Reviewerly/exHarmony huggingface dataset:
A Fast and Energy Efficient Computing-in-Memory Architecture for Few-Shot Learning Applications

Among few-shot learning methods, prototypical networks (PNs) are one of the most popular approaches due to their excellent classification accuracies and network simplicity. Test examples are classified based on their distances from class prototypes. Despite the application-level advantages of PNs, the latency of transferring data from memory to compute units is much higher than the PN computation time. Thus, PNs performance is limited by memory bandwidth. Computing-in-memory addresses this bandwidth-bottleneck problem by bringing a subset of compute units closer to memory. In this work, we propose a CiM-PN framework that enables the computation of distance metrics and prototypes inside the memory. CiM-PN replaces the computationally intensive Euclidean distance metric by the CiM-friendly Manhattan distance metric. Additionally, prototypes are computed using an in-memory mean operation realized by accumulation and division by powers of two, which enables few-shot learning implementations where shots are powers of two. The CiM-PN hardware uses CMOS memory cells, as well as CMOS peripherals such as customized sense amplifiers, carry-look-ahead adders, in-place copy buffers and a logarithmic shifter. Compared with a GPU implementation, a CMOS-based CiM-PN achieves speedups of 2808x/111x and energy savings of 2372x/5170x at iso-accuracy for the prototype and nearest-neighbor computation, respectively, and over 2x end-to-end speedup and energy improvements. We also gain 3--14% accuracy improvement when compared to existing non-GPU hardware approaches due to the floating-point CiM operations.
A study of deep learning methods for same-genre and cross-genre author profiling

The aim of the author profiling task is to automatically predict various traits of an author (e.g. age, gender, etc.) from written text. The problem of author profiling has been mainly treated as a supervised text classification task. Initially, traditional machine learning algorithms were used by the researchers to address the problem of author profiling. However, in recent years, deep learning has emerged as a state-of-the-art method for a range of classification problems related to image, audio, video, and text. No previous study has carried out a detailed comparison of deep learning methods to identify which method(s) are most suitable for same-genre and cross-genre author profiling. To fulfill this gap, the main aim of this study is to carry out an in-depth and detailed comparison of state-of-the-art deep learning methods, i.e. CNN, Bi-LSTM, GRU, and CRNN along with proposed ensemble methods, on four PAN Author Profiling corpora. PAN 2015 corpus, PAN 2017 corpus and PAN 2018 Author Profiling corpus were used for same-genre author profiling whereas PAN 2016 Author Profiling corpus was used for cross-genre author profiling. Our extensive experimentation showed that for same-genre author profiling, our proposed ensemble methods produced best results for gender identification task whereas CNN model performed best for age identification task. For cross-genre author profiling, the GRU model outperformed all other approaches for both age and gender.
Does Gender and Accent of Voice Matter?

We explore the impact of the gender and accent of the voice recording in Interactive Voice Response (IVR) systems in low-literate and patriarchal contexts. We conducted a small randomized control trial (RCT) with 62 participants, prompting them to identify myths and factual statements from a list of 10 prompts. One of four sets of recordings were randomized for each participant: Male formal (MF), Male Informal (MI), Female Formal (FF), and Female Informal (FI). We found that (a) male participants found male voices as providing more accurate information, (b) formal voice made myths seem accurate to male participants, (c) there was a significant impact of participant education level on correctly identifying myths, and (d) female participants were more knowledgeable about maternal health facts. Our study provides some basic guidelines on the potential characteristics of the voice used in IVR systems when deployment is in low-literate, and patriarchal communities.
Delay Sensitive Large-scale Parked Vehicular Computing via Software Defined Blockchain

To utilize the potential commutating resources of parked vehicles (PVs) in the large parking lot, we design a large-scale parked vehicular computing system via software defined blockchain. However, the parking time for PVs is uncertain and some computational services have delay requirements. Therefore, in this paper, we propose a delay-sensitive joint blockchain parameters and resource optimization framework including block size and block generation time, as well as the offloading strategy and computing frequency adjustment. Such a design causes the problem to be highly coupled and non-convex, for which we use an alternating optimization (AO) strategy and perform multiple transformations to ensure convexity. Finally, the simulation results show the effectiveness of the proposed scheme.
Planning and Designing UPI Science and Techno Park as a Green Campus Center in Universitas Pendidikan Indonesia

