigation, seat row ingress/egress and embarkment/disembarkment) and with the user in a seating position (PSU and try table reachability and usability tests) were carried out in order to improve and optimize the features of the aircraft interiors.The protocol followed by CIRA for the human-in-the-loop subjective assessment of innovative design of regional aircraft interiors included two user questionnaires: the first one specific for the purposes of the core evaluation, the second one aiming at validating the user acceptance of VR, of the immersive approach, and of the specific VR application itself. In this paper, the results of a subjective test campaign made up of #24 testing subjects are presented.
Analysis and Design of CRM System for PT. Askrindo

Marketing is vital in the process of selling the company's products. Based on the interview results, the CRM system at PT Askrindo has several problems that include a lot of redundant data, the number of users who use the CRM system has not reached 100%, and there is a mismatch in CRM system reports with the financial information from the company. This research aims to identify problems, analyze needs, and design new prototype CRM websites to provide solutions to companies to solve the issues. The process of creating CRM is carried out by prototyping the model method. The new proposed system has core features related to marketing business processes in addition to key account structure. The proposed system separates roles in marketing and customers to fit the condition of the new marketing division structure. The result of the proposed CRM application shows a promising system in helping the marketing department in this company.
Extractive Summarization of Text Using Supervised and Unsupervised Techniques

The information has grown at a rapid pace in the recent past. The overwhelming amount of data available may be a lot for the consumers to absorb and retain. Various methods have been developed over the years to tackle the issue of over-burdening consumers. The research has led to the topic of text summarization. Text summarization is the method of producing a concise version of text by retaining the most relevant information. The paper focuses on the extractive summarization of single documents. Majority of the previous studies revolve around unsupervised methods while the supervised methods mainly comprise of models trained on the DUC datasets. The proposed work in this paper provides a comparison of supervised and unsupervised methods. The base of the supervised model is on a convolutional neural network (CNN) whereas the base of the unsupervised method is on K-Means clustering. The proposed model is used to evaluate the BBC News Summary dataset. The performance is a comparison with the previously proposed systems.
Attention-Based Multi-Filter Convolutional Neural Network for Inappropriate Speech Detection

Nowadays, there are numerous social platforms for people to discuss and share information. However, certain users may post contents that discriminate against specific groups or spread inappropriate behaviors. Therefore, it is desired to have a detection mechanism that is able to accurately identify these offensive comments and inappropriate messages before appearing on social platforms. To address this need, we propose a novel model called Attention-based Multi-filter Convolutional Neural Network (AMCNN) for inappropriate speech detection. Our model combines CNN and BiLSTM to acquire the advantages of the two. AM CNN leverages CNN filters of various kernel sizes for convolution operations to capture the relation between words of different lengths. The convolutional features are then used as the inputs of BiLSTM, which is able to obtain the bidirectional semantics via the forward and backward directions. AMCNN further incorporates the attention mechanism to highlight the important parts of the hidden layer output for better classification. Our model can be applied to various types and lengths of text, and it is also able to support different languages. To evaluate the model performance, we conduct extensive experiments on various real-world datasets. According to the experimental results, our model achieves the best performance, which is on average 12.63% higher than the state-of-the-art approach on the F1-score indicator. We also conduct ablation analysis, and the results show that the attention mechanism and the usage of BiLSTM can considerably improve the classification performance and effectively detect inappropriate speech in sentences.
An Adaptive Sharing Framework for Efficient Multi-source Shortest Path Computation

Many real-world applications need to know the distances between some or all pairs of vertices in a large graph, but the existing distributed single-source shortest path (SSSP) solution can not efficiently handle the problem of such multi-source computation. While, distributed multi-source shortest path (MSSP) has not attracted enough attention. This paper thereby proposes a distributed multi-source shortest path algorithm by scheduling source vertices in different batches and sharing graph traversal operations in the same batch for efficiency (B-MSSP), which effectively solves the performance bottleneck of MSSP on big graphs. To correctly run multi source vertices in the same batch, a message cluster mechanism is designed to effectively manage the communication. A further analysis reveals that the overall performance of B-MSSP is affected by the batch size. Thus, an adaptive cost-benefit model is proposed to quickly and easily select a proper batch size for better performance. Extensive experiments over lots of real graphs validate the efficiency and effectiveness of our proposals.
Learning-Based QoE Prediction and Optimization for Video Streaming

It is always the top priority for network service providers to provide better Quality of Experience (QoE) for users around the world. Huge efforts have been devoted to Adaptive Bit Rate (ABR) streaming, but it is only a passive QoE amelioration method by adapting to bandwidth variations. In this paper, we design a Hierarchical Navigable Small World (HNSW) query algorithm and a classify method to help the content provider improve the users' QoE under the amount of users' playback logs with different network-context features. To make a better prediction for users' QoE and QoE metrics, we also designed a Long Short-term Memory (LSTM) prediction algorithm which can help us make a more optimal changeable features selection by an accuracy prediction capacity. To evaluate the performances, the extensive network streaming experiments are carried out over a controllable network test-bed and real Internet trace. The results demonstrate that the proposed learning-based QoE prediction and optimization video streaming algorithm is able to improve the average video bitrate up to 12%, decrease the startup delay up to 28%, reduce the stall ratio up to 29%, and increase the QoE up to 17% compared to the default scheme, which is the widely used scheme for the most video content providers.
FAccTRec 2021: The 4th Workshop on Responsible Recommendation

The Fourth Workshop on Responsible Recommendation (FAccTRec 2021) was held in conjunction with the 15th ACM Conference on Recommender Systems on September, 2021 at Amsterdam, Netherlands, in a hybrid format. This workshop brought together researchers and practitioners to discuss several topics under the banner of social responsibility in recommender systems: fairness, accountability, transparency, privacy, and other ethical and social concerns. It served to advance research and discussion of these topics in the recommender systems space, and incubate ideas for future development and refinement.
Self Regulated Learning Mechanism for Data Efficient Knowledge Distillation

Existing methods for distillation do not efficiently utilize the training data. This work presents a novel approach to perform distillation using only a subset of the training data, making it more data-efficient. For this purpose, the training of the teacher model is modified to include self-regulation wherein a sample in the training set is used for updating model parameters in the backward pass either if it is misclassified or the model is not confident enough in its prediction. This modification restricts the participation of samples, unlike the conventional training method. The number of times a sample participates in the self-regulated training process is a measure of its significance towards the model's knowledge. The significance values are used to weigh the losses incurred on the corresponding samples in the distillation process. This method is named significance-based distillation. Two other methods are proposed for comparison where the student model learns by distillation and incorporating self-regulation as the teacher model, either utilizing the significance information computed during the teacher's training or not. These methods are named hybrid and regulated distillations, respectively. Experiments on benchmark datasets show that the proposed methods achieve similar performance as other state-of-the-art methods for knowledge distillation while utilizing a significantly less number of samples.