FIT

The lack of quality career guidance in African high schools, caused by shortage of human and time resources that the process demands, has led to the choice of unsuitable careers resulting in widespread poor performance at the workplace. The focus of this paper is the use of Expert Systems that store the highly specialized knowledge of career counseling and dispenses it to the needy youth in a cheap and quick process. Initial results of the evaluation of the model, has shown a career diagnosis accuracy of 71%. 124 Information Systems 124 Information Systems

If the growth of communication systems’ energy consumption continues at the present pace, it will endanger ambitious plans to reduce CO2 emissions and tackle climate change. Increasing the energy efficiency of such systems is thus clearly a major challenge. Green computing seeks to offer energy efficient solutions to Communication systems. This paper presents a brief overview of the application of green technology in wireless communication industries.

There has been a growing need of handyman services in most countries around the world which is attributed to several contributing factors which include, local demand, market influence, having second homes, commercial property maintenance and people’s lives becoming busier and more hectic with more people now looking for help with odd-jobs around their home, like changing light fittings, or putting up shelves. The existing handyman services solutions striving to solve this problem are defragmented offering contacts scattered in the web and there is no structured method of determining the Location and quality of service provided by these handymen. The proposed model is a mobile application for locating handyman services within a locality to help in streamlining this process and provide a structured approach for determining location and quality of service to be provided by the handymen. The application was developed in android operating system because of its popularity among many mobil...

The last several years have witnessed a surge of activity in lightweight cryptographic design. Many lightweight block ciphers have been proposed, targeted mostly at hardware applications. Typically software performance has not been a priority, and consequently software performance for many of these algorithms is unexceptional. Simon and Speck are lightweight block cipher families developed by the U.S. National Security Agency for high performance in constrained hardware and software environments. In this paper, we discuss software performance and demonstrate how to achieve high performance implementations of Simon and Speck on the AVR family of 8-bit microcontrollers. Both ciphers compare favorably to other lightweight block ciphers on this platform. Indeed, Speck seems to have better overall performance than any existing block cipher — lightweight or not.

Gradual pattern mining allows for extraction of attribute correlations through gradual rules such as:"the more X, the more Y". Such correlations are useful in identifying and isolating relationships among the attributes that may not be obvious through quick scans on a data set. For instance, a researcher may apply gradual pattern mining to determine which attributes of a data set exhibit unfamiliar correlations in order to isolate them for deeper exploration or analysis. In this work, we propose an ant colony optimization technique which uses a popular probabilistic approach that mimics the behavior biological ants as they search for the shortest path to find food in order to solve combinatorial problems. In our second contribution, we extend an existing gradual pattern mining technique to allow for extraction of gradual patterns together with an approximated temporal lag between the affected gradual item sets. Such a pattern is referred to as a fuzzy-temporal gradual patt...

Existing approaches for extracting gradual patterns become inefficient in terms of memory usage when applied on data sets with huge numbers of objects. This inefficiency is caused by the contiguous nature of loading binary matrices into main memory as single blocks when validating candidate gradual patterns. This paper proposes an efficient storage layout that allows these matrices to be split and loaded into/from memory in multiple smaller chunks. We show how HDF5 (Hierarchical Data Format version 5) may be used to implement this chunked layout and our experiments reveal a great improvement in memory usage efficiency especially on huge data sets.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Gradual Pattern Mining Tool on Cloud Dickson Owuor, Anne Laurent, Joseph Onderi Orero, Olivier Lobry

Swarm intelligence is a discipline that studies the collective behavior that is produced by local interactions of a group of individuals with each other and with their environment. In Computer Science domain, numerous swarm intelligence techniques are applied to optimization problems that seek to efficiently find best solutions within a search space. Gradual pattern mining is another Computer Science field that could benefit from the efficiency of swarm based optimization techniques in the task of finding gradual patterns from a huge search space. A gradual pattern is a rule-based correlation that describes the gradual relationship among the attributes of a data set. For example, given attributes {G, H} of a data set a gradual pattern may take the form: "the less G, the more H" (simply denoted as {G ↓ , H ↑ }). In this paper, we propose a numeric encoding for gradual pattern candidates that we use to define an effective search space. In addition, we present a systematic study of several meta-heuristic optimization techniques as efficient solutions to the problem of finding gradual patterns using our search space.