Abstract Universitas Pendidikan Indonesia (UPI) plans Science and Techno Park to improve UPI’s ranking in the Green Campus Ranking in the world. Currently, UPI is ranked 44th in Indonesia, or equivalent to 100 in Asia (1000 in the world). So, to improve UPI’s ranking, an area is needed to develop research in the Green Campus field. The developed Science and Techno Park have a research focus in the fields of infrastructure, energy, waste, water, and transportation management. UPI Science and Techno Park use the High Technology Architecture Theme. So, Science and Techno Park can support the UPI area to become Green Campus based on criteria issued by Green Metric UI. Science and Techno Park UPI facilitates research collaboration with outside companies for further research by researchers. Science and Techno Park can facilitate commercialized, produced, and marketed to the public.
GridMask Based Data Augmentation For Bengali Handwritten Grapheme Classification

In this paper, we describe the deep learning-based Bengali handwritten grapheme classification. Specifically, our recognition approach is based on the convolutional neural networks (CNNs) as deep CNNs have achieved splendid performance on many different visual recognition tasks. Moreover, we employ GridMask-based data augmentation to improve the recognition performance further. We compare the GridMask-based data augmentation with conventional data augmentations (such as flip, rotation, mixup) on three widely-used CNN architectures: ResNet101, DenseNet169 and EfficientNet B0. Extensive experiments demonstrate GridMask can utilize the information removal to improve the robustness of the neural networks, and the boost of hierarchical macro-averaged recall on the validation set suggest that GridMask data augmentation can be efficiently used for the Bengali handwritten grapheme analysis without any prior grapheme segmentation.
Static Timing Analysis (STA) with Timing Bleed: Certifying Much Higher Performance for Rapid Single Flux Quantum (RSFQ) Logic

Abstract Josephson-junction based technologies, such as RSFQ, are receiving growing investments due to their high speeds and low power. As the time interval between the data input and the clock becomes smaller, clock-to-Q delay of a flip-flop increases. In conventional timing analysis, setup time is defined as the interval where clock-to-Q delay increases by 10%. This allows the pipeline to be divided into separate stages and STA to be performed independently for each stage. However, since RSFQ is pipelined at gate-level, setup time is a significant portion of the clock period and makes this timing constraint extremely conservative. Instead, we define setup time based on the probability of causing a logic error and develop a new STA method that allows larger increases in clock-to-Q delay, i.e., timing bleed, whenever the data input arrives late. We present results of simulations for benchmark circuits with process variations to demonstrate that our new method certifies much higher speeds for RSFQ logic.
Multi-Key Homomorphic Encryption Create new Multiple Logic Gates and Arithmetic Circuit

This is a feasibility study on homomorphic encryption using the MK-TFHE library in daily computing using cloud services. Logic gates OR, AND, XOR, XNOR, NOR were created. A basic set of arithmetic operations namely - addition, subtraction, multiplication and division were also created. This research is a continuation of a previous work and this peeks into the newly created logic gates on these arithmetic operations.
On the Cyber Security of Lebanon: A Large Scale Empirical Study of Critical Vulnerabilities

In this paper, we uncover 1645 critical vulnerabilities in the perimeter of Lebanon affecting the majority of its sectors, including critical infrastructure. Given the enormous economic and personal damage imposed by critical vulnerabilities, we use a novel framework to regularly identify these vulnerabilities in time on a large scale. We show that the root cause of the uncovered vulnerabilities is the lack of a core security best practice, namely, patch management. All the 1645 vulnerable systems had a patch offered by the vendor at the time they were found vulnerable. In addition to that, the poor reaction to our notification efforts to the owners of vulnerable systems underlines another lack of a proper incident handling process. To this end, this research shall be considered as a first step towards continuous attack surface evaluation of Lebanon, which shall involve different parties from public and private sectors in order to better perform risk analysis and mitigation.
Sticks, Ropes, Land: Confronting Colonial Practices in Public Space Design

Common participatory design and community consultation practices often tokenize participants' input. This can limit the depth of information shared, lead to disconnected understandings of site, and perpetuate hierarchical structures between designers, planners and the communities they seek to serve. Sticks, Ropes, Land proposes alternative approaches for engaging with community stewards and groups who get marginalized in the design of public space. Through the development of practices that pair material-based methods of making with activities grounded in direct connections to place, Sticks, Ropes, Land puts forward approaches to participatory design that aim to question and problematize colonial structures in relation to public space design. This paper examines a series of three approaches that designers might consider towards the work of serving and supporting the agency and rights of place-based communities.
Making the Relationship between Uncertainty Estimation and Safety Less Uncertain

Uncertainty estimation is an intense subject of research in object detection. A commonly mentioned motivation is the usage of object detection in safety-critical applications. However, the precise connection between uncertainty estimation and safety engineering is seldom done: how shall uncertainty estimation really support safety engineering? How does uncertainty relate to classical safety engineering activities? Which problems does uncertainty estimation solve? Which new challenges does it pose? This paper sketches those connections more precisely and provides perspectives for future work in this direction.
Intent Pattern Discovery for Academic Chatbot - A Comparison between N-gram model and Frequent Pattern-Growth method

We present our findings in user's Intent Pattern Discovery for designing a conversational agent (chatbot) in the academic-related domain. We developed our chatbot corpus using social messaging dataset of the conversation between students and the internship coordinator to train our Academic Chatbot model. We compare our unsupervised approach FIP or Frequent Intent Pattern against the N-gram model to investigate the efficiency of our model to extract significant and descriptive user's intent pattern with limited vector size. Experiment results show promising intent rules discovery with 0.9 confidence value with term vector reduction size of 78% against the Bigram model. This finding has given the insight to discover the essence from a social conversation for our Academic Chatbot in order to understand a user's intention in a social conversation.
Multi-feature Counting of Dense Crowd Image Based on Multi-column Convolutional Neural Network

The crowd counting task is an important research problem. Now more and more people are concerned about safety issues. When the population density reaches a very high peak, the population density counts, the alarm is sent out, and the crowds are diverted. The trampling of the Shanghai New Year's stampede will not happen again. The final density map is produced by two steps: at first, extract feature maps from multiple layers, and then adjust their output so that they are all the same size, all these resized layers are combined into the final density map. We also used texture features and target edge detection to reduce the loss of density map detail to better integrate with our convolutional neural network. We tested on several commonly used datasets. Our model achieved good results in crowd counting.
Decentralised Ecosystem for Journalism based on Blockchain

Agrawal, Vaibhav Agarwal, Aishwarya Shah, Shailja Parmar, Dilay Pratap Rao, UdaiSignificant improvement and adaptation of the blockchain technology have led to various implementations and use cases, which utilise the power of decentralisation, immutability, scalability and secure the flow of data between two parties. Journalism is controlling a huge part of society and if manipulated can lead to disastrous results. In the recent past, it has been noted how journalism or media can influence the mass population and be used in favour or opposition of certain entities. The ecosystem we are proposing would tackle the problem of news authentication by a voting system. The traditional system used in journalism today uses a centralised platform. In this paper, we propose a novel decentralised platform to make the world of journalism democratic by giving power to the people to filter the authenticity of a news article through a majority consensus.
New Blind Filter Protocol: An Improved Privacy-Preserving Scheme for Location-Based Services

Abstract Location-based services have attracted much attention in both academia and industry. However, protecting user’s privacy while providing accurate service for users remains challenging. In most of the existing research works, a semi-trusted proxy is employed to act on behalf of a user to minimize the computation and communication costs of the user. However, user privacy, e.g. location privacy, cannot be protected against the proxy. In this paper, we design a new blind filter protocol where a user can employ a semi-trusted proxy to determine whether a point of interest is within a circular area centered at the user’s location. During the protocol, neither the proxy nor the location-based service provider can obtain the location of the user and the query results. Moreover, each type of query is controlled by an access tree and only the users whose attributes satisfy this access tree can complete the specific type of query. Security analysis and efficiency experiments validate that the proposed protocol is secure and efficient in terms of the computation and communication overhead.
Homomorphic Authentication Scheme for Proof of Retrievability with Public Verifiability

Cloud storages are the cloud computing model that stores, manages, and operates data storage as a service. These are widely used nowadays and are being adopted by many organizations or individuals. The ease of using cloud storage is that the users can upload the data in remote cloud storage and can be accessed by any device through the internet. These cloud storage are managed by cloud service providers. Despite having benefits, users don’t get physical access to the servers that store their data. Thus, users need some integrity verification techniques that confirm that data stored in the data center is intact. This paper presents the public verifiability service that provides integrity verification of data placed in the cloud. The proposed system is built on pseudorandom functions (PRFs) which rely on homomorphic linear property. We also considered the third party auditor (TPA) to confirm the integrity of cloud data through auditing. Our experimental study shows that the proposed method is provably secure.
Bag of Models Based Embeddings for Assessment of Neurological Disorders Using Speech Intelligibility