We present EpidBioBERT, a biosurveillance epidemiological document tagger for disease surveillance over PADI-Web system. Our model is trained on PADI-Web corpus which contains news articles on Animal Diseases Outbreak extracted from the web. We train a classifier to discriminate between relevant and irrelevant documents based on their epidemiological thematic feature content in preparation for further epidemiology information extraction. Our approach proposes a new way to perform epidemiological document classification by enriching epidemiological thematic features namely disease, host, location and date, which are used as inputs to our epidemiological document classifier. We adopt a pre-trained biomedical language model with a novel fine tuning approach that enriches these epidemiological thematic features. We find these thematic features rich enough to improve epidemiological document classification over a smaller data set than initially used in PADI-Web classifier. This improves the classifiers ability to avoid false positive alerts on disease surveillance systems. To further understand information encoded in EpidBioBERT, we experiment the impact of each epidemiology thematic feature on the classifier under ablation studies. We compare our biomedical pre-trained approach with a general language model based model finding that thematic feature embeddings pre-trained on general English documents are not rich enough for epidemiology classification task. Our model achieves an F1-score of 95.5% over an unseen test set, with an improvement of +5.5 points on F1-Score on the PADI-Web classifier with nearly half the training data set.

Mobile payments support a range of services in many less developed countries including everyday payments, migrant remittances, credit, tax collection, and welfare benefits. These services depend entirely on the mobile phone network as their carrier, so they stop where the network does. This leaves millions of the very poorest people stranded – people living in remote areas where there is little to no network service. It also leaves urban users at the mercy of network congestion. We developed a prototype system, DigiTally, which lets users make offline payments by copying short strings of digits from one mobile handset to another. Offline payments are already used for electricity (both in prepayment meters and pay-asyou-go solar); can we extend them into a general-purpose payment system, to increase service resilience in the face of network congestion or outage, and provide service to currently excluded areas? We report the results of a preliminary study with an early prototype of Di...

Resumo: Vários algoritmos de agrupamentos foram propostos na literatura. O uso de diferentes algoritmos de agrupamento, ou até mesmo de um único algoritmo, pode obter diferentes resultados quando aplicados em um mesmo conjunto de dados. A combinação de resultados, obtidos de uma técnica de classificação ou de técnicas distintas, é utilizada com sucesso para melhorar a estabilidade ou desempenho dessas técnicas. Por isto, nos últimos anos houve um aumento crescente no interesse do uso de combinação de agrupamentos de dados. Neste trabalho, é feita uma revisão sobre os principais métodos de combinação de agrupamentos encontrados na literatura. Para isso, a revisão começa com uma descrição do problema de combinação e uma análise dos objetivos comumente adotados por métodos de combinação. Em seguida, discorre-se sobre a necessidade da diversidade nos agrupamentos a serem combinados e métodos para medi-la. Também é definido um critério para medir a informação mútua entre agrupamentos e são apresentados exemplos de seu uso. O desempenho dos métodos foi comparado por vários autores na literatura e uma análise dessas comparações é realizada neste trabalho.

An abnormal human gait has been identified as one of the key behavioral indicators used by CCTV operators to detect concealed firearm carrying. Gait has however been found to be significantly different in male and female more so that it has been proposed as a gender determinant. Using skeletal tracking and machine learning, this study sought to compare the performance of various machine learning algorithms to classify armed and unarmed features obtained from male and female depth datasets. The findings indicate that features selected from the datasets are significantly different. However, the classification ability of machine learning algorithms was almost similar for both datasets with K-nearest neighbor algorithm outperforming other algorithms for both datasets. These findings suggest that concealed firearm carrying affects male and females differently therefore a concealed firearm detector based on human motion should factor in these differences for a more holistic system.

Crimes involving the use of firearms have been on the increase in the past few years. One of the measures adopted to prevent these crimes is the use of CCTV operators at video surveillance centers to detect persons carrying concealed firearms on their bodies by monitoring their behavior. This paper has found that this technique has challenges associated with human weaknesses and errors. A review of the current attempts to automate video surveillance for concealed firearm detection has found that they have the limitation that the techniques can only be employed on stationary and cooperative persons. This makes them inappropriate for real-life surveillance. This paper highlights the need for automated video surveillance solutions that can detect persons carrying concealed firearms when they are not stationary and aware of the scanning process. We further explore automated behavioral analysis and specifically gait analysis as a possible technique for concealed firearm detection on vide...

There has been an increase in crimes involving illegal firearms in the last couple of years. Previous studies have found that most illegal firearms are carried in a concealed manner. The detection therefore of persons carrying concealed firearms is critical in maintaining security especially in public places. Literature indicates that disruption in gait is a major indicator used by security personnel to detect persons carrying concealed firearms especially those tucked on the hip. However, the specific gait parameters that are indicative have not yet been quantitatively determined. The purpose of this study therefore is to analyze the gait of persons carrying a concealed firearm tucked on the right hip and to quantitatively determine the gait characteristics associated with carrying the firearm. A simulation of persons walking while carrying a concealed firearm and when unarmed was recorded using Kinect V2 depth camera. The depth camera provided 3D spatial skeletal joint position fe...