Intelligibility assessment of impaired speech is a tedious task because of the slow speaking rate, slurry speech, distorted vowels, and consonants. Hence, learning discriminative and compact embeddings to assess speech quality is essential. We propose a bag of models (BoM)-based framework that utilizes adapted Gaussian mixture models (AGMMs)-based embeddings. As opposed to AGMMs-based i-vector and supervector representations which use a universal background model (UBM) as a basic model and the model parameters of AGMMs for representations, we propose to use GMMs built for each intelligibility level as base-class GMMs to build a bag of models. The performance of the proposed approach is evaluated using two datasets namely 10-digits and 100-common words of UA-Speech database belonging to four intelligibility levels namely very low, low, mid, and high. The proposed BoM-based approach performs significantly better than the supervector, hybrid GMM/SVM, i-vector, and x-vector-based approaches for both datasets. In the case of prediction of intelligibility scores, the prediction error is reduced to 0.0726 and 0.1054 for 10-digits and 100-common words datasets, respectively. The discriminative power and the compactness of the proposed BoM-based embeddings lead to the best accuracy for both assessment of intelligibility levels and prediction of intelligibility scores.
CESS-A System to Categorize Bangla Web Text Documents

Technology has evolved remarkably, which has led to an exponential increase in the availability of digital text documents of disparate domains over the Internet. This makes the retrieval of the information a very much time- and resource-consuming task. Thus, a system that can categorize such documents based on their domains can truly help the users in obtaining the required information with relative ease and also reduce the workload of the search engines. This article presents a text categorization system (CESS) that categorizes text document using newly proposed hybrid features that combines term frequency-inverse document frequency-inverse class frequency and modified chi-square methods. Experiments were performed on real-world Bangla documents from eight domains comprises of 24,29,857 tokens, and the highest accuracy of 99.91% has been obtained with multilayer perceptron-based classification. Also, the experiments were tested on Reuters-21578 and 20 Newsgroups datasets and obtained accuracies of 97.29% and 94.67%, respectively, to show the language-independent nature of the system.
Stabilization of Neuro-Control Structure using Lyapunov Functional Based Approach

In this paper, a neuro-control structure is proposed for the speed control of a nonlinear motor drive system. The neural network is trained to learn the inverse dynamics of the considered system from observation of the input-output data. After achieving the training process, a direct adaptive control approach with a model following controller is performed. The Lyapunov BackPropagation algorithm (LBP) is developed and used to adjust online the neural network so that the neural model output follows the desired one and maintains the stability of the neurocontrol scheme for a large variation of the motor speed drive system. The obtained simulation results verify the effectiveness of the developed Lyapunov BackPropagation algorithm to have a fast error convergence and highlight its performance to guarantee the stability of the designed approach.
LDA_RAD: A Spam review detection method based on topic model and reviewer anomaly degree

Abstract Online shopping has become a popular activity among consumers. Reviews have become important standards for consumers to buy things. To detect the spam re-views that do not correctly response the right information of goods, a method based on topic model and reviewer anomaly degree is raised. This method divides spam reviews into content-type and deceptive spam review respectively. Firstly, the experimental data set is modelled by LDA topic, which detects the content-type spam reviews with different themes. Then the deceptive spam reviews are detected by the reviewer’s abnormality degree index. It assigns a score to each review according to extracted features and related weights. A score is finally combined with an adaptive weight calculation based on the abnormal period and the reviewer’s similarity to obtain the review score. The review with a high score is considered to be a spam review, and the review with a low score is a true review. Experiments show that this method has a certain improvement on the recognition rate of spam reviews.
Challenges of acquiring mobile devices while minimizing the loss of usable forensics data

The move from computers to mobile devices has placed forensic professionals in a tough spot when performing analysis of seized hardware. Forensics specialists must contend with hardware that is designed to be secure as it is highly mobile, is capable of receiving commands from a suspect after it has been seized which could include commands that are capable of destroying evidence remotely, obsolescence of certain forensics methods such as deleted file recovery from block storage while having to purchase expensive software and hardware packages to extract any usable data.
CLIK: Cloud‐based Linux kernel practice environment and judgment system

Abstract Assignments on kernel programming are essential parts of operating system (OS) courses taught to computer science students to provide them a deep understanding of real‐world OSs. However, these assignments require tremendous effort from both students and instructors. Students are routinely flustered by the daunting task of building a practice environment from scratch; instructors are pressurized for time while validating a student's work that requires several kernel installations and reboots. To minimize this effort, we propose CLIK, a cloud‐based Linux kernel practice environment supporting automatic judgment. It provides students with an individual and easy‐to‐use kernel practice environment and instructors with a fast and easy evaluation of students' work with live feedback. Our experiences with two assignments from a real‐world OS course carried out on CLIK show that CLIK can successfully provide Linux kernel environments for 40 students and help instructors greatly by validating their kernels within one minute using parallel and automated judgments. We also describe detailed lessons learned from developing CLIK that will help both researchers and instructors building similar systems